notebooks/dt_surrogate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# License \n",
    "***\n",
    "Copyright 2017 J. Patrick Hall, phall@h2o.ai & Navdeep Gill, navdeep@h2o.ai\n",
    "\n",
    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
    "\n",
    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
    "\n",
    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Surrogates (Global Surrogates)\n",
    "***\n",
    "Based on: Craven, Mark W. and Shavlik, Jude W. Extracting tree structured representations of trained networks. Advances in Neural Information Processing Systems, pp. 24–30, 1996.\n",
    "\n",
    "https://papers.nips.cc/paper/1152-extracting-tree-structured-representations-of-trained-networks.pdf\n",
    "\n",
    "** Requires GraphViz **\n",
    "\n",
    "GraphViz: http://www.graphviz.org/\n",
    "\n",
    "For MacOS: \n",
    "* Install Brew: https://brew.sh/ \n",
    "* `brew install graphviz`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminaries: imports, start H2O, load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "# import h2o module and certain needed classes\n",
    "import h2o\n",
    "from h2o.estimators.gbm import H2OGradientBoostingEstimator\n",
    "from h2o.estimators.deeplearning import H2ODeepLearningEstimator\n",
    "from h2o.backend import H2OLocalServer\n",
    "\n",
    "# for in-notebook display \n",
    "from IPython.display import Image\n",
    "from IPython.display import display\n",
    "\n",
    "# for calling external processes\n",
    "import os\n",
    "import re\n",
    "import subprocess\n",
    "from subprocess import CalledProcessError\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "  Java Version: java version \"1.8.0_181\"; Java(TM) SE Runtime Environment (build 1.8.0_181-b13); Java HotSpot(TM) 64-Bit Server VM (build 25.181-b13, mixed mode)\n",
      "  Starting server from /home/patrickh/anaconda3/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /tmp/tmpo17o6sd8\n",
      "  JVM stdout: /tmp/tmpo17o6sd8/h2o_patrickh_started_from_python.out\n",
      "  JVM stderr: /tmp/tmpo17o6sd8/h2o_patrickh_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54321\n",
      "Connecting to H2O server at http://127.0.0.1:54321... successful.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>01 secs</td></tr>\n",
       "<tr><td>H2O cluster timezone:</td>\n",
       "<td>America/New_York</td></tr>\n",
       "<tr><td>H2O data parsing timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.20.0.4</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>1 month and 20 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_patrickh_buru4a</td></tr>\n",
       "<tr><td>H2O cluster total nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster free memory:</td>\n",
       "<td>10.67 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O cluster status:</td>\n",
       "<td>accepting new members, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://127.0.0.1:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>H2O internal security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>H2O API Extensions:</td>\n",
       "<td>XGBoost, Algos, AutoML, Core V3, Core V4</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>3.6.4 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ----------------------------------------\n",
       "H2O cluster uptime:         01 secs\n",
       "H2O cluster timezone:       America/New_York\n",
       "H2O data parsing timezone:  UTC\n",
       "H2O cluster version:        3.20.0.4\n",
       "H2O cluster version age:    1 month and 20 days\n",
       "H2O cluster name:           H2O_from_python_patrickh_buru4a\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    10.67 Gb\n",
       "H2O cluster total cores:    8\n",
       "H2O cluster allowed cores:  8\n",
       "H2O cluster status:         accepting new members, healthy\n",
       "H2O connection url:         http://127.0.0.1:54321\n",
       "H2O connection proxy:\n",
       "H2O internal security:      False\n",
       "H2O API Extensions:         XGBoost, Algos, AutoML, Core V3, Core V4\n",
       "Python version:             3.6.4 final\n",
       "--------------------------  ----------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# start and connect to h2o server\n",
    "h2o.init(max_mem_size='12G')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# load clean data\n",
    "path = '../data/train.csv'\n",
    "frame = h2o.import_file(path=path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean and prepare data for modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assign target and inputs\n",
    "y = 'SalePrice'\n",
    "X = [name for name in frame.columns if name not in [y, 'Id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# determine column types\n",
    "# impute\n",
    "reals, enums = [], []\n",
    "for key, val in frame.types.items():\n",
    "    if key in X:\n",
    "        if val == 'enum':\n",
    "            enums.append(key)\n",
    "        else: \n",
    "            reals.append(key)\n",
    "            \n",
    "_ = frame[reals].impute(method='median')\n",
    "_ = frame[enums].impute(method='mode')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into training, validation, and 30% test\n",
    "train, valid = frame.split_frame([0.7])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a \"black box\" model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# initialize pre-tuned nn model\n",
    "nn_model = H2ODeepLearningEstimator(\n",
    "    epochs=50,                    # read over the data 50 times, but in mini-batches\n",
    "    hidden=[100],                 # 100 hidden units in 1 hidden layer\n",
    "    activation='Tanh',            # bounded activation function that allows for dropout, tanh  \n",
    "    l2=0.007,                     # L2 penalty can increase stability in presence of highly correlated inputs\n",
    "    adaptive_rate=True,           # adjust magnitude of weight updates automatically (+stability, +accuracy)\n",
    "    stopping_rounds=2,            # stop after validation error does not decrease for 5 iterations\n",
    "    score_each_iteration=True,    # score validation error on every iteration\n",
    "    reproducible=True,\n",
    "    seed=12345)                   \n",
    "    \n",
    "# train nn model\n",
    "nn_model.train(\n",
    "    x=X,\n",
    "    y=y,\n",
    "    training_frame=train,\n",
    "    validation_frame=valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1055395942558104\n",
      "0.17196763577524285\n"
     ]
    }
   ],
   "source": [
    "# measure nn AUC\n",
    "print(nn_model.rmsle(train=True))\n",
    "print(nn_model.rmsle(valid=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use a decision tree surrogate to generate explanations of the \"black box\" model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First bind the \"black box\" model predictions onto the training frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning prediction progress: |███████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# cbind predictions to training frame\n",
    "# give them a nice name\n",
    "preds = nn_model.predict(frame)\n",
    "preds.columns = ['predicted_SalePrice']\n",
    "frame_yhat = frame.cbind(preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train decision tree surrogate model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "Model Details\n",
      "=============\n",
      "H2OGradientBoostingEstimator :  Gradient Boosting Machine\n",
      "Model Key:  dt_surrogate_mojo\n",
      "\n",
      "\n",
      "ModelMetricsRegression: gbm\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 5088845423.26723\n",
      "RMSE: 71336.14387719054\n",
      "MAE: 53990.482603809935\n",
      "RMSLE: 0.37110304191861543\n",
      "Mean Residual Deviance: 5088845423.26723\n",
      "Scoring History: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>timestamp</b></td>\n",
       "<td><b>duration</b></td>\n",
       "<td><b>number_of_trees</b></td>\n",
       "<td><b>training_rmse</b></td>\n",
       "<td><b>training_mae</b></td>\n",
       "<td><b>training_deviance</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-09-21 14:42:38</td>\n",
       "<td> 0.005 sec</td>\n",
       "<td>0.0</td>\n",
       "<td>77247.1503077</td>\n",
       "<td>58648.1442489</td>\n",
       "<td>5967122230.6633215</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-09-21 14:42:39</td>\n",
       "<td> 0.117 sec</td>\n",
       "<td>1.0</td>\n",
       "<td>71336.1438772</td>\n",
       "<td>53990.4826038</td>\n",
       "<td>5088845423.2672300</td></tr></table></div>"
      ],
      "text/plain": [
       "    timestamp            duration    number_of_trees    training_rmse    training_mae    training_deviance\n",
       "--  -------------------  ----------  -----------------  ---------------  --------------  -------------------\n",
       "    2018-09-21 14:42:38  0.005 sec   0                  77247.2          58648.1         5.96712e+09\n",
       "    2018-09-21 14:42:39  0.117 sec   1                  71336.1          53990.5         5.08885e+09"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable Importances: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>variable</b></td>\n",
       "<td><b>relative_importance</b></td>\n",
       "<td><b>scaled_importance</b></td>\n",
       "<td><b>percentage</b></td></tr>\n",
       "<tr><td>OverallQual</td>\n",
       "<td>6443843977216.0000000</td>\n",
       "<td>1.0</td>\n",
       "<td>0.9548046</td></tr>\n",
       "<tr><td>GrLivArea</td>\n",
       "<td>305017716736.0000000</td>\n",
       "<td>0.0473347</td>\n",
       "<td>0.0451954</td></tr>\n",
       "<tr><td>MSSubClass</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>MSZoning</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>LotFrontage</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td></tr>\n",
       "<tr><td>MiscVal</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>MoSold</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>YrSold</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>SaleType</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>SaleCondition</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr></table></div>"
      ],
      "text/plain": [
       "variable       relative_importance    scaled_importance    percentage\n",
       "-------------  ---------------------  -------------------  -------------------\n",
       "OverallQual    6443843977216.0        1.0                  0.9548045684490257\n",
       "GrLivArea      305017716736.0         0.04733474581545966  0.04519543155097429\n",
       "MSSubClass     0.0                    0.0                  0.0\n",
       "MSZoning       0.0                    0.0                  0.0\n",
       "LotFrontage    0.0                    0.0                  0.0\n",
       "---            ---                    ---                  ---\n",
       "MiscVal        0.0                    0.0                  0.0\n",
       "MoSold         0.0                    0.0                  0.0\n",
       "YrSold         0.0                    0.0                  0.0\n",
       "SaleType       0.0                    0.0                  0.0\n",
       "SaleCondition  0.0                    0.0                  0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "See the whole table with table.as_data_frame()\n",
      "\n",
      "Generated MOJO path:\n",
      " /home/patrickh/workspace/mli-resources/notebooks/dt_surrogate_mojo.zip\n"
     ]
    }
   ],
   "source": [
    "yhat = 'predicted_SalePrice'\n",
    "model_id = 'dt_surrogate_mojo'\n",
    "\n",
    "# train single tree surrogate model\n",
    "surrogate = H2OGradientBoostingEstimator(ntrees=1,\n",
    "                                         sample_rate=1,\n",
    "                                         col_sample_rate=1,\n",
    "                                         max_depth=3,\n",
    "                                         seed=12345,\n",
    "                                         model_id=model_id)\n",
    "\n",
    "_ = surrogate.train(x=X, y=yhat, training_frame=frame_yhat)\n",
    "\n",
    "# persist MOJO (compiled, representation of trained model)\n",
    "# from which to generate plot of surrogate\n",
    "mojo_path = surrogate.download_mojo(path='.')\n",
    "\n",
    "print(surrogate)\n",
    "print('Generated MOJO path:\\n', mojo_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate GraphViz representation of MOJO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discovered H2O jar path:\n",
      " /home/patrickh/anaconda3/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n",
      "\n",
      "Calling external process ...\n",
      "java -cp /home/patrickh/anaconda3/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar hex.genmodel.tools.PrintMojo --tree 0 -i /home/patrickh/workspace/mli-resources/notebooks/dt_surrogate_mojo.zip -o dt_surrogate_mojo.gv --title Home Prices Decision Tree Surrogate\n"
     ]
    }
   ],
   "source": [
    "details = False # print more info on tree, details = True\n",
    "title = 'Home Prices Decision Tree Surrogate'\n",
    "\n",
    "hs = H2OLocalServer()\n",
    "h2o_jar_path = hs._find_jar()\n",
    "print('Discovered H2O jar path:\\n', h2o_jar_path)\n",
    "\n",
    "gv_file_name = model_id + '.gv'\n",
    "gv_args = str('-cp ' + h2o_jar_path +\n",
    "              ' hex.genmodel.tools.PrintMojo --tree 0 -i '\n",
    "              + mojo_path + ' -o').split()\n",
    "gv_args.insert(0, 'java')\n",
    "gv_args.append(gv_file_name)\n",
    "\n",
    "if details:\n",
    "    gv_args.append('--detail')\n",
    "\n",
    "if title is not None:\n",
    "    gv_args = gv_args + ['--title', title]\n",
    "    \n",
    "print()\n",
    "print('Calling external process ...')\n",
    "print(' '.join(gv_args))\n",
    "    \n",
    "_ = subprocess.call(gv_args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate PNG from GraphViz representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling external process ...\n",
      "dot -Tpng dt_surrogate_mojo.gv -o dt_surrogate_mojo.png\n"
     ]
    }
   ],
   "source": [
    "png_file_name = model_id + '.png'\n",
    "png_args = str('dot -Tpng ' + gv_file_name + ' -o ' + png_file_name)\n",
    "png_args = png_args.split()\n",
    "\n",
    "print('Calling external process ...')\n",
    "print(' '.join(png_args))\n",
    "\n",
    "_ = subprocess.call(png_args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display decision tree surrogate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Image((png_file_name)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From this surrogate model we can see ...\n",
    "* Some of the most important variables\n",
    "* Important interactions \n",
    "* The decision path for the most expensive houses\n",
    "* The decision path for the least expensive houses\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Are you sure you want to shutdown the H2O instance running at http://127.0.0.1:54321 (Y/N)? Y\n",
      "H2O session _sid_ba62 closed.\n"
     ]
    }
   ],
   "source": [
    "# shutdown h2o\n",
    "h2o.cluster().shutdown(prompt=True)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}