model-walkthrough.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time-Series Prediction: Kaggle Bike Sharing Demand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "@satishkt @sialan\n",
    "\n",
    "Lorem ipsum delorum..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# This tells matplotlib not to try opening a new window for each plot.\n",
    "%matplotlib inline\n",
    "\n",
    "import math\n",
    "import random\n",
    "from datetime import datetime, date, time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "from sklearn import cross_validation\n",
    "from sklearn import feature_extraction\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn import linear_model\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "# xgboost\n",
    "# theano\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10886, 12)\n",
      "(100, 9) (100, 3)\n",
      "Index([u'datetime', u'season', u'holiday', u'workingday', u'weather', u'temp', u'atemp', u'humidity', u'windspeed'], dtype='object')\n",
      "datetime       object\n",
      "season          int64\n",
      "holiday         int64\n",
      "workingday      int64\n",
      "weather         int64\n",
      "temp          float64\n",
      "atemp         float64\n",
      "humidity        int64\n",
      "windspeed     float64\n",
      "dtype: object\n",
      "Index([u'casual', u'registered', u'count'], dtype='object')\n",
      "casual        int64\n",
      "registered    int64\n",
      "count         int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Load the data, which is included in sklearn.\n",
    "bike_sharing_demand = pd.read_csv('./data/input/train.csv')\n",
    "prediction_data = pd.read_csv('./data/input/test.csv')\n",
    "print bike_sharing_demand.shape\n",
    "\n",
    "train_data, train_labels = bike_sharing_demand.ix[:, 'datetime':'windspeed'], bike_sharing_demand.ix[:, 'casual':]\n",
    "prediction_data = prediction_data.ix[:, 'datetime':'windspeed']\n",
    "\n",
    "# Set the randomizer seed so results are the same each time.\n",
    "np.random.seed(0)\n",
    "\n",
    "# Shuffle the input: create a random permutation of the integers between 0 and the number of data points and apply this\n",
    "# permutation to X and Y.\n",
    "# NOTE: Each time you run this cell, you'll re-shuffle the data, resulting in a different ordering.\n",
    "shuffle = np.random.permutation(np.arange(train_data.shape[0]))\n",
    "mini_bike_sharing_demand = bike_sharing_demand.ix[shuffle[:100], :]\n",
    "mini_train_data, mini_train_labels = train_data.ix[shuffle[:100], :], train_labels.ix[shuffle[:100], :]\n",
    "print mini_train_data.shape, mini_train_labels.shape\n",
    "\n",
    "print train_data.columns\n",
    "print train_data.dtypes\n",
    "print train_labels.columns\n",
    "print train_labels.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get a better handle on what we're working with..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# TODO: Data Exploration\n",
    "# g = sns.pairplot(mini_bike_sharing_demand.ix[:, 'temp':'casual'], hue=\"casual\", palette=\"Set2\", diag_kind=\"kde\", size=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x1f2e2198>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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OipGpZfTGl+FmTA3NDgQEAABaNPWqsX0I7KVCMZQPO8miVdwuts5rjlw4HpZDRMTZbJoI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\nAAAhIAAAAABCQAAAAABE9P87VokxfU53kAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f310978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "indexed_df = bike_sharing_demand.set_index('datetime').ix[:, 'count']\n",
    "indexed_df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to get the value of the information in the timestamp, we need to extract it. We'll cover some more advanced feature engineering later on but this should help us out of the gate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 2011-01-01 00:00:00</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> 9.84</td>\n",
       "      <td> 14.395</td>\n",
       "      <td> 81</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 5</td>\n",
       "      <td> 1</td>\n",
       "      <td> 2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2011-01-01 01:00:00</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> 9.02</td>\n",
       "      <td> 13.635</td>\n",
       "      <td> 80</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> 5</td>\n",
       "      <td> 1</td>\n",
       "      <td> 2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2011-01-01 02:00:00</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> 9.02</td>\n",
       "      <td> 13.635</td>\n",
       "      <td> 80</td>\n",
       "      <td> 0</td>\n",
       "      <td> 2</td>\n",
       "      <td> 5</td>\n",
       "      <td> 1</td>\n",
       "      <td> 2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 2011-01-01 03:00:00</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> 9.84</td>\n",
       "      <td> 14.395</td>\n",
       "      <td> 75</td>\n",
       "      <td> 0</td>\n",
       "      <td> 3</td>\n",
       "      <td> 5</td>\n",
       "      <td> 1</td>\n",
       "      <td> 2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 2011-01-01 04:00:00</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> 9.84</td>\n",
       "      <td> 14.395</td>\n",
       "      <td> 75</td>\n",
       "      <td> 0</td>\n",
       "      <td> 4</td>\n",
       "      <td> 5</td>\n",
       "      <td> 1</td>\n",
       "      <td> 2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
       "0  2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "1  2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2  2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "3  2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "4  2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "   humidity  windspeed  hour  weekday  month  year  \n",
       "0        81          0     0        5      1  2011  \n",
       "1        80          0     1        5      1  2011  \n",
       "2        80          0     2        5      1  2011  \n",
       "3        75          0     3        5      1  2011  \n",
       "4        75          0     4        5      1  2011  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's extract the information\n",
    "for dataset in (train_data, prediction_data):\n",
    "    dataset['hour'] = dataset['datetime'].map(lambda x: (datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\")).hour)\n",
    "    dataset['weekday'] = dataset['datetime'].map(lambda x: (datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\")).weekday())\n",
    "    dataset['month'] = dataset['datetime'].map(lambda x: (datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\")).month)\n",
    "    dataset['year'] = dataset['datetime'].map(lambda x: (datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\")).year)\n",
    "\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we move onto modelling, we can define a few functions to use going forward..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('TRAIN:', array([ 4677,  4448,  5705, ...,  9845, 10799,  2732]), 'TEST:', array([6638, 7975, 5915, ..., 6860, 9596, 2663]))\n",
      "('TRAIN:', array([10386,  7496,  9182, ...,  1139,  2328,  2219]), 'TEST:', array([6395, 6832, 9781, ..., 2491, 6925, 6305]))\n",
      "('TRAIN:', array([ 1287,  6686,  3958, ...,  4151,   665, 10420]), 'TEST:', array([ 2075,  2707,  6480, ..., 10843,  7669,  2231]))\n",
      "('TRAIN:', array([5623, 5232, 5102, ..., 9615,  495, 1377]), 'TEST:', array([4024, 5941,   16, ..., 6469,  573, 3649]))\n",
      "('TRAIN:', array([ 121, 2774, 6844, ..., 9362, 6073, 8646]), 'TEST:', array([5481, 8121, 7550, ..., 5653, 6203, 3524]))\n",
      "('TRAIN:', array([3211, 3162, 1709, ...,  459, 1169, 3559]), 'TEST:', array([7341, 6238, 9430, ..., 9801, 5258, 5785]))\n",
      "('TRAIN:', array([ 2695,  8909,  7979, ...,  3635, 10203,  2596]), 'TEST:', array([7930, 1021, 4776, ..., 9878, 8776, 1113]))\n",
      "('TRAIN:', array([3614, 5140,  928, ...,  839, 1852, 1606]), 'TEST:', array([6553, 9310, 1568, ..., 6010,  439, 2633]))\n",
      "('TRAIN:', array([4720, 1323, 4715, ..., 2609, 9758, 9604]), 'TEST:', array([10826,  8944,  5650, ...,  4131,  4755,  2856]))\n",
      "('TRAIN:', array([5584, 6556,  450, ..., 5275,  210,  456]), 'TEST:', array([7766, 3685, 7400, ..., 9496, 2389, 7535]))\n"
     ]
    }
   ],
   "source": [
    "# Define the Root-Mean-Squared-Log Error function for scoring predictions\n",
    "def rmsle(actual_values, predicted_values):\n",
    "    squared_log_errors = (np.log(np.array(predicted_values) + 1) - np.log(np.array(actual_values) + 1)) ** 2\n",
    "    mean_squared_errors = np.nansum(squared_log_errors) / len(squared_log_errors)\n",
    "    return np.sqrt(mean_squared_errors)\n",
    "\n",
    "# Define cross-validation loop for training and testing\n",
    "rs = cross_validation.ShuffleSplit(train_data.shape[0], n_iter=10, random_state=0)\n",
    "for train_index, test_index in rs:\n",
    "    print(\"TRAIN:\", train_index, \"TEST:\", test_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Baseline Model (Linear Regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validated Error Loop 0: 1.17262495536\n",
      "Cross-Validated Error Loop 1: 1.18423803286\n",
      "Cross-Validated Error Loop 2: 1.17035666194\n",
      "Cross-Validated Error Loop 3: 1.15128236819\n",
      "Cross-Validated Error Loop 4: 1.1208367118\n",
      "Cross-Validated Error Loop 5: 1.13114804469\n",
      "Cross-Validated Error Loop 6: 1.14718152976\n",
      "Cross-Validated Error Loop 7: 1.15876066646\n",
      "Cross-Validated Error Loop 8: 1.12558267782\n",
      "Cross-Validated Error Loop 9: 1.15743021669\n"
     ]
    }
   ],
   "source": [
    "# Create linear regression object\n",
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "# Train the model using the training sets\n",
    "for i, (train_index, test_index) in enumerate(rs):\n",
    "    regr.fit(train_data.ix[train_index, 'season':], train_labels.ix[train_index, 'count'])\n",
    "    prediction_values = regr.predict(train_data.ix[test_index, 'season':])\n",
    "    print(\"Cross-Validated Error Loop {0}: {1}\".format(i, rmsle(train_labels.ix[test_index, 'count'], prediction_values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Classical Statistical Forecasting Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Traditional approaches to time-series forecasting assume an underlying stochastic process with some degree of stationarity. Using this assumption, auto-regressive (AR) approaches using VAR, ARIMA, or GARCH models are very common. However, unable to capture non-linear feature interaction...ADD MORE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Let's try some traditional statistical forecasting models, ARIMA\n",
    "# arma = sm.tsa.ARIMA(src_data_model, order=(1,1,1), freq='W').fit(full_output=False, disp=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Machine Learning Approach To Time-Series Forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modern approaches to time-series forecasting leverage chaos theory and the assumption that the underlying process is not random, but ultimately deterministic and highly non-linear...ADD MORE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K Nearest Neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A non-parametric approach based on ________ paper...ADD MORE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validated Error Loop 0: 0.870329339042\n",
      "Cross-Validated Error Loop 1: 0.813035720167\n",
      "Cross-Validated Error Loop 2: 0.841401302924\n",
      "Cross-Validated Error Loop 3: 0.868199951255\n",
      "Cross-Validated Error Loop 4: 0.826519114775\n",
      "Cross-Validated Error Loop 5: 0.837798330481\n",
      "Cross-Validated Error Loop 6: 0.86842735387\n",
      "Cross-Validated Error Loop 7: 0.833777351734\n",
      "Cross-Validated Error Loop 8: 0.843276624515\n",
      "Cross-Validated Error Loop 9: 0.840713253162\n"
     ]
    }
   ],
   "source": [
    "# Create knn regression object\n",
    "neigh = KNeighborsRegressor(n_neighbors=5)\n",
    "\n",
    "# Train the model using the training sets\n",
    "for i, (train_index, test_index) in enumerate(rs):\n",
    "    neigh.fit(train_data.ix[train_index, 'season':], train_labels.ix[train_index, 'count'])\n",
    "    prediction_values = neigh.predict(train_data.ix[test_index, 'season':])\n",
    "    print(\"Cross-Validated Error Loop {0}: {1}\".format(i, rmsle(train_labels.ix[test_index, 'count'], prediction_values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A kernel method based on ____ paper...ADD MORE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create svr objects\n",
    "svr_lin = SVR(kernel='linear', C=1e3)\n",
    "svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)\n",
    "svr_poly = SVR(kernel='poly', C=1e3, degree=2)\n",
    "\n",
    "print(\"For the SVR using a linear kernel:\")\n",
    "for i, (train_index, test_index) in enumerate(rs):\n",
    "    svr_lin.fit(train_data.ix[train_index, 'season':], train_labels.ix[train_index, 'count'])\n",
    "    prediction_values = svr_lin.predict(train_data.ix[test_index, 'season':])\n",
    "    print(\"Cross-Validated Error Loop {0}: {1}\".format(i, rmsle(train_labels.ix[test_index, 'count'], prediction_values)))\n",
    "\"\"\"\n",
    "# Train the model using the training sets\n",
    "print(\"For the SVR using an rbf kernel:\")\n",
    "for i, (train_index, test_index) in enumerate(rs):\n",
    "    svr_rbf.fit(train_data.ix[train_index, 'season':], train_labels.ix[train_index, 'count'])\n",
    "    prediction_values = svr_rbf.predict(train_data.ix[test_index, 'season':])\n",
    "    print(\"Cross-Validated Error Loop {0}: {1}\".format(i, rmsle(train_labels.ix[test_index, 'count'], prediction_values)))\n",
    "    \n",
    "# Train the model using the training sets\n",
    "print(\"For the SVR using a polynomial kernel or degree 2:\")\n",
    "for i, (train_index, test_index) in enumerate(rs):\n",
    "    svr_poly.fit(train_data.ix[train_index, 'season':], train_labels.ix[train_index, 'count'])\n",
    "    prediction_values = svr_poly.predict(train_data.ix[test_index, 'season':])\n",
    "    print(\"Cross-Validated Error Loop {0}: {1}\".format(i, rmsle(train_labels.ix[test_index, 'count'], prediction_values)))\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision-Tree Based Approaches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RF and GBM extend decision trees....really ensemble methods but we'll cover ensembling multiple models later..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the Random Forest model:\n",
      "Cross-Validated Error Loop 0: 0.357062993623\n",
      "Cross-Validated Error Loop 1: 0.319521364968\n",
      "Cross-Validated Error Loop 2: 0.320574357334\n",
      "Cross-Validated Error Loop 3: 0.340111790106\n",
      "Cross-Validated Error Loop 4: 0.32492139945\n",
      "Cross-Validated Error Loop 5: 0.324845814757\n",
      "Cross-Validated Error Loop 6: 0.325664160057\n",
      "Cross-Validated Error Loop 7: 0.340137989483\n",
      "Cross-Validated Error Loop 8: 0.328012599016\n",
      "Cross-Validated Error Loop 9: 0.32580457893\n",
      "\n",
      "For the Gradient Boosted Trees model:\n",
      "Cross-Validated Error Loop 0: 0.59509561285\n",
      "Cross-Validated Error Loop 1: 0.595152375499\n",
      "Cross-Validated Error Loop 2: 0.569883383723\n",
      "Cross-Validated Error Loop 3: 0.613558059052\n",
      "Cross-Validated Error Loop 4: 0.566935489885\n",
      "Cross-Validated Error Loop 5: 0.569727251467\n",
      "Cross-Validated Error Loop 6: 0.550634120313\n",
      "Cross-Validated Error Loop 7: 0.585498709599\n",
      "Cross-Validated Error Loop 8: 0.53910777445\n",
      "Cross-Validated Error Loop 9: 0.551717866881\n"
     ]
    }
   ],
   "source": [
    "# TODO: Random Forest and GBM Regression\n",
    "# Create rf and gbm regression object\n",
    "rf = RandomForestRegressor(random_state=0, n_estimators=100)\n",
    "gbm = GradientBoostingRegressor(loss='ls', alpha=0.95, n_estimators=500, max_depth=4, learning_rate=.01, min_samples_leaf=9, min_samples_split=9)\n",
    "\n",
    "# Train the model using the training sets\n",
    "print(\"For the Random Forest model:\")\n",
    "for i, (train_index, test_index) in enumerate(rs):\n",
    "    rf.fit(train_data.ix[train_index, 'season':], train_labels.ix[train_index, 'count'])\n",
    "    prediction_values = rf.predict(train_data.ix[test_index, 'season':])\n",
    "    print(\"Cross-Validated Error Loop {0}: {1}\".format(i, rmsle(train_labels.ix[test_index, 'count'], prediction_values)))\n",
    "    \n",
    "# Train the model using the training sets\n",
    "print(\"\\nFor the Gradient Boosted Trees model:\")\n",
    "for i, (train_index, test_index) in enumerate(rs):\n",
    "    gbm.fit(train_data.ix[train_index, 'season':], train_labels.ix[train_index, 'count'])\n",
    "    prediction_values = gbm.predict(train_data.ix[test_index, 'season':])\n",
    "    print(\"Cross-Validated Error Loop {0}: {1}\".format(i, rmsle(train_labels.ix[test_index, 'count'], prediction_values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can start playing with the features..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "def one_hot_dataframe(data, cols, replace=False):\n",
    "    \"\"\" Takes a dataframe and a list of columns that need to be encoded.\n",
    "    Returns a 3-tuple comprising the data, the vectorized data,\n",
    "    and the fitted vectorizer.\n",
    "    Modified from https://gist.github.com/kljensen/5452382\n",
    "    \"\"\"\n",
    "    enc = OneHotEncoder()\n",
    "    e = enc.fit_transform(data[cols].values)\n",
    "    vecData = pd.DataFrame(e.toarray())\n",
    "    if replace is True:\n",
    "        data = data.drop(cols, axis=1)\n",
    "        data = data.join(vecData)\n",
    "    return (data, vecData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>hour</th>\n",
       "      <th>...</th>\n",
       "      <th>humidity_w3</th>\n",
       "      <th>humidity_w6</th>\n",
       "      <th>humidity_w12</th>\n",
       "      <th>humidity_w24</th>\n",
       "      <th>humidity_w48</th>\n",
       "      <th>windspeed_w3</th>\n",
       "      <th>windspeed_w6</th>\n",
       "      <th>windspeed_w12</th>\n",
       "      <th>windspeed_w24</th>\n",
       "      <th>windspeed_w48</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 2011-01-01 00:00:00</td>\n",
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       "      <td> 0.0000</td>\n",
       "      <td> 0.0000</td>\n",
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       "    <tr>\n",
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       "      <td> 1</td>\n",
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       "      <td> 80.000000</td>\n",
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       "      <td> 0.0000</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 0.0000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>  9.02</td>\n",
       "      <td> 13.635</td>\n",
       "      <td> 80</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 2</td>\n",
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       "      <td> 80.333333</td>\n",
       "      <td> 80.000000</td>\n",
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       "      <td> 80</td>\n",
       "      <td> 0.000000</td>\n",
       "      <td> 0.000000</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 0.0000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 2011-01-01 03:00:00</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td>  9.84</td>\n",
       "      <td> 14.395</td>\n",
       "      <td> 75</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 3</td>\n",
       "      <td>...</td>\n",
       "      <td> 78.333333</td>\n",
       "      <td> 75.000000</td>\n",
       "      <td> 75</td>\n",
       "      <td> 75</td>\n",
       "      <td> 75</td>\n",
       "      <td> 0.000000</td>\n",
       "      <td> 0.000000</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 0.0000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 2011-01-01 04:00:00</td>\n",
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       "      <td>  9.84</td>\n",
       "      <td> 14.395</td>\n",
       "      <td> 75</td>\n",
       "      <td> 0.0000</td>\n",
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       "      <td> 75</td>\n",
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       "      <td> 0.000000</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 0.0000</td>\n",
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       "    <tr>\n",
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       "      <td> 2011-01-01 05:00:00</td>\n",
       "      <td> 1</td>\n",
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       "      <td>  9.84</td>\n",
       "      <td> 12.880</td>\n",
       "      <td> 75</td>\n",
       "      <td> 6.0032</td>\n",
       "      <td> 5</td>\n",
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       "      <td> 75.000000</td>\n",
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       "      <td> 2.001067</td>\n",
       "      <td> 1.000533</td>\n",
       "      <td> 6.0032</td>\n",
       "      <td> 6.0032</td>\n",
       "      <td> 6.0032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td> 2011-01-01 06:00:00</td>\n",
       "      <td> 1</td>\n",
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       "      <td>  9.02</td>\n",
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       "      <td> 80</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 6</td>\n",
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       "      <td> 76.666667</td>\n",
       "      <td> 77.500000</td>\n",
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       "      <td> 80</td>\n",
       "      <td> 80</td>\n",
       "      <td> 2.001067</td>\n",
       "      <td> 1.000533</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td> 2011-01-01 07:00:00</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td>  8.20</td>\n",
       "      <td> 12.880</td>\n",
       "      <td> 86</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 7</td>\n",
       "      <td>...</td>\n",
       "      <td> 80.333333</td>\n",
       "      <td> 78.500000</td>\n",
       "      <td> 86</td>\n",
       "      <td> 86</td>\n",
       "      <td> 86</td>\n",
       "      <td> 2.001067</td>\n",
       "      <td> 1.000533</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td> 2011-01-01 08:00:00</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td>  9.84</td>\n",
       "      <td> 14.395</td>\n",
       "      <td> 75</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 8</td>\n",
       "      <td>...</td>\n",
       "      <td> 80.333333</td>\n",
       "      <td> 77.666667</td>\n",
       "      <td> 75</td>\n",
       "      <td> 75</td>\n",
       "      <td> 75</td>\n",
       "      <td> 0.000000</td>\n",
       "      <td> 1.000533</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td> 2011-01-01 09:00:00</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> 13.12</td>\n",
       "      <td> 17.425</td>\n",
       "      <td> 76</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 9</td>\n",
       "      <td>...</td>\n",
       "      <td> 79.000000</td>\n",
       "      <td> 77.833333</td>\n",
       "      <td> 76</td>\n",
       "      <td> 76</td>\n",
       "      <td> 76</td>\n",
       "      <td> 0.000000</td>\n",
       "      <td> 1.000533</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 0.0000</td>\n",
       "      <td> 0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  season  holiday  workingday  weather   temp   atemp  \\\n",
       "0  2011-01-01 00:00:00       1        0           0        1   9.84  14.395   \n",
       "1  2011-01-01 01:00:00       1        0           0        1   9.02  13.635   \n",
       "2  2011-01-01 02:00:00       1        0           0        1   9.02  13.635   \n",
       "3  2011-01-01 03:00:00       1        0           0        1   9.84  14.395   \n",
       "4  2011-01-01 04:00:00       1        0           0        1   9.84  14.395   \n",
       "5  2011-01-01 05:00:00       1        0           0        2   9.84  12.880   \n",
       "6  2011-01-01 06:00:00       1        0           0        1   9.02  13.635   \n",
       "7  2011-01-01 07:00:00       1        0           0        1   8.20  12.880   \n",
       "8  2011-01-01 08:00:00       1        0           0        1   9.84  14.395   \n",
       "9  2011-01-01 09:00:00       1        0           0        1  13.12  17.425   \n",
       "\n",
       "   humidity  windspeed  hour      ...        humidity_w3  humidity_w6  \\\n",
       "0        81     0.0000     0      ...          81.000000    81.000000   \n",
       "1        80     0.0000     1      ...          80.000000    80.000000   \n",
       "2        80     0.0000     2      ...          80.333333    80.000000   \n",
       "3        75     0.0000     3      ...          78.333333    75.000000   \n",
       "4        75     0.0000     4      ...          76.666667    75.000000   \n",
       "5        75     6.0032     5      ...          75.000000    77.666667   \n",
       "6        80     0.0000     6      ...          76.666667    77.500000   \n",
       "7        86     0.0000     7      ...          80.333333    78.500000   \n",
       "8        75     0.0000     8      ...          80.333333    77.666667   \n",
       "9        76     0.0000     9      ...          79.000000    77.833333   \n",
       "\n",
       "   humidity_w12  humidity_w24  humidity_w48  windspeed_w3  windspeed_w6  \\\n",
       "0            81            81            81      0.000000      0.000000   \n",
       "1            80            80            80      0.000000      0.000000   \n",
       "2            80            80            80      0.000000      0.000000   \n",
       "3            75            75            75      0.000000      0.000000   \n",
       "4            75            75            75      0.000000      0.000000   \n",
       "5            75            75            75      2.001067      1.000533   \n",
       "6            80            80            80      2.001067      1.000533   \n",
       "7            86            86            86      2.001067      1.000533   \n",
       "8            75            75            75      0.000000      1.000533   \n",
       "9            76            76            76      0.000000      1.000533   \n",
       "\n",
       "   windspeed_w12  windspeed_w24  windspeed_w48  \n",
       "0         0.0000         0.0000         0.0000  \n",
       "1         0.0000         0.0000         0.0000  \n",
       "2         0.0000         0.0000         0.0000  \n",
       "3         0.0000         0.0000         0.0000  \n",
       "4         0.0000         0.0000         0.0000  \n",
       "5         6.0032         6.0032         6.0032  \n",
       "6         0.0000         0.0000         0.0000  \n",
       "7         0.0000         0.0000         0.0000  \n",
       "8         0.0000         0.0000         0.0000  \n",
       "9         0.0000         0.0000         0.0000  \n",
       "\n",
       "[10 rows x 33 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: Add Sliding Windows\n",
    "# Add windowed features (simple moving average) for weather, temp, atemp, humidity, and windspeed\n",
    "train_data_windowed_features_three = pd.rolling_window(train_data.ix[:, 'temp':'windspeed'], 3, 'boxcar')\n",
    "train_data_windowed_features_six = pd.rolling_window(train_data.ix[:, 'temp':'windspeed'], 6, 'boxcar')\n",
    "train_data_windowed_features_twelve = pd.rolling_window(train_data.ix[:, 'temp':'windspeed'], 12, 'boxcar')\n",
    "train_data_windowed_features_twentyfour = pd.rolling_window(train_data.ix[:, 'temp':'windspeed'], 24, 'boxcar')\n",
    "train_data_windowed_features_fourtyeight = pd.rolling_window(train_data.ix[:, 'temp':'windspeed'], 48, 'boxcar')\n",
    "\n",
    "prediction_data_windowed_features_three = pd.rolling_window(prediction_data.ix[:, 'temp':'windspeed'], 3, 'boxcar')\n",
    "prediction_data_windowed_features_six = pd.rolling_window(prediction_data.ix[:, 'temp':'windspeed'], 6, 'boxcar')\n",
    "prediction_data_windowed_features_twelve = pd.rolling_window(prediction_data.ix[:, 'temp':'windspeed'], 12, 'boxcar')\n",
    "prediction_data_windowed_features_twentyfour = pd.rolling_window(prediction_data.ix[:, 'temp':'windspeed'], 24, 'boxcar')\n",
    "prediction_data_windowed_features_fourtyeight = pd.rolling_window(prediction_data.ix[:, 'temp':'windspeed'], 48, 'boxcar')\n",
    "\n",
    "train_data_combined = train_data.copy(deep=False)\n",
    "\n",
    "train_data_combined['temp_w3'] = train_data_windowed_features_three['temp']\n",
    "train_data_combined['temp_w6'] = train_data_windowed_features_six['temp']\n",
    "train_data_combined['temp_w12'] = train_data_windowed_features_twelve['temp']\n",
    "train_data_combined['temp_w24'] = train_data_windowed_features_twentyfour['temp']\n",
    "train_data_combined['temp_w48'] = train_data_windowed_features_fourtyeight['temp']\n",
    "train_data_combined = train_data_combined.apply(lambda x: x.fillna(value=train_data_combined['temp']))\n",
    "\n",
    "train_data_combined['atemp_w3'] = train_data_windowed_features_three['atemp']\n",
    "train_data_combined['atemp_w6'] = train_data_windowed_features_six['atemp']\n",
    "train_data_combined['atemp_w12'] = train_data_windowed_features_twelve['atemp']\n",
    "train_data_combined['atemp_w24'] = train_data_windowed_features_twentyfour['atemp']\n",
    "train_data_combined['atemp_w48'] = train_data_windowed_features_fourtyeight['atemp']\n",
    "train_data_combined = train_data_combined.apply(lambda x: x.fillna(value=train_data_combined['atemp']))\n",
    "\n",
    "train_data_combined['humidity_w3'] = train_data_windowed_features_three['humidity']\n",
    "train_data_combined['humidity_w6'] = train_data_windowed_features_six['humidity']\n",
    "train_data_combined['humidity_w12'] = train_data_windowed_features_twelve['humidity']\n",
    "train_data_combined['humidity_w24'] = train_data_windowed_features_twentyfour['humidity']\n",
    "train_data_combined['humidity_w48'] = train_data_windowed_features_fourtyeight['humidity']\n",
    "train_data_combined = train_data_combined.apply(lambda x: x.fillna(value=train_data_combined['humidity']))\n",
    "\n",
    "train_data_combined['windspeed_w3'] = train_data_windowed_features_three['windspeed']\n",
    "train_data_combined['windspeed_w6'] = train_data_windowed_features_six['windspeed']\n",
    "train_data_combined['windspeed_w12'] = train_data_windowed_features_twelve['windspeed']\n",
    "train_data_combined['windspeed_w24'] = train_data_windowed_features_twentyfour['windspeed']\n",
    "train_data_combined['windspeed_w48'] = train_data_windowed_features_fourtyeight['windspeed']\n",
    "train_data_combined = train_data_combined.apply(lambda x: x.fillna(value=train_data_combined['windspeed']))\n",
    "\n",
    "train_data_combined.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One hot encoding!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_data_processed, _ = one_hot_dataframe(train_data_combined, ['season', 'holiday', 'workingday', 'weather', 'hour', 'weekday', 'month', 'year'], replace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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evZ9LyfO/Hi0IDQ4AdHg02EFJSSm/qqqKo6Kiwp84ceL5zp07ZwIAa+rUqaeK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\nYTAYYk9Pz1/c3d0V9gY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c6L127dpo2fPQIdQecMIMIYTQvxwcHH7/4osv0nfv3h06fPjw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FhrZWVV4OTkdD4wMHDjpUuX3G7cuDF5//79biKRSGXnzp3fP378GEaMGEF1KkII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b26CiotL492PVV61alezj47Ohqxvk9/Pz84s+f/78rNDQUKf2NLS1QyAQ6Dx//nzAgQMH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      "text/plain": [
       "<matplotlib.figure.Figure at 0x174832b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Add some techniques from signal processing\n",
    "sns.set_context(rc={\"figure.figsize\": (24, 24)})\n",
    "sns.corrplot(train_data_combined);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've built more features, we need to do some feature selection...ADD MORE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling: Round Two!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time to try some of these models again with the advanced features...this time we'll optimize the parameters also...ADD MORE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the Random Forest model, using 9 principle components:\n",
      "Cross-Validated Error Loop 0: 0.988901513735\n",
      "Cross-Validated Error Loop 1: 0.915728329319\n",
      "Cross-Validated Error Loop 2: 0.939468237106\n",
      "Cross-Validated Error Loop 3: 0.928092177782\n",
      "Cross-Validated Error Loop 4: 0.881143103466\n",
      "Cross-Validated Error Loop 5: 0.884459394897\n",
      "Cross-Validated Error Loop 6: 0.943681786889\n",
      "Cross-Validated Error Loop 7: 0.929069050374\n",
      "Cross-Validated Error Loop 8: 0.902285646983\n",
      "Cross-Validated Error Loop 9: 0.9279309146\n"
     ]
    }
   ],
   "source": [
    "for d in range(9,10):\n",
    "    # Initialize and run PCA for d number of components\n",
    "    pca = PCA(n_components=d)\n",
    "    train_data_r = pd.DataFrame(pca.fit_transform(train_data_combined.ix[:, 'season':]))\n",
    "\n",
    "    rf = RandomForestRegressor(random_state=0, n_estimators=100)\n",
    "\n",
    "    # Train the model using the training sets\n",
    "    print(\"For the Random Forest model, using {0} principle components:\".format(d))\n",
    "    for i, (train_index, test_index) in enumerate(rs):\n",
    "        rf.fit(train_data_r.ix[train_index, :], train_labels.ix[train_index, 'count'])\n",
    "        prediction_values = rf.predict(train_data_r.ix[test_index, :])\n",
    "        print(\"Cross-Validated Error Loop {0}: {1}\".format(i, rmsle(train_labels.ix[test_index, 'count'], prediction_values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensembling!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our models all tuned, we can ensemble them together for superior performance...ADD MORE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "estimators = [(\"Linear Regression\", regr),\n",
    "              (\"K Nearest Neighbors\", neigh),\n",
    "              (\"Random Forest\", rf),\n",
    "              (\"Gradient Boosted Machine\", gbm)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For kaggle, we can simply build a csv using the sample submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# For now train using the RF model on the entire training set\n",
    "rf = RandomForestRegressor(random_state=0)\n",
    "\n",
    "# Set the parameters by cross-validation\n",
    "param_grid = {'n_estimators': [100, 300, 500, 700, 1000], 'max_depth': [None, 1, 2, 3, 5], 'min_samples_split': [1, 2, 3, 5]}\n",
    "model = GridSearchCV(rf, param_grid=param_grid)\n",
    "model.fit(train_data.ix[:, 'season':], train_labels.ix[:, 'count'])\n",
    "\n",
    "# Make predictions\n",
    "prediction_values = model.predict(prediction_data.ix[:, 'season':])\n",
    "\n",
    "# Create submission from sample_submission file\n",
    "submission_df = pd.read_csv('./data/output/sampleSubmission.csv')\n",
    "submission_df['count'] = prediction_values\n",
    "submission_df.to_csv('./data/output/rf_tuned_windowedfeatures.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What have we learned?..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## APPENDIX: Deep Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So using supervised learning, the sliding window approaches proved to be instrumental and feature engineering is obviously super important. However, with the modern advances in ML over the last few years, we can achieve the same (or superior) performance without domain-specific features. See LINK for tutorial, but ultimately the importance of Deep Learning is representation learning...ADD MORE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# TODO: RBM, Stacked Auto-encoders, DBN, CNN and RNN?"
   ]
  }
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