ml-finance-python

01_gbm_baseline.ipynb

(861776B)

 {
  "cells": [
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Adaptive and Gradient Boosting"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "In this notebook, we demonstrate the use of AdaBoost and gradient boosting, incuding several state-of-the-art implementations of this very powerful and flexible algorithm that greatly speed up training. \n",
     "\n",
     "We use the stock return dataset with a few engineered factors created in [Chapter 4 on Alpha Factor Research](../04_alpha_factor_research) in the notebook [feature_engineering](../04_alpha_factor_research/00_data/feature_engineering.ipynb)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Imports and Settings"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:17.011966Z",
      "start_time": "2018-10-24T13:41:17.008175Z"
     }
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "\n",
     "import warnings\n",
     "import os\n",
     "from datetime import datetime\n",
     "from pathlib import Path\n",
     "import quandl\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
     "from mpl_toolkits.mplot3d import Axes3D\n",
     "import seaborn as sns\n",
     "import graphviz\n",
     "\n",
     "from xgboost import XGBClassifier, XGBRegressor\n",
     "from lightgbm import LGBMClassifier, LGBMRegressor\n",
     "from catboost import CatBoostClassifier, CatBoostRegressor\n",
     "from sklearn.model_selection import cross_val_score, cross_validate\n",
     "from sklearn.dummy import DummyClassifier\n",
     "from sklearn.tree import DecisionTreeClassifier\n",
     "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
     "from sklearn.ensemble.partial_dependence import partial_dependence, plot_partial_dependence\n",
     "from sklearn.externals import joblib\n",
     "from sklearn.metrics import roc_auc_score, roc_curve, mean_squared_error, precision_recall_curve"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "results_path = Path('results')\n",
     "if not results_path.exists():\n",
     "    results_path.mkdir(exist_ok=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:17.029896Z",
      "start_time": "2018-10-24T13:41:17.013276Z"
     }
    },
    "outputs": [],
    "source": [
     "warnings.filterwarnings('ignore')\n",
     "sns.set_style(\"whitegrid\")\n",
     "idx = pd.IndexSlice\n",
     "np.random.seed(42)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Prepare Data"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Get source"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We use the `engineered_features` dataset created in [Chapter 4, Alpha Factor Research](../04_alpha_factor_research)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Set data store location:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "DATA_STORE = '../data/assets.h5'"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:17.040793Z",
      "start_time": "2018-10-24T13:41:17.031105Z"
     }
    },
    "outputs": [],
    "source": [
     "def get_data(start='2000', end='2018', task='classification', holding_period=1, dropna=False):\n",
     "    \n",
     "    idx = pd.IndexSlice\n",
     "    target = f'target_{holding_period}m'\n",
     "    with pd.HDFStore(DATA_STORE) as store:\n",
     "        df = store['engineered_features']\n",
     "\n",
     "    if start is not None and end is not None:\n",
     "        df = df.loc[idx[:, start: end], :]\n",
     "    if dropna:\n",
     "        df = df.dropna()\n",
     "        \n",
     "    y = (df[target]>0).astype(int)\n",
     "    X = df.drop([c for c in df.columns if c.startswith('target')], axis=1)\n",
     "    return y, X"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Factorize Categories"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Define columns with categorical data:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:17.051508Z",
      "start_time": "2018-10-24T13:41:17.042435Z"
     }
    },
    "outputs": [],
    "source": [
     "cat_cols = ['year', 'month', 'age', 'msize', 'sector']"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Integer-encode categorical columns:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:17.059194Z",
      "start_time": "2018-10-24T13:41:17.053062Z"
     }
    },
    "outputs": [],
    "source": [
     "def factorize_cats(df, cats=['sector']):\n",
     "    cat_cols = ['year', 'month', 'age', 'msize'] + cats\n",
     "    for cat in cats:\n",
     "        df[cat] = pd.factorize(df[cat])[0]\n",
     "    df.loc[:, cat_cols] = df.loc[:, cat_cols].fillna(-1)\n",
     "    return df"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### One-Hot Encoding"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Create dummy variables from categorical columns if needed:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:17.067090Z",
      "start_time": "2018-10-24T13:41:17.060174Z"
     }
    },
    "outputs": [],
    "source": [
     "def get_one_hot_data(df, cols=cat_cols[:-1]):\n",
     "    df = pd.get_dummies(df,\n",
     "                        columns=cols + ['sector'],\n",
     "                        prefix=cols + [''],\n",
     "                        prefix_sep=['_'] * len(cols) + [''])\n",
     "    return df.rename(columns={c: c.replace('.0', '') for c in df.columns})"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Get Holdout Set"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Create holdout test set to estimate generalization error after cross-validation:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:17.078223Z",
      "start_time": "2018-10-24T13:41:17.068011Z"
     }
    },
    "outputs": [],
    "source": [
     "def get_holdout_set(target, features, period=6):\n",
     "    idx = pd.IndexSlice\n",
     "    label = target.name\n",
     "    dates = np.sort(y.index.get_level_values('date').unique())\n",
     "    cv_start, cv_end = dates[0], dates[-period - 2]\n",
     "    holdout_start, holdout_end = dates[-period - 1], dates[-1]\n",
     "\n",
     "    df = features.join(target.to_frame())\n",
     "    train = df.loc[idx[:, cv_start: cv_end], :]\n",
     "    y_train, X_train = train[label], train.drop(label, axis=1)\n",
     "\n",
     "    test = df.loc[idx[:, holdout_start: holdout_end], :]\n",
     "    y_test, X_test = test[label], test.drop(label, axis=1)\n",
     "    return y_train, X_train, y_test, X_test"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Load Data"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The algorithms in this chapter use a dataset generated in [Chapter 4 on Alpha Factor Research](../04_alpha_factor_research) in the notebook [feature-engineering](../04_alpha_factor_research/00_data/feature_engineering.ipynb) that needs to be executed first."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:23.585278Z",
      "start_time": "2018-10-24T13:41:17.079339Z"
     }
    },
    "outputs": [],
    "source": [
     "y, features = get_data()\n",
     "X_dummies = get_one_hot_data(features)\n",
     "X_factors = factorize_cats(features)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:29.358596Z",
      "start_time": "2018-10-24T13:41:23.586375Z"
     }
    },
    "outputs": [],
    "source": [
     "y_clean, features_clean = get_data(dropna=True)\n",
     "X_dummies_clean = get_one_hot_data(features_clean)\n",
     "X_factors_clean = factorize_cats(features_clean)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Cross-Validation Setup"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Custom Time Series KFold Generator"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Custom Time Series KFold generator introduced in [Chapter 10](../10_decision_trees_random_forests) on [Decision Trees and Random Forests](../10_decision_trees_random_forests/01_decision_trees.ipynb#custom_kfold)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:29.363212Z",
      "start_time": "2018-10-24T13:41:29.359511Z"
     }
    },
    "outputs": [],
    "source": [
     "class OneStepTimeSeriesSplit:\n",
     "    \"\"\"Generates tuples of train_idx, test_idx pairs\n",
     "    Assumes the index contains a level labeled 'date'\"\"\"\n",
     "\n",
     "    def __init__(self, n_splits=3, test_period_length=1, shuffle=False):\n",
     "        self.n_splits = n_splits\n",
     "        self.test_period_length = test_period_length\n",
     "        self.shuffle = shuffle\n",
     "\n",
     "    @staticmethod\n",
     "    def chunks(l, n):\n",
     "        for i in range(0, len(l), n):\n",
     "            yield l[i:i + n]\n",
     "\n",
     "    def split(self, X, y=None, groups=None):\n",
     "        unique_dates = (X.index\n",
     "                        .get_level_values('date')\n",
     "                        .unique()\n",
     "                        .sort_values(ascending=False)\n",
     "                        [:self.n_splits*self.test_period_length])\n",
     "\n",
     "        dates = X.reset_index()[['date']]\n",
     "        for test_date in self.chunks(unique_dates, self.test_period_length):\n",
     "            train_idx = dates[dates.date < min(test_date)].index\n",
     "            test_idx = dates[dates.date.isin(test_date)].index\n",
     "            if self.shuffle:\n",
     "                np.random.shuffle(list(train_idx))\n",
     "            yield train_idx, test_idx\n",
     "\n",
     "    def get_n_splits(self, X, y, groups=None):\n",
     "        return self.n_splits"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:29.387167Z",
      "start_time": "2018-10-24T13:41:29.364226Z"
     }
    },
    "outputs": [],
    "source": [
     "cv = OneStepTimeSeriesSplit(n_splits=12, test_period_length=1, shuffle=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### CV Metrics"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Define some metrics for use with cross-validation:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:29.401821Z",
      "start_time": "2018-10-24T13:41:29.389751Z"
     }
    },
    "outputs": [],
    "source": [
     "metrics = {'balanced_accuracy': 'Accuracy' ,\n",
     "           'roc_auc': 'AUC',\n",
     "           'neg_log_loss': 'Log Loss',\n",
     "           'f1_weighted': 'F1',\n",
     "           'precision_weighted': 'Precision',\n",
     "           'recall_weighted': 'Recall'\n",
     "}"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Helper function that runs cross-validation for the various algorithms."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:29.413658Z",
      "start_time": "2018-10-24T13:41:29.402898Z"
     }
    },
    "outputs": [],
    "source": [
     "def run_cv(clf, X=X_dummies, y=y, metrics=metrics, cv=cv, fit_params=None):\n",
     "    return cross_validate(estimator=clf,\n",
     "                          X=X,\n",
     "                          y=y,\n",
     "                          scoring=list(metrics.keys()),\n",
     "                          cv=cv,\n",
     "                          return_train_score=True,\n",
     "                          n_jobs=-1,\n",
     "                          verbose=1,\n",
     "                          fit_params=fit_params)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### CV Result Handler Functions"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The following helper functions manipulate and plot the cross-validation results to produce the outputs below."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:29.424738Z",
      "start_time": "2018-10-24T13:41:29.415982Z"
     }
    },
    "outputs": [],
    "source": [
     "def stack_results(scores):\n",
     "    columns = pd.MultiIndex.from_tuples(\n",
     "        [tuple(m.split('_', 1)) for m in scores.keys()],\n",
     "        names=['Dataset', 'Metric'])\n",
     "    data = np.array(list(scores.values())).T\n",
     "    df = (pd.DataFrame(data=data,\n",
     "                       columns=columns)\n",
     "          .iloc[:, 2:])\n",
     "    results = pd.melt(df, value_name='Value')\n",
     "    results.Metric = results.Metric.apply(lambda x: metrics.get(x))\n",
     "    results.Dataset = results.Dataset.str.capitalize()\n",
     "    return results"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:29.436140Z",
      "start_time": "2018-10-24T13:41:29.426363Z"
     }
    },
    "outputs": [],
    "source": [
     "def plot_result(df, model=None):\n",
     "    m = list(metrics.values())\n",
     "    g = sns.catplot(x='Dataset', \n",
     "                    y='Value', \n",
     "                    hue='Dataset', \n",
     "                    col='Metric',\n",
     "                    data=df, \n",
     "                    col_order=m,\n",
     "                    order=['Train', 'Test'],\n",
     "                    kind=\"box\", \n",
     "                    col_wrap=3,\n",
     "                    sharey=False,\n",
     "                    height=4, aspect=1.2)\n",
     "    df = df.groupby(['Metric', 'Dataset']).Value.mean().unstack().loc[m]\n",
     "    for i, ax in enumerate(g.axes.flat):\n",
     "        s = f\"Train: {df.loc[m[i], 'Train']:>7.4f}\\nTest:  {df.loc[m[i], 'Test'] :>7.4f}\"\n",
     "        ax.text(0.05, 0.85, s, fontsize=10, transform=ax.transAxes, \n",
     "                bbox=dict(facecolor='white', edgecolor='grey', boxstyle='round,pad=0.5'))\n",
     "    g.fig.suptitle(model, fontsize=16)\n",
     "    g.fig.subplots_adjust(top=.9);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Baseline Classifier"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "`sklearn` provides the [DummyClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html) that makes predictions using simple rule and is useful as a simple baseline to compare with the other (real) classifiers we use below.\n",
     "\n",
     "The `stratified` rule generates predictions based on the training set’s class distribution, i.e. always predicts the most frequent class."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:29.447165Z",
      "start_time": "2018-10-24T13:41:29.437491Z"
     }
    },
    "outputs": [],
    "source": [
     "dummy_clf = DummyClassifier(strategy='stratified',\n",
     "                            random_state=42)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:33.225052Z",
      "start_time": "2018-10-24T13:41:29.448148Z"
     }
    },
    "outputs": [],
    "source": [
     "fname = 'results/dummy_cv_result.joblib'\n",
     "if not Path(fname).exists():\n",
     "    dummy_cv_result = run_cv(dummy_clf)\n",
     "    joblib.dump(dummy_cv_result, fname)\n",
     "else:\n",
     "    dummy_cv_result = joblib.load(fname)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Unsurprisingly, it produces results near the AUC threshold for arbitrary predictions of 0.5:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:33.240259Z",
      "start_time": "2018-10-24T13:41:33.226421Z"
     }
    },
    "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",
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        "    }\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>Dataset</th>\n",
        "      <th>Test</th>\n",
        "      <th>Train</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Metric</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AUC</th>\n",
        "      <td>0.494516</td>\n",
        "      <td>0.499862</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Accuracy</th>\n",
        "      <td>0.494516</td>\n",
        "      <td>0.499862</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>F1</th>\n",
        "      <td>0.501068</td>\n",
        "      <td>0.502171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Log Loss</th>\n",
        "      <td>-17.594264</td>\n",
        "      <td>-17.195080</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Precision</th>\n",
        "      <td>0.534767</td>\n",
        "      <td>0.502186</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Recall</th>\n",
        "      <td>0.490601</td>\n",
        "      <td>0.502157</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "Dataset         Test      Train\n",
        "Metric                         \n",
        "AUC         0.494516   0.499862\n",
        "Accuracy    0.494516   0.499862\n",
        "F1          0.501068   0.502171\n",
        "Log Loss  -17.594264 -17.195080\n",
        "Precision   0.534767   0.502186\n",
        "Recall      0.490601   0.502157"
       ]
      },
      "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "dummy_result = stack_results(dummy_cv_result)\n",
     "dummy_result.groupby(['Metric', 'Dataset']).Value.mean().unstack()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:33.946536Z",
      "start_time": "2018-10-24T13:41:33.241863Z"
     },
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 1036.8x576 with 6 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plot_result(dummy_result, model='Dummy Classifier')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## RandomForest"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "For comparison, we train a `RandomForestClassifier` as presented in [Chapter 10 on Decision Trees and Random Forests](../10_decision_trees_random_forests/02_random_forest.ipynb)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Configure"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T13:41:33.951360Z",
      "start_time": "2018-10-24T13:41:33.947953Z"
     }
    },
    "outputs": [],
    "source": [
     "rf_clf = RandomForestClassifier(n_estimators=200,             # will change from 10 to 100 in version 0.22 \n",
     "                                criterion='gini', \n",
     "                                max_depth=None, \n",
     "                                min_samples_split=2, \n",
     "                                min_samples_leaf=1, \n",
     "                                min_weight_fraction_leaf=0.0, \n",
     "                                max_features='auto',\n",
     "                                max_leaf_nodes=None, \n",
     "                                min_impurity_decrease=0.0, \n",
     "                                min_impurity_split=None, \n",
     "                                bootstrap=True, \n",
     "                                oob_score=True, \n",
     "                                n_jobs=-1,\n",
     "                                random_state=42, \n",
     "                                verbose=1)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Cross-validate"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:09:53.041686Z",
      "start_time": "2018-10-24T13:41:33.952323Z"
     }
    },
    "outputs": [],
    "source": [
     "fname = 'results/rf_cv_result.joblib'\n",
     "if not Path(fname).exists():\n",
     "    rf_cv_result = run_cv(rf_clf, y=y_clean, X=X_dummies_clean)\n",
     "    joblib.dump(rf_cv_result, fname)\n",
     "else:\n",
     "    rf_cv_result = joblib.load(fname)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Plot Results"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:09:53.059337Z",
      "start_time": "2018-10-24T14:09:53.043262Z"
     }
    },
    "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>Dataset</th>\n",
        "      <th>Test</th>\n",
        "      <th>Train</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Metric</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AUC</th>\n",
        "      <td>0.659692</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Accuracy</th>\n",
        "      <td>0.616270</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>F1</th>\n",
        "      <td>0.592029</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Log Loss</th>\n",
        "      <td>-0.611774</td>\n",
        "      <td>-0.169561</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Precision</th>\n",
        "      <td>0.665563</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Recall</th>\n",
        "      <td>0.602865</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "Dataset        Test     Train\n",
        "Metric                       \n",
        "AUC        0.659692  1.000000\n",
        "Accuracy   0.616270  1.000000\n",
        "F1         0.592029  1.000000\n",
        "Log Loss  -0.611774 -0.169561\n",
        "Precision  0.665563  1.000000\n",
        "Recall     0.602865  1.000000"
       ]
      },
      "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "rf_result = stack_results(rf_cv_result)\n",
     "rf_result.groupby(['Metric', 'Dataset']).Value.mean().unstack()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:09:53.758347Z",
      "start_time": "2018-10-24T14:09:53.061268Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 1036.8x576 with 6 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plot_result(rf_result, model='Random Forest')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## sklearn: AdaBoost"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "As part of its [ensemble module](https://scikit-learn.org/stable/modules/ensemble.html#adaboost), sklearn provides an [AdaBoostClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html) implementation that supports two or more classes. The code examples for this section are in the notebook gbm_baseline that compares the performance of various algorithms with a dummy classifier that always predicts the most frequent class."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Base Estimator"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We need to first define a base_estimator as a template for all ensemble members and then configure the ensemble itself. We'll use the default DecisionTreeClassifier with max_depth=1—that is, a stump with a single split. The complexity of the base_estimator is a key tuning parameter because it depends on the nature of the data. \n",
     "\n",
     "As demonstrated in the [previous chapter](../../10_decision_trees_random_forests), changes to `max_depth` should be combined with appropriate regularization constraints using adjustments to, for example, `min_samples_split`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:11:56.869863Z",
      "start_time": "2018-10-24T14:11:56.866657Z"
     }
    },
    "outputs": [],
    "source": [
     "base_estimator = DecisionTreeClassifier(criterion='gini', \n",
     "                                        splitter='best',\n",
     "                                        max_depth=1, \n",
     "                                        min_samples_split=2, \n",
     "                                        min_samples_leaf=20, \n",
     "                                        min_weight_fraction_leaf=0.0,\n",
     "                                        max_features=None, \n",
     "                                        random_state=None, \n",
     "                                        max_leaf_nodes=None, \n",
     "                                        min_impurity_decrease=0.0, \n",
     "                                        min_impurity_split=None, \n",
     "                                        class_weight=None, \n",
     "                                        presort=False)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### AdaBoost Configuration"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "In the second step, we'll design the ensemble. The n_estimators parameter controls the number of weak learners and the learning_rate determines the contribution of each weak learner, as shown in the following code. By default, weak learners are decision tree stumps:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:11:56.886698Z",
      "start_time": "2018-10-24T14:11:56.871208Z"
     }
    },
    "outputs": [],
    "source": [
     "ada_clf = AdaBoostClassifier(base_estimator=base_estimator,\n",
     "                             n_estimators=200,\n",
     "                             learning_rate=1.0,\n",
     "                             algorithm='SAMME.R',\n",
     "                             random_state=42)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The main tuning parameters that are responsible for good results are `n_estimators` and the base estimator complexity because the depth of the tree controls the extent of the interaction among the features. "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Cross-validate"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We will cross-validate the AdaBoost ensemble using a custom 12-fold rolling time-series split to predict 1 month ahead for the last 12 months in the sample, using all available prior data for training, as shown in the following code:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:27:13.002984Z",
      "start_time": "2018-10-24T14:11:56.888141Z"
     }
    },
    "outputs": [],
    "source": [
     "fname = 'results/ada_cv_result.joblib'\n",
     "if not Path(fname).exists():\n",
     "    ada_cv_result = run_cv(ada_clf, y=y_clean, X=X_dummies_clean)\n",
     "    joblib.dump(ada_cv_result, fname)\n",
     "else:\n",
     "    ada_cv_result = joblib.load(fname)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Plot Result"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:27:13.019271Z",
      "start_time": "2018-10-24T14:27:13.005126Z"
     }
    },
    "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>Dataset</th>\n",
        "      <th>Test</th>\n",
        "      <th>Train</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Metric</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AUC</th>\n",
        "      <td>0.668890</td>\n",
        "      <td>0.613425</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Accuracy</th>\n",
        "      <td>0.626251</td>\n",
        "      <td>0.574614</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>F1</th>\n",
        "      <td>0.590490</td>\n",
        "      <td>0.574779</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Log Loss</th>\n",
        "      <td>-0.692288</td>\n",
        "      <td>-0.692875</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Precision</th>\n",
        "      <td>0.707423</td>\n",
        "      <td>0.585429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Recall</th>\n",
        "      <td>0.619776</td>\n",
        "      <td>0.587690</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "Dataset        Test     Train\n",
        "Metric                       \n",
        "AUC        0.668890  0.613425\n",
        "Accuracy   0.626251  0.574614\n",
        "F1         0.590490  0.574779\n",
        "Log Loss  -0.692288 -0.692875\n",
        "Precision  0.707423  0.585429\n",
        "Recall     0.619776  0.587690"
       ]
      },
      "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "ada_result = stack_results(ada_cv_result)\n",
     "ada_result.groupby(['Metric', 'Dataset']).Value.mean().unstack()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:27:13.738655Z",
      "start_time": "2018-10-24T14:27:13.020788Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 1036.8x576 with 6 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plot_result(ada_result, model='AdaBoost')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## GradientBoostingClassifier"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The ensemble module of sklearn contains an implementation of gradient boosting trees for regression and classification, both binary and multiclass. "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Configure"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The following [GradientBoostingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html) initialization code illustrates the key tuning parameters that we previously introduced, in addition to those that we are familiar with from looking at standalone decision tree models."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The available loss functions include the exponential loss that leads to the AdaBoost algorithm and the deviance that corresponds to the logistic regression for probabilistic outputs. \n",
     "\n",
     "The `friedman_mse` node quality measure is a variation on the mean squared error that includes an improvement score (see GitHub references for links to original papers), as shown in the following code:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:27:13.744803Z",
      "start_time": "2018-10-24T14:27:13.739880Z"
     }
    },
    "outputs": [],
    "source": [
     "gb_clf = GradientBoostingClassifier(loss='deviance',                # deviance = logistic reg; exponential: AdaBoost\n",
     "                                    learning_rate=0.1,              # shrinks the contribution of each tree\n",
     "                                    n_estimators=100,               # number of boosting stages\n",
     "                                    subsample=1.0,                  # fraction of samples used t fit base learners\n",
     "                                    criterion='friedman_mse',       # measures the quality of a split\n",
     "                                    min_samples_split=2,            \n",
     "                                    min_samples_leaf=1, \n",
     "                                    min_weight_fraction_leaf=0.0,   # min. fraction of sum of weights\n",
     "                                    max_depth=3,                    # opt value depends on interaction\n",
     "                                    min_impurity_decrease=0.0, \n",
     "                                    min_impurity_split=None, \n",
     "                                    init=None, \n",
     "                                    random_state=None, \n",
     "                                    max_features=None, \n",
     "                                    verbose=0, \n",
     "                                    max_leaf_nodes=None, \n",
     "                                    warm_start=False, \n",
     "                                    presort='auto', \n",
     "                                    validation_fraction=0.1, \n",
     "                                    n_iter_no_change=None, \n",
     "                                    tol=0.0001)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Cross-validate"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:40:37.193171Z",
      "start_time": "2018-10-24T14:27:13.745804Z"
     }
    },
    "outputs": [],
    "source": [
     "fname = 'results/gb_cv_result.joblib'\n",
     "if not Path(fname).exists():\n",
     "    gb_cv_result = run_cv(gb_clf, y=y_clean, X=X_dummies_clean)\n",
     "    joblib.dump(gb_cv_result, fname)\n",
     "else:\n",
     "    gb_cv_result = joblib.load(fname)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Plot Results"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:40:37.205529Z",
      "start_time": "2018-10-24T14:40:37.194614Z"
     }
    },
    "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>Dataset</th>\n",
        "      <th>Test</th>\n",
        "      <th>Train</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Metric</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AUC</th>\n",
        "      <td>0.675116</td>\n",
        "      <td>0.694941</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Accuracy</th>\n",
        "      <td>0.627907</td>\n",
        "      <td>0.629854</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>F1</th>\n",
        "      <td>0.589335</td>\n",
        "      <td>0.631810</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Log Loss</th>\n",
        "      <td>-0.626003</td>\n",
        "      <td>-0.638505</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Precision</th>\n",
        "      <td>0.714278</td>\n",
        "      <td>0.645043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Recall</th>\n",
        "      <td>0.625646</td>\n",
        "      <td>0.642046</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "Dataset        Test     Train\n",
        "Metric                       \n",
        "AUC        0.675116  0.694941\n",
        "Accuracy   0.627907  0.629854\n",
        "F1         0.589335  0.631810\n",
        "Log Loss  -0.626003 -0.638505\n",
        "Precision  0.714278  0.645043\n",
        "Recall     0.625646  0.642046"
       ]
      },
      "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "gb_result = stack_results(gb_cv_result)\n",
     "gb_result.groupby(['Metric', 'Dataset']).Value.mean().unstack()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:40:37.942770Z",
      "start_time": "2018-10-24T14:40:37.209110Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 1036.8x576 with 6 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plot_result(gb_result, model='Gradient Boosting Classifier')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Partial Dependence Plot"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:42:51.001549Z",
      "start_time": "2018-10-24T14:40:37.944681Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
        "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
        "              max_features=None, max_leaf_nodes=None,\n",
        "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
        "              min_samples_leaf=1, min_samples_split=2,\n",
        "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
        "              n_iter_no_change=None, presort='auto', random_state=None,\n",
        "              subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
        "              verbose=0, warm_start=False)"
       ]
      },
      "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "gb_clf.fit(y=y_clean, X=X_dummies_clean)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 36,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:42:51.948213Z",
      "start_time": "2018-10-24T14:42:51.002501Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "0.6404773585987626"
       ]
      },
      "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# mean accuracy\n",
     "gb_clf.score(X=X_dummies_clean, y=y_clean)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:42:52.959647Z",
      "start_time": "2018-10-24T14:42:51.949309Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "0.6893357756405963"
       ]
      },
      "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "y_score = gb_clf.predict_proba(X_dummies_clean)[:, 1]\n",
     "roc_auc_score(y_score=y_score, y_true=y_clean)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Feature Importance"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 38,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:42:53.118825Z",
      "start_time": "2018-10-24T14:42:52.960771Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "(pd.Series(gb_clf.feature_importances_, \n",
     "          index=X_dummies_clean.columns)\n",
     " .sort_values(ascending=False)\n",
     " .head(15)).sort_values().plot.barh(title='Feature Importance');"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 39,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:42:54.857327Z",
      "start_time": "2018-10-24T14:42:53.119954Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x504 with 4 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig, axes = plot_partial_dependence(gbrt=gb_clf,\n",
     "                                    X=X_dummies_clean,\n",
     "                                    features=['month_9', 'return_1m', 'return_3m', ('return_1m', 'return_3m')],\n",
     "                                    feature_names=['month_9','return_1m', 'return_3m'],\n",
     "                                    percentiles=(0.01, 0.99),\n",
     "                                    n_jobs=-1,\n",
     "                                    n_cols=2,\n",
     "                                    grid_resolution=250)\n",
     "fig.suptitle('Partial Dependence Plot', fontsize=18)\n",
     "fig.tight_layout()\n",
     "fig.set_size_inches(12, 7)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 40,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:42:58.248709Z",
      "start_time": "2018-10-24T14:42:54.858678Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 1008x576 with 2 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "targets = ['return_1m', 'return_3m']\n",
     "target_feature = [X_dummies_clean.columns.get_loc(t) for t in targets]\n",
     "pdp, axes = partial_dependence(gb_clf,\n",
     "                               target_feature,\n",
     "                               X=X_dummies_clean,\n",
     "                               grid_resolution=100)\n",
     "\n",
     "XX, YY = np.meshgrid(axes[0], axes[1])\n",
     "Z = pdp[0].reshape(list(map(np.size, axes))).T\n",
     "\n",
     "fig = plt.figure(figsize=(14, 8))\n",
     "ax = Axes3D(fig)\n",
     "surf = ax.plot_surface(XX, YY, Z,\n",
     "                       rstride=1,\n",
     "                       cstride=1,\n",
     "                       cmap=plt.cm.BuPu,\n",
     "                       edgecolor='k')\n",
     "ax.set_xlabel(' '.join(targets[0].split('_')).capitalize())\n",
     "ax.set_ylabel(' '.join(targets[1].split('_')).capitalize())\n",
     "ax.set_zlabel('Partial Dependence')\n",
     "ax.view_init(elev=22, azim=30)\n",
     "\n",
     "fig.colorbar(surf)\n",
     "fig.suptitle('Partial Dependence by 1- and 3-months returns')\n",
     "fig.tight_layout()\n",
     "fig.savefig('partial_plot', dpi=300);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## XGBoost"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "See XGBoost [docs](https://xgboost.readthedocs.io/en/latest/python/python_api.html) for details on parameters and usage."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Configure"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 41,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:42:58.255441Z",
      "start_time": "2018-10-24T14:42:58.249732Z"
     }
    },
    "outputs": [],
    "source": [
     "xgb_clf = XGBClassifier(max_depth=3,                  # Maximum tree depth for base learners.\n",
     "                        learning_rate=0.1,            # Boosting learning rate (xgb's \"eta\")\n",
     "                        n_estimators=100,             # Number of boosted trees to fit.\n",
     "                        silent=True,                  # Whether to print messages while running\n",
     "                        objective='binary:logistic',  # Task and objective or custom objective function\n",
     "                        booster='gbtree',             # Select booster: gbtree, gblinear or dart\n",
     "#                         tree_method='gpu_hist',\n",
     "                        n_jobs=-1,                    # Number of parallel threads\n",
     "                        gamma=0,                      # Min loss reduction for further splits\n",
     "                        min_child_weight=1,           # Min sum of sample weight(hessian) needed\n",
     "                        max_delta_step=0,             # Max delta step for each tree's weight estimation\n",
     "                        subsample=1,                  # Subsample ratio of training samples\n",
     "                        colsample_bytree=1,           # Subsample ratio of cols for each tree\n",
     "                        colsample_bylevel=1,          # Subsample ratio of cols for each split\n",
     "                        reg_alpha=0,                  # L1 regularization term on weights\n",
     "                        reg_lambda=1,                 # L2 regularization term on weights\n",
     "                        scale_pos_weight=1,           # Balancing class weights\n",
     "                        base_score=0.5,               # Initial prediction score; global bias\n",
     "                        random_state=42)              # random seed"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Cross-validate"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 42,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:43:36.974259Z",
      "start_time": "2018-10-24T14:42:58.256842Z"
     },
     "scrolled": false
    },
    "outputs": [],
    "source": [
     "fname = 'results/xgb_cv_result.joblib'\n",
     "if not Path(fname).exists():\n",
     "    xgb_cv_result = run_cv(xgb_clf)\n",
     "    joblib.dump(xgb_cv_result, fname)\n",
     "else:\n",
     "    xgb_cv_result = joblib.load(fname)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Plot Results"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 43,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:43:36.987450Z",
      "start_time": "2018-10-24T14:43:36.975595Z"
     }
    },
    "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>Dataset</th>\n",
        "      <th>Test</th>\n",
        "      <th>Train</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Metric</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AUC</th>\n",
        "      <td>0.690214</td>\n",
        "      <td>0.690331</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Accuracy</th>\n",
        "      <td>0.626261</td>\n",
        "      <td>0.624804</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>F1</th>\n",
        "      <td>0.570426</td>\n",
        "      <td>0.624806</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Log Loss</th>\n",
        "      <td>-0.590711</td>\n",
        "      <td>-0.638768</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Precision</th>\n",
        "      <td>0.700872</td>\n",
        "      <td>0.640821</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Recall</th>\n",
        "      <td>0.592593</td>\n",
        "      <td>0.636230</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "Dataset        Test     Train\n",
        "Metric                       \n",
        "AUC        0.690214  0.690331\n",
        "Accuracy   0.626261  0.624804\n",
        "F1         0.570426  0.624806\n",
        "Log Loss  -0.590711 -0.638768\n",
        "Precision  0.700872  0.640821\n",
        "Recall     0.592593  0.636230"
       ]
      },
      "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "xbg_result = stack_results(xgb_cv_result)\n",
     "xbg_result.groupby(['Metric', 'Dataset']).Value.mean().unstack()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 44,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:43:37.706386Z",
      "start_time": "2018-10-24T14:43:36.989850Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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       "text/plain": [
        "<Figure size 1036.8x576 with 6 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plot_result(xbg_result, model='XG Boost')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 45,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:43:40.295084Z",
      "start_time": "2018-10-24T14:43:37.707698Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
        "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
        "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
        "       n_jobs=-1, nthread=None, objective='binary:logistic',\n",
        "       random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
        "       seed=None, silent=True, subsample=1)"
       ]
      },
      "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "xgb_clf.fit(X=X_dummies, y=y)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 46,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:43:40.300921Z",
      "start_time": "2018-10-24T14:43:40.296214Z"
     }
    },
    "outputs": [],
    "source": [
     "fi = pd.Series(xgb_clf.feature_importances_, index=X_dummies.columns)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 47,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:43:40.319165Z",
      "start_time": "2018-10-24T14:43:40.302182Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "year_2008           0.054534\n",
        "month_4             0.050602\n",
        "year_2010           0.041430\n",
        "year_2012           0.039757\n",
        "month_9             0.039299\n",
        "year_2007           0.037836\n",
        "month_7             0.033265\n",
        "year_2011           0.032926\n",
        "year_2005           0.031873\n",
        "year_2014           0.031700\n",
        "month_8             0.030093\n",
        "month_10            0.029192\n",
        "year_2015           0.028653\n",
        "month_5             0.026768\n",
        "year_2003           0.026737\n",
        "month_6             0.026244\n",
        "month_1             0.026009\n",
        "year_2018           0.024712\n",
        "year_2013           0.023958\n",
        "month_11            0.023091\n",
        "year_2002           0.022950\n",
        "month_12            0.021967\n",
        "year_2004           0.021956\n",
        "month_2             0.021665\n",
        "year_2016           0.021070\n",
        "year_2009           0.021010\n",
        "year_2017           0.020547\n",
        "month_3             0.016647\n",
        "momentum_12         0.011454\n",
        "return_12m          0.010553\n",
        "return_6m           0.010244\n",
        "return_1m_t-3       0.009592\n",
        "return_1m_t-6       0.008697\n",
        "return_1m_t-4       0.008140\n",
        "return_2m           0.007653\n",
        "return_1m_t-2       0.007380\n",
        "return_3m           0.007291\n",
        "return_1m_t-5       0.007066\n",
        "CMA                 0.006816\n",
        "age_5               0.005803\n",
        "return_1m           0.005731\n",
        "momentum_9          0.005605\n",
        "msize_1             0.005375\n",
        "momentum_2          0.005295\n",
        "Health Care         0.004494\n",
        "RMW                 0.004481\n",
        "momentum_3_12       0.004194\n",
        "SMB                 0.003891\n",
        "return_9m           0.003780\n",
        "return_1m_t-1       0.003524\n",
        "Public Utilities    0.003251\n",
        "Energy              0.003126\n",
        "momentum_3          0.003038\n",
        "msize_10            0.002805\n",
        "momentum_6          0.002791\n",
        "Finance             0.002748\n",
        "Mkt-RF              0.002721\n",
        "Technology          0.002414\n",
        "year_2001           0.002396\n",
        "msize_6             0.001158\n",
        "dtype: float32"
       ]
      },
      "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "fi[fi>0].sort_values(ascending=False)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## LightGBM"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "See LightGBM [docs](https://lightgbm.readthedocs.io/en/latest/Parameters.html) for details on parameters and usage."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Configure"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 48,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:43:40.326512Z",
      "start_time": "2018-10-24T14:43:40.320364Z"
     }
    },
    "outputs": [],
    "source": [
     "lgb_clf = LGBMClassifier(boosting_type='gbdt',\n",
     "#                          device='gpu',\n",
     "                         objective='binary',          # learning task\n",
     "                         metric='auc',\n",
     "                         num_leaves=31,               # Maximum tree leaves for base learners.\n",
     "                         max_depth=-1,                # Maximum tree depth for base learners, -1 means no limit.\n",
     "                         learning_rate=0.1,          # Adaptive lr via callback override in .fit() method  \n",
     "                         n_estimators=100,            # Number of boosted trees to fit\n",
     "                         subsample_for_bin=200000,    # Number of samples for constructing bins.\n",
     "                         class_weight=None,           # dict, 'balanced' or None\n",
     "                         min_split_gain=0.0,          # Minimum loss reduction for further split\n",
     "                         min_child_weight=0.001,      # Minimum sum of instance weight(hessian)\n",
     "                         min_child_samples=20,        # Minimum number of data need in a child(leaf)\n",
     "                         subsample=1.0,               # Subsample ratio of training samples\n",
     "                         subsample_freq=0,            # Frequency of subsampling, <=0: disabled\n",
     "                         colsample_bytree=1.0,        # Subsampling ratio of features\n",
     "                         reg_alpha=0.0,               # L1 regularization term on weights\n",
     "                         reg_lambda=0.0,              # L2 regularization term on weights\n",
     "                         random_state=42,             # Random number seed; default: C++ seed\n",
     "                         n_jobs=-1,                   # Number of parallel threads.\n",
     "                         silent=False,\n",
     "                         importance_type='gain',      # default: 'split' or 'gain'\n",
     "                        )"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Cross-Validate"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Using categorical features"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 49,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:44:42.945824Z",
      "start_time": "2018-10-24T14:43:40.327931Z"
     }
    },
    "outputs": [],
    "source": [
     "fname = 'results/lgb_factor_cv_result.joblib'\n",
     "if not Path(fname).exists():\n",
     "    lgb_factor_cv_result = run_cv(lgb_clf, X=X_factors, fit_params={'categorical_feature': cat_cols})\n",
     "    joblib.dump(lgb_factor_cv_result, fname)\n",
     "else:\n",
     "    lgb_factor_cv_result = joblib.load(fname)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##### Plot Results"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 50,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:44:42.963928Z",
      "start_time": "2018-10-24T14:44:42.947706Z"
     }
    },
    "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>Dataset</th>\n",
        "      <th>Test</th>\n",
        "      <th>Train</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Metric</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AUC</th>\n",
        "      <td>0.685903</td>\n",
        "      <td>0.753829</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Accuracy</th>\n",
        "      <td>0.591003</td>\n",
        "      <td>0.682658</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>F1</th>\n",
        "      <td>0.530207</td>\n",
        "      <td>0.685766</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Log Loss</th>\n",
        "      <td>-0.603009</td>\n",
        "      <td>-0.587044</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Precision</th>\n",
        "      <td>0.608764</td>\n",
        "      <td>0.692368</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Recall</th>\n",
        "      <td>0.578861</td>\n",
        "      <td>0.690098</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "Dataset        Test     Train\n",
        "Metric                       \n",
        "AUC        0.685903  0.753829\n",
        "Accuracy   0.591003  0.682658\n",
        "F1         0.530207  0.685766\n",
        "Log Loss  -0.603009 -0.587044\n",
        "Precision  0.608764  0.692368\n",
        "Recall     0.578861  0.690098"
       ]
      },
      "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "lgb_factor_result = stack_results(lgb_factor_cv_result)\n",
     "lgb_factor_result.groupby(['Metric', 'Dataset']).Value.mean().unstack()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 51,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:44:43.676629Z",
      "start_time": "2018-10-24T14:44:42.965262Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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5UhkZGdqyZYu6deumV155RaGhoXrjjTcUFxen2tpa32cBQFtDPwUA/6CforUjFEDQ2717t1wul0JCQtTU1KTp06erd+/emj17tkpLSxUREaFevXrp5MmTio2N1YoVKzRw4ECNHTtWaWlp6tChgzp37qyTJ0/qhhtu0LJly1RUVKTQ0FDNmDFD0dHReuCBB+RyudTU1KTu3btr1KhR+s9//qNDhw5pzZo1euCBB8z+ZwCAH41+CgD+QT9FMLF5vV6v2UUAAAAAAADjsdAgAAAAAAAWRSgAAAAAAIBFEQoAAAAAAGBRhAIAAAAAAFgUoQAAAAAAABZFKAAAAAAAgEURCgAAAAAAYFGEAgAAAAAAWNT/AaxQLVBmZvHmAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 1036.8x576 with 6 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plot_result(lgb_factor_result, model='Light GBM | Factors')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Using dummy variables"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 52,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:45:37.396152Z",
      "start_time": "2018-10-24T14:44:43.677951Z"
     }
    },
    "outputs": [],
    "source": [
     "fname = 'results/lgb_dummy_cv_result.joblib'\n",
     "if not Path(fname).exists():\n",
     "    lgb_dummy_cv_result = run_cv(lgb_clf)\n",
     "    joblib.dump(lgb_dummy_cv_result, fname)\n",
     "else:\n",
     "    lgb_dummy_cv_result = joblib.load(fname)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "##### Plot results"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 53,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:45:37.408355Z",
      "start_time": "2018-10-24T14:45:37.397371Z"
     }
    },
    "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>Dataset</th>\n",
        "      <th>Test</th>\n",
        "      <th>Train</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Metric</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AUC</th>\n",
        "      <td>0.690086</td>\n",
        "      <td>0.747246</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Accuracy</th>\n",
        "      <td>0.624242</td>\n",
        "      <td>0.675954</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>F1</th>\n",
        "      <td>0.570777</td>\n",
        "      <td>0.678493</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Log Loss</th>\n",
        "      <td>-0.581760</td>\n",
        "      <td>-0.594757</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Precision</th>\n",
        "      <td>0.655675</td>\n",
        "      <td>0.690174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Recall</th>\n",
        "      <td>0.599825</td>\n",
        "      <td>0.685142</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "Dataset        Test     Train\n",
        "Metric                       \n",
        "AUC        0.690086  0.747246\n",
        "Accuracy   0.624242  0.675954\n",
        "F1         0.570777  0.678493\n",
        "Log Loss  -0.581760 -0.594757\n",
        "Precision  0.655675  0.690174\n",
        "Recall     0.599825  0.685142"
       ]
      },
      "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "lgb_dummy_result = stack_results(lgb_dummy_cv_result)\n",
     "lgb_dummy_result.groupby(['Metric', 'Dataset']).Value.mean().unstack()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Catboost"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "See CatBoost [docs](https://catboost.ai/docs/concepts/python-reference_catboostclassifier.html) for details on parameters and usage."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Configure"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
       "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
       "[Parallel(n_jobs=-1)]: Done  10 out of  12 | elapsed: 33.6min remaining:  6.7min\n",
       "[Parallel(n_jobs=-1)]: Done  12 out of  12 | elapsed: 33.7min finished\n"
      ]
     }
    ],
    "source": [
     "cat_clf = CatBoostClassifier()\n",
     "cat_cv_result = run_cv(cat_clf, X=X_factors, fit_params={'cat_features': cat_cols_idx})"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Cross-Validate"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 62,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:45:37.453778Z",
      "start_time": "2018-10-24T14:45:37.440472Z"
     }
    },
    "outputs": [],
    "source": [
     "s = pd.Series(X_factors.columns.tolist())\n",
     "cat_cols_idx = s[s.isin(cat_cols)].index.tolist()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 63,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:47:24.250746Z",
      "start_time": "2018-10-24T14:45:37.454841Z"
     }
    },
    "outputs": [],
    "source": [
     "fname = 'results/cat_cv_result.joblib'\n",
     "if not Path(fname).exists():\n",
     "    cat_cv_result = run_cv(cat_clf, X=X_factors, fit_params={'cat_features': cat_cols_idx})\n",
     "    joblib.dump(cat_cv_result, fname)\n",
     "else:\n",
     "    cat_cv_result = joblib.load(fname)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Plot Results"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 64,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:47:24.264208Z",
      "start_time": "2018-10-24T14:47:24.252151Z"
     }
    },
    "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>Dataset</th>\n",
        "      <th>Test</th>\n",
        "      <th>Train</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Metric</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AUC</th>\n",
        "      <td>0.697064</td>\n",
        "      <td>0.632887</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Accuracy</th>\n",
        "      <td>0.595647</td>\n",
        "      <td>0.583538</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>F1</th>\n",
        "      <td>0.525387</td>\n",
        "      <td>0.580220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Log Loss</th>\n",
        "      <td>-0.610451</td>\n",
        "      <td>-0.660919</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Precision</th>\n",
        "      <td>0.618131</td>\n",
        "      <td>0.598573</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Recall</th>\n",
        "      <td>0.580783</td>\n",
        "      <td>0.596744</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "Dataset        Test     Train\n",
        "Metric                       \n",
        "AUC        0.697064  0.632887\n",
        "Accuracy   0.595647  0.583538\n",
        "F1         0.525387  0.580220\n",
        "Log Loss  -0.610451 -0.660919\n",
        "Precision  0.618131  0.598573\n",
        "Recall     0.580783  0.596744"
       ]
      },
      "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "cat_result = stack_results(cat_cv_result)\n",
     "cat_result.groupby(['Metric', 'Dataset']).Value.mean().unstack()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 67,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:47:24.997404Z",
      "start_time": "2018-10-24T14:47:24.266467Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 1036.8x576 with 6 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plot_result(cat_result, model='CatBoost')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Compare Results"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 68,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:57:19.610665Z",
      "start_time": "2018-10-24T14:57:19.575866Z"
     }
    },
    "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>Metric</th>\n",
        "      <th>AUC</th>\n",
        "      <th>Accuracy</th>\n",
        "      <th>F1</th>\n",
        "      <th>Log Loss</th>\n",
        "      <th>Precision</th>\n",
        "      <th>Recall</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CatBoost</th>\n",
        "      <td>0.697064</td>\n",
        "      <td>0.595647</td>\n",
        "      <td>0.525387</td>\n",
        "      <td>-0.610451</td>\n",
        "      <td>0.618131</td>\n",
        "      <td>0.580783</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>XG Boost</th>\n",
        "      <td>0.690214</td>\n",
        "      <td>0.626261</td>\n",
        "      <td>0.570426</td>\n",
        "      <td>-0.590711</td>\n",
        "      <td>0.700872</td>\n",
        "      <td>0.592593</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LightGBM Dummies</th>\n",
        "      <td>0.690086</td>\n",
        "      <td>0.624242</td>\n",
        "      <td>0.570777</td>\n",
        "      <td>-0.581760</td>\n",
        "      <td>0.655675</td>\n",
        "      <td>0.599825</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LightGBM Factors</th>\n",
        "      <td>0.685903</td>\n",
        "      <td>0.591003</td>\n",
        "      <td>0.530207</td>\n",
        "      <td>-0.603009</td>\n",
        "      <td>0.608764</td>\n",
        "      <td>0.578861</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Gradient Booster</th>\n",
        "      <td>0.675116</td>\n",
        "      <td>0.627907</td>\n",
        "      <td>0.589335</td>\n",
        "      <td>-0.626003</td>\n",
        "      <td>0.714278</td>\n",
        "      <td>0.625646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AdaBoost</th>\n",
        "      <td>0.668890</td>\n",
        "      <td>0.626251</td>\n",
        "      <td>0.590490</td>\n",
        "      <td>-0.692288</td>\n",
        "      <td>0.707423</td>\n",
        "      <td>0.619776</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Random Forest</th>\n",
        "      <td>0.659692</td>\n",
        "      <td>0.616270</td>\n",
        "      <td>0.592029</td>\n",
        "      <td>-0.611774</td>\n",
        "      <td>0.665563</td>\n",
        "      <td>0.602865</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Baseline</th>\n",
        "      <td>0.494516</td>\n",
        "      <td>0.494516</td>\n",
        "      <td>0.501068</td>\n",
        "      <td>-17.594264</td>\n",
        "      <td>0.534767</td>\n",
        "      <td>0.490601</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "Metric                 AUC  Accuracy        F1   Log Loss  Precision    Recall\n",
        "CatBoost          0.697064  0.595647  0.525387  -0.610451   0.618131  0.580783\n",
        "XG Boost          0.690214  0.626261  0.570426  -0.590711   0.700872  0.592593\n",
        "LightGBM Dummies  0.690086  0.624242  0.570777  -0.581760   0.655675  0.599825\n",
        "LightGBM Factors  0.685903  0.591003  0.530207  -0.603009   0.608764  0.578861\n",
        "Gradient Booster  0.675116  0.627907  0.589335  -0.626003   0.714278  0.625646\n",
        "AdaBoost          0.668890  0.626251  0.590490  -0.692288   0.707423  0.619776\n",
        "Random Forest     0.659692  0.616270  0.592029  -0.611774   0.665563  0.602865\n",
        "Baseline          0.494516  0.494516  0.501068 -17.594264   0.534767  0.490601"
       ]
      },
      "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "results = {'Baseline': dummy_result,\n",
     "           'Random Forest': rf_result,\n",
     "           'AdaBoost': ada_result,\n",
     "           'Gradient Booster': gb_result,\n",
     "           'XG Boost': xbg_result,\n",
     "           'LightGBM Dummies': lgb_dummy_result,\n",
     "           'LightGBM Factors': lgb_factor_result,\n",
     "           'CatBoost': cat_result}\n",
     "df = pd.DataFrame()\n",
     "for model, result in results.items():\n",
     "    df = pd.concat([df, result.groupby(['Metric', 'Dataset']\n",
     "                                       ).Value.mean().unstack()['Test'].to_frame(model)], axis=1)\n",
     "\n",
     "df.T.sort_values('AUC', ascending=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 69,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T14:57:33.119457Z",
      "start_time": "2018-10-24T14:57:32.988175Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "df.T['AUC'].sort_values().plot.barh(title='AUC Test Score');"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-24T15:10:05.262082Z",
      "start_time": "2018-10-24T15:10:04.992263Z"
     }
    },
    "outputs": [],
    "source": [
     "fig, ax = plt.subplots(figsize=(14, 8))\n",
     "auc = pd.concat([v.loc[(v.Dataset=='Test') & (v.Metric=='AUC'), 'Value'].to_frame('AUC').assign(Model=k) \n",
     "                 for k, v in results.items()])\n",
     "auc = auc[auc.Model != 'Baseline']\n",
     "sns.barplot(x='Model', y='AUC', data=auc, ax=ax)\n",
     "ax.set_ylim(.5, .8);"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 2",
    "language": "python",
    "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
     "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.8"
   },
   "toc": {
    "base_numbering": 1,
    "nav_menu": {},
    "number_sections": true,
    "sideBar": true,
    "skip_h1_title": true,
    "title_cell": "Table of Contents",
    "title_sidebar": "Contents",
    "toc_cell": false,
    "toc_position": {
     "height": "calc(100% - 180px)",
     "left": "10px",
     "top": "150px",
     "width": "326.4px"
    },
    "toc_section_display": true,
    "toc_window_display": true
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }