Big Mart Sales -AV.ipynb

{
  "cells": [
    {
      "metadata": {
        "_uuid": "8f87c1fed41020a01cdeba441f3ab12eb0412410"
      },
      "cell_type": "markdown",
      "source": "# Data Exploration & Feature Engineering"
    },
    {
      "metadata": {
        "_uuid": "f24a5423196f8405db213033fc007f791f826d7b"
      },
      "cell_type": "markdown",
      "source": "# 1. Data Exploration"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "94ae8a98b29c3c0e71334752fbbe949d33b34cdf"
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np",
      "execution_count": 56,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "35e134609b606f2765f7d207e0f4dde7fdcc0bbb"
      },
      "cell_type": "markdown",
      "source": "### Loading data:\n"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "ff24cc93a5e3003e7081340f28f75b4ba4666eda"
      },
      "cell_type": "code",
      "source": "#Read files:\ntrain = pd.read_csv(\"../input/Test.csv\")\ntest = pd.read_csv(\"../input/Train.csv\")",
      "execution_count": 57,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "febc7b13c739a7df8cbda024ee174dd3ff4227bc"
      },
      "cell_type": "code",
      "source": "#Combine test and train into one file\ntrain['source']='train'\ntest['source']='test'\ndata = pd.concat([train, test],ignore_index=True)\nprint (train.shape, test.shape, data.shape)",
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": "(5681, 12) (8523, 13) (14204, 13)\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:4: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\nof pandas will change to not sort by default.\n\nTo accept the future behavior, pass 'sort=False'.\n\nTo retain the current behavior and silence the warning, pass 'sort=True'.\n\n  after removing the cwd from sys.path.\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "ddffdd9a41af7758e7d4ea8166906b9c399031d0"
      },
      "cell_type": "code",
      "source": "data.tail()",
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 59,
          "data": {
            "text/plain": "      Item_Fat_Content Item_Identifier   ...          Outlet_Type  source\n14199          Low Fat           FDF22   ...    Supermarket Type1    test\n14200          Regular           FDS36   ...    Supermarket Type1    test\n14201          Low Fat           NCJ29   ...    Supermarket Type1    test\n14202          Regular           FDN46   ...    Supermarket Type2    test\n14203          Low Fat           DRG01   ...    Supermarket Type1    test\n\n[5 rows x 13 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Item_Fat_Content</th>\n      <th>Item_Identifier</th>\n      <th>Item_MRP</th>\n      <th>Item_Outlet_Sales</th>\n      <th>Item_Type</th>\n      <th>Item_Visibility</th>\n      <th>Item_Weight</th>\n      <th>Outlet_Establishment_Year</th>\n      <th>Outlet_Identifier</th>\n      <th>Outlet_Location_Type</th>\n      <th>Outlet_Size</th>\n      <th>Outlet_Type</th>\n      <th>source</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>14199</th>\n      <td>Low Fat</td>\n      <td>FDF22</td>\n      <td>214.5218</td>\n      <td>2778.3834</td>\n      <td>Snack Foods</td>\n      <td>0.056783</td>\n      <td>6.865</td>\n      <td>1987</td>\n      <td>OUT013</td>\n      <td>Tier 3</td>\n      <td>High</td>\n      <td>Supermarket Type1</td>\n      <td>test</td>\n    </tr>\n    <tr>\n      <th>14200</th>\n      <td>Regular</td>\n      <td>FDS36</td>\n      <td>108.1570</td>\n      <td>549.2850</td>\n      <td>Baking Goods</td>\n      <td>0.046982</td>\n      <td>8.380</td>\n      <td>2002</td>\n      <td>OUT045</td>\n      <td>Tier 2</td>\n      <td>NaN</td>\n      <td>Supermarket Type1</td>\n      <td>test</td>\n    </tr>\n    <tr>\n      <th>14201</th>\n      <td>Low Fat</td>\n      <td>NCJ29</td>\n      <td>85.1224</td>\n      <td>1193.1136</td>\n      <td>Health and Hygiene</td>\n      <td>0.035186</td>\n      <td>10.600</td>\n      <td>2004</td>\n      <td>OUT035</td>\n      <td>Tier 2</td>\n      <td>Small</td>\n      <td>Supermarket Type1</td>\n      <td>test</td>\n    </tr>\n    <tr>\n      <th>14202</th>\n      <td>Regular</td>\n      <td>FDN46</td>\n      <td>103.1332</td>\n      <td>1845.5976</td>\n      <td>Snack Foods</td>\n      <td>0.145221</td>\n      <td>7.210</td>\n      <td>2009</td>\n      <td>OUT018</td>\n      <td>Tier 3</td>\n      <td>Medium</td>\n      <td>Supermarket Type2</td>\n      <td>test</td>\n    </tr>\n    <tr>\n      <th>14203</th>\n      <td>Low Fat</td>\n      <td>DRG01</td>\n      <td>75.4670</td>\n      <td>765.6700</td>\n      <td>Soft Drinks</td>\n      <td>0.044878</td>\n      <td>14.800</td>\n      <td>1997</td>\n      <td>OUT046</td>\n      <td>Tier 1</td>\n      <td>Small</td>\n      <td>Supermarket Type1</td>\n      <td>test</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "86a5e9c0b47d478988a73572a17eab9d62013427"
      },
      "cell_type": "code",
      "source": "#Check missing values:\ndata.apply(lambda x: sum(x.isnull()))",
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 60,
          "data": {
            "text/plain": "Item_Fat_Content                0\nItem_Identifier                 0\nItem_MRP                        0\nItem_Outlet_Sales            5681\nItem_Type                       0\nItem_Visibility                 0\nItem_Weight                  2439\nOutlet_Establishment_Year       0\nOutlet_Identifier               0\nOutlet_Location_Type            0\nOutlet_Size                  4016\nOutlet_Type                     0\nsource                          0\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "bdbe7eb7252ab8f62ab39d623a3cffa9ffc21141"
      },
      "cell_type": "code",
      "source": "#Numerical data summary:\ndata.describe()",
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 61,
          "data": {
            "text/plain": "           Item_MRP            ...              Outlet_Establishment_Year\ncount  14204.000000            ...                           14204.000000\nmean     141.004977            ...                            1997.830681\nstd       62.086938            ...                               8.371664\nmin       31.290000            ...                            1985.000000\n25%       94.012000            ...                            1987.000000\n50%      142.247000            ...                            1999.000000\n75%      185.855600            ...                            2004.000000\nmax      266.888400            ...                            2009.000000\n\n[8 rows x 5 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Item_MRP</th>\n      <th>Item_Outlet_Sales</th>\n      <th>Item_Visibility</th>\n      <th>Item_Weight</th>\n      <th>Outlet_Establishment_Year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>14204.000000</td>\n      <td>8523.000000</td>\n      <td>14204.000000</td>\n      <td>11765.000000</td>\n      <td>14204.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>141.004977</td>\n      <td>2181.288914</td>\n      <td>0.065953</td>\n      <td>12.792854</td>\n      <td>1997.830681</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>62.086938</td>\n      <td>1706.499616</td>\n      <td>0.051459</td>\n      <td>4.652502</td>\n      <td>8.371664</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>31.290000</td>\n      <td>33.290000</td>\n      <td>0.000000</td>\n      <td>4.555000</td>\n      <td>1985.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>94.012000</td>\n      <td>834.247400</td>\n      <td>0.027036</td>\n      <td>8.710000</td>\n      <td>1987.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>142.247000</td>\n      <td>1794.331000</td>\n      <td>0.054021</td>\n      <td>12.600000</td>\n      <td>1999.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>185.855600</td>\n      <td>3101.296400</td>\n      <td>0.094037</td>\n      <td>16.750000</td>\n      <td>2004.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>266.888400</td>\n      <td>13086.964800</td>\n      <td>0.328391</td>\n      <td>21.350000</td>\n      <td>2009.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "23462bc6a26c30d17c433624a12174a55f6096c4"
      },
      "cell_type": "code",
      "source": "#Number of unique values in each:\ndata.apply(lambda x: len(x.unique()))",
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 62,
          "data": {
            "text/plain": "Item_Fat_Content                 5\nItem_Identifier               1559\nItem_MRP                      8052\nItem_Outlet_Sales             3494\nItem_Type                       16\nItem_Visibility              13006\nItem_Weight                    416\nOutlet_Establishment_Year        9\nOutlet_Identifier               10\nOutlet_Location_Type             3\nOutlet_Size                      4\nOutlet_Type                      4\nsource                           2\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true,
        "_uuid": "b7a2b7c894b58e649b9410e87cf3930e89b78918"
      },
      "cell_type": "code",
      "source": "#Filter categorical variables\ncategorical_columns = [x for x in data.dtypes.index if data.dtypes[x]=='object']\n#Exclude ID cols and source:\ncategorical_columns = [x for x in categorical_columns if x not in ['Item_Identifier','Outlet_Identifier','source']]\n#Print frequency of categories\nfor col in categorical_columns:\n    print ('\\nFrequency of Categories for varible %s'%col)\n    print (data[col].value_counts())",
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": "\nFrequency of Categories for varible Item_Fat_Content\nLow Fat    8485\nRegular    4824\nLF          522\nreg         195\nlow fat     178\nName: Item_Fat_Content, dtype: int64\n\nFrequency of Categories for varible Item_Type\nFruits and Vegetables    2013\nSnack Foods              1989\nHousehold                1548\nFrozen Foods             1426\nDairy                    1136\nBaking Goods             1086\nCanned                   1084\nHealth and Hygiene        858\nMeat                      736\nSoft Drinks               726\nBreads                    416\nHard Drinks               362\nOthers                    280\nStarchy Foods             269\nBreakfast                 186\nSeafood                    89\nName: Item_Type, dtype: int64\n\nFrequency of Categories for varible Outlet_Location_Type\nTier 3    5583\nTier 2    4641\nTier 1    3980\nName: Outlet_Location_Type, dtype: int64\n\nFrequency of Categories for varible Outlet_Size\nMedium    4655\nSmall     3980\nHigh      1553\nName: Outlet_Size, dtype: int64\n\nFrequency of Categories for varible Outlet_Type\nSupermarket Type1    9294\nGrocery Store        1805\nSupermarket Type3    1559\nSupermarket Type2    1546\nName: Outlet_Type, dtype: int64\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "83c20dd0ebf06112fb7ed4450f07d190ac44c4a9"
      },
      "cell_type": "markdown",
      "source": "# 2. Data Cleaning"
    },
    {
      "metadata": {
        "_uuid": "a86afbb943d61609c1863f6c37d7755714986f09"
      },
      "cell_type": "markdown",
      "source": "### Imputation"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "cda7668959f93ca1f097a342a92a66b7a9976094"
      },
      "cell_type": "code",
      "source": "\ndata['Item_Weight'] = data['Item_Weight'].fillna(data['Item_Weight'].dropna().mean())\n",
      "execution_count": 64,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "34b1d994f9dad6ab226bf98fdcf0a452a21b10c7"
      },
      "cell_type": "code",
      "source": "data['Outlet_Size'].fillna('Medium', inplace=True)",
      "execution_count": 65,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "b7efa052f4d487a1d7bb266019cda5bbbf1f2dc7"
      },
      "cell_type": "markdown",
      "source": "# 2. Feature Engineering:"
    },
    {
      "metadata": {
        "_uuid": "08507a3b9652ca57262394a29613ecb1754fa29a"
      },
      "cell_type": "markdown",
      "source": "### Step1: Consider combining categories in Outlet_Type"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "ca70be83ec367ad6e7966be044c1d40924d8a43b"
      },
      "cell_type": "code",
      "source": "#Check the mean sales by type:\ndata.pivot_table(values='Item_Outlet_Sales',index='Outlet_Type')",
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 66,
          "data": {
            "text/plain": "                   Item_Outlet_Sales\nOutlet_Type                         \nGrocery Store             339.828500\nSupermarket Type1        2316.181148\nSupermarket Type2        1995.498739\nSupermarket Type3        3694.038558",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Item_Outlet_Sales</th>\n    </tr>\n    <tr>\n      <th>Outlet_Type</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Grocery Store</th>\n      <td>339.828500</td>\n    </tr>\n    <tr>\n      <th>Supermarket Type1</th>\n      <td>2316.181148</td>\n    </tr>\n    <tr>\n      <th>Supermarket Type2</th>\n      <td>1995.498739</td>\n    </tr>\n    <tr>\n      <th>Supermarket Type3</th>\n      <td>3694.038558</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "43b1fd5b139f07c0e935b254488acdd5fe4d3b6e"
      },
      "cell_type": "markdown",
      "source": "### Step2: Modify Item_Visibility"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "72c9445acdd5fea0a71f56826571a88dad895773"
      },
      "cell_type": "code",
      "source": "#Determine average visibility of a product\nif (data['Item_Visibility'].empty):\n    data['Item_Visibility'].fillna(data['Item_Visibility'].mean(), inplace=True)\n\n",
      "execution_count": 67,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "3ff6124416229352e82f1d193194caf9749c465f"
      },
      "cell_type": "code",
      "source": "#Check missing values:\ndata.apply(lambda x: sum(x.isnull()))",
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 68,
          "data": {
            "text/plain": "Item_Fat_Content                0\nItem_Identifier                 0\nItem_MRP                        0\nItem_Outlet_Sales            5681\nItem_Type                       0\nItem_Visibility                 0\nItem_Weight                     0\nOutlet_Establishment_Year       0\nOutlet_Identifier               0\nOutlet_Location_Type            0\nOutlet_Size                     0\nOutlet_Type                     0\nsource                          0\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "259a6f1c02e137a270a14b9dd5d447811b511ae7"
      },
      "cell_type": "markdown",
      "source": "### Step 3: Create a broad category of Type of Item"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "33f902e837d0a06dde8f0d2661d721734ccfc602"
      },
      "cell_type": "code",
      "source": "#Item type combine:\ndata['Item_Identifier'].value_counts()\ndata['Item_Type_Combined'] = data['Item_Identifier'].apply(lambda x: x[0:2])\ndata['Item_Type_Combined'] = data['Item_Type_Combined'].map({'FD':'Food',\n                                                             'NC':'Non-Consumable',\n                                                             'DR':'Drinks'})\ndata['Item_Type_Combined'].value_counts()",
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 69,
          "data": {
            "text/plain": "Food              10201\nNon-Consumable     2686\nDrinks             1317\nName: Item_Type_Combined, dtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "08138ab87f50d2e58db8f1078c46239c3e85fa96"
      },
      "cell_type": "markdown",
      "source": "### Step 4: Determine the years of operation of a store"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "cae455f11b84f387294c209bf7220c600d919190"
      },
      "cell_type": "code",
      "source": "#Years:\ndata['Outlet_Years'] = 2013 - data['Outlet_Establishment_Year']\ndata['Outlet_Years'].describe()",
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 70,
          "data": {
            "text/plain": "count    14204.000000\nmean        15.169319\nstd          8.371664\nmin          4.000000\n25%          9.000000\n50%         14.000000\n75%         26.000000\nmax         28.000000\nName: Outlet_Years, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "6b0094d22393c6b201c6561e03a8f888a4ad2f38"
      },
      "cell_type": "markdown",
      "source": "### Step 5: Modify categories of Item_Fat_Content"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "d0004011539373e6f03ae402d47e0f1336f43cd2"
      },
      "cell_type": "code",
      "source": "#Change categories of low fat:\nprint ('Original Categories:')\nprint (data['Item_Fat_Content'].value_counts())\n\nprint ('\\nModified Categories:')\ndata['Item_Fat_Content'] = data['Item_Fat_Content'].replace({'LF':'Low Fat',\n                                                             'reg':'Regular',\n                                                             'low fat':'Low Fat'})\nprint (data['Item_Fat_Content'].value_counts())",
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Original Categories:\nLow Fat    8485\nRegular    4824\nLF          522\nreg         195\nlow fat     178\nName: Item_Fat_Content, dtype: int64\n\nModified Categories:\nLow Fat    9185\nRegular    5019\nName: Item_Fat_Content, dtype: int64\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "1a57e5bcaa700bb95d7f2437aeb88640bb6502cf"
      },
      "cell_type": "code",
      "source": "#Mark non-consumables as separate category in low_fat:\ndata.loc[data['Item_Type_Combined']==\"Non-Consumable\",'Item_Fat_Content'] = \"Non-Edible\"\ndata['Item_Fat_Content'].value_counts()",
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 72,
          "data": {
            "text/plain": "Low Fat       6499\nRegular       5019\nNon-Edible    2686\nName: Item_Fat_Content, dtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "3ac781cfcc1e450f37a8a37fe3fa754872d45147"
      },
      "cell_type": "markdown",
      "source": "### Step 6: Numerical and One-Hot Coding of Categorical variables"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "55b47945399d4f66e48a98f7057b3c4092fb80db"
      },
      "cell_type": "code",
      "source": "#Import library:\nfrom sklearn.preprocessing import LabelEncoder\nle = LabelEncoder()\n#New variable for outlet\ndata['Outlet'] = le.fit_transform(data['Outlet_Identifier'])\nvar_mod = ['Item_Fat_Content','Outlet_Location_Type','Outlet_Size','Item_Type_Combined','Outlet_Type','Outlet']\nle = LabelEncoder()\nfor i in var_mod:\n    data[i] = le.fit_transform(data[i])",
      "execution_count": 73,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "f7ee501cde5dcaf86d61351829493f70f3698b7d"
      },
      "cell_type": "code",
      "source": "#One Hot Coding:\ndata = pd.get_dummies(data, columns=['Item_Fat_Content','Outlet_Location_Type','Outlet_Size','Outlet_Type',\n                              'Item_Type_Combined','Outlet'])",
      "execution_count": 74,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "fa91c22a669e1721687ae7083d6388b5472d5d66"
      },
      "cell_type": "code",
      "source": "data.dtypes",
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 75,
          "data": {
            "text/plain": "Item_Identifier               object\nItem_MRP                     float64\nItem_Outlet_Sales            float64\nItem_Type                     object\nItem_Visibility              float64\nItem_Weight                  float64\nOutlet_Establishment_Year      int64\nOutlet_Identifier             object\nsource                        object\nOutlet_Years                   int64\nItem_Fat_Content_0             uint8\nItem_Fat_Content_1             uint8\nItem_Fat_Content_2             uint8\nOutlet_Location_Type_0         uint8\nOutlet_Location_Type_1         uint8\nOutlet_Location_Type_2         uint8\nOutlet_Size_0                  uint8\nOutlet_Size_1                  uint8\nOutlet_Size_2                  uint8\nOutlet_Type_0                  uint8\nOutlet_Type_1                  uint8\nOutlet_Type_2                  uint8\nOutlet_Type_3                  uint8\nItem_Type_Combined_0           uint8\nItem_Type_Combined_1           uint8\nItem_Type_Combined_2           uint8\nOutlet_0                       uint8\nOutlet_1                       uint8\nOutlet_2                       uint8\nOutlet_3                       uint8\nOutlet_4                       uint8\nOutlet_5                       uint8\nOutlet_6                       uint8\nOutlet_7                       uint8\nOutlet_8                       uint8\nOutlet_9                       uint8\ndtype: object"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "96d6d482bb5ee674922c44b5eb91169df136b1bd"
      },
      "cell_type": "code",
      "source": "data[['Item_Fat_Content_0','Item_Fat_Content_1','Item_Fat_Content_2']].head(10)",
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 76,
          "data": {
            "text/plain": "   Item_Fat_Content_0         ...          Item_Fat_Content_2\n0                   1         ...                           0\n1                   0         ...                           1\n2                   0         ...                           0\n3                   1         ...                           0\n4                   0         ...                           1\n5                   0         ...                           1\n6                   0         ...                           1\n7                   1         ...                           0\n8                   0         ...                           1\n9                   1         ...                           0\n\n[10 rows x 3 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Item_Fat_Content_0</th>\n      <th>Item_Fat_Content_1</th>\n      <th>Item_Fat_Content_2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "c65eb2c82494d3637a51e8a0ad08878a3dce2ffc"
      },
      "cell_type": "markdown",
      "source": "### Step7: Exporting Data"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "bdb630fe8f7da98e351a9ad22caccbb4f299f900"
      },
      "cell_type": "code",
      "source": "#Drop the columns which have been converted to different types:\ndata.drop(['Item_Type','Outlet_Establishment_Year'],axis=1,inplace=True)\n\n#Divide into test and train:\ntrain = data.loc[data['source']==\"train\"]\ntest = data.loc[data['source']==\"test\"]\n\n#Drop unnecessary columns:\ntest.drop(['Item_Outlet_Sales','source'],axis=1,inplace=True)\ntrain.drop(['source'],axis=1,inplace=True)\n\n#Export files as modified versions:\ntrain.to_csv(\"train_modified.csv\",index=False)\ntest.to_csv(\"test_modified.csv\",index=False)",
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": "/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py:3697: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n  errors=errors)\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "_uuid": "10119db7f3bd9ba37de486ed9b51d399e0093c90"
      },
      "cell_type": "markdown",
      "source": "# Model Building"
    },
    {
      "metadata": {
        "_uuid": "97fde80105fc6603c7d14bea3f9c44caaf754287"
      },
      "cell_type": "markdown",
      "source": "## Load libraries and data:\nThe data will be the one exported from the 'exploration_and_feature_engineering' file"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "_uuid": "c875433782c48e54f44b80dc057572b9d189d006"
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np\n%matplotlib inline\nfrom matplotlib.pylab import rcParams\nrcParams['figure.figsize'] = 12, 8",
      "execution_count": 78,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "_uuid": "44a18355aab1d60ac27653d06d82081da52616b9"
      },
      "cell_type": "code",
      "source": "train = pd.read_csv(\"train_modified.csv\")\ntest = pd.read_csv(\"test_modified.csv\")",
      "execution_count": 79,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "4c4117145b6d19035b12020a74508f0254cd7786"
      },
      "cell_type": "code",
      "source": "print( train.shape)\ntrain.dtypes",
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": "(5681, 33)\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 81,
          "data": {
            "text/plain": "Item_Identifier            object\nItem_MRP                  float64\nItem_Outlet_Sales         float64\nItem_Visibility           float64\nItem_Weight               float64\nOutlet_Identifier          object\nOutlet_Years                int64\nItem_Fat_Content_0          int64\nItem_Fat_Content_1          int64\nItem_Fat_Content_2          int64\nOutlet_Location_Type_0      int64\nOutlet_Location_Type_1      int64\nOutlet_Location_Type_2      int64\nOutlet_Size_0               int64\nOutlet_Size_1               int64\nOutlet_Size_2               int64\nOutlet_Type_0               int64\nOutlet_Type_1               int64\nOutlet_Type_2               int64\nOutlet_Type_3               int64\nItem_Type_Combined_0        int64\nItem_Type_Combined_1        int64\nItem_Type_Combined_2        int64\nOutlet_0                    int64\nOutlet_1                    int64\nOutlet_2                    int64\nOutlet_3                    int64\nOutlet_4                    int64\nOutlet_5                    int64\nOutlet_6                    int64\nOutlet_7                    int64\nOutlet_8                    int64\nOutlet_9                    int64\ndtype: object"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "00be2eb3c54b634cae6da5e69122b738d2ceb094"
      },
      "cell_type": "markdown",
      "source": "## Baseline models:"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "dea09ebedf8c4a146e73f78e74461b821526eb3d"
      },
      "cell_type": "code",
      "source": "#Mean based:\nmean_sales = train['Item_Outlet_Sales'].mean()\n\n#Define a dataframe with IDs for submission:\nbase1 = test[['Item_Identifier','Outlet_Identifier']]\nbase1['Item_Outlet_Sales'] = mean_sales\n\n#Export submission file\nbase1.to_csv(\"alg0.csv\",index=False)",
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n  \n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "2327843b2bc37915b4cfd72693dc63d12d417d35"
      },
      "cell_type": "code",
      "source": "#Define target and ID columns:\ntarget = 'Item_Outlet_Sales'\nIDcol = ['Item_Identifier','Outlet_Identifier']\nfrom sklearn import cross_validation, metrics\ndef modelfit(alg, dtrain, dtest, predictors, target, IDcol, filename):\n    #Fit the algorithm on the data\n    alg.fit(dtrain[predictors], dtrain[target])\n        \n    #Predict training set:\n    dtrain_predictions = alg.predict(dtrain[predictors])\n\n    #Perform cross-validation:\n    cv_score = cross_validation.cross_val_score(alg, dtrain[predictors], dtrain[target], cv=20, scoring='mean_squared_error')\n    cv_score = np.sqrt(np.abs(cv_score))\n    \n    #Print model report:\n    print (\"\\nModel Report\")\n    print (\"RMSE : %.4g\" % np.sqrt(metrics.mean_squared_error(dtrain[target].values, dtrain_predictions)))\n    print( \"CV Score : Mean - %.4g | Std - %.4g | Min - %.4g | Max - %.4g\" % (np.mean(cv_score),np.std(cv_score),np.min(cv_score),np.max(cv_score)))\n    #Predict on testing data:\n    dtest[target] = alg.predict(dtest[predictors])\n    \n    #Export submission file:\n    IDcol.append(target)\n    submission = pd.DataFrame({ x: dtest[x] for x in IDcol})\n    submission.to_csv(filename, index=False)",
      "execution_count": 92,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "17071a2bca078d5c3639f3b315cefc0f2d3e5eaf"
      },
      "cell_type": "markdown",
      "source": "## Linear Regression Model:"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "_uuid": "d749cdb98285967e20d03fa0c9542210ff0e4cae"
      },
      "cell_type": "code",
      "source": "from sklearn.linear_model import LinearRegression, Ridge",
      "execution_count": 93,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "9c5a8124fed205ca342176a9ae7a559ddf4bcd02"
      },
      "cell_type": "code",
      "source": "predictors = [x for x in train.columns if x not in [target]+IDcol]\n# print predictors\nalg1 = LinearRegression(normalize=True)\nmodelfit(alg1, train, test, predictors, target, IDcol, 'alg1.csv')\ncoef1 = pd.Series(alg1.coef_, predictors).sort_values()\ncoef1.plot(kind='bar', title='Model Coefficients')",
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-94-8f290dd9f3ca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# print predictors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0malg1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmodelfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIDcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'alg1.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mcoef1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mcoef1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Model Coefficients'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-92-b80db1714aa7>\u001b[0m in \u001b[0;36mmodelfit\u001b[0;34m(alg, dtrain, dtest, predictors, target, IDcol, filename)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmodelfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIDcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;31m#Fit the algorithm on the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0malg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpredictors\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\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      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;31m#Predict training set:\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/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    480\u001b[0m         \u001b[0mn_jobs_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],\n\u001b[0;32m--> 482\u001b[0;31m                          y_numeric=True, multi_output=True)\n\u001b[0m\u001b[1;32m    483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\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/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    672\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    673\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n\u001b[0;32m--> 674\u001b[0;31m                         dtype=None)\n\u001b[0m\u001b[1;32m    675\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    676\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    536\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 538\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[1;32m     49\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'infinity'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'NaN, infinity'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype_err\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "ed8a6930a0b10090a30c7c96da8a5ee122deeaf7"
      },
      "cell_type": "code",
      "source": "predictors = [x for x in train.columns if x not in [target]+IDcol]\nalg2 = Ridge(alpha=0.05,normalize=True)\nmodelfit(alg2, train, test, predictors, target, IDcol, 'alg2.csv')\ncoef2 = pd.Series(alg2.coef_, predictors).sort_values()\ncoef2.plot(kind='bar', title='Model Coefficients')",
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-95-775c897dc2a4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpredictors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mIDcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0malg2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRidge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodelfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIDcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'alg2.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mcoef2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mcoef2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Model Coefficients'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-92-b80db1714aa7>\u001b[0m in \u001b[0;36mmodelfit\u001b[0;34m(alg, dtrain, dtest, predictors, target, IDcol, filename)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmodelfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIDcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;31m#Fit the algorithm on the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0malg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpredictors\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\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      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;31m#Predict training set:\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/sklearn/linear_model/ridge.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    666\u001b[0m         \u001b[0mself\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mreturns\u001b[0m \u001b[0man\u001b[0m \u001b[0minstance\u001b[0m \u001b[0mof\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    667\u001b[0m         \"\"\"\n\u001b[0;32m--> 668\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRidge\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    669\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/ridge.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    477\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    478\u001b[0m         X, y = check_X_y(X, y, ['csr', 'csc', 'coo'], dtype=_dtype,\n\u001b[0;32m--> 479\u001b[0;31m                          multi_output=True, y_numeric=True)\n\u001b[0m\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m         if ((sample_weight is not None) and\n",
            "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    672\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    673\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n\u001b[0;32m--> 674\u001b[0;31m                         dtype=None)\n\u001b[0m\u001b[1;32m    675\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    676\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    536\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 538\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[1;32m     49\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'infinity'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'NaN, infinity'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype_err\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "_uuid": "e6f1bcea29f883ce19a7787375b6720b8ac9b19f"
      },
      "cell_type": "code",
      "source": "from sklearn.tree import DecisionTreeRegressor",
      "execution_count": 96,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "bee5c60c7fd4a6b41012380debcf6c7d5e4632d5"
      },
      "cell_type": "code",
      "source": "predictors = [x for x in train.columns if x not in [target]+IDcol]\nalg3 = DecisionTreeRegressor(max_depth=15, min_samples_leaf=100)\nmodelfit(alg3, train, test, predictors, target, IDcol, 'alg3.csv')\ncoef3 = pd.Series(alg3.feature_importances_, predictors).sort_values(ascending=False)\ncoef3.plot(kind='bar', title='Feature Importances')",
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-97-db49d32cea70>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpredictors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mIDcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0malg3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDecisionTreeRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_samples_leaf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodelfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIDcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'alg3.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mcoef3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_importances_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mascending\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[1;32m      5\u001b[0m \u001b[0mcoef3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Feature Importances'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-92-b80db1714aa7>\u001b[0m in \u001b[0;36mmodelfit\u001b[0;34m(alg, dtrain, dtest, predictors, target, IDcol, filename)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmodelfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIDcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;31m#Fit the algorithm on the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0malg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpredictors\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\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      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;31m#Predict training set:\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/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m   1129\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1130\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1131\u001b[0;31m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[1;32m   1132\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m                 \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_indices\u001b[0m\u001b[0;34m(\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/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    536\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 538\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[1;32m     49\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'infinity'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'NaN, infinity'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype_err\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "8d927ace82e3dbacb7bca41d1005c803ecaa18f2"
      },
      "cell_type": "code",
      "source": "predictors = ['Item_MRP','Outlet_Type_0','Outlet_5','Outlet_Years']\nalg4 = DecisionTreeRegressor(max_depth=8, min_samples_leaf=150)\nmodelfit(alg4, train, test, predictors, target, IDcol, 'alg4.csv')\ncoef4 = pd.Series(alg4.feature_importances_, predictors).sort_values(ascending=False)\ncoef4.plot(kind='bar', title='Feature Importances')",
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-98-6c09810e6320>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpredictors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Item_MRP'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Outlet_Type_0'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Outlet_5'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Outlet_Years'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0malg4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDecisionTreeRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_samples_leaf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodelfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIDcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'alg4.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mcoef4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_importances_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mascending\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[1;32m      5\u001b[0m \u001b[0mcoef4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Feature Importances'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-92-b80db1714aa7>\u001b[0m in \u001b[0;36mmodelfit\u001b[0;34m(alg, dtrain, dtest, predictors, target, IDcol, filename)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmodelfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIDcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;31m#Fit the algorithm on the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0malg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpredictors\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\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      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;31m#Predict training set:\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/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m   1129\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1130\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1131\u001b[0;31m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[1;32m   1132\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m                 \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_indices\u001b[0m\u001b[0;34m(\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/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    536\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 538\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[1;32m     49\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'infinity'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'NaN, infinity'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype_err\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "b7163dcc847250f66d9da40f29d1651ae9737ae7"
      },
      "cell_type": "markdown",
      "source": "## Random Forest Model:\nNote: random forest models are not 100% replicable. So the outputs might differ very slightly but should be around the ballpark"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "_uuid": "8994e4e4e967706c261e544a54bc452c56d8695d"
      },
      "cell_type": "code",
      "source": "from sklearn.ensemble import RandomForestRegressor",
      "execution_count": 99,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "f278af433a825ae6bf6d615931fabf12422e0471"
      },
      "cell_type": "code",
      "source": "predictors = [x for x in train.columns if x not in [target]+IDcol]\nalg5 = RandomForestRegressor(n_estimators=200,max_depth=5, min_samples_leaf=100,n_jobs=4)\nmodelfit(alg5, train, test, predictors, target, IDcol, 'alg5.csv')\ncoef5 = pd.Series(alg5.feature_importances_, predictors).sort_values(ascending=False)\ncoef5.plot(kind='bar', title='Feature Importances')",
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-100-358f7ce36a30>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpredictors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mIDcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0malg5\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_samples_leaf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodelfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIDcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'alg5.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mcoef5\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_importances_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mascending\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[1;32m      5\u001b[0m \u001b[0mcoef5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Feature Importances'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-92-b80db1714aa7>\u001b[0m in \u001b[0;36mmodelfit\u001b[0;34m(alg, dtrain, dtest, predictors, target, IDcol, filename)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmodelfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIDcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;31m#Fit the algorithm on the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0malg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpredictors\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\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      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;31m#Predict training set:\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/sklearn/ensemble/forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0;31m# Validate or convert input data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\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[1;32m    249\u001b[0m             \u001b[0msample_weight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\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[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    536\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 538\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[1;32m     49\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'infinity'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'NaN, infinity'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype_err\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "866ca1e295fd1b8952243cabb30a7939a61b05ab"
      },
      "cell_type": "code",
      "source": "predictors = [x for x in train.columns if x not in [target]+IDcol]\nalg6 = RandomForestRegressor(n_estimators=400,max_depth=6, min_samples_leaf=100,n_jobs=4)\nmodelfit(alg6, train, test, predictors, target, IDcol, 'alg6.csv')\ncoef6 = pd.Series(alg6.feature_importances_, predictors).sort_values(ascending=False)\ncoef6.plot(kind='bar', title='Feature Importances')",
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-101-70096f3c9d06>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpredictors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mIDcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0malg6\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m400\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_samples_leaf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodelfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIDcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'alg6.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mcoef6\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg6\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_importances_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mascending\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[1;32m      5\u001b[0m \u001b[0mcoef6\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Feature Importances'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-92-b80db1714aa7>\u001b[0m in \u001b[0;36mmodelfit\u001b[0;34m(alg, dtrain, dtest, predictors, target, IDcol, filename)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmodelfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIDcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;31m#Fit the algorithm on the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0malg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpredictors\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\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      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;31m#Predict training set:\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/sklearn/ensemble/forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0;31m# Validate or convert input data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\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[1;32m    249\u001b[0m             \u001b[0msample_weight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\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[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    536\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 538\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[1;32m     49\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'infinity'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'NaN, infinity'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype_err\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
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