Feature Selection.ipynb

{
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  {
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    "_uuid": "748e245881e9a26d54da4c9431786b3f874734de"
   },
   "source": [
    "# Introduction: Feature Selection\n",
    "\n",
    "In this notebook we will test the manual engineered features built in two previous kernels. First, we will attempt to reduce the number of features then we will test the features performance using several models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pandas and numpy for data manipulation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# featuretools for automated feature engineering\n",
    "import featuretools as ft\n",
    "\n",
    "# matplotlit and seaborn for visualizations\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.size'] = 22\n",
    "import seaborn as sns\n",
    "\n",
    "# Suppress warnings from pandas\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "06695931219858e57330b8574af5d9028c9e50c2"
   },
   "source": [
    "We first will see how many features we built over the manual engineering process. Here we use a couple of set operations to find the columns that are only in the `bureau`, only in the `previous`, and in both dataframes, indicating that there are `original` features from the `application` dataframe. Here we are working with a small subset of the data in order to not overwhelm the kernel. This code has been run on the full dataset (we will take a look at the results)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "File b'../input/train_previous_raw.csv' does not exist",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
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      "\u001b[1;32mD:\\miniconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    676\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 678\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    679\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    680\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\miniconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    439\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 440\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    441\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    442\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\miniconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    785\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    786\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 787\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    788\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    789\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\miniconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1012\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1013\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1014\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1015\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1016\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\miniconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1706\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'usecols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1707\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1708\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1709\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1710\u001b[0m         \u001b[0mpassed_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnames\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: File b'../input/train_previous_raw.csv' does not exist"
     ]
    }
   ],
   "source": [
    "# Read in data\n",
    "train_bureau = pd.read_csv('../input/train_bureau_raw.csv', nrows = 1000)\n",
    "test_bureau = pd.read_csv('../input/test_bureau_raw.csv', nrows = 1000)\n",
    "\n",
    "train_previous = pd.read_csv('../input/train_previous_raw.csv', nrows = 1000)\n",
    "test_previous = pd.read_csv('../input/test_previous_raw.csv', nrows = 1000)\n",
    "\n",
    "# All columns in dataframes\n",
    "bureau_columns = list(train_bureau.columns)\n",
    "previous_columns = list(train_previous.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "af78939941b510855d5a972ca24e33037c003977",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Bureau only features\n",
    "bureau_features = list(set(bureau_columns) - set(previous_columns))\n",
    "\n",
    "# Previous only features\n",
    "previous_features = list(set(previous_columns) - set(bureau_columns))\n",
    "\n",
    "# Original features will be in both datasets\n",
    "original_features = list(set(previous_columns) & set(bureau_columns))\n",
    "\n",
    "print('There are %d original features.' % len(original_features))\n",
    "print('There are %d bureau and bureau balance features.' % len(bureau_features))\n",
    "print('There are %d previous Home Credit loan features.' % len(previous_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "951caa5d7f4a0033ff4148039d73ba153034cb61"
   },
   "source": [
    "That gives us the number of features in each dataframe. Now we want to combine the data without creating any duplicate rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "b0da702050879ddfb8aecc4c23186e7ee97923ba",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_labels = train_bureau['TARGET']\n",
    "previous_features.append('SK_ID_CURR')\n",
    "\n",
    "train_ids = train_bureau['SK_ID_CURR']\n",
    "test_ids = test_bureau['SK_ID_CURR']\n",
    "\n",
    "# Merge the dataframes avoiding duplicating columns by subsetting train_previous\n",
    "train = train_bureau.merge(train_previous[previous_features], on = 'SK_ID_CURR')\n",
    "test = test_bureau.merge(test_previous[previous_features], on = 'SK_ID_CURR')\n",
    "\n",
    "print('Training shape: ', train.shape)\n",
    "print('Testing shape: ', test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f20574a7e1edf8fc4398c79fcec85a261edc1c44"
   },
   "source": [
    "Then we want to one-hot encode the dataframes. This is not quite realistic since we are only working with a sample of the data. This therefore will not create as many columns as one-hot encoding the entire dataset would. I provide the actual numbers after applying this to the full data below the code cell. An important note in the code cell is where we __align the dataframes by the columns.__ This ensures we have the same columns in the training and testing datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "356f67ea6d19d408bc19e1a58ee5f23693a3944a",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# One hot encoding\n",
    "train = pd.get_dummies(train)\n",
    "test = pd.get_dummies(test)\n",
    "\n",
    "# Match the columns in the dataframes\n",
    "train, test = train.align(test, join = 'inner', axis = 1)\n",
    "print('Training shape: ', train.shape)\n",
    "print('Testing shape: ', test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c2031dd11cb58242acb31db9b3557603e1de5dce"
   },
   "source": [
    "When we do this to the full dataset, we get __1465__ features. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "ecd8a30162d43fe05e14ef93cb88de29f933f8e9"
   },
   "source": [
    "### Admit and Correct Mistakes\n",
    "\n",
    "When doing manual feature engineering, I accidentally created some columns derived from the client id, `SK_ID_CURR`. As this is a unique identifier for each client, it should not have any predictive power, and we would not want to build a model trained on this \"feature\". Let's remove any columns built on the `SK_ID_CURR`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "330d43c9cf3e7236fbd4a6852a10efd014b433ce",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols_with_id = [x for x in train.columns if 'SK_ID_CURR' in x]\n",
    "cols_with_bureau_id = [x for x in train.columns if 'SK_ID_BUREAU' in x]\n",
    "cols_with_previous_id = [x for x in train.columns if 'SK_ID_PREV' in x]\n",
    "print('There are %d columns that contain SK_ID_CURR' % len(cols_with_id))\n",
    "print('There are %d columns that contain SK_ID_BUREAU' % len(cols_with_bureau_id))\n",
    "print('There are %d columns that contain SK_ID_PREV' % len(cols_with_previous_id))\n",
    "\n",
    "train = train.drop(columns = cols_with_id)\n",
    "test = test.drop(columns = cols_with_id)\n",
    "print('Training shape: ', train.shape)\n",
    "print('Testing shape: ', test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "d6f0e15b600f90d4062faee276640c371cb655eb"
   },
   "source": [
    "After applying this to the full dataset, we end up with __1416 __ features. More features might seem like a good thing, and they can be if they help our model learn. However, irrelevant features, and highly correlated features, can prevent the model from learning and decrease generalization performance on the testing data. For that reason, we like to remove highly collinear variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c402926c8d265ad46e8f9bc5b1683563352cb496"
   },
   "source": [
    "# Remove Collinear Variables\n",
    "\n",
    "Collinear variables are those which are highly correlated with one another. These can decrease the model's availablility to learn, decrease model interpretability, and decrease generalization performance on the test set. Clearly, these are three things we want to increase, so removing collinear variables is a useful step. We will establish an admittedly arbitrary threshold for removing collinear variables, and then remove one out of any pair of variables that is above that threshold. \n",
    "\n",
    "The code below identifies the highly correlated variables based on the absolute magnitude of the Pearson correlation coefficient being greater than 0.9. Again, this is not entirely accurate since we are dealing with such a limited section of the data. This code is for illustration purposes, but if we read in the entire dataset, it would work (if the kernels allowed it)! \n",
    "\n",
    "This code is adapted from [work by Chris Albon](https://chrisalbon.com/machine_learning/feature_selection/drop_highly_correlated_features/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "5100a4ad6c6586b2637139db8ef4e67df5ad49a8"
   },
   "source": [
    "### Identify Correlated Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "59259cf2be69f0c5e699109f9177287599836354",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Threshold for removing correlated variables\n",
    "threshold = 0.9\n",
    "\n",
    "# Absolute value correlation matrix\n",
    "corr_matrix = train.corr().abs()\n",
    "corr_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "7c33392948540b626cf213fa6dea316766627eb9",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Upper triangle of correlations\n",
    "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
    "upper.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "1517d554f80a8eed2c0e03303fe7d1fb4a469131",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Select columns with correlations above threshold\n",
    "to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n",
    "\n",
    "print('There are %d columns to remove.' % (len(to_drop)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "2c18834968dac305317a5a881308a0c9a3cce353",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = train.drop(columns = to_drop)\n",
    "test = test.drop(columns = to_drop)\n",
    "\n",
    "print('Training shape: ', train.shape)\n",
    "print('Testing shape: ', test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "52eada9c579b09cc97c338ea372554aa8d52f9df"
   },
   "source": [
    "Applying this on the entire dataset results in __ __ collinear features removed.  The total dataset size is therefore __ __. This has reduced the number of features singificantly, but it is likely still too many. At this point, we can test the dataset to see how it does in a fairly simple LightGBM model. This will serve as a baseline before we do more feature selection. Moreover, we can actually use the feature importances returned from the LightGBM model for subsequent feature selection! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "15973fdfb3169e1d816136c68764c80d1368000e"
   },
   "source": [
    "## Read in Full Dataset\n",
    "\n",
    "Now we are ready to move on to the full set of features. These were built by applying the above steps to the entire `train_bureau` and `train_previous` files (you can do the same if you want and have the computational resources)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "e8384e3033ee466ab3c426d8330822e974ad3c1b",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('../input/m_train_combined.csv')\n",
    "test = pd.read_csv('../input/m_test_combined.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "18d3be4bd305a82d6bba1c2a6b7d2876abb098d9",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set full shape:  (307511, 865)\n",
      "Testing set full shape:  (48744, 864)\n"
     ]
    }
   ],
   "source": [
    "print('Training set full shape: ', train.shape)\n",
    "print('Testing set full shape: ' , test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e9c652ca3eb3a8770195469abf77ad71d15b3fe0"
   },
   "source": [
    "## Missing Values\n",
    "\n",
    "A relatively simple choice of feature selection is removing missing values. Well, it seems simple, at least until we have to decide what percentage of missing values is the minimum threshold for removing a column. Like many choices in machine learning, there is no right answer, and not even a general rule of thumb for making this choice. In this implementation, if any columns have greater than 75% missing values, they will be removed. \n",
    "\n",
    "Most models (including those in Sk-Learn) cannot handle missing values, so we will have to fill these in before machine learning. The Gradient Boosting Machine ([at least in LightGBM](https://github.com/Microsoft/LightGBM/blob/master/docs/Advanced-Topics.rst)) can handle missing values. Imputing missing values always makes me a little uncomfortable because we are adding information that actually isn't in the dataset. Since we are going to be evaluating several models (in a later notebook), we will have to use some form of imputation. For now, we will focus on removing columns above the threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "7849b6e3f53cd08850e9e5797e7256b08a823acb",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "client_credit_AMT_PAYMENT_CURRENT_min_min            0.801438\n",
       "client_credit_AMT_PAYMENT_CURRENT_mean_max           0.801438\n",
       "client_credit_AMT_DRAWINGS_OTHER_CURRENT_min_mean    0.801178\n",
       "client_credit_CNT_DRAWINGS_OTHER_CURRENT_min_max     0.801178\n",
       "client_credit_AMT_DRAWINGS_ATM_CURRENT_mean_max      0.801178\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train missing values (in percent)\n",
    "train_missing = (train.isnull().sum() / len(train)).sort_values(ascending = False)\n",
    "train_missing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "f7b485689bedd322ea3eb9083822f814719add0b",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "client_credit_CNT_DRAWINGS_OTHER_CURRENT_max_max     0.773223\n",
       "client_credit_CNT_DRAWINGS_POS_CURRENT_mean_max      0.773223\n",
       "client_credit_CNT_DRAWINGS_ATM_CURRENT_min_max       0.773223\n",
       "client_credit_AMT_DRAWINGS_OTHER_CURRENT_mean_min    0.773223\n",
       "client_credit_CNT_DRAWINGS_POS_CURRENT_min_mean      0.773223\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test missing values (in percent)\n",
    "test_missing = (test.isnull().sum() / len(test)).sort_values(ascending = False)\n",
    "test_missing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "9ab5d9001ab54692f0e2bc4e1d976d734771891c",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 19 columns with more than 75% missing values\n"
     ]
    }
   ],
   "source": [
    "# Identify missing values above threshold\n",
    "train_missing = train_missing.index[train_missing > 0.75]\n",
    "test_missing = test_missing.index[test_missing > 0.75]\n",
    "\n",
    "all_missing = list(set(set(train_missing) | set(test_missing)))\n",
    "print('There are %d columns with more than 75%% missing values' % len(all_missing))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "fc8e7bdb14e7e65a257c93d5c45a882098971360"
   },
   "source": [
    "Let's drop the columns, one-hot encode the dataframes, and then align the columns of the dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "9478bfc3069bf9b4e192c45cf323c1ea84ca7b2c",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set full shape:  (307511, 846)\n",
      "Testing set full shape:  (48744, 845)\n"
     ]
    }
   ],
   "source": [
    "# Need to save the labels because aligning will remove this column\n",
    "train_labels = train[\"TARGET\"]\n",
    "train_ids = train['SK_ID_CURR']\n",
    "test_ids = test['SK_ID_CURR']\n",
    "\n",
    "train = pd.get_dummies(train.drop(columns = all_missing))\n",
    "test = pd.get_dummies(test.drop(columns = all_missing))\n",
    "\n",
    "train, test = train.align(test, join = 'inner', axis = 1)\n",
    "\n",
    "print('Training set full shape: ', train.shape)\n",
    "print('Testing set full shape: ' , test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(columns = ['SK_ID_CURR', 'TARGET'])\n",
    "test = test.drop(columns = ['SK_ID_CURR'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "d4d38ab010cddd88e714894e09d8733cc60daafd"
   },
   "source": [
    "# Feature Selection through Feature Importances\n",
    "\n",
    "The next method we can employ for feature selection is to use the feature importances of a model. Tree-based models (and consequently ensembles of trees) can determine an \"importance\" for each feature by measuring the reduction in impurity for including the feature in the model. I'm not really sure what that means (any explanations would be welcome) and the absolute value of the importance can be difficult to interpret. However, the relative value of the importances can be used as an approximation of the \"relevance\" of different features in a model. Moreover, we can use the feature importances to remove features that the model does not consider important. \n",
    "\n",
    "One method for doing this automatically is the [Recursive Feature Elimination method](http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html) in Scikit-Learn. This accepts an estimator (one that either returns feature weights such as a linear regression, or feature importances such as a random forest) and a desired number of features. In then fits the model repeatedly on the data and iteratively removes the lowest importance features until the desired number of features is left. This means we have another arbitrary hyperparameter to use in out pipeline: the number of features to keep! \n",
    "\n",
    "Instead of doing this automatically, we can perform our own feature removal by first removing all zero importance features from the model. If this leaves too many features, then we can consider removing the features with the lowest importance. We will use a Gradient Boosted Model from the LightGBM library to assess feature importances. If you're used to the Scikit-Learn library, the LightGBM library has an API that makes deploying the model very similar to using a Scikit-Learn model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "1623797e0258c25f0276974c68f941505fbafb21",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6525f690933a50ab1eb7f360629b98d9a2f6df37"
   },
   "source": [
    "Since the LightGBM model does not need missing values to be imputed, we can directly `fit` on the training data. We will use Early Stopping to determine the optimal number of iterations and run the model twice, averaging the feature importances to try and avoid overfitting to a certain set of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "4741055961c566f3a2e9d60893950cade5a57330",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Initialize an empty array to hold feature importances\n",
    "feature_importances = np.zeros(train.shape[1])\n",
    "\n",
    "# Create the model with several hyperparameters\n",
    "model = lgb.LGBMClassifier(objective='binary', boosting_type = 'goss', n_estimators = 10000, class_weight = 'balanced')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "4535a84ea5b2c55f7b443832c7a5b2b77894e511",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid_0's auc: 0.782063\n",
      "Early stopping, best iteration is:\n",
      "[144]\tvalid_0's auc: 0.782491\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid_0's auc: 0.782875\n",
      "Early stopping, best iteration is:\n",
      "[147]\tvalid_0's auc: 0.783545\n"
     ]
    }
   ],
   "source": [
    "# Fit the model twice to avoid overfitting\n",
    "for i in range(2):\n",
    "    \n",
    "    # Split into training and validation set\n",
    "    train_features, valid_features, train_y, valid_y = train_test_split(train, train_labels, test_size = 0.25, random_state = i)\n",
    "    \n",
    "    # Train using early stopping\n",
    "    model.fit(train_features, train_y, early_stopping_rounds=100, eval_set = [(valid_features, valid_y)], \n",
    "              eval_metric = 'auc', verbose = 200)\n",
    "    \n",
    "    # Record the feature importances\n",
    "    feature_importances += model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "68a0ce264d8182bffa27fca668d81040fdc3b9ba",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "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>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>EXT_SOURCE_1</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>EXT_SOURCE_2</td>\n",
       "      <td>136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>client_installments_AMT_PAYMENT_min_sum</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>EXT_SOURCE_3</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DAYS_BIRTH</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     feature  importance\n",
       "24                              EXT_SOURCE_1       146.0\n",
       "25                              EXT_SOURCE_2       136.0\n",
       "368  client_installments_AMT_PAYMENT_min_sum       121.0\n",
       "26                              EXT_SOURCE_3       118.0\n",
       "5                                 DAYS_BIRTH        91.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make sure to average feature importances! \n",
    "feature_importances = feature_importances / 2\n",
    "feature_importances = pd.DataFrame({'feature': list(train.columns), 'importance': feature_importances}).sort_values('importance', ascending = False)\n",
    "\n",
    "feature_importances.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "68f738649fcc774a3eeceb2c602d00d9f4ff841d"
   },
   "source": [
    "We see that one of our features made it into the top 5 most important! That's a good sign for all of our hard work making the features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "144732dd9d88d13b06983590b9d34970ff6c9a69",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_feature_importances(df, threshold = 0.9):\n",
    "    \"\"\"\n",
    "    Plots 15 most important features and the cumulative importance of features.\n",
    "    Prints the number of features needed to reach threshold cumulative importance.\n",
    "    \n",
    "    Parameters\n",
    "    --------\n",
    "    df : dataframe\n",
    "        Dataframe of feature importances. Columns must be feature and importance\n",
    "    threshold : float, default = 0.9\n",
    "        Threshold for prining information about cumulative importances\n",
    "        \n",
    "    Return\n",
    "    --------\n",
    "    df : dataframe\n",
    "        Dataframe ordered by feature importances with a normalized column (sums to 1)\n",
    "        and a cumulative importance column\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    plt.rcParams['font.size'] = 18\n",
    "    \n",
    "    # Sort features according to importance\n",
    "    df = df.sort_values('importance', ascending = False).reset_index()\n",
    "    \n",
    "    # Normalize the feature importances to add up to one\n",
    "    df['importance_normalized'] = df['importance'] / df['importance'].sum()\n",
    "    df['cumulative_importance'] = np.cumsum(df['importance_normalized'])\n",
    "\n",
    "    # Make a horizontal bar chart of feature importances\n",
    "    plt.figure(figsize = (10, 6))\n",
    "    ax = plt.subplot()\n",
    "    \n",
    "    # Need to reverse the index to plot most important on top\n",
    "    ax.barh(list(reversed(list(df.index[:15]))), \n",
    "            df['importance_normalized'].head(15), \n",
    "            align = 'center', edgecolor = 'k')\n",
    "    \n",
    "    # Set the yticks and labels\n",
    "    ax.set_yticks(list(reversed(list(df.index[:15]))))\n",
    "    ax.set_yticklabels(df['feature'].head(15))\n",
    "    \n",
    "    # Plot labeling\n",
    "    plt.xlabel('Normalized Importance'); plt.title('Feature Importances')\n",
    "    plt.show()\n",
    "    \n",
    "    # Cumulative importance plot\n",
    "    plt.figure(figsize = (8, 6))\n",
    "    plt.plot(list(range(len(df))), df['cumulative_importance'], 'r-')\n",
    "    plt.xlabel('Number of Features'); plt.ylabel('Cumulative Importance'); \n",
    "    plt.title('Cumulative Feature Importance');\n",
    "    plt.show();\n",
    "    \n",
    "    importance_index = np.min(np.where(df['cumulative_importance'] > threshold))\n",
    "    print('%d features required for %0.2f of cumulative importance' % (importance_index + 1, threshold))\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "ea0dcbff339b6ffdfd654d165cdb96268311ba95",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZT/+cB2gYqglxP034m92H8KF2PiFBXIGH2oS9gIui7fYjXNPPCJ159/CV+yHVqrbX0t2/I7wX7iV0ED6QMMnaYMLMkoUMVYwTz7eE/2u/J3z470p4j65LSIxuydh+GvHf842GRZmUiIiUAdOtmaUeqSZBREREslKSICIiIlkpSRAREZGs1CdBREREslJNgoiIiGSlaZmB9u3be5cuXZIOQ0REpF68/vrr1e5e683ilCQAXbp0YeLEiUmHISIiUi/MLOtdRDOpuUFERESyUpIgIiIiWSlJEBERkayUJIiIiEhWShJEREQkKyUJIiIikpWSBBEREckq0STBzP5oZsPNbIqZuZlNq+NxTjSzN81soZl9Y2Z3m1mtk0SIiIhIbknXJFwF7AV8CsyqywHM7HzgPuAH4FzgTuAY4EUzW71IcYqIiDQ6Sc+4uIm7TwEws/eANeLsbGbtgSuBCcDe7r40Wj8BeJKQNFxV1IhFREQaiURrElIJwio4BGgNDEolCNFxRwJTgN6reHwREZFGK+mahFXVPVq+mqXsNeBYM1vD3efVY0wi0hi5w/z58P33MGtWWNbUJB2VVIouXWCLLer9tOWeJHSKll9mKfsSsGibTzILzawv0Begc+fOpYpPRMrNsmXwww/hQz7zkfrwz/VYvDjp6KVSnXce3HhjvZ+23JOE1tHyxyxlNRnbrMDd7wLuAqiqqvLihyYiiVq8uPYP9Wwf/LNmhVqBXNZYA9q2Xf7YaqsVf2/bFtq0CctWrerv+Upl69gxkdOWe5KwIFq2ABZmlLXM2EZEKsmiRTB1KkyeDJMmhWXq8e23MHdu7n3Nln+Qpz7UN9lk5Q/7zMc660Dz5vX3HEUSVu5JwlfRcn1gckbZ+oCnbSMi5aamJnsiMGkSfP55aBpIWWst2HRTqKqCTp1WTAIyH2uvDU2SHgEu0vCVe5IwgdCvYEdWThJ6Ah+r06JIA7dwIUyZsnJtwKRJMH36ilX/66wTEoEdd4QTT4SuXcNj002hXbtQQyAiRVM2SYKZdSb0L/jU3VO9g54AbgH6mdlDafMkHAxsAvwpkWBFZGVffw0TJsBHH62YCHzxxYrbtWsXPvh33TV8+KcSga5dQ5mI1JtEkwQzOwHYMPq1A9DczC6Lfv/M3R9I2/x+YHdgI2AagLvPNLM/AdcBz5nZw4Rmht8BHwE3lfxJiMjK5s2D11+H//4Xxo8Pj+nTl5e3bx8SgD33XF4TkEoE2rRJLm4RWUHSNQmnEj740/05Wr4EPEAt3P16M/sOOJ9QqzAHGAb8QU0NIvVgyRJ4//0VE4L331/eX2DjjWHnnaFHj/DYaqvQbCAiDV6iSYK771GMbd39XuDeVQ5IRPJzDx0G0xOC11+HBdEgorZtoWdPOOywkBB07w4ddK81kXKVdE2CiDR0P/wAzz0Ho0fDmDHwZTR3WYsWsP32cPrpITHo0SPUGqjzoEjFUJIgIityh3feCUnB6NHwyiuhSWHttWHffWGPPUJS8POfa84AkQqnJEFEltcWjBoVagu+iqYX6dYNLrwQ9t8/DDtcTf8yRBoT/cWLNEa11Rbsvz/stx+st17SkYpIgpQkiDQWc+bAs8+qtkBECqb/BiKV7McfQ03BQw/ByJFhmmPVFohIgZQkiFSaZctg7NiQGAwfDrNnh2GIp50GRx4JO+2k2gIRKYj+U4hUAnd4+20YOhQefjgMU1x9dTj0UDj+eNh7b2jWLOkoRaTMKEkQKWdz5sC998Kdd8IHH4Qagv32g7/9DX7965AoiIjUkZIEkXI0aRLceiv8/e8wd26YyOj220NzQvv2SUcnIhVCSYJIuXAPoxNuvjmMUGjWDI4+Gs4+OyQJIiJFpiRBpKGbNw/uvx8GDQq3WV53Xbj8cvjtb6Fjx6SjE5EKpiRBpKGaMgVuuw2GDAkzIlZVwQMPhCaFFi2Sjk5EGgElCSINiTu88EJoUhg5Epo2hSOOgHPOgR120M2TRKReKUkQaQhqakItwS23wHvvhc6Hl1wCZ5wB66+fdHQi0kgpSRBJUk1NaE64+uowt0G3bmHEwjHHQMuWSUcnIo2ckgSRJGQmB7vsAvfdB3vtpSYFEWkwlCSI1KdsycH998Oeeyo5EJEGR0mCSH348ceQHFx1VUgOdt5ZNQci0uApSRAppUWL4O67lRyISFlSkiBSCu5hCOPvfgeTJ4fk4N57w42WlByISJloknQAIhXnvfdg332hV69ww6V//QvGjYN99lGCICJlRUmCSLFUV8OZZ8K228LEiWFCpHfegQMOUHIgImVJzQ0iq2rx4jB98hVXhDsynnFG+Lldu6QjExFZJUoSRFbFqFFwwQXw8cehieGGG2CrrZKOSkSkKNTcIFIXn3wC++8PBx4Iy5aFTopjxihBEJGKoiRBJI7Fi8Nwxm22gVdegeuvDx0VDzpI/Q5EpOKouUGkUBMmwGmnhc6IRx4ZbsbUsWPSUYmIlIxqEkRqM28enH9+uFVzdTU8/jgMG6YEQUQqnmoSRPJ59lk4/XT47DP47W/hmmtg7bWTjkpEpF6oJkEkm5qaUHuw777hls1jx8IddyhBEJFGRTUJIpneew+OOw7efRf69YNrr4VWrZKOSkSk3qkmQSTFHW69Faqq4JtvwnTKgwYpQRCRRks1CSIA334LJ58cJkfaf3/4+99h3XWTjkpEJFFKEkReew2OOCKMXBg0CM46S3MeiIig5gZpzNxDZ8TddoPmzUOy0K+fEgQRkUisJMHM1jSz/mb2splNMrMdo/Xto/U/K02YIkW2cGFoXjjzzHAL54kToVu3pKMSEWlQCm5uMLMOwMvAxsDkaNkKwN2rzawPsA5wQQniFCmeqVPh8MPhzTfh8suhf39ooko1EZFMcfokXAl0BHoCnwPfZpQ/AexdpLhESmPMmDC80R2eeircoElERLKK8/XpIOB2d38D8CzlU4ANihKVSLEtWwZXXgkHHAAbbBCaF5QgiIjkFacmoT2hmSGXZUDLVQtHpAQWLoQ+fWD4cDj+eLjrLmjdOumoREQavDhJwgxgkzzl2xGaIUQajhkzoFevcAfHa6+F3/9eoxdERAoUp7lhFHCqma2XWWBmPYETCf0SRBqGd96Bnj3DNMsjRsCFFypBEBGJIU6ScAWwBHgTuJrQL6GPmT0MjAW+Av5a9AhF6mLUKNh5Z1iyBMaNg0MOSToiEZGyU3CS4O4zgB2A/wKnAAacABwFPAPs6u7flyJIkVjuuQcOPhg23RTGj4ftt086IhGRshRrWmZ3nw70MrO1gM0JicJkJQfSYFx7LVx8cbjF86OPwhprJB2RiEjZqtO9G9x9DjChyLGI1J07XHQRXHcdHH003H9/mGpZRETqrODmBjM72szuz1N+n5kdEefkZtbEzM43s4/MrMbMppvZ9Wa2eoH7r2Fml5jZu2Y218yqzewVMzvJTD3UGo0lS+DUU0OCcOaZMHSoEgQRkSKI03GxH2EuhFyWAmfHPP+NwA3AB9G+w4FzgJFmlje2qHw08GdCrcbvCLNCNgX+DlwTMxYpR4sWwZFHhls7X3453HorNG2adFQiIhUhTnPDFsA/85S/CRxc6MHMbCtCYjDC3Q9PWz8VuAU4BngozyF6ArsAN7n7+Wn73w58BPwGuLjQeKQM/fhjuMXzU0/BzTfDOeckHZGISEWJU5OwOqG2IBcH1oxxvGMJHR9vylg/GFgA9K5l/7Wi5VcrBOG+CKgG5seIRcpNTQ0cdlhIEO64QwmCiEgJxKlJmEr45n5rjvJdiDfjYndC88X49JXuXmNmb0Xl+YwHZgMXmdk0wtDMVsBJwC+A38aIRcpJTQ0cemi4WdOdd0LfvklHJCJSkeLUJDwGHGlmp2YWmNkpwJHAiBjH6wRUu/uPWcq+BNqbWc7eZ+4+C/g18D0wDPiM0MxwFnC4uw+OEYuUi5qaMDHS00/D3XcrQRARKaE4NQnXAL2Au8zsfOAtQhNDN2BL4GPgqhjHaw1kSxAAatK2WZTnGPOA94AngVeAtoQk4SEz6+Xuz+ba0cz6An0BOnfuHCNsScySJXDssSFBGDIETjkl6YhERCpanBkX5wI7A3cC6wHHAccTagTuAHaK5k8o1AKgRY6ylmnbZGVmPyckBs+6+4Xu/pi7DyE0e8wABptZzm7u7n6Xu1e5e1WHDh1ihC2JWLYMTjsNHn8cBg1SgiAiUg/iNDfg7j+4+5mE20avC3QE2rt7P3efHfPcXxGaFLIlCusTmiLy1SKcT0gmhmfEuAD4F7Ah0CVmTNIQucMFF8B998HAgdCvX9IRiYg0CrGShBQPZrr7t+7udTz3hOj8PdJXmllLQhPGxFr2Xz9aZqstWC1jKeXsyivDEMfzzoPLLks6GhGRRiP2h6iZbQZ0BdoRhjCuwN1zzsqY4RHgEuA8YFza+tMJfRGGpp1zE6CZu3+Utt0HwL6E0QzXpm27DqHvxCzg0wJjkYbq1luhf3/o0weuv163ehYRqUcFJwlmti5wH/DL1KosmzlQUJLg7u+a2W1APzMbAYwiTNh0DvASK06k9G9C80H6OW8CTgSuifon/IfQcfF0Qp+Js9x9SWHPThqkoUPh7LPDaIa774Ymdar4EhGROopTk3ArIUG4A3ge+K4I5z8PmEYYZXAgYRKkQUB/d883BTTu/pmZ9QD6A3sTZmhcSBh18Tt3jzMcUxqakSND7cFee8HDD8NqajkSEalvVmiXAjObDQx197NKG1L9q6qq8okTa+sCIfVm3Lhwq+ef/xz+/W9YM85EniIiUhsze93dq2rbLk79bRPg7bqHJFKAjz6CXr1gww1h9GglCCIiCYqTJIwDti1VICJ88w3svz80axYShHbtko5IRKRRi5MkXAAcamaH17qlSFzz58NBB8G338K//gUbbZR0RCIijV6c3mB3EKZBHmZmXwFTWPmukO7uexcrOGkkliyBY46BN94IMypW1dpMJiIi9SBOkrAxYYhj6k6PuuGBrDr3cJvnp56C226Dgw9OOiIREYkUnCS4e5cSxiGN1XXXwR13wIUXwplnJh2NiIik0ew0kpwRI+Cii+Coo+Caa5KORkREMihJkGS8+SaccAL07Blu3KTZFEVEGpxY09hF91A4H+gJtGHlJMPdfZMixSaVasaMMBdC27aho2LLlrXvIyIi9S7OvRt+DrwMtAA+JnRkfJ9wo6eOhJspfVGCGKWS1NTAoYfCd9/Byy9Dx45JRyQiIjnEqeMdCCwiTKiUGuZ4rrt3An4DrANU3JTNUkTu0LcvvPYa3H8/bLdd0hGJiEgecZKEXYC73P1jwlBIiO7K6O6DgdGAep9JbtdeCw88AAMHwuGak0tEpKGLkySsSWhSgFCjALB6Wvl/CImEyMqefhr++Ec4+mi47LKkoxERkQLESRK+IfQ9wN3nAvOBzdLK2wBNixeaVIxp0+C448JdHe+5B8ySjkhERAoQZ3TDW0D3tN9fAs41s/GEZKMfukukZKqpCU0LS5fCo49C69ZJRyQiIgWKU5PwENDOzFpFv/8JWBt4Afg3oePiJcUNT8pev37hngwPPABduyYdjYiIxBBnWuZHgEfSfn/TzLYCDiXc6Gm0u08pfohStu6+G4YMgUsv1T0ZRETKUKzJlDK5+3TgliLFIpVkwgQ46yz45S/hiiuSjkZEROqg4OYGM1tqZsflKT/azDJvHS2N0XffwRFHhImSHnoImqo/q4hIOYpTk1Bbl3R1WZcwYdJJJ4Wpl19+Gdq3TzoiERGpo1VqbsjQGZhbxONJObrpJnjqKbj5ZujevfbtRUSkwcqbJJhZL6BX2qq+ZrZPlk3bAvsQ7u0gjdXEiXDxxeHmTWefnXQ0IiKyimqrSegGnBT97MBu0SPTPOAVwlwJ0hj98EOYTbFjR02YJCJSIfJ2XHT3K9y9ibs3IfQ56J36PeOxlrvv6+6T6ydsaVBSN2767DN4+OFwC2gRESl7BfVJMLMWwMnAu6UNR8rS4MEwbBj85S+w885JRyMiIkVS6BDIpcDdwJ4ljEXK0XvvwbnnhvkQ/vCHpKMREZEiKihJcPclwAw0zFHSLVoEvXvDWmuFaZebxJnlW0REGro4/9WHA0eZmT4JJLjqKnj7bbhqtu7VAAAgAElEQVTrLlh33aSjERGRIoszT0KqueFZM7sJmAQsyNzI3T8vUmzSkL31VuiDcPzxYcijiIhUnDhJwnuEYZAG7JFnO83BW+kWLQqzKrZrFyZNEhGRihQnSRhISBKksbv66tDM8PjjIVEQEZGKFOdW0QNKGIeUi7fegiuvVDODiEgjoE6IUrglS+Dkk9XMICLSSMS6wVM0sqEPcCiwcbR6CjACuN/dlxU3PGlQBg0KNQn//KeaGUREGoGCkwQzawWMIty7wYGvo6IDgAOBE83sAHevKXqUkrwvvoD+/eHAA+Gww5KORkRE6kGc5obLgN2B64EO7r6Bu28AtAeuI4x4uLToEUrDcN55sHRpqE3QzZtERBqFOEnC0cAwd7/I3WelVrr7bHe/GBgGHFvsAKUB+Ne/4NFH4U9/go02SjoaERGpJ3GShJ8CL+YpfynaRirJggXQrx9ssQX87ndJRyMiIvUoTsfF2cCmecq7RttIJfnLX2DaNHjxRWjePOloRESkHsWpSXgWONPMfpVZYGb7AmcATxcrMGkAJk2C666DE06A3XdPOhoREalncWoSLgN+BYwyszeB96P1WwHbAdVA/+KGJ4m64AJo0QL++tekIxERkQTEmXHxMzOrAq4GDga2j4rmAg8Dl+jmThVk9Gh46in4299gvfWSjkZERBJg7vFvx2BmBnQg3OzpW6/LQRqQqqoqnzhxYtJhNByLFsHPfx5+fvdd9UUQEakwZva6u1fVtl2sGRdToqTg27rsK2Xgllvgk09g1CglCCIijVjsJMHMjmLlaZkfc/dhxQxMEvL113DFFXDQQbD//klHIyIiCYozLXNr4AlgL0Izw+xo2R04ysx+A/za3eeXIlCpJ3/8Y2huuPHGpCMREZGExRkCeRWwNzAI6OTubd29DdApWrcn8Jfihyj15rXX4L77wqiGrl2TjkZERBJWcMdFM/saGOvuR+coHw7s4u5l1xVeHRcBd9h55zBx0scfw5prJh2RiIiUSKEdF+PUJKwFvJCn/Plom4KZWRMzO9/MPjKzGjObbmbXm9nqMY7R1syuM7PJ0TFmmtkLZrZrnFgavcceg1dfhYEDlSCIiAgQr+PiO+SflnlT4N2Y578ROAd4jHB3yS2i37czs33cfVm+nc1sQ8L9JNYAhgCfAGsD2wDrx4yl8Vq8OPRF2HJLOOmkpKMREZEGIu6Mi4+Z2YvuPjK9wMx6AacBhxR6MDPbCjgbGOHuh6etnwrcAhwDPFTLYR4kPIdt3P3rQs8tGYYMCUMen3wSVqvTqFgREalAcT4RjgemAo+b2cfAh4ADWwKbE2oReptZ77R93N1PzXG8YwmjI27KWD8YuAboTZ4kwcx2A3YBznH3r82sGdDM3RfEeE4ybx4MGAC77RaGPYqIiETiJAknpf38s+iRbpvokc6BXElCd2AZMH6FHdxrzOytqDyfA6Ll52Y2EtgfaGpmk4CB7v5gLfsLhKGO33wDTzwBZklHIyIiDUjBHRfdvUkdHk3zHLITUO3uP2Yp+xJob2b5pvvbPFoOBtoCfQgJySLgATM7Od/zMbO+ZjbRzCbOnDkz36aV6/vvw10eDz0UevZMOhoREWlg4oxuKLbWQLYEAaAmbZtcUl3w5wJ7uvtQd78H2JUw0dNVZpbz+bn7Xe5e5e5VHTp0iBl6hbjuOpg7N8ywKCIikiHJJGEB0CJHWcu0bXJZGC0fdvdFqZXuPgt4EujI8toGyfTtt3DzzXDMMctv5iQiIpImVld2M9sJOIsw3LEdoeNhOnf3TQo83FfAlmbWIkuTw/qEpohFWfZL+SJazshSlhrp0KbAWBqfa66BmprQaVFERCSLgmsSzOx0YBxwGNAc+Bz4LOPxeYxzT4jO3yPjPC2BbkBtUyCmOjz+NEtZap3uVJnNl1/C7bdDnz6w2WZJRyMiIg1UnOaGS4C3gA3cvZu775ntEeN4jxBGP5yXsf50Ql+EoakVZraJmWWOpnic0B+ht5mtkbbteoT5Gia5++QY8TQeV14Jy5ZB//5JRyIiIg1YnOaGdYG/uXt1MU7s7u+a2W1APzMbAYxi+YyLL7HiHAn/BjYkrXnD3WeZ2e+BO4HXzOweQg3HGdGyXzHirDhTp8Ldd0PfvtClS9LRiIhIAxYnSfiQ4rfxnwdMA/oCBwLVhDtK9q9tSmYIIxTMrBq4CPgzYd6FV4Hj3P0/RY61MgwcGGZVvPTSpCMREZEGLs5dIA8jfID3cPcvSxpVPWs0d4H8+ONwf4bzzoPrr086GhERSUihd4EsuCbB3UeYWWvgAzN7nFADsHTlzfzPsSKV+jNgALRqBRdfnHQkIiJSBgpOEsxsM2AgYRKjE3Js5oRqf2lo3nkH/vEPuOQS+MlPko5GRETKQJw+CbcDPwHOJQyFnFWSiKQ0BgyAtdeG3/8+6UhERKRMxEkSdgCuc/dBpQpGSuSDD+Cxx+BPf4I2ml9KREQKE2eehDlAI70TUpm79trQF+Gcc5KOREREykicJGEYYbZFKSeffw5Dh8Lpp0P79klHIyIiZSROc8OdwH3RyIZbgKmsPLoBd48zNbOUWmqo4+9+l2wcIiJSduIkCe8TRi9UAQfn2a7pKkUkxVNdDYMHw/HHQ+fOSUcjIiJlJk6SMJCQJEi5uOUWWLhQ8yKIiEidxJlMaUAJ45BimzsXbr0VDjkEttgi6WhERKQMxem4KOVk8GCYNQv+8IekIxERkTKVtybBzNrGPaC7f1/3cKQoliyBm26C3XeHnj2TjkZERMpUbc0N1cTrh+AFHFNK7dFHYfr00NwgIiJSR7V9oN+POiuWF3e44Qbo2hUOOijpaEREpIzlTRLc/aR6ikOK5dVXYfx4uO02aKIuJyIiUnf6FKk0N9wQ7s/Qp0/SkYiISJlTklBJpk4NN3L6zW9g9dWTjkZERMqckoRKcsstoYmhX7+kIxERkQqgJKFSzJ0LQ4bA0UfD+usnHY2IiFQAJQmV4sEHQ6Jw9tlJRyIiIhVCSUIlcA+jGbbfHnr0SDoaERGpEJr4qBKMGwfvvx+aG8ySjkZERCpErJoEM1vTzPqb2ctmNsnMdozWt4/W/6w0YUpet98O66wDxxyTdCQiIlJBCq5JMLMOwMvAxsDkaNkKwN2rzawPsA5wQQnilFxmzAjTMJ99NrRunXQ0IiJSQeI0N1wJdAR6Ap8D32aUPwHsXaS4pFCDB4cbOp1xRtKRiIhIhYnT3HAQcLu7v0H2+zlMATYoSlRSmCVL4M47Yd99YdNNk45GREQqTJwkoT2hmSGXZUDLVQtHYnnqKfjyS9UiiIhIScRJEmYAm+Qp347QDCH15e67oVMn3e1RRERKIk6SMAo41czWyywws57AiYR+CVIfvvgCRo+Gk0+G1TSSVUREii9OknAFsAR4E7ia0C+hj5k9DIwFvgL+WvQIJbt774Vly+CUU5KOREREKlTBSYK7zwB2AP4LnAIYcAJwFPAMsKu7f1+KICXDsmVh4qS99oKNN046GhERqVCx6qndfTrQy8zWAjYnJAqTlRzUs+efh2nT4Kqrko5EREQqWJzJlNq5+3cA7j4HmFCyqCS/IUOgTRs49NCkIxERkQoWp0/CV2Y2wsx6mZl6yiXlu+9gxAg44QRoqRGnIiJSOnGShBHAr6Ll12Z2s5lVlSYsyenBB2HRIjj11KQjERGRChen4+KxhGmZ+wIfAP2A/5rZ+2Z2oZl1KlGMkuIe5kbo0QO22SbpaEREpMLFuguku8919yHuvjvhBk8DgGaEoY+fmdmY4oco/zNxIrz3nmoRRESkXsRKEtK5+2fu/md33ww4HpgP/LJokcnKhg6F5s3hqKOSjkRERBqBOndANLM1gSMJMy3uQkg43itSXJJpyRL4xz/CFMzrrJN0NCIi0gjEShLMzAidF08EegGtgJnArcB97v5m0SOU4Pnn4Ztv4Pjjk45EREQaiTjzJFwHHAesCywG/gXcB4xy9yWlCU/+58EHYe214YADko5EREQaiTg1CRcQJlC6EnjY3WeVJiRZyYIF8NhjcPTRmhtBRETqTZwkYUt3/6hkkUhuTz4J8+apqUFEROpVnHkSlCAkZehQ+OlPYffdk45EREQakZw1CWZ2YvTjA+7uab/n5e73FyUyCb7/HsaMgfPPhyZ1HrEqIiISW77mhnsBB/4BLEr73fLs44CShGJ6/PEw/PHoo5OOREREGpl8ScKeAO6+KP13qWfDh8NGG8H22ycdiYiINDI5kwR3fynf78VgZk2Ac4HfAF0Icy4MA/q7+/yYx2oNvB8d5zZ371fUYJPw/ffw3HNwwQVg+SpwREREiq/gRm4zu8fMeuYp72Fm98Q8/43ADYQbRp0NDAfOAUZGCUQcA4H2Mfdp2J54IjQ1HHFE0pGIiEgjFOeD+CRgkzzlGwF9Cj2YmW1FSAxGuPth7j7Y3S8gzMewJ3BMjGNtD5wHXF7oPmVh+HDo0gWqdEduERGpf8XsLr86YSbGQh1L6AR5U8b6wcACoHchBzGzptE+Y4ARMc7fsM2aFZoajjhCTQ0iIpKIvJMpmVlnQht/ys/MbLcsm7YFzgAmxzh3d2AZMD59pbvXmNlbUXkhzgd+Bhwe49wN35NPwuLFamoQEZHE1Dbj4smEKnyPHpdGj0xG+MA/Oca5OwHV7v5jlrIvgZ3MrHna6IqVT2q2EXAFMNDdp5lZlxjnb9iGD4fOnaFHj6QjERGRRqq2JOFxYBohCbgHuAt4NWMbB+YBE9x9eoxztwayJQgANWnb5EwSgDuAqYTOj7GYWV+gL0Dnzp3j7l5ac+bAs8/CWWepqUFERBKTN0lw97eBtwHMbEPgUXd/r0jnXgD8JEdZy7RtsjKz3sC+wG7uHqcvBADufhch6aGqqsrj7l9SzzwDixZBr15JRyIiIo1YwTd4cvcrinzur4AtzaxFliaH9QlNEVlrEcysBaH2YBQww8y6pu0HsHa0rtrdZxc57tIbORLatIGdd046EhERacTi3AUSADNbF6gC2pBldESMezdMINQE9ADGpR2/JdANGJtn31ZAB+DA6JGpd/S4ELiuwHgahqVLYdQoOOAAWC32yyMiIlI0BX8KRZMb3QacRv6hk4UmCY8AlxDmNxiXtv50Ql+EoWnn3gRolnYnyvnAkVmO2QG4nTAccgjwToGxNByvvQbV1XDwwUlHIiIijVycr6q/J0yf/CDwDCEZuBiYS/ig/wH4Y6EHc/d3zew2oJ+ZjSA0HWxBmHHxJeChtM3/DWxIdHOpqA/CPzOPmTa64VN3X6m8LDz5ZKhB2G+/pCMREZFGLk6S0Ad42t1PNLN20brX3f15M3uA8K39F8DzMY55HmH0RF9Cs0E1MIhw74ZlMY5TOUaOhN13h7XXTjoSERFp5OIkCRsDd0Y/pz7AmwG4+3wz+zuhKeJvhR7Q3ZcC10ePfNt1KfB408h/K+uGbfJk+PBD+O1vk45EREQk1rTMC1k+7fI8wvwI6UMYZwAbFCmuxmnkyLBUfwQREWkA4iQJnxHd4CnqEzAZSG843wf4pnihNUIjR8LWW8NGGyUdiYiISKwk4Xng0LTfHwCONbMXzOxFwmiDYUWMrXGZOxfGjYMDs43oFBERqX9x+iRcBzyTNvnR1YTmht7AUsLshZV1q+b69PzzsGQJ7L9/0pGIiIgA8WZc/Br4Ou33pYThiueUIK7GZ8wYWHNN2HHHpCMREREB4jU3SKm4w+jRsPfe0Lx50tGIiIgAeWoSzKxOt0Z098/rHk4j9fHH8Nln8MeC56ISEREpuXzNDdMIwxzjalq3UBqxMWPCUrMsiohIA5IvSRhI3ZIEiWvMGNhiC9hww6QjERER+Z+cSYK7D6jHOBqvhQvhpZfgjDOSjkRERGQF6riYtJdegpoaNTWIiEiDE+dW0QV1ZFTHxZhGj4ZWrWC33ZKOREREZAVxJlOaRmF9FNRxMY4xY2CPPaBly6QjERERWUGcJCFbR8bVCPdz6AW8C4wuUlyNw9Sp8Mkn0K9f0pGIiIisJM6MiwNylZnZxsCrwMQixNR4vPhiWO61V6JhiIiIZFOUjovuPgW4E7iiGMdrNMaOhfbtYcstk45ERERkJcUc3fAloE+7OMaOhV13BbOkIxEREVlJMZOEQ4BZRTxeZfviC5gyRaMaRESkwYozBLJ/jqK2wF7A1sC1xQiqURg7NiyVJIiISAMVZ3TDgDxlM4DLgL+uUjSNydix4dbQ226bdCQiIiJZxUkSNsqyzoHv3X1ekeJpPMaOhV12gaaaVkJERBqmOEMgPytlII3KzJnw4YdwwglJRyIiIpKT7t2QhPHjw3LnnZONQ0REJI84zQ2Y2U7AWcCmQDsgc+yeu/smRYqtco0fD02awPbbJx2JiIhITnFGN5wO/B+wCPgY0I2c6mr8eNh6a1hjjaQjERERySlOTcIlwFvAr9y9ukTxVD73kCQcdljSkYiIiOQVp0/CusAQJQir6NNP4fvvoUePpCMRERHJK06S8CHQplSBNBqpTotKEkREpIGLkyT8BTjTzNYvVTCNwvjx0KoVbLVV0pGIiIjkFWeehBFm1hr4wMweB6YBS1fezP9cxPgqz/jx8ItfwGqxBpaIiIjUuzijGzYDBgJrArlmAXJASUIuixfDG29Av35JRyIiIlKrOF9nbwd+ApwLjEN3fIzv3Xfhxx/VH0FERMpCnCRhB+A6dx9UqmAqnjotiohIGYnTcXEOMLNUgTQK48dDhw6w4YZJRyIiIlKrOEnCMEAzAK2K8eNDLYJlzmYtIiLS8MRJEu4E1jSzx81sLzPbyMw6Zz5KFWjZmzcPPvgAundPOhIREZGCxOmT8D5h9EIVcHCe7ZquUkSV6t13w5TM222XdCQiIiIFiZMkDCQkCVIXb78dlt26JRuHiIhIgeJMpjSghHFUvrfegnXWgQ02SDoSERGRgsTpkyCr4u23Ydtt1WlRRETKRpwZF3crZDt3H1v3cCrU0qWhT8KppyYdiYiISMHi9El4kcL6JKjjYqZPP4X589UfQUREykqcJOHkHPtvApxEuOHTnaseUgVKdVrcdttk4xAREYkhTsfF+3KVmdnfgDeKElEleu89aNIEttwy6UhEREQKVpSOi+4+C7gbuKgYx6s4kyZB587QsmXSkYiIiBSsmKMbZgEbF/F4leOTT2CzzZKOQkREJJaiJAlm1hI4AZhRjONVFPdQk7DppklHIiIiEkucIZD35ChqC+wIdAAujHNyM2sCnAv8BuhCuMvkMKC/u8+vZd/NgN7AvoTOky2BT4HhwE217V9vZs6EOXOUJIiISNmJM7rhpBzrvwc+Ac5394dinv9G4BzgMeB6YIvo9+3MbB93X5Zn31OAs4AngaHAYmBP4ErgKDPbwd0Xxoyn+CZNCkslCSIiUmbijG4o6uyMZrYVcDYwwt0PT1s/FbgFOAbIl3T8E7ja3X9IW/d/ZjYJuBQ4Fbi1mDHXiZIEEREpU0lOy3wsYMBNGesHAwsITQk5ufvEjAQh5ZFoufUqR1gMkyZB06bQpUvSkYiIiMSSN0kws6Zmdo2Z/baW7c4ws6vMYt2YoDuwDBifvtLda4C3ovK6+Gm0/KaO+xfXpEmw0UbQrFnSkYiIiMRSW01Cb0JnxAm1bDceuJhQO1CoTkC1u/+YpexLoL2ZNY9xPMysKdAfWEL+por6o5ENIiJSpmpLEo4CnnP31/NtFJU/TbwkoTWQLUEAqEnbJo6bgB0IoyM+zrehmfU1s4lmNnHmzJkxT1MgDX8UEZEyVluS8AvguQKP9QJQFePcC4AWOcpapm1TEDP7M9APuMvdr65te3e/y92r3L2qQ4cOhZ4mnq+/Djd20kRKIiJShmpLEtoC3xZ4rJnR9oX6itCkkC1RWJ/QFLGokAOZ2QDgMuDvQN7+E/VKIxtERKSM1ZYkzAXaF3isdsC8GOeeEJ2/R/rKaPbGbsDEQg5iZpcDlwP3A6e5eyG3s64fkyeHZdeuycYhIiJSB7UlCe8TZjQsxC+j7Qv1CODAeRnrTyf0RRiaWmFmm5jZzzIPYGb9gQHAA8DJtUy+VP8+/xzMYIMNko5EREQkttomUxoBXG9mvdz9iVwbmdmvCUnCBYWe2N3fNbPbgH5mNgIYxfIZF19ixdEJ/wY2JMyrkDrnWcAVwOeEfhPHZYzA/Mbdny00npKYPh06dtTwRxERKUu1JQl3AmcAw8zsOmCwu09LFZpZF+A04PeEqZnvjHn+84BpQF/gQKAaGEQYnVBbrUBqHoXOwH1Zyl8Ckk8SVIsgIiJlymprwjezrsBTwGaE5oG5wBxgTWAtwrf7j4GD3P3TkkZbIlVVVT5xYkFdIOL52c9g663hn/8s/rFFRETqyMxed/daRyTWOi2zu08mdCQ8F3iZMFFRR2ApMC5av325Jggl4w5ffKGaBBERKVsF3eApmip5UPSQQsyeHeZIUJIgIiJlKskbPFW26dPDUkmCiIiUKSUJpaIkQUREypyShFL5+uuw7NQp2ThERETqSElCqVRXh2X7QiesFBERaViUJJRKdTW0bh0eIiIiZUhJQqlUV6sWQUREypqShFJRkiAiImVOSUKpKEkQEZEypyShVL77Dtq1SzoKERGROlOSUCqzZkGbNklHISIiUmdKEkrBPUzLvM46SUciIiJSZ0oSSmHuXFi6VDUJIiJS1pQklMKsWWGpJEFERMqYkoRSmD07LJUkiIhIGVOSUAqpmgT1SRARkTKmJKEU1NwgIiIVQElCKShJEBGRCqAkoRSUJIiISAVQklAKs2eDGay5ZtKRiIiI1JmShFKYNSt0WmyiyysiIuVLn2KloCmZRUSkAihJKAUlCSIiUgGUJJRCqrlBRESkjClJKIU5c5QkiIhI2VOSUApz5mhkg4iIlD0lCaUwZw6stVbSUYiIiKwSJQnF5h5uFa0kQUREypyShGKbPz8kCmpuEBGRMqckodjmzg1L1SSIiEiZU5JQbHPmhKWSBBERKXNKEootlSSouUFERMqckoRiU3ODiIhUCCUJxabmBhERqRBKEootVZOg5gYRESlzShKKTTUJIiJSIZQkFJuSBBERqRBKEopt7lxYbTVo0SLpSERERFaJkoRiS923wSzpSERERFaJkoRi030bRESkQihJKDbdJlpERCqEkoRi022iRUSkQqyWdAAVp1u30HFRRESkzOnTrNiuvz7pCERERIpCzQ0iIiKSlZIEERERyUpJgoiIiGSVeJJgZk3M7Hwz+8jMasxsupldb2ar18f+IiIikl3iSQJwI3AD8AFwNjAcOAcYaWaFxLeq+4uIiEgWiY5uMLOtCB/sI9z98LT1U4FbgGOAh0q1v4iIiOSW9DftYwEDbspYPxhYAPQu8f4iIiKSQ9JJQndgGTA+faW71wBvReWl3F9ERERySDpJ6ARUu/uPWcq+BNqbWfNS7G9mfc1soplNnDlzZuzARUREKl3SSUJrINsHPEBN2jZF39/d73L3Knev6tChQ62BioiINDZJJwkLgBY5ylqmbVOq/UVERCSHpJOErwhNAtk+6NcnNCUsKuH+IiIikkPSScKEKIYe6SvNrCXQDZhY4v1FREQkh6TvAvkIcAlwHjAubf3phL4EQ1MrzGwToJm7f1SX/fN5/fXXq83ss7o8gRzaA9VFPJ7Eo+ufPL0GydL1T1Y5XP8NC9nI3L3UgeQPwGwQ0A94DBgFbEGYMfE/wF7uvizabhqwobtbXfavT2Y20d2r6vu8Euj6J0+vQbJ0/ZNVSdc/6ZoECLUA04C+wIGE7GsQ0L/AD/hV3V9ERESySDxJcPelwPXRI992XVZlfxEREYkn6Y6LlequpANo5HT9k6fXIFm6/smqmOufeJ8EERERaZhUkyAiIiJZKUkQERGRrJQkFIGZNTGz883sIzOrMbPpZna9ma2edGzlysw2M7OBZvaamc00s7lm9paZXZrtuprZ5mb2uJnNMrP5ZjbOzPbKcey1zWyQmX0ZvV7vm9kZZmbZthcws9ZmNtXM3MxuzVKu618CZtbWzK4zs8nRtZppZi+Y2a4Z2/U0s+eiv5M5ZjbGzLrlOGYnM7s/OtbC6EZ3R9bPMyofZraGmV1iZu9G17XazF4xs5My36uVfP0TH91QIW4kzM3wGGGURWquhu3MbB8NxayTU4CzgCcJk2ItBvYErgSOMrMd3H0h/G+irVeAJcC1wA+ECbWeNrP93f251EGju4I+C2xHGCr7IbA/cDuwLjCgPp5cGRpImCBmJbr+pWFmGwIvAmsAQ4BPgLWBbQjTzqe22yHa7kugf7S6HzDOzHZy93fTtm0LvAz8BLgB+AI4DhhmZqe4+99L+6zKg5k1AUYDOwH3Ed6rrYFjgb8T/sdfHG1b2dff3fVYhQewFbAMeDRj/dmAA8clHWM5PoAqYO0s66+Mrmu/tHXDgKVAt7R1awCfAR8TddCN1p8Z7X92xnEfBRYRJuxK/Pk3pAewPSEBuCC6drdmlOv6l+a6jwOmA+vVst14YA6wftq69aN1z2Rse210/Q9OW9c0OsZ3wBpJP++G8AB2jK7TjRnrmwNTgNmN5fqruWHVHQsYcFPG+sGEO1D2rveIKoC7T3T3H7IUPRIttwaImh5+Dbzo7m+l7T8PuBvYDOietv9xhNdlcMZxbwKaAUcX5QlUCDNrSrhWY4ARWcp1/UvAzHYDdgGudfevzayZma1023sz60q4vsPd/cvU+ujn4cA+ZtYxbZfjgE/dfWTatksJ35TbAgeU5AmVn7Wi5VfpKz3cMLAamA+N4/orSVh13Qk1CePTV7p7DfAWK/6DlFX302j5TbTchnC78FezbPtatOwO/6tC3B54M3p90o0nvI56vVZ0PvAzQvVpNrr+pZH6sPjczEYCC4H5ZvaJmaV/8Uhdr1zX34BfAJjZeoRvuK/l2Db9eI3deGA2cJGZHWlmnaN+N1cTrpWWIeQAAAyISURBVOeAaLuKv/5KElZdJ8ItqX/MUvYl4VbWzes5pooUfavtT6j6fiha3Slafplll9S6VPttG6BVtm2j1++7tG0bPTPbCLgCGOju03JsputfGptHy8GEb5h9gFMJTTIPmNn/t3fmQVZUVxz+foIbxCgBImrKLahxRSlDMGoiKdCKSbngmgIMlsRyyeISBXHDPS5llDLBxERRIURNohiipWI0EbcSISVubAqWBiIILoBscvLHuY1N029mHszzwcz5qm71e6dP3z597ry5p+96ajpfjf+r0W3VmNlCvIVsAd6dNht4Ex8ndZyZZS1hLd7/MXBx/WkHlAUIAEtzOsu/GHNaNLcAPYGhZjY1ybIm2LIyWFrQaUg301+rSbcVMwJ4Gx9gVYnwf23YKh0/AXqlZm4kPYj3iV8r6W6az/9F3QAWAa/ig6efw4O1s4E/STrazJ6gFfg/goT1Zwk+UrWMLXI6wXog6Sq8yfv3ZnZd7lTm281LLiv6vyHdTD/KCkhN2ocD3zGzFQ2ohv9rw6fpOCYLEMDfcCU9DJyCtzY0l//jf1UOSfvigcG5ZnZ7Tj4GDxzuSLN6Wrz/o7th/fkv3qVQVvA74F0R0YqwHkgaBlyCTz06o3A6G1hU1kyXybKmvIX4P9+1dFP5daS8KbBVkXxxM771+lxJXdMArWz/+a2TbBvC/7Xi3XScW3JuTjp2oDr/V6Pb2jkXr7gfyAvNbAnwD/y3sDOtwP8RJKw/L+F+7JEXStoC2B+YWA+jWgqSLgcuB+4BBlmaM5RjCt58d1DJ5T3TcSKA+XoVk/D1K4pBXQ+8HKO8fNxAZ3zr9em59HQ63z99H0T4v1ZkA6G/VnIuk72P//+Byv434GUAM5uDV0I9K+hC+D8jq7TblJxrmzu2fP/Xew7mxp6AfWl4nYT+9bZxY034IEXDA4RNGtB7AJ+n3y0ny+bpT2PNefpnU3me/gpgl3o/d70TPhXx+JJ0ZvLdo+n77uH/mpVBB3ye/bvk5s4D2+F95dNyspeS7vY52fZJNr6Q741Unqe/ENiq3s++ISR8gTwDLizIs9azBUDb1uD/uhvQEhI+x9XweeSD8FUXV+BvXhUrt0gN+jSrTGbj/a/9C6lPTrdr+tH+DxiCL9gzGZ8FcUQh383waH1FKqdBqdwMuKrez70hJ7x5tWwxpfB/bfx9evLLq/hCVkPS72E5cHhO79t4a85M4JyUZqZgolshz47ALHxA5BXpHk+l+5xW72feUBLenfAB/gJ4L97NORQfyGvAWa3F/3U3oCWkFAmej68utwxvUrqZDXD1rI0lASPTD6dSerqgvycwFp/bvARf+rR3hby3AW7D3wiWAa/jgyJV6+famFOlICH8X1Of98Xn0C9OFcvjwMElegcBT6aK6RPgMaB7hTx3SBXffHxU/STgpHo/64aWgK/jSzK/iwe1HwP/Bvq2Jv8rGR0EQRAEQbAGMXAxCIIgCIJSIkgIgiAIgqCUCBKCIAiCICglgoQgCIIgCEqJICEIgiAIglIiSAiCIAiCoJQIEoIgCIIgKCWChCBoAUgySSPrbce6IKmdpOGS3pH0maRZ9bYpCAIngoQgqICkw1Lla5IGVdAxSeO+aNtaGIPxvU7uAwbiy9pWJFcmZWlIrY2V1FfSZbW+TxBsCLRtXCUIAuAKSaPN7NN6G9IC6QNMMbMLqrjmP/jeD0UmN49JDdIXOBm48gu4VxDUlQgSgqBxJgIH4m+419XZlrojqQ2wuZktaaYsuwDvVHnNe2Y2qpnuv8EgSUB7M1tUb1uCAKK7IQiawv34nvCDJXVsTLnS+ABJA9O5w3KyYUm2l6RbJM2RtFjSk5L2SDp9JU2S9KmkWZJOb+DevSW9IGmJpLmSbpXUvkRva0nXS5ohaZmkeZLGSNq1gs29JV0qaSa+Kc2JjfigraTBkl6XtFTSB5IelLRvMW9gF+C7uS6DYQ3lXQ2Sekgam+6/TNJUSRelQCev11PS3ZKmJ999IukZSUcV9CYA/YA2hW6O/tl5STNK7Oia9C7JyXon2QBJP5P0Br7h1Tk5nT0kjU5luVzS25JukNSukP9Oku6SNDs95/uSnpU0oDn8GLReoiUhCBrH8H7z8cDF+La9zc3d+A5y1wKd8V1FH5N0KXADMAK4EzgN+J2k181sQiGP7sDxwB3APUAv4OfAPpL6mNkq8AABeA7YMeX5GrAdvsXzi5IONLPZhbxvAjZNeX+M73jaEKPxQOKJZHsXfPvv5yUdamaT8R31BgC/xnfEuyZd+0ojeQNsKqlTQbbKzBZkX1IF/5dk643AQuDgdJ/9gB/lrj0O2B34M96q0Qn4MTBW0klmdn/SuxK4HOiZzmc82wSbK3E+0AH4IzAX3w4aST3wv7kFuA/nAN3wIOIgSb3MbKWkzXA/dwF+C0zHd9rcDzgE33EwCNaNem9DGSnShpqAw/AA4Zfp++P4W/ROOR0DxhWuM2BkSX4D07nDcrJhSfZ3clsl45W74dvO7piTd042jCm5pwHHFOS3JvnJBdmnrL3X/U54ADCyxOapQLsm+q1Puua+wjPtB6wEninoz6Kw9Xcj+VfaPnxuTqcdMA94CmhTuP6CpH9ITta+5D7t8Qr3lYJ8FLCygm0TgBkl8q7pnpfkZL2TbD7QqaAv4FV8G+0vFc6dkK7rn753T9/Pq/dvJlLLS9HdEARNZzCwGXBVDfIebmb5fdufScexZra6v97M5uEV9m4leUw1s4cKsl+l47Gwus+7H/4W/56kTlkCFgMvAIeX5D3Cmj4G4dh0vCb/TGb2CjAOOERS5ybmVYkX8WAkn07InT8Cbw24E+hQeM5Hks7q5zSzxdln+ZTMjsCWwNN4S8xaXTbNyEgzm1+Q7Q/sjbfIbFGw/194oJjZ/1E6fq8Z/BoEaxDdDUHQRMxssqQxQD9JN6VKr7l4q/B9YTq+XaK7EH/rL/JGUWBmcyR9CGRjDToDHfEKZl4FW1aVyKZV0C1jl5THWvbgb8dHJ51K928K881sfAPn90zHexrQ2Tb7IKkLcDVwFO6jIlvjQVQtKPNtZv/VKZWxLYCZzZR0PXAhMFfSZOBJ4AEzm9jcxgatiwgSgqA6LsH7/a8Hvl/ltQ393j6rUq4SmZXIirrZ5/H4MzSVamYylNn2RZPZcB4wpYLOewCSNsH79HfDu2Jext/OPwMGASfR9EHelcqgobIv821m/w3JtjI+WH1TsyGS7gB+ABwKnA5cKOk6MxvasMlBUJkIEoKgCszsbUkjgF9I6lVBbQHwlRL5riWy5mSvokDSdvhbcNZSMQ/4EPhyI2/i68NMvLl/T9YehJjZWNZC0pxMT8dFTXjOA4B9gMvMbI2uJElnlOhXCgTAy37vEnm1ZZ/Zv7Kp5WRmM4HhwHBJW+LBxZDU6rWg4auDoJwYkxAE1XM1PsCv0pv4NHz0+eppapI6AKfW2K49JB1TkA1Ox4cAzGc4jAZ6SDq+LBNJX11PO7JxERelMRBZvvvgzfkT0tiKWvII/qZ9UfL9GkjaUtJW6WvWWqOCTjfc3iKL8CmQXy45Nw0fA9E9l88mNLKKZAkT8e6asyTtXGL/ptlzyaezrvHCZ77o15v4M631/EHQVKIlIQiqxMzmS7qRygMYb8NHwP9T0r34dLSf4FPbutTQtCnAqNTsPB2fAnk8PtDtvpzexfhUwPsl3Y8PVlyOj3M4Em9uH7iuRpjZEynfk/EKcxyfT4Fcis/cqClmtkjSKcDfgKmS7gJm4BXmN/BVE3+Iz0Z4Da9QL0qBw7Skczru0+6F7F8AzgBul/QosAJ43nza6O14QPCwpFvTuROANlSBma1KaxyMB6ZIuhOf6dAenylxHD51chQ+aPM3kv6abF+ML/51KvBsamEIgnUigoQgWDduxtcV2K54wsxGS9oe+GnSewufX78K+FYNbZqE98Ffg1diH+MBy9DUgpDZ95Gkg/FK5kR8IOFK4F280vxDM9jSL9kzEF8+eTEerFxqZpXGCDQrZvaIpG8CQ/D1GDrhgz5n4OsmvJb0Vkg6El8LYiBeEU9Jz9CDtYOEUfh6BSfx+XiFAcDsNIjwWLwMrsG7d+7GW29erdL+lyUdAAwFjgHOxMt0Fl5GTyfVyXjrTa9kh/C1Hq7C//6CYJ3RmrOugiAIgiAInBiTEARBEARBKREkBEEQBEFQSgQJQRAEQRCUEkFCEARBEASlRJAQBEEQBEEpESQEQRAEQVBKBAlBEARBEJQSQUIQBEEQBKVEkBAEQRAEQSkRJARBEARBUMr/AV6shMyfUNQ7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "270 features required for 0.90 of cumulative importance\n"
     ]
    }
   ],
   "source": [
    "norm_feature_importances = plot_feature_importances(feature_importances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "f9db2a2ad8865d5626dca94ccf15fedc5549e9e1",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 308 features with 0.0 importance\n"
     ]
    },
    {
     "data": {
      "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>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>previous_loans_NAME_GOODS_CATEGORY_Insurance_c...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>previous_loans_PRODUCT_COMBINATION_Cash Street...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>previous_loans_NAME_CONTRACT_STATUS_Unused off...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>previous_loans_NAME_CASH_LOAN_PURPOSE_Car repa...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>EMERGENCYSTATE_MODE_Yes</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               feature  importance\n",
       "593  previous_loans_NAME_GOODS_CATEGORY_Insurance_c...         0.0\n",
       "261  previous_loans_PRODUCT_COMBINATION_Cash Street...         0.0\n",
       "262  previous_loans_NAME_CONTRACT_STATUS_Unused off...         0.0\n",
       "267  previous_loans_NAME_CASH_LOAN_PURPOSE_Car repa...         0.0\n",
       "843                            EMERGENCYSTATE_MODE_Yes         0.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the features with zero importance\n",
    "zero_features = list(feature_importances[feature_importances['importance'] == 0.0]['feature'])\n",
    "print('There are %d features with 0.0 importance' % len(zero_features))\n",
    "feature_importances.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "ba3df39a57a398f799ae1e4552083ce733b71a5e"
   },
   "source": [
    "Well, it also looks like many of the features we made have literally 0 importance. For the gradient boosting machine, features with 0 importance are not used at all to make any splits. Therefore, we can remove these features from the model with no effect on performance (except for faster training). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "e3849b51615d52eee53e3c919c17c6066ae29397",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training shape:  (307511, 536)\n",
      "Testing shape:  (48744, 536)\n"
     ]
    }
   ],
   "source": [
    "train = train.drop(columns = zero_features)\n",
    "test = test.drop(columns = zero_features)\n",
    "\n",
    "print('Training shape: ', train.shape)\n",
    "print('Testing shape: ', test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c67926fdcb81d3f438510f06cadbba1edbc70879"
   },
   "source": [
    "At this point, we can re-run the model to see if it identifies any more features with zero importance. In a way, we are implementing our own form of recursive feature elimination. Since we are repeating work, we should probably put the zero feature importance identification code in a function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "d09ec2ad863688243bc95c51aa43a559f56ad4fb",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def identify_zero_importance_features(train, train_labels, iterations = 2):\n",
    "    \"\"\"\n",
    "    Identify zero importance features in a training dataset based on the \n",
    "    feature importances from a gradient boosting model. \n",
    "    \n",
    "    Parameters\n",
    "    --------\n",
    "    train : dataframe\n",
    "        Training features\n",
    "        \n",
    "    train_labels : np.array\n",
    "        Labels for training data\n",
    "        \n",
    "    iterations : integer, default = 2\n",
    "        Number of cross validation splits to use for determining feature importances\n",
    "    \"\"\"\n",
    "    \n",
    "    # Initialize an empty array to hold feature importances\n",
    "    feature_importances = np.zeros(train.shape[1])\n",
    "\n",
    "    # Create the model with several hyperparameters\n",
    "    model = lgb.LGBMClassifier(objective='binary', boosting_type = 'goss', n_estimators = 10000, class_weight = 'balanced')\n",
    "    \n",
    "    # Fit the model multiple times to avoid overfitting\n",
    "    for i in range(iterations):\n",
    "\n",
    "        # Split into training and validation set\n",
    "        train_features, valid_features, train_y, valid_y = train_test_split(train, train_labels, test_size = 0.25, random_state = i)\n",
    "\n",
    "        # Train using early stopping\n",
    "        model.fit(train_features, train_y, early_stopping_rounds=100, eval_set = [(valid_features, valid_y)], \n",
    "                  eval_metric = 'auc', verbose = 200)\n",
    "\n",
    "        # Record the feature importances\n",
    "        feature_importances += model.feature_importances_ / iterations\n",
    "    \n",
    "    feature_importances = pd.DataFrame({'feature': list(train.columns), 'importance': feature_importances}).sort_values('importance', ascending = False)\n",
    "    \n",
    "    # Find the features with zero importance\n",
    "    zero_features = list(feature_importances[feature_importances['importance'] == 0.0]['feature'])\n",
    "    print('\\nThere are %d features with 0.0 importance' % len(zero_features))\n",
    "    \n",
    "    return zero_features, feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "f69eede0e8e04f1f4c850e5c102dcdebcdb107e7",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid_0's auc: 0.781395\n",
      "Early stopping, best iteration is:\n",
      "[151]\tvalid_0's auc: 0.782591\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid_0's auc: 0.782666\n",
      "Early stopping, best iteration is:\n",
      "[147]\tvalid_0's auc: 0.783545\n",
      "\n",
      "There are 0 features with 0.0 importance\n"
     ]
    }
   ],
   "source": [
    "second_round_zero_features, feature_importances = identify_zero_importance_features(train, train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c877ed509374d9494c508a8b920e92c926453f84"
   },
   "source": [
    "There are now no 0 importance features left (I guess we should have expected this). If we want to remove more features, we will have to start with features that have a non-zero importance. One way we could do this is by retaining enough features to account for a threshold percentage of importance, such as 95%. At this point, let's keep enough features to account for 95% of the importance. Again, this is an arbitrary decision! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "bb6dd17a7417f3b67017e7e8e18c09725df6e434",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "343 features required for 0.95 of cumulative importance\n"
     ]
    }
   ],
   "source": [
    "norm_feature_importances = plot_feature_importances(feature_importances, threshold = 0.95)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1cfc6829b62a44efcc7aeba227db4b293b76bfd5"
   },
   "source": [
    "We can keep only the features needed for 95% importance. This step seems to me to have the greatest chance of harming the model's learning ability, so rather than changing the original dataset, we will make smaller copies. Then, we can test both versions of the data to see if the extra feature removal step is worthwhile. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "55a534e48b21d4038413a11ff3aad5e025bba5bd",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Threshold for cumulative importance\n",
    "threshold = 0.95\n",
    "\n",
    "# Extract the features to keep\n",
    "features_to_keep = list(norm_feature_importances[norm_feature_importances['cumulative_importance'] < threshold]['feature'])\n",
    "\n",
    "# Create new datasets with smaller features\n",
    "train_small = train[features_to_keep]\n",
    "test_small = test[features_to_keep]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "b004fb636fee6ad313296c185273a5c0058afc78",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_small['TARGET'] = train_labels\n",
    "train_small['SK_ID_CURR'] = train_ids\n",
    "test_small['SK_ID_CURR'] = test_ids\n",
    "\n",
    "train_small.to_csv('m_train_small.csv', index = False)\n",
    "test_small.to_csv('m_test_small.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b4f2c70170ca1ec372e9646edecac79bba561cd6"
   },
   "source": [
    "# Test New Featuresets\n",
    "\n",
    "The last step of feature removal we did seems like it may have the potential to hurt the model the most. Therefore we want to test the effect of this removal. To do that, we can use a standard model and change the features. \n",
    "\n",
    "We will use a fairly standard LightGBM model, similar to the one we used for feature selection. The main difference is this model uses five-fold cross validation for training and we  use it to make predictions. There's a lot of code here, but that's because I included documentation and a few extras (such as feature importances) that aren't strictly necessary. For now, understanding the entire model isn't critical, just know that we are using the same model with two different datasets to see which one performs the best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "12cf6a94d72b87b20bcc2cb6217471aaa50ee016",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Imports used by model\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "import gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "cf8d66553230c1a535438f3a453ca3a05da7bc29",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model(features, test_features, encoding = 'ohe', n_folds = 5):\n",
    "    \n",
    "    \"\"\"Train and test a light gradient boosting model using\n",
    "    cross validation. \n",
    "    \n",
    "    Parameters\n",
    "    --------\n",
    "        features (pd.DataFrame): \n",
    "            dataframe of training features to use \n",
    "            for training a model. Must include the TARGET column.\n",
    "        test_features (pd.DataFrame): \n",
    "            dataframe of testing features to use\n",
    "            for making predictions with the model. \n",
    "        encoding (str, default = 'ohe'): \n",
    "            method for encoding categorical variables. Either 'ohe' for one-hot encoding or 'le' for integer label encoding\n",
    "            n_folds (int, default = 5): number of folds to use for cross validation\n",
    "        \n",
    "    Return\n",
    "    --------\n",
    "        submission (pd.DataFrame): \n",
    "            dataframe with `SK_ID_CURR` and `TARGET` probabilities\n",
    "            predicted by the model.\n",
    "        feature_importances (pd.DataFrame): \n",
    "            dataframe with the feature importances from the model.\n",
    "        valid_metrics (pd.DataFrame): \n",
    "            dataframe with training and validation metrics (ROC AUC) for each fold and overall.\n",
    "        \n",
    "    \"\"\"\n",
    "    \n",
    "    # Extract the ids\n",
    "    train_ids = features['SK_ID_CURR']\n",
    "    test_ids = test_features['SK_ID_CURR']\n",
    "    \n",
    "    # Extract the labels for training\n",
    "    labels = features['TARGET']\n",
    "    \n",
    "    # Remove the ids and target\n",
    "    features = features.drop(columns = ['SK_ID_CURR', 'TARGET'])\n",
    "    test_features = test_features.drop(columns = ['SK_ID_CURR'])\n",
    "    \n",
    "    \n",
    "    # One Hot Encoding\n",
    "    if encoding == 'ohe':\n",
    "        features = pd.get_dummies(features)\n",
    "        test_features = pd.get_dummies(test_features)\n",
    "        \n",
    "        # Align the dataframes by the columns\n",
    "        features, test_features = features.align(test_features, join = 'inner', axis = 1)\n",
    "        \n",
    "        # No categorical indices to record\n",
    "        cat_indices = 'auto'\n",
    "    \n",
    "    # Integer label encoding\n",
    "    elif encoding == 'le':\n",
    "        \n",
    "        # Create a label encoder\n",
    "        label_encoder = LabelEncoder()\n",
    "        \n",
    "        # List for storing categorical indices\n",
    "        cat_indices = []\n",
    "        \n",
    "        # Iterate through each column\n",
    "        for i, col in enumerate(features):\n",
    "            if features[col].dtype == 'object':\n",
    "                # Map the categorical features to integers\n",
    "                features[col] = label_encoder.fit_transform(np.array(features[col].astype(str)).reshape((-1,)))\n",
    "                test_features[col] = label_encoder.transform(np.array(test_features[col].astype(str)).reshape((-1,)))\n",
    "\n",
    "                # Record the categorical indices\n",
    "                cat_indices.append(i)\n",
    "    \n",
    "    # Catch error if label encoding scheme is not valid\n",
    "    else:\n",
    "        raise ValueError(\"Encoding must be either 'ohe' or 'le'\")\n",
    "        \n",
    "    print('Training Data Shape: ', features.shape)\n",
    "    print('Testing Data Shape: ', test_features.shape)\n",
    "    \n",
    "    # Extract feature names\n",
    "    feature_names = list(features.columns)\n",
    "    \n",
    "    # Convert to np arrays\n",
    "    features = np.array(features)\n",
    "    test_features = np.array(test_features)\n",
    "    \n",
    "    # Create the kfold object\n",
    "    k_fold = KFold(n_splits = n_folds, shuffle = False, random_state = 50)\n",
    "    \n",
    "    # Empty array for feature importances\n",
    "    feature_importance_values = np.zeros(len(feature_names))\n",
    "    \n",
    "    # Empty array for test predictions\n",
    "    test_predictions = np.zeros(test_features.shape[0])\n",
    "    \n",
    "    # Empty array for out of fold validation predictions\n",
    "    out_of_fold = np.zeros(features.shape[0])\n",
    "    \n",
    "    # Lists for recording validation and training scores\n",
    "    valid_scores = []\n",
    "    train_scores = []\n",
    "    \n",
    "    # Iterate through each fold\n",
    "    for train_indices, valid_indices in k_fold.split(features):\n",
    "        \n",
    "        # Training data for the fold\n",
    "        train_features, train_labels = features[train_indices], labels[train_indices]\n",
    "        # Validation data for the fold\n",
    "        valid_features, valid_labels = features[valid_indices], labels[valid_indices]\n",
    "        \n",
    "        # Create the model\n",
    "        model = lgb.LGBMClassifier(n_estimators=10000, objective = 'binary', boosting_type='goss',\n",
    "                                   class_weight = 'balanced', learning_rate = 0.05, \n",
    "                                   reg_alpha = 0.1, reg_lambda = 0.1, n_jobs = -1, random_state = 50)\n",
    "        \n",
    "        # Train the model\n",
    "        model.fit(train_features, train_labels, eval_metric = 'auc',\n",
    "                  eval_set = [(valid_features, valid_labels), (train_features, train_labels)],\n",
    "                  eval_names = ['valid', 'train'], categorical_feature = cat_indices,\n",
    "                  early_stopping_rounds = 100, verbose = 200)\n",
    "        \n",
    "        # Record the best iteration\n",
    "        best_iteration = model.best_iteration_\n",
    "        \n",
    "        # Record the feature importances\n",
    "        feature_importance_values += model.feature_importances_ / k_fold.n_splits\n",
    "        \n",
    "        # Make predictions\n",
    "        test_predictions += model.predict_proba(test_features, num_iteration = best_iteration)[:, 1] / k_fold.n_splits\n",
    "        \n",
    "        # Record the out of fold predictions\n",
    "        out_of_fold[valid_indices] = model.predict_proba(valid_features, num_iteration = best_iteration)[:, 1]\n",
    "        \n",
    "        # Record the best score\n",
    "        valid_score = model.best_score_['valid']['auc']\n",
    "        train_score = model.best_score_['train']['auc']\n",
    "        \n",
    "        valid_scores.append(valid_score)\n",
    "        train_scores.append(train_score)\n",
    "        \n",
    "        # Clean up memory\n",
    "        gc.enable()\n",
    "        del model, train_features, valid_features\n",
    "        gc.collect()\n",
    "        \n",
    "    # Make the submission dataframe\n",
    "    submission = pd.DataFrame({'SK_ID_CURR': test_ids, 'TARGET': test_predictions})\n",
    "    \n",
    "    # Make the feature importance dataframe\n",
    "    feature_importances = pd.DataFrame({'feature': feature_names, 'importance': feature_importance_values})\n",
    "    \n",
    "    # Overall validation score\n",
    "    valid_auc = roc_auc_score(labels, out_of_fold)\n",
    "    \n",
    "    # Add the overall scores to the metrics\n",
    "    valid_scores.append(valid_auc)\n",
    "    train_scores.append(np.mean(train_scores))\n",
    "    \n",
    "    # Needed for creating dataframe of validation scores\n",
    "    fold_names = list(range(n_folds))\n",
    "    fold_names.append('overall')\n",
    "    \n",
    "    # Dataframe of validation scores\n",
    "    metrics = pd.DataFrame({'fold': fold_names,\n",
    "                            'train': train_scores,\n",
    "                            'valid': valid_scores}) \n",
    "    \n",
    "    return submission, feature_importances, metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e116f579927c9dbb930ca2cf313e1a42fe0e61c5"
   },
   "source": [
    "### Test \"Full\" Dataset\n",
    "\n",
    "This is the expanded dataset. To recap the process to make this dataset we:\n",
    "\n",
    "* Removed collinear features as measured by the correlation coefficient greater than 0.9\n",
    "* Removed any columns with greater than 80% missing values in the train or test set\n",
    "* Removed all features with non-zero feature importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "3560addd3c31f5c21235062257bf022e31a872d3",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Shape:  (307511, 536)\n",
      "Testing Data Shape:  (48744, 536)\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.781029\ttrain's auc: 0.829007\n",
      "Early stopping, best iteration is:\n",
      "[292]\tvalid's auc: 0.782706\ttrain's auc: 0.847775\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.784264\ttrain's auc: 0.828528\n",
      "[400]\tvalid's auc: 0.785755\ttrain's auc: 0.866165\n",
      "Early stopping, best iteration is:\n",
      "[423]\tvalid's auc: 0.785998\ttrain's auc: 0.870008\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.776897\ttrain's auc: 0.829924\n",
      "[400]\tvalid's auc: 0.779461\ttrain's auc: 0.867629\n",
      "Early stopping, best iteration is:\n",
      "[467]\tvalid's auc: 0.779866\ttrain's auc: 0.877945\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.783129\ttrain's auc: 0.828608\n",
      "[400]\tvalid's auc: 0.784313\ttrain's auc: 0.866548\n",
      "Early stopping, best iteration is:\n",
      "[353]\tvalid's auc: 0.784522\ttrain's auc: 0.858774\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.78412\ttrain's auc: 0.828442\n",
      "[400]\tvalid's auc: 0.78643\ttrain's auc: 0.866312\n",
      "Early stopping, best iteration is:\n",
      "[406]\tvalid's auc: 0.786593\ttrain's auc: 0.867249\n"
     ]
    }
   ],
   "source": [
    "train['TARGET'] = train_labels\n",
    "train['SK_ID_CURR'] = train_ids\n",
    "test['SK_ID_CURR'] = test_ids\n",
    "\n",
    "submission, feature_importances, metrics = model(train, test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "400cf0845130330e64dd59705a16488d077f4d27",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>train</th>\n",
       "      <th>valid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.847775</td>\n",
       "      <td>0.782706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.870008</td>\n",
       "      <td>0.785998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.877945</td>\n",
       "      <td>0.779866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.858774</td>\n",
       "      <td>0.784522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.867249</td>\n",
       "      <td>0.786593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>overall</td>\n",
       "      <td>0.864350</td>\n",
       "      <td>0.783799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fold     train     valid\n",
       "0        0  0.847775  0.782706\n",
       "1        1  0.870008  0.785998\n",
       "2        2  0.877945  0.779866\n",
       "3        3  0.858774  0.784522\n",
       "4        4  0.867249  0.786593\n",
       "5  overall  0.864350  0.783799"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_uuid": "bd41dd2fc42440ce495fd42e4819b00e5658b656",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission.to_csv('selected_features_submission.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "fe3efd3457d02bf99dfa033d01f870487f3314c1"
   },
   "source": [
    "The full features after feature selection score __0.783__ when submitted to the public leaderboard. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b5e46e877e2be014cb9a821de7c93efb6d90f036"
   },
   "source": [
    "### Test \"Small\" Dataset\n",
    "\n",
    "The small dataset requires one additional step over the ful l dataset:\n",
    "\n",
    "* Keep only features needed to reach 95% cumulative importance in the gradient boosting machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_uuid": "a3a34bde5e4be879d334fff74a246c6c11b679e6",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Shape:  (307511, 342)\n",
      "Testing Data Shape:  (48744, 342)\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.782346\ttrain's auc: 0.828859\n",
      "[400]\tvalid's auc: 0.783798\ttrain's auc: 0.866426\n",
      "Early stopping, best iteration is:\n",
      "[379]\tvalid's auc: 0.784002\ttrain's auc: 0.863014\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.784287\ttrain's auc: 0.828735\n",
      "[400]\tvalid's auc: 0.785738\ttrain's auc: 0.86653\n",
      "Early stopping, best iteration is:\n",
      "[330]\tvalid's auc: 0.786148\ttrain's auc: 0.854718\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.777313\ttrain's auc: 0.829525\n",
      "[400]\tvalid's auc: 0.780317\ttrain's auc: 0.867304\n",
      "[600]\tvalid's auc: 0.780608\ttrain's auc: 0.89533\n",
      "Early stopping, best iteration is:\n",
      "[548]\tvalid's auc: 0.780609\ttrain's auc: 0.888595\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.782972\ttrain's auc: 0.828592\n",
      "[400]\tvalid's auc: 0.784681\ttrain's auc: 0.865942\n",
      "Early stopping, best iteration is:\n",
      "[351]\tvalid's auc: 0.784868\ttrain's auc: 0.857985\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "[200]\tvalid's auc: 0.783889\ttrain's auc: 0.828017\n",
      "[400]\tvalid's auc: 0.785853\ttrain's auc: 0.865712\n",
      "Early stopping, best iteration is:\n",
      "[404]\tvalid's auc: 0.785956\ttrain's auc: 0.866399\n"
     ]
    }
   ],
   "source": [
    "submission_small, feature_importances_small, metrics_small = model(train_small, test_small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_uuid": "beed5d5aed49e7a61db907778b8b717729697d76",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>fold</th>\n",
       "      <th>train</th>\n",
       "      <th>valid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.863014</td>\n",
       "      <td>0.784002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.854718</td>\n",
       "      <td>0.786148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.888595</td>\n",
       "      <td>0.780609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.857985</td>\n",
       "      <td>0.784868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.866399</td>\n",
       "      <td>0.785956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>overall</td>\n",
       "      <td>0.866142</td>\n",
       "      <td>0.784168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fold     train     valid\n",
       "0        0  0.863014  0.784002\n",
       "1        1  0.854718  0.786148\n",
       "2        2  0.888595  0.780609\n",
       "3        3  0.857985  0.784868\n",
       "4        4  0.866399  0.785956\n",
       "5  overall  0.866142  0.784168"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_uuid": "edb183a37eaeff22deb3f5afb04b6da480997e63",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission_small.to_csv('selected_features_small_submission.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "510e06476d8106c2fda7eb857ff55e5caf57ccfe"
   },
   "source": [
    "The smaller featureset scores __0.782__ when submitted to the public leaderboard."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "5055adbaffab2f8ce2ee2e062995f19a0e15ceb7"
   },
   "source": [
    "# Other Options for Dimensionality Reduction\n",
    "\n",
    "We only covered a small portion of the techniques used for feature selection/dimensionality reduction. There are many other methods such as:\n",
    "\n",
    "* PCA: Principle Components Analysis (PCA)\n",
    "* ICA: Independent Components Analysis (ICA)\n",
    "* Manifold learning: [also called non-linear dimensionality reduction](https://stats.stackexchange.com/questions/247907/what-is-the-difference-between-manifold-learning-and-non-linear-dimensionality-r)\n",
    "\n",
    "PCA is a great method for reducing the number of features provided that you do not care about model interpretability. It projects the original set of features onto a lower dimension, in the process, eliminating any physical representation behind the features. Here's a pretty thorough introduction to the math for anyone interested. PCA also assumes that the data is Gaussian distributed, which may not be the case, especially when dealing with real-world human generated data. \n",
    "\n",
    "ICA representations also obscure any physical meaning behind the variables and presevere the most \"independent\" dimensions of the data (which is different than the dimensions with the most variance). \n",
    "\n",
    "Manifold learning is more often used for low-dimensional visualizations (such as with T-SNE or LLE) rather than for dimensionality reduction for a classifier. These methods are heavily dependent on several hyperparameters and are not deterministic which means that there is no way to apply it to new data (in other words you cannot `fit` it to the training data and then separately `transform` the testing data). The learned representation of a dataset will change every time you apply manifold learning so it is not generally a stable method for feature selection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "be760a6dd27ed9a4bcc3040953ffd5b5a4b87877"
   },
   "source": [
    "## PCA Example\n",
    "\n",
    "We can go through a quick example to show how PCA is implemented. Without going through too many details, PCA finds a new set of axis (the principal components) that maximize the amount of variance captured in the data. The original data is then projected down onto these principal components. The idea is that we can use fewer principal components than the original number of features while still capturing most of the variance. PCA is implemented in Scikit-Learn in the same way as preprocessing methods. We can either select the number of new components, or the fraction of variance we want explained in the data. If we pass in no argument, the number of principal components will be the same as the number of original features. We can then use the `variance_explained_ratio_` to determine the number of components needed for different threshold of variance retained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_uuid": "abd2c85ee9efd0c28afe9a27fb4cc283ce987f18",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# Make sure to drop the ids and target\n",
    "train = train.drop(columns = ['SK_ID_CURR', 'TARGET'])\n",
    "test = test.drop(columns = ['SK_ID_CURR'])\n",
    "\n",
    "# Make a pipeline with imputation and pca\n",
    "pipeline = Pipeline(steps = [('imputer', Imputer(strategy = 'median')),\n",
    "             ('pca', PCA())])\n",
    "\n",
    "# Fit and transform on the training data\n",
    "train_pca = pipeline.fit_transform(train)\n",
    "\n",
    "# transform the testing data\n",
    "test_pca = pipeline.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_uuid": "5fb2b6315382ab104c8d9dcef031931fd6cbdb9d",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract the pca object\n",
    "pca = pipeline.named_steps['pca']\n",
    "\n",
    "# Plot the cumulative variance explained\n",
    "\n",
    "plt.figure(figsize = (10, 8))\n",
    "plt.plot(list(range(train.shape[1])), np.cumsum(pca.explained_variance_ratio_), 'r-')\n",
    "plt.xlabel('Number of PC'); plt.ylabel('Cumulative Variance Explained');\n",
    "plt.title('Cumulative Variance Explained with PCA');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We only need a few prinicipal components to account for the majority of variance in the data. We can use the first two principal components to visualize the entire dataset. We will color the datapoints by the value of the target to see if using two principal components clearly separates the classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 796.175x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dataframe of pca results\n",
    "pca_df = pd.DataFrame({'pc_1': train_pca[:, 0], 'pc_2': train_pca[:, 1], 'target': train_labels})\n",
    "\n",
    "# Plot pc2 vs pc1 colored by target\n",
    "sns.lmplot('pc_1', 'pc_2', data = pca_df, hue = 'target', fit_reg=False, size = 10)\n",
    "plt.title('PC2 vs PC1 by Target');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 principal components account for 98.7933% of the variance.\n"
     ]
    }
   ],
   "source": [
    "print('2 principal components account for {:.4f}% of the variance.'.format(100 * np.sum(pca.explained_variance_ratio_[:2])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though we have accounted for most of the variance, that does not mean the pca decomposition makes the problem of identifying loans repaid vs not repaid any easier. PCA does not consider the value of the label when projecting the features to a lower dimension. Feel free to try a classifier on top of this data, but when I have done so, I noticed that it was not very accurate. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "054116a7cd95f8baeecf4ada05a21eddaaaad89e"
   },
   "source": [
    "# Conclusions\n",
    "\n",
    "In this notebook we employed a number of feature selection methods. These methods are necessary to reduce the number of features to increase model interpretability, decrease model runtime, and increase generalization performance on the test set. The methods of feature selection we used are:\n",
    "\n",
    "1. Remove highly collinear variables as measured by a correlation coefficient greater than 0.9\n",
    "2. Remove any columns with more than 75% missing values.\n",
    "3. Remove any features with a zero importance as determined by a gradient boosting machine.\n",
    "4. (Optional) keep only enough features to account for 95% of the importance in the gradient boosting machine.\n",
    "\n",
    "Using the first three methods, we reduced the number of features from __1465__ to __536__ with a 5-fold cv AUC ROC score of 0.7838 and a public leaderboard score of 0.783.\n",
    "\n",
    "After applying the fourth method, we end up with 342 features with a 5-fold cv AUC SCORE of 0.7482 and a public leaderboard score of 0.782. \n",
    "\n",
    "Going forward, we might actually want to add _more_ features except this time, instead of naively applying aggregations, think about what features are actually important from a domain point of view. There are a number of kernels that have created useful features that we can add to our set here to improve performance. The process of feature engineering - feature selection is iterative, and it may require several more passes before we get it completely right! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "07501aa5454fd60c183efb98c733b953ba6ae10d",
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}