Predict_Bidding_Price.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import linear_model\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "import csv\n",
    "from sklearn import cross_validation\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def agg_competitor(data, index_start, index_end, skip):\n",
    "    copy = data.copy()\n",
    "    copy['Competitor'] = copy.iloc[:,1].map(lambda x: 0)\n",
    "    i=index_start\n",
    "    while i < index_end:\n",
    "        copy.ix[:,i] = ~copy.ix[:,i].isnull()*1\n",
    "        copy['Competitor'] = copy.iloc[:,i] + copy['Competitor']\n",
    "        i=i+skip\n",
    "    return copy['Competitor']\n",
    "\n",
    "p_mol_mean_price_pre_loe = {}\n",
    "\n",
    "def max_price_per_presentation():\n",
    "    data = pd.read_csv('data/data.csv')\n",
    "    return data['Winning_price_per_standard_unit'].max()   \n",
    "\n",
    "def days_since_loe(molecule_name, start_date):\n",
    "    if molecule_name =='Molecule3':\n",
    "        return float((start_date - datetime(2009,3,1,0,0,0)).days)\n",
    "    else:\n",
    "        return float((start_date - datetime(2011,1,1,0,0,0)).days)\n",
    "\n",
    "def build_presentation_price_dict():\n",
    "    data = pd.read_csv('data/data.csv')\n",
    "    p_presentation_dict_train  = {}\n",
    "    for item in data['Presentation'].unique():\n",
    "        row = data.ix[data[data['Presentation'] == item]['Winning_price_per_standard_unit'].idxmax()]\n",
    "        p_presentation_dict_train[row['Presentation']] = row['Winning_price_per_standard_unit']\n",
    "    return p_presentation_dict_train\n",
    "\n",
    "def transform_length_of_contract(row):\n",
    "    value = 0\n",
    "    if(row['Length_of_contract_in_Months'] < 12):\n",
    "        value = np.log(row['Length_of_contract_in_Months'])\n",
    "    return value\n",
    "\n",
    "def apply_featur_for_no_competition(row):\n",
    "    value = 0\n",
    "    if(row['Competitor'] == 0):\n",
    "        value = p_presentation_dict_train[row['Presentation']]\n",
    "    return value\n",
    "\n",
    "def impute_competitor_price(row):\n",
    "    if pd.isnull(row['Competitor_2nd_Lowest_Price_per_standard_unit']):\n",
    "        return 0\n",
    "    return row['Competitor_2nd_Lowest_Price_per_standard_unit']\n",
    "\n",
    "def process_df(file, cols, is_test):\n",
    "    copy =pd.read_csv(file)\n",
    "    copy['Start_date'] = pd.to_datetime(copy['Start_date'])\n",
    "    copy['End_date_including_extension'] = pd.to_datetime(copy['End_date_including_extension'])\n",
    "    if is_test == False:\n",
    "        copy = copy[copy['ID'] != 680]\n",
    "    copy['Competitor'] = agg_competitor(copy, 16, 34, 2)\n",
    "    copy = copy[cols]\n",
    "    copy['Competitor_2nd_Lowest_Price_per_standard_unit'] = copy.apply(lambda row: impute_competitor_price(row), axis=1)\n",
    "#only drop for train data\n",
    "    if is_test == False:\n",
    "        copy = copy.dropna()\n",
    "    copy.loc[:,'Tenure'] = copy.apply(lambda row: days_since_loe(row['Molecule'], row['Start_date'])/30, axis=1)\n",
    "    copy['Feature_No_Competition'] = copy.apply(lambda x: apply_featur_for_no_competition(x),axis=1)\n",
    "    copy.loc[:,'Presentation'] = copy.apply(lambda row: p_presentation_dict_train[row['Presentation']], axis=1)\n",
    "#    copy['Feature_Small_Contract'] = copy.apply(lambda x: transform_length_of_contract(x),axis=1)\n",
    "    copy.loc[:,'Contract_Length'] = copy.apply(lambda row: (row['End_date_including_extension'] - row['Start_date']).days/30 , axis=1)\n",
    "    copy.loc[:,'Length_of_contract_in_Months'] = copy.apply(lambda row: np.log(1./row['Length_of_contract_in_Months']), axis=1)\n",
    "    copy.loc[:,'Tender_Standard_Unit_per_year'] = copy.apply(lambda row: np.log(row['Tender_Standard_Unit_per_year']), axis=1)\n",
    "#only drop for train data\n",
    "    if is_test == False:\n",
    "        copy=copy[copy['Tenure'] >= 0]\n",
    "    copy.loc[:,'Tenure'] = copy.apply(lambda row: np.log(row['Tenure']), axis=1)\n",
    "    copy = copy.drop(['Molecule', 'Start_date', 'End_date_including_extension', 'Account','Province', 'Tender_Type', 'Contract_Length'],1)\n",
    "    return copy\n",
    "\n",
    "def generate_feature(is_test):\n",
    "    col=['Molecule','Account','Winning_price_per_standard_unit','Province','Region','Tender_Type', 'Tender_Standard_Unit_per_year', 'Length_of_contract_in_Months', 'Presentation', 'Start_date', 'End_date_including_extension', 'Competitor', 'Competitor_2nd_Lowest_Price_per_standard_unit']\n",
    "    file_location = 'data/data.csv'\n",
    "    if is_test:\n",
    "        col=['Molecule','Account','Province','Region','Tender_Type', 'Tender_Standard_Unit_per_year', 'Length_of_contract_in_Months', 'Presentation', 'Start_date', 'End_date_including_extension', 'Competitor','Competitor_2nd_Lowest_Price_per_standard_unit']\n",
    "        file_location = 'data/CAX_Bidding_TEST_Molecule_6_Post_LOE.csv'\n",
    "    data = process_df(file_location, col, is_test)\n",
    "    return data.copy()\n",
    "\n",
    "def prepare_model_X(data):\n",
    "    data=pd.get_dummies(data)\n",
    "    y=data.as_matrix()[:,0]\n",
    "    X=data.as_matrix()[:,1:]\n",
    "    return X,y\n",
    "\n",
    "def run_linear_reg(X_train, X_test, y_train, y_test):\n",
    "    reg=linear_model.LinearRegression()\n",
    "    reg.fit(X_train,y_train)\n",
    "    print \"Score:\" , reg.score(X_test,y_test)\n",
    "    print \"MSE:\", mean_squared_error(y_test,reg.predict(X_test))\n",
    "    plot_scatter(y_test, reg.predict(X_test))\n",
    "    return reg\n",
    "\n",
    "def run_gradient_boost(X_train, X_test, y_train, y_test):\n",
    "    # Fit regression model\n",
    "    params = {'n_estimators': 500, 'max_depth': 6, 'min_samples_split': 3,\n",
    "          'learning_rate': 0.01, 'loss': 'ls'}\n",
    "    clf = GradientBoostingRegressor(**params)\n",
    "    clf.fit(X_train, y_train)\n",
    "\n",
    "    print \"mse:\", mean_squared_error(y_test, clf.predict(X_test))\n",
    "    print \"score:\", clf.score(X_test, y_test)\n",
    "    plot_scatter(y_test, clf.predict(X_test))\n",
    "    plot_gb(params, clf)\n",
    "    return clf\n",
    "\n",
    "def plot_gb(params, clf):\n",
    "    test_score = np.zeros((params['n_estimators'],), dtype=np.float64)\n",
    "    for i, y_pred in enumerate(clf.staged_predict(X_test)):\n",
    "        test_score[i] = clf.loss_(y_test, y_pred)\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.title('Deviance')\n",
    "    plt.plot(np.arange(params['n_estimators']) + 1, clf.train_score_, 'b-',\n",
    "         label='Training Set Deviance')\n",
    "    plt.plot(np.arange(params['n_estimators']) + 1, test_score, 'r-',\n",
    "         label='Test Set Deviance')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.xlabel('Boosting Iterations')\n",
    "    plt.ylabel('Deviance')\n",
    "    \n",
    "def plot_scatter(y_test, y_pred):\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.scatter(y_test, y_pred)\n",
    "    ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
    "    ax.set_xlabel('Measured')\n",
    "    ax.set_ylabel('Predicted')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#prepare some globals.\n",
    "p_presentation_dict_train = build_presentation_price_dict()\n",
    "max_presentation_train = max_price_per_presentation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "639 9\n",
      "152 9\n"
     ]
    }
   ],
   "source": [
    "#generate features\n",
    "features = generate_feature(False)\n",
    "print features.shape[0], len(features.columns)\n",
    "feature_test = generate_feature(True)\n",
    "print feature_test.shape[0], len(features.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape Y: (639,)\n",
      "Shape X: (639, 27)\n"
     ]
    }
   ],
   "source": [
    "train_input = prepare_model_X(features)\n",
    "print \"Shape Y:\", train_input[1].shape\n",
    "print \"Shape X:\", train_input[0].shape\n",
    "X_train,X_test,y_train,y_test=train_test_split(train_input[0], train_input[1], test_size=0.33, random_state=42)\n",
    "\n",
    "feature_test=pd.get_dummies(feature_test)\n",
    "X_test_mtx=feature_test.as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.780837017382\n",
      "MSE: 1.54125793516\n"
     ]
    },
    {
     "data": {
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gHXAM8DHwFWADQZfQ2vC5VwF/weyDZJ2CiGQYdQ0l2Ukn9QgTQWxF7y3AIKCU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\n5f4//O/z6YHkPukAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c8aae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse: 0.665170480174\n",
      "score: 0.905414439038\n"
     ]
    },
    {
     "data": {
      "image/png": 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7LjO7kGDltELgjsQkkMtyMbmJSPbSzGIRkRyjZahFRKRDlAhERPKcEoGISJ5T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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dcbe090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XiqmJHfOIB/X7ikgaiablOwY4GFgL4O4zgJ6xXMTdJ7j7ibGc06dPDYKvWr4i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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ccf0dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_reg = run_linear_reg(X_train, X_test, y_train, y_test)\n",
    "gradient_boost = run_gradient_boost(X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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k4WzdOqFTbKspIh2nRJBh9fX1LRZVmz59Aqeddto+k0G6LSKvuOKbvPrqGwRN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102329c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual/Competitor\n"
     ]
    },
    {
     "data": {
      "image/png": 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EEZHfA7/E6aZ6KM5TwV9U9Vch56hX8R2Iszh6TyDQZbQnhYUfBde0NQe2dOlS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\nkgpxpkKMYHE2N4vTW54kAlW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x102329790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear_Predictor/Competitor\n"
     ]
    },
    {
     "data": {
      "image/png": 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yk2PGjLGLv2n9omlQSPaGNRa3SnPmzFEI7Q3kbN9WKFNovrdNc43FeXmjNCOj\nZ915MjJ6al7eqEYaiUdao65JGbTixmLTRjz88J+BrsDnDfa0p127dRw8+CHwQ9LSijnhhCH84hfh\nq1Qau9sOLg9UAX3yyads2rSfDRsm4ywmb4yJlGcDyiJhK5S1Tjk5g9i162xggVtyDE5SOMDAgUcw\nYMDXgfgOnho9eiIVFeMJNCI7c/84ayk1rBrKzJxhUySYNi3aFcrsicC0iKoiUv/vrW/fw9m16xGg\nADgK+CtOu8Bkdu++jVWrnkhwVGOAIrKzb+PEE0+guPhQd1WwRl1jGrJEYGKi6qwd/Mgjj7Bq1Sra\nt29ft2/ixHG88sqrqH4FbMNZ2uJ/gA/p2/eokHPFY4qF0Mblh/n97+tf8L28+Df8GQGbVsL4RzQN\nCsnesMZiX3rwwQc1J+ewugbg2267rW7foWkXJiocrtDDfe005jZsFI7nNA1+nWit4c+YkdFTMzK6\n29QUJmFojVNMNBqcJQJfqa2t1alTp4b0CEpPT9dXX31VVQPTLhTX6xUE3TQvb1TYi10qTNMQ7mdM\ndC8mvyZFkxzRJgKrGjIRe/rpp1mwYEFIeW2t8u9//5thw4a5Jc8Dh0YFgzPQy6o/kqOx6Tns928a\n4+nCNKZ1SU9Pp127Dg1Kj+Kb3zyzbsro4uIppKW9EfE5E7GISmBR+dGjJ0a9TGVLPtuYhj9jRsZN\nZGRsJZ4/c7D603M4CaGli9ebNi6ax4dkb/i8aiiVHr/LysrcwVvzFDq61UIdFTJDZuUsKSnRtLQe\nbhVIsaal5WheXr6WlZWFndo5nr/HQ/XxxQojNS0tJ+LF4J2fsbsGFpLPyOjeZDzRxN3w2ET+7aRC\ndZtpGtZGkByHLoyHGgDbSjKora3VrVu31iurf3F5SmFu3QUz3EXGWS1sVFBCWKrp6d0Usuo+B1kR\nX6SDz9vUBTRcG0VaWo+I/tvk5Y1SyA1q28jVvLxRjcbh17UI/BybSQ5LBEmSl5cfcteVl5fvdVgt\ntn37dh09erR27txZly1bVnfRDffzNpUIVMPdmR4bcqHt3PmIRmMJdxfd3AXO+c6m1xpoTLipKbKz\nB0b4s/nrrjuVnlZNqGgTgTUWx2j79ncjKmstVJ1xAdOmTWPv3r0ATJo0mdra3wFCRsYNpKcXcyCw\nwiM/AfaRkZFGcfHseucqLS1l/vwH2LNnLyAcajT+guDZQgG++qr+jJ4B4Ro8jz322LBTUwc3ghYX\nT+HZZy87sBhTAAAQ9klEQVSmtjb630HfvkeFrF8QbtxDa2CT4ZmoRJM1kr3h6yeCyKsR/O6hhx6q\nNy6g/nZf0BPPKM3Ly9cuXfpo585HhO0SGrqyV5bCQPcuvXPIXfTAgd8IG1OkK46Fuws/1EYRXTtB\nNNV9Vv1i/AyrGkqOaBsW/SS42qCkpEQ7dMhWaBcmCRyj8Ga9i25zVQ71L9ZlblIY6V6Qu2haWpeg\nNoOcRn9n4dcgHhXxxbd+g3XkF+qWNAAb4xeWCJKoNV4IGt7JHrpzvq0uAaSltdNrrrlGO3Y8rN6F\ntKioqNmLa5cufYIu4KFPTQMHDo2o90xjd9yR/M7LysrchDTSTUaNPz0Y0xZZIjBNanyU6z6F4QrH\n6KhRY1T1UM+f7OyBOnDgUIXssNU1wRf19PROQVVDR4Ucn55+WN3xzd21x5JoGyYQp/dQmSUCk1Ki\nTQSeNRaLyNeAB4HD3DvR+1T1bq/iSQXV1dVs2rQeZ2WvB4C3gf/grBo2ADiHjh2fYNy4Sxg9eiKf\nfPIRmza9zr59d7Jr12+Bo0POuWtXTyoqxvPssxfRq1c3Dhz4NfA3gtcDDnbgwH4qKoTVqy9h//4D\nqB5asjJc42+0Gq517JhNZubbtp6wMY3wstfQfuBGVX1FRDoD60SkQlW3eBhTm6Tq9AiaOnUqX375\nJTAeGM6hxdyHuq8X06NHf37+87uorb0MJ2HciXNRXQn0B2YEnXkq8EdgDLW18MEHNwBVOEtUzgcO\nBy4JOn4acDmwiH37CoE1IbF+8smnIUkIWjZNQnb2zpCZSI0xQaJ5fEjkBjwJnNGgLK6PS6koMC6A\neo3AvRQ+catOznOrbYq1fm+fXm4df3DDb25dTxynmuioMFVMOVq/H3+Ze1z9+no4TKFTvTYEkW5B\ns3LGNhbAevMY04qqhoKJSD8gD3jB20jalrVr1zJ27Ni6cQGHfISznGNwH/nngbupX6XyAM54gYAv\ncVb/6kx6+kEOHvwM1UB1ywycVcE6AAdwnhYC/gNchbNgTMB+t6wQuA94n44dM6ipCX4Cid6YMWNY\nsWKZLUJjTBQ8TwRutdDjwFRV/aLh/tmzZ9e9LigooKCgIGmxtUbBC6D8+MeX0rNnzzCJ4IfAkRyq\nGloGvB7mbP8lI+MAgwc/QG5uDvn5N7N69XoAiotn8/LLL/PznxdTW/t195yLCFQ1ZWTcQE7O7Xz8\n8W4OHvzS/a6A63ESwPM4A8zG4Ey+dlvdwjIwxT2nw5mYLbI6fhtMZVJNZWUllZWVMX/e0zWLRaQ9\n8BTwV1X9VZj96mV8rU3D0bgZGTfRp08vtm17zT3iGOBbpKU9Sa9eOXzwwXacBzGAjUBHnLp9SEu7\nkRNOOJ5f/OJ/mryoBiee/PzhQYmi/qpbgwYN5s039+A8MUwCjiItrZja2nmAc6GfOfM6SkvvCYr/\nBgYPPoHc3BxbxcuYKES7ZrFniUCchW6XAZ+q6o2NHGOJIEK1tbWMHXt+0ALu4Px6fwvkAJXAcmAc\nsIzCwpXk5w93G4a/DowiI+P+hF14GyapwIW/YeKIx7KVxqS61pQIvgX8HedWNBDEz1S1LOgYXycC\nP1y01O0R9Lvf/Y6uXY/kuee+jtMtFJxePm8DT+AkhZV1rwsLV7Jq1RNJ/Rn88PsyJhVEmwg87y3U\n1IaPew35oXdKwx5Bp556aoOeP10VShr06rGeNMa0dUTZa8jTNoLm+PmJYPToiSHVMIG77ERTDZ0p\n9JCZQEldTE7V0FVkZNzE4MFHk5vby+7GjWnjon0i8LzXkIne6tWrmTx5ckh5WloatbWf1ivLzt7J\niSeupLj4Ibv4G2PCsieCGJWXlzN+/CV1I18zMm5i5crkXWwvuOACHnvssaCSY2jf/n1EOtbFlJk5\nwxYtNyYF2RNBEh048B/glqDXybNw4UJWrPgz+/crTpfPq9m//yHy8haRm+sMxrLBVMaYSNgTQYwG\nDRrGm29+gDMgCuAnDBx4BNu2bYzbd6gqVVVVDBs2LOz+ESNOZ926c4Cb3JLktVMYY/wr2ieCtEQG\n05Zt3/4Rh5ZdLAJ+6ZbFR3V1NWPHjuWkk05i8+bNYY8pLZ1JZuY8nEbhZe7o2ylxi8EYkxosEcQo\nM7NDRGXRUlUWL17MkCFDWLVqFfv27WPSpEkcOLRYcJ3AvDqFhSspLFxp7QHGmJhY1VCMSktLueWW\nuTgTtQFcT0nJdGbOnBnzOd99911+9KMfsWrVqpB9c+fO5aabbgrzKWOMqc8ai5NkxIgRpKcf5MAB\np7E4Pf0gI0aMaNE59+/fz/PPPx9Snp+fz8SJE1t0bmOMaYxVDcVo3rz7OHDgKuBk4GQOHLiqbvqE\nWPXv35+5c+fWvc/KymLhwoU899xzDBgwoGUBG2NMI+yJIEaffPIRzkRu892SaXzyyXEtPu9VV13F\n8uXLUVWWLFliCcAYk3DWRhCjnJx+7Np1K8FTTGRnz+LTT99p9rPV1dUsWbKEWbNm4UzCWt9nn31G\n165dSUuzBzZjTPSsjSBJdu0KWUMnbFmwhnME9evXj8suuyzkuO7du8crTGOMaZY9EcSoXbvu1Na2\nJ3hAWVrafg4e/Czs8dXV1UyePLlej6Bu3bqxadMmevfunfiAjTEpwwaUJckll3wXZw3f37rbl25Z\nqHXr1tWNCwj2+eefc++99yY6VGOMaZI9EbTAZZddxiOP/BWAiy8ex9KlS8Met2/fPk466SQ2bjw0\n/URWVhZz587l6quvtrYAY0xctZoVyiLh90QQjQ0bNnDyySdz4MAB8vPzrUeQMSZhLBF47ODBg7Rr\n1y7svjvuuIMuXbrYU4AxJqEsEXgk0CNo/vz5rF271nr+GGM806oai0VkrIhsFZE3RGSGl7G0RGCm\n0MmTJ7NlyxZuvPFGr0MyxpiIefZEICLtgH8DZwLvAS8BF6nqlqBjfP1E0NTawU899RRnnXWWR5EZ\nY1JZaxpQdjKwTVXfARCRR4FzgS1NfchP1q1bF3bt4KysLD799NMwnzDGGP/xsmqoN7Aj6P27blmr\nMWLECK688sp6Zfn5+VRVVXHppZd6FJUxxkTHyyeCiOp8Zs+eXfe6oKCAgoKCBIUTmzvvvJOysjJ2\n7txp4wKMMZ6orKyksrIy5s972UYwEpitqmPd9z8DalV1TtAxvm4jCHjxxRfJzc21cQHGGF9oNd1H\nRSQdp7H4DOB94EVaWWOxMcb4UatpLFbVAyJyLVAOtAPuD04CxhhjksMGlBljTBvTqgaUGWOM8Z4l\nAmOMSXGWCIwxJsVZIjDGmBRnicAYY1KcJQJjjElxlgiMMSbFWSIwxpgUZ4nAGGNSnCUCY4xJcZYI\njDEmxVkiMMaYFGeJwBhjUpwlAmOMSXGWCIwxJsVZIjDGmBRnicAYY1KcJQJjjElxniQCEblTRLaI\nyKsi8icR6eZFHMYYY7x7IlgFDFbVE4DXgZ95FEdcVFZWeh1CRFpDnK0hRrA4483i9JYniUBVK1S1\n1n37AnCUF3HES2v542gNcbaGGMHijDeL01t+aCO4HHjG6yCMMSZVpSfqxCJSARweZtfNqvoX95iZ\nwD5V/X2i4jDGGNM0UVVvvljkMmAycIaqftXIMd4EZ4wxrZyqSqTHJuyJoCkiMha4CchvLAlAdD+I\nMcaY2HjyRCAibwAZwC636J+qek3SAzHGGONd1ZAxxhh/8EOvoSb5efCZiIwVka0i8oaIzPA6nnBE\n5Gsi8jcR2SQir4nI9V7H1BQRaSciG0TkL17H0hgR6S4ij7t/l5tFZKTXMYUjIje6/82rROT3ItLB\nBzEtEZGPRKQqqCxbRCpE5HURWSUi3b2M0Y0pXJy+uxaFizNoX7GI1IpIdnPn8X0iwKeDz0SkHbAQ\nGAscD1wkIsd5G1VY+4EbVXUwMBL4sU/jDJgKbAb8/Ki6AHhGVY8DhgFbPI4nhIj0Bq4DTlTVoUA7\n4EJvowLgAZz/Z4L9FKhQ1aOBZ933XgsXpx+vReHiRES+BhQC2yM5ie8TgY8Hn50MbFPVd1R1P/Ao\ncK7HMYVQ1Q9V9RX39Rc4F60jvY0qPBE5CvgOsBjwZUcB9y7wNFVdAqCqB1T1c4/Dakw6kCUi6UAW\n8J7H8aCq/wB2NygeDyxzXy8DvpvUoMIIF6cfr0WN/D4B5gPTIz2P7xNBA34afNYb2BH0/l23zLdE\npB+Qh/NH7Ed34fQmq23uQA/1B3aKyAMisl5EFolIltdBNaSq7wHzgGrgfeAzVf0/b6NqVC9V/ch9\n/RHQy8tgIuSna1E9InIu8K6qboz0M75IBG79YFWY7ZygY/w2+MzPVRchRKQz8Dgw1X0y8BURORv4\nWFU34NOnAVc6MBy4V1WHA//BH1UZ9YhID5w77X44T4CdReRiT4OKgDq9V3z9/5YPr0V13JuSm4FZ\nwcXNfc6TcQQNqWphU/vdwWffAc5ISkCReQ/4WtD7r+E8FfiOiLQHngAeVtUnvY6nEacC40XkO0BH\noKuIPKiql3ocV0Pv4txtveS+fxwfJgLgTOBtVf0UQET+hPM7fsTTqML7SEQOV9UPReQI4GOvA2qM\nT69FwQbiJP9XRQSc6qt1InKyqjb6e/XFE0FTggafndvU4DMPvAx8XUT6iUgGcAGw0uOYQojz13A/\nsFlVf+V1PI1R1ZtV9Wuq2h+nUfM5HyYBVPVDYIeIHO0WnQls8jCkxmwHRopIpvs3cCZOI7wfrQSK\n3NdFgC9vVnx8LaqjqlWq2ktV+7v/L70LDG8qCUArSATAPUBnoMLtVniv1wGB00gIXAuU4/wP9kdV\n9V3vEWAU8EPg2+7vb4P7B+13fq4euA54RERexek1dLvH8YRQ1RdxnlbWA4G64vu8i8ghIn8A1gLH\niMgOEZkE3AEUisjrwP9z33sqTJyX48NrUVCcRwf9PoNF9P+RDSgzxpgU1xqeCIwxxiSQJQJjjElx\nlgiMMSbFWSIwxpgUZ4nAGGNSnCUCY4xJcZYITJvlTsH7UND7dBHZ6ecprgFExHdTgJi2zRKBacv+\nAwwWkY7u+0KckZZJHzzjTlseKRvcY5LKEoFp654BznJfXwT8AXcSLhHp5C7s8YI7k+h4t7yfiPxd\nRNa52zfd8iPc8g3upIij3PK6O3gR+Z6IPOC+XioivxWRfwFzRGSgiPxVRF52z3OMe1x/EfmniGwU\nkZIk/V6MqWOJwLR1fwQuFGd1rqHUn4J7JvCsqp6CM7XBne7sjR8Bhap6Is68R3e7x/8AKFPVPOAE\n4FW3PPgOvuHd/JHAN1X1JzhTPFynqiNw5qwJTFGwAPi1qg7DmTLamKTyxeyjxiSKqla56zBcBDzd\nYPdo4BwR+Yn7vgPOLLIfAgtF5ATgIPB1d/+LwBJ3NtcnVfVVmqbAclVVdxrwbwLL3VkhATLcf08F\nJrivHwbmRPVDGtNClghMKlgJ/BLIB3o22Heeqr4RXCAis4EPVPUSt27/K3BWgxKR04CzgaUiMl9V\nH6L+U0Bmg/N/6f6bhrM4TF48fiBj4smqhkwqWALMVtWG00WXA9cH3ohI4CLdFeepAOBSnPV+EZE+\nwE5VXYwztXfg+I9E5FgRScO5sw9p7FXVPcDbIvI991wiIsPc3c9zaD1h3y8eY9oeSwSmLVNwlm1U\n1YVBZYEL9W1Ae7eR9jXgVrf8XqBIRF4BjgECjcHfBl4RkfXA+Th1++AsTPMUzgW9YR1/cFK4GPiR\ne97XcFYQA5gK/FhENuK0KVivIZNUNg21McakOHsiMMaYFGeJwBhjUpwlAmOMSXGWCIwxJsVZIjDG\nmBRnicAYY1KcJQJjjElxlgiMMSbF/X8UndaCo1NzwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102329b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient_Predictor/Competitor\n"
     ]
    }
   ],
   "source": [
    "y_test_pred_reg = linear_reg.predict(X_test_mtx)\n",
    "y_test_pred_gb = gradient_boost.predict(X_test_mtx)\n",
    "zipped = zip(y_test_pred_reg, y_test_pred_gb)\n",
    "plot_scatter(features['Competitor_2nd_Lowest_Price_per_standard_unit'], features['Winning_price_per_standard_unit'])\n",
    "print \"Actual/Competitor\"\n",
    "plot_scatter(feature_test['Competitor_2nd_Lowest_Price_per_standard_unit'], y_test_pred_reg)\n",
    "print \"Linear_Predictor/Competitor\"\n",
    "plot_scatter(feature_test['Competitor_2nd_Lowest_Price_per_standard_unit'], y_test_pred_gb)\n",
    "print \"Gradient_Predictor/Competitor\"\n",
    "\n",
    "def merge_gb_linear(row):\n",
    "    if row['Competitor'] == 0:\n",
    "        return row['gb']\n",
    "    return row['linear']\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2PsAkqkhMQx1Jv8FZG7CTx3GYVtSUqZnVHRdQUFDAvn3Oonfjxo1jz55DAW0N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\nY2JR3DQWq2q1iNwM+IB2wLz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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e101e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lr = pd.DataFrame(y_test_pred_reg)\n",
    "feature_test['linear'] = y_test_pred_reg\n",
    "feature_test['gb'] = y_test_pred_gb\n",
    "\n",
    "feature_test['linear'] = feature_test.apply(lambda x: merge_gb_linear(x),axis=1)\n",
    "y_test_merged = feature_test['linear'].as_matrix()\n",
    "plot_scatter(feature_test['Competitor_2nd_Lowest_Price_per_standard_unit'], y_test_merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5.0934674468831194, 10.55893730683645, 10.55893730683645, 0.0)\n",
      "(5.0580123570850031, 9.2173245606837124, 9.2173245606837124, 0.0)\n",
      "(11.259013996137831, 8.1600254209296104, 11.259013996137831, 11.989000000000001)\n",
      "(10.854125237292678, 8.1698161022943463, 10.854125237292678, 10.984999999999999)\n",
      "(11.056023976904402, 5.9914015358868706, 11.056023976904402, 10.984999999999999)\n",
      "(12.311061279332971, 10.447134612924641, 12.311061279332971, 12.49)\n",
      "(4.7267841978458289, 8.9928558663564768, 8.9928558663564768, 0.0)\n",
      "(10.851941854302201, 6.1009169688874545, 10.851941854302201, 11.0)\n",
      "(10.223999735650818, 6.0441453692292324, 10.223999735650818, 11.289999999999999)\n",
      "(8.4922151981544651, 5.8576409578219275, 8.4922151981544651, 9.25)\n",
      "(4.4520995905834218, 8.4046257650491558, 8.4046257650491558, 0.0)\n",
      "(10.364642216949306, 5.9663794088456648, 10.364642216949306, 10.484349999999999)\n",
      "(11.792624893149039, 6.0922039535390118, 11.792624893149039, 11.989000000000001)\n",
      "(10.846413301294049, 8.1583562640012346, 10.846413301294049, 10.984999999999999)\n",
      "(4.6014968961509091, 8.8062508650662998, 8.8062508650662998, 0.0)\n",
      "(4.7443229502013367, 9.0057792288738163, 9.0057792288738163, 0.0)\n",
      "(9.1452530146690485, 5.9594100195697628, 9.1452530146690485, 9.75)\n",
      "(7.7518967957965952, 5.7379931359697629, 7.7518967957965952, 7.3799999999999999)\n",
      "(8.4903144612186985, 5.8601230998496012, 8.4903144612186985, 9.0)\n",
      "(4.1345085146025538, 8.4320682546551549, 8.4320682546551549, 0.0)\n",
      "(7.1673058477713223, 4.9442885769229656, 7.1673058477713223, 6.75)\n",
      "(9.4632638749181961, 8.155797691145569, 9.4632638749181961, 9.6999999999999993)\n",
      "(3.8996820900821865, 8.4014887441628865, 8.4014887441628865, 0.0)\n",
      "(8.423610564630085, 5.8585971191579986, 8.423610564630085, 9.0)\n",
      "(6.3932898327459569, 4.2262745025859401, 6.3932898327459569, 7.0)\n",
      "(6.4045087845893143, 4.2316124588905009, 6.4045087845893143, 7.0)\n",
      "(3.6490587372727816, 0.74177418570035025, 3.6490587372727816, 0.0)\n",
      "(4.7434858919531528, 3.7782317829661793, 4.7434858919531528, 5.2750000000000004)\n",
      "(5.4400181888438714, 4.2480445867135588, 5.4400181888438714, 6.1289999999999996)\n",
      "(5.8033588952343171, 2.9973204761957266, 5.8033588952343171, 6.8499999999999996)\n",
      "(8.1130144675198608, 4.3293949664706215, 8.1130144675198608, 9.0)\n",
      "(7.2750911515022594, 4.3602141571228765, 7.2750911515022594, 8.0)\n",
      "(5.5562819051872054, 4.3096440311681405, 5.5562819051872054, 6.2957999999999998)\n",
      "(5.2137671097991607, 3.7285918886032867, 5.2137671097991607, 5.7539999999999996)\n",
      "(5.223336054283938, 3.7322034138953577, 5.223336054283938, 5.7539999999999996)\n",
      "(6.1948105070203905, 4.1729534609676495, 6.1948105070203905, 7.0)\n",
      "(6.9876126418525324, 4.1988290109086126, 6.9876126418525324, 8.0)\n",
      "(5.3400824961618909, 3.7876338598232739, 5.3400824961618909, 6.2000000000000002)\n",
      "(4.4744509484479149, 3.8236491676017041, 4.4744509484479149, 5.1749999999999998)\n",
      "(8.0024960387925965, 4.4520784290659305, 8.0024960387925965, 9.0)\n",
      "(4.3822661825960063, 3.786667865382114, 4.3822661825960063, 5.3499999999999996)\n",
      "(4.4380986495438499, 3.7872669787510524, 4.4380986495438499, 5.0099999999999998)\n",
      "(3.2715361134995034, 3.5861583956678813, 3.5861583956678813, 3.5649999999999999)\n",
      "(4.1264048215029518, 3.3230767170627478, 4.1264048215029518, 4.7999999999999998)\n",
      "(4.5759809279591996, 3.4316693260351725, 4.5759809279591996, 5.1200000000000001)\n",
      "(3.6521826158288668, 0.67820326537168507, 3.6521826158288668, 0.0)\n",
      "(4.3526859432151719, 3.4226084523299996, 4.3526859432151719, 4.9500000000000002)\n",
      "(4.3963555752430672, 2.7443649896784308, 4.3963555752430672, 4.8869999999999996)\n",
      "(3.4569850384886225, 0.90975399966385551, 3.4569850384886225, 0.0)\n",
      "(5.4019443566447993, 2.7227013391548893, 5.4019443566447993, 5.5899999999999999)\n",
      "(4.4643543225198279, 2.6801747577745147, 4.4643543225198279, 4.9199999999999999)\n",
      "(4.9433338336107244, 2.6625578702211858, 4.9433338336107244, 5.6200000000000001)\n",
      "(3.5697308189094388, 2.4717872675957753, 3.5697308189094388, 4.2249999999999996)\n",
      "(4.0940465821426333, 2.5915797073416327, 4.0940465821426333, 4.4500000000000002)\n",
      "(3.6772117776064404, 0.67718093437204574, 3.6772117776064404, 0.0)\n",
      "(5.7807464976747704, 2.9853874222502372, 5.7807464976747704, 6.6799999999999997)\n",
      "(4.1055550794512108, 0.77134273397619935, 4.1055550794512108, 0.0)\n",
      "(3.0814969410548274, 2.1563075492346768, 3.0814969410548274, 3.8180000000000001)\n",
      "(2.9781793619168049, 2.1650830703028503, 2.9781793619168049, 3.6299999999999999)\n",
      "(3.0503690579370346, 1.9938792239613685, 3.0503690579370346, 3.8199999999999998)\n",
      "(3.4626555067935616, 2.0150843219580246, 3.4626555067935616, 4.0780000000000003)\n",
      "(2.7876033937084226, 2.1284426242592676, 2.7876033937084226, 3.52)\n",
      "(2.8320477400724213, 2.1281922833654296, 2.8320477400724213, 3.4700000000000002)\n",
      "(2.6669433089698331, 2.0488668180744187, 2.6669433089698331, 3.2799999999999998)\n",
      "(5.5538169073299191, 2.6130729386155407, 5.5538169073299191, 6.1320000000000006)\n",
      "(4.5961003672302567, 2.3684072501250619, 4.5961003672302567, 5.0)\n",
      "(2.7672465536823987, 1.9633325412929521, 2.7672465536823987, 3.6400000000000001)\n",
      "(6.6124756427321651, 2.8533813500547351, 6.6124756427321651, 8.0)\n",
      "(3.3009140989596903, 2.1772793767171463, 3.3009140989596903, 3.4500000000000002)\n",
      "(2.4322584123613455, 2.1908401720520847, 2.4322584123613455, 3.2898000000000001)\n",
      "(2.3705000794773121, 2.0630500527057674, 2.3705000794773121, 3.1299999999999999)\n",
      "(2.515178622484683, 2.1368240312686235, 2.515178622484683, 3.0869999999999997)\n",
      "(3.5588347289410702, 2.0418341039579588, 3.5588347289410702, 4.1984699999999995)\n",
      "(2.9226670589128476, 2.0923142211778689, 2.9226670589128476, 3.6000000000000001)\n",
      "(2.7607112562532357, 2.1140519768981165, 2.7607112562532357, 3.4100000000000001)\n",
      "(4.3985833253801179, 2.2394864559641188, 4.3985833253801179, 4.9000000000000004)\n",
      "(2.9592694419406618, 2.0452082697155824, 2.9592694419406618, 3.5800000000000001)\n",
      "(2.2303784173347152, 2.1468170068554291, 2.2303784173347152, 2.8879999999999999)\n",
      "(6.4592558014790731, 7.2542589721801098, 7.2542589721801098, 6.4770000000000003)\n",
      "(6.7100969186296213, 4.9233358373563423, 6.7100969186296213, 5.9804599999999999)\n",
      "(6.8806934009561376, 4.9284745524097104, 6.8806934009561376, 6.2450000000000001)\n",
      "(3.1490685368781381, 9.2404399720798516, 9.2404399720798516, 0.0)\n",
      "(6.4824850886350678, 5.0972342627526457, 6.4824850886350678, 6.0)\n",
      "(5.5686538346462457, 5.093244198625591, 5.5686538346462457, 5.9299999999999997)\n",
      "(2.886736111194804, 8.3249053159278841, 8.3249053159278841, 0.0)\n",
      "(6.9988899021303315, 5.0517936015577805, 6.9988899021303315, 6.4770000000000003)\n",
      "(6.0630365898314782, 7.1044150354091595, 7.1044150354091595, 5.4924999999999997)\n",
      "(2.3677434377462783, 8.3054329034621386, 8.3054329034621386, 0.0)\n",
      "(6.7858691688880137, 5.0972342627526457, 6.7858691688880137, 6.4770500000000002)\n",
      "(4.783944729151103, 3.0265565958647058, 4.783944729151103, 3.9350000000000001)\n",
      "(4.9204497155304772, 4.1254274512693572, 4.9204497155304772, 4.8700000000000001)\n",
      "(4.2608207726991676, 2.9744213658888903, 4.2608207726991676, 3.375)\n",
      "(5.3885345530314943, 6.9229522917788859, 6.9229522917788859, 5.0)\n",
      "(2.3518589532123291, 8.3054329034621386, 8.3054329034621386, 0.0)\n",
      "(5.9582264446350219, 5.0805657239209854, 5.9582264446350219, 6.1220000000000008)\n",
      "(3.3621215468421162, 3.6099995984647468, 3.6099995984647468, 3.5)\n",
      "(3.3733406891642868, 3.5922198311481481, 3.5922198311481481, 3.5)\n",
      "(2.0779958102903593, 1.2084228098110952, 2.0779958102903593, 0.0)\n",
      "(3.2374064171265857, 3.591859132052666, 3.591859132052666, 3.4900000000000002)\n",
      "(3.189872667652089, 3.5845933575749975, 3.5845933575749975, 3.5)\n",
      "(3.9998250735097156, 3.6698104941835554, 3.9998250735097156, 4.2000000000000002)\n",
      "(4.2816921298849637, 3.65120985347904, 4.2816921298849637, 4.5)\n",
      "(3.2830399063031499, 3.5675023793751812, 3.5675023793751812, 3.5)\n",
      "(3.3017091584336291, 3.5675023793751812, 3.5675023793751812, 3.5)\n",
      "(3.3313071229766678, 3.51409670745638, 3.51409670745638, 3.6800000000000002)\n",
      "(4.0984511428826789, 3.5795796617042823, 4.0984511428826789, 4.6500000000000004)\n",
      "(3.1675079207237395, 3.2674196733812111, 3.2674196733812111, 3.5)\n",
      "(3.0332912902455389, 3.1949727791300755, 3.1949727791300755, 3.5)\n",
      "(2.961463308215861, 3.3209001298617178, 3.3209001298617178, 3.3960000000000004)\n",
      "(4.3695630276747348, 3.8766133682842616, 4.3695630276747348, 4.7999999999999998)\n",
      "(3.0739707190261134, 3.5788682936328802, 3.5788682936328802, 3.7999999999999998)\n",
      "(3.0763870886536888, 3.3294163192496433, 3.3294163192496433, 3.3900000000000001)\n",
      "(3.318275577236907, 3.6567504358098684, 3.6567504358098684, 3.5649999999999999)\n",
      "(2.0353409248784322, 2.3670167108705469, 2.3670167108705469, 2.3999999999999999)\n",
      "(3.1204189300862288, 2.963119092207243, 3.1204189300862288, 3.3999999999999999)\n",
      "(2.0835139192770837, 1.3175304429266448, 2.0835139192770837, 0.0)\n",
      "(2.661288691193521, 2.9084248826608601, 2.9084248826608601, 3.0)\n",
      "(2.7774202169417164, 2.7025667648486316, 2.7774202169417164, 2.968)\n",
      "(3.6171068892412941, 2.6719002738749129, 3.6171068892412941, 3.7119999999999997)\n",
      "(1.8965539553982116, 1.5676894579884164, 1.8965539553982116, 0.0)\n",
      "(3.6191439573676512, 2.6093703177190126, 3.6191439573676512, 3.4900000000000002)\n",
      "(2.7639879434231758, 2.7102504189340366, 2.7639879434231758, 2.9199999999999999)\n",
      "(3.3161252606731551, 2.6123305470734137, 3.3161252606731551, 3.7000000000000002)\n",
      "(2.2790031381160984, 2.5110330524002857, 2.5110330524002857, 2.6889999999999996)\n",
      "(2.0947537267037646, 1.199420246597847, 2.0947537267037646, 0.0)\n",
      "(3.2096673527340784, 2.5462021899082328, 3.2096673527340784, 3.7000000000000002)\n",
      "(3.0190811717451913, 2.6825689637174484, 3.0190811717451913, 2.8900000000000001)\n",
      "(2.1135567324449456, 2.313702862879043, 2.313702862879043, 2.218)\n",
      "(3.5297067705449598, 2.4195116644252774, 3.5297067705449598, 3.3500000000000001)\n",
      "(2.5673881379714176, 2.5663620379050176, 2.5673881379714176, 0.0)\n",
      "(1.7795443826531976, 2.0845567626865589, 2.0845567626865589, 2.2719999999999998)\n",
      "(1.903223165870555, 2.0902746307218938, 2.0902746307218938, 2.3369999999999997)\n",
      "(3.2513803668814285, 2.4356623812468956, 3.2513803668814285, 3.3500000000000001)\n",
      "(1.8256733704449501, 1.955711230423995, 1.955711230423995, 2.3599999999999999)\n",
      "(2.2177419465031409, 2.0493995414610016, 2.2177419465031409, 2.5899999999999999)\n",
      "(1.7839969750622175, 2.0845567626865589, 2.0845567626865589, 2.3100000000000001)\n",
      "(1.878458216363293, 1.9725885035285, 1.9725885035285, 2.3119999999999998)\n",
      "(2.5379869859511266, 2.6427907811887486, 2.6427907811887486, 0.0)\n",
      "(3.3553097842929334, 2.2485302274717811, 3.3553097842929334, 3.8900000000000001)\n",
      "(1.6390458635944281, 1.7789142859309071, 1.7789142859309071, 2.0499999999999998)\n",
      "(3.3237729062558237, 2.3477752026362575, 3.3237729062558237, 3.5)\n",
      "(3.2939308094652326, 2.253033080864741, 3.2939308094652326, 3.5)\n",
      "(1.6363543965960063, 2.0504101882532435, 2.0504101882532435, 2.2999999999999998)\n",
      "(1.5230184696531834, 2.0815090666735072, 2.0815090666735072, 2.1869999999999998)\n",
      "(1.085235200413359, 1.3512460819221084, 1.3512460819221084, 1.6000000000000001)\n",
      "(1.576361221569164, 1.7313967438093631, 1.7313967438093631, 1.95)\n",
      "(2.3203056418906378, 2.116860693191807, 2.3203056418906378, 2.7168900000000002)\n",
      "(1.386482537120469, 1.7849941780858307, 1.7849941780858307, 1.8)\n",
      "(2.6962536887646698, 2.2394839878491921, 2.6962536887646698, 2.8999999999999999)\n",
      "(2.0824435552176519, 1.9870971259804386, 2.0824435552176519, 2.5099999999999998)\n",
      "(2.7987126850338075, 2.2950514486140516, 2.7987126850338075, 2.9500000000000002)\n",
      "(1.3130252479058964, 1.5943638591856264, 1.5943638591856264, 1.78)\n"
     ]
    }
   ],
   "source": [
    "zipped = zip(y_test_pred_reg, y_test_pred_gb, y_test_merged, feature_test['Competitor_2nd_Lowest_Price_per_standard_unit'])\n",
    "for k in zipped:\n",
    "    print k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "submission = pd.read_csv('/Users/ssing25/Desktop/CAX_Bidding_SubmissionFormat.csv')\n",
    "submission['Winning_price_per_standard_unit'] = y_test_merged\n",
    "submission.to_csv('/Users/ssing25/Desktop/CAX_Bidding_SubmissionFormat.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 1,\n",
    "#          'learning_rate': 0.01, 'loss': 'ls'}\n",
    "\n",
    "gradient_boosting_grid = {'max_depth': [3, 6, 10],\n",
    "                     'min_samples_split': [1, 4, 8],\n",
    "                     'learning_rate': [0.01],\n",
    "                     'loss': ['ls'],\n",
    "                     'n_estimators': [100, 300, 500]}\n",
    "\n",
    "rf_gridsearch = GridSearchCV(GradientBoostingRegressor(),\n",
    "                            gradient_boosting_grid,\n",
    "                            n_jobs=-1,\n",
    "                            verbose=True,\n",
    "                            scoring='mean_squared_error')\n",
    "#rf_gridsearch.fit(train_x, train_y)\n",
    "k_fold = cross_validation.KFold(len(X), 10)\n",
    "for k, (train, test) in enumerate(k_fold):\n",
    "    rf_gridsearch.fit(X[train], y[train])\n",
    "    print(\"[fold {0}] mse: {1:.5f}, score: {2:.5f}\".\n",
    "          format(k, mean_squared_error(y[test], rf_gridsearch.predict(X[test])), rf_gridsearch.score(X[test], y[test])))\n",
    "\n",
    "print \"best parameters:\", rf_gridsearch.best_params_\n",
    "\n",
    "#best_rf_model = rf_gridsearch.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "\n",
    "# Fit regression model\n",
    "params = {'n_estimators': 500, 'max_depth': 6, 'min_samples_split': 3,\n",
    "          'learning_rate': 0.01, 'loss': 'ls'}\n",
    "clf2 = GradientBoostingRegressor(**params)\n",
    "k_fold = cross_validation.KFold(len(X), 10)\n",
    "print(\"Alpha parameters maximising the generalization score on different\")\n",
    "print(\"subsets of the data:\")\n",
    "for k, (train, test) in enumerate(k_fold):\n",
    "    clf2.fit(X[train], y[train])\n",
    "    print(\"[fold {0}] mse: {1:.5f}, score: {2:.5f}\".\n",
    "          format(k, mean_squared_error(y[test], clf2.predict(X[test])), clf2.score(X[test], y[test])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Plot feature importance\n",
    "#feature_importance = clf.feature_importances_\n",
    "# make importances relative to max importance\n",
    "#feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "#sorted_idx = np.argsort(feature_importance)\n",
    "#pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "#plt.subplot(1, 2, 2)\n",
    "#plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "#plt.yticks(pos, df2.feature_importance[sorted_idx])\n",
    "#plt.xlabel('Relative Importance')\n",
    "#plt.title('Variable Importance')\n",
    "#plt.show()"
   ]
  }
 ],
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