ieee-fraud-detection-automl-h2o (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About this Kernel\n",
    "This is my first attempt to publish a kernel in Kaggle. I will try H2O AutoML  on this IEEE Fraud transaction dataset. Primary goal for me is to work on a dataset that has combination of features (numbers, categorical, datetime) and the features are not explanatory, so i could experiment with pre-processing & Feature engineering concepts\n",
    "\n",
    "\n",
    "## Credits\n",
    "1. Thanks to kernels submitted by tunguz. I have used some of the concepts in this Kernel\n",
    "https://www.kaggle.com/tunguz/ieee-with-h2o-automl\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import os\n",
    "import gc\n",
    "import itertools\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.26.0.2\n",
      "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: openjdk version \"1.8.0_222\"; OpenJDK Runtime Environment (build 1.8.0_222-8u222-b10-1~deb9u1-b10); OpenJDK 64-Bit Server VM (build 25.222-b10, mixed mode)\n",
      "  Starting server from /opt/conda/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /tmp/tmp67tep73u\n",
      "  JVM stdout: /tmp/tmp67tep73u/h2o_unknownUser_started_from_python.out\n",
      "  JVM stderr: /tmp/tmp67tep73u/h2o_unknownUser_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>02 secs</td></tr>\n",
       "<tr><td>H2O cluster timezone:</td>\n",
       "<td>Etc/UTC</td></tr>\n",
       "<tr><td>H2O data parsing timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.26.0.2</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>19 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_unknownUser_ra237c</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>4</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>4</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>Amazon S3, XGBoost, Algos, AutoML, Core V3, Core V4</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>3.6.6 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ---------------------------------------------------\n",
       "H2O cluster uptime:         02 secs\n",
       "H2O cluster timezone:       Etc/UTC\n",
       "H2O data parsing timezone:  UTC\n",
       "H2O cluster version:        3.26.0.2\n",
       "H2O cluster version age:    19 days\n",
       "H2O cluster name:           H2O_from_python_unknownUser_ra237c\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    10.67 Gb\n",
       "H2O cluster total cores:    4\n",
       "H2O cluster allowed cores:  4\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:         Amazon S3, XGBoost, Algos, AutoML, Core V3, Core V4\n",
       "Python version:             3.6.6 final\n",
       "--------------------------  ---------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import h2o\n",
    "print(h2o.__version__)\n",
    "from h2o.automl import H2OAutoML\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",
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "train_transaction=h2o.import_file(\"../input/train_transaction.csv\")\n",
    "train_identity=h2o.import_file(\"../input/train_identity.csv\")\n",
    "test_transaction=h2o.import_file(\"../input/test_transaction.csv\")\n",
    "test_identity=h2o.import_file(\"../input/test_identity.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train= train_transaction.merge(train_identity, all_x=True)\n",
    "test= test_transaction.merge(test_identity, all_x=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((590540, 434),\n",
       " (590540, 394),\n",
       " (144233, 41),\n",
       " (506691, 433),\n",
       " (506691, 393),\n",
       " (141907, 41))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, train_transaction.shape,train_identity.shape, test.shape, test_transaction.shape, test_identity.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's treat the TransactionDT feature, per the dataset description given\n",
    "References: https://www.kaggle.com/wajihullahbaig/up-sampling-on-every-hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creates an hour of the day feature, encoded as 0-23. \n",
    "train.hours=((train['TransactionDT']/(3600)).floor())%24\n",
    "test.hours=((test['TransactionDT']/(3600)).floor())%24"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drop TransactionDT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train.drop(['TransactionDT'], axis=1)\n",
    "X_test = test.drop(['TransactionDT'], axis=1)\n",
    "del train_transaction, train_identity, test_transaction, test_identity\n",
    "del train, test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find & tag categorical variables as H2O factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for f in X_train.columns:\n",
    "    if X_train[f].columns_by_type(coltype='categorical'): \n",
    "        X_train[f]=X_train[f].asfactor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for f in X_test.columns:\n",
    "    if X_test[f].columns_by_type(coltype='categorical'): \n",
    "        X_test[f]=X_test[f].asfactor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  TransactionID</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">    3.66355e+06</td></tr>\n",
       "<tr><td style=\"text-align: right;\">    3.66355e+06</td></tr>\n",
       "<tr><td style=\"text-align: right;\">    3.66355e+06</td></tr>\n",
       "<tr><td style=\"text-align: right;\">    3.66355e+06</td></tr>\n",
       "<tr><td style=\"text-align: right;\">    3.66355e+06</td></tr>\n",
       "<tr><td style=\"text-align: right;\">    3.66355e+06</td></tr>\n",
       "<tr><td style=\"text-align: right;\">    3.66356e+06</td></tr>\n",
       "<tr><td style=\"text-align: right;\">    3.66356e+06</td></tr>\n",
       "<tr><td style=\"text-align: right;\">    3.66356e+06</td></tr>\n",
       "<tr><td style=\"text-align: right;\">    3.66356e+06</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.pop('TransactionID')\n",
    "X_test.pop('TransactionID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=X_train.columns[1:]\n",
    "y='isFraud'\n",
    "X_train[y]=X_train[y].asfactor()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lets try H2O autoML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "H2OResponseError",
     "evalue": "Server error java.lang.IllegalArgumentException:\n  Error: Field = stopping_metric cannot be set to value = aucpr\n  Request: POST /99/AutoMLBuilder\n    json: {'input_spec': {'response_column': 'isFraud', 'training_frame': 'py_933_sid_be32', 'ignored_columns': []}, 'build_control': {'stopping_criteria': {'max_runtime_secs': 18000, 'max_models': 20, 'stopping_metric': 'aucpr', 'stopping_tolerance': 0.01, 'stopping_rounds': 5, 'seed': 18}, 'nfolds': 5, 'keep_cross_validation_predictions': False, 'keep_cross_validation_models': False, 'keep_cross_validation_fold_assignment': False, 'project_name': 'automl_py_933_sid_be32'}, 'build_models': {'exclude_algos': None}}\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mH2OResponseError\u001b[0m                          Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/h2o/automl/autoh2o.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, x, y, training_frame, fold_column, weights_column, validation_frame, leaderboard_frame, blending_frame)\u001b[0m\n\u001b[1;32m    434\u001b[0m         \u001b[0mautoml_build_params\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'build_models'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_models\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 436\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh2o\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'POST /99/AutoMLBuilder'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mautoml_build_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    437\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m'job'\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Exception from the back end: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/h2o/h2o.py\u001b[0m in \u001b[0;36mapi\u001b[0;34m(endpoint, data, json, filename, save_to)\u001b[0m\n\u001b[1;32m    102\u001b[0m     \u001b[0;31m# type checks are performed in H2OConnection class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     \u001b[0m_check_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mh2oconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msave_to\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/h2o/backend/connection.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, endpoint, data, json, filename, save_to)\u001b[0m\n\u001b[1;32m    405\u001b[0m                                     auth=self._auth, verify=self._verify_ssl_cert, proxies=self._proxies)\n\u001b[1;32m    406\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_log_end_transaction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 407\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_to\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConnectionError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTTPError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/h2o/backend/connection.py\u001b[0m in \u001b[0;36m_process_response\u001b[0;34m(response, save_to)\u001b[0m\n\u001b[1;32m    741\u001b[0m         \u001b[0;31m# Client errors (400 = \"Bad Request\", 404 = \"Not Found\", 412 = \"Precondition Failed\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    742\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstatus_code\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;36m400\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m404\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m412\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mH2OErrorV3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mH2OModelBuilderErrorV3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 743\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mH2OResponseError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    744\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    745\u001b[0m         \u001b[0;31m# Server errors (notably 500 = \"Server Error\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mH2OResponseError\u001b[0m: Server error java.lang.IllegalArgumentException:\n  Error: Field = stopping_metric cannot be set to value = aucpr\n  Request: POST /99/AutoMLBuilder\n    json: {'input_spec': {'response_column': 'isFraud', 'training_frame': 'py_933_sid_be32', 'ignored_columns': []}, 'build_control': {'stopping_criteria': {'max_runtime_secs': 18000, 'max_models': 20, 'stopping_metric': 'aucpr', 'stopping_tolerance': 0.01, 'stopping_rounds': 5, 'seed': 18}, 'nfolds': 5, 'keep_cross_validation_predictions': False, 'keep_cross_validation_models': False, 'keep_cross_validation_fold_assignment': False, 'project_name': 'automl_py_933_sid_be32'}, 'build_models': {'exclude_algos': None}}\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "aml = H2OAutoML(max_models=20, seed=18, max_runtime_secs=18000, stopping_rounds=5,stopping_metric=\"aucpr\",stopping_tolerance=1e-2)\n",
    "aml.train(x=x, y=y, training_frame=X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "CPU times: user 44 ms, sys: 8 ms, total: 52 ms\n",
      "Wall time: 69.8 ms\n",
      "This H2OFrame is empty.\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "lb = aml.leaderboard\n",
    "lb.head(rows=lb.nrows) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 18.4 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "aml.leader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'NoneType' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/h2o/automl/autoh2o.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, test_data)\u001b[0m\n\u001b[1;32m    471\u001b[0m         \u001b[0mleader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    472\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mleader\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 473\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    474\u001b[0m             \u001b[0mleader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    475\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mleader\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/h2o/automl/autoh2o.py\u001b[0m in \u001b[0;36m_fetch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    519\u001b[0m     \u001b[0;31m#-------------------------------------------------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_fetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m         \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mH2OAutoML\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetch_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproject_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_leader_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'leader_id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_leaderboard\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'leaderboard'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/h2o/automl/autoh2o.py\u001b[0m in \u001b[0;36m_fetch_state\u001b[0;34m(project_name, properties)\u001b[0m\n\u001b[1;32m    585\u001b[0m         \u001b[0mleaderboard\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    586\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mshould_fetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'leaderboard'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 587\u001b[0;31m             \u001b[0mleaderboard\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mH2OAutoML\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetch_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate_json\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'leaderboard_table'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mproject_name\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"_leaderboard\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    588\u001b[0m             \u001b[0mleaderboard\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh2o\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleaderboard\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproject_name\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"_leaderboard\"\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# removing index and reassign id to ensure persistence on backend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    589\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'NoneType' and 'str'"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "y_preds = aml.predict(X_test)\n",
    "y_preds['p1'].as_data_frame().values.flatten().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 100 ms, sys: 92 ms, total: 192 ms\n",
      "Wall time: 193 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import pandas as pd\n",
    "sample_submission = pd.read_csv('../input/sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_preds' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'y_preds' is not defined"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "sample_submission['isFraud'] = y_preds['p1'].as_data_frame().values\n",
    "sample_submission.to_csv('H2O_AutoML_submission_1.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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