diff --git a/.gitignore b/.gitignore index 894a44c..a609af7 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,7 @@ +# Folders for cluster / local results +examples/icml_2018_experiments/CAVE_reports +examples/icml_2018_experiments/log + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] diff --git a/examples/icml_2018_experiments/bnn.ipynb b/examples/icml_2018_experiments/bnn.ipynb index fed010f..2c450a4 100644 --- a/examples/icml_2018_experiments/bnn.ipynb +++ b/examples/icml_2018_experiments/bnn.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this series of notebooks, we will replicate and analyze the ICML 2018 experiments that where used for benchmarking in BOHB (Falkner et al. 2018).\n", + "In this series of notebooks, we will replicate and analyze the ICML 2018 experiments that were used for benchmarking in BOHB (Falkner et al. 2018).\n", "In addition to HpBandSter, we will use CAVE to analyze and visualize the optimization process." ] }, @@ -19,7 +19,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## About the frameworks\n", + "## About this experiment\n", "\n", "### Bayesian Neural Networks (BNN)\n", "\n", @@ -42,53 +42,13 @@ "## Run the experiment\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We deactivate logging to ensure readability\n", - "import logging\n", - "logging.basicConfig(level=logging.ERROR)\n", - "# Also, we suppress warnings.\n", - "# If there are problems for you executing this notebook, you might want to comment this out.\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.1) References\n", - "*Worker*: We need a Worker to define the kind of computation we want to optimize. The worker used for the experiments is located in `workers/bnn_worker.py`\n", - "\n", - "\n", - "\n", - "*ConfigSpace*: Every problem needs a description of the search space to be complete. In HpBandSter, a ConfigurationSpace-object defines all hyperparameters, their ranges and dependencies between them.\n", - "\n", - "In our example here, the search space consists of the hyperparameters:\n", - "\n", - "\n", - "| Name | Type | Values |\n", - "|:---------:|:-------:|:------------:|\n", - "| burn_in | float | [0.0, 0.8] |\n", - "| l_rate | float | [1e-06, 0.1] |\n", - "| mdecay | float | [0.0, 1.0] |\n", - "| n_units_1 | float | [16, 512] |\n", - "| n_units_2 | float | [16, 512] |" - ] - }, { "cell_type": "markdown", "metadata": { "scrolled": false }, "source": [ - "### 1.3) Setting up the experiment and running the optimizer(s)\n", - "\n", - "Please note that the execution of the experiment with all datasets might take up to a few days, depending on your hardware. You can also skip this step and process with the precomputed results saved in `opt_results/bnn`." + "This examples ships with all the code necessary to reproduce the experiment. Because it takes a few days to generate the data, the results of the optimization are provided in `examples/icml_2018_experiments/opt_results/`. If you want to generate the data (from examples/icml_2018_experiments), just run:" ] }, { @@ -99,79 +59,16 @@ }, "outputs": [], "source": [ - "import os\n", - "from itertools import product\n", - "import numpy as np\n", - "\n", - "import hpbandster.core.nameserver as hpns\n", - "import hpbandster.core.result as hpres\n", - "from hpbandster.optimizers import BOHB, RandomSearch, HyperBand\n", - "\n", - "from workers.bnn_worker import BNNWorker\n", - "\n", - "# Run the experiment, evaluate each dataset on each optimizer\n", - "\n", - "datasets = ['toyfunction', 'bostonhousing', 'proteinstructure']\n", - "opt_methods = [\"smac\", \"bohb\", \"randomsearch\", \"hyperband\"]\n", - "num_iterations = 4\n", - "min_budget = 300\n", - "max_budget = 10000\n", - "\n", - "eta = 3\n", - "\n", - "for dataset, opt_method in product(datasets, opt_methods):\n", - "\n", - " print(dataset, opt_method)\n", - " \n", - " output_dir = \"opt_results/bnn/{}/{}\".format(dataset, opt_method)\n", - " if not os.path.exists(output_dir):\n", - " os.makedirs(output_dir)\n", - "\n", - " run_id = '0' # Every run has to have a unique (at runtime) id.\n", - " \n", - " # create a worker\n", - " worker = BNNWorker(dataset=dataset, measure_test_loss=False, run_id=run_id, max_budget=max_budget)\n", - " configspace = worker.configspace\n", - "\n", - " if opt_method in ['randomsearch', 'bohb', 'hyperband']:\n", - " # setup a nameserver\n", - " NS = hpns.NameServer(run_id=run_id, host='localhost', port=0, working_directory=output_dir)\n", - " ns_host, ns_port = NS.start()\n", - "\n", - " worker.load_nameserver_credentials(output_dir)\n", - " worker.run(background=True)\n", - "\n", - " # instantiate and run optimizer\n", - " opt = RandomSearch if opt_method == 'randomsearch' else BOHB if opt_method == 'bohb' else HyperBand\n", - "\n", - " result_logger = hpres.json_result_logger(directory=output_dir, overwrite=True)\n", - "\n", - " opt = opt(configspace, eta=3,\n", - " working_directory=output_dir,\n", - " run_id=run_id,\n", - " min_budget=min_budget, max_budget=max_budget,\n", - " host=ns_host,\n", - " nameserver=ns_host,\n", - " nameserver_port = ns_port,\n", - " ping_interval=3600,\n", - " result_logger=result_logger)\n", - "\n", - " result = opt.run(n_iterations=num_iterations)\n", - " \n", - " # **NOTE:** Unfortunately, the configuration space is *not yet saved automatically* to file\n", - " # but this step is mandatory for the analysis with CAVE. \n", - " # We recommend to save the configuration space every time you use BOHB.\n", - " # We do this by using the ConfigSpace-to-json-writer.\n", - "\n", - " from ConfigSpace.read_and_write import pcs_new\n", - " with open(os.path.join(output_dir, 'configspace.pcs'), 'w') as fh:\n", - " fh.write(pcs_new.write(opt.config_generator.configspace))\n", - " \n", - " else:\n", - " # the number of iterations for the blackbox optimizers must be increased so they have comparable total budgets\n", - " bb_iterations = int(num_iterations * (1+(np.log(max_budget) - np.log(min_budget))/np.log(eta)))\n", - " if opt_method == 'smac':\n", - " result = worker.run_smac(bb_iterations, deterministic=True, working_directory=output_dir)" + "%%capture\n", + "! python scripts/run_experiment.py --exp_name bnn --num_iterations 4 --min_budget 300 --max_budget 1000 --n_workers 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can use this script to generate the optimization data for a variety of optimizers, the results will (by default) be stored in `examples/icml_2018_experiments/EXPERIMENT/DATASET/OPTIMIZER`. Use `python run_experiment --help` to see how to use the script.\n", + "If you have access to a cluster, take a look at the scripts provided for cluster computation on a SLURM cluster at `examples/icml_2018_experiments/scripts/cluster/`." ] }, { @@ -182,12 +79,12 @@ "\n", "### Instantiate CAVE\n", "\n", - "Analyzing the optimization results with CAVE is very straight-forward. If you want to use CAVE interactively in a notebook, set `show_jupyter=True`. Specify which optimization you want to analyze via the `folders` argument and specify `file_format==SMAC3` or `file_format==BOHB`, depending on which optimizer was used for the results. To analyze how BOHB optimized on the *bostenhousing* dataset, run:" + "Analyzing the optimization results with CAVE is very straight-forward. If you want to use CAVE interactively in a notebook, set `show_jupyter=True`. Specify which optimization you want to analyze via the `folders` argument and specify `file_format==SMAC3` or `file_format==BOHB`, depending on which optimizer was used for the results. To analyze how BOHB optimized the bnn-problem for the bostonhousing dataset, run:" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "scrolled": false }, @@ -213,19 +110,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/shuki/Repos/BOHBsCAVE/.ve_BOHBsCAVE/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n", - " return ptp(axis=axis, out=out, **kwargs)\n" - ] - } - ], + "outputs": [], "source": [ + "%%capture\n", "cave.analyze()\n", "! firefox CAVE_reports/bnn_notebook/report.html" ] @@ -234,474 +123,29 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "CAVE is fully compatible with Jupyter notebooks. You can invoke the individual analysis methods as follows.\n", + "CAVE is fully compatible with Jupyter notebooks. You can invoke the individual analysis methods.\n", "\n", "The most interesting plot for BOHB might be a visualization of the learning curves:" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"14224\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"14224\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '14224' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"14224\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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var data_multiline = source_multiline.data;\\n var data_scatter = source_scatter.data;\\n var min_perf = 4.992416977283878;\\n var max_perf = 31586347444678.41;\\n var min_iter = 0;\\n var max_iter = 3;\\n if (cb_obj.value == 'performance') {\\n data_multiline['colors'] = data_multiline['colors_performance'];\\n data_scatter['colors'] = data_scatter['colors_performance'];\\n cm.low = min_perf;\\n cm.high = max_perf;\\n } else {\\n data_multiline['colors'] = data_multiline['colors_iteration'];\\n data_scatter['colors'] = data_scatter['colors_iteration'];\\n cm.low = min_iter;\\n cm.high = max_iter;\\n }\\n source.change.emit();\\n \"},\"id\":\"14346\",\"type\":\"CustomJS\"},{\"attributes\":{\"children\":[{\"id\":\"14348\",\"type\":\"WidgetBox\"},{\"id\":\"14351\",\"type\":\"Row\"},{\"id\":\"14352\",\"type\":\"WidgetBox\"}]},\"id\":\"14353\",\"type\":\"Column\"},{\"attributes\":{},\"id\":\"14471\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"field\":\"colors\",\"transform\":{\"id\":\"14228\",\"type\":\"LinearColorMapper\"}},\"line_color\":{\"field\":\"colors\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"times\"},\"y\":{\"field\":\"losses\"}},\"id\":\"14337\",\"type\":\"Circle\"},{\"attributes\":{\"active\":[0,1,2,3],\"callback\":{\"id\":\"14340\",\"type\":\"CustomJS\"},\"labels\":[\"0\",\"1\",\"2\",\"3\"]},\"id\":\"14341\",\"type\":\"CheckboxButtonGroup\"},{\"attributes\":{\"args\":{\"checkbox\":{\"id\":\"14341\",\"type\":\"CheckboxButtonGroup\"},\"glyph_renderer0\":{\"id\":\"14265\",\"type\":\"GlyphRenderer\"},\"glyph_renderer1\":{\"id\":\"14272\",\"type\":\"GlyphRenderer\"},\"glyph_renderer10\":{\"id\":\"14332\",\"type\":\"GlyphRenderer\"},\"glyph_renderer11\":{\"id\":\"14339\",\"type\":\"GlyphRenderer\"},\"glyph_renderer2\":{\"id\":\"14279\",\"type\":\"GlyphRenderer\"},\"glyph_renderer3\":{\"id\":\"14285\",\"type\":\"GlyphRenderer\"},\"glyph_renderer4\":{\"id\":\"14292\",\"type\":\"GlyphRenderer\"},\"glyph_renderer5\":{\"id\":\"14299\",\"type\":\"GlyphRenderer\"},\"glyph_renderer6\":{\"id\":\"14305\",\"type\":\"GlyphRenderer\"},\"glyph_renderer7\":{\"id\":\"14312\",\"type\":\"GlyphRenderer\"},\"glyph_renderer8\":{\"id\":\"14319\",\"type\":\"GlyphRenderer\"},\"glyph_renderer9\":{\"id\":\"14325\",\"type\":\"GlyphRenderer\"}},\"code\":\"var labels = []; checkbox.active = labels;len_labels = 4;glyph_renderers = [[glyph_renderer0,glyph_renderer1,glyph_renderer2],[glyph_renderer3,glyph_renderer4,glyph_renderer5],[glyph_renderer6,glyph_renderer7,glyph_renderer8],[glyph_renderer9,glyph_renderer10,glyph_renderer11]];\\n for (i = 0; i < len_labels; i++) {\\n if (checkbox.active.includes(i)) {\\n // console.log('Setting to true: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = true;\\n // console.log('Setting to true: ' + i + ' : ' + j)\\n }\\n } else {\\n // console.log('Setting to false: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = false;\\n // console.log('Setting to false: ' + i + ' : ' + j)\\n }\\n }\\n }\\n 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"execution_count": 17, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
# aggregated parallel BOHB runs1
# parameters5
Deterministic target algorithmTrue
Optimized run objectivequality
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
budget 370.4budget 1111.1budget 3333.3budget 10000
Total time spent evaluating configurations156.08 sec196.55 sec231.21 sec238.56 sec
Average time per configuration (mean / std)8.21 sec (± 2.00)10.92 sec (± 3.19)19.27 sec (± 15.45)29.82 sec (± 10.66)
# evaluated configurations1918128
# changed parameters (default to incumbent)5555
Configuration originsAcquisition Function : 11, Random : 8Acquisition Function : 14, Random : 4Acquisition Function : 8, Random : 4Acquisition Function : 7, Random : 1
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cave.overview_table();" ] @@ -715,78 +159,11 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
budget 370.4budget 1111.1budget 3333.3budget 10000
burn_in0.3896640.05379280.05379280.0537928
l_rate0.02974140.06953450.06953450.0695345
mdecay0.3288010.5863020.5863020.586302
n_units_167228228228
n_units_2184242424
Cost39.1625.4135.8514.992
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cave.bohb_incumbents_per_budget();" ] @@ -807,129 +184,22 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
-------------------- Single importance: ----------------------------------------
l_rate59.6515 +/- 46.4956
burn_in28.4864 +/- 43.246
mdecay7.1975 +/- 24.2353
n_units_21.3038 +/- 3.8476
n_units_10.4141 +/- 1.579
-------------------- Pairwise importance: ----------------------------------------
burn_in & mdecay1.9159 +/- 7.4185
l_rate & n_units_20.6959 +/- 1.8439
l_rate & n_units_10.2079 +/- 0.7715
l_rate & burn_in0.1036 +/- 0.2745
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\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"14033\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error() {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (var i = 0; i < css_urls.length; i++) {\n", - " var url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };var element = document.getElementById(\"14033\");\n", - " if (element == null) {\n", - " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '14033' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n", - " var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {} // ensure no trailing comma for IE\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"14033\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"14033\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"14033\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '14033' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"14033\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function embed_document(root) {\n", - " \n", - " var docs_json = {\"551ccf55-e190-4029-8f29-b21dfa5f6f16\":{\"roots\":{\"references\":[{\"attributes\":{\"columns\":[{\"id\":\"14035\",\"type\":\"TableColumn\"},{\"id\":\"14036\",\"type\":\"TableColumn\"},{\"id\":\"14037\",\"type\":\"TableColumn\"},{\"id\":\"14038\",\"type\":\"TableColumn\"},{\"id\":\"14039\",\"type\":\"TableColumn\"},{\"id\":\"14040\",\"type\":\"TableColumn\"}],\"height\":170,\"index_position\":null,\"source\":{\"id\":\"14034\",\"type\":\"ColumnDataSource\"},\"view\":{\"id\":\"14042\",\"type\":\"CDSView\"}},\"id\":\"14041\",\"type\":\"DataTable\"},{\"attributes\":{},\"id\":\"14152\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"14140\",\"type\":\"StringFormatter\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"14147\",\"type\":\"StringEditor\"},\"field\":\"fANOVA\",\"formatter\":{\"id\":\"14146\",\"type\":\"StringFormatter\"},\"title\":\"fANOVA\",\"width\":100},\"id\":\"14038\",\"type\":\"TableColumn\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"14145\",\"type\":\"StringEditor\"},\"field\":\"LPI\",\"formatter\":{\"id\":\"14144\",\"type\":\"StringFormatter\"},\"title\":\"LPI\",\"width\":100},\"id\":\"14037\",\"type\":\"TableColumn\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"14151\",\"type\":\"StringEditor\"},\"field\":\"LPI\",\"formatter\":{\"id\":\"14150\",\"type\":\"StringFormatter\"},\"title\":\"LPI\",\"width\":100},\"id\":\"14040\",\"type\":\"TableColumn\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"14141\",\"type\":\"StringEditor\"},\"field\":\"Parameters\",\"formatter\":{\"id\":\"14140\",\"type\":\"StringFormatter\"},\"sortable\":false,\"title\":\"Parameters\",\"width\":150},\"id\":\"14035\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"14151\",\"type\":\"StringEditor\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"14149\",\"type\":\"StringEditor\"},\"field\":\"fANOVA\",\"formatter\":{\"id\":\"14148\",\"type\":\"StringFormatter\"},\"title\":\"fANOVA\",\"width\":100},\"id\":\"14039\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"14141\",\"type\":\"StringEditor\"},{\"attributes\":{\"source\":{\"id\":\"14034\",\"type\":\"ColumnDataSource\"}},\"id\":\"14042\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"14153\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"callback\":null,\"data\":{\"LPI\":[\"LPI\",\"LPI\",\"LPI\",\"LPI\"],\"Parameters\":[\"l_rate\",\"burn_in\",\"mdecay\",\"n_units_1\",\"n_units_2\"],\"fANOVA\":[\"fANOVA\",\"fANOVA\",\"fANOVA\",\"fANOVA\",\"fANOVA\",\"fANOVA\",\"fANOVA\",\"fANOVA\",\"fANOVA\"]},\"selected\":{\"id\":\"14152\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"14153\",\"type\":\"UnionRenderers\"}},\"id\":\"14034\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"14146\",\"type\":\"StringFormatter\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"14143\",\"type\":\"StringEditor\"},\"field\":\"fANOVA\",\"formatter\":{\"id\":\"14142\",\"type\":\"StringFormatter\"},\"title\":\"fANOVA\",\"width\":100},\"id\":\"14036\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"14144\",\"type\":\"StringFormatter\"},{\"attributes\":{},\"id\":\"14148\",\"type\":\"StringFormatter\"},{\"attributes\":{},\"id\":\"14147\",\"type\":\"StringEditor\"},{\"attributes\":{},\"id\":\"14150\",\"type\":\"StringFormatter\"},{\"attributes\":{},\"id\":\"14145\",\"type\":\"StringEditor\"},{\"attributes\":{},\"id\":\"14142\",\"type\":\"StringFormatter\"},{\"attributes\":{},\"id\":\"14143\",\"type\":\"StringEditor\"},{\"attributes\":{},\"id\":\"14149\",\"type\":\"StringEditor\"}],\"root_ids\":[\"14041\"]},\"title\":\"Bokeh Application\",\"version\":\"1.1.0\"}};\n", - " var render_items = [{\"docid\":\"551ccf55-e190-4029-8f29-b21dfa5f6f16\",\"roots\":{\"14041\":\"cbb12c10-9b3f-41d1-9037-e3f6c9771188\"}}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - "\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " var attempts = 0;\n", - " var timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " clearInterval(timer);\n", - " }\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", - " clearInterval(timer);\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "14041" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cave.pimp_comparison_table(run='budget_10000');" ] @@ -1325,801 +229,36 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"18151\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error() {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (var i = 0; i < css_urls.length; i++) {\n", - " var url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };var element = document.getElementById(\"18151\");\n", - " if (element == null) {\n", - " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '18151' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n", - " var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {} // ensure no trailing comma for IE\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"18151\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"18151\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"18151\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '18151' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"18151\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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glyph209, glyph210, glyph211, glyph212, glyph213, glyph214, glyph215, glyph216, glyph217, glyph218, glyph219, glyph220, glyph221, glyph222, glyph223, glyph224, glyph225, glyph226, glyph227, glyph228, glyph229, glyph230, glyph231, glyph232, glyph233, glyph234, glyph235, glyph236, glyph237, glyph238, glyph239];var overtime = [[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],[48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95],[96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143],[144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191],[192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239]];var runs = [[0,4,8,12,16,20,24,28,32,36,40,44,48,52,56,60,64,68,72,76,80,84,88,92,96,100,104,108,112,116,120,124,128,132,136,140,144,148,152,156,160,164,168,172,176,180,184,188,192,196,200,204,208,212,216,220,224,228,232,236],[1,5,9,13,17,21,25,29,33,37,41,45,49,53,57,61,65,69,73,77,81,85,89,93,97,101,105,109,113,117,121,125,129,133,137,141,145,149,153,157,161,165,169,173,177,181,185,189,193,197,201,205,209,213,217,221,225,229,233,237],[2,6,10,14,18,22,26,30,34,38,42,46,50,54,58,62,66,70,74,78,82,86,90,94,98,102,106,110,114,118,122,126,130,134,138,142,146,150,154,158,162,166,170,174,178,182,186,190,194,198,202,206,210,214,218,222,226,230,234,238],[3,7,11,15,19,23,27,31,35,39,43,47,51,55,59,63,67,71,75,79,83,87,91,95,99,103,107,111,115,119,123,127,131,135,139,143,147,151,155,159,163,167,171,175,179,183,187,191,195,199,203,207,211,215,219,223,227,231,235,239]];\\n glyphs.forEach(function(g) {\\n g.visible = false;\\n })\\n // union function\\n function union_arrays(x, y) {\\n var obj = {};\\n for (var i = x.length-1; i >= 0; -- i)\\n obj[x[i]] = x[i];\\n for (var i = y.length-1; i >= 0; -- i)\\n obj[y[i]] = y[i];\\n var res = []\\n for (var k in obj) {\\n if (obj.hasOwnProperty(k)) // <-- optional\\n res.push(obj[k]);\\n }\\n return res;\\n }\\n console.log(\\\"Timeslider: \\\" + time_slider.value);\\n console.log(\\\"Checkbox: \\\" + checkbox.active);var slider_labels = ['39.12', '53.78', '126.60', '323.54', '895.87'];console.log(\\\"Detected slider_labels: \\\" + slider_labels);time_slider.title = \\\"Until wallclocktime \\\" + slider_labels[time_slider.value - 1] + \\\". Step no.\\\"; \\n var activate = [];\\n // if we want multiple checkboxes at the same time, we need to combine the arrays\\n checkbox.active.forEach(function(c) {\\n activate = union_arrays(activate, runs[c]);\\n })\\n // now the intersection of timeslider-activated and checkbox-activated\\n activate = activate.filter(value => -1 !== overtime[time_slider.value - 1].indexOf(value));\\n activate.forEach(function(idx) {\\n glyphs[idx].visible = true;\\n })\\n 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(var i = x.length-1; i >= 0; -- i)\\n obj[x[i]] = x[i];\\n for (var i = y.length-1; i >= 0; -- i)\\n obj[y[i]] = y[i];\\n var res = []\\n for (var k in obj) {\\n if (obj.hasOwnProperty(k)) // <-- optional\\n res.push(obj[k]);\\n }\\n return res;\\n }\\n console.log(\\\"Timeslider: \\\" + time_slider.value);\\n console.log(\\\"Checkbox: \\\" + checkbox.active);var slider_labels = ['39.12', '53.78', '126.60', '323.54', '895.87'];console.log(\\\"Detected slider_labels: \\\" + slider_labels);time_slider.title = \\\"Until wallclocktime \\\" + slider_labels[time_slider.value - 1] + \\\". Step no.\\\"; \\n var activate = [];\\n // if we want multiple checkboxes at the same time, we need to combine the arrays\\n checkbox.active.forEach(function(c) {\\n activate = union_arrays(activate, runs[c]);\\n })\\n // now the intersection of timeslider-activated and checkbox-activated\\n activate = activate.filter(value => -1 !== overtime[time_slider.value - 1].indexOf(value));\\n activate.forEach(function(idx) {\\n glyphs[idx].visible = true;\\n })\\n 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upFilter\"},{\"attributes\":{\"fill_color\":{\"field\":\"color\"},\"marker\":{\"value\":\"square_x\"},\"size\":{\"field\":\"size\",\"units\":\"screen\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"16172\",\"type\":\"Scatter\"},{\"attributes\":{\"column_name\":\"type\",\"group\":\"Default\"},\"id\":\"15444\",\"type\":\"GroupFilter\"},{\"attributes\":{\"column_name\":\"type\",\"group\":\"Incumbent\"},\"id\":\"17278\",\"type\":\"GroupFilter\"},{\"attributes\":{\"data_source\":{\"id\":\"14545\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"16212\",\"type\":\"Scatter\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"16213\",\"type\":\"Scatter\"},\"selection_glyph\":null,\"view\":{\"id\":\"16006\",\"type\":\"CDSView\"}},\"id\":\"16214\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"14546\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"16817\",\"type\":\"Scatter\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"16818\",\"type\":\"Scatter\"},\"selection_glyph\":null,\"view\":{\"id\":\"16599\",\"type\":\"CDSView\"}},\"id\":\"16819\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"marker\":{\"value\":\"triangle\"},\"size\":{\"field\":\"size\",\"units\":\"screen\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"15608\",\"type\":\"Scatter\"},{\"attributes\":{\"column_name\":\"zorder\",\"group\":\"0\"},\"id\":\"17300\",\"type\":\"GroupFilter\"},{\"attributes\":{\"data_source\":{\"id\":\"14544\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"15467\",\"type\":\"Scatter\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"15468\",\"type\":\"Scatter\"},\"selection_glyph\":null,\"view\":{\"id\":\"15233\",\"type\":\"CDSView\"}},\"id\":\"15469\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_color\":{\"field\":\"color\"},\"marker\":{\"value\":\"triangle\"},\"size\":{\"field\":\"size\",\"units\":\"screen\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"15631\",\"type\":\"Scatter\"},{\"attributes\":{\"fill_color\":{\"field\":\"color\"},\"marker\":{\"value\":\"square\"},\"size\":{\"field\":\"size\",\"units\":\"screen\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"16164\",\"type\":\"Scatter\"},{\"attributes\":{\"fill_color\":{\"field\":\"color\"},\"marker\":{\"value\":\"square_x\"},\"size\":{\"field\":\"size\",\"units\":\"screen\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"16176\",\"type\":\"Scatter\"},{\"attributes\":{\"column_name\":\"origin\",\"group\":\"Acquisition 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\",\"dtype\":\"float64\",\"shape\":[25,26]},{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[25,26]},{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[25,26]}]},\"selected\":{\"id\":\"15047\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"15048\",\"type\":\"UnionRenderers\"}},\"id\":\"14593\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"column_name\":\"zorder\",\"group\":\"0\"},\"id\":\"16607\",\"type\":\"GroupFilter\"},{\"attributes\":{\"column_name\":\"origin\",\"group\":\"Unknown\"},\"id\":\"16621\",\"type\":\"GroupFilter\"},{\"attributes\":{\"color_mapper\":{\"id\":\"14577\",\"type\":\"LinearColorMapper\"},\"dh\":{\"units\":\"data\",\"value\":4.990325024476247},\"dw\":{\"units\":\"data\",\"value\":5.114014413376522},\"image\":{\"field\":\"image\"},\"x\":{\"value\":-2.7324138021809112},\"y\":{\"value\":-2.626210846366635}},\"id\":\"14594\",\"type\":\"Image\"},{\"attributes\":{\"color_mapper\":{\"id\":\"14577\",\"type\":\"LinearColorMapper\"},\"dh\":{\"units\":\"data\",\"value\":4.990325024476247},\"dw\":{\"units\":\"data\",\"value\":5.114014413376522},\"image\":{\"field\":\"image\"},\"x\":{\"value\":-2.7324138021809112},\"y\":{\"value\":-2.626210846366635}},\"id\":\"14595\",\"type\":\"Image\"},{\"attributes\":{\"filters\":[{\"id\":\"16620\",\"type\":\"GroupFilter\"},{\"id\":\"16621\",\"type\":\"GroupFilter\"},{\"id\":\"16622\",\"type\":\"GroupFilter\"},{\"id\":\"16623\",\"type\":\"BooleanFilter\"}],\"source\":{\"id\":\"14546\",\"type\":\"ColumnDataSource\"}},\"id\":\"16624\",\"type\":\"CDSView\"},{\"attributes\":{\"data_source\":{\"id\":\"14593\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"14594\",\"type\":\"Image\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"14595\",\"type\":\"Image\"},\"selection_glyph\":null,\"view\":{\"id\":\"14597\",\"type\":\"CDSView\"},\"visible\":false},\"id\":\"14596\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"column_name\":\"type\",\"group\":\"Incumbent\"},\"id\":\"16600\",\"type\":\"GroupFilter\"},{\"attributes\":{\"source\":{\"id\":\"14593\",\"type\":\"ColumnDataSource\"}},\"id\":\"14597\",\"type\":\"CDSView\"},{\"attributes\":{\"filters\":[{\"id\":\"16625\",\"type\":\"GroupFilter\"},{\"id\":\"16626\",\"type\":\"GroupFilter\"},{\"id\":\"16627\",\"type\":\"GroupFilter\"},{\"id\":\"16628\",\"type\":\"BooleanFilter\"}],\"source\":{\"id\":\"14546\",\"type\":\"ColumnDataSource\"}},\"id\":\"16629\",\"type\":\"CDSView\"},{\"attributes\":{\"column_name\":\"type\",\"group\":\"Final 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clearInterval(timer);\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "17889" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cave.configurator_footprint(use_timeslider=True, num_quantiles=5);" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"18456\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error() {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (var i = 0; i < css_urls.length; i++) {\n", - " var url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };var element = document.getElementById(\"18456\");\n", - " if (element == null) {\n", - " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '18456' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n", - " var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {} // ensure no trailing comma for IE\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"18456\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"18456\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"18456\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '18456' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"18456\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function embed_document(root) {\n", - " \n", - " var docs_json = {\"c2174755-313a-4046-8cc5-cf96e2e8ec61\":{\"roots\":{\"references\":[{\"attributes\":{\"children\":[{\"id\":\"18459\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"id\":\"18563\",\"type\":\"Column\"}]},\"id\":\"18564\",\"type\":\"Row\"},{\"attributes\":{\"filters\":[{\"id\":\"18490\",\"type\":\"GroupFilter\"}],\"source\":{\"id\":\"18457\",\"type\":\"ColumnDataSource\"}},\"id\":\"18491\",\"type\":\"CDSView\"},{\"attributes\":{\"children\":[{\"id\":\"18550\",\"type\":\"Button\"}],\"width\":50},\"id\":\"18560\",\"type\":\"WidgetBox\"},{\"attributes\":{},\"id\":\"18572\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"18483\",\"type\":\"ResetTool\"},{\"attributes\":{\"overlay\":{\"id\":\"18570\",\"type\":\"BoxAnnotation\"}},\"id\":\"18481\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"18577\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"18574\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[50]},\"y\":{\"__ndarray__\":\"A5IGlA5G/kEDkgaUDkb+QQjj/lHVLrRACOP+UdUutEBBygeI1HqsQEHKB4jUeqxAs3g3ORvOUUCzeDc5G85RQJ0PbsVArUpAnQ9uxUCtSkA/QaILwpRDQD9BogvClENAuTXOfPt2MkC5Nc58+3YyQEEZvNYrSR1AQRm81itJHUCjFvgTnwdXQKMW+BOfB1dAM3iuOnlVG0AzeK46eVUbQPGKRc724BdA8YpFzvbgF0C9t/UnPPgTQL239Sc8+BNAvbf1Jzz4E0C9t/UnPPgTQL239Sc8+BNAvbf1Jzz4E0DxikXO9uAXQPGKRc724BdAM3iuOnlVG0AzeK46eVUbQKMW+BOfB1dAoxb4E58HV0BBGbzWK0kdQEEZvNYrSR1AuTXOfPt2MkC5Nc58+3YyQD9BogvClENAP0GiC8KUQ0CdD27FQK1KQJ0PbsVArUpAs3g3ORvOUUCzeDc5G85RQEHKB4jUeqxAQcoHiNR6rEAI4/5R1S60QAjj/lHVLrRAA5IGlA5G/kEDkgaUDkb+QQ==\",\"dtype\":\"float64\",\"shape\":[50]}},\"selected\":{\"id\":\"18573\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"18574\",\"type\":\"UnionRenderers\"}},\"id\":\"18496\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"renderers\":[{\"id\":\"18495\",\"type\":\"GlyphRenderer\"},{\"id\":\"18499\",\"type\":\"GlyphRenderer\"},{\"id\":\"18506\",\"type\":\"GlyphRenderer\"},{\"id\":\"18510\",\"type\":\"GlyphRenderer\"},{\"id\":\"18517\",\"type\":\"GlyphRenderer\"},{\"id\":\"18521\",\"type\":\"GlyphRenderer\"},{\"id\":\"18528\",\"type\":\"GlyphRenderer\"},{\"id\":\"18532\",\"type\":\"GlyphRenderer\"},{\"id\":\"18539\",\"type\":\"GlyphRenderer\"},{\"id\":\"18543\",\"type\":\"GlyphRenderer\"}],\"tooltips\":[[\"estimated performance\",\"@mean\"],[\"at-time\",\"@time\"]]},\"id\":\"18545\",\"type\":\"HoverTool\"},{\"attributes\":{\"children\":[{\"id\":\"18559\",\"type\":\"WidgetBox\"},{\"id\":\"18562\",\"type\":\"Row\"}]},\"id\":\"18563\",\"type\":\"Column\"},{\"attributes\":{\"children\":[{\"id\":\"18552\",\"type\":\"Button\"}],\"width\":50},\"id\":\"18561\",\"type\":\"WidgetBox\"},{\"attributes\":{},\"id\":\"18580\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"data_source\":{\"id\":\"18496\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"18497\",\"type\":\"Patch\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"18498\",\"type\":\"Patch\"},\"selection_glyph\":null,\"view\":{\"id\":\"18500\",\"type\":\"CDSView\"}},\"id\":\"18499\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"18570\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"fill_alpha\":0.1,\"fill_color\":\"#1f77b4\",\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"18498\",\"type\":\"Patch\"},{\"attributes\":{\"children\":[{\"id\":\"18560\",\"type\":\"WidgetBox\"},{\"id\":\"18561\",\"type\":\"WidgetBox\"}]},\"id\":\"18562\",\"type\":\"Row\"},{\"attributes\":{},\"id\":\"18480\",\"type\":\"PanTool\"},{\"attributes\":{\"ticker\":null},\"id\":\"18569\",\"type\":\"LogTickFormatter\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"18475\",\"type\":\"BasicTicker\"}},\"id\":\"18478\",\"type\":\"Grid\"},{\"attributes\":{\"label\":{\"value\":\"budget 10000\"},\"renderers\":[{\"id\":\"18539\",\"type\":\"GlyphRenderer\"},{\"id\":\"18543\",\"type\":\"GlyphRenderer\"}]},\"id\":\"18558\",\"type\":\"LegendItem\"},{\"attributes\":{\"data_source\":{\"id\":\"18457\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"18493\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"18494\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"18491\",\"type\":\"CDSView\"}},\"id\":\"18495\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"18578\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"18479\",\"type\":\"SaveTool\"},{\"id\":\"18480\",\"type\":\"PanTool\"},{\"id\":\"18481\",\"type\":\"BoxZoomTool\"},{\"id\":\"18482\",\"type\":\"WheelZoomTool\"},{\"id\":\"18483\",\"type\":\"ResetTool\"},{\"id\":\"18545\",\"type\":\"HoverTool\"}]},\"id\":\"18484\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"18475\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"18567\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"18479\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"18573\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"18482\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"args\":{\"glyph_renderer0\":{\"id\":\"18495\",\"type\":\"GlyphRenderer\"},\"glyph_renderer1\":{\"id\":\"18499\",\"type\":\"GlyphRenderer\"},\"glyph_renderer2\":{\"id\":\"18506\",\"type\":\"GlyphRenderer\"},\"glyph_renderer3\":{\"id\":\"18510\",\"type\":\"GlyphRenderer\"},\"glyph_renderer4\":{\"id\":\"18517\",\"type\":\"GlyphRenderer\"},\"glyph_renderer5\":{\"id\":\"18521\",\"type\":\"GlyphRenderer\"},\"glyph_renderer6\":{\"id\":\"18528\",\"type\":\"GlyphRenderer\"},\"glyph_renderer7\":{\"id\":\"18532\",\"type\":\"GlyphRenderer\"},\"glyph_renderer8\":{\"id\":\"18539\",\"type\":\"GlyphRenderer\"},\"glyph_renderer9\":{\"id\":\"18543\",\"type\":\"GlyphRenderer\"}},\"code\":\"len_labels = 5;glyph_renderers = [[glyph_renderer0,glyph_renderer1],[glyph_renderer2,glyph_renderer3],[glyph_renderer4,glyph_renderer5],[glyph_renderer6,glyph_renderer7],[glyph_renderer8,glyph_renderer9]];\\n for (i = 0; i < len_labels; i++) {\\n if (cb_obj.active.includes(i)) {\\n // console.log('Setting to true: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = true;\\n // console.log('Setting to true: ' + i + ' : ' + j)\\n }\\n } else {\\n // console.log('Setting to false: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = false;\\n // console.log('Setting to false: ' + i + ' : ' + j)\\n }\\n }\\n }\\n \"},\"id\":\"18547\",\"type\":\"CustomJS\"},{\"attributes\":{\"label\":{\"value\":\"budget 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"cave.cost_over_time();" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/shuki/Repos/BOHBsCAVE/.ve_BOHBsCAVE/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n", - " return ptp(axis=axis, out=out, **kwargs)\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cave.parallel_coordinates(run='budget_10000');" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -2127,6 +266,18 @@ "display_name": "BOHBsCAVE", "language": "python", "name": "bohbscave" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" } }, "nbformat": 4, diff --git a/examples/icml_2018_experiments/cartpole.ipynb b/examples/icml_2018_experiments/cartpole.ipynb index 59adaa9..3e73ead 100644 --- a/examples/icml_2018_experiments/cartpole.ipynb +++ b/examples/icml_2018_experiments/cartpole.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this series of notebooks, we will replicate and analyze the ICML 2018 experiments that where used for benchmarking in BOHB (Falkner et al. 2018).\n", + "In this series of notebooks, we will replicate and analyze the ICML 2018 experiments that were used for benchmarking in BOHB (Falkner et al. 2018).\n", "In addition to HpBandSter, we will use CAVE to analyze and visualize the optimization process." ] }, @@ -19,7 +19,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## About the frameworks\n", + "## About this experiment\n", "\n", "### Cartpole\n", "\n", @@ -38,43 +38,19 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# We deactivate logging to ensure readability\n", - "import logging\n", - "logging.basicConfig(level=logging.ERROR)\n", - "# Also, we suppress warnings.\n", - "# If there are problems for you executing this notebook, you might want to comment this out.\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")" + "This examples ships with all the code necessary to reproduce the experiment. Because it takes a few days to generate the data, the results of the optimization are provided in `examples/icml_2018_experiments/opt_results/`. If you want to generate the data (from examples/icml_2018_experiments), just run:" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "### 1.1) References\n", - "*Worker*: We need a Worker to define the kind of computation we want to optimize. The worker used for the experiments is located in `workers/cartpole_worker.py`\n", - "\n", - "\n", - "\n", - "*ConfigSpace*: Every problem needs a description of the search space to be complete. In HpBandSter, a ConfigurationSpace-object defines all hyperparameters, their ranges and dependencies between them.\n", - "\n", - "In our example here, the search space consists of the hyperparameters:\n", - " \n", - "\n", - "| Name | Type | Values |\n", - "|:-------------------------:|:-------:|:----------------:|\n", - "| batch_size | integer | [8, 256] log |\n", - "| discount | real | [0.0, 1.0] |\n", - "| entropy_regularization | real | [0.0, 1.0] |\n", - "| learning_rate | real | [1e-07, 0.1] log |\n", - "| likelihood_ratio_clipping | real | [0.0, 1.0] |\n", - "| n_units_1 | float | [8, 128] |\n", - "| n_units_2 | float | [8, 1<28] |" + "! python scripts/run_experiment.py --exp_name cartpole --num_iterations 1 --min_budget 1 --max_budget 3 --n_workers 1" ] }, { @@ -83,92 +59,8 @@ "scrolled": false }, "source": [ - "### 1.3) Setting up the experiment and running the optimizer(s)\n", - "\n", - "Please note that the execution of the experiment with all datasets might take up to a few days, depending on your hardware. You can also skip this step and process with the precomputed results saved in `opt_results/cartpole`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "import os\n", - "from itertools import product\n", - "import numpy as np\n", - "\n", - "import hpbandster.core.nameserver as hpns\n", - "import hpbandster.core.result as hpres\n", - "from hpbandster.optimizers import BOHB, RandomSearch, HyperBand\n", - "\n", - "from workers.cartpole_worker import CartpoleReducedWorker\n", - "\n", - "# Run the experiment, evaluate each dataset on each optimizer\n", - "\n", - "datasets = ['toyfunction', 'bostonhousing', 'proteinstructure']\n", - "opt_methods = [\"smac\", \"bohb\", \"randomsearch\", \"hyperband\"]\n", - "num_iterations = 4\n", - "min_budget = 300\n", - "max_budget = 10000\n", - "\n", - "eta = 3\n", - "\n", - "for dataset, opt_method in product(datasets, opt_methods):\n", - "\n", - " print(dataset, opt_method)\n", - " \n", - " output_dir = \"opt_results/bnn/{}/{}\".format(dataset, opt_method)\n", - " if not os.path.exists(output_dir):\n", - " os.makedirs(output_dir)\n", - "\n", - " run_id = '0' # Every run has to have a unique (at runtime) id.\n", - " \n", - " # create a worker\n", - " worker = BNNWorker(dataset=dataset, measure_test_loss=False, run_id=run_id, max_budget=max_budget)\n", - " configspace = worker.configspace\n", - "\n", - " if opt_method in ['randomsearch', 'bohb', 'hyperband']:\n", - " # setup a nameserver\n", - " NS = hpns.NameServer(run_id=run_id, host='localhost', port=0, working_directory=output_dir)\n", - " ns_host, ns_port = NS.start()\n", - "\n", - " worker.load_nameserver_credentials(output_dir)\n", - " worker.run(background=True)\n", - "\n", - " # instantiate and run optimizer\n", - " opt = RandomSearch if opt_method == 'randomsearch' else BOHB if opt_method == 'bohb' else HyperBand\n", - "\n", - " result_logger = hpres.json_result_logger(directory=output_dir, overwrite=True)\n", - "\n", - " opt = opt(configspace, eta=3,\n", - " working_directory=output_dir,\n", - " run_id=run_id,\n", - " min_budget=min_budget, max_budget=max_budget,\n", - " host=ns_host,\n", - " nameserver=ns_host,\n", - " nameserver_port = ns_port,\n", - " ping_interval=3600,\n", - " result_logger=result_logger)\n", - "\n", - " result = opt.run(n_iterations=num_iterations)\n", - " \n", - " # **NOTE:** Unfortunately, the configuration space is *not yet saved automatically* to file\n", - " # but this step is mandatory for the analysis with CAVE. \n", - " # We recommend to save the configuration space every time you use BOHB.\n", - " # We do this by using the ConfigSpace-to-json-writer.\n", - "\n", - " from ConfigSpace.read_and_write import pcs_new\n", - " with open(os.path.join(output_dir, 'configspace.pcs'), 'w') as fh:\n", - " fh.write(pcs_new.write(opt.config_generator.configspace))\n", - " \n", - " else:\n", - " # the number of iterations for the blackbox optimizers must be increased so they have comparable total budgets\n", - " bb_iterations = int(num_iterations * (1+(np.log(max_budget) - np.log(min_budget))/np.log(eta)))\n", - " if opt_method == 'smac':\n", - " result = worker.run_smac(bb_iterations, deterministic=True, working_directory=output_dir)" + "You can use this script to generate the optimization data for a variety of optimizers, the results will (by default) be stored in `examples/icml_2018_experiments/EXPERIMENT/OPTIMIZER`. Use `python run_experiment --help` to see how to use the script.\n", + "If you have access to a cluster, take a look at the scripts provided for cluster computation on a SLURM cluster at `examples/icml_2018_experiments/scripts/cluster/`." ] }, { @@ -184,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "scrolled": false }, @@ -192,10 +84,10 @@ "source": [ "from cave.cavefacade import CAVE\n", "\n", - "cave = CAVE(folders=[\"opt_results/cart/bohb\"], # .../bnn/DATASET/OPTIMIZER\n", - " output_dir=\"CAVE_reports/bnn_notebook\", # output for debug/images/etc\n", - " ta_exec_dir=[\".\"], # Only important for SMAC-results\n", - " file_format='BOHB', # BOHB or SMAC3\n", + "cave = CAVE(folders=[\"opt_results/cartpole/bohb\"],\n", + " output_dir=\"CAVE_reports/cartpole_notebook\", # output for debug/images/etc\n", + " ta_exec_dir=[\".\"], # Only important for SMAC-results\n", + " file_format='BOHB', # BOHB or SMAC3\n", " verbose_level='OFF',\n", " show_jupyter=True,\n", " )" @@ -205,26 +97,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To generate the HTML-report you can use the `analyze`-method. The report is located in `output_dir/report.html`, so in this case in `CAVE_reports/bnn_notebook/report.html`." + "To generate the HTML-report you can use the `analyze`-method. The report is located in `output_dir/report.html`, so in this case in `CAVE_reports/cartpole_notebook/report.html`." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/shuki/Repos/BOHBsCAVE/.ve_BOHBsCAVE/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n", - " return ptp(axis=axis, out=out, **kwargs)\n" - ] - } - ], + "outputs": [], "source": [ + "%%capture\n", "cave.analyze()\n", - "! firefox CAVE_reports/bnn_notebook/report.html" + "! firefox CAVE_reports/cartpole_notebook/report.html" ] }, { @@ -238,467 +122,22 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
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\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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var data_multiline = source_multiline.data;\\n var data_scatter = source_scatter.data;\\n var min_perf = 4.992416977283878;\\n var max_perf = 31586347444678.41;\\n var min_iter = 0;\\n var max_iter = 3;\\n if (cb_obj.value == 'performance') {\\n data_multiline['colors'] = data_multiline['colors_performance'];\\n data_scatter['colors'] = data_scatter['colors_performance'];\\n cm.low = min_perf;\\n cm.high = max_perf;\\n } else {\\n data_multiline['colors'] = data_multiline['colors_iteration'];\\n data_scatter['colors'] = data_scatter['colors_iteration'];\\n cm.low = min_iter;\\n cm.high = max_iter;\\n }\\n source.change.emit();\\n \"},\"id\":\"14346\",\"type\":\"CustomJS\"},{\"attributes\":{\"children\":[{\"id\":\"14348\",\"type\":\"WidgetBox\"},{\"id\":\"14351\",\"type\":\"Row\"},{\"id\":\"14352\",\"type\":\"WidgetBox\"}]},\"id\":\"14353\",\"type\":\"Column\"},{\"attributes\":{},\"id\":\"14471\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"field\":\"colors\",\"transform\":{\"id\":\"14228\",\"type\":\"LinearColorMapper\"}},\"line_color\":{\"field\":\"colors\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"times\"},\"y\":{\"field\":\"losses\"}},\"id\":\"14337\",\"type\":\"Circle\"},{\"attributes\":{\"active\":[0,1,2,3],\"callback\":{\"id\":\"14340\",\"type\":\"CustomJS\"},\"labels\":[\"0\",\"1\",\"2\",\"3\"]},\"id\":\"14341\",\"type\":\"CheckboxButtonGroup\"},{\"attributes\":{\"args\":{\"checkbox\":{\"id\":\"14341\",\"type\":\"CheckboxButtonGroup\"},\"glyph_renderer0\":{\"id\":\"14265\",\"type\":\"GlyphRenderer\"},\"glyph_renderer1\":{\"id\":\"14272\",\"type\":\"GlyphRenderer\"},\"glyph_renderer10\":{\"id\":\"14332\",\"type\":\"GlyphRenderer\"},\"glyph_renderer11\":{\"id\":\"14339\",\"type\":\"GlyphRenderer\"},\"glyph_renderer2\":{\"id\":\"14279\",\"type\":\"GlyphRenderer\"},\"glyph_renderer3\":{\"id\":\"14285\",\"type\":\"GlyphRenderer\"},\"glyph_renderer4\":{\"id\":\"14292\",\"type\":\"GlyphRenderer\"},\"glyph_renderer5\":{\"id\":\"14299\",\"type\":\"GlyphRenderer\"},\"glyph_renderer6\":{\"id\":\"14305\",\"type\":\"GlyphRenderer\"},\"glyph_renderer7\":{\"id\":\"14312\",\"type\":\"GlyphRenderer\"},\"glyph_renderer8\":{\"id\":\"14319\",\"type\":\"GlyphRenderer\"},\"glyph_renderer9\":{\"id\":\"14325\",\"type\":\"GlyphRenderer\"}},\"code\":\"var labels = []; checkbox.active = labels;len_labels = 4;glyph_renderers = [[glyph_renderer0,glyph_renderer1,glyph_renderer2],[glyph_renderer3,glyph_renderer4,glyph_renderer5],[glyph_renderer6,glyph_renderer7,glyph_renderer8],[glyph_renderer9,glyph_renderer10,glyph_renderer11]];\\n for (i = 0; i < len_labels; i++) {\\n if (checkbox.active.includes(i)) {\\n // console.log('Setting to true: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = true;\\n // console.log('Setting to true: ' + i + ' : ' + j)\\n }\\n } else {\\n // console.log('Setting to false: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = false;\\n // console.log('Setting to false: ' + i + ' : ' + j)\\n }\\n }\\n }\\n 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\"],[\"burn_in\",\"@burn_in\"],[\"l_rate\",\"@l_rate\"],[\"mdecay\",\"@mdecay\"],[\"n_units_1\",\"@n_units_1\"],[\"n_units_2\",\"@n_units_2\"]]},\"id\":\"14225\",\"type\":\"HoverTool\"},{\"attributes\":{\"callback\":{\"id\":\"14342\",\"type\":\"CustomJS\"},\"icon\":null,\"label\":\"All\"},\"id\":\"14343\",\"type\":\"Button\"},{\"attributes\":{\"children\":[{\"id\":\"14345\",\"type\":\"Button\"}],\"width\":50},\"id\":\"14350\",\"type\":\"WidgetBox\"},{\"attributes\":{\"filters\":[{\"id\":\"14333\",\"type\":\"GroupFilter\"},{\"id\":\"14334\",\"type\":\"GroupFilter\"}],\"source\":{\"id\":\"14227\",\"type\":\"ColumnDataSource\"}},\"id\":\"14335\",\"type\":\"CDSView\"},{\"attributes\":{\"children\":[{\"id\":\"14349\",\"type\":\"WidgetBox\"},{\"id\":\"14350\",\"type\":\"WidgetBox\"}]},\"id\":\"14351\",\"type\":\"Row\"},{\"attributes\":{\"column_name\":\"config_info\",\"group\":\"model_based_pick=True\"},\"id\":\"14327\",\"type\":\"GroupFilter\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"times\"},\"y\":{\"field\":\"losses\"}},\"id\":\"14331\",\"type\":\"CircleX\"},{\"attributes\":{\"args\":{\"glyph_renderer0\":{\"id\":\"14265\",\"type\":\"GlyphRenderer\"},\"glyph_renderer1\":{\"id\":\"14272\",\"type\":\"GlyphRenderer\"},\"glyph_renderer10\":{\"id\":\"14332\",\"type\":\"GlyphRenderer\"},\"glyph_renderer11\":{\"id\":\"14339\",\"type\":\"GlyphRenderer\"},\"glyph_renderer2\":{\"id\":\"14279\",\"type\":\"GlyphRenderer\"},\"glyph_renderer3\":{\"id\":\"14285\",\"type\":\"GlyphRenderer\"},\"glyph_renderer4\":{\"id\":\"14292\",\"type\":\"GlyphRenderer\"},\"glyph_renderer5\":{\"id\":\"14299\",\"type\":\"GlyphRenderer\"},\"glyph_renderer6\":{\"id\":\"14305\",\"type\":\"GlyphRenderer\"},\"glyph_renderer7\":{\"id\":\"14312\",\"type\":\"GlyphRenderer\"},\"glyph_renderer8\":{\"id\":\"14319\",\"type\":\"GlyphRenderer\"},\"glyph_renderer9\":{\"id\":\"14325\",\"type\":\"GlyphRenderer\"}},\"code\":\"len_labels 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[{\"docid\":\"29192e80-202c-4814-8944-ba76b9d398e9\",\"roots\":{\"14354\":\"3e9cee61-b4fe-4729-a422-b5509a4b3ffb\"}}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - "\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " var attempts = 0;\n", - " var timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " clearInterval(timer);\n", - " }\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", - " clearInterval(timer);\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "14354" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cave.bohb_learning_curves();" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
# aggregated parallel BOHB runs1
# parameters5
Deterministic target algorithmTrue
Optimized run objectivequality
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
budget 370.4budget 1111.1budget 3333.3budget 10000
Total time spent evaluating configurations156.08 sec196.55 sec231.21 sec238.56 sec
Average time per configuration (mean / std)8.21 sec (± 2.00)10.92 sec (± 3.19)19.27 sec (± 15.45)29.82 sec (± 10.66)
# evaluated configurations1918128
# changed parameters (default to incumbent)5555
Configuration originsAcquisition Function : 11, Random : 8Acquisition Function : 14, Random : 4Acquisition Function : 8, Random : 4Acquisition Function : 7, Random : 1
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cave.overview_table();" ] @@ -712,78 +151,11 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
budget 370.4budget 1111.1budget 3333.3budget 10000
burn_in0.3896640.05379280.05379280.0537928
l_rate0.02974140.06953450.06953450.0695345
mdecay0.3288010.5863020.5863020.586302
n_units_167228228228
n_units_2184242424
Cost39.1625.4135.8514.992
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cave.bohb_incumbents_per_budget();" ] @@ -804,131 +176,24 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
-------------------- Single importance: ----------------------------------------
l_rate59.6515 +/- 46.4956
burn_in28.4864 +/- 43.246
mdecay7.1975 +/- 24.2353
n_units_21.3038 +/- 3.8476
n_units_10.4141 +/- 1.579
-------------------- Pairwise importance: ----------------------------------------
burn_in & mdecay1.9159 +/- 7.4185
l_rate & n_units_20.6959 +/- 1.8439
l_rate & n_units_10.2079 +/- 0.7715
l_rate & burn_in0.1036 +/- 0.2745
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"14033\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error() {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (var i = 0; i < css_urls.length; i++) {\n", - " var url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };var element = document.getElementById(\"14033\");\n", - " if (element == null) {\n", - " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '14033' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n", - " var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {} // ensure no trailing comma for IE\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"14033\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"14033\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"14033\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '14033' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"14033\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function embed_document(root) {\n", - " \n", - " var docs_json = {\"551ccf55-e190-4029-8f29-b21dfa5f6f16\":{\"roots\":{\"references\":[{\"attributes\":{\"columns\":[{\"id\":\"14035\",\"type\":\"TableColumn\"},{\"id\":\"14036\",\"type\":\"TableColumn\"},{\"id\":\"14037\",\"type\":\"TableColumn\"},{\"id\":\"14038\",\"type\":\"TableColumn\"},{\"id\":\"14039\",\"type\":\"TableColumn\"},{\"id\":\"14040\",\"type\":\"TableColumn\"}],\"height\":170,\"index_position\":null,\"source\":{\"id\":\"14034\",\"type\":\"ColumnDataSource\"},\"view\":{\"id\":\"14042\",\"type\":\"CDSView\"}},\"id\":\"14041\",\"type\":\"DataTable\"},{\"attributes\":{},\"id\":\"14152\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"14140\",\"type\":\"StringFormatter\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"14147\",\"type\":\"StringEditor\"},\"field\":\"fANOVA\",\"formatter\":{\"id\":\"14146\",\"type\":\"StringFormatter\"},\"title\":\"fANOVA\",\"width\":100},\"id\":\"14038\",\"type\":\"TableColumn\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"14145\",\"type\":\"StringEditor\"},\"field\":\"LPI\",\"formatter\":{\"id\":\"14144\",\"type\":\"StringFormatter\"},\"title\":\"LPI\",\"width\":100},\"id\":\"14037\",\"type\":\"TableColumn\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"14151\",\"type\":\"StringEditor\"},\"field\":\"LPI\",\"formatter\":{\"id\":\"14150\",\"type\":\"StringFormatter\"},\"title\":\"LPI\",\"width\":100},\"id\":\"14040\",\"type\":\"TableColumn\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"14141\",\"type\":\"StringEditor\"},\"field\":\"Parameters\",\"formatter\":{\"id\":\"14140\",\"type\":\"StringFormatter\"},\"sortable\":false,\"title\":\"Parameters\",\"width\":150},\"id\":\"14035\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"14151\",\"type\":\"StringEditor\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"14149\",\"type\":\"StringEditor\"},\"field\":\"fANOVA\",\"formatter\":{\"id\":\"14148\",\"type\":\"StringFormatter\"},\"title\":\"fANOVA\",\"width\":100},\"id\":\"14039\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"14141\",\"type\":\"StringEditor\"},{\"attributes\":{\"source\":{\"id\":\"14034\",\"type\":\"ColumnDataSource\"}},\"id\":\"14042\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"14153\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"callback\":null,\"data\":{\"LPI\":[\"LPI\",\"LPI\",\"LPI\",\"LPI\"],\"Parameters\":[\"l_rate\",\"burn_in\",\"mdecay\",\"n_units_1\",\"n_units_2\"],\"fANOVA\":[\"fANOVA\",\"fANOVA\",\"fANOVA\",\"fANOVA\",\"fANOVA\",\"fANOVA\",\"fANOVA\",\"fANOVA\",\"fANOVA\"]},\"selected\":{\"id\":\"14152\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"14153\",\"type\":\"UnionRenderers\"}},\"id\":\"14034\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"14146\",\"type\":\"StringFormatter\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"14143\",\"type\":\"StringEditor\"},\"field\":\"fANOVA\",\"formatter\":{\"id\":\"14142\",\"type\":\"StringFormatter\"},\"title\":\"fANOVA\",\"width\":100},\"id\":\"14036\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"14144\",\"type\":\"StringFormatter\"},{\"attributes\":{},\"id\":\"14148\",\"type\":\"StringFormatter\"},{\"attributes\":{},\"id\":\"14147\",\"type\":\"StringEditor\"},{\"attributes\":{},\"id\":\"14150\",\"type\":\"StringFormatter\"},{\"attributes\":{},\"id\":\"14145\",\"type\":\"StringEditor\"},{\"attributes\":{},\"id\":\"14142\",\"type\":\"StringFormatter\"},{\"attributes\":{},\"id\":\"14143\",\"type\":\"StringEditor\"},{\"attributes\":{},\"id\":\"14149\",\"type\":\"StringEditor\"}],\"root_ids\":[\"14041\"]},\"title\":\"Bokeh Application\",\"version\":\"1.1.0\"}};\n", - " var render_items = [{\"docid\":\"551ccf55-e190-4029-8f29-b21dfa5f6f16\",\"roots\":{\"14041\":\"cbb12c10-9b3f-41d1-9037-e3f6c9771188\"}}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - "\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " var attempts = 0;\n", - " var timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " clearInterval(timer);\n", - " }\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", - " clearInterval(timer);\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "14041" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "cave.pimp_comparison_table(run='budget_10000');" + "cave.pimp_comparison_table(run='budget_3');" ] }, { @@ -1322,801 +221,36 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"18151\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error() {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (var i = 0; i < css_urls.length; i++) {\n", - " var url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };var element = document.getElementById(\"18151\");\n", - " if (element == null) {\n", - " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '18151' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n", - " var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {} // ensure no trailing comma for IE\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"18151\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"18151\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"18151\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '18151' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"18151\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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glyph209, glyph210, glyph211, glyph212, glyph213, glyph214, glyph215, glyph216, glyph217, glyph218, glyph219, glyph220, glyph221, glyph222, glyph223, glyph224, glyph225, glyph226, glyph227, glyph228, glyph229, glyph230, glyph231, glyph232, glyph233, glyph234, glyph235, glyph236, glyph237, glyph238, glyph239];var overtime = [[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],[48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95],[96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143],[144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191],[192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239]];var runs = [[0,4,8,12,16,20,24,28,32,36,40,44,48,52,56,60,64,68,72,76,80,84,88,92,96,100,104,108,112,116,120,124,128,132,136,140,144,148,152,156,160,164,168,172,176,180,184,188,192,196,200,204,208,212,216,220,224,228,232,236],[1,5,9,13,17,21,25,29,33,37,41,45,49,53,57,61,65,69,73,77,81,85,89,93,97,101,105,109,113,117,121,125,129,133,137,141,145,149,153,157,161,165,169,173,177,181,185,189,193,197,201,205,209,213,217,221,225,229,233,237],[2,6,10,14,18,22,26,30,34,38,42,46,50,54,58,62,66,70,74,78,82,86,90,94,98,102,106,110,114,118,122,126,130,134,138,142,146,150,154,158,162,166,170,174,178,182,186,190,194,198,202,206,210,214,218,222,226,230,234,238],[3,7,11,15,19,23,27,31,35,39,43,47,51,55,59,63,67,71,75,79,83,87,91,95,99,103,107,111,115,119,123,127,131,135,139,143,147,151,155,159,163,167,171,175,179,183,187,191,195,199,203,207,211,215,219,223,227,231,235,239]];\\n glyphs.forEach(function(g) {\\n g.visible = false;\\n })\\n // union function\\n function union_arrays(x, y) {\\n var obj = {};\\n for (var i = x.length-1; i >= 0; -- i)\\n obj[x[i]] = x[i];\\n for (var i = y.length-1; i >= 0; -- i)\\n obj[y[i]] = y[i];\\n var res = []\\n for (var k in obj) {\\n if (obj.hasOwnProperty(k)) // <-- optional\\n res.push(obj[k]);\\n }\\n return res;\\n }\\n console.log(\\\"Timeslider: \\\" + time_slider.value);\\n console.log(\\\"Checkbox: \\\" + checkbox.active);var slider_labels = ['39.12', '53.78', '126.60', '323.54', '895.87'];console.log(\\\"Detected slider_labels: \\\" + slider_labels);time_slider.title = \\\"Until wallclocktime \\\" + slider_labels[time_slider.value - 1] + \\\". Step no.\\\"; \\n var activate = [];\\n // if we want multiple checkboxes at the same time, we need to combine the arrays\\n checkbox.active.forEach(function(c) {\\n activate = union_arrays(activate, runs[c]);\\n })\\n // now the intersection of timeslider-activated and checkbox-activated\\n activate = activate.filter(value => -1 !== overtime[time_slider.value - 1].indexOf(value));\\n activate.forEach(function(idx) {\\n glyphs[idx].visible = true;\\n })\\n 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(var i = x.length-1; i >= 0; -- i)\\n obj[x[i]] = x[i];\\n for (var i = y.length-1; i >= 0; -- i)\\n obj[y[i]] = y[i];\\n var res = []\\n for (var k in obj) {\\n if (obj.hasOwnProperty(k)) // <-- optional\\n res.push(obj[k]);\\n }\\n return res;\\n }\\n console.log(\\\"Timeslider: \\\" + time_slider.value);\\n console.log(\\\"Checkbox: \\\" + checkbox.active);var slider_labels = ['39.12', '53.78', '126.60', '323.54', '895.87'];console.log(\\\"Detected slider_labels: \\\" + slider_labels);time_slider.title = \\\"Until wallclocktime \\\" + slider_labels[time_slider.value - 1] + \\\". Step no.\\\"; \\n var activate = [];\\n // if we want multiple checkboxes at the same time, we need to combine the arrays\\n checkbox.active.forEach(function(c) {\\n activate = union_arrays(activate, runs[c]);\\n })\\n // now the intersection of timeslider-activated and checkbox-activated\\n activate = activate.filter(value => -1 !== overtime[time_slider.value - 1].indexOf(value));\\n activate.forEach(function(idx) {\\n glyphs[idx].visible = true;\\n })\\n 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upFilter\"},{\"attributes\":{\"fill_color\":{\"field\":\"color\"},\"marker\":{\"value\":\"square_x\"},\"size\":{\"field\":\"size\",\"units\":\"screen\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"16172\",\"type\":\"Scatter\"},{\"attributes\":{\"column_name\":\"type\",\"group\":\"Default\"},\"id\":\"15444\",\"type\":\"GroupFilter\"},{\"attributes\":{\"column_name\":\"type\",\"group\":\"Incumbent\"},\"id\":\"17278\",\"type\":\"GroupFilter\"},{\"attributes\":{\"data_source\":{\"id\":\"14545\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"16212\",\"type\":\"Scatter\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"16213\",\"type\":\"Scatter\"},\"selection_glyph\":null,\"view\":{\"id\":\"16006\",\"type\":\"CDSView\"}},\"id\":\"16214\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"14546\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"16817\",\"type\":\"Scatter\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"16818\",\"type\":\"Scatter\"},\"selection_glyph\":null,\"view\":{\"id\":\"16599\",\"type\":\"CDSView\"}},\"id\":\"16819\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"marker\":{\"value\":\"triangle\"},\"size\":{\"field\":\"size\",\"units\":\"screen\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"15608\",\"type\":\"Scatter\"},{\"attributes\":{\"column_name\":\"zorder\",\"group\":\"0\"},\"id\":\"17300\",\"type\":\"GroupFilter\"},{\"attributes\":{\"data_source\":{\"id\":\"14544\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"15467\",\"type\":\"Scatter\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"15468\",\"type\":\"Scatter\"},\"selection_glyph\":null,\"view\":{\"id\":\"15233\",\"type\":\"CDSView\"}},\"id\":\"15469\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_color\":{\"field\":\"color\"},\"marker\":{\"value\":\"triangle\"},\"size\":{\"field\":\"size\",\"units\":\"screen\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"15631\",\"type\":\"Scatter\"},{\"attributes\":{\"fill_color\":{\"field\":\"color\"},\"marker\":{\"value\":\"square\"},\"size\":{\"field\":\"size\",\"units\":\"screen\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"16164\",\"type\":\"Scatter\"},{\"attributes\":{\"fill_color\":{\"field\":\"color\"},\"marker\":{\"value\":\"square_x\"},\"size\":{\"field\":\"size\",\"units\":\"screen\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"16176\",\"type\":\"Scatter\"},{\"attributes\":{\"column_name\":\"origin\",\"group\":\"Acquisition 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EvdKDj9vzzsS90oOP2/POxL3Sg4/b8IuRiq9QT6vwi5GKr1BPq/CLkYqvUE+r8IuRiq9QT6vwi5GKr1BPq/CLkYqvUE+r8IuRiq9QT6vwi5GKr1BPq/CLkYqvUE+r8IuRiq9QT6vwi5GKr1BPq/CLkYqvUE+r8IuRiq9QT6vwi5GKr1BPq/CLkYqvUE+r8IuRiq9QT6vwi5GKr1BPq/CLkYqvUE+r8IuRiq9QT6vwi5GKr1BPq/CLkYqvUE+r8IuRiq9QT6vwi5GKr1BPq/CLkYqvUE+r8IuRiq9QT6vwi5GKr1BPq/1IXldsLR9r/UheV2wtH2v9SF5XbC0fa/1IXldsLR9r/UheV2wtH2v9SF5XbC0fa/1IXldsLR9r/UheV2wtH2v9SF5XbC0fa/1IXldsLR9r/UheV2wtH2v9SF5XbC0fa/1IXldsLR9r/UheV2wtH2v9SF5XbC0fa/1IXldsLR9r/UheV2wtH2v9SF5XbC0fa/1IXldsLR9r/UheV2wtH2v9SF5XbC0fa/1IXldsLR9r/UheV2wtH2v9SF5XbC0fa/1IXldsLR9r/UheV2wtH2v6BSskOPnvO/oFKyQ4+e87+gUrJDj57zv6BSskOPnvO/oFKyQ4+e87+gUrJDj57zv6BSskOPnvO/oFKyQ4+e87+gUrJDj57zv6BSskOPnvO/oFKyQ4+e87+gUrJDj57zv6BSskOPnvO/oFKyQ4+e87+gUrJDj57zv6BSskOPnvO/oFKyQ4+e87+gUrJDj57zv6BSskOPnvO/oFKyQ4+e87+gUrJDj57zv6BSskOPnvO/oFKyQ4+e87+gUrJDj57zv6BSskOPnvO/oFKyQ4+e879sH38QXGvwv2wffxBca/C/bB9/EFxr8L9sH38QXGvwv2wffxBca/C/bB9/EFxr8L9sH38QXGvwv2wffxBca/C/bB9/EFxr8L9sH38QXGvwv2wffxBca/C/bB9/EFxr8L9sH38QXGvwv2wffxBca/C/bB9/EFxr8L9sH38QXGvwv2wffxBca/C/bB9/EFxr8L9sH38QXGvwv2wffxBca/C/bB9/EFxr8L9sH38QXGvwv2wffxBca/C/bB9/EFxr8L9sH38QXGvwv2wffxBca/C/cNiXulFw6r9w2Je6UXDqv3DYl7pRcOq/cNiXulFw6r9w2Je6UXDqv3DYl7pRcOq/cNiXulFw6r9w2Je6UXDqv3DYl7pRcOq/cNiXulFw6r9w2Je6UXDqv3DYl7pRcOq/cNiXulFw6r9w2Je6UXDqv3DYl7pRcOq/cNiXulFw6r9w2Je6UXDqv3DYl7pRcOq/cNiXulFw6r9w2Je6UXDqv3DYl7pRcOq/cNiXulFw6r9w2Je6UXDqv3DYl7pRcOq/cNiXulFw6r9w2Je6UXDqvwhyMVTrCeS/CHIxVOsJ5L8IcjFU6wnkvwhyMVTrCeS/CHIxVOsJ5L8IcjFU6wnkvwhyMVTrCeS/CHIxVOsJ5L8IcjFU6wnkvwhyMVTrCeS/CHIxVOsJ5L8IcjFU6wnkvwhyMVTrCeS/CHIxVOsJ5L8IcjFU6wnkvwhyMVTrCeS/CHIxVOsJ5L8IcjFU6wnkvwhyMVTrCeS/CHIxVOsJ5L8IcjFU6wnkvwhyMVTrCeS/CHIxVOsJ5L8IcjFU6wnkvwhyMVTrCeS/CHIxVOsJ5L9AF5bbCUfbv0AXltsJR9u/QBeW2wlH279AF5bbCUfbv0AXltsJR9u/QBeW2wlH279AF5bbCUfbv0AXltsJR9u/QBeW2wlH279AF5bbCUfbv0AXltsJR9u/QBeW2wlH279AF5bbCUfbv0AXltsJR9u/QBeW2wlH279AF5bbCUfbv0AXltsJR9u/QBeW2wlH279AF5bbCUfbv0AXltsJR9u/QBeW2wlH279AF5bbCUfbv0AXltsJR9u/QBeW2wlH279AF5bbCUfbv0AXltsJR9u/4JSSHXr0zL/glJIdevTMv+CUkh169My/4JSSHXr0zL/glJIdevTMv+CUkh169My/4JSSHXr0zL/glJIdevTMv+CUkh169My/4JSSHXr0zL/glJIdevTMv+CUkh169My/4JSSHXr0zL/glJIdevTMv+CUkh169My/4JSSHXr0zL/glJIdevTMv+CUkh169My/4JSSHXr0zL/glJIdevTMv+CUkh169My/4JSSHXr0zL/glJIdevTMv+CUkh169My/4JSSHXr0zL/glJIdevTMvwDaxx8E15q/ANrHHwTXmr8A2scfBNeavwDaxx8E15q/ANrHHwTXmr8A2scfBNeavwDaxx8E15q/ANrHHwTXmr8A2scfBNeavwDaxx8E15q/ANrHHwTXmr8A2scfBNeavwDaxx8E15q/ANrHHwTXmr8A2scfBNeavwDaxx8E15q/ANrHHwTXmr8A2scfBNeavwDaxx8E15q/ANrHHwTXmr8A2scfBNeavwDaxx8E15q/ANrHHwTXmr8A2scfBNeavwDaxx8E15q/ANrHHwTXmr9gnqAVuT7GP2CeoBW5PsY/YJ6gFbk+xj9gnqAVuT7GP2CeoBW5PsY/YJ6gFbk+xj9gnqAVuT7GP2CeoBW5PsY/YJ6gFbk+xj9gnqAVuT7GP2CeoBW5PsY/YJ6gFbk+xj9gnqAVuT7GP2CeoBW5PsY/YJ6gFbk+xj9gnqAVuT7GP2CeoBW5PsY/YJ6gFbk+xj9gnqAVuT7GP2CeoBW5PsY/YJ6gFbk+xj9gnqAVuT7GP2CeoBW5PsY/YJ6gFbk+xj9gnqAVuT7GP2CeoBW5PsY/ABydVyns1z8AHJ1XKezXPwAcnVcp7Nc/ABydVyns1z8AHJ1XKezXPwAcnVcp7Nc/ABydVyns1z8AHJ1XKezXPwAcnVcp7Nc/ABydVyns1z8AHJ1XKezXPwAcnVcp7Nc/ABydVyns1z8AHJ1XKezXPwAcnVcp7Nc/ABydVyns1z8AHJ1XKezXPwAcnVcp7Nc/ABydVyns1z8AHJ1XKezXPwAcnVcp7Nc/ABydVyns1z8AHJ1XKezXPwAcnVcp7Nc/ABydVyns1z8AHJ1XKezXP2j0NBJ7XOI/aPQ0Entc4j9o9DQSe1ziP2j0NBJ7XOI/aPQ0Entc4j9o9DQSe1ziP2j0NBJ7XOI/aPQ0Entc4j9o9DQSe1ziP2j0NBJ7XOI/aPQ0Entc4j9o9DQSe1ziP2j0NBJ7XOI/aPQ0Entc4j9o9DQSe1ziP2j0NBJ7XOI/aPQ0Entc4j9o9DQSe1ziP2j0NBJ7XOI/aPQ0Entc4j9o9DQSe1ziP2j0NBJ7XOI/aPQ0Entc4j9o9DQSe1ziP2j0NBJ7XOI/aPQ0Entc4j/QWpt44cLoP9Bam3jhwug/0FqbeOHC6D/QWpt44cLoP9Bam3jhwug/0FqbeOHC6D/QWpt44cLoP9Bam3jhwug/0FqbeOHC6D/QWpt44cLoP9Bam3jhwug/0FqbeOHC6D/QWpt44cLoP9Bam3jhwug/0FqbeOHC6D/QWpt44cLoP9Bam3jhwug/0FqbeOHC6D/QWpt44cLoP9Bam3jhwug/0FqbeOHC6D/QWpt44cLoP9Bam3jhwug/0FqbeOHC6D/QWpt44cLoP9Bam3jhwug/OMEB30cp7z84wQHfRynvPzjBAd9HKe8/OMEB30cp7z84wQHfRynvPzjBAd9HKe8/OMEB30cp7z84wQHfRynvPzjBAd9HKe8/OMEB30cp7z84wQHfRynvPzjBAd9HKe8/OMEB30cp7z84wQHfRynvPzjBAd9HKe8/OMEB30cp7z84wQHfRynvPzjBAd9HKe8/OMEB30cp7z84wQHfRynvPzjBAd9HKe8/OMEB30cp7z84wQHfRynvPzjBAd9HKe8/OMEB30cp7z84wQHfRynvP9ATtCLXx/I/0BO0ItfH8j/QE7Qi18fyP9ATtCLXx/I/0BO0ItfH8j/QE7Qi18fyP9ATtCLXx/I/0BO0ItfH8j/QE7Qi18fyP9ATtCLXx/I/0BO0ItfH8j/QE7Qi18fyP9ATtCLXx/I/0BO0ItfH8j/QE7Qi18fyP9ATtCLXx/I/0BO0ItfH8j/QE7Qi18fyP9ATtCLXx/I/0BO0ItfH8j/QE7Qi18fyP9ATtCLXx/I/0BO0ItfH8j/QE7Qi18fyP9ATtCLXx/I/0BO0ItfH8j8ER+dVCvv1PwRH51UK+/U/BEfnVQr79T8ER+dVCvv1PwRH51UK+/U/BEfnVQr79T8ER+dVCvv1PwRH51UK+/U/BEfnVQr79T8ER+dVCvv1PwRH51UK+/U/BEfnVQr79T8ER+dVCvv1PwRH51UK+/U/BEfnVQr79T8ER+dVCvv1PwRH51UK+/U/BEfnVQr79T8ER+dVCvv1PwRH51UK+/U/BEfnVQr79T8ER+dVCvv1PwRH51UK+/U/BEfnVQr79T8ER+dVCvv1PwRH51UK+/U/OHoaiT0u+T84ehqJPS75Pzh6Gok9Lvk/OHoaiT0u+T84ehqJPS75Pzh6Gok9Lvk/OHoaiT0u+T84ehqJPS75Pzh6Gok9Lvk/OHoaiT0u+T84ehqJPS75Pzh6Gok9Lvk/OHoaiT0u+T84ehqJPS75Pzh6Gok9Lvk/OHoaiT0u+T84ehqJPS75Pzh6Gok9Lvk/OHoaiT0u+T84ehqJPS75Pzh6Gok9Lvk/OHoaiT0u+T84ehqJPS75Pzh6Gok9Lvk/OHoaiT0u+T84ehqJPS75P2ytTbxwYfw/bK1NvHBh/D9srU28cGH8P2ytTbxwYfw/bK1NvHBh/D9srU28cGH8P2ytTbxwYfw/bK1NvHBh/D9srU28cGH8P2ytTbxwYfw/bK1NvHBh/D9srU28cGH8P2ytTbxwYfw/bK1NvHBh/D9srU28cGH8P2ytTbxwYfw/bK1NvHBh/D9srU28cGH8P2ytTbxwYfw/bK1NvHBh/D9srU28cGH8P2ytTbxwYfw/bK1NvHBh/D9srU28cGH8P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\",\"dtype\":\"float64\",\"shape\":[25,26]},{\"__ndarray__\":\"2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEHZpDdb8jWkQdmkN1vyNaRB2aQ3W/I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRBUvb8T7I1pEFS9vxPsjWkQVL2/E+yNaRB8qDCYvFAmEHyoMJi8UCYQfKgwmLxQJhB8qDCYvFAmEHyoMJi8UCYQfKgwmLxQJhB8qDCYvFAmEHyoMJi8UCYQfKgwmLxQJhB8qDCYvFAmEHyoMJi8UCYQfKgwmLxQJhBbrxqmfFAmEFuvGqZ8UCYQW68apnxQJhBbrxqmfFAmEHAMKeRPkGYQcAwp5E+QZhB18w5nkNFmEHXzDmeQ0WYQdfMOZ5DRZhB18w5nkNFmEHXzDmeQ0WYQdfMOZ5DRZhB18w5nkNFmEHXzDmeQ0WYQbLfdqd3K5BBst92p3crkEGy33andyuQQbLfdqd3K5BBst92p3crkEGy33andyuQQbLfdqd3K5BBst92p3crkEGy33andyuQQbLfdqd3K5BBst92p3crkEGy33andyuQQS77Ht53K5BBLvse3ncrkEEu+x7edyuQQS77Ht53K5BBgG9b1sQrkEGAb1vWxCuQQYzLSfzQNZBBjMtJ/NA1kEGMy0n80DWQQYzLSfzQNZBBjMtJ/NA1kEGMy0n80DWQQYzLSfzQNZBBjMtJ/NA1kEHUR2w5ooHAQNRHbDmigcBA1EdsOaKBwEDUR2w5ooHAQNRHbDmigcBA1EdsOaKBwEDUR2w5ooHAQNRHbDmigcBA1EdsOaKBwEDUR2w5ooHAQNRHbDmigcBA1EdsOaKBwEAaqds8d4jAQBqp2zx3iMBAGqnbPHeIwEAaqds8d4jAQB7+Zct+J8pAHv5ly34nykAagSuqAmgRQRqBK6oCaBFBGoErqgJoEUEagSuqAmgRQRqBK6oCaBFBGoErqgJoEUEagSuqAmgRQRqBK6oCaBFBAXOXUpSt0EABc5dSlK3QQAFzl1KUrdBAAXOXUpSt0EABc5dSlK3QQAFzl1KUrdBAAXOXUpSt0EABc5dSlK3QQAFzl1KUrdBAAXOXUpSt0EABc5dSlK3QQAFzl1KUrdBApCNP1P6w0ECkI0/U/rDQQKQjT9T+sNBApCNP1P6w0EAmTpSbgoDVQCZOlJuCgNVACpaJ3c7uEUEKlondzu4RQQqWid3O7hFBCpaJ3c7uEUEKlondzu4RQQqWid3O7hFBCpaJ3c7uEUEKlondzu4RQWBpZ2I++cBAYGlnYj75wEBgaWdiPvnAQGBpZ2I++cBAYGlnYj75wEBgaWdiPvnAQGBpZ2I++cBAYGlnYj75wEBgaWdiPvnAQGBpZ2I++cBAYGlnYj75wEBgaWdiPvnAQKbK1mUTAMFApsrWZRMAwUCmytZlEwDBQKbK1mUTAMFAqh9h9BqfykCqH2H0Gp/KQGFwo88OtRFBYXCjzw61EUFhcKPPDrURQWFwo88OtRFBYXCjzw61EUFhcKPPDrURQWFwo88OtRFBYXCjzw61EUGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGLA+axBewWQYsD5rEF7BZBiwPmsQXsFkGLA+axBewWQQ55X3+2OBdBDnlff7Y4F0HzgSvgAwwkQfOBK+ADDCRB84Er4AMMJEHzgSvgAwwkQfOBK+ADDCRB84Er4AMMJEHzgSvgAwwkQfOBK+ADDCRBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBiwPmsQXsFkGLA+axBewWQYsD5rEF7BZBiwPmsQXsFkEOeV9/tjgXQQ55X3+2OBdB84Er4AMMJEHzgSvgAwwkQfOBK+ADDCRB84Er4AMMJEHzgSvgAwwkQfOBK+ADDCRB84Er4AMMJEHzgSvgAwwkQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYsD5rEF7BZBiwPmsQXsFkGLA+axBewWQYsD5rEF7BZBDnlff7Y4F0EOeV9/tjgXQfOBK+ADDCRB84Er4AMMJEHzgSvgAwwkQfOBK+ADDCRB84Er4AMMJEHzgSvgAwwkQfOBK+ADDCRB84Er4AMMJEGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGLA+axBewWQYsD5rEF7BZBiwPmsQXsFkGLA+axBewWQQ55X3+2OBdBDnlff7Y4F0HzgSvgAwwkQfOBK+ADDCRB84Er4AMMJEHzgSvgAwwkQfOBK+ADDCRB84Er4AMMJEHzgSvgAwwkQfOBK+ADDCRBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBiwPmsQXsFkGLA+axBewWQYsD5rEF7BZBiwPmsQXsFkEOeV9/tjgXQQ55X3+2OBdB84Er4AMMJEHzgSvgAwwkQfOBK+ADDCRB84Er4AMMJEHzgSvgAwwkQfOBK+ADDCRB84Er4AMMJEHzgSvgAwwkQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYCIygnP6xZBgIjKCc/rFkGAiMoJz+sWQYsD5rEF7BZBiwPmsQXsFkGLA+axBewWQYsD5rEF7BZBDnlff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\",\"dtype\":\"float64\",\"shape\":[25,26]},{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[25,26]}]},\"selected\":{\"id\":\"15047\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"15048\",\"type\":\"UnionRenderers\"}},\"id\":\"14593\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"column_name\":\"zorder\",\"group\":\"0\"},\"id\":\"16607\",\"type\":\"GroupFilter\"},{\"attributes\":{\"column_name\":\"origin\",\"group\":\"Unknown\"},\"id\":\"16621\",\"type\":\"GroupFilter\"},{\"attributes\":{\"color_mapper\":{\"id\":\"14577\",\"type\":\"LinearColorMapper\"},\"dh\":{\"units\":\"data\",\"value\":4.990325024476247},\"dw\":{\"units\":\"data\",\"value\":5.114014413376522},\"image\":{\"field\":\"image\"},\"x\":{\"value\":-2.7324138021809112},\"y\":{\"value\":-2.626210846366635}},\"id\":\"14594\",\"type\":\"Image\"},{\"attributes\":{\"color_mapper\":{\"id\":\"14577\",\"type\":\"LinearColorMapper\"},\"dh\":{\"units\":\"data\",\"value\":4.990325024476247},\"dw\":{\"units\":\"data\",\"value\":5.114014413376522},\"image\":{\"field\":\"image\"},\"x\":{\"value\":-2.7324138021809112},\"y\":{\"value\":-2.626210846366635}},\"id\":\"14595\",\"type\":\"Image\"},{\"attributes\":{\"filters\":[{\"id\":\"16620\",\"type\":\"GroupFilter\"},{\"id\":\"16621\",\"type\":\"GroupFilter\"},{\"id\":\"16622\",\"type\":\"GroupFilter\"},{\"id\":\"16623\",\"type\":\"BooleanFilter\"}],\"source\":{\"id\":\"14546\",\"type\":\"ColumnDataSource\"}},\"id\":\"16624\",\"type\":\"CDSView\"},{\"attributes\":{\"data_source\":{\"id\":\"14593\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"14594\",\"type\":\"Image\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"14595\",\"type\":\"Image\"},\"selection_glyph\":null,\"view\":{\"id\":\"14597\",\"type\":\"CDSView\"},\"visible\":false},\"id\":\"14596\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"column_name\":\"type\",\"group\":\"Incumbent\"},\"id\":\"16600\",\"type\":\"GroupFilter\"},{\"attributes\":{\"source\":{\"id\":\"14593\",\"type\":\"ColumnDataSource\"}},\"id\":\"14597\",\"type\":\"CDSView\"},{\"attributes\":{\"filters\":[{\"id\":\"16625\",\"type\":\"GroupFilter\"},{\"id\":\"16626\",\"type\":\"GroupFilter\"},{\"id\":\"16627\",\"type\":\"GroupFilter\"},{\"id\":\"16628\",\"type\":\"BooleanFilter\"}],\"source\":{\"id\":\"14546\",\"type\":\"ColumnDataSource\"}},\"id\":\"16629\",\"type\":\"CDSView\"},{\"attributes\":{\"column_name\":\"type\",\"group\":\"Final 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clearInterval(timer);\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "17889" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cave.configurator_footprint(use_timeslider=True, num_quantiles=5);" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"18456\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error() {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (var i = 0; i < css_urls.length; i++) {\n", - " var url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };var element = document.getElementById(\"18456\");\n", - " if (element == null) {\n", - " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '18456' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n", - " var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {} // ensure no trailing comma for IE\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"18456\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"18456\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"18456\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '18456' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"18456\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function embed_document(root) {\n", - " \n", - " var docs_json = {\"c2174755-313a-4046-8cc5-cf96e2e8ec61\":{\"roots\":{\"references\":[{\"attributes\":{\"children\":[{\"id\":\"18459\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"id\":\"18563\",\"type\":\"Column\"}]},\"id\":\"18564\",\"type\":\"Row\"},{\"attributes\":{\"filters\":[{\"id\":\"18490\",\"type\":\"GroupFilter\"}],\"source\":{\"id\":\"18457\",\"type\":\"ColumnDataSource\"}},\"id\":\"18491\",\"type\":\"CDSView\"},{\"attributes\":{\"children\":[{\"id\":\"18550\",\"type\":\"Button\"}],\"width\":50},\"id\":\"18560\",\"type\":\"WidgetBox\"},{\"attributes\":{},\"id\":\"18572\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"18483\",\"type\":\"ResetTool\"},{\"attributes\":{\"overlay\":{\"id\":\"18570\",\"type\":\"BoxAnnotation\"}},\"id\":\"18481\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"18577\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"18574\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[50]},\"y\":{\"__ndarray__\":\"A5IGlA5G/kEDkgaUDkb+QQjj/lHVLrRACOP+UdUutEBBygeI1HqsQEHKB4jUeqxAs3g3ORvOUUCzeDc5G85RQJ0PbsVArUpAnQ9uxUCtSkA/QaILwpRDQD9BogvClENAuTXOfPt2MkC5Nc58+3YyQEEZvNYrSR1AQRm81itJHUCjFvgTnwdXQKMW+BOfB1dAM3iuOnlVG0AzeK46eVUbQPGKRc724BdA8YpFzvbgF0C9t/UnPPgTQL239Sc8+BNAvbf1Jzz4E0C9t/UnPPgTQL239Sc8+BNAvbf1Jzz4E0DxikXO9uAXQPGKRc724BdAM3iuOnlVG0AzeK46eVUbQKMW+BOfB1dAoxb4E58HV0BBGbzWK0kdQEEZvNYrSR1AuTXOfPt2MkC5Nc58+3YyQD9BogvClENAP0GiC8KUQ0CdD27FQK1KQJ0PbsVArUpAs3g3ORvOUUCzeDc5G85RQEHKB4jUeqxAQcoHiNR6rEAI4/5R1S60QAjj/lHVLrRAA5IGlA5G/kEDkgaUDkb+QQ==\",\"dtype\":\"float64\",\"shape\":[50]}},\"selected\":{\"id\":\"18573\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"18574\",\"type\":\"UnionRenderers\"}},\"id\":\"18496\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"renderers\":[{\"id\":\"18495\",\"type\":\"GlyphRenderer\"},{\"id\":\"18499\",\"type\":\"GlyphRenderer\"},{\"id\":\"18506\",\"type\":\"GlyphRenderer\"},{\"id\":\"18510\",\"type\":\"GlyphRenderer\"},{\"id\":\"18517\",\"type\":\"GlyphRenderer\"},{\"id\":\"18521\",\"type\":\"GlyphRenderer\"},{\"id\":\"18528\",\"type\":\"GlyphRenderer\"},{\"id\":\"18532\",\"type\":\"GlyphRenderer\"},{\"id\":\"18539\",\"type\":\"GlyphRenderer\"},{\"id\":\"18543\",\"type\":\"GlyphRenderer\"}],\"tooltips\":[[\"estimated performance\",\"@mean\"],[\"at-time\",\"@time\"]]},\"id\":\"18545\",\"type\":\"HoverTool\"},{\"attributes\":{\"children\":[{\"id\":\"18559\",\"type\":\"WidgetBox\"},{\"id\":\"18562\",\"type\":\"Row\"}]},\"id\":\"18563\",\"type\":\"Column\"},{\"attributes\":{\"children\":[{\"id\":\"18552\",\"type\":\"Button\"}],\"width\":50},\"id\":\"18561\",\"type\":\"WidgetBox\"},{\"attributes\":{},\"id\":\"18580\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"data_source\":{\"id\":\"18496\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"18497\",\"type\":\"Patch\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"18498\",\"type\":\"Patch\"},\"selection_glyph\":null,\"view\":{\"id\":\"18500\",\"type\":\"CDSView\"}},\"id\":\"18499\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"18570\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"fill_alpha\":0.1,\"fill_color\":\"#1f77b4\",\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"18498\",\"type\":\"Patch\"},{\"attributes\":{\"children\":[{\"id\":\"18560\",\"type\":\"WidgetBox\"},{\"id\":\"18561\",\"type\":\"WidgetBox\"}]},\"id\":\"18562\",\"type\":\"Row\"},{\"attributes\":{},\"id\":\"18480\",\"type\":\"PanTool\"},{\"attributes\":{\"ticker\":null},\"id\":\"18569\",\"type\":\"LogTickFormatter\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"18475\",\"type\":\"BasicTicker\"}},\"id\":\"18478\",\"type\":\"Grid\"},{\"attributes\":{\"label\":{\"value\":\"budget 10000\"},\"renderers\":[{\"id\":\"18539\",\"type\":\"GlyphRenderer\"},{\"id\":\"18543\",\"type\":\"GlyphRenderer\"}]},\"id\":\"18558\",\"type\":\"LegendItem\"},{\"attributes\":{\"data_source\":{\"id\":\"18457\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"18493\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"18494\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"18491\",\"type\":\"CDSView\"}},\"id\":\"18495\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"18578\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"18479\",\"type\":\"SaveTool\"},{\"id\":\"18480\",\"type\":\"PanTool\"},{\"id\":\"18481\",\"type\":\"BoxZoomTool\"},{\"id\":\"18482\",\"type\":\"WheelZoomTool\"},{\"id\":\"18483\",\"type\":\"ResetTool\"},{\"id\":\"18545\",\"type\":\"HoverTool\"}]},\"id\":\"18484\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"18475\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"18567\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"18479\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"18573\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"18482\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"args\":{\"glyph_renderer0\":{\"id\":\"18495\",\"type\":\"GlyphRenderer\"},\"glyph_renderer1\":{\"id\":\"18499\",\"type\":\"GlyphRenderer\"},\"glyph_renderer2\":{\"id\":\"18506\",\"type\":\"GlyphRenderer\"},\"glyph_renderer3\":{\"id\":\"18510\",\"type\":\"GlyphRenderer\"},\"glyph_renderer4\":{\"id\":\"18517\",\"type\":\"GlyphRenderer\"},\"glyph_renderer5\":{\"id\":\"18521\",\"type\":\"GlyphRenderer\"},\"glyph_renderer6\":{\"id\":\"18528\",\"type\":\"GlyphRenderer\"},\"glyph_renderer7\":{\"id\":\"18532\",\"type\":\"GlyphRenderer\"},\"glyph_renderer8\":{\"id\":\"18539\",\"type\":\"GlyphRenderer\"},\"glyph_renderer9\":{\"id\":\"18543\",\"type\":\"GlyphRenderer\"}},\"code\":\"len_labels = 5;glyph_renderers = [[glyph_renderer0,glyph_renderer1],[glyph_renderer2,glyph_renderer3],[glyph_renderer4,glyph_renderer5],[glyph_renderer6,glyph_renderer7],[glyph_renderer8,glyph_renderer9]];\\n for (i = 0; i < len_labels; i++) {\\n if (cb_obj.active.includes(i)) {\\n // console.log('Setting to true: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = true;\\n // console.log('Setting to true: ' + i + ' : ' + j)\\n }\\n } else {\\n // console.log('Setting to false: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = false;\\n // console.log('Setting to false: ' + i + ' : ' + j)\\n }\\n }\\n }\\n \"},\"id\":\"18547\",\"type\":\"CustomJS\"},{\"attributes\":{\"label\":{\"value\":\"budget 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"cave.cost_over_time();" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/shuki/Repos/BOHBsCAVE/.ve_BOHBsCAVE/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n", - " return ptp(axis=axis, out=out, **kwargs)\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "cave.parallel_coordinates(run='budget_10000');" + "cave.parallel_coordinates(run='budget_1');" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/bostonhousing/bohb/configs.json b/examples/icml_2018_experiments/opt_results/bnn/small_bnn/bostonhousing/bohb/configs.json deleted file mode 100644 index 878ee08..0000000 --- a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/bostonhousing/bohb/configs.json +++ /dev/null @@ -1,3 +0,0 @@ -[[0, 0, 0], {"burn_in": 0.020168911164951543, "l_rate": 7.132250896709421e-05, "mdecay": 0.1590515141746459, "n_units_1": 83, "n_units_2": 57}, {"model_based_pick": false}] -[[0, 0, 1], {"burn_in": 0.44213073623986665, "l_rate": 0.0001683861887471032, "mdecay": 0.4970541633208484, "n_units_1": 241, "n_units_2": 224}, {"model_based_pick": false}] -[[0, 0, 2], {"burn_in": 0.7005031306081873, "l_rate": 1.2137966323520124e-05, "mdecay": 0.020898391794446747, "n_units_1": 221, "n_units_2": 164}, {"model_based_pick": false}] diff --git a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/bostonhousing/bohb/configspace.pcs b/examples/icml_2018_experiments/opt_results/bnn/small_bnn/bostonhousing/bohb/configspace.pcs deleted file mode 100644 index 5b925e2..0000000 --- a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/bostonhousing/bohb/configspace.pcs +++ /dev/null @@ -1,5 +0,0 @@ -burn_in real [0.0, 0.8] [0.3] -l_rate real [1e-06, 0.1] [0.01]log -mdecay real [0.0, 1.0] [0.05] -n_units_1 integer [16, 512] [64]log -n_units_2 integer [16, 512] [64]log \ No newline at end of file diff --git a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/bostonhousing/bohb/results.json b/examples/icml_2018_experiments/opt_results/bnn/small_bnn/bostonhousing/bohb/results.json deleted file mode 100644 index a331a2e..0000000 --- a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/bostonhousing/bohb/results.json +++ /dev/null @@ -1,4 +0,0 @@ -[[0, 0, 0], 333.3333333333333, {"submitted": 1556453474.4188616, "started": 1556453474.419506, "finished": 1556453481.6137702}, {"loss": 95414411.10679102, "info": {"function_value": 95414411.10679102, "cost": 7.1828773021698}}, null] -[[0, 0, 1], 333.3333333333333, {"submitted": 1556453481.6190689, "started": 1556453481.619701, "finished": 1556453492.118984}, {"loss": 23043673.394927647, "info": {"function_value": 23043673.394927647, "cost": 10.488747358322144}}, null] -[[0, 0, 2], 333.3333333333333, {"submitted": 1556453492.122976, "started": 1556453492.1236098, "finished": 1556453501.7618592}, {"loss": NaN, "info": {"function_value": 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5b925e2..0000000 --- a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/bostonhousing/randomsearch/configspace.pcs +++ /dev/null @@ -1,5 +0,0 @@ -burn_in real [0.0, 0.8] [0.3] -l_rate real [1e-06, 0.1] [0.01]log -mdecay real [0.0, 1.0] [0.05] -n_units_1 integer [16, 512] [64]log -n_units_2 integer [16, 512] [64]log \ No newline at end of file diff --git a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/bostonhousing/randomsearch/results.json b/examples/icml_2018_experiments/opt_results/bnn/small_bnn/bostonhousing/randomsearch/results.json deleted file mode 100644 index 28b857d..0000000 --- a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/bostonhousing/randomsearch/results.json +++ /dev/null @@ -1,2 +0,0 @@ -[[0, 0, 0], 1000, {"submitted": 1556453519.836012, "started": 1556453519.836674, "finished": 1556453539.5065682}, {"loss": 207214043.50446814, "info": {"function_value": 207214043.50446814, "cost": 19.658586263656616}}, null] -[[0, 0, 1], 1000, 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[16, 512] [64]log \ No newline at end of file diff --git a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/proteinstructure/hyperband/results.json b/examples/icml_2018_experiments/opt_results/bnn/small_bnn/proteinstructure/hyperband/results.json deleted file mode 100644 index cdf1c0b..0000000 --- a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/proteinstructure/hyperband/results.json +++ /dev/null @@ -1,4 +0,0 @@ -[[0, 0, 0], 333.3333333333333, {"submitted": 1556453661.3982754, "started": 1556453661.399149, "finished": 1556453671.4784374}, {"loss": 4718502.848513162, "info": {"function_value": 4718502.848513162, "cost": 10.072045803070068}}, null] -[[0, 0, 1], 333.3333333333333, {"submitted": 1556453671.480846, "started": 1556453671.4814599, "finished": 1556453679.9797187}, {"loss": 672640.5519018316, "info": {"function_value": 672640.5519018316, "cost": 8.49052906036377}}, null] -[[0, 0, 2], 333.3333333333333, {"submitted": 1556453679.982271, "started": 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a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/proteinstructure/randomsearch/results.json +++ /dev/null @@ -1,2 +0,0 @@ -[[0, 0, 0], 1000, {"submitted": 1556453627.8613157, "started": 1556453627.8620806, "finished": 1556453639.4028065}, {"loss": 19670046.795678582, "info": {"function_value": 19670046.795678582, "cost": 11.536272525787354}}, null] -[[0, 0, 1], 1000, {"submitted": 1556453639.4052353, "started": 1556453639.4058053, "finished": 1556453660.5042295}, {"loss": 10.264865580625292, "info": {"function_value": 10.264865580625292, "cost": 21.090742111206055}}, null] diff --git a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/proteinstructure/smac/run_168048252/configspace.json b/examples/icml_2018_experiments/opt_results/bnn/small_bnn/proteinstructure/smac/run_168048252/configspace.json deleted file mode 100644 index 2603ccd..0000000 --- a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/proteinstructure/smac/run_168048252/configspace.json +++ 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file diff --git a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/bohb/results.json b/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/bohb/results.json deleted file mode 100644 index 7a428c0..0000000 --- a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/bohb/results.json +++ /dev/null @@ -1,4 +0,0 @@ -[[0, 0, 0], 333.3333333333333, {"submitted": 1556459067.8340747, "started": 1556459067.8381813, "finished": 1556459086.7028923}, {"loss": NaN, "info": {"function_value": NaN, "cost": 18.8304283618927}}, null] -[[0, 0, 1], 333.3333333333333, {"submitted": 1556459086.720635, "started": 1556459086.7254136, "finished": 1556459101.4217832}, {"loss": NaN, "info": {"function_value": NaN, "cost": 14.660250425338745}}, null] -[[0, 0, 2], 333.3333333333333, {"submitted": 1556459101.4800384, "started": 1556459101.489848, "finished": 1556459114.5818865}, {"loss": NaN, "info": {"function_value": NaN, "cost": 13.037848949432373}}, null] 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b/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/hyperband/configspace.pcs deleted file mode 100644 index 5b925e2..0000000 --- a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/hyperband/configspace.pcs +++ /dev/null @@ -1,5 +0,0 @@ -burn_in real [0.0, 0.8] [0.3] -l_rate real [1e-06, 0.1] [0.01]log -mdecay real [0.0, 1.0] [0.05] -n_units_1 integer [16, 512] [64]log -n_units_2 integer [16, 512] [64]log \ No newline at end of file diff --git a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/hyperband/results.json b/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/hyperband/results.json deleted file mode 100644 index e69de29..0000000 diff --git a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/randomsearch/configs.json b/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/randomsearch/configs.json deleted file mode 100644 index a7c0c40..0000000 --- a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/randomsearch/configs.json +++ /dev/null @@ -1,2 +0,0 @@ -[[0, 0, 0], {"burn_in": 0.404088404358502, "l_rate": 4.029552714910519e-05, "mdecay": 0.18952899682541624, "n_units_1": 226, "n_units_2": 76}, {}] -[[0, 0, 1], {"burn_in": 0.17147447220545714, "l_rate": 7.467518482329436e-05, "mdecay": 0.9903970055286138, "n_units_1": 23, "n_units_2": 31}, {}] diff --git a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/randomsearch/configspace.pcs b/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/randomsearch/configspace.pcs deleted file mode 100644 index 5b925e2..0000000 --- a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/randomsearch/configspace.pcs +++ /dev/null @@ -1,5 +0,0 @@ -burn_in real [0.0, 0.8] [0.3] -l_rate real [1e-06, 0.1] [0.01]log -mdecay real [0.0, 1.0] [0.05] -n_units_1 integer [16, 512] [64]log -n_units_2 integer [16, 512] [64]log \ No newline at end of file diff --git a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/randomsearch/results.json b/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/randomsearch/results.json deleted file mode 100644 index 48cfbbc..0000000 --- a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/randomsearch/results.json +++ /dev/null @@ -1,2 +0,0 @@ -[[0, 0, 0], 1000, {"submitted": 1556459160.5593321, "started": 1556459160.56659, "finished": 1556459178.8378327}, {"loss": 4863768.795786699, "info": {"function_value": 4863768.795786699, "cost": 18.239877223968506}}, null] -[[0, 0, 1], 1000, {"submitted": 1556459178.8554738, "started": 1556459178.8592591, "finished": 1556459191.0689545}, {"loss": 877052297.1778479, "info": {"function_value": 877052297.1778479, "cost": 12.178157329559326}}, null] diff --git a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/smac/configspace.pcs b/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/smac/configspace.pcs deleted file mode 100644 index e69de29..0000000 diff --git a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/smac/run_1731960763/configspace.json b/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/smac/run_1731960763/configspace.json deleted file mode 100644 index 2603ccd..0000000 --- a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/toyfunction/smac/run_1731960763/configspace.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "hyperparameters": [ - { - "name": "burn_in", - "type": "uniform_float", - "log": false, - "lower": 0.0, - "upper": 0.8, - "default": 0.3 - }, - { - "name": "l_rate", - "type": "uniform_float", - "log": true, - "lower": 1e-06, - "upper": 0.1, - "default": 0.01 - }, - { - "name": "mdecay", - "type": "uniform_float", - "log": false, - "lower": 0.0, - "upper": 1.0, - "default": 0.05 - }, - { - "name": "n_units_1", - "type": 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b/examples/icml_2018_experiments/opt_results/paramnet_surrogate/adult/smac/run_1712039260/stats.json deleted file mode 100644 index b570bf7..0000000 --- a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/adult/smac/run_1712039260/stats.json +++ /dev/null @@ -1 +0,0 @@ -{"ta_runs": 15, "n_configs": 15, "wallclock_time_used": 10.532288074493408, "ta_time_used": 0.40616369247436523, "inc_changed": 3, "_n_configs_per_intensify": 14, "_n_calls_of_intensify": 7, "_ema_n_configs_per_intensifiy": 2.0, "_EMA_ALPHA": 0.2} \ No newline at end of file diff --git a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/adult/smac/run_1712039260/traj_aclib2.json b/examples/icml_2018_experiments/opt_results/paramnet_surrogate/adult/smac/run_1712039260/traj_aclib2.json deleted file mode 100644 index e43b5d1..0000000 --- a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/adult/smac/run_1712039260/traj_aclib2.json +++ /dev/null @@ -1,4 +0,0 @@ -{"cpu_time": 0, 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a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/mnist/smac/run_1720134895/scenario.txt +++ /dev/null @@ -1,12 +0,0 @@ -execdir = . -deterministic = True -run_obj = quality -overall_obj = par10 -par_factor = 10 -cost_for_crash = 2147483647.0 -algo_runs_timelimit = inf -wallclock_limit = inf -always_race_default = False -ta_run_limit = 15.0 -initial_incumbent = RANDOM -pcs_fn = opt_results/paramnet_surrogate/mnist/smac/run_1720134895/configspace.json diff --git a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/mnist/smac/run_1720134895/stats.json b/examples/icml_2018_experiments/opt_results/paramnet_surrogate/mnist/smac/run_1720134895/stats.json deleted file mode 100644 index bbde77b..0000000 --- a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/mnist/smac/run_1720134895/stats.json +++ /dev/null @@ -1 +0,0 @@ -{"ta_runs": 15, "n_configs": 15, "wallclock_time_used": 11.381669521331787, "ta_time_used": 0.46224379539489746, "inc_changed": 2, "_n_configs_per_intensify": 14, "_n_calls_of_intensify": 7, "_ema_n_configs_per_intensifiy": 2.0, "_EMA_ALPHA": 0.2} \ No newline at end of file diff --git a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/mnist/smac/run_1720134895/traj_aclib2.json b/examples/icml_2018_experiments/opt_results/paramnet_surrogate/mnist/smac/run_1720134895/traj_aclib2.json deleted file mode 100644 index 06a5aa2..0000000 --- a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/mnist/smac/run_1720134895/traj_aclib2.json +++ /dev/null @@ -1,3 +0,0 @@ -{"cpu_time": 0, "total_cpu_time": null, "wallclock_time": 6.937980651855469e-05, "evaluations": 0, "cost": 2147483648, "incumbent": ["x0='-4.206747670116752'", "x1='5.9407555020237'", "x2='4.272656104363868'", "x3='-2.04014694348734'", "x4='3.6051647150286685'", "x5='0.17220570897159826'"], "origin": "Random initial design."} -{"cpu_time": 0.027514219284057617, "total_cpu_time": null, "wallclock_time": 0.05218505859375, "evaluations": 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} -} \ No newline at end of file diff --git a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/optdigits/smac/run_1726927288/scenario.txt b/examples/icml_2018_experiments/opt_results/paramnet_surrogate/optdigits/smac/run_1726927288/scenario.txt deleted file mode 100644 index 2b729b9..0000000 --- a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/optdigits/smac/run_1726927288/scenario.txt +++ /dev/null @@ -1,12 +0,0 @@ -execdir = . -deterministic = True -run_obj = quality -overall_obj = par10 -par_factor = 10 -cost_for_crash = 2147483647.0 -algo_runs_timelimit = inf -wallclock_limit = inf -always_race_default = False -ta_run_limit = 16.0 -initial_incumbent = RANDOM -pcs_fn = opt_results/paramnet_surrogate/optdigits/smac/run_1726927288/configspace.json diff --git a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/optdigits/smac/run_1726927288/stats.json b/examples/icml_2018_experiments/opt_results/paramnet_surrogate/optdigits/smac/run_1726927288/stats.json deleted file mode 100644 index 6e0c254..0000000 --- a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/optdigits/smac/run_1726927288/stats.json +++ /dev/null @@ -1 +0,0 @@ -{"ta_runs": 16, "n_configs": 16, "wallclock_time_used": 13.698112487792969, "ta_time_used": 0.5590310096740723, "inc_changed": 3, "_n_configs_per_intensify": 14, "_n_calls_of_intensify": 7, "_ema_n_configs_per_intensifiy": 2.0, "_EMA_ALPHA": 0.2} \ No newline at end of file diff --git a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/optdigits/smac/run_1726927288/traj_aclib2.json b/examples/icml_2018_experiments/opt_results/paramnet_surrogate/optdigits/smac/run_1726927288/traj_aclib2.json deleted file mode 100644 index 4f22d6a..0000000 --- a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/optdigits/smac/run_1726927288/traj_aclib2.json +++ /dev/null @@ -1,4 +0,0 @@ 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a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/poker/smac/run_389996652/stats.json b/examples/icml_2018_experiments/opt_results/paramnet_surrogate/poker/smac/run_389996652/stats.json deleted file mode 100644 index 3ed93f7..0000000 --- a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/poker/smac/run_389996652/stats.json +++ /dev/null @@ -1 +0,0 @@ -{"ta_runs": 16, "n_configs": 16, "wallclock_time_used": 13.914662599563599, "ta_time_used": 0.6413352489471436, "inc_changed": 4, "_n_configs_per_intensify": 14, "_n_calls_of_intensify": 7, "_ema_n_configs_per_intensifiy": 2.0, "_EMA_ALPHA": 0.2} \ No newline at end of file diff --git a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/poker/smac/run_389996652/traj_aclib2.json b/examples/icml_2018_experiments/opt_results/paramnet_surrogate/poker/smac/run_389996652/traj_aclib2.json deleted file mode 100644 index e364a49..0000000 --- a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/poker/smac/run_389996652/traj_aclib2.json +++ /dev/null @@ -1,5 +0,0 @@ -{"cpu_time": 0, "total_cpu_time": null, "wallclock_time": 7.033348083496094e-05, "evaluations": 0, "cost": 2147483648, "incumbent": ["x0='-4.143064484620699'", "x1='3.41201617926935'", "x2='5.885390375676876'", "x3='-1.3996332638421483'", "x4='2.4787544701344886'", "x5='0.06315585129981383'"], "origin": "Random initial design."} -{"cpu_time": 0.04063248634338379, "total_cpu_time": null, "wallclock_time": 0.08260107040405273, "evaluations": 1, "cost": 0.32103207657334343, "incumbent": ["x0='-4.143064484620699'", "x1='3.41201617926935'", "x2='5.885390375676876'", "x3='-1.3996332638421483'", "x4='2.4787544701344886'", "x5='0.06315585129981383'"], "origin": "Random initial design."} -{"cpu_time": 0.07454848289489746, "total_cpu_time": null, "wallclock_time": 0.42035937309265137, "evaluations": 2, "cost": 0.20234037692749465, "incumbent": ["x0='-2.6469587013784497'", "x1='4.0777176776897335'", "x2='4.894130669530188'", "x3='-1.5950978305698338'", "x4='1.8817094889080397'", "x5='0.11818460275329984'"], "origin": "Random Search"} -{"cpu_time": 0.19159531593322754, "total_cpu_time": null, "wallclock_time": 1.3232083320617676, "evaluations": 5, "cost": 0.09220535231272806, "incumbent": ["x0='-3.5515296284958286'", "x1='3.4159337556575404'", "x2='6.850258132478894'", "x3='-1.0137966424437348'", "x4='4.663335884295854'", "x5='0.2607717284028018'"], "origin": "Random Search"} -{"cpu_time": 0.5986759662628174, "total_cpu_time": null, "wallclock_time": 11.62325644493103, "evaluations": 15, "cost": 0.029007192667982497, "incumbent": ["x0='-3.900032196859244'", "x1='5.562547296496028'", "x2='7.188160010744838'", "x3='-0.6017059649237835'", "x4='3.8842385319756785'", "x5='0.23351191896088325'"], "origin": "Random Search (sorted)"} diff --git a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/poker/smac/run_389996652/traj_old.csv b/examples/icml_2018_experiments/opt_results/paramnet_surrogate/poker/smac/run_389996652/traj_old.csv deleted file mode 100644 index e6db07e..0000000 --- a/examples/icml_2018_experiments/opt_results/paramnet_surrogate/poker/smac/run_389996652/traj_old.csv +++ /dev/null @@ -1,6 +0,0 @@ -"CPU Time Used","Estimated Training Performance","Wallclock Time","Incumbent ID","Automatic Configurator (CPU) Time","Configuration..." -0.000000, 2147483648.000000, 0.000070, 1, 0.000070, x0='-4.143064484620699', x1='3.41201617926935', x2='5.885390375676876', x3='-1.3996332638421483', x4='2.4787544701344886', x5='0.06315585129981383' -0.040632, 0.321032, 0.082601, 1, 0.041969, x0='-4.143064484620699', x1='3.41201617926935', x2='5.885390375676876', x3='-1.3996332638421483', x4='2.4787544701344886', x5='0.06315585129981383' -0.074548, 0.202340, 0.420359, 2, 0.345811, x0='-2.6469587013784497', 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examples/icml_2018_experiments/opt_results/bnn/small_bnn/proteinstructure/smac/run_168048252/scenario.txt rename to examples/icml_2018_experiments/opt_results/paramnet_surrogates/adult/smac/run_1569982670/scenario.txt index 2be26d5..b3ab022 100644 --- a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/proteinstructure/smac/run_168048252/scenario.txt +++ b/examples/icml_2018_experiments/opt_results/paramnet_surrogates/adult/smac/run_1569982670/scenario.txt @@ -7,6 +7,6 @@ cost_for_crash = 2147483647.0 algo_runs_timelimit = inf wallclock_limit = inf always_race_default = False -ta_run_limit = 2.0 +ta_run_limit = 39.0 initial_incumbent = RANDOM -pcs_fn = opt_results/small_bnn/proteinstructure/smac/run_168048252/configspace.json +pcs_fn = ../opt_results/paramnet_surrogates/adult/smac/run_1569982670/configspace.json diff --git a/examples/icml_2018_experiments/opt_results/paramnet_surrogates/adult/smac/run_1569982670/stats.json b/examples/icml_2018_experiments/opt_results/paramnet_surrogates/adult/smac/run_1569982670/stats.json new file mode 100644 index 0000000..2115942 --- /dev/null +++ b/examples/icml_2018_experiments/opt_results/paramnet_surrogates/adult/smac/run_1569982670/stats.json @@ -0,0 +1 @@ +{"ta_runs": 39, "n_configs": 39, "wallclock_time_used": 38.68742895126343, "ta_time_used": 0.9097795486450195, "inc_changed": 2, "_n_configs_per_intensify": 38, "_n_calls_of_intensify": 19, "_ema_n_configs_per_intensifiy": 2.0, "_EMA_ALPHA": 0.2} \ No newline at end of file diff --git a/examples/icml_2018_experiments/opt_results/paramnet_surrogates/adult/smac/run_1569982670/traj_aclib2.json b/examples/icml_2018_experiments/opt_results/paramnet_surrogates/adult/smac/run_1569982670/traj_aclib2.json new file mode 100644 index 0000000..d8d7dc3 --- /dev/null +++ b/examples/icml_2018_experiments/opt_results/paramnet_surrogates/adult/smac/run_1569982670/traj_aclib2.json @@ -0,0 +1,3 @@ +{"cpu_time": 0, "total_cpu_time": null, "wallclock_time": 8.988380432128906e-05, "evaluations": 0, "cost": 2147483648, "incumbent": ["x0='-4.427184146847615'", "x1='5.468409116159567'", "x2='5.166697446737537'", "x3='-2.4737392169558943'", "x4='4.557793026011394'", "x5='0.26048328742862104'"], "origin": "Random initial design."} +{"cpu_time": 0.014215469360351562, "total_cpu_time": null, "wallclock_time": 0.03326892852783203, "evaluations": 1, "cost": 0.16898147900181787, "incumbent": ["x0='-4.427184146847615'", "x1='5.468409116159567'", "x2='5.166697446737537'", "x3='-2.4737392169558943'", "x4='4.557793026011394'", "x5='0.26048328742862104'"], "origin": "Random initial design."} +{"cpu_time": 0.02863907814025879, "total_cpu_time": null, "wallclock_time": 0.3213036060333252, "evaluations": 2, "cost": 0.14852777666984882, "incumbent": ["x0='-3.0208079181949494'", "x1='4.302339512022793'", "x2='6.682814513749159'", "x3='-1.5860107594761308'", "x4='1.7579816943137954'", "x5='0.29784322077691755'"], "origin": "Random Search"} diff --git a/examples/icml_2018_experiments/opt_results/paramnet_surrogates/adult/smac/run_1569982670/traj_old.csv b/examples/icml_2018_experiments/opt_results/paramnet_surrogates/adult/smac/run_1569982670/traj_old.csv new file mode 100644 index 0000000..dcc5eb5 --- /dev/null +++ b/examples/icml_2018_experiments/opt_results/paramnet_surrogates/adult/smac/run_1569982670/traj_old.csv @@ -0,0 +1,4 @@ +"CPU Time Used","Estimated Training Performance","Wallclock Time","Incumbent ID","Automatic Configurator (CPU) Time","Configuration..." +0.000000, 2147483648.000000, 0.000090, 1, 0.000090, x0='-4.427184146847615', x1='5.468409116159567', x2='5.166697446737537', x3='-2.4737392169558943', x4='4.557793026011394', x5='0.26048328742862104' +0.014215, 0.168981, 0.033269, 1, 0.019053, x0='-4.427184146847615', x1='5.468409116159567', x2='5.166697446737537', x3='-2.4737392169558943', x4='4.557793026011394', x5='0.26048328742862104' +0.028639, 0.148528, 0.321304, 2, 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opt_results/small_bnn/toyfunction/smac/run_1731960763/configspace.json +pcs_fn = ../opt_results/paramnet_surrogates/higgs/smac/run_2040386885/configspace.json diff --git a/examples/icml_2018_experiments/opt_results/paramnet_surrogates/higgs/smac/run_2040386885/stats.json b/examples/icml_2018_experiments/opt_results/paramnet_surrogates/higgs/smac/run_2040386885/stats.json new file mode 100644 index 0000000..56f33ae --- /dev/null +++ b/examples/icml_2018_experiments/opt_results/paramnet_surrogates/higgs/smac/run_2040386885/stats.json @@ -0,0 +1 @@ +{"ta_runs": 39, "n_configs": 39, "wallclock_time_used": 39.5362651348114, "ta_time_used": 0.8841092586517334, "inc_changed": 3, "_n_configs_per_intensify": 38, "_n_calls_of_intensify": 19, "_ema_n_configs_per_intensifiy": 2.0, "_EMA_ALPHA": 0.2} \ No newline at end of file diff --git a/examples/icml_2018_experiments/opt_results/paramnet_surrogates/higgs/smac/run_2040386885/traj_aclib2.json 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a/examples/icml_2018_experiments/opt_results/bnn/small_bnn/bostonhousing/smac/run_1630317213/scenario.txt +++ b/examples/icml_2018_experiments/opt_results/paramnet_surrogates/poker/smac/run_965337418/scenario.txt @@ -7,6 +7,6 @@ cost_for_crash = 2147483647.0 algo_runs_timelimit = inf wallclock_limit = inf always_race_default = False -ta_run_limit = 2.0 +ta_run_limit = 40.0 initial_incumbent = RANDOM -pcs_fn = opt_results/small_bnn/bostonhousing/smac/run_1630317213/configspace.json +pcs_fn = ../opt_results/paramnet_surrogates/poker/smac/run_965337418/configspace.json diff --git a/examples/icml_2018_experiments/opt_results/paramnet_surrogates/poker/smac/run_965337418/stats.json b/examples/icml_2018_experiments/opt_results/paramnet_surrogates/poker/smac/run_965337418/stats.json new file mode 100644 index 0000000..a4239bf --- /dev/null +++ b/examples/icml_2018_experiments/opt_results/paramnet_surrogates/poker/smac/run_965337418/stats.json @@ -0,0 +1 @@ +{"ta_runs": 40, "n_configs": 40, "wallclock_time_used": 55.47499489784241, "ta_time_used": 1.5194895267486572, "inc_changed": 4, "_n_configs_per_intensify": 38, "_n_calls_of_intensify": 19, "_ema_n_configs_per_intensifiy": 2.0, "_EMA_ALPHA": 0.2} \ No newline at end of file diff --git a/examples/icml_2018_experiments/opt_results/paramnet_surrogates/poker/smac/run_965337418/traj_aclib2.json b/examples/icml_2018_experiments/opt_results/paramnet_surrogates/poker/smac/run_965337418/traj_aclib2.json new file mode 100644 index 0000000..a03d344 --- /dev/null +++ b/examples/icml_2018_experiments/opt_results/paramnet_surrogates/poker/smac/run_965337418/traj_aclib2.json @@ -0,0 +1,5 @@ +{"cpu_time": 0, "total_cpu_time": null, "wallclock_time": 0.0001914501190185547, "evaluations": 0, "cost": 2147483648, "incumbent": ["x0='-2.763456783161529'", "x1='6.681601775093551'", "x2='7.777465262058573'", "x3='-1.614322334367868'", "x4='1.0714981172735687'", "x5='0.012826150125409497'"], "origin": "Random initial design."} +{"cpu_time": 0.030979394912719727, "total_cpu_time": null, "wallclock_time": 0.07370328903198242, "evaluations": 1, "cost": 0.26680712789807987, "incumbent": ["x0='-2.763456783161529'", "x1='6.681601775093551'", "x2='7.777465262058573'", "x3='-1.614322334367868'", "x4='1.0714981172735687'", "x5='0.012826150125409497'"], "origin": "Random initial design."} +{"cpu_time": 0.08523941040039062, "total_cpu_time": null, "wallclock_time": 0.7302227020263672, "evaluations": 3, "cost": 0.08035564384324528, "incumbent": ["x0='-2.975028821874204'", "x1='3.37687820329722'", "x2='5.20362478305125'", "x3='-2.4549191822839265'", "x4='3.6406619843765307'", "x5='0.09726420263601987'"], "origin": "Random Search"} +{"cpu_time": 0.16112351417541504, "total_cpu_time": null, "wallclock_time": 4.5691819190979, "evaluations": 6, "cost": 0.002223006958301432, "incumbent": ["x0='-3.3296390336760147'", "x1='5.593861737903584'", "x2='7.723454971546828'", "x3='-2.4347001816089113'", "x4='2.8808660453391677'", "x5='0.14284223976501176'"], "origin": "Random Search (sorted)"} +{"cpu_time": 1.4907829761505127, "total_cpu_time": null, "wallclock_time": 52.70077872276306, "evaluations": 39, "cost": 0.001607906271525239, "incumbent": ["x0='-3.6253867093927665'", "x1='5.500766337200364'", "x2='7.597126214974917'", "x3='-2.21662246745676'", "x4='2.8558631974361828'", "x5='0.08394208264413566'"], "origin": "Local Search"} diff --git a/examples/icml_2018_experiments/opt_results/paramnet_surrogates/poker/smac/run_965337418/traj_old.csv b/examples/icml_2018_experiments/opt_results/paramnet_surrogates/poker/smac/run_965337418/traj_old.csv new file mode 100644 index 0000000..7438613 --- /dev/null +++ b/examples/icml_2018_experiments/opt_results/paramnet_surrogates/poker/smac/run_965337418/traj_old.csv @@ -0,0 +1,6 @@ +"CPU Time Used","Estimated Training Performance","Wallclock Time","Incumbent ID","Automatic Configurator (CPU) Time","Configuration..." +0.000000, 2147483648.000000, 0.000191, 1, 0.000191, x0='-2.763456783161529', x1='6.681601775093551', x2='7.777465262058573', x3='-1.614322334367868', x4='1.0714981172735687', x5='0.012826150125409497' +0.030979, 0.266807, 0.073703, 1, 0.042724, x0='-2.763456783161529', x1='6.681601775093551', x2='7.777465262058573', x3='-1.614322334367868', x4='1.0714981172735687', x5='0.012826150125409497' +0.085239, 0.080356, 0.730223, 2, 0.644983, x0='-2.975028821874204', x1='3.37687820329722', x2='5.20362478305125', x3='-2.4549191822839265', x4='3.6406619843765307', x5='0.09726420263601987' +0.161124, 0.002223, 4.569182, 3, 4.408058, x0='-3.3296390336760147', x1='5.593861737903584', x2='7.723454971546828', x3='-2.4347001816089113', x4='2.8808660453391677', x5='0.14284223976501176' +1.490783, 0.001608, 52.700779, 4, 51.209996, x0='-3.6253867093927665', x1='5.500766337200364', x2='7.597126214974917', x3='-2.21662246745676', x4='2.8558631974361828', x5='0.08394208264413566' diff --git a/examples/icml_2018_experiments/paramnet_surrogates.ipynb b/examples/icml_2018_experiments/paramnet_surrogates.ipynb index 95f8667..b693424 100644 --- a/examples/icml_2018_experiments/paramnet_surrogates.ipynb +++ b/examples/icml_2018_experiments/paramnet_surrogates.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this series of notebooks, we will replicate and analyze the ICML 2018 experiments that where used for benchmarking in BOHB (Falkner et al. 2018).\n", + "In this series of notebooks, we will replicate and analyze the ICML 2018 experiments that were used for benchmarking in BOHB (Falkner et al. 2018).\n", "In addition to HpBandSter, we will use CAVE to analyze and visualize the optimization process." ] }, @@ -19,11 +19,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## About the frameworks\n", + "## About this experiment\n", "\n", "### Paramnet Surrogates\n", - "\n", - "coming soon\n", + "(... coming soon ...)\n", "\n", "### Installation requirements\n", "\n", @@ -38,40 +37,19 @@ ] }, { - "cell_type": "code", - "execution_count": 1, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# We deactivate logging to ensure readability\n", - "import logging\n", - "logging.basicConfig(level=logging.ERROR)\n", - "# Also, we suppress warnings.\n", - "# If there are problems for you executing this notebook, you might want to comment this out.\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")" + "This examples ships with all the code necessary to reproduce the experiment. Because it takes a few days to generate the data, the results of the optimization are provided in `examples/icml_2018_experiments/opt_results/`. If you want to generate the data (from examples/icml_2018_experiments), just run:" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "### 1.1) References\n", - "*Worker*: We need a Worker to define the kind of computation we want to optimize. The worker used for the experiments is located in `workers/paramnet_surrogates.py`\n", - "\n", - "*ConfigSpace*: Every problem needs a description of the search space to be complete. In HpBandSter, a ConfigurationSpace-object defines all hyperparameters, their ranges and dependencies between them.\n", - "\n", - "In our example here, the search space consists of the hyperparameters:\n", - "\n", - "\n", - "| Name | Type | Values |\n", - "|:---------:|:-------:|:------------:|\n", - "| x0 | real | [-6.0, -2.0] |\n", - "| x1 | real | [3.0, 8.0] |\n", - "| x2 | real | [4.0, 8.0] |\n", - "| x3 | real | [-4.0, 0.0] |\n", - "| x4 | real | [1.0, 5.0] |\n", - "| x5 | real | [0.0, 0.5] |" + "! python scripts/run_experiment.py --exp_name paramnet_surrogates --dataset_paramnet_surrogates higgs --num_iterations 1 --n_workers 1" ] }, { @@ -80,92 +58,8 @@ "scrolled": false }, "source": [ - "### 1.3) Setting up the experiment and running the optimizer(s)\n", - "\n", - "Please note that the execution of the experiment with all datasets might take up to a few days, depending on your hardware. You can also skip this step and process with the precomputed results saved in `opt_results/paramnet_surrogate`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import os\n", - "from itertools import product\n", - "import numpy as np\n", - "\n", - "import hpbandster.core.nameserver as hpns\n", - "import hpbandster.core.result as hpres\n", - "from hpbandster.optimizers import BOHB, RandomSearch, HyperBand\n", - "\n", - "from workers.paramnet_surrogates import ParamNetSurrogateWorker\n", - "\n", - "# Run the experiment, evaluate each dataset on each optimizer\n", - "datasets = ['adult', 'higgs', 'letter', 'mnist', 'optdigits', 'poker']\n", - "opt_methods = [\"smac\", \"bohb\", \"randomsearch\", \"hyperband\"]\n", - "num_iterations = 4\n", - "\n", - "\n", - "eta = 3\n", - "\n", - "for dataset, opt_method in product(datasets, opt_methods):\n", - "\n", - " print(dataset, opt_method)\n", - " \n", - " output_dir = \"opt_results/paramnet_surrogate/{}/{}\".format(dataset, opt_method)\n", - " if not os.path.exists(output_dir):\n", - " os.makedirs(output_dir)\n", - "\n", - " run_id = '0' # Every run has to have a unique (at runtime) id.\n", - " \n", - " # create a worker\n", - " worker = ParamNetSurrogateWorker(dataset=dataset, measure_test_loss=False, run_id=run_id, surrogate_path=None)\n", - " min_budget, max_budget = worker.budgets[dataset]\n", - " \n", - " configspace = worker.configspace\n", - "\n", - " if opt_method in ['randomsearch', 'bohb', 'hyperband']:\n", - " # setup a nameserver\n", - " NS = hpns.NameServer(run_id=run_id, host='localhost', port=0, working_directory=output_dir)\n", - " ns_host, ns_port = NS.start()\n", - "\n", - " worker.load_nameserver_credentials(output_dir)\n", - " worker.run(background=True)\n", - "\n", - " # instantiate and run optimizer\n", - " opt = RandomSearch if opt_method == 'randomsearch' else BOHB if opt_method == 'bohb' else HyperBand\n", - "\n", - " result_logger = hpres.json_result_logger(directory=output_dir, overwrite=True)\n", - "\n", - " opt = opt(configspace, eta=3,\n", - " working_directory=output_dir,\n", - " run_id=run_id,\n", - " min_budget=min_budget, max_budget=max_budget,\n", - " host=ns_host,\n", - " nameserver=ns_host,\n", - " nameserver_port = ns_port,\n", - " ping_interval=3600,\n", - " result_logger=result_logger)\n", - "\n", - " result = opt.run(n_iterations=num_iterations)\n", - " \n", - " # **NOTE:** Unfortunately, the configuration space is *not yet saved automatically* to file\n", - " # but this step is mandatory for the analysis with CAVE. \n", - " # We recommend to save the configuration space every time you use BOHB.\n", - " # We do this by using the ConfigSpace-to-json-writer.\n", - "\n", - " from ConfigSpace.read_and_write import pcs_new\n", - " with open(os.path.join(output_dir, 'configspace.pcs'), 'w') as fh:\n", - " fh.write(pcs_new.write(opt.config_generator.configspace))\n", - " \n", - " else:\n", - " # the number of iterations for the blackbox optimizers must be increased so they have comparable total budgets\n", - " bb_iterations = int(num_iterations * (1+(np.log(max_budget) - np.log(min_budget))/np.log(eta)))\n", - " if opt_method == 'smac':\n", - " result = worker.run_smac(bb_iterations, deterministic=True, working_directory=output_dir)" + "You can use this script to generate the optimization data for a variety of optimizers, the results will (by default) be stored in `examples/icml_2018_experiments/EXPERIMENT/DATASET/OPTIMIZER`. Use `python run_experiment --help` to see how to use the script.\n", + "If you have access to a cluster, take a look at the scripts provided for cluster computation on a SLURM cluster at `examples/icml_2018_experiments/scripts/cluster/`." ] }, { @@ -176,12 +70,12 @@ "\n", "### Instantiate CAVE\n", "\n", - "Analyzing the optimization results with CAVE is very straight-forward. If you want to use CAVE interactively in a notebook, set `show_jupyter=True`. Specify which optimization you want to analyze via the `folders` argument and specify `file_format==SMAC3` or `file_format==BOHB`, depending on which optimizer was used for the results. To analyze how BOHB optimized on the *bostenhousing* dataset, run:" + "Analyzing the optimization results with CAVE is very straight-forward. If you want to use CAVE interactively in a notebook, set `show_jupyter=True`. Specify which optimization you want to analyze via the `folders` argument and specify `file_format==SMAC3` or `file_format==BOHB`, depending on which optimizer was used for the results. To analyze how BOHB optimized on the *higgs* dataset, run:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "scrolled": false }, @@ -189,10 +83,10 @@ "source": [ "from cave.cavefacade import CAVE\n", "\n", - "cave = CAVE(folders=[\"opt_results/paramnet_surrogate/higgs/bohb\"],\n", - " output_dir=\"CAVE_reports/higgs_notebook\", # output for debug/images/etc\n", - " ta_exec_dir=[\".\"], # Only important for SMAC-results\n", - " file_format='BOHB', # BOHB or SMAC3\n", + "cave = CAVE(folders=[\"opt_results/paramnet_surrogates/higgs/bohb\"],\n", + " output_dir=\"CAVE_reports/paramnet_surrogates/higgs_notebook\", # output for debug/images/etc\n", + " ta_exec_dir=[\".\"], # Only important for SMAC-results\n", + " file_format='BOHB', # BOHB or SMAC3\n", " verbose_level='OFF',\n", " show_jupyter=True,\n", " )" @@ -202,18 +96,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To generate the HTML-report you can use the `analyze`-method. The report is located in `output_dir/report.html`, so in this case in `CAVE_reports/higgs_notebook/report.html`." + "To generate the HTML-report you can use the `analyze`-method. The report is located in `output_dir/report.html`, so in this case in `CAVE_reports/paramnet_surrogates/higgs_notebook/report.html`." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%capture\n", "cave.analyze()\n", - "! firefox CAVE_reports/higgs_notebook/report.html" + "! firefox CAVE_reports/paramnet_surrogates/higgs_notebook/report.html" ] }, { @@ -227,467 +121,22 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
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\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"33601\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"33601\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '33601' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"33601\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function embed_document(root) {\n", - " \n", - " var docs_json = {\"e508c477-6165-454c-a384-1337366cf007\":{\"roots\":{\"references\":[{\"attributes\":{\"children\":[{\"id\":\"33608\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"id\":\"33730\",\"type\":\"Column\"}]},\"id\":\"33731\",\"type\":\"Row\"},{\"attributes\":{\"ticker\":{\"id\":\"33616\",\"type\":\"BasicTicker\"}},\"id\":\"33619\",\"type\":\"Grid\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"times\"},\"y\":{\"field\":\"losses\"}},\"id\":\"33708\",\"type\":\"CircleX\"},{\"attributes\":{\"column_name\":\"HB_iteration\",\"group\":\"1\"},\"id\":\"33663\",\"type\":\"GroupFilter\"},{\"attributes\":{\"axis_label\":\"Cost\",\"formatter\":{\"id\":\"33734\",\"type\":\"LogTickFormatter\"},\"ticker\":{\"id\":\"33621\",\"type\":\"LogTicker\"}},\"id\":\"33620\",\"type\":\"LogAxis\"},{\"attributes\":{\"data_source\":{\"id\":\"33604\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"33707\",\"type\":\"CircleX\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"33708\",\"type\":\"CircleX\"},\"selection_glyph\":null,\"view\":{\"id\":\"33705\",\"type\":\"CDSView\"}},\"id\":\"33709\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"column_name\":\"config_info\",\"group\":\"model_based_pick=True\"},\"id\":\"33664\",\"type\":\"GroupFilter\"},{\"attributes\":{\"num_minor_ticks\":10},\"id\":\"33621\",\"type\":\"LogTicker\"},{\"attributes\":{\"filters\":[{\"id\":\"33663\",\"type\":\"GroupFilter\"},{\"id\":\"33664\",\"type\":\"GroupFilter\"}],\"source\":{\"id\":\"33604\",\"type\":\"ColumnDataSource\"}},\"id\":\"33665\",\"type\":\"CDSView\"},{\"attributes\":{\"column_name\":\"HB_iteration\",\"group\":\"3\"},\"id\":\"33710\",\"type\":\"GroupFilter\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"33621\",\"type\":\"LogTicker\"}},\"id\":\"33624\",\"type\":\"Grid\"},{\"attributes\":{\"column_name\":\"config_info\",\"group\":\"model_based_pick=False\"},\"id\":\"33711\",\"type\":\"GroupFilter\"},{\"attributes\":{\"filters\":[{\"id\":\"33703\",\"type\":\"GroupFilter\"},{\"id\":\"33704\",\"type\":\"GroupFilter\"}],\"source\":{\"id\":\"33604\",\"type\":\"ColumnDataSource\"}},\"id\":\"33705\",\"type\":\"CDSView\"},{\"attributes\":{\"filters\":[{\"id\":\"33637\",\"type\":\"GroupFilter\"}],\"source\":{\"id\":\"33603\",\"type\":\"ColumnDataSource\"}},\"id\":\"33638\",\"type\":\"CDSView\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"field\":\"colors\",\"transform\":{\"id\":\"33605\",\"type\":\"LinearColorMapper\"}},\"line_color\":{\"field\":\"colors\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"times\"},\"y\":{\"field\":\"losses\"}},\"id\":\"33667\",\"type\":\"CircleX\"},{\"attributes\":{\"filters\":[{\"id\":\"33710\",\"type\":\"GroupFilter\"},{\"id\":\"33711\",\"type\":\"GroupFilter\"}],\"source\":{\"id\":\"33604\",\"type\":\"ColumnDataSource\"}},\"id\":\"33712\",\"type\":\"CDSView\"},{\"attributes\":{\"column_name\":\"HB_iteration\",\"group\":\"0\"},\"id\":\"33637\",\"type\":\"GroupFilter\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"times\"},\"y\":{\"field\":\"losses\"}},\"id\":\"33668\",\"type\":\"CircleX\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"33602\",\"type\":\"HoverTool\"},{\"id\":\"33625\",\"type\":\"SaveTool\"},{\"id\":\"33626\",\"type\":\"PanTool\"},{\"id\":\"33627\",\"type\":\"WheelZoomTool\"},{\"id\":\"33628\",\"type\":\"BoxZoomTool\"},{\"id\":\"33629\",\"type\":\"ResetTool\"}]},\"id\":\"33630\",\"type\":\"Toolbar\"},{\"attributes\":{\"data_source\":{\"id\":\"33604\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"33667\",\"type\":\"CircleX\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"33668\",\"type\":\"CircleX\"},\"selection_glyph\":null,\"view\":{\"id\":\"33665\",\"type\":\"CDSView\"}},\"id\":\"33669\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"field\":\"colors\",\"transform\":{\"id\":\"33605\",\"type\":\"LinearColorMapper\"}},\"line_color\":{\"field\":\"colors\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"times\"},\"y\":{\"field\":\"losses\"}},\"id\":\"33714\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"33625\",\"type\":\"SaveTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"times\"},\"y\":{\"field\":\"losses\"}},\"id\":\"33715\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"33626\",\"type\":\"PanTool\"},{\"attributes\":{\"column_name\":\"HB_iteration\",\"group\":\"1\"},\"id\":\"33670\",\"type\":\"GroupFilter\"},{\"attributes\":{\"data_source\":{\"id\":\"33604\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"33714\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"33715\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"33712\",\"type\":\"CDSView\"}},\"id\":\"33716\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"33627\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"column_name\":\"config_info\",\"group\":\"model_based_pick=False\"},\"id\":\"33671\",\"type\":\"GroupFilter\"},{\"attributes\":{\"filters\":[{\"id\":\"33670\",\"type\":\"GroupFilter\"},{\"id\":\"33671\",\"type\":\"GroupFilter\"}],\"source\":{\"id\":\"33604\",\"type\":\"ColumnDataSource\"}},\"id\":\"33672\",\"type\":\"CDSView\"},{\"attributes\":{\"overlay\":{\"id\":\"33742\",\"type\":\"BoxAnnotation\"}},\"id\":\"33628\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"args\":{\"checkbox\":{\"id\":\"33718\",\"type\":\"CheckboxButtonGroup\"},\"glyph_renderer0\":{\"id\":\"33642\",\"type\":\"GlyphRenderer\"},\"glyph_renderer1\":{\"id\":\"33649\",\"type\":\"GlyphRenderer\"},\"glyph_renderer10\":{\"id\":\"33709\",\"type\":\"GlyphRenderer\"},\"glyph_renderer11\":{\"id\":\"33716\",\"type\":\"GlyphRenderer\"},\"glyph_renderer2\":{\"id\":\"33656\",\"type\":\"GlyphRenderer\"},\"glyph_renderer3\":{\"id\":\"33662\",\"type\":\"GlyphRenderer\"},\"glyph_renderer4\":{\"id\":\"33669\",\"type\":\"GlyphRenderer\"},\"glyph_renderer5\":{\"id\":\"33676\",\"type\":\"GlyphRenderer\"},\"glyph_renderer6\":{\"id\":\"33682\",\"type\":\"GlyphRenderer\"},\"glyph_renderer7\":{\"id\":\"33689\",\"type\":\"GlyphRenderer\"},\"glyph_renderer8\":{\"id\":\"33696\",\"type\":\"GlyphRenderer\"},\"glyph_renderer9\":{\"id\":\"33702\",\"type\":\"GlyphRenderer\"}},\"code\":\"var labels = [0, 1, 2, 3]; checkbox.active = labels;len_labels = 4;glyph_renderers = [[glyph_renderer0,glyph_renderer1,glyph_renderer2],[glyph_renderer3,glyph_renderer4,glyph_renderer5],[glyph_renderer6,glyph_renderer7,glyph_renderer8],[glyph_renderer9,glyph_renderer10,glyph_renderer11]];\\n for (i = 0; i < len_labels; i++) {\\n if (checkbox.active.includes(i)) {\\n // console.log('Setting to true: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = true;\\n // console.log('Setting to true: ' + i + ' : ' + j)\\n }\\n } else {\\n // console.log('Setting to false: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = false;\\n // console.log('Setting to false: ' + i + ' : ' + j)\\n }\\n }\\n }\\n \"},\"id\":\"33719\",\"type\":\"CustomJS\"},{\"attributes\":{\"ticker\":null},\"id\":\"33734\",\"type\":\"LogTickFormatter\"},{\"attributes\":{},\"id\":\"33629\",\"type\":\"ResetTool\"},{\"attributes\":{\"column_name\":\"config_info\",\"group\":\"model_based_pick=True\"},\"id\":\"33704\",\"type\":\"GroupFilter\"},{\"attributes\":{\"args\":{\"glyph_renderer0\":{\"id\":\"33642\",\"type\":\"GlyphRenderer\"},\"glyph_renderer1\":{\"id\":\"33649\",\"type\":\"GlyphRenderer\"},\"glyph_renderer10\":{\"id\":\"33709\",\"type\":\"GlyphRenderer\"},\"glyph_renderer11\":{\"id\":\"33716\",\"type\":\"GlyphRenderer\"},\"glyph_renderer2\":{\"id\":\"33656\",\"type\":\"GlyphRenderer\"},\"glyph_renderer3\":{\"id\":\"33662\",\"type\":\"GlyphRenderer\"},\"glyph_renderer4\":{\"id\":\"33669\",\"type\":\"GlyphRenderer\"},\"glyph_renderer5\":{\"id\":\"33676\",\"type\":\"GlyphRenderer\"},\"glyph_renderer6\":{\"id\":\"33682\",\"type\":\"GlyphRenderer\"},\"glyph_renderer7\":{\"id\":\"33689\",\"type\":\"GlyphRenderer\"},\"glyph_renderer8\":{\"id\":\"33696\",\"type\":\"GlyphRenderer\"},\"glyph_renderer9\":{\"id\":\"33702\",\"type\":\"GlyphRenderer\"}},\"code\":\"len_labels = 4;glyph_renderers = [[glyph_renderer0,glyph_renderer1,glyph_renderer2],[glyph_renderer3,glyph_renderer4,glyph_renderer5],[glyph_renderer6,glyph_renderer7,glyph_renderer8],[glyph_renderer9,glyph_renderer10,glyph_renderer11]];\\n for (i = 0; i < len_labels; i++) {\\n if (cb_obj.active.includes(i)) {\\n // console.log('Setting to true: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = true;\\n // console.log('Setting to true: ' + i + ' : ' + j)\\n }\\n } else {\\n // console.log('Setting to false: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = false;\\n // console.log('Setting to false: ' + i + ' : ' + j)\\n }\\n }\\n }\\n \"},\"id\":\"33717\",\"type\":\"CustomJS\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"field\":\"colors\",\"transform\":{\"id\":\"33605\",\"type\":\"LinearColorMapper\"}},\"line_color\":{\"field\":\"colors\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"times\"},\"y\":{\"field\":\"losses\"}},\"id\":\"33674\",\"type\":\"Circle\"},{\"attributes\":{\"active\":[0,1,2,3],\"callback\":{\"id\":\"33717\",\"type\":\"CustomJS\"},\"labels\":[\"0\",\"1\",\"2\",\"3\"]},\"id\":\"33718\",\"type\":\"CheckboxButtonGroup\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"times\"},\"y\":{\"field\":\"losses\"}},\"id\":\"33675\",\"type\":\"Circle\"},{\"attributes\":{\"filters\":[{\"id\":\"33697\",\"type\":\"GroupFilter\"}],\"source\":{\"id\":\"33603\",\"type\":\"ColumnDataSource\"}},\"id\":\"33698\",\"type\":\"CDSView\"},{\"attributes\":{\"callback\":{\"id\":\"33719\",\"type\":\"CustomJS\"},\"icon\":null,\"label\":\"All\"},\"id\":\"33720\",\"type\":\"Button\"},{\"attributes\":{\"data_source\":{\"id\":\"33604\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"33674\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"33675\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"33672\",\"type\":\"CDSView\"}},\"id\":\"33676\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_alpha\":{\"value\":0.5},\"line_color\":{\"field\":\"colors\",\"transform\":{\"id\":\"33605\",\"type\":\"LinearColorMapper\"}},\"line_width\":{\"value\":5},\"xs\":{\"field\":\"times\"},\"ys\":{\"field\":\"losses\"}},\"id\":\"33640\",\"type\":\"MultiLine\"},{\"attributes\":{\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"line_width\":{\"value\":5},\"xs\":{\"field\":\"times\"},\"ys\":{\"field\":\"losses\"}},\"id\":\"33641\",\"type\":\"MultiLine\"},{\"attributes\":{\"column_name\":\"HB_iteration\",\"group\":\"2\"},\"id\":\"33677\",\"type\":\"GroupFilter\"},{\"attributes\":{\"args\":{\"checkbox\":{\"id\":\"33718\",\"type\":\"CheckboxButtonGroup\"},\"glyph_renderer0\":{\"id\":\"33642\",\"type\":\"GlyphRenderer\"},\"glyph_renderer1\":{\"id\":\"33649\",\"type\":\"GlyphRenderer\"},\"glyph_renderer10\":{\"id\":\"33709\",\"type\":\"GlyphRenderer\"},\"glyph_renderer11\":{\"id\":\"33716\",\"type\":\"GlyphRenderer\"},\"glyph_renderer2\":{\"id\":\"33656\",\"type\":\"GlyphRenderer\"},\"glyph_renderer3\":{\"id\":\"33662\",\"type\":\"GlyphRenderer\"},\"glyph_renderer4\":{\"id\":\"33669\",\"type\":\"GlyphRenderer\"},\"glyph_renderer5\":{\"id\":\"33676\",\"type\":\"GlyphRenderer\"},\"glyph_renderer6\":{\"id\":\"33682\",\"type\":\"GlyphRenderer\"},\"glyph_renderer7\":{\"id\":\"33689\",\"type\":\"GlyphRenderer\"},\"glyph_renderer8\":{\"id\":\"33696\",\"type\":\"GlyphRenderer\"},\"glyph_renderer9\":{\"id\":\"33702\",\"type\":\"GlyphRenderer\"}},\"code\":\"var labels = []; checkbox.active = labels;len_labels = 4;glyph_renderers = [[glyph_renderer0,glyph_renderer1,glyph_renderer2],[glyph_renderer3,glyph_renderer4,glyph_renderer5],[glyph_renderer6,glyph_renderer7,glyph_renderer8],[glyph_renderer9,glyph_renderer10,glyph_renderer11]];\\n for (i = 0; i < len_labels; i++) {\\n if (checkbox.active.includes(i)) {\\n // console.log('Setting to true: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = true;\\n // console.log('Setting to true: ' + i + ' : ' + j)\\n }\\n } else {\\n // console.log('Setting to false: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = false;\\n // console.log('Setting to false: ' + i + ' : ' + j)\\n }\\n }\\n }\\n \"},\"id\":\"33721\",\"type\":\"CustomJS\"},{\"attributes\":{\"data_source\":{\"id\":\"33603\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"33640\",\"type\":\"MultiLine\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"33641\",\"type\":\"MultiLine\"},\"selection_glyph\":null,\"view\":{\"id\":\"33638\",\"type\":\"CDSView\"}},\"id\":\"33642\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"filters\":[{\"id\":\"33677\",\"type\":\"GroupFilter\"}],\"source\":{\"id\":\"33603\",\"type\":\"ColumnDataSource\"}},\"id\":\"33678\",\"type\":\"CDSView\"},{\"attributes\":{\"callback\":{\"id\":\"33721\",\"type\":\"CustomJS\"},\"icon\":null,\"label\":\"None\"},\"id\":\"33722\",\"type\":\"Button\"},{\"attributes\":{\"column_name\":\"HB_iteration\",\"group\":\"0\"},\"id\":\"33643\",\"type\":\"GroupFilter\"},{\"attributes\":{\"column_name\":\"HB_iteration\",\"group\":\"3\"},\"id\":\"33703\",\"type\":\"GroupFilter\"},{\"attributes\":{\"args\":{\"cm\":{\"id\":\"33605\",\"type\":\"LinearColorMapper\"},\"source_multiline\":{\"id\":\"33603\",\"type\":\"ColumnDataSource\"},\"source_scatter\":{\"id\":\"33604\",\"type\":\"ColumnDataSource\"}},\"code\":\"\\n 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# aggregated parallel BOHB runs1
# parameters6
Deterministic target algorithmTrue
Optimized run objectivequality
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budget 9budget 27budget 81budget 243
Total time spent evaluating configurations0.21 sec0.10 sec0.06 sec0.04 sec
Average time per configuration (mean / std)0.01 sec (± 0.01)0.01 sec (± 0.00)0.01 sec (± 0.00)0.01 sec (± 0.00)
# evaluated configurations2718128
# changed parameters (default to incumbent)6666
Configuration originsAcquisition Function : 6, Random : 21Acquisition Function : 9, Random : 9Acquisition Function : 9, Random : 3Acquisition Function : 4, Random : 4
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budget 9budget 27budget 81budget 243
x0-2.5975-2.43896-2.43896-2.43896
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x3-0.724677-0.250866-0.250866-0.250866
x44.463313.576073.576073.57607
x50.02753890.03086070.03086070.0308607
Cost0.2960.2880.2860.286
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cave.local_parameter_importance(run='budget_9');" ] @@ -852,375 +204,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"1056\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error() {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (var i = 0; i < css_urls.length; i++) {\n", - " var url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };var element = document.getElementById(\"1056\");\n", - " if (element == null) {\n", - " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1056' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n", - " var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {} // ensure no trailing comma for IE\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"1056\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"1056\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"1056\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1056' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1056\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function embed_document(root) {\n", - " \n", - " var docs_json = {\"d7e6a015-2086-4778-96fa-0e5a382774be\":{\"roots\":{\"references\":[{\"attributes\":{\"columns\":[{\"id\":\"1058\",\"type\":\"TableColumn\"},{\"id\":\"1059\",\"type\":\"TableColumn\"},{\"id\":\"1060\",\"type\":\"TableColumn\"}],\"height\":140,\"index_position\":null,\"source\":{\"id\":\"1057\",\"type\":\"ColumnDataSource\"},\"view\":{\"id\":\"1062\",\"type\":\"CDSView\"}},\"id\":\"1061\",\"type\":\"DataTable\"},{\"attributes\":{\"callback\":null,\"data\":{\"LPI\":[\"57.30 +/- 36.10\",\"33.79 +/- 36.61\",\"06.75 +/- 20.06\",\"00.00 +/- 0.00\"],\"Parameters\":[\"x0\",\"x1\",\"x2\",\"x4\"],\"fANOVA\":[\"26.27 +/- 27.92\",\"26.23 +/- 34.71\",\"13.51 +/- 31.68\",\"08.77 +/- 15.48\"]},\"selected\":{\"id\":\"1078\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1077\",\"type\":\"UnionRenderers\"}},\"id\":\"1057\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1072\",\"type\":\"StringEditor\"},{\"attributes\":{},\"id\":\"1073\",\"type\":\"StringFormatter\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"1076\",\"type\":\"StringEditor\"},\"field\":\"LPI\",\"formatter\":{\"id\":\"1075\",\"type\":\"StringFormatter\"},\"title\":\"LPI\",\"width\":100},\"id\":\"1060\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"1076\",\"type\":\"StringEditor\"},{\"attributes\":{\"source\":{\"id\":\"1057\",\"type\":\"ColumnDataSource\"}},\"id\":\"1062\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1077\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"1074\",\"type\":\"StringEditor\"},\"field\":\"fANOVA\",\"formatter\":{\"id\":\"1073\",\"type\":\"StringFormatter\"},\"title\":\"fANOVA\",\"width\":100},\"id\":\"1059\",\"type\":\"TableColumn\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"1072\",\"type\":\"StringEditor\"},\"field\":\"Parameters\",\"formatter\":{\"id\":\"1071\",\"type\":\"StringFormatter\"},\"sortable\":false,\"title\":\"Parameters\",\"width\":150},\"id\":\"1058\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"1074\",\"type\":\"StringEditor\"},{\"attributes\":{},\"id\":\"1078\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1071\",\"type\":\"StringFormatter\"},{\"attributes\":{},\"id\":\"1075\",\"type\":\"StringFormatter\"}],\"root_ids\":[\"1061\"]},\"title\":\"Bokeh Application\",\"version\":\"1.1.0\"}};\n", - " var render_items = [{\"docid\":\"d7e6a015-2086-4778-96fa-0e5a382774be\",\"roots\":{\"1061\":\"e325c62f-83a6-40c9-8edb-c7d6a9e88c44\"}}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - "\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " var attempts = 0;\n", - " var timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " clearInterval(timer);\n", - " }\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", - " clearInterval(timer);\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "1061" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cave.pimp_comparison_table(run='budget_9');" ] @@ -1234,793 +220,36 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"37574\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error() {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (var i = 0; i < css_urls.length; i++) {\n", - " var url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };var element = document.getElementById(\"37574\");\n", - " if (element == null) {\n", - " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '37574' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n", - " var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {} // ensure no trailing comma for IE\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"37574\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"37574\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"37574\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '37574' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"37574\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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\",\"dtype\":\"float64\",\"shape\":[27,30]},{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[27,30]},{\"__ndarray__\":\"X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z/2HpnXO/vSP+BAeEj1vdI/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z/2HpnXO/vSP+BAeEj1vdI/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z/2HpnXO/vSP+BAeEj1vdI/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z/2HpnXO/vSP+BAeEj1vdI/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z/2HpnXO/vSP+BAeEj1vdI/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z/2HpnXO/vSP+BAeEj1vdI/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/1JyyxXXH0j/UnLLFdcfSP9ScssV1x9I/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WUvDVzTP1/tZS8NXNM/X+1lLw1c0z9f7WU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6r9YVk0m2ELkv+DfzX/juNu/ICYCZi3Yzb8AMqIxT/qgvyANMc0FW8U/YFNls0961z8YEBlAjiPiP4B2f6b0ieg/6NzlDFvw7j+oIaa5YKvyP9xU2eyT3vU/EIgMIMcR+T9Euz9T+kT8P3jucoYteP8/1hDTXLBVAUBwqmz2Se8CQApEBpDjiARAaCJg1oLdCcDOiMY86UMIwDTvLKNPqgbAmlWTCbYQBcAAvPlvHHcDwGYiYNaC3QHAzIjGPOlDAMBk3llGn1T9vzCrJhNsIfq//Hfz3zju9r/IRMCsBbvzv5QRjXnSh/C/wLyzjD6p6r9YVk0m2ELkv+DfzX/juNu/ICYCZi3Yzb8AMqIxT/qgvyANMc0FW8U/YFNls0961z8YEBlAjiPiP4B2f6b0ieg/6NzlDFvw7j+oIaa5YKvyP9xU2eyT3vU/EIgMIMcR+T9Euz9T+kT8P3jucoYteP8/1hDTXLBVAUBwqmz2Se8CQApEBpDjiARAaCJg1oLdCcDOiMY86UMIwDTvLKNPqgbAmlWTCbYQBcAAvPlvHHcDwGYiYNaC3QHAzIjGPOlDAMBk3llGn1T9vzCrJhNsIfq//Hfz3zju9r/IRMCsBbvzv5QRjXnSh/C/wLyzjD6p6r9YVk0m2ELkv+DfzX/juNu/ICYCZi3Yzb8AMqIxT/qgvyANMc0FW8U/YFNls0961z8YEBlAjiPiP4B2f6b0ieg/6NzlDFvw7j+oIaa5YKvyP9xU2eyT3vU/EIgMIMcR+T9Euz9T+kT8P3jucoYteP8/1hDTXLBVAUBwqmz2Se8CQApEBpDjiARAaCJg1oLdCcDOiMY86UMIwDTvLKNPqgbAmlWTCbYQBcAAvPlvHHcDwGYiYNaC3QHAzIjGPOlDAMBk3llGn1T9vzCrJhNsIfq//Hfz3zju9r/IRMCsBbvzv5QRjXnSh/C/wLyzjD6p6r9YVk0m2ELkv+DfzX/juNu/ICYCZi3Yzb8AMqIxT/qgvyANMc0FW8U/YFNls0961z8YEBlAjiPiP4B2f6b0ieg/6NzlDFvw7j+oIaa5YKvyP9xU2eyT3vU/EIgMIMcR+T9Euz9T+kT8P3jucoYteP8/1hDTXLBVAUBwqmz2Se8CQApEBpDjiARAaCJg1oLdCcDOiMY86UMIwDTvLKNPqgbAmlWTCbYQBcAAvPlvHHcDwGYiYNaC3QHAzIjGPOlDAMBk3llGn1T9vzCrJhNsIfq//Hfz3zju9r/IRMCsBbvzv5QRjXnSh/C/wLyzjD6p6r9YVk0m2ELkv+DfzX/juNu/ICYCZi3Yzb8AMqIxT/qgvyANMc0FW8U/YFNls0961z8YEBlAjiPiP4B2f6b0ieg/6NzlDFvw7j+oIaa5YKvyP9xU2eyT3vU/EIgMIMcR+T9Euz9T+kT8P3jucoYteP8/1hDTXLBVAUBwqmz2Se8CQApEBpDjiARAaCJg1oLdCcDOiMY86UMIwDTvLKNPqgbAmlWTCbYQBcAAvPlvHHcDwGYiYNaC3QHAzIjGPOlDAMBk3llGn1T9vzCrJhNsIfq//Hfz3zju9r/IRMCsBbvzv5QRjXnSh/C/wLyzjD6p6r9YVk0m2ELkv+DfzX/juNu/ICYCZi3Yzb8AMqIxT/qgvyANMc0FW8U/YFNls0961z8YEBlAjiPiP4B2f6b0ieg/6NzlDFvw7j+oIaa5YKvyP9xU2eyT3vU/EIgMIMcR+T9Euz9T+kT8P3jucoYteP8/1hDTXLBVAUBwqmz2Se8CQApEBpDjiARAaCJg1oLdCcDOiMY86UMIwDTvLKNPqgbAmlWTCbYQBcAAvPlvHHcDwGYiYNaC3QHAzIjGPOlDAMBk3llGn1T9vzCrJhNsIfq//Hfz3zju9r/IRMCsBbvzv5QRjXnSh/C/wLyzjD6p6r9YVk0m2ELkv+DfzX/juNu/ICYCZi3Yzb8AMqIxT/qgvyANMc0FW8U/YFNls0961z8YEBlAjiPiP4B2f6b0ieg/6NzlDFvw7j+oIaa5YKvyP9xU2eyT3vU/EIgMIMcR+T9Euz9T+kT8P3jucoYteP8/1hDTXLBVAUBwqmz2Se8CQApEBpDjiARAaCJg1oLdCcDOiMY86UMIwDTvLKNPqgbAmlWTCbYQBcAAvPlvHHcDwGYiYNaC3QHAzIjGPOlDAMBk3llGn1T9vzCrJhNsIfq//Hfz3zju9r/IRMCsBbvzv5QRjXnSh/C/wLyzjD6p6r9YVk0m2ELkv+DfzX/juNu/ICYCZi3Yzb8AMqIxT/qgvyANMc0FW8U/YFNls0961z8YEBlAjiPiP4B2f6b0ieg/6NzlDFvw7j+oIaa5YKvyP9xU2eyT3vU/EIgMIMcR+T9Euz9T+kT8P3jucoYteP8/1hDTXLBVAUBwqmz2Se8CQApEBpDjiARAaCJg1oLdCcDOiMY86UMIwDTvLKNPqgbAmlWTCbYQBcAAvPlvHHcDwGYiYNaC3QHAzIjGPOlDAMBk3llGn1T9vzCrJhNsIfq//Hfz3zju9r/IRMCsBbvzv5QRjXnSh/C/wLyzjD6p6r9YVk0m2ELkv+DfzX/juNu/ICYCZi3Yzb8AMqIxT/qgvyANMc0FW8U/YFNls0961z8YEBlAjiPiP4B2f6b0ieg/6NzlDFvw7j+oIaa5YKvyP9xU2eyT3vU/EIgMIMcR+T9Euz9T+kT8P3jucoYteP8/1hDTXLBVAUBwqmz2Se8CQApEBpDjiARAaCJg1oLdCcDOiMY86UMIwDTvLKNPqgbAmlWTCbYQBcAAvPlvHHcDwGYiYNaC3QHAzIjGPOlDAMBk3llGn1T9vzCrJhNsIfq//Hfz3zju9r/IRMCsBbvzv5QRjXnSh/C/wLyzjD6p6r9YVk0m2ELkv+DfzX/juNu/ICYCZi3Yzb8AMqIxT/qgvyANMc0FW8U/YFNls0961z8YEBlAjiPiP4B2f6b0ieg/6NzlDFvw7j+oIaa5YKvyP9xU2eyT3vU/EIgMIMcR+T9Euz9T+kT8P3jucoYteP8/1hDTXLBVAUBwqmz2Se8CQApEBpDjiARAaCJg1oLdCcDOiMY86UMIwDTvLKNPqgbAmlWTCbYQBcAAvPlvHHcDwGYiYNaC3QHAzIjGPOlDAMBk3llGn1T9vzCrJhNsIfq//Hfz3zju9r/IRMCsBbvzv5QRjXnSh/C/wLyzjD6p6r9YVk0m2ELkv+DfzX/juNu/ICYCZi3Yzb8AMqIxT/qgvyANMc0FW8U/YFNls0961z8YEBlAjiPiP4B2f6b0ieg/6NzlDFvw7j+oIaa5YKvyP9xU2eyT3vU/EIgMIMcR+T9Euz9T+kT8P3jucoYteP8/1hDTXLBVAUBwqmz2Se8CQApEBpDjiARAaCJg1oLdCcDOiMY86UMIwDTvLKNPqgbAmlWTCbYQBcAAvPlvHHcDwGYiYNaC3QHAzIjGPOlDAMBk3llGn1T9vzCrJhNsIfq//Hfz3zju9r/IRMCsBbvzv5QRjXnSh/C/wLyzjD6p6r9YVk0m2ELkv+DfzX/juNu/ICYCZi3Yzb8AMqIxT/qgvyANMc0FW8U/YFNls0961z8YEBlAjiPiP4B2f6b0ieg/6NzlDFvw7j+oIaa5YKvyP9xU2eyT3vU/EIgMIMcR+T9Euz9T+kT8P3jucoYteP8/1hDTXLBVAUBwqmz2Se8CQApEBpDjiARAaCJg1oLdCcDOiMY86UMIwDTvLKNPqgbAmlWTCbYQBcAAvPlvHHcDwGYiYNaC3QHAzIjGPOlDAMBk3llGn1T9vzCrJhNsIfq//Hfz3zju9r/IRMCsBbvzv5QRjXnSh/C/wLyzjD6p6r9YVk0m2ELkv+DfzX/juNu/ICYCZi3Yzb8AMqIxT/qgvyANMc0FW8U/YFNls0961z8YEBlAjiPiP4B2f6b0ieg/6NzlDFvw7j+oIaa5YKvyP9xU2eyT3vU/EIgMIMcR+T9Euz9T+kT8P3jucoYteP8/1hDTXLBVAUBwqmz2Se8CQApEBpDjiARAaCJg1oLdCcDOiMY86UMIwDTvLKNPqgbAmlWTCbYQBcAAvPlvHHcDwGYiYNaC3QHAzIjGPOlDAMBk3llGn1T9vzCrJhNsIfq//Hfz3zju9r/IRMCsBbvzv5QRjXnSh/C/wLyzjD6p6r9YVk0m2ELkv+DfzX/juNu/ICYCZi3Yzb8AMqIxT/qgvyANMc0FW8U/YFNls0961z8YEBlAjiPiP4B2f6b0ieg/6NzlDFvw7j+oIaa5YKvyP9xU2eyT3vU/EIgMIMcR+T9Euz9T+kT8P3jucoYteP8/1hDTXLBVAUBwqmz2Se8CQApEBpDjiARAaCJg1oLdCcDOiMY86UMIwDTvLKNPqgbAmlWTCbYQBcAAvPlvHHcDwGYiYNaC3QHAzIjGPOlDAMBk3llGn1T9vzCrJhNsIfq//Hfz3zju9r/IRMCsBbvzv5QRjXnSh/C/wLyzjD6p6r9YVk0m2ELkv+DfzX/juNu/ICYCZi3Yzb8AMqIxT/qgvyANMc0FW8U/YFNls0961z8YEBlAjiPiP4B2f6b0ieg/6NzlDFvw7j+oIaa5YKvyP9xU2eyT3vU/EIgMIMcR+T9Euz9T+kT8P3jucoYteP8/1hDTXLBVAUBwqmz2Se8CQApEBpDjiARAaCJg1oLdCcDOiMY86UMIwDTvLKNPqgbAmlWTCbYQBcAAvPlvHHcDwGYiYNaC3QHAzIjGPOlDAMBk3llGn1T9vzCrJhNsIfq//Hfz3zju9r/IRMCsBbvzv5QRjXnSh/C/wLyzjD6p6r9YVk0m2ELkv+DfzX/juNu/ICYCZi3Yzb8AMqIxT/qgvyANMc0FW8U/YFNls0961z8YEBlAjiPiP4B2f6b0ieg/6NzlDFvw7j+oIaa5YKvyP9xU2eyT3vU/EIgMIMcR+T9Euz9T+kT8P3jucoYteP8/1hDTXLBVAUBwqmz2Se8CQApEBpDjiARAaCJg1oLdCcDOiMY8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\",\"dtype\":\"float64\",\"shape\":[27,30]},{\"__ndarray__\":\"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(var i = x.length-1; i >= 0; -- i)\\n obj[x[i]] = x[i];\\n for (var i = y.length-1; i >= 0; -- i)\\n obj[y[i]] = y[i];\\n var res = []\\n for (var k in obj) {\\n if (obj.hasOwnProperty(k)) // <-- optional\\n res.push(obj[k]);\\n }\\n return res;\\n }\\n console.log(\\\"Timeslider: \\\" + time_slider.value);\\n console.log(\\\"Checkbox: \\\" + checkbox.active);var slider_labels = ['0.01', '0.03', '0.13', '0.32', '1.33'];console.log(\\\"Detected slider_labels: \\\" + slider_labels);time_slider.title = \\\"Until wallclocktime \\\" + slider_labels[time_slider.value - 1] + \\\". 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(var i = x.length-1; i >= 0; -- i)\\n obj[x[i]] = x[i];\\n for (var i = y.length-1; i >= 0; -- i)\\n obj[y[i]] = y[i];\\n var res = []\\n for (var k in obj) {\\n if (obj.hasOwnProperty(k)) // <-- optional\\n res.push(obj[k]);\\n }\\n return res;\\n }\\n console.log(\\\"Timeslider: \\\" + time_slider.value);\\n console.log(\\\"Checkbox: \\\" + checkbox.active);var slider_labels = ['0.01', '0.03', '0.13', '0.32', '1.33'];console.log(\\\"Detected slider_labels: \\\" + slider_labels);time_slider.title = \\\"Until wallclocktime \\\" + slider_labels[time_slider.value - 1] + \\\". 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"37790\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error() {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (var i = 0; i < css_urls.length; i++) {\n", - " var url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };var element = document.getElementById(\"37790\");\n", - " if (element == null) {\n", - " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '37790' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n", - " var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {} // ensure no trailing comma for IE\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"37790\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"37790\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"37790\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '37790' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"37790\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function embed_document(root) {\n", - " \n", - " var docs_json = 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9\"},\"renderers\":[{\"id\":\"37840\",\"type\":\"GlyphRenderer\"},{\"id\":\"37844\",\"type\":\"GlyphRenderer\"}]},\"id\":\"37889\",\"type\":\"LegendItem\"},{\"attributes\":{\"fill_alpha\":0.1,\"fill_color\":\"#1f77b4\",\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"37876\",\"type\":\"Patch\"},{\"attributes\":{\"line_color\":\"#66a61e\",\"x\":{\"field\":\"time\"},\"y\":{\"field\":\"mean\"}},\"id\":\"37871\",\"type\":\"Line\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"time\"},\"y\":{\"field\":\"mean\"}},\"id\":\"37839\",\"type\":\"Line\"},{\"attributes\":{\"callback\":{\"id\":\"37885\",\"type\":\"CustomJS\"},\"icon\":null,\"label\":\"None\"},\"id\":\"37886\",\"type\":\"Button\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"37916\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"line_color\":\"#d95f02\",\"x\":{\"field\":\"time\"},\"y\":{\"field\":\"mean\"}},\"id\":\"37838\",\"type\":\"Line\"},{\"attributes\":{\"column_name\":\"name\",\"group\":\"budget 243\"},\"id\":\"37868\",\"type\":\"GroupFilter\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"AAAAgNcw5T8AAACA2pflPwAAAIDal+U/AAAAQB6N9D8AAABAHo30PwAAAEAejfQ/AAAAQB6N9D8AAACA2pflPwAAAIDal+U/AAAAgNcw5T8=\",\"dtype\":\"float64\",\"shape\":[10]},\"y\":{\"__ndarray__\":\"yOkTY9Za0j/I6RNj1lrSPyueCr+uSdI/K54Kv65J0j8rngq/rknSPyueCr+uSdI/K54Kv65J0j8rngq/rknSP8jpE2PWWtI/yOkTY9Za0j8=\",\"dtype\":\"float64\",\"shape\":[10]}},\"selected\":{\"id\":\"37912\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"37913\",\"type\":\"UnionRenderers\"}},\"id\":\"37863\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"line_color\":\"#1b9e77\",\"x\":{\"field\":\"time\"},\"y\":{\"field\":\"mean\"}},\"id\":\"37827\",\"type\":\"Line\"},{\"attributes\":{\"column_name\":\"name\",\"group\":\"budget 81\"},\"id\":\"37857\",\"type\":\"GroupFilter\"},{\"attributes\":{},\"id\":\"37915\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"data_source\":{\"id\":\"37791\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"37838\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"37839\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"37836\",\"type\":\"CDSView\"},\"visible\":false},\"id\":\"37840\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":0.1,\"fill_color\":\"#1f77b4\",\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"37832\",\"type\":\"Patch\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[50]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[50]}},\"selected\":{\"id\":\"37906\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"37907\",\"type\":\"UnionRenderers\"}},\"id\":\"37830\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"children\":[{\"id\":\"37884\",\"type\":\"Button\"}],\"width\":50},\"id\":\"37894\",\"type\":\"WidgetBox\"},{\"attributes\":{\"callback\":null,\"end\":1.3263158798217773,\"start\":0.004393339157104492},\"id\":\"37792\",\"type\":\"Range1d\"},{\"attributes\":{\"data_source\":{\"id\":\"37791\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"37860\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"37861\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"37858\",\"type\":\"CDSView\"},\"visible\":false},\"id\":\"37862\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"click_policy\":\"hide\",\"items\":[{\"id\":\"37888\",\"type\":\"LegendItem\"},{\"id\":\"37889\",\"type\":\"LegendItem\"},{\"id\":\"37890\",\"type\":\"LegendItem\"},{\"id\":\"37891\",\"type\":\"LegendItem\"},{\"id\":\"37892\",\"type\":\"LegendItem\"}],\"label_text_font_size\":{\"value\":\"8pt\"},\"location\":\"bottom_left\"},\"id\":\"37887\",\"type\":\"Legend\"},{\"attributes\":{\"line_color\":\"#e7298a\",\"x\":{\"field\":\"time\"},\"y\":{\"field\":\"mean\"}},\"id\":\"37860\",\"type\":\"Line\"},{\"attributes\":{\"label\":{\"value\":\"budget 81\"},\"renderers\":[{\"id\":\"37862\",\"type\":\"GlyphRenderer\"},{\"id\":\"37866\",\"type\":\"GlyphRenderer\"}]},\"id\":\"37891\",\"type\":\"LegendItem\"},{\"attributes\":{\"args\":{\"checkbox\":{\"id\":\"37882\",\"type\":\"CheckboxButtonGroup\"},\"glyph_renderer0\":{\"id\":\"37829\",\"type\":\"GlyphRenderer\"},\"glyph_renderer1\":{\"id\":\"37833\",\"type\":\"GlyphRenderer\"},\"glyph_renderer2\":{\"id\":\"37840\",\"type\":\"GlyphRenderer\"},\"glyph_renderer3\":{\"id\":\"37844\",\"type\":\"GlyphRenderer\"},\"glyph_renderer4\":{\"id\":\"37851\",\"type\":\"GlyphRenderer\"},\"glyph_renderer5\":{\"id\":\"37855\",\"type\":\"GlyphRenderer\"},\"glyph_renderer6\":{\"id\":\"37862\",\"type\":\"GlyphRenderer\"},\"glyph_renderer7\":{\"id\":\"37866\",\"type\":\"GlyphRenderer\"},\"glyph_renderer8\":{\"id\":\"37873\",\"type\":\"GlyphRenderer\"},\"glyph_renderer9\":{\"id\":\"37877\",\"type\":\"GlyphRenderer\"}},\"code\":\"var labels = [0, 1, 2, 3, 4]; checkbox.active = labels;len_labels = 5;glyph_renderers = [[glyph_renderer0,glyph_renderer1],[glyph_renderer2,glyph_renderer3],[glyph_renderer4,glyph_renderer5],[glyph_renderer6,glyph_renderer7],[glyph_renderer8,glyph_renderer9]];\\n for (i = 0; i < len_labels; i++) {\\n if (checkbox.active.includes(i)) {\\n // console.log('Setting to true: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = true;\\n // console.log('Setting to true: ' + i + ' : ' + j)\\n }\\n } else {\\n // console.log('Setting to false: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = false;\\n // console.log('Setting to false: ' + i + ' : ' + j)\\n }\\n }\\n }\\n \"},\"id\":\"37883\",\"type\":\"CustomJS\"},{\"attributes\":{\"data_source\":{\"id\":\"37791\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"37849\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"37850\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"37847\",\"type\":\"CDSView\"},\"visible\":false},\"id\":\"37851\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"time\"},\"y\":{\"field\":\"mean\"}},\"id\":\"37828\",\"type\":\"Line\"},{\"attributes\":{\"column_name\":\"name\",\"group\":\"budget 27\"},\"id\":\"37846\",\"type\":\"GroupFilter\"},{\"attributes\":{\"fill_alpha\":0.1,\"fill_color\":\"#1f77b4\",\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"37854\",\"type\":\"Patch\"},{\"attributes\":{\"data_source\":{\"id\":\"37830\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"37831\",\"type\":\"Patch\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"37832\",\"type\":\"Patch\"},\"selection_glyph\":null,\"view\":{\"id\":\"37834\",\"type\":\"CDSView\"}},\"id\":\"37833\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"args\":{\"glyph_renderer0\":{\"id\":\"37829\",\"type\":\"GlyphRenderer\"},\"glyph_renderer1\":{\"id\":\"37833\",\"type\":\"GlyphRenderer\"},\"glyph_renderer2\":{\"id\":\"37840\",\"type\":\"GlyphRenderer\"},\"glyph_renderer3\":{\"id\":\"37844\",\"type\":\"GlyphRenderer\"},\"glyph_renderer4\":{\"id\":\"37851\",\"type\":\"GlyphRenderer\"},\"glyph_renderer5\":{\"id\":\"37855\",\"type\":\"GlyphRenderer\"},\"glyph_renderer6\":{\"id\":\"37862\",\"type\":\"GlyphRenderer\"},\"glyph_renderer7\":{\"id\":\"37866\",\"type\":\"GlyphRenderer\"},\"glyph_renderer8\":{\"id\":\"37873\",\"type\":\"GlyphRenderer\"},\"glyph_renderer9\":{\"id\":\"37877\",\"type\":\"GlyphRenderer\"}},\"code\":\"len_labels = 5;glyph_renderers = [[glyph_renderer0,glyph_renderer1],[glyph_renderer2,glyph_renderer3],[glyph_renderer4,glyph_renderer5],[glyph_renderer6,glyph_renderer7],[glyph_renderer8,glyph_renderer9]];\\n for (i = 0; i < len_labels; i++) {\\n if (cb_obj.active.includes(i)) {\\n // console.log('Setting to true: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = true;\\n // console.log('Setting to true: ' + i + ' : ' + j)\\n }\\n } else {\\n // console.log('Setting to false: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = false;\\n // console.log('Setting to false: ' + i + ' : ' + j)\\n }\\n }\\n }\\n \"},\"id\":\"37881\",\"type\":\"CustomJS\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"time\"},\"y\":{\"field\":\"mean\"}},\"id\":\"37850\",\"type\":\"Line\"},{\"attributes\":{\"callback\":{\"id\":\"37883\",\"type\":\"CustomJS\"},\"icon\":null,\"label\":\"All\"},\"id\":\"37884\",\"type\":\"Button\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"time\"},\"y\":{\"field\":\"mean\"}},\"id\":\"37872\",\"type\":\"Line\"},{\"attributes\":{\"fill_alpha\":0.1,\"fill_color\":\"#1f77b4\",\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"37865\",\"type\":\"Patch\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"time\"},\"y\":{\"field\":\"mean\"}},\"id\":\"37861\",\"type\":\"Line\"},{\"attributes\":{\"data_source\":{\"id\":\"37791\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"37827\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"37828\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"37825\",\"type\":\"CDSView\"}},\"id\":\"37829\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"AAAAAA344j8AAAAAYi3jPwAAAABiLeM/AAAAAKNe4z8AAAAAo17jPwAAAICj2+M/AAAAgKPb4z8AAAAAqObkPwAAAACo5uQ/AAAAgKcS8D8AAACApxLwPwAAAICnEvA/AAAAgKcS8D8AAAAAqObkPwAAAACo5uQ/AAAAgKPb4z8AAACAo9vjPwAAAACjXuM/AAAAAKNe4z8AAAAAYi3jPwAAAABiLeM/AAAAAA344j8=\",\"dtype\":\"float64\",\"shape\":[22]},\"y\":{\"__ndarray__\":\"npjPwkjW0z+emM/CSNbTP5aaY7mRSdM/lppjuZFJ0z9ywH2Tpa7SP3LAfZOlrtI/+5sgBJd10j/7myAEl3XSPzLXh4ILadI/MteHggtp0j8y14eCC2nSPzLXh4ILadI/MteHggtp0j8y14eCC2nSP/ubIASXddI/+5sgBJd10j9ywH2Tpa7SP3LAfZOlrtI/lppjuZFJ0z+WmmO5kUnTP56Yz8JI1tM/npjPwkjW0z8=\",\"dtype\":\"float64\",\"shape\":[22]}},\"selected\":{\"id\":\"37910\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"37911\",\"type\":\"UnionRenderers\"}},\"id\":\"37852\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"37841\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"37842\",\"type\":\"Patch\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"37843\",\"type\":\"Patch\"},\"selection_glyph\":null,\"view\":{\"id\":\"37845\",\"type\":\"CDSView\"},\"visible\":false},\"id\":\"37844\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":0.2,\"fill_color\":\"#7570B3\",\"line_color\":\"#7570B3\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"37875\",\"type\":\"Patch\"},{\"attributes\":{\"line_color\":\"#7570b3\",\"x\":{\"field\":\"time\"},\"y\":{\"field\":\"mean\"}},\"id\":\"37849\",\"type\":\"Line\"},{\"attributes\":{\"source\":{\"id\":\"37874\",\"type\":\"ColumnDataSource\"}},\"id\":\"37878\",\"type\":\"CDSView\"},{\"attributes\":{\"source\":{\"id\":\"37841\",\"type\":\"ColumnDataSource\"}},\"id\":\"37845\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"37914\",\"type\":\"Selection\"},{\"attributes\":{\"label\":{\"value\":\"budget 243\"},\"renderers\":[{\"id\":\"37873\",\"type\":\"GlyphRenderer\"},{\"id\":\"37877\",\"type\":\"GlyphRenderer\"}]},\"id\":\"37892\",\"type\":\"LegendItem\"},{\"attributes\":{\"label\":{\"value\":\"all budgets\"},\"renderers\":[{\"id\":\"37829\",\"type\":\"GlyphRenderer\"},{\"id\":\"37833\",\"type\":\"GlyphRenderer\"}]},\"id\":\"37888\",\"type\":\"LegendItem\"},{\"attributes\":{\"fill_alpha\":0.1,\"fill_color\":\"#1f77b4\",\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"37843\",\"type\":\"Patch\"},{\"attributes\":{\"fill_alpha\":0.2,\"fill_color\":\"#7570B3\",\"line_color\":\"#7570B3\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"37842\",\"type\":\"Patch\"},{\"attributes\":{\"children\":[{\"id\":\"37886\",\"type\":\"Button\"}],\"width\":50},\"id\":\"37895\",\"type\":\"WidgetBox\"},{\"attributes\":{\"fill_alpha\":0.2,\"fill_color\":\"#7570B3\",\"line_color\":\"#7570B3\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"37831\",\"type\":\"Patch\"},{\"attributes\":{\"children\":[{\"id\":\"37882\",\"type\":\"CheckboxButtonGroup\"}],\"width\":100},\"id\":\"37893\",\"type\":\"WidgetBox\"},{\"attributes\":{\"fill_alpha\":0.2,\"fill_color\":\"#7570B3\",\"line_color\":\"#7570B3\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"37853\",\"type\":\"Patch\"},{\"attributes\":{\"data_source\":{\"id\":\"37863\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"37864\",\"type\":\"Patch\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"37865\",\"type\":\"Patch\"},\"selection_glyph\":null,\"view\":{\"id\":\"37867\",\"type\":\"CDSView\"},\"visible\":false},\"id\":\"37866\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"children\":[{\"id\":\"37894\",\"type\":\"WidgetBox\"},{\"id\":\"37895\",\"type\":\"WidgetBox\"}]},\"id\":\"37896\",\"type\":\"Row\"},{\"attributes\":{\"data_source\":{\"id\":\"37852\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"37853\",\"type\":\"Patch\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"37854\",\"type\":\"Patch\"},\"selection_glyph\":null,\"view\":{\"id\":\"37856\",\"type\":\"CDSView\"},\"visible\":false},\"id\":\"37855\",\"type\":\"GlyphRenderer\"}],\"root_ids\":[\"37898\"]},\"title\":\"Bokeh Application\",\"version\":\"1.1.0\"}};\n", - " var render_items = [{\"docid\":\"bc523d1b-3a81-4094-a478-c93e30fd29d9\",\"roots\":{\"37898\":\"d5336ad4-2559-4e20-81f5-480a7d8068c5\"}}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - "\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " var attempts = 0;\n", - " var timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " clearInterval(timer);\n", - " }\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", - " clearInterval(timer);\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "37898" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cave.cost_over_time();" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cave.parallel_coordinates(run='budget_9');" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples/icml_2018_experiments/run_experiment.py b/examples/icml_2018_experiments/run_experiment.py deleted file mode 100644 index b5f1c83..0000000 --- a/examples/icml_2018_experiments/run_experiment.py +++ /dev/null @@ -1,126 +0,0 @@ -import os -import pickle - -import numpy as np - -import hpbandster.core.nameserver as hpns -from hpbandster.optimizers import RandomSearch, BOHB, HyperBand -import hpbandster.core.result as hpres - -from workers.bnn_worker import BNNWorker -from workers.cartpole_worker import CartpoleReducedWorker as CartpoleWorker - -def standard_parser_args(parser): - parser.add_argument('--dest_dir', type=str, help='the destination directory. A new subfolder is created for each benchmark/dataset.', default='../results/') - parser.add_argument('--num_iterations', type=int, help='number of Hyperband iterations performed.', default=4) - parser.add_argument('--exp_name', type=str, default='bnn', help='Possible choices: bnn, cartpole, svm_surrogate, paramnet_surrogate') - parser.add_argument('--method', type=str, default='randomsearch', help='Possible choices: randomsearch, bohb, hyperband, tpe, smac') - parser.add_argument('--min_budget', type=float, help='Minimum budget for Hyperband and BOHB.') - parser.add_argument('--max_budget', type=float, help='Maximum budget for all methods.') - parser.add_argument('--eta', type=float, help='Eta value for Hyperband/BOHB.', default=3) - # Network / cluster args for HpBandSter-methods - parser.add_argument('--n_workers', type=int, help='Number of workers to run in parallel.', default=4) - parser.add_argument('--worker', help='Flag to turn this into a worker process', action='store_true') - parser.add_argument('--nic_name', type=str, default='lo', help='name of the network interface used for communication. Note: default is only for local execution on *nix!') - parser.add_argument('--run_id', type=str, default=0) - parser.add_argument('--working_directory', type=str, help='Directory holding live rundata. Should be shared across all nodes for parallel optimization.', default='/tmp/') - # Only relevant for some experiments - parser.add_argument('--dataset_bnn', type=str, help='Only for bnn. Choose from toyfunction, bostonhousing, proteinstructure.', default=None) - parser.add_argument('--dataset_paramnet_surrogates', choices=['adult', 'higgs', 'letter', 'mnist', 'optdigits', 'poker'], help="name of the dataset used", default='mnist') - parser.add_argument('--surrogate_path', type=str, help='Path to the pickled surrogate models. If None, HPOlib2 will automatically download the surrogates to the .hpolib directory in your home directory.', default=None) - return parser - -def get_optimizer(parsed_args, config_space, **kwargs): - eta = parsed_args.eta - opt = None - if parsed_args.method == 'randomsearch': - opt = RandomSearch - if parsed_args.method == 'bohb': - opt = BOHB - if parsed_args.method == 'hyperband': - opt = HyperBand - if opt is None: - raise ValueError("Unknown method %s"%parsed_args.method) - return opt(config_space, eta=eta, **kwargs) - -def get_worker(args, host=None): - exp_name = args.exp_name - if exp_name == 'bnn': - if not args.dataset_bnn: - raise ValueError("Specify a dataset for bnn experiment!") - worker = BNNWorker(dataset=args.dataset_bnn, measure_test_loss=False, run_id=args.run_id, - max_budget=args.max_budget, host=host) - elif exp_name == 'cartpole': - worker = CartpoleWorker(measure_test_loss=False, run_id=args.run_id, host=host) - elif exp_name == 'svm_surrogate': - # this is a synthetic benchmark, so we will use the run_id to separate the independent runs JM: what's that supposed to mean? - worker = Worker(surrogate_path=args.surrogate_path, measure_test_loss=True, run_id=args.run_id, host=host) - elif exp_name == 'paramnet_surrogates': - worker = Worker(dataset=args.dataset_paramnet_surrogates, surrogate_path=args.surrogate_path, - measure_test_loss=False, run_id=args.run_id, host=host) - else: - raise ValueError("{} not a valid experiment name".format(exp_name)) - return worker - -def run_experiment(args, worker, dest_dir, smac_deterministic, store_all_runs=False): - # make sure the working and dest directory exist - os.makedirs(args.working_directory, exist_ok=True) - os.makedirs(dest_dir, exist_ok=True) - - if args.method in ['randomsearch', 'bohb', 'hyperband']: - # Every process has to lookup the hostname - host = hpns.nic_name_to_host(args.nic_name) - - # setup a nameserver - NS = hpns.NameServer(run_id=args.run_id, nic_name=args.nic_name, host=host, working_directory=args.working_directory) - ns_host, ns_port = NS.start() - - if args.worker: - time.sleep(5) # short artificial delay to make sure the nameserver is already running - worker = get_worker(args, host=host) - worker.load_nameserver_credentials(working_directory=args.working_directory) - worker.run(background=False) - exit(0) - - # start worker in the background - worker.load_nameserver_credentials(args.working_directory) - worker.run(background=True) - - configspace = worker.configspace - - result_logger = hpres.json_result_logger(directory=dest_dir, overwrite=True) - - opt = get_optimizer(args, configspace, working_directory=args.dest_dir, - run_id = args.run_id, - min_budget=args.min_budget, max_budget=args.max_budget, - host=ns_host, - nameserver=ns_host, - nameserver_port = ns_port, - ping_interval=3600, - result_logger=result_logger, - ) - - result = opt.run(n_iterations=args.num_iterations, min_n_workers=args.n_workers) - - # shutdown the worker and the dispatcher - opt.shutdown(shutdown_workers=True) - NS.shutdown() - - - from ConfigSpace.read_and_write import pcs_new - with open(os.path.join(dest_dir, 'configspace.pcs'), 'w') as fh: - fh.write(pcs_new.write(opt.config_generator.configspace)) - - # the number of iterations for the blackbox optimizers must be increased so they have comparable total budgets - bb_iterations = int(args.num_iterations * (1+(np.log(args.max_budget) - np.log(args.min_budget))/np.log(args.eta))) - - if args.method == 'tpe': - result = worker.run_tpe(bb_iterations) - - if args.method == 'smac': - result = worker.run_smac(bb_iterations, deterministic=smac_deterministic) - - if result is None: - raise ValueError("Unknown method %s!"%args.method) - - return result diff --git a/examples/icml_2018_experiments/scripts/cluster/job_bnn.sh b/examples/icml_2018_experiments/scripts/cluster/job_bnn.sh index 45c052f..485bfd1 100644 --- a/examples/icml_2018_experiments/scripts/cluster/job_bnn.sh +++ b/examples/icml_2018_experiments/scripts/cluster/job_bnn.sh @@ -1,5 +1,5 @@ #!/bin/bash -#SBATCH -p cpu_ivy # partition (queue) +#SBATCH -p queue-name # partition (queue) #SBATCH --mem 4000 # memory pool for all cores (4GB) #SBATCH -t 0-10:00 # time (D-HH:MM) #SBATCH -c 4 # number of cores @@ -19,9 +19,9 @@ source ~/BOHBsCAVE/.ve_BOHBsCAVE/bin/activate; # Job to perform if [ $SLURM_ARRAY_TASK_ID -eq 1 ] - then python run_experiment.py --exp_name bnn --run_id 42 --nic_name eth0 --dataset bostonhousing --opt_method bohb --max_budget 10000 --min_budget 300 + then python run_experiment.py --exp_name bnn --run_id 42 --nic_name eth0 --dataset_bnn bostonhousing --opt_method bohb --max_budget 10000 --min_budget 300 --n_workers 3 else - python run_experiment.py --exp_name bnn --run_id 42 --nic_name eth0 --worker --dataset bostonhousing --opt_method bohb --max_budget 10000 --min_budget 300 + python run_experiment.py --exp_name bnn --run_id 42 --nic_name eth0 --worker --dataset_bnn bostonhousing --opt_method bohb --max_budget 10000 --min_budget 300 --n_workers 3 fi # Print some Information about the end-time to STDOUT diff --git a/examples/icml_2018_experiments/scripts/cluster/job_cartpole.sh b/examples/icml_2018_experiments/scripts/cluster/job_cartpole.sh index 8a5fe72..51b2516 100644 --- a/examples/icml_2018_experiments/scripts/cluster/job_cartpole.sh +++ b/examples/icml_2018_experiments/scripts/cluster/job_cartpole.sh @@ -1,13 +1,13 @@ #!/bin/bash -#SBATCH -p cpu_ivy # partition (queue) -#SBATCH --mem 6000 # memory pool for all cores (4GB) -#SBATCH -t 2-10:00 # time (D-HH:MM) -#SBATCH -c 6 # number of cores +#SBATCH -p queue-name # partition (queue) +#SBATCH --mem 4000 # memory pool for all cores (4GB) +#SBATCH -t 1-10:00 # time (D-HH:MM) +#SBATCH -c 4 # number of cores #SBATCH -o log/%x.%N.%j.out # STDOUT (the folder log has to be created prior to running or this won't work) #SBATCH -e log/%x.%N.%j.err # STDERR (the folder log has to be created prior to running or this won't work) #SBATCH -J cartpole # sets the job name. If not specified, the file name will be used as job name #SBATCH --mail-type=END,FAIL # (recive mails about end and timeouts/crashes of your job) -#SBATCH --array 0-5 +#SBATCH --array 0-3 # Print some information about the job to STDOUT echo "Workingdir: $PWD"; echo "NOTE: Always submit from dir with run_experiment.py"; @@ -18,10 +18,11 @@ echo "SLURM_ARRAY_TASK_ID: $SLURM_ARRAY_TASK_ID"; source ~/BOHBsCAVE/.ve_BOHBsCAVE/bin/activate; # Job to perform -if [ $SLURM_ARRAY_TASK_ID -eq 1 ] - then python run_experiment.py --exp_name cartpole --run_id 43 --nic_name eth0 --opt_method bohb --max_budget 9 --min_budget 1 +if [ $SLURM_ARRAY_TASK_ID -eq 0 ] +then + python run_experiment.py --exp_name cartpole --run_id 43 --nic_name eth0 --no_worker --opt_method bohb --dest_dir opt_results/cartpole/bohb --max_budget 9 --min_budget 1 --n_workers 4 else - python run_experiment.py --exp_name cartpole --run_id 43 --nic_name eth0 --worker --opt_method bohb --max_budget 9 --min_budget 1 + python run_experiment.py --exp_name cartpole --run_id 43 --nic_name eth0 --worker --opt_method bohb --dest_dir opt_results/cartpole/bohb --max_budget 9 --min_budget 1 --n_workers 4 fi # Print some Information about the end-time to STDOUT diff --git a/examples/icml_2018_experiments/scripts/generate_all_cave_reports.py b/examples/icml_2018_experiments/scripts/generate_all_cave_reports.py new file mode 100644 index 0000000..f9fd459 --- /dev/null +++ b/examples/icml_2018_experiments/scripts/generate_all_cave_reports.py @@ -0,0 +1,20 @@ +import os + +from cave.cavefacade import CAVE + +def analyze_all(): + for dirpath, dirnames, filenames in os.walk('../opt_results/'): + if not dirnames: + print(dirpath) + cave = CAVE(folders=[dirpath], + output_dir=os.path.join("../CAVE_reports", dirpath[15:]), # output for debug/images/etc + ta_exec_dir=["."], # Only important for SMAC-results + file_format='BOHB' if os.path.exists(os.path.join(dirpath, 'configs.json')) else 'SMAC3', + #verbose_level='DEV_DEBUG', + verbose_level='OFF', + show_jupyter=False, + ) + cave.analyze() + +if __name__ == '__main__': + analyze_all() diff --git a/examples/icml_2018_experiments/scripts/run_all_cave_results.sh b/examples/icml_2018_experiments/scripts/run_all_cave_results.sh deleted file mode 100644 index 923ca89..0000000 --- a/examples/icml_2018_experiments/scripts/run_all_cave_results.sh +++ /dev/null @@ -1,56 +0,0 @@ -#!/bin/bash - -cave --folders opt_results/bnn/bostonhousing/bohb --file_format BOHB --output CAVE_reports/bnn_bostonhousing_bohb --verbose_level OFF -cave --folders opt_results/bnn/bostonhousing/hyperband --file_format BOHB --output CAVE_reports/bnn_bostonhousing_hyperband --verbose_level OFF -cave --folders opt_results/bnn/bostonhousing/randomsearch --file_format BOHB --output CAVE_reports/bnn_bostonhousing_randomsearch --verbose_level OFF -cave --folders opt_results/bnn/bostonhousing/smac/* --file_format SMAC3 --output CAVE_reports/bnn_bostonhousing_smac --verbose_level OFF - -cave --folders opt_results/bnn/toyfunction/bohb --file_format BOHB --output CAVE_reports/bnn_toyfunction_bohb --verbose_level OFF -cave --folders opt_results/bnn/toyfunction/hyperband --file_format BOHB --output CAVE_reports/bnn_toyfunction_hyperband --verbose_level OFF -cave --folders opt_results/bnn/toyfunction/randomsearch --file_format BOHB --output CAVE_reports/bnn_toyfunction_randomsearch --verbose_level OFF -cave --folders opt_results/bnn/toyfunction/smac/* --file_format SMAC3 --output CAVE_reports/bnn_toyfunction_smac --verbose_level OFF - -cave --folders opt_results/bnn/proteinstructure/bohb --file_format BOHB --output CAVE_reports/bnn_proteinstructure_bohb --verbose_level OFF -cave --folders opt_results/bnn/proteinstructure/hyperband --file_format BOHB --output CAVE_reports/bnn_proteinstructure_hyperband --verbose_level OFF -cave --folders opt_results/bnn/proteinstructure/randomsearch --file_format BOHB --output CAVE_reports/bnn_proteinstructure_randomsearch --verbose_level OFF -cave --folders opt_results/bnn/proteinstructure/smac/* --file_format SMAC3 --output CAVE_reports/bnn_proteinstructure_smac --verbose_level OFF - -cave --folders opt_results/cartpole/bohb --file_format BOHB --output CAVE_reports/cartpole_bohb --verbose_level OFF -cave --folders opt_results/cartpole/hyperband --file_format BOHB --output CAVE_reports/cartpole_hyperband --verbose_level OFF -cave --folders opt_results/cartpole/randomsearch --file_format BOHB --output CAVE_reports/cartpole_randomsearch --verbose_level OFF -cave --folders opt_results/cartpole/smac/* --file_format SMAC3 --output CAVE_reports/cartpole_smac --verbose_level OFF - -cave --folders opt_results/svm_surrogate/bohb --file_format BOHB --output CAVE_reports/svm_surrogate_bohb --verbose_level OFF -cave --folders opt_results/svm_surrogate/hyperband --file_format BOHB --output CAVE_reports/svm_surrogate_hyperband --verbose_level OFF -cave --folders opt_results/svm_surrogate/randomsearch --file_format BOHB --output CAVE_reports/svm_surrogate_randomsearch --verbose_level OFF -cave --folders opt_results/svm_surrogate/smac/* --file_format SMAC3 --output CAVE_reports/svm_surrogate_smac --verbose_level OFF - -cave --folders opt_results/paramnet_surrogate/adult/bohb --file_format BOHB --output CAVE_reports/paramnet_surrogate_adult_bohb --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/adult/hyperband --file_format BOHB --output CAVE_reports/paramnet_surrogate_adult_hyperband --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/adult/randomsearch --file_format BOHB --output CAVE_reports/paramnet_surrogate_adult_randomsearch --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/adult/smac/* --file_format SMAC3 --output CAVE_reports/paramnet_surrogate_adult_smac --verbose_level OFF - -cave --folders opt_results/paramnet_surrogate/higgs/bohb --file_format BOHB --output CAVE_reports/paramnet_surrogate_higgs_bohb --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/higgs/hyperband --file_format BOHB --output CAVE_reports/paramnet_surrogate_higgs_hyperband --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/higgs/randomsearch --file_format BOHB --output CAVE_reports/paramnet_surrogate_higgs_randomsearch --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/higgs/smac/* --file_format SMAC3 --output CAVE_reports/paramnet_surrogate_higgs_smac --verbose_level OFF - -cave --folders opt_results/paramnet_surrogate/letter/bohb --file_format BOHB --output CAVE_reports/paramnet_surrogate_letter_bohb --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/letter/hyperband --file_format BOHB --output CAVE_reports/paramnet_surrogate_letter_hyperband --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/letter/randomsearch --file_format BOHB --output CAVE_reports/paramnet_surrogate_letter_randomsearch --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/letter/smac/* --file_format SMAC3 --output CAVE_reports/paramnet_surrogate_letter_smac --verbose_level OFF - -cave --folders opt_results/paramnet_surrogate/mnist/bohb --file_format BOHB --output CAVE_reports/paramnet_surrogate_mnist_bohb --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/mnist/hyperband --file_format BOHB --output CAVE_reports/paramnet_surrogate_mnist_hyperband --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/mnist/randomsearch --file_format BOHB --output CAVE_reports/paramnet_surrogate_mnist_randomsearch --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/mnist/smac/* --file_format SMAC3 --output CAVE_reports/paramnet_surrogate_mnist_smac --verbose_level OFF - -cave --folders opt_results/paramnet_surrogate/optdigits/bohb --file_format BOHB --output CAVE_reports/paramnet_surrogate_optdigits_bohb --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/optdigits/hyperband --file_format BOHB --output CAVE_reports/paramnet_surrogate_optdigits_hyperband --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/optdigits/randomsearch --file_format BOHB --output CAVE_reports/paramnet_surrogate_optdigits_randomsearch --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/optdigits/smac/* --file_format SMAC3 --output CAVE_reports/paramnet_surrogate_optdigits_smac --verbose_level OFF - -cave --folders opt_results/paramnet_surrogate/poker/bohb --file_format BOHB --output CAVE_reports/paramnet_surrogate_poker_bohb --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/poker/hyperband --file_format BOHB --output CAVE_reports/paramnet_surrogate_poker_hyperband --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/poker/randomsearch --file_format BOHB --output CAVE_reports/paramnet_surrogate_poker_randomsearch --verbose_level OFF -cave --folders opt_results/paramnet_surrogate/poker/smac/* --file_format SMAC3 --output CAVE_reports/paramnet_surrogate_poker_smac --verbose_level OFF diff --git a/examples/icml_2018_experiments/scripts/run_all_exps.py b/examples/icml_2018_experiments/scripts/run_all_exps.py new file mode 100644 index 0000000..37ac064 --- /dev/null +++ b/examples/icml_2018_experiments/scripts/run_all_exps.py @@ -0,0 +1,116 @@ +# This script will generate job-files for execution on a SLURM cluster. +# You can modify the template (you'll have to set the queue at the very least). +# It is also possible to just run this for a couple of days on a local machine (set submit2cluster=False). +# You might need to change variables when executing the script from a different folder than (...)/scripts/ +# To change the number of iterations etc. just set the variables here: + +queue_name = ? +n_iterations = { + 'bnn' : 4, + 'cartpole' : 4, + 'paramnet_surrogates' : 10, + 'svm_surrogate' : 10 + } +submit2cluster = True +optimizers = ['randomsearch', 'bohb', 'hyperband', 'smac'] +experiments = ['bnn', 'cartpole', 'svm_surrogate', 'paramnet_surrogates'] + +############################################################################################################## +############################################################################################################## +##### no modifications should be necessary from here on to run on SLURM cluster, but adapt what you need ##### +############################################################################################################## +############################################################################################################## + +cluster_job_template = """#!/bin/bash +#SBATCH -p {queue_name} # partition (queue) +#SBATCH --mem 4000 # memory pool for all cores (4GB) +#SBATCH -t 1-10:00 # time (D-HH:MM) +#SBATCH -c 4 # number of cores +#SBATCH -o log/%x.%N.%j.out # STDOUT (the folder log has to be created prior to running or this won't work) +#SBATCH -e log/%x.%N.%j.err # STDERR (the folder log has to be created prior to running or this won't work) +#SBATCH -J {job} # sets the job name. If not specified, the file name will be used as job name +#SBATCH --mail-type=END,FAIL # (recive mails about end and timeouts/crashes of your job) +#SBATCH --array 0-3 + +# Print some information about the job to STDOUT +echo "Workingdir: $PWD"; +echo "NOTE: Always submit from dir with run_experiment.py"; +echo "Started at $(date)"; +echo "Running job $SLURM_JOB_NAME using $SLURM_JOB_CPUS_PER_NODE cpus per node with given JID $SLURM_JOB_ID on queue $SLURM_JOB_PARTITION"; +echo "SLURM_ARRAY_TASK_ID: $SLURM_ARRAY_TASK_ID"; + +# Activate virtual-environment +source ~/BOHBsCAVE/.ve_BOHBsCAVE/bin/activate; + +# Job to perform +if [ $SLURM_ARRAY_TASK_ID -eq 0 ] +then + python run_experiment.py --exp_name {exp} --run_id {run_id} --nic_name eth0 --no_worker --opt_method {opt} --dest_dir opt_results/ --max_budget {max_b} --min_budget {min_b} --n_workers 4 {additional} --num_iterations {num_it} +else + python run_experiment.py --exp_name {exp} --run_id {run_id} --nic_name eth0 --worker --opt_method {opt} --dest_dir opt_results/ --max_budget {max_b} --min_budget {min_b} --n_workers 4 {additional} --num_iterations {num_it} +fi + +# Print some Information about the end-time to STDOUT +echo "DONE"; +echo "Finished at $(date)"; +""" + +import os +import random + +def gen_script(script, job_str): + """ This will write the script to file """ + path = 'cluster/job_' + job_str + with open(path, 'w') as fh: + fh.write(script) + if submit2cluster: + os.system('sbatch ' + path) + +def run_cartpole(): + for opt in optimizers: + job_str = '_'.join(['cartpole', opt]) + script = cluster_job_template.format(job=job_str, exp='cartpole', run_id=random.randint(1, 1000000), opt=opt, + max_b=9, min_b=1, additional='', + num_it=n_iterations['cartpole'], queue_name=queue_name) + gen_script(script, job_str) + +def run_svm_surrogate(): + for opt in optimizers: + job_str = '_'.join(['svm_surrogate', opt]) + script = cluster_job_template.format(job=job_str, exp='svm_surrogate', run_id=random.randint(1, 1000000), opt=opt, + max_b=1, min_b=0.001953125, additional='', + num_it=n_iterations['svm_surrogate'], queue_name=queue_name) + gen_script(script, job_str) + +def run_paramnet_surrogates(): + for dataset in ['adult', 'higgs', 'letter', 'mnist', 'optdigits', 'poker']: + for opt in optimizers: + job_str = '_'.join(['paramnet_surrogates', dataset, opt]) + script = cluster_job_template.format(job=job_str, exp='paramnet_surrogates', run_id=random.randint(1, 1000000), opt=opt, + max_b=0, min_b=0, additional='--dataset_paramnet_surrogates ' + dataset, + num_it=n_iterations['paramnet_surrogates'], queue_name=queue_name) + gen_script(script, job_str) + if not submit2cluster: + os.system("python run_experiment.py --exp_name paramnet_surrogates --opt_method {opt} " + "--dataset_paramnet_surrogates {dataset} --num_iterations 10 --n_workers 1 " + "--dest_dir ../opt_results".format(opt=opt, dataset=dataset)) + +def run_bnn(): + for dataset in ['bostonhousing', 'toyfunction', 'proteinstructure']: + for opt in optimizers: + job_str = '_'.join(['bnn', dataset, opt]) + script = cluster_job_template.format(job=job_str, exp='bnn', run_id=random.randint(1, 1000000), opt=opt, + max_b=10000, min_b=300, additional='--dataset_bnn ' + dataset, + num_it=n_iterations['bnn'], queue_name=queue_name) + gen_script(script, job_str) + + +if __name__ == '__main__': + if 'bnn' in experiments: + run_bnn() + if 'cartpole' in experiments: + run_cartpole() + if 'paramnet_surrogates' in experiments: + run_paramnet_surrogates() + if 'svm_surrogate' in experiments: + run_svm_surrogate() diff --git a/examples/icml_2018_experiments/scripts/run_experiment.py b/examples/icml_2018_experiments/scripts/run_experiment.py new file mode 100644 index 0000000..f0958c0 --- /dev/null +++ b/examples/icml_2018_experiments/scripts/run_experiment.py @@ -0,0 +1,190 @@ +# This script will run any of the experiments described in HpBandSter's icml_2018_experiments +# Use the script with the --help flag to receive info on usage. + +import os +import pickle +import argparse +import copy + +import numpy as np + +import hpbandster.core.nameserver as hpns +from hpbandster.optimizers import RandomSearch, BOHB, HyperBand +import hpbandster.core.result as hpres + +from workers.bnn_worker import BNNWorker +from workers.cartpole_worker import CartpoleReducedWorker as CartpoleWorker +from workers.paramnet_surrogates import ParamNetSurrogateWorker +from workers.svm_surrogate import SVMSurrogateWorker + +def standard_parser_args(parser): + parser.add_argument('--exp_name', type=str, required=True, help='Possible choices: bnn, cartpole, svm_surrogate, paramnet_surrogates') + parser.add_argument('--opt_method', type=str, default='bohb', help='Possible choices: randomsearch, bohb, hyperband, smac') + + parser.add_argument('--dest_dir', type=str, help='the destination directory. A new subfolder is created for each benchmark/dataset.', + default='../opt_results') + parser.add_argument('--num_iterations', type=int, help='number of Hyperband iterations performed.', default=4) + parser.add_argument('--min_budget', type=float, help='Minimum budget for Hyperband and BOHB.') + parser.add_argument('--max_budget', type=float, help='Maximum budget for all methods.') + parser.add_argument('--eta', type=float, help='Eta value for Hyperband/BOHB.', default=3) + # Network / cluster args for HpBandSter-methods + parser.add_argument('--n_workers', type=int, help='Number of workers to run in parallel.', default=1) + parser.add_argument('--worker', help='Flag to turn this into a worker process', action='store_true') + parser.add_argument('--no_worker', help='Flag to turn this into a worker process', + dest='worker', action='store_false') + parser.add_argument('--nic_name', type=str, default='lo', help='name of the network interface used for communication. Note: default is only for local execution on *nix!') + parser.add_argument('--run_id', type=str, default=0) + parser.add_argument('--working_directory', type=str, help='Directory holding live rundata. Should be shared across all nodes for parallel optimization.', + default='./tmp/') + # Only relevant for some experiments + parser.add_argument('--dataset_bnn', choices=['toyfunction', 'bostonhousing', 'proteinstructure'], help='Only for bnn. ', default=None) + parser.add_argument('--dataset_paramnet_surrogates', choices=['adult', 'higgs', 'letter', 'mnist', 'optdigits', 'poker'], + help="Only for paramnet_surrogates. ", default=None) + parser.add_argument('--surrogate_path', type=str, help="Path to the pickled surrogate models. If None, HPOlib2 " + "will automatically download the surrogates to the .hpolib " + "directory in your home directory.", default=None) + return parser + +def get_optimizer(parsed_args, config_space, **kwargs): + """ Get the right hpbandster-optimizer """ + eta = parsed_args.eta + opt = None + if parsed_args.opt_method == 'randomsearch': + opt = RandomSearch + if parsed_args.opt_method == 'bohb': + opt = BOHB + if parsed_args.opt_method == 'hyperband': + opt = HyperBand + if opt is None: + raise ValueError("Unknown method %s" % parsed_args.method) + return opt(config_space, eta=eta, **kwargs) + +def get_worker(args, host=None): + exp_name = args.exp_name + if exp_name == 'bnn': + if not args.dataset_bnn: + raise ValueError("Specify a dataset for bnn experiment!") + worker = BNNWorker(dataset=args.dataset_bnn, measure_test_loss=False, run_id=args.run_id, + max_budget=args.max_budget, host=host) + elif exp_name == 'cartpole': + worker = CartpoleWorker(measure_test_loss=False, run_id=args.run_id, host=host) + elif exp_name == 'svm_surrogate': + # this is a synthetic benchmark, so we will use the run_id to separate the independent runs (JM: what's that supposed to mean?) + worker = SVMSurrogateWorker(surrogate_path=args.surrogate_path, measure_test_loss=True, run_id=args.run_id, host=host) + elif exp_name == 'paramnet_surrogates': + if not args.dataset_paramnet_surrogates: + raise ValueError("Specify a dataset for paramnet surrogates experiment!") + worker = ParamNetSurrogateWorker(dataset=args.dataset_paramnet_surrogates, surrogate_path=args.surrogate_path, + measure_test_loss=False, run_id=args.run_id, host=host) + else: + raise ValueError("{} not a valid experiment name".format(exp_name)) + return worker + +def run_experiment(args, worker, dest_dir, smac_deterministic, store_all_runs=False): + print("Running experiment (args: %s)" % str(args)) + # make sure the working and dest directory exist + os.makedirs(args.working_directory, exist_ok=True) + os.makedirs(dest_dir, exist_ok=True) + + if args.opt_method in ['randomsearch', 'bohb', 'hyperband']: + print("Using hpbandster-optimizer (%s)" % args.opt_method) + # Every process has to lookup the hostname + host = hpns.nic_name_to_host(args.nic_name) + print("Host: %s" % str(host)) + + # setup a nameserver + NS = hpns.NameServer(run_id=args.run_id, + nic_name=args.nic_name, + port=0, + host=host, + working_directory=args.working_directory) + ns_host, ns_port = NS.start() + print("Initialized nameserver (ns_host: %s; ns_port: %s)" % (str(ns_host), str(ns_port))) + + if args.worker: + print("This is a pure worker-thread.") + worker = get_worker(args, host=host) + worker.load_nameserver_credentials(working_directory=args.working_directory) + worker.run(background=False) + print("Exiting...") + exit(0) + + print("This is the name-server thread, however there will be a worker running in the background.") + worker = get_worker(args, host=host) + + # start worker in the background + worker.load_nameserver_credentials(working_directory=args.working_directory) + worker.run(background=True) + + if args.exp_name == 'paramnet_surrogates': + print("This is the paramnet_surrogates experiment, so any custom budgets will be replaced by the " + "dataset-specific budgets.") + args.min_budget, args.max_budget = worker.budgets[args.dataset_paramnet_surrogates] + + print("Background-worker is running, grabbing configspace from worker and initializing result_logger " + "(with dest_dir %s)" % dest_dir) + configspace = worker.configspace + + result_logger = hpres.json_result_logger(directory=dest_dir, overwrite=True) + + print("Getting optimizer.") + + opt = get_optimizer(args, configspace, working_directory=args.working_directory, + run_id=args.run_id, + min_budget=args.min_budget, max_budget=args.max_budget, + host=host, + nameserver=ns_host, + nameserver_port = ns_port, + ping_interval=30, + result_logger=result_logger, + ) + + print("Initialization successful, starting optimization.") + + from ConfigSpace.read_and_write import pcs_new + with open(os.path.join(dest_dir, 'configspace.pcs'), 'w') as fh: + fh.write(pcs_new.write(opt.config_generator.configspace)) + + result = opt.run(n_iterations=args.num_iterations, min_n_workers=args.n_workers) + + print("Finished optimization") + # shutdown the worker and the dispatcher + opt.shutdown(shutdown_workers=True) + NS.shutdown() + + if args.exp_name == 'paramnet_surrogates': + # This if block is necessary to set budgets for paramnet_surrogates - for nothing else + args_tmp = copy.deepcopy(args) + args_tmp.opt_method = 'bohb' + worker = get_worker(args_tmp) + args.min_budget, args.max_budget = worker.budgets[args.dataset_paramnet_surrogates] + + # the number of iterations for the blackbox optimizers must be increased so they have comparable total budgets + bb_iterations = int(args.num_iterations * (1+(np.log(args.max_budget) - np.log(args.min_budget))/np.log(args.eta))) + + #if args.opt_method == 'tpe': + # result = worker.run_tpe(bb_iterations) + + if args.opt_method == 'smac': + result = worker.run_smac(bb_iterations, deterministic=smac_deterministic, working_directory=args.dest_dir) + + if result is None: + raise ValueError("Unknown method %s!"%args.method) + + return result + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='Running one of the icml 2018 experiments.', conflict_handler='resolve') + parser = standard_parser_args(parser) + + # Parsing args and creating sub-folders for experiments + args = parser.parse_args() + args.dest_dir = os.path.join(args.dest_dir, args.exp_name) + if args.exp_name == 'bnn': + args.dest_dir = os.path.join(args.dest_dir, args.dataset_bnn) + if args.exp_name == 'paramnet_surrogates': + args.dest_dir = os.path.join(args.dest_dir, args.dataset_paramnet_surrogates) + args.dest_dir = os.path.join(args.dest_dir, args.opt_method) + + run_experiment(args, args.worker, args.dest_dir, smac_deterministic=True, store_all_runs=False) diff --git a/examples/icml_2018_experiments/workers/base_worker.py b/examples/icml_2018_experiments/scripts/workers/base_worker.py similarity index 98% rename from examples/icml_2018_experiments/workers/base_worker.py rename to examples/icml_2018_experiments/scripts/workers/base_worker.py index a87c496..03ea64e 100644 --- a/examples/icml_2018_experiments/workers/base_worker.py +++ b/examples/icml_2018_experiments/scripts/workers/base_worker.py @@ -125,7 +125,7 @@ def smac_objective(config, **kwargs): return loss, [] - scenario = Scenario({ "run_obj": "quality", + scenario = Scenario({ "run_obj": "quality", "runcount-limit": num_iterations, "cs": self.configspace, "deterministic": deterministic, diff --git a/examples/icml_2018_experiments/workers/bnn_worker.py b/examples/icml_2018_experiments/scripts/workers/bnn_worker.py similarity index 100% rename from examples/icml_2018_experiments/workers/bnn_worker.py rename to examples/icml_2018_experiments/scripts/workers/bnn_worker.py diff --git a/examples/icml_2018_experiments/workers/cartpole_worker.py b/examples/icml_2018_experiments/scripts/workers/cartpole_worker.py similarity index 100% rename from examples/icml_2018_experiments/workers/cartpole_worker.py rename to examples/icml_2018_experiments/scripts/workers/cartpole_worker.py diff --git a/examples/icml_2018_experiments/workers/paramnet_surrogates.py b/examples/icml_2018_experiments/scripts/workers/paramnet_surrogates.py similarity index 100% rename from examples/icml_2018_experiments/workers/paramnet_surrogates.py rename to examples/icml_2018_experiments/scripts/workers/paramnet_surrogates.py diff --git a/examples/icml_2018_experiments/workers/svm_surrogate.py b/examples/icml_2018_experiments/scripts/workers/svm_surrogate.py similarity index 100% rename from examples/icml_2018_experiments/workers/svm_surrogate.py rename to examples/icml_2018_experiments/scripts/workers/svm_surrogate.py diff --git a/examples/icml_2018_experiments/svm_surrogate.ipynb b/examples/icml_2018_experiments/svm_surrogate.ipynb index bfd25a9..61dbf23 100644 --- a/examples/icml_2018_experiments/svm_surrogate.ipynb +++ b/examples/icml_2018_experiments/svm_surrogate.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this series of notebooks, we will replicate and analyze the ICML 2018 experiments that where used for benchmarking in BOHB (Falkner et al. 2018).\n", + "In this series of notebooks, we will replicate and analyze the ICML 2018 experiments that were used for benchmarking in BOHB (Falkner et al. 2018).\n", "In addition to HpBandSter, we will use CAVE to analyze and visualize the optimization process." ] }, @@ -19,7 +19,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## About the frameworks\n", + "## About this experiment\n", "\n", "### SVM Surrogate\n", "\n", @@ -44,38 +44,19 @@ ] }, { - "cell_type": "code", - "execution_count": 3, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# We deactivate logging to ensure readability\n", - "import logging\n", - "logging.basicConfig(level=logging.ERROR)\n", - "# Also, we suppress warnings.\n", - "# If there are problems for you executing this notebook, you might want to comment this out.\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")" + "This examples ships with all the code necessary to reproduce the experiment. Because it takes a few days to generate the data, the results of the optimization are provided in `examples/icml_2018_experiments/opt_results/`. If you want to generate the data (from examples/icml_2018_experiments), just run:" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "### 1.1) References\n", - "*Worker*: We need a Worker to define the kind of computation we want to optimize. The worker used for the experiments is located in `workers/svm_surrogate.py`\n", - "\n", - "\n", - "\n", - "*ConfigSpace*: Every problem needs a description of the search space to be complete. In HpBandSter, a ConfigurationSpace-object defines all hyperparameters, their ranges and dependencies between them.\n", - "\n", - "In our example here, the search space consists of the hyperparameters:\n", - "\n", - "\n", - "| Name | Type | Values |\n", - "|:---------:|:-------:|:------------:|\n", - "| x0 | real | [-10.0, 10.0] |\n", - "| x1 | real | [-10.0, 10.0] |" + "! python scripts/run_experiment.py --exp_name svm_surrogate --num_iterations 1 --min_budget 0.001953125 --max_budget 1 --n_workers 1" ] }, { @@ -84,90 +65,8 @@ "scrolled": false }, "source": [ - "### 1.3) Setting up the experiment and running the optimizer(s)\n", - "\n", - "Please note that the execution of the experiment with all datasets might take up to a few days, depending on your hardware. You can also skip this step and process with the precomputed results saved in `opt_results/svm_surrogate`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import os\n", - "from itertools import product\n", - "import numpy as np\n", - "\n", - "import hpbandster.core.nameserver as hpns\n", - "import hpbandster.core.result as hpres\n", - "from hpbandster.optimizers import BOHB, RandomSearch, HyperBand\n", - "\n", - "from workers.svm_surrogate import SVMSurrogateWorker\n", - "\n", - "# Run the experiment\n", - "opt_methods = [\"smac\", \"bohb\", \"randomsearch\", \"hyperband\"]\n", - "num_iterations = 32\n", - "min_budget = 1/512\n", - "max_budget = 1\n", - "\n", - "eta = 3\n", - "\n", - "for opt_method in opt_methods:\n", - "\n", - " print(opt_method)\n", - " \n", - " output_dir = \"opt_results/svm_surrogate/{}\".format(opt_method)\n", - " if not os.path.exists(output_dir):\n", - " os.makedirs(output_dir)\n", - "\n", - " run_id = '0' # Every run has to have a unique (at runtime) id.\n", - " \n", - " # create a worker\n", - " worker = SVMSurrogateWorker(surrogate_path=None, measure_test_loss=True, run_id=run_id)\n", - " configspace = worker.configspace\n", - "\n", - " if opt_method in ['randomsearch', 'bohb', 'hyperband']:\n", - " # setup a nameserver\n", - " NS = hpns.NameServer(run_id=run_id, host='localhost', port=0, working_directory=output_dir)\n", - " ns_host, ns_port = NS.start()\n", - "\n", - " worker.load_nameserver_credentials(output_dir)\n", - " worker.run(background=True)\n", - "\n", - " # instantiate and run optimizer\n", - " opt = RandomSearch if opt_method == 'randomsearch' else BOHB if opt_method == 'bohb' else HyperBand\n", - "\n", - " result_logger = hpres.json_result_logger(directory=output_dir, overwrite=True)\n", - "\n", - " opt = opt(configspace, eta=3,\n", - " working_directory=output_dir,\n", - " run_id=run_id,\n", - " min_budget=min_budget, max_budget=max_budget,\n", - " host=ns_host,\n", - " nameserver=ns_host,\n", - " nameserver_port = ns_port,\n", - " ping_interval=3600,\n", - " result_logger=result_logger)\n", - "\n", - " result = opt.run(n_iterations=num_iterations)\n", - " \n", - " # **NOTE:** Unfortunately, the configuration space is *not yet saved automatically* to file\n", - " # but this step is mandatory for the analysis with CAVE. \n", - " # We recommend to save the configuration space every time you use BOHB.\n", - " # We do this by using the ConfigSpace-to-json-writer.\n", - "\n", - " from ConfigSpace.read_and_write import pcs_new\n", - " with open(os.path.join(output_dir, 'configspace.pcs'), 'w') as fh:\n", - " fh.write(pcs_new.write(opt.config_generator.configspace))\n", - " \n", - " else:\n", - " # the number of iterations for the blackbox optimizers must be increased so they have comparable total budgets\n", - " bb_iterations = int(num_iterations * (1+(np.log(max_budget) - np.log(min_budget))/np.log(eta)))\n", - " if opt_method == 'smac':\n", - " result = worker.run_smac(bb_iterations, deterministic=True, working_directory=output_dir)" + "You can use this script to generate the optimization data for a variety of optimizers, the results will (by default) be stored in `examples/icml_2018_experiments/EXPERIMENT/OPTIMIZER`. Use `python run_experiment --help` to see how to use the script.\n", + "If you have access to a cluster, take a look at the scripts provided for cluster computation on a SLURM cluster at `examples/icml_2018_experiments/scripts/cluster/`." ] }, { @@ -178,12 +77,12 @@ "\n", "### Instantiate CAVE\n", "\n", - "Analyzing the optimization results with CAVE is very straight-forward. If you want to use CAVE interactively in a notebook, set `show_jupyter=True`. Specify which optimization you want to analyze via the `folders` argument and specify `file_format==SMAC3` or `file_format==BOHB`, depending on which optimizer was used for the results. To analyze how BOHB optimized on the *bostenhousing* dataset, run:" + "Analyzing the optimization results with CAVE is very straight-forward. If you want to use CAVE interactively in a notebook, set `show_jupyter=True`. Specify which optimization you want to analyze via the `folders` argument and specify `file_format==SMAC3` or `file_format==BOHB`, depending on which optimizer was used for the results. To analyze how BOHB optimized on the *svm_surrogate* experiment, run:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "scrolled": false }, @@ -213,6 +112,7 @@ "metadata": {}, "outputs": [], "source": [ + "%%capture\n", "cave.analyze()\n", "! firefox CAVE_reports/svm_notebook/report.html" ] @@ -228,467 +128,22 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"10413\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error() {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (var i = 0; i < css_urls.length; i++) {\n", - " var url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };var element = document.getElementById(\"10413\");\n", - " if (element == null) {\n", - " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '10413' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n", - " var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {} // ensure no trailing comma for IE\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"10413\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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"},\"glyph_renderer31\":{\"id\":\"10501\",\"type\":\"GlyphRenderer\"},\"glyph_renderer32\":{\"id\":\"10508\",\"type\":\"GlyphRenderer\"},\"glyph_renderer33\":{\"id\":\"10514\",\"type\":\"GlyphRenderer\"},\"glyph_renderer34\":{\"id\":\"10521\",\"type\":\"GlyphRenderer\"},\"glyph_renderer35\":{\"id\":\"10528\",\"type\":\"GlyphRenderer\"},\"glyph_renderer36\":{\"id\":\"10534\",\"type\":\"GlyphRenderer\"},\"glyph_renderer37\":{\"id\":\"10541\",\"type\":\"GlyphRenderer\"},\"glyph_renderer38\":{\"id\":\"10548\",\"type\":\"GlyphRenderer\"},\"glyph_renderer39\":{\"id\":\"10554\",\"type\":\"GlyphRenderer\"},\"glyph_renderer4\":{\"id\":\"10481\",\"type\":\"GlyphRenderer\"},\"glyph_renderer40\":{\"id\":\"10561\",\"type\":\"GlyphRenderer\"},\"glyph_renderer41\":{\"id\":\"10568\",\"type\":\"GlyphRenderer\"},\"glyph_renderer42\":{\"id\":\"10574\",\"type\":\"GlyphRenderer\"},\"glyph_renderer43\":{\"id\":\"10581\",\"type\":\"GlyphRenderer\"},\"glyph_renderer44\":{\"id\":\"10588\",\"type\":\"GlyphRenderer\"},\"glyph_renderer45\":{\"id\":\"10594\",\"type\":\"GlyphRenderer\"},\"glyph_renderer46\":{\"id\":\"10601\",\"type\":\"GlyphRenderer\"},\"glyph_renderer47\":{\"id\":\"10608\",\"type\":\"GlyphRenderer\"},\"glyph_renderer48\":{\"id\":\"10614\",\"type\":\"GlyphRenderer\"},\"glyph_renderer49\":{\"id\":\"10621\",\"type\":\"GlyphRenderer\"},\"glyph_renderer5\":{\"id\":\"10488\",\"type\":\"GlyphRenderer\"},\"glyph_renderer50\":{\"id\":\"10628\",\"type\":\"GlyphRenderer\"},\"glyph_renderer51\":{\"id\":\"10634\",\"type\":\"GlyphRenderer\"},\"glyph_renderer52\":{\"id\":\"10641\",\"type\":\"GlyphRenderer\"},\"glyph_renderer53\":{\"id\":\"10648\",\"type\":\"GlyphRenderer\"},\"glyph_renderer54\":{\"id\":\"10654\",\"type\":\"GlyphRenderer\"},\"glyph_renderer55\":{\"id\":\"10661\",\"type\":\"GlyphRenderer\"},\"glyph_renderer56\":{\"id\":\"10668\",\"type\":\"GlyphRenderer\"},\"glyph_renderer57\":{\"id\":\"10674\",\"type\":\"GlyphRenderer\"},\"glyph_renderer58\":{\"id\":\"10681\",\"type\":\"GlyphRenderer\"},\"glyph_renderer59\":{\"id\":\"10688\",\"type\":\"GlyphRenderer\"},\"glyph_renderer6\":{\"id\":\"10694\",\"type\":\"GlyphRenderer\"},\"glyph_renderer60\":{\"id\":\"10714\",\"type\":\"GlyphRenderer\"},\"glyph_renderer61\":{\"id\":\"10721\",\"type\":\"GlyphRenderer\"},\"glyph_renderer62\":{\"id\":\"10728\",\"type\":\"GlyphRenderer\"},\"glyph_renderer63\":{\"id\":\"10734\",\"type\":\"GlyphRenderer\"},\"glyph_renderer64\":{\"id\":\"10741\",\"type\":\"GlyphRenderer\"},\"glyph_renderer65\":{\"id\":\"10748\",\"type\":\"GlyphRenderer\"},\"glyph_renderer66\":{\"id\":\"10754\",\"type\":\"GlyphRenderer\"},\"glyph_renderer67\":{\"id\":\"10761\",\"type\":\"GlyphRenderer\"},\"glyph_renderer68\":{\"id\":\"10768\",\"type\":\"GlyphRenderer\"},\"glyph_renderer69\":{\"id\":\"10774\",\"type\":\"GlyphRenderer\"},\"glyph_renderer7\":{\"id\":\"10701\",\"type\":\"GlyphRenderer\"},\"glyph_renderer70\":{\"id\":\"10781\",\"type\":\"GlyphRenderer\"},\"glyph_renderer71\":{\"id\":\"10788\",\"type\":\"GlyphRenderer\"},\"glyph_renderer72\":{\"id\":\"10794\",\"type\":\"GlyphRenderer\"},\"glyph_renderer73\":{\"id\":\"10801\",\"type\":\"GlyphRenderer\"},\"glyph_renderer74\":{\"id\":\"10808\",\"type\":\"GlyphRenderer\"},\"glyph_renderer75\":{\"id\":\"10814\",\"type\":\"GlyphRenderer\"},\"glyph_renderer76\":{\"id\":\"10821\",\"type\":\"GlyphRenderer\"},\"glyph_renderer77\":{\"id\":\"10828\",\"type\":\"GlyphRenderer\"},\"glyph_renderer78\":{\"id\":\"10834\",\"type\":\"GlyphRenderer\"},\"glyph_renderer79\":{\"id\":\"10841\",\"type\":\"GlyphRenderer\"},\"glyph_renderer8\":{\"id\":\"10708\",\"type\":\"GlyphRenderer\"},\"glyph_renderer80\":{\"id\":\"10848\",\"type\":\"GlyphRenderer\"},\"glyph_renderer81\":{\"id\":\"10854\",\"type\":\"GlyphRenderer\"},\"glyph_renderer82\":{\"id\":\"10861\",\"type\":\"GlyphRenderer\"},\"glyph_renderer83\":{\"id\":\"10868\",\"type\":\"GlyphRenderer\"},\"glyph_renderer84\":{\"id\":\"10874\",\"type\":\"GlyphRenderer\"},\"glyph_renderer85\":{\"id\":\"10881\",\"type\":\"GlyphRenderer\"},\"glyph_renderer86\":{\"id\":\"10888\",\"type\":\"GlyphRenderer\"},\"glyph_renderer87\":{\"id\":\"10894\",\"type\":\"GlyphRenderer\"},\"glyph_renderer88\":{\"id\":\"10901\",\"type\":\"GlyphRenderer\"},\"glyph_renderer89\":{\"id\":\"10908\",\"type\":\"GlyphRenderer\"},\"glyph_renderer9\":{\"id\":\"10914\",\"type\":\"GlyphRenderer\"},\"glyph_renderer90\":{\"id\":\"10934\",\"type\":\"GlyphRenderer\"},\"glyph_renderer91\":{\"id\":\"10941\",\"type\":\"GlyphRenderer\"},\"glyph_renderer92\":{\"id\":\"10948\",\"type\":\"GlyphRenderer\"},\"glyph_renderer93\":{\"id\":\"10954\",\"type\":\"GlyphRenderer\"},\"glyph_renderer94\":{\"id\":\"10961\",\"type\":\"GlyphRenderer\"},\"glyph_renderer95\":{\"id\":\"10968\",\"type\":\"GlyphRenderer\"}},\"code\":\"var labels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]; checkbox.active = labels;len_labels = 32;glyph_renderers = [[glyph_renderer0,glyph_renderer1,glyph_renderer2],[glyph_renderer3,glyph_renderer4,glyph_renderer5],[glyph_renderer6,glyph_renderer7,glyph_renderer8],[glyph_renderer9,glyph_renderer10,glyph_renderer11],[glyph_renderer12,glyph_renderer13,glyph_renderer14],[glyph_renderer15,glyph_renderer16,glyph_renderer17],[glyph_renderer18,glyph_renderer19,glyph_renderer20],[glyph_renderer21,glyph_renderer22,glyph_renderer23],[glyph_renderer24,glyph_renderer25,glyph_renderer26],[glyph_renderer27,glyph_renderer28,glyph_renderer29],[glyph_renderer30,glyph_renderer31,glyph_renderer32],[glyph_renderer33,glyph_renderer34,glyph_renderer35],[glyph_renderer36,glyph_renderer37,glyph_renderer38],[glyph_renderer39,glyph_renderer40,glyph_renderer41],[glyph_renderer42,glyph_renderer43,glyph_renderer44],[glyph_renderer45,glyph_renderer46,glyph_renderer47],[glyph_renderer48,glyph_renderer49,glyph_renderer50],[glyph_renderer51,glyph_renderer52,glyph_renderer53],[glyph_renderer54,glyph_renderer55,glyph_renderer56],[glyph_renderer57,glyph_renderer58,glyph_renderer59],[glyph_renderer60,glyph_renderer61,glyph_renderer62],[glyph_renderer63,glyph_renderer64,glyph_renderer65],[glyph_renderer66,glyph_renderer67,glyph_renderer68],[glyph_renderer69,glyph_renderer70,glyph_renderer71],[glyph_renderer72,glyph_renderer73,glyph_renderer74],[glyph_renderer75,glyph_renderer76,glyph_renderer77],[glyph_renderer78,glyph_renderer79,glyph_renderer80],[glyph_renderer81,glyph_renderer82,glyph_renderer83],[glyph_renderer84,glyph_renderer85,glyph_renderer86],[glyph_renderer87,glyph_renderer88,glyph_renderer89],[glyph_renderer90,glyph_renderer91,glyph_renderer92],[glyph_renderer93,glyph_renderer94,glyph_renderer95]];\\n for (i = 0; i < len_labels; i++) {\\n if (checkbox.active.includes(i)) {\\n // console.log('Setting to true: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = true;\\n // console.log('Setting to true: ' + i + ' : ' + j)\\n }\\n } else {\\n // console.log('Setting to false: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = false;\\n // console.log('Setting to false: ' + i + ' : ' + j)\\n }\\n }\\n }\\n \"},\"id\":\"11091\",\"type\":\"CustomJS\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"times\"},\"y\":{\"field\":\"losses\"}},\"id\":\"10480\",\"type\":\"CircleX\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"times\"},\"y\":{\"field\":\"losses\"}},\"id\":\"10680\",\"type\":\"CircleX\"},{\"attributes\":{\"callback\":{\"id\":\"11091\",\"type\":\"CustomJS\"},\"icon\":null,\"label\":\"All\"},\"id\":\"11092\",\"type\":\"Button\"},{\"attributes\":{\"data_source\":{\"id\":\"10416\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"10479\",\"type\":\"CircleX\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"10480\",\"type\":\"CircleX\"},\"selection_glyph\":null,\"view\":{\"id\":\"10477\",\"type\":\"CDSView\"}},\"id\":\"10481\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"10416\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"10679\",\"type\":\"CircleX\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"10680\",\"type\":\"CircleX\"},\"selection_glyph\":null,\"view\":{\"id\":\"10677\",\"type\":\"CDSView\"}},\"id\":\"10681\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"args\":{\"checkbox\":{\"id\":\"11090\",\"type\":\"CheckboxButtonGroup\"},\"glyph_renderer0\":{\"id\":\"10454\",\"type\":\"GlyphRenderer\"},\"glyph_renderer1\":{\"id\":\"10461\",\"type\":\"GlyphRenderer\"},\"glyph_renderer10\":{\"id\":\"10921\",\"type\":\"GlyphRenderer\"},\"glyph_renderer11\":{\"id\":\"10928\",\"type\":\"GlyphRenderer\"},\"glyph_renderer12\":{\"id\":\"10974\",\"type\":\"GlyphRenderer\"},\"glyph_renderer13\":{\"id\":\"10981\",\"type\":\"GlyphRenderer\"},\"glyph_renderer14\":{\"id\":\"10988\",\"type\":\"GlyphRenderer\"},\"glyph_renderer15\":{\"id\":\"10994\",\"type\":\"GlyphRenderer\"},\"glyph_renderer16\":{\"id\":\"11001\",\"type\":\"GlyphRenderer\"},\"glyph_renderer17\":{\"id\":\"11008\",\"type\":\"GlyphRenderer\"},\"glyph_renderer18\":{\"id\":\"11014\",\"type\":\"GlyphRenderer\"},\"glyph_renderer19\":{\"id\":\"11021\",\"type\":\"GlyphRenderer\"},\"glyph_renderer2\":{\"id\":\"10468\",\"type\":\"GlyphRenderer\"},\"glyph_renderer20\":{\"id\":\"11028\",\"type\":\"GlyphRenderer\"},\"glyph_renderer21\":{\"id\":\"11034\",\"type\":\"GlyphRenderer\"},\"glyph_renderer22\":{\"id\":\"11041\",\"type\":\"GlyphRenderer\"},\"glyph_renderer23\":{\"id\":\"11048\",\"type\":\"GlyphRenderer\"},\"glyph_renderer24\":{\"id\":\"11054\",\"type\":\"GlyphRenderer\"},\"glyph_renderer25\":{\"id\":\"11061\",\"type\":\"GlyphRenderer\"},\"glyph_renderer26\":{\"id\":\"11068\",\"type\":\"GlyphRenderer\"},\"glyph_renderer27\":{\"id\":\"11074\",\"type\":\"GlyphRenderer\"},\"glyph_renderer28\":{\"id\":\"11081\",\"type\":\"GlyphRenderer\"},\"glyph_renderer29\":{\"id\":\"11088\",\"type\":\"GlyphRenderer\"},\"glyph_renderer3\":{\"id\":\"10474\",\"type\":\"GlyphRenderer\"},\"glyph_renderer30\":{\"id\":\"10494\",\"type\":\"GlyphRenderer\"},\"glyph_renderer31\":{\"id\":\"10501\",\"type\":\"GlyphRenderer\"},\"glyph_renderer32\":{\"id\":\"10508\",\"type\":\"GlyphRenderer\"},\"glyph_renderer33\":{\"id\":\"10514\",\"type\":\"GlyphRenderer\"},\"glyph_renderer34\":{\"id\":\"10521\",\"type\":\"GlyphRenderer\"},\"glyph_renderer35\":{\"id\":\"10528\",\"type\":\"GlyphRenderer\"},\"glyph_renderer36\":{\"id\":\"10534\",\"type\":\"GlyphRenderer\"},\"glyph_renderer37\":{\"id\":\"10541\",\"type\":\"GlyphRenderer\"},\"glyph_renderer38\":{\"id\":\"10548\",\"type\":\"GlyphRenderer\"},\"glyph_renderer39\":{\"id\":\"10554\",\"type\":\"GlyphRenderer\"},\"glyph_renderer4\":{\"id\":\"10481\",\"type\":\"GlyphRenderer\"},\"glyph_renderer40\":{\"id\":\"10561\",\"type\":\"GlyphRenderer\"},\"glyph_renderer41\":{\"id\":\"10568\",\"type\":\"GlyphRenderer\"},\"glyph_renderer42\":{\"id\":\"10574\",\"type\":\"GlyphRenderer\"},\"glyph_renderer43\":{\"id\":\"10581\",\"type\":\"GlyphRenderer\"},\"glyph_renderer44\":{\"id\":\"10588\",\"type\":\"GlyphRenderer\"},\"glyph_renderer45\":{\"id\":\"10594\",\"type\":\"GlyphRenderer\"},\"glyph_renderer46\":{\"id\":\"10601\",\"type\":\"GlyphRenderer\"},\"glyph_renderer47\":{\"id\":\"10608\",\"type\":\"GlyphRenderer\"},\"glyph_renderer48\":{\"id\":\"10614\",\"type\":\"GlyphRenderer\"},\"glyph_renderer49\":{\"id\":\"10621\",\"type\":\"GlyphRenderer\"},\"glyph_renderer5\":{\"id\":\"10488\",\"type\":\"GlyphRenderer\"},\"glyph_renderer50\":{\"id\":\"10628\",\"type\":\"GlyphRenderer\"},\"glyph_renderer51\":{\"id\":\"10634\",\"type\":\"GlyphRenderer\"},\"glyph_renderer52\":{\"id\":\"10641\",\"type\":\"GlyphRenderer\"},\"glyph_renderer53\":{\"id\":\"10648\",\"type\":\"GlyphRenderer\"},\"glyph_renderer54\":{\"id\":\"10654\",\"type\":\"GlyphRenderer\"},\"glyph_renderer55\":{\"id\":\"10661\",\"type\":\"GlyphRenderer\"},\"glyph_renderer56\":{\"id\":\"10668\",\"type\":\"GlyphRenderer\"},\"glyph_renderer57\":{\"id\":\"10674\",\"type\":\"GlyphRenderer\"},\"glyph_renderer58\":{\"id\":\"10681\",\"type\":\"GlyphRenderer\"},\"glyph_renderer59\":{\"id\":\"10688\",\"type\":\"GlyphRenderer\"},\"glyph_renderer6\":{\"id\":\"10694\",\"type\":\"GlyphRenderer\"},\"glyph_renderer60\":{\"id\":\"10714\",\"type\":\"GlyphRenderer\"},\"glyph_renderer61\":{\"id\":\"10721\",\"type\":\"GlyphRenderer\"},\"glyph_renderer62\":{\"id\":\"10728\",\"type\":\"GlyphRenderer\"},\"glyph_renderer63\":{\"id\":\"10734\",\"type\":\"GlyphRenderer\"},\"glyph_renderer64\":{\"id\":\"10741\",\"type\":\"GlyphRenderer\"},\"glyph_renderer65\":{\"id\":\"10748\",\"type\":\"GlyphRenderer\"},\"glyph_renderer66\":{\"id\":\"10754\",\"type\":\"GlyphRenderer\"},\"glyph_renderer67\":{\"id\":\"10761\",\"type\":\"GlyphRenderer\"},\"glyph_renderer68\":{\"id\":\"10768\",\"type\":\"GlyphRenderer\"},\"glyph_renderer69\":{\"id\":\"10774\",\"type\":\"GlyphRenderer\"},\"glyph_renderer7\":{\"id\":\"10701\",\"type\":\"GlyphRenderer\"},\"glyph_renderer70\":{\"id\":\"10781\",\"type\":\"GlyphRenderer\"},\"glyph_renderer71\":{\"id\":\"10788\",\"type\":\"GlyphRenderer\"},\"glyph_renderer72\":{\"id\":\"10794\",\"type\":\"GlyphRenderer\"},\"glyph_renderer73\":{\"id\":\"10801\",\"type\":\"GlyphRenderer\"},\"glyph_renderer74\":{\"id\":\"10808\",\"type\":\"GlyphRenderer\"},\"glyph_renderer75\":{\"id\":\"10814\",\"type\":\"GlyphRenderer\"},\"glyph_renderer76\":{\"id\":\"10821\",\"type\":\"GlyphRenderer\"},\"glyph_renderer77\":{\"id\":\"10828\",\"type\":\"GlyphRenderer\"},\"glyph_renderer78\":{\"id\":\"10834\",\"type\":\"GlyphRenderer\"},\"glyph_renderer79\":{\"id\":\"10841\",\"type\":\"GlyphRenderer\"},\"glyph_renderer8\":{\"id\":\"10708\",\"type\":\"GlyphRenderer\"},\"glyph_renderer80\":{\"id\":\"10848\",\"type\":\"GlyphRenderer\"},\"glyph_renderer81\":{\"id\":\"10854\",\"type\":\"GlyphRenderer\"},\"glyph_renderer82\":{\"id\":\"10861\",\"type\":\"GlyphRenderer\"},\"glyph_renderer83\":{\"id\":\"10868\",\"type\":\"GlyphRenderer\"},\"glyph_renderer84\":{\"id\":\"10874\",\"type\":\"GlyphRenderer\"},\"glyph_renderer85\":{\"id\":\"10881\",\"type\":\"GlyphRenderer\"},\"glyph_renderer86\":{\"id\":\"10888\",\"type\":\"GlyphRenderer\"},\"glyph_renderer87\":{\"id\":\"10894\",\"type\":\"GlyphRenderer\"},\"glyph_renderer88\":{\"id\":\"10901\",\"type\":\"GlyphRenderer\"},\"glyph_renderer89\":{\"id\":\"10908\",\"type\":\"GlyphRenderer\"},\"glyph_renderer9\":{\"id\":\"10914\",\"type\":\"GlyphRenderer\"},\"glyph_renderer90\":{\"id\":\"10934\",\"type\":\"GlyphRenderer\"},\"glyph_renderer91\":{\"id\":\"10941\",\"type\":\"GlyphRenderer\"},\"glyph_renderer92\":{\"id\":\"10948\",\"type\":\"GlyphRenderer\"},\"glyph_renderer93\":{\"id\":\"10954\",\"type\":\"GlyphRenderer\"},\"glyph_renderer94\":{\"id\":\"10961\",\"type\":\"GlyphRenderer\"},\"glyph_renderer95\":{\"id\":\"10968\",\"type\":\"GlyphRenderer\"}},\"code\":\"var labels = []; checkbox.active = labels;len_labels = 32;glyph_renderers = [[glyph_renderer0,glyph_renderer1,glyph_renderer2],[glyph_renderer3,glyph_renderer4,glyph_renderer5],[glyph_renderer6,glyph_renderer7,glyph_renderer8],[glyph_renderer9,glyph_renderer10,glyph_renderer11],[glyph_renderer12,glyph_renderer13,glyph_renderer14],[glyph_renderer15,glyph_renderer16,glyph_renderer17],[glyph_renderer18,glyph_renderer19,glyph_renderer20],[glyph_renderer21,glyph_renderer22,glyph_renderer23],[glyph_renderer24,glyph_renderer25,glyph_renderer26],[glyph_renderer27,glyph_renderer28,glyph_renderer29],[glyph_renderer30,glyph_renderer31,glyph_renderer32],[glyph_renderer33,glyph_renderer34,glyph_renderer35],[glyph_renderer36,glyph_renderer37,glyph_renderer38],[glyph_renderer39,glyph_renderer40,glyph_renderer41],[glyph_renderer42,glyph_renderer43,glyph_renderer44],[glyph_renderer45,glyph_renderer46,glyph_renderer47],[glyph_renderer48,glyph_renderer49,glyph_renderer50],[glyph_renderer51,glyph_renderer52,glyph_renderer53],[glyph_renderer54,glyph_renderer55,glyph_renderer56],[glyph_renderer57,glyph_renderer58,glyph_renderer59],[glyph_renderer60,glyph_renderer61,glyph_renderer62],[glyph_renderer63,glyph_renderer64,glyph_renderer65],[glyph_renderer66,glyph_renderer67,glyph_renderer68],[glyph_renderer69,glyph_renderer70,glyph_renderer71],[glyph_renderer72,glyph_renderer73,glyph_renderer74],[glyph_renderer75,glyph_renderer76,glyph_renderer77],[glyph_renderer78,glyph_renderer79,glyph_renderer80],[glyph_renderer81,glyph_renderer82,glyph_renderer83],[glyph_renderer84,glyph_renderer85,glyph_renderer86],[glyph_renderer87,glyph_renderer88,glyph_renderer89],[glyph_renderer90,glyph_renderer91,glyph_renderer92],[glyph_renderer93,glyph_renderer94,glyph_renderer95]];\\n for (i = 0; i < len_labels; i++) {\\n if (checkbox.active.includes(i)) {\\n // console.log('Setting to true: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = true;\\n // console.log('Setting to true: ' + i + ' : ' + j)\\n }\\n } else {\\n // console.log('Setting to false: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = false;\\n // console.log('Setting to false: ' + i + ' : ' + j)\\n }\\n }\\n }\\n \"},\"id\":\"11093\",\"type\":\"CustomJS\"},{\"attributes\":{\"column_name\":\"HB_iteration\",\"group\":\"19\"},\"id\":\"10682\",\"type\":\"GroupFilter\"},{\"attributes\":{\"callback\":{\"id\":\"11093\",\"type\":\"CustomJS\"},\"icon\":null,\"label\":\"None\"},\"id\":\"11094\",\"type\":\"Button\"},{\"attributes\":{\"column_name\":\"HB_iteration\",\"group\":\"1\"},\"id\":\"10482\",\"type\":\"GroupFilter\"},{\"attributes\":{\"column_name\":\"config_info\",\"group\":\"model_based_pick=False\"},\"id\":\"10483\",\"type\":\"GroupFilter\"},{\"attributes\":{\"column_name\":\"config_info\",\"group\":\"model_based_pick=False\"},\"id\":\"10683\",\"type\":\"GroupFilter\"},{\"attributes\":{\"args\":{\"cm\":{\"id\":\"10417\",\"type\":\"LinearColorMapper\"},\"source_multiline\":{\"id\":\"10415\",\"type\":\"ColumnDataSource\"},\"source_scatter\":{\"id\":\"10416\",\"type\":\"ColumnDataSource\"}},\"code\":\"\\n var data_multiline = source_multiline.data;\\n var data_scatter = source_scatter.data;\\n var min_perf = 0.015045801948051959;\\n var max_perf = 0.90683;\\n var min_iter = 0;\\n var max_iter = 31;\\n if (cb_obj.value == 'performance') {\\n data_multiline['colors'] = data_multiline['colors_performance'];\\n data_scatter['colors'] = data_scatter['colors_performance'];\\n cm.low = min_perf;\\n cm.high = max_perf;\\n } else {\\n data_multiline['colors'] = data_multiline['colors_iteration'];\\n data_scatter['colors'] = data_scatter['colors_iteration'];\\n cm.low = min_iter;\\n cm.high = max_iter;\\n }\\n source.change.emit();\\n 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= 32;glyph_renderers = [[glyph_renderer0,glyph_renderer1,glyph_renderer2],[glyph_renderer3,glyph_renderer4,glyph_renderer5],[glyph_renderer6,glyph_renderer7,glyph_renderer8],[glyph_renderer9,glyph_renderer10,glyph_renderer11],[glyph_renderer12,glyph_renderer13,glyph_renderer14],[glyph_renderer15,glyph_renderer16,glyph_renderer17],[glyph_renderer18,glyph_renderer19,glyph_renderer20],[glyph_renderer21,glyph_renderer22,glyph_renderer23],[glyph_renderer24,glyph_renderer25,glyph_renderer26],[glyph_renderer27,glyph_renderer28,glyph_renderer29],[glyph_renderer30,glyph_renderer31,glyph_renderer32],[glyph_renderer33,glyph_renderer34,glyph_renderer35],[glyph_renderer36,glyph_renderer37,glyph_renderer38],[glyph_renderer39,glyph_renderer40,glyph_renderer41],[glyph_renderer42,glyph_renderer43,glyph_renderer44],[glyph_renderer45,glyph_renderer46,glyph_renderer47],[glyph_renderer48,glyph_renderer49,glyph_renderer50],[glyph_renderer51,glyph_renderer52,glyph_renderer53],[glyph_renderer54,glyph_renderer55,glyph_renderer56],[glyph_renderer57,glyph_renderer58,glyph_renderer59],[glyph_renderer60,glyph_renderer61,glyph_renderer62],[glyph_renderer63,glyph_renderer64,glyph_renderer65],[glyph_renderer66,glyph_renderer67,glyph_renderer68],[glyph_renderer69,glyph_renderer70,glyph_renderer71],[glyph_renderer72,glyph_renderer73,glyph_renderer74],[glyph_renderer75,glyph_renderer76,glyph_renderer77],[glyph_renderer78,glyph_renderer79,glyph_renderer80],[glyph_renderer81,glyph_renderer82,glyph_renderer83],[glyph_renderer84,glyph_renderer85,glyph_renderer86],[glyph_renderer87,glyph_renderer88,glyph_renderer89],[glyph_renderer90,glyph_renderer91,glyph_renderer92],[glyph_renderer93,glyph_renderer94,glyph_renderer95]];\\n for (i = 0; i < len_labels; i++) {\\n if (cb_obj.active.includes(i)) {\\n // console.log('Setting to true: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = true;\\n // console.log('Setting to true: ' + i + ' : ' + j)\\n }\\n } else {\\n // console.log('Setting to false: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = false;\\n // console.log('Setting to false: ' + i + ' : ' + j)\\n }\\n }\\n }\\n 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# aggregated parallel BOHB runs1
# parameters2
Deterministic target algorithmTrue
Optimized run objectivequality
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budget 0.0budget 0.1budget 0.3budget 1
Total time spent evaluating configurations11.35 sec5.60 sec2.85 sec1.60 sec
Average time per configuration (mean / std)0.02 sec (± 0.01)0.02 sec (± 0.01)0.02 sec (± 0.01)0.02 sec (± 0.01)
# evaluated configurations45924312672
# changed parameters (default to incumbent)2222
Configuration originsAcquisition Function : 405, Random : 54Acquisition Function : 209, Random : 34Acquisition Function : 108, Random : 18Acquisition Function : 62, Random : 10
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cave.bohb_incumbents_per_budget();" ] @@ -787,8 +188,7 @@ }, "outputs": [], "source": [ - "%%capture\n", - "cave.cave_fanova(run='budget_0.0');" + "cave.cave_fanova(run='budget_0.00');" ] }, { @@ -799,7 +199,7 @@ }, "outputs": [], "source": [ - "cave.local_parameter_importance(run='budget_0.0');" + "cave.local_parameter_importance(run='budget_0.00');" ] }, { @@ -811,377 +211,11 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"11192\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error() {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (var i = 0; i < css_urls.length; i++) {\n", - " var url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };var element = document.getElementById(\"11192\");\n", - " if (element == null) {\n", - " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '11192' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n", - " var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {} // ensure no trailing comma for IE\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"11192\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"11192\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"11192\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '11192' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"11192\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function embed_document(root) {\n", - " \n", - " var docs_json = {\"a2c99529-e48f-4096-b018-fc2fdf459cf7\":{\"roots\":{\"references\":[{\"attributes\":{\"columns\":[{\"id\":\"11194\",\"type\":\"TableColumn\"},{\"id\":\"11195\",\"type\":\"TableColumn\"},{\"id\":\"11196\",\"type\":\"TableColumn\"}],\"height\":80,\"index_position\":null,\"source\":{\"id\":\"11193\",\"type\":\"ColumnDataSource\"},\"view\":{\"id\":\"11198\",\"type\":\"CDSView\"}},\"id\":\"11197\",\"type\":\"DataTable\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"11223\",\"type\":\"StringEditor\"},\"field\":\"Parameters\",\"formatter\":{\"id\":\"11224\",\"type\":\"StringFormatter\"},\"sortable\":false,\"title\":\"Parameters\",\"width\":150},\"id\":\"11194\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"11228\",\"type\":\"StringFormatter\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"11227\",\"type\":\"StringEditor\"},\"field\":\"fANOVA\",\"formatter\":{\"id\":\"11228\",\"type\":\"StringFormatter\"},\"title\":\"fANOVA\",\"width\":100},\"id\":\"11196\",\"type\":\"TableColumn\"},{\"attributes\":{\"callback\":null,\"data\":{\"LPI\":[\"63.34 +/- 27.69\",\"36.66 +/- 27.69\"],\"Parameters\":[\"x1\",\"x0\"],\"fANOVA\":[\"39.89 +/- 15.86\",\"30.48 +/- 16.14\"]},\"selected\":{\"id\":\"11221\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"11222\",\"type\":\"UnionRenderers\"}},\"id\":\"11193\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"11225\",\"type\":\"StringEditor\"},{\"attributes\":{},\"id\":\"11221\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"11227\",\"type\":\"StringEditor\"},{\"attributes\":{},\"id\":\"11224\",\"type\":\"StringFormatter\"},{\"attributes\":{\"default_sort\":\"descending\",\"editor\":{\"id\":\"11225\",\"type\":\"StringEditor\"},\"field\":\"LPI\",\"formatter\":{\"id\":\"11226\",\"type\":\"StringFormatter\"},\"title\":\"LPI\",\"width\":100},\"id\":\"11195\",\"type\":\"TableColumn\"},{\"attributes\":{},\"id\":\"11223\",\"type\":\"StringEditor\"},{\"attributes\":{},\"id\":\"11226\",\"type\":\"StringFormatter\"},{\"attributes\":{\"source\":{\"id\":\"11193\",\"type\":\"ColumnDataSource\"}},\"id\":\"11198\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"11222\",\"type\":\"UnionRenderers\"}],\"root_ids\":[\"11197\"]},\"title\":\"Bokeh Application\",\"version\":\"1.1.0\"}};\n", - " var render_items = [{\"docid\":\"a2c99529-e48f-4096-b018-fc2fdf459cf7\",\"roots\":{\"11197\":\"99434858-a0c1-4835-85bc-f8538bc2d71a\"}}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - "\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " var attempts = 0;\n", - " var timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " clearInterval(timer);\n", - " }\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", - " clearInterval(timer);\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "11197" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "cave.pimp_comparison_table(run='budget_0.0');" + "cave.pimp_comparison_table(run='budget_0.00');" ] }, { @@ -1193,754 +227,22 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"14877\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error() {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (var i = 0; i < css_urls.length; i++) {\n", - " var url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };var element = document.getElementById(\"14877\");\n", - " if (element == null) {\n", - " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '14877' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n", - " var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {} // ensure no trailing comma for IE\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"14877\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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[[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],[48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95],[96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143],[144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191],[192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239]];var runs = 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(var i = x.length-1; i >= 0; -- i)\\n obj[x[i]] = x[i];\\n for (var i = y.length-1; i >= 0; -- i)\\n obj[y[i]] = y[i];\\n var res = []\\n for (var k in obj) {\\n if (obj.hasOwnProperty(k)) // <-- optional\\n res.push(obj[k]);\\n }\\n return res;\\n }\\n console.log(\\\"Timeslider: \\\" + time_slider.value);\\n console.log(\\\"Checkbox: \\\" + checkbox.active);var slider_labels = ['41.46', '42.74', '76.94', '141.07', '212.23'];console.log(\\\"Detected slider_labels: \\\" + slider_labels);time_slider.title = \\\"Until wallclocktime \\\" + slider_labels[time_slider.value - 1] + \\\". Step no.\\\"; \\n var activate = [];\\n // if we want multiple checkboxes at the same time, we need to combine the arrays\\n checkbox.active.forEach(function(c) {\\n activate = union_arrays(activate, runs[c]);\\n })\\n // now the intersection of timeslider-activated and checkbox-activated\\n activate = activate.filter(value => -1 !== overtime[time_slider.value - 1].indexOf(value));\\n activate.forEach(function(idx) {\\n glyphs[idx].visible = true;\\n })\\n \"},\"id\":\"14600\",\"type\":\"CustomJS\"},{\"attributes\":{\"filters\":[{\"id\":\"12758\",\"type\":\"GroupFilter\"},{\"id\":\"12759\",\"type\":\"GroupFilter\"},{\"id\":\"12760\",\"type\":\"GroupFilter\"},{\"id\":\"12761\",\"type\":\"BooleanFilter\"}],\"source\":{\"id\":\"11271\",\"type\":\"ColumnDataSource\"}},\"id\":\"12762\",\"type\":\"CDSView\"},{\"attributes\":{\"args\":{\"colormapper\":{\"id\":\"11303\",\"type\":\"LinearColorMapper\"},\"glyph0\":{\"id\":\"11307\",\"type\":\"GlyphRenderer\"},\"glyph1\":{\"id\":\"11312\",\"type\":\"GlyphRenderer\"},\"glyph2\":{\"id\":\"11317\",\"type\":\"GlyphRenderer\"},\"glyph3\":{\"id\":\"11322\",\"type\":\"GlyphRenderer\"}},\"code\":\"var len_labels = 4,glyphs = [ glyph0,glyph1,glyph2,glyph3],mins = [0.054565000000000016, 0.03125666666666668, 0.024544166666666672, 0.015045801948051959],maxs = [0.8995867000000001, 0.8942918856413712, 0.8935998210426346, 0.5807446514467143];\\n for (i = 0; i < len_labels; i++) {\\n if (cb_obj.active === i) {\\n // console.log('Setting to true: ' + i);\\n glyphs[i].visible = true;\\n colormapper.low = mins[i];\\n colormapper.high = maxs[i];\\n } else {\\n // console.log('Setting to false: ' + i);\\n glyphs[i].visible = false;\\n }\\n }\\n \"},\"id\":\"14602\",\"type\":\"CustomJS\"},{\"attributes\":{\"column_name\":\"type\",\"group\":\"Default\"},\"id\":\"12783\",\"type\":\"GroupFilter\"},{\"attributes\":{\"column_name\":\"origin\",\"group\":\"Random\"},\"id\":\"12774\",\"type\":\"GroupFilter\"},{\"attributes\":{\"callback\":{\"id\":\"14598\",\"type\":\"CustomJS\"},\"icon\":null,\"label\":\"All\"},\"id\":\"14599\",\"type\":\"Button\"},{\"attributes\":{\"booleans\":[true,true,true,true,true,true,true,true,true,false,true,true,true,true,true,false,false,false,false,true,false,false,true,true,false,false,false,false,false,true,false,false,true,true,false,true,true,false,false,true,true,true,false,true,true,false,true,true,false,true,true,false,false,true,false,false,false,false,false,false,true,false,false,false,true,false,false,false,true,false,false,true,false,false,true,true,false,false,true,true,false,false,true,false,true,true,false,true,true,true,true,true,true,true,true,true,true,false,false,true,false,true,false,true,true,false,false,false,true,false,false,true,false,true,false,false,false,false,false,false,false,true,false,true,false,true,false,true,false,true,false,true,false,false,false,true,false,true,false,true,false,true,false,true,false,true,false,false,true,false,true,true,false,true,true,false,true,false,false,false,true,true,true,true,false,true,true,false,true,false,true,false,false,true,true,true,false,true,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false]},\"id\":\"12761\",\"type\":\"BooleanFilter\"},{\"attributes\":{\"children\":[{\"id\":\"14595\",\"type\":\"CheckboxButtonGroup\"}]},\"id\":\"14608\",\"type\":\"WidgetBox\"},{\"attributes\":{\"children\":[{\"id\":\"14601\",\"type\":\"Button\"}],\"width\":100},\"id\":\"14610\",\"type\":\"WidgetBox\"},{\"attributes\":{\"column_name\":\"zorder\",\"group\":\"0\"},\"id\":\"12760\",\"type\":\"GroupFilter\"},{\"attributes\":{\"filters\":[{\"id\":\"12763\",\"type\":\"GroupFilter\"},{\"id\":\"12764\",\"type\":\"GroupFilter\"},{\"id\":\"12765\",\"type\":\"GroupFilter\"},{\"id\":\"12766\",\"type\":\"BooleanFilter\"}],\"source\":{\"id\":\"11271\",\"type\":\"ColumnDataSource\"}},\"id\":\"12767\",\"type\":\"CDSView\"},{\"attributes\":{\"column_name\":\"zorder\",\"group\":\"0\"},\"id\":\"12775\",\"type\":\"GroupFilter\"},{\"attributes\":{\"children\":[{\"id\":\"14594\",\"type\":\"Slider\"}]},\"id\":\"14605\",\"type\":\"WidgetBox\"},{\"attributes\":{\"booleans\":[false,true,true,false,true,true,true,false,true,false,false,true,true,false,true,true,false,true,false,true,true,false,false,true,false,false,false,false,false,true,false,false,false,true,false,false,true,false,false,false,true,true,false,false,true,false,false,true,false,false,true,false,true,true,false,true,true,true,true,false,false,true,true,false,false,true,false,false,false,true,false,false,false,false,false,true,false,false,false,true,false,false,false,false,false,true,false,false,true,false,false,true,false,true,false,true,true,false,false,true,false,true,false,false,false,true,true,false,false,true,false,false,false,false,true,false,false,true,false,true,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false]},\"id\":\"12766\",\"type\":\"BooleanFilter\"},{\"attributes\":{\"filters\":[{\"id\":\"12783\",\"type\":\"GroupFilter\"},{\"id\":\"12784\",\"type\":\"GroupFilter\"},{\"id\":\"12785\",\"type\":\"GroupFilter\"},{\"id\":\"12786\",\"type\":\"BooleanFilter\"}],\"source\":{\"id\":\"11271\",\"type\":\"ColumnDataSource\"}},\"id\":\"12787\",\"type\":\"CDSView\"},{\"attributes\":{\"column_name\":\"zorder\",\"group\":\"0\"},\"id\":\"12765\",\"type\":\"GroupFilter\"},{\"attributes\":{\"booleans\":[true,true,true,true,true,true,true,true,false,true,true,true,false,true,true,false,true,false,true,true,false,true,true,true,false,true,false,true,false,false,false,true,true,false,false,true,false,false,true,true,false,true,true,true,true,true,true,true,true,true,false,true,false,false,true,false,false,false,false,true,true,false,false,true,true,false,false,true,true,false,false,true,false,true,true,true,false,true,true,true,false,true,false,false,false,true,true,false,true,false,false,true,false,true,false,false,false,true,true,false,true,false,true,false,false,false,false,true,false,false,true,false,true,false,false,true,true,false,true,false,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,false,false,false,false,true,false,false,true,false,true,false,true,true,false,false,false,true,false,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true,true]},\"id\":\"12776\",\"type\":\"BooleanFilter\"},{\"attributes\":{\"active\":[0,1,2,3],\"callback\":{\"id\":\"14596\",\"type\":\"CustomJS\"},\"labels\":[\"budget 0.0\",\"budget 0.1\",\"budget 0.3\",\"budget 1\"]},\"id\":\"14595\",\"type\":\"CheckboxButtonGroup\"},{\"attributes\":{\"column_name\":\"origin\",\"group\":\"Unknown\"},\"id\":\"12764\",\"type\":\"GroupFilter\"},{\"attributes\":{\"children\":[{\"id\":\"11274\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"id\":\"14605\",\"type\":\"WidgetBox\"}]},\"id\":\"14606\",\"type\":\"Column\"},{\"attributes\":{\"column_name\":\"zorder\",\"group\":\"0\"},\"id\":\"12770\",\"type\":\"GroupFilter\"},{\"attributes\":{\"column_name\":\"type\",\"group\":\"Default\"},\"id\":\"12773\",\"type\":\"GroupFilter\"},{\"attributes\":{\"children\":[{\"id\":\"14597\",\"type\":\"Div\"}]},\"id\":\"14607\",\"type\":\"WidgetBox\"},{\"attributes\":{\"column_name\":\"origin\",\"group\":\"Unknown\"},\"id\":\"12769\",\"type\":\"GroupFilter\"},{\"attributes\":{\"column_name\":\"type\",\"group\":\"Default\"},\"id\":\"12768\",\"type\":\"GroupFilter\"},{\"attributes\":{\"column_name\":\"origin\",\"group\":\"Random\"},\"id\":\"12784\",\"type\":\"GroupFilter\"},{\"attributes\":{\"column_name\":\"type\",\"group\":\"Default\"},\"id\":\"12778\",\"type\":\"GroupFilter\"},{\"attributes\":{\"booleans\":[false,false,true,false,false,true,true,false,true,false,false,false,true,false,false,true,false,true,false,false,true,false,false,false,true,false,true,false,true,false,true,false,false,false,true,false,false,true,false,false,false,true,false,false,true,false,false,true,false,false,true,false,false,true,false,false,true,false,true,false,false,false,true,false,false,false,true,false,false,false,true,false,true,false,false,false,true,false,false,false,true,false,false,true,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false]},\"id\":\"12771\",\"type\":\"BooleanFilter\"},{\"attributes\":{\"filters\":[{\"id\":\"12768\",\"type\":\"GroupFilter\"},{\"id\":\"12769\",\"type\":\"GroupFilter\"},{\"id\":\"12770\",\"type\":\"GroupFilter\"},{\"id\":\"12771\",\"type\":\"BooleanFilter\"}],\"source\":{\"id\":\"11271\",\"type\":\"ColumnDataSource\"}},\"id\":\"12772\",\"type\":\"CDSView\"},{\"attributes\":{\"booleans\":[false,true,true,false,true,true,true,false,true,false,false,true,true,false,true,true,false,true,false,true,true,false,false,true,false,false,false,false,false,true,false,false,false,true,false,false,true,false,false,false,true,true,false,false,true,false,false,true,false,false,true,false,true,true,false,true,true,true,true,false,false,true,true,false,false,true,false,false,false,true,false,false,false,false,false,true,false,false,false,true,false,false,false,false,false,true,false,false,true,false,false,true,false,true,false,true,true,false,false,true,false,true,false,false,false,true,true,false,false,true,false,false,false,false,true,false,false,true,false,true,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false]},\"id\":\"12786\",\"type\":\"BooleanFilter\"},{\"attributes\":{\"column_name\":\"zorder\",\"group\":\"0\"},\"id\":\"12780\",\"type\":\"GroupFilter\"},{\"attributes\":{\"text\":\"Showing only configurations evaluated in:\"},\"id\":\"14597\",\"type\":\"Div\"},{\"attributes\":{\"children\":[{\"id\":\"14609\",\"type\":\"WidgetBox\"},{\"id\":\"14610\",\"type\":\"WidgetBox\"}]},\"id\":\"14611\",\"type\":\"Row\"},{\"attributes\":{\"callback\":{\"id\":\"14600\",\"type\":\"CustomJS\"},\"icon\":null,\"label\":\"None\"},\"id\":\"14601\",\"type\":\"Button\"},{\"attributes\":{\"column_name\":\"zorder\",\"group\":\"0\"},\"id\":\"12785\",\"type\":\"GroupFilter\"},{\"attributes\":{\"fill_color\":{\"field\":\"color\"},\"marker\":{\"value\":\"triangle\"},\"size\":{\"field\":\"size\",\"units\":\"screen\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"13639\",\"type\":\"Scatter\"},{\"attributes\":{\"column_name\":\"origin\",\"group\":\"Random\"},\"id\":\"12779\",\"type\":\"GroupFilter\"},{\"attributes\":{\"active\":0,\"callback\":{\"id\":\"14602\",\"type\":\"CustomJS\"},\"labels\":[\"budget 0.0\",\"budget 0.1\",\"budget 0.3\",\"budget 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[[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],[48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95],[96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143],[144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191],[192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239]];var runs = [[0,4,8,12,16,20,24,28,32,36,40,44,48,52,56,60,64,68,72,76,80,84,88,92,96,100,104,108,112,116,120,124,128,132,136,140,144,148,152,156,160,164,168,172,176,180,184,188,192,196,200,204,208,212,216,220,224,228,232,236],[1,5,9,13,17,21,25,29,33,37,41,45,49,53,57,61,65,69,73,77,81,85,89,93,97,101,105,109,113,117,121,125,129,133,137,141,145,149,153,157,161,165,169,173,177,181,185,189,193,197,201,205,209,213,217,221,225,229,233,237],[2,6,10,14,18,22,26,30,34,38,42,46,50,54,58,62,66,70,74,78,82,86,90,94,98,102,106,110,114,118,122,126,130,134,138,142,146,150,154,158,162,166,170,174,178,182,186,190,194,198,202,206,210,214,218,222,226,230,234,238],[3,7,11,15,19,23,27,31,35,39,43,47,51,55,59,63,67,71,75,79,83,87,91,95,99,103,107,111,115,119,123,127,131,135,139,143,147,151,155,159,163,167,171,175,179,183,187,191,195,199,203,207,211,215,219,223,227,231,235,239]];\\n glyphs.forEach(function(g) {\\n g.visible = false;\\n })\\n // union function\\n function union_arrays(x, y) {\\n var obj = {};\\n for (var i = x.length-1; i >= 0; -- i)\\n obj[x[i]] = x[i];\\n for (var i = y.length-1; i >= 0; -- i)\\n obj[y[i]] = y[i];\\n var res = []\\n for (var k in obj) {\\n if (obj.hasOwnProperty(k)) // <-- optional\\n res.push(obj[k]);\\n }\\n return res;\\n }\\n console.log(\\\"Timeslider: \\\" + time_slider.value);\\n console.log(\\\"Checkbox: \\\" + checkbox.active);var slider_labels = ['41.46', '42.74', '76.94', '141.07', '212.23'];console.log(\\\"Detected slider_labels: \\\" + slider_labels);time_slider.title = \\\"Until wallclocktime \\\" + slider_labels[time_slider.value - 1] + \\\". Step no.\\\"; \\n var activate = [];\\n // if we want multiple checkboxes at the same time, we need to combine the arrays\\n checkbox.active.forEach(function(c) {\\n activate = union_arrays(activate, runs[c]);\\n })\\n // now the intersection of timeslider-activated and checkbox-activated\\n activate = activate.filter(value => -1 !== overtime[time_slider.value - 1].indexOf(value));\\n activate.forEach(function(idx) {\\n glyphs[idx].visible = true;\\n })\\n 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wallclocktime 212.23. Step no. 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"Scatter\"},{\"attributes\":{\"fill_color\":{\"field\":\"color\"},\"marker\":{\"value\":\"inverted_triangle\"},\"size\":{\"field\":\"size\",\"units\":\"screen\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"11708\",\"type\":\"Scatter\"},{\"attributes\":{\"fill_color\":{\"field\":\"color\"},\"marker\":{\"value\":\"triangle\"},\"size\":{\"field\":\"size\",\"units\":\"screen\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"13627\",\"type\":\"Scatter\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"marker\":{\"value\":\"inverted_triangle\"},\"size\":{\"field\":\"size\",\"units\":\"screen\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"14281\",\"type\":\"Scatter\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"marker\":{\"value\":\"square\"},\"size\":{\"field\":\"size\",\"units\":\"screen\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"11621\",\"type\":\"Scatter\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"marker\":{\"value\":\"inverted_triangle\"},\"size\":{\"field\":\"size\",\"units\":\"screen\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"11709\",\"type\":\"Scatter\"},{\"attributes\":{},\"id\":\"11775\",\"type\":\"Selection\"},{\"attributes\":{\"data_source\":{\"id\":\"11269\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"11620\",\"type\":\"Scatter\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"11621\",\"type\":\"Scatter\"},\"selection_glyph\":null,\"view\":{\"id\":\"11396\",\"type\":\"CDSView\"}},\"id\":\"11622\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"11269\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"11708\",\"type\":\"Scatter\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"11709\",\"type\":\"Scatter\"},\"selection_glyph\":null,\"view\":{\"id\":\"11506\",\"type\":\"CDSView\"}},\"id\":\"11710\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"11273\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"14252\",\"type\":\"Scatter\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"14253\",\"type\":\"Scatter\"},\"selection_glyph\":null,\"view\":{\"id\":\"14043\",\"type\":\"CDSView\"}},\"id\":\"14254\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"11272\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"13627\",\"type\":\"Scatter\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"13628\",\"type\":\"Scatter\"},\"selection_glyph\":null,\"view\":{\"id\":\"13430\",\"type\":\"CDSView\"}},\"id\":\"13629\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_color\":{\"field\":\"color\"},\"marker\":{\"value\":\"inverted_triangle\"},\"size\":{\"field\":\"size\",\"units\":\"screen\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"14280\",\"type\":\"Scatter\"}],\"root_ids\":[\"14615\"]},\"title\":\"Bokeh Application\",\"version\":\"1.1.0\"}};\n", - " var render_items = [{\"docid\":\"4feb7cc8-6485-44b1-b3ae-5602ead37507\",\"roots\":{\"14615\":\"4e61ce0d-bb9e-4e9c-9fa5-cbbf61791e0a\"}}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - "\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " var attempts = 0;\n", - " var timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " clearInterval(timer);\n", - " }\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", - " clearInterval(timer);\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "14615" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cave.configurator_footprint(use_timeslider=True, num_quantiles=5);" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"15090\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error() {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (var i = 0; i < css_urls.length; i++) {\n", - " var url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };var element = document.getElementById(\"15090\");\n", - " if (element == null) {\n", - " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '15090' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n", - " var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {} // ensure no trailing comma for IE\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"15090\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"15090\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"15090\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '15090' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"15090\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function embed_document(root) {\n", - " \n", - " var docs_json = {\"d597344f-b899-4d73-9a8d-bdc26a1da961\":{\"roots\":{\"references\":[{\"attributes\":{\"children\":[{\"id\":\"15093\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"id\":\"15221\",\"type\":\"Column\"}]},\"id\":\"15222\",\"type\":\"Row\"},{\"attributes\":{},\"id\":\"15101\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"15233\",\"type\":\"Selection\"},{\"attributes\":{\"label\":{\"value\":\"budget 1\"},\"renderers\":[{\"id\":\"15195\",\"type\":\"GlyphRenderer\"},{\"id\":\"15199\",\"type\":\"GlyphRenderer\"}]},\"id\":\"15216\",\"type\":\"LegendItem\"},{\"attributes\":{\"fill_alpha\":0.2,\"fill_color\":\"#7570B3\",\"line_color\":\"#7570B3\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"15186\",\"type\":\"Patch\"},{\"attributes\":{\"children\":[{\"id\":\"15206\",\"type\":\"Button\"}],\"width\":50},\"id\":\"15218\",\"type\":\"WidgetBox\"},{\"attributes\":{\"label\":{\"value\":\"budget 0.1111111111111111\"},\"renderers\":[{\"id\":\"15173\",\"type\":\"GlyphRenderer\"},{\"id\":\"15177\",\"type\":\"GlyphRenderer\"}]},\"id\":\"15214\",\"type\":\"LegendItem\"},{\"attributes\":{\"children\":[{\"id\":\"15217\",\"type\":\"WidgetBox\"},{\"id\":\"15220\",\"type\":\"Row\"}]},\"id\":\"15221\",\"type\":\"Column\"},{\"attributes\":{\"line_color\":\"#1b9e77\",\"x\":{\"field\":\"time\"},\"y\":{\"field\":\"mean\"}},\"id\":\"15127\",\"type\":\"Line\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"15109\",\"type\":\"BasicTicker\"}},\"id\":\"15112\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"15241\",\"type\":\"Selection\"},{\"attributes\":{\"ticker\":{\"id\":\"15104\",\"type\":\"LogTicker\"}},\"id\":\"15107\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":{\"id\":\"15205\",\"type\":\"CustomJS\"},\"icon\":null,\"label\":\"All\"},\"id\":\"15206\",\"type\":\"Button\"},{\"attributes\":{},\"id\":\"15234\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"15113\",\"type\":\"SaveTool\"},{\"id\":\"15114\",\"type\":\"PanTool\"},{\"id\":\"15115\",\"type\":\"BoxZoomTool\"},{\"id\":\"15116\",\"type\":\"WheelZoomTool\"},{\"id\":\"15117\",\"type\":\"ResetTool\"},{\"id\":\"15201\",\"type\":\"HoverTool\"}]},\"id\":\"15118\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"15231\",\"type\":\"Selection\"},{\"attributes\":{\"label\":{\"value\":\"budget 0.3333333333333333\"},\"renderers\":[{\"id\":\"15184\",\"type\":\"GlyphRenderer\"},{\"id\":\"15188\",\"type\":\"GlyphRenderer\"}]},\"id\":\"15215\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"15116\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"15232\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"overlay\":{\"id\":\"15228\",\"type\":\"BoxAnnotation\"}},\"id\":\"15115\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"15228\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"children\":[{\"id\":\"15204\",\"type\":\"CheckboxButtonGroup\"}],\"width\":100},\"id\":\"15217\",\"type\":\"WidgetBox\"},{\"attributes\":{\"line_color\":\"#d95f02\",\"x\":{\"field\":\"time\"},\"y\":{\"field\":\"mean\"}},\"id\":\"15193\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"15237\",\"type\":\"Selection\"},{\"attributes\":{\"column_name\":\"name\",\"group\":\"budget 1\"},\"id\":\"15190\",\"type\":\"GroupFilter\"},{\"attributes\":{\"data_source\":{\"id\":\"15174\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"15175\",\"type\":\"Patch\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"15176\",\"type\":\"Patch\"},\"selection_glyph\":null,\"view\":{\"id\":\"15178\",\"type\":\"CDSView\"},\"visible\":false},\"id\":\"15177\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"args\":{\"checkbox\":{\"id\":\"15204\",\"type\":\"CheckboxButtonGroup\"},\"glyph_renderer0\":{\"id\":\"15129\",\"type\":\"GlyphRenderer\"},\"glyph_renderer1\":{\"id\":\"15133\",\"type\":\"GlyphRenderer\"},\"glyph_renderer10\":{\"id\":\"15184\",\"type\":\"GlyphRenderer\"},\"glyph_renderer11\":{\"id\":\"15188\",\"type\":\"GlyphRenderer\"},\"glyph_renderer12\":{\"id\":\"15195\",\"type\":\"GlyphRenderer\"},\"glyph_renderer13\":{\"id\":\"15199\",\"type\":\"GlyphRenderer\"},\"glyph_renderer2\":{\"id\":\"15140\",\"type\":\"GlyphRenderer\"},\"glyph_renderer3\":{\"id\":\"15144\",\"type\":\"GlyphRenderer\"},\"glyph_renderer4\":{\"id\":\"15151\",\"type\":\"GlyphRenderer\"},\"glyph_renderer5\":{\"id\":\"15155\",\"type\":\"GlyphRenderer\"},\"glyph_renderer6\":{\"id\":\"15162\",\"type\":\"GlyphRenderer\"},\"glyph_renderer7\":{\"id\":\"15166\",\"type\":\"GlyphRenderer\"},\"glyph_renderer8\":{\"id\":\"15173\",\"type\":\"GlyphRenderer\"},\"glyph_renderer9\":{\"id\":\"15177\",\"type\":\"GlyphRenderer\"}},\"code\":\"var labels = []; checkbox.active = labels;len_labels = 7;glyph_renderers = [[glyph_renderer0,glyph_renderer1],[glyph_renderer2,glyph_renderer3],[glyph_renderer4,glyph_renderer5],[glyph_renderer6,glyph_renderer7],[glyph_renderer8,glyph_renderer9],[glyph_renderer10,glyph_renderer11],[glyph_renderer12,glyph_renderer13]];\\n for (i = 0; i < len_labels; i++) {\\n if (checkbox.active.includes(i)) {\\n // console.log('Setting to true: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = true;\\n // console.log('Setting to true: ' + i + ' : ' + j)\\n }\\n } else {\\n // console.log('Setting to false: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = false;\\n // console.log('Setting to false: ' + i + ' : ' + j)\\n }\\n }\\n }\\n \"},\"id\":\"15207\",\"type\":\"CustomJS\"},{\"attributes\":{\"axis_label\":\"estimated cost\",\"axis_label_text_font_size\":{\"value\":\"15pt\"},\"formatter\":{\"id\":\"15227\",\"type\":\"BasicTickFormatter\"},\"major_label_text_font_size\":{\"value\":\"12pt\"},\"ticker\":{\"id\":\"15109\",\"type\":\"BasicTicker\"}},\"id\":\"15108\",\"type\":\"LinearAxis\"},{\"attributes\":{\"data_source\":{\"id\":\"15130\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"15131\",\"type\":\"Patch\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"15132\",\"type\":\"Patch\"},\"selection_glyph\":null,\"view\":{\"id\":\"15134\",\"type\":\"CDSView\"}},\"id\":\"15133\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"label\":{\"value\":\"budget 0.037037037037037035\"},\"renderers\":[{\"id\":\"15162\",\"type\":\"GlyphRenderer\"},{\"id\":\"15166\",\"type\":\"GlyphRenderer\"}]},\"id\":\"15213\",\"type\":\"LegendItem\"},{\"attributes\":{\"callback\":null,\"renderers\":[{\"id\":\"15129\",\"type\":\"GlyphRenderer\"},{\"id\":\"15133\",\"type\":\"GlyphRenderer\"},{\"id\":\"15140\",\"type\":\"GlyphRenderer\"},{\"id\":\"15144\",\"type\":\"GlyphRenderer\"},{\"id\":\"15151\",\"type\":\"GlyphRenderer\"},{\"id\":\"15155\",\"type\":\"GlyphRenderer\"},{\"id\":\"15162\",\"type\":\"GlyphRenderer\"},{\"id\":\"15166\",\"type\":\"GlyphRenderer\"},{\"id\":\"15173\",\"type\":\"GlyphRenderer\"},{\"id\":\"15177\",\"type\":\"GlyphRenderer\"},{\"id\":\"15184\",\"type\":\"GlyphRenderer\"},{\"id\":\"15188\",\"type\":\"GlyphRenderer\"},{\"id\":\"15195\",\"type\":\"GlyphRenderer\"},{\"id\":\"15199\",\"type\":\"GlyphRenderer\"}],\"tooltips\":[[\"estimated performance\",\"@mean\"],[\"at-time\",\"@time\"]]},\"id\":\"15201\",\"type\":\"HoverTool\"},{\"attributes\":{\"source\":{\"id\":\"15174\",\"type\":\"ColumnDataSource\"}},\"id\":\"15178\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"15113\",\"type\":\"SaveTool\"},{\"attributes\":{\"children\":[{\"id\":\"15218\",\"type\":\"WidgetBox\"},{\"id\":\"15219\",\"type\":\"WidgetBox\"}]},\"id\":\"15220\",\"type\":\"Row\"},{\"attributes\":{\"axis_label\":\"time (sec)\",\"axis_label_text_font_size\":{\"value\":\"15pt\"},\"formatter\":{\"id\":\"15225\",\"type\":\"LogTickFormatter\"},\"major_label_orientation\":0.75,\"major_label_text_font_size\":{\"value\":\"12pt\"},\"ticker\":{\"id\":\"15104\",\"type\":\"LogTicker\"}},\"id\":\"15103\",\"type\":\"LogAxis\"},{\"attributes\":{\"callback\":null},\"id\":\"15097\",\"type\":\"DataRange1d\"},{\"attributes\":{\"ticker\":null},\"id\":\"15225\",\"type\":\"LogTickFormatter\"},{\"attributes\":{\"filters\":[{\"id\":\"15179\",\"type\":\"GroupFilter\"}],\"source\":{\"id\":\"15091\",\"type\":\"ColumnDataSource\"}},\"id\":\"15180\",\"type\":\"CDSView\"},{\"attributes\":{\"filters\":[{\"id\":\"15124\",\"type\":\"GroupFilter\"}],\"source\":{\"id\":\"15091\",\"type\":\"ColumnDataSource\"}},\"id\":\"15125\",\"type\":\"CDSView\"},{\"attributes\":{\"fill_alpha\":0.2,\"fill_color\":\"#7570B3\",\"line_color\":\"#7570B3\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"15131\",\"type\":\"Patch\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"AAAAEDPKPUAAAAAQM8o9QA==\",\"dtype\":\"float64\",\"shape\":[2]},\"y\":{\"__ndarray__\":\"i8h4WlXQjj+LyHhaVdCOPw==\",\"dtype\":\"float64\",\"shape\":[2]}},\"selected\":{\"id\":\"15243\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"15244\",\"type\":\"UnionRenderers\"}},\"id\":\"15196\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"label\":{\"value\":\"budget 0.00411522633744856\"},\"renderers\":[{\"id\":\"15140\",\"type\":\"GlyphRenderer\"},{\"id\":\"15144\",\"type\":\"GlyphRenderer\"}]},\"id\":\"15211\",\"type\":\"LegendItem\"},{\"attributes\":{\"data_source\":{\"id\":\"15091\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"15127\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"15128\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"15125\",\"type\":\"CDSView\"}},\"id\":\"15129\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_color\":\"#66a61e\",\"x\":{\"field\":\"time\"},\"y\":{\"field\":\"mean\"}},\"id\":\"15171\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"15239\",\"type\":\"Selection\"},{\"attributes\":{\"filters\":[{\"id\":\"15190\",\"type\":\"GroupFilter\"}],\"source\":{\"id\":\"15091\",\"type\":\"ColumnDataSource\"}},\"id\":\"15191\",\"type\":\"CDSView\"},{\"attributes\":{\"data_source\":{\"id\":\"15091\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"15182\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"15183\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"15180\",\"type\":\"CDSView\"},\"visible\":false},\"id\":\"15184\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"15117\",\"type\":\"ResetTool\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"time\"},\"y\":{\"field\":\"mean\"}},\"id\":\"15194\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"15109\",\"type\":\"BasicTicker\"},{\"attributes\":{\"args\":{\"checkbox\":{\"id\":\"15204\",\"type\":\"CheckboxButtonGroup\"},\"glyph_renderer0\":{\"id\":\"15129\",\"type\":\"GlyphRenderer\"},\"glyph_renderer1\":{\"id\":\"15133\",\"type\":\"GlyphRenderer\"},\"glyph_renderer10\":{\"id\":\"15184\",\"type\":\"GlyphRenderer\"},\"glyph_renderer11\":{\"id\":\"15188\",\"type\":\"GlyphRenderer\"},\"glyph_renderer12\":{\"id\":\"15195\",\"type\":\"GlyphRenderer\"},\"glyph_renderer13\":{\"id\":\"15199\",\"type\":\"GlyphRenderer\"},\"glyph_renderer2\":{\"id\":\"15140\",\"type\":\"GlyphRenderer\"},\"glyph_renderer3\":{\"id\":\"15144\",\"type\":\"GlyphRenderer\"},\"glyph_renderer4\":{\"id\":\"15151\",\"type\":\"GlyphRenderer\"},\"glyph_renderer5\":{\"id\":\"15155\",\"type\":\"GlyphRenderer\"},\"glyph_renderer6\":{\"id\":\"15162\",\"type\":\"GlyphRenderer\"},\"glyph_renderer7\":{\"id\":\"15166\",\"type\":\"GlyphRenderer\"},\"glyph_renderer8\":{\"id\":\"15173\",\"type\":\"GlyphRenderer\"},\"glyph_renderer9\":{\"id\":\"15177\",\"type\":\"GlyphRenderer\"}},\"code\":\"var 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budgets\"},\"id\":\"15124\",\"type\":\"GroupFilter\"},{\"attributes\":{\"label\":{\"value\":\"budget 0.012345679012345678\"},\"renderers\":[{\"id\":\"15151\",\"type\":\"GlyphRenderer\"},{\"id\":\"15155\",\"type\":\"GlyphRenderer\"}]},\"id\":\"15212\",\"type\":\"LegendItem\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"AAAA7PGkPUAAAADoqrE9QAAAAOiqsT1AAAAAYA6+PUAAAABgDr49QAAAACbPs0JAAAAAJs+zQkAAAICXv4VqQAAAgJe/hWpAAACAl7+FakAAAICXv4VqQAAAACbPs0JAAAAAJs+zQkAAAABgDr49QAAAAGAOvj1AAAAA6KqxPUAAAADoqrE9QAAAAOzxpD1A\",\"dtype\":\"float64\",\"shape\":[18]},\"y\":{\"__ndarray__\":\"ZuOItfgUoD9m44i1+BSgP8ZgN2xblJk/xmA3bFuUmT9+17P6/XWZP37Xs/r9dZk/2EmQJBsimT/YSZAkGyKZP9hJkCQbIpk/2EmQJBsimT/YSZAkGyKZP9hJkCQbIpk/ftez+v11mT9+17P6/XWZP8ZgN2xblJk/xmA3bFuUmT9m44i1+BSgP2bjiLX4FKA/\",\"dtype\":\"float64\",\"shape\":[18]}},\"selected\":{\"id\":\"15241\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"15242\",\"type\":\"UnionRenderers\"}},\"id\":\"15185\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"num_minor_ticks\":10},\"id\":\"15104\",\"type\":\"LogTicker\"},{\"attributes\":{},\"id\":\"15236\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"args\":{\"glyph_renderer0\":{\"id\":\"15129\",\"type\":\"GlyphRenderer\"},\"glyph_renderer1\":{\"id\":\"15133\",\"type\":\"GlyphRenderer\"},\"glyph_renderer10\":{\"id\":\"15184\",\"type\":\"GlyphRenderer\"},\"glyph_renderer11\":{\"id\":\"15188\",\"type\":\"GlyphRenderer\"},\"glyph_renderer12\":{\"id\":\"15195\",\"type\":\"GlyphRenderer\"},\"glyph_renderer13\":{\"id\":\"15199\",\"type\":\"GlyphRenderer\"},\"glyph_renderer2\":{\"id\":\"15140\",\"type\":\"GlyphRenderer\"},\"glyph_renderer3\":{\"id\":\"15144\",\"type\":\"GlyphRenderer\"},\"glyph_renderer4\":{\"id\":\"15151\",\"type\":\"GlyphRenderer\"},\"glyph_renderer5\":{\"id\":\"15155\",\"type\":\"GlyphRenderer\"},\"glyph_renderer6\":{\"id\":\"15162\",\"type\":\"GlyphRenderer\"},\"glyph_renderer7\":{\"id\":\"15166\",\"type\":\"GlyphRenderer\"},\"glyph_renderer8\":{\"id\":\"15173\",\"type\":\"GlyphRenderer\"},\"glyph_renderer9\":{\"id\":\"15177\",\"type\":\"GlyphRenderer\"}},\"code\":\"len_labels = 7;glyph_renderers = [[glyph_renderer0,glyph_renderer1],[glyph_renderer2,glyph_renderer3],[glyph_renderer4,glyph_renderer5],[glyph_renderer6,glyph_renderer7],[glyph_renderer8,glyph_renderer9],[glyph_renderer10,glyph_renderer11],[glyph_renderer12,glyph_renderer13]];\\n for (i = 0; i < len_labels; i++) {\\n if (cb_obj.active.includes(i)) {\\n // console.log('Setting to true: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = true;\\n // console.log('Setting to true: ' + i + ' : ' + j)\\n }\\n } else {\\n // console.log('Setting to false: ' + i + '(' + glyph_renderers[i].length + ')')\\n for (j = 0; j < glyph_renderers[i].length; j++) {\\n glyph_renderers[i][j].visible = false;\\n // console.log('Setting to false: ' + i + ' : ' + j)\\n }\\n }\\n }\\n \"},\"id\":\"15203\",\"type\":\"CustomJS\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"time\"},\"y\":{\"field\":\"mean\"}},\"id\":\"15183\",\"type\":\"Line\"},{\"attributes\":{\"active\":[0],\"callback\":{\"id\":\"15203\",\"type\":\"CustomJS\"},\"labels\":[\"all budgets\",\"budget 0.00411522633744856\",\"budget 0.012345679012345678\",\"budget 0.037037037037037035\",\"budget 0.1111111111111111\",\"budget 0.3333333333333333\",\"budget 1\"]},\"id\":\"15204\",\"type\":\"CheckboxButtonGroup\"},{\"attributes\":{\"fill_alpha\":0.1,\"fill_color\":\"#1f77b4\",\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"15198\",\"type\":\"Patch\"},{\"attributes\":{\"callback\":{\"id\":\"15207\",\"type\":\"CustomJS\"},\"icon\":null,\"label\":\"None\"},\"id\":\"15208\",\"type\":\"Button\"},{\"attributes\":{\"line_color\":\"#1b9e77\",\"x\":{\"field\":\"time\"},\"y\":{\"field\":\"mean\"}},\"id\":\"15182\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"15227\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"15230\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"label\":{\"value\":\"all budgets\"},\"renderers\":[{\"id\":\"15129\",\"type\":\"GlyphRenderer\"},{\"id\":\"15133\",\"type\":\"GlyphRenderer\"}]},\"id\":\"15210\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"15235\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"15229\",\"type\":\"Selection\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"time\"},\"y\":{\"field\":\"mean\"}},\"id\":\"15128\",\"type\":\"Line\"},{\"attributes\":{\"children\":[{\"id\":\"15208\",\"type\":\"Button\"}],\"width\":50},\"id\":\"15219\",\"type\":\"WidgetBox\"},{\"attributes\":{\"column_name\":\"name\",\"group\":\"budget 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