07.03-05_K-means_featurization.ipynb

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    "import numpy as np\n",
    "from scipy.spatial import Voronoi, voronoi_plot_2d\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import make_moons\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import sklearn\n",
    "import scipy\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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    "class KMeansFeaturizer:\n",
    "    \"\"\"Transforms numeric data into k-means cluster memberships.\n",
    "    \n",
    "    This transformer runs k-means on the input data and converts each data point\n",
    "    into the id of the closest cluster. If a target variable is present, it is \n",
    "    scaled and included as input to k-means in order to derive clusters that\n",
    "    obey the classification boundary as well as group similar points together.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    k: integer, optional, default 100\n",
    "        The number of clusters to group data into.\n",
    "\n",
    "    target_scale: float, [0, infty], optional, default 5.0\n",
    "        The scaling factor for the target variable. Set this to zero to ignore\n",
    "        the target. For classification problems, larger `target_scale` values \n",
    "        will produce clusters that better respect the class boundary.\n",
    "\n",
    "    random_state : integer or numpy.RandomState, optional\n",
    "        This is passed to k-means as the generator used to initialize the \n",
    "        kmeans centers. If an integer is given, it fixes the seed. Defaults to \n",
    "        the global numpy random number generator.\n",
    "\n",
    "    Attributes\n",
    "    ----------\n",
    "    cluster_centers_ : array, [k, n_features]\n",
    "        Coordinates of cluster centers. n_features does count the target column.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, k=100, target_scale=5.0, random_state=None):\n",
    "        self.k = k\n",
    "        self.target_scale = target_scale\n",
    "        self.random_state = random_state\n",
    "        self.cluster_encoder = OneHotEncoder().fit(np.array(range(k)).reshape(-1,1))\n",
    "        \n",
    "    def fit(self, X, y=None):\n",
    "        \"\"\"Runs k-means on the input data and find centroids.\n",
    "\n",
    "        If no target is given (`y` is None) then run vanilla k-means on input\n",
    "        `X`. \n",
    "\n",
    "        If target `y` is given, then include the target (weighted by \n",
    "        `target_scale`) as an extra dimension for k-means clustering. In this \n",
    "        case, run k-means twice, first with the target, then an extra iteration\n",
    "        without.\n",
    "\n",
    "        After fitting, the attribute `cluster_centers_` are set to the k-means\n",
    "        centroids in the input space represented by `X`.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array-like or sparse matrix, shape=(n_data_points, n_features)\n",
    "\n",
    "        y : vector of length n_data_points, optional, default None\n",
    "            If provided, will be weighted with `target_scale` and included in \n",
    "            k-means clustering as hint.\n",
    "        \"\"\"\n",
    "        if y is None:\n",
    "            # No target variable, just do plain k-means\n",
    "            km_model = KMeans(n_clusters=self.k, \n",
    "                              n_init=20, \n",
    "                              random_state=self.random_state)\n",
    "            km_model.fit(X)\n",
    "\n",
    "            self.km_model_ = km_model\n",
    "            self.cluster_centers_ = km_model.cluster_centers_\n",
    "            return self\n",
    "\n",
    "        # There is target information. Apply appropriate scaling and include\n",
    "        # into input data to k-means            \n",
    "        data_with_target = np.hstack((X, y[:,np.newaxis]*self.target_scale))\n",
    "\n",
    "        # Build a pre-training k-means model on data and target\n",
    "        km_model_pretrain = KMeans(n_clusters=self.k, \n",
    "                                   n_init=20, \n",
    "                                   random_state=self.random_state)\n",
    "        km_model_pretrain.fit(data_with_target)\n",
    "\n",
    "        # Run k-means a second time to get the clusters in the original space\n",
    "        # without target info. Initialize using centroids found in pre-training.\n",
    "        # Go through a single iteration of cluster assignment and centroid \n",
    "        # recomputation.\n",
    "        km_model = KMeans(n_clusters=self.k, \n",
    "                          init=km_model_pretrain.cluster_centers_[:,:2], \n",
    "                          n_init=1, \n",
    "                          max_iter=1)\n",
    "        km_model.fit(X)\n",
    "        \n",
    "        self.km_model = km_model\n",
    "        self.cluster_centers_ = km_model.cluster_centers_\n",
    "        return self\n",
    "        \n",
    "    def transform(self, X, y=None):\n",
    "        \"\"\"Outputs the closest cluster id for each input data point.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array-like or sparse matrix, shape=(n_data_points, n_features)\n",
    "\n",
    "        y : vector of length n_data_points, optional, default None\n",
    "            Target vector is ignored even if provided.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        cluster_ids : array, shape[n_data_points,1]\n",
    "        \"\"\"\n",
    "        clusters = self.km_model.predict(X)\n",
    "        return self.cluster_encoder.transform(clusters.reshape(-1,1))\n",
    "    \n",
    "    def fit_transform(self, X, y=None):\n",
    "        \"\"\"Runs fit followed by transform.\n",
    "        \"\"\"\n",
    "        self.fit(X, y)\n",
    "        return self.transform(X, y)"
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    "seed = 1\n",
    "training_data, training_labels = make_moons(n_samples=2000, noise=0.2, random_state=seed)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "kmf_hint = KMeansFeaturizer(k=100, target_scale=10, random_state=seed).fit(training_data, training_labels)\n",
    "kmf_no_hint = KMeansFeaturizer(k=100, target_scale=0, random_state=seed).fit(training_data, training_labels)"
   ]
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   "source": [
    "def kmeans_voronoi_plot(X, y, cluster_centers, ax):\n",
    "    \"\"\"Plots the Voronoi diagram of the kmeans clusters overlayed with the data\"\"\"\n",
    "    ax.scatter(X[:, 0], X[:, 1], c=y, cmap='Set1', alpha=0.2)\n",
    "    vor = Voronoi(cluster_centers)\n",
    "    voronoi_plot_2d(vor, ax=ax, show_vertices=False, alpha=0.5)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
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       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1eb95b38b00>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.subplot(211, aspect='equal')\n",
    "kmeans_voronoi_plot(training_data, training_labels, kmf_hint.cluster_centers_, ax)\n",
    "ax.set_title('K-Means with Target Hint')\n",
    "ax2 = plt.subplot(212, aspect='equal')\n",
    "kmeans_voronoi_plot(training_data, training_labels, kmf_no_hint.cluster_centers_, ax2)\n",
    "ax2.set_title('K-Means without Target Hint')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification with KMeans clustering features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_data, test_labels = make_moons(n_samples=2000, noise=0.3, random_state=seed+5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "training_cluster_features = kmf_hint.transform(training_data)\n",
    "test_cluster_features = kmf_hint.transform(test_data)\n",
    "\n",
    "training_with_cluster = scipy.sparse.hstack((training_data, training_cluster_features))\n",
    "test_with_cluster = scipy.sparse.hstack((test_data, test_cluster_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lr_cluster = LogisticRegression(random_state=seed).fit(training_with_cluster, training_labels)\n",
    "\n",
    "classifier_names = ['LR',\n",
    "                    'kNN',\n",
    "                    'RBF SVM',\n",
    "                    'Random Forest',\n",
    "                    'Boosted Trees']\n",
    "classifiers = [LogisticRegression(random_state=seed),\n",
    "               KNeighborsClassifier(5),\n",
    "               SVC(gamma=2, C=1, random_state=seed),\n",
    "               RandomForestClassifier(max_depth=5, n_estimators=10, \n",
    "                                      max_features=1, random_state=seed),\n",
    "               GradientBoostingClassifier(n_estimators=10, learning_rate=1.0,\n",
    "                                          max_depth=5, random_state=seed)]\n",
    "for model in classifiers:\n",
    "    model.fit(training_data, training_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_roc(model, data, labels):\n",
    "    if hasattr(model, \"decision_function\"):\n",
    "        predictions = model.decision_function(data)\n",
    "    else:\n",
    "        predictions = model.predict_proba(data)[:,1]\n",
    "    fpr, tpr, _ = sklearn.metrics.roc_curve(labels, predictions)\n",
    "    return fpr, tpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1eb97b821d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "fpr_cluster, tpr_cluster = test_roc(lr_cluster, test_with_cluster, test_labels)\n",
    "plt.plot(fpr_cluster, tpr_cluster, 'r-', label='LR with k-means')\n",
    "\n",
    "for i, model in enumerate(classifiers):\n",
    "    fpr, tpr = test_roc(model, test_data, test_labels)\n",
    "    plt.plot(fpr, tpr, label=classifier_names[i])\n",
    "    \n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.legend()\n",
    "plt.xlabel('False Positive Rate', fontsize=14)\n",
    "plt.ylabel('True Positive Rate', fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 216)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifiers[2].dual_coef_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}