ml-finance-python

01_decision_trees.ipynb

(982298B)

 {
  "cells": [
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# How to use decision trees in practice"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "In this notebook, we illustrate how to use tree-based models to gain insight and make predictions. \n",
     "\n",
     "To demonstrate regression trees we predict returns, and for the classification case, we return to the example of positive and negative asset price moves."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 116,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:03:40.814238Z",
      "start_time": "2018-10-31T22:03:40.490481Z"
     }
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "\n",
     "import warnings\n",
     "import os\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 matplotlib.ticker import FuncFormatter\n",
     "from matplotlib import cm\n",
     "import seaborn as sns\n",
     "import graphviz\n",
     "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor, export_graphviz, _tree\n",
     "from sklearn.linear_model import LinearRegression, Ridge, LogisticRegression\n",
     "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, learning_curve, KFold\n",
     "from sklearn.metrics import roc_auc_score, roc_curve, mean_squared_error, precision_recall_curve,  accuracy_score\n",
     "from sklearn.preprocessing import Imputer\n",
     "import statsmodels.api as sm\n",
     "from scipy.interpolate import interp1d, interp2d"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 117,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-01T20:00:02.808595Z",
      "start_time": "2018-10-01T20:00:02.793749Z"
     }
    },
    "outputs": [],
    "source": [
     "warnings.filterwarnings('ignore')\n",
     "plt.style.use('fivethirtyeight')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Get Data"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We use a simplified version of the data set constructed in Chapter 4, Alpha factor research. It consists of daily stock prices provided by Quandl for the 2010-2017 period and various engineered features. The details can be found in the notebook [data_prep](00_data_prep.ipynb) in the GitHub repo for this chapter."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The decision tree models in this chapter are not equipped to handle missing or categorical variables, so we will apply dummy encoding to the latter after dropping any of the former."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
       "MultiIndex: 171162 entries, (A, 2011-03-31 00:00:00) to (ZUMZ, 2018-02-28 00:00:00)\n",
       "Data columns (total 61 columns):\n",
       "returns                  171162 non-null float64\n",
       "t-1                      171162 non-null float64\n",
       "t-2                      171162 non-null float64\n",
       "t-3                      171162 non-null float64\n",
       "t-4                      171162 non-null float64\n",
       "t-5                      171162 non-null float64\n",
       "t-6                      171162 non-null float64\n",
       "t-7                      171162 non-null float64\n",
       "t-8                      171162 non-null float64\n",
       "t-9                      171162 non-null float64\n",
       "t-10                     171162 non-null float64\n",
       "t-11                     171162 non-null float64\n",
       "t-12                     171162 non-null float64\n",
       "year_2011                171162 non-null uint8\n",
       "year_2012                171162 non-null uint8\n",
       "year_2013                171162 non-null uint8\n",
       "year_2014                171162 non-null uint8\n",
       "year_2015                171162 non-null uint8\n",
       "year_2016                171162 non-null uint8\n",
       "year_2017                171162 non-null uint8\n",
       "year_2018                171162 non-null uint8\n",
       "month_1                  171162 non-null uint8\n",
       "month_2                  171162 non-null uint8\n",
       "month_3                  171162 non-null uint8\n",
       "month_4                  171162 non-null uint8\n",
       "month_5                  171162 non-null uint8\n",
       "month_6                  171162 non-null uint8\n",
       "month_7                  171162 non-null uint8\n",
       "month_8                  171162 non-null uint8\n",
       "month_9                  171162 non-null uint8\n",
       "month_10                 171162 non-null uint8\n",
       "month_11                 171162 non-null uint8\n",
       "month_12                 171162 non-null uint8\n",
       "size_1                   171162 non-null int8\n",
       "size_2                   171162 non-null int8\n",
       "size_3                   171162 non-null int8\n",
       "size_4                   171162 non-null int8\n",
       "size_5                   171162 non-null int8\n",
       "size_6                   171162 non-null int8\n",
       "size_7                   171162 non-null int8\n",
       "size_8                   171162 non-null int8\n",
       "size_9                   171162 non-null int8\n",
       "size_10                  171162 non-null int8\n",
       "age_0                    171162 non-null int8\n",
       "age_1                    171162 non-null int8\n",
       "age_2                    171162 non-null int8\n",
       "age_3                    171162 non-null int8\n",
       "age_4                    171162 non-null int8\n",
       "age_5                    171162 non-null int8\n",
       "Basic Industries         171162 non-null int8\n",
       "Capital Goods            171162 non-null int8\n",
       "Consumer Durables        171162 non-null int8\n",
       "Consumer Non-Durables    171162 non-null int8\n",
       "Consumer Services        171162 non-null int8\n",
       "Energy                   171162 non-null int8\n",
       "Finance                  171162 non-null int8\n",
       "Health Care              171162 non-null int8\n",
       "Miscellaneous            171162 non-null int8\n",
       "Public Utilities         171162 non-null int8\n",
       "Technology               171162 non-null int8\n",
       "Transportation           171162 non-null int8\n",
       "dtypes: float64(13), int8(28), uint8(20)\n",
       "memory usage: 25.4+ MB\n"
      ]
     }
    ],
    "source": [
     "with pd.HDFStore('data.h5') as store:\n",
     "    data = store['data']\n",
     "data.info()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Stock Prices"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 119,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:06:14.046946Z",
      "start_time": "2018-10-31T22:06:14.023537Z"
     }
    },
    "outputs": [],
    "source": [
     "y = data.returns\n",
     "X = data.drop('returns', axis=1)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Binary Outcome"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 120,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:06:14.560201Z",
      "start_time": "2018-10-31T22:06:14.556970Z"
     }
    },
    "outputs": [],
    "source": [
     "y_binary = (y>0).astype(int)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### 2 Lags Only"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 121,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:06:15.185101Z",
      "start_time": "2018-10-31T22:06:15.177458Z"
     }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
       "MultiIndex: 171162 entries, (A, 2011-03-31 00:00:00) to (ZUMZ, 2018-02-28 00:00:00)\n",
       "Data columns (total 2 columns):\n",
       "t-1    171162 non-null float64\n",
       "t-2    171162 non-null float64\n",
       "dtypes: float64(2)\n",
       "memory usage: 3.2+ MB\n"
      ]
     }
    ],
    "source": [
     "X2 = X.loc[:, ['t-1', 't-2']]\n",
     "X2.info()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Explore Data"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 122,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:06:17.839683Z",
      "start_time": "2018-10-31T22:06:17.789405Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "count    171162.000000\n",
        "mean          0.009827\n",
        "std           0.068340\n",
        "min          -0.152427\n",
        "10%          -0.080626\n",
        "20%          -0.048064\n",
        "30.0%        -0.025061\n",
        "40%          -0.006481\n",
        "50%           0.009259\n",
        "60%           0.025641\n",
        "70%           0.043847\n",
        "80%           0.066246\n",
        "90%           0.100111\n",
        "max           0.185083\n",
        "Name: returns, dtype: float64"
       ]
      },
      "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "y.describe(percentiles=np.arange(.1, .91, .1).round(1))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "<a id=\"custom_kfold\"></a>\n",
     "## Custom KFold"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We also construct a custom cross-validation class tailored to the format of the data just created, which has pandas MultiIndex with two levels, one for the ticker and one for the data."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "`OneStepTimeSeriesSplit` ensures a split of training and validation sets that avoids a lookahead bias by training models using only data up to period T-1 for each stock when validating using data for month T. We will only use one-step-ahead forecasts."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 123,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:06:23.748287Z",
      "start_time": "2018-10-31T22:06:23.739908Z"
     }
    },
    "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",
     "        self.test_end = n_splits * test_period_length\n",
     "\n",
     "    @staticmethod\n",
     "    def chunks(l, chunk_size):\n",
     "        for i in range(0, len(l), chunk_size):\n",
     "            yield l[i:i + chunk_size]\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)[:self.test_end])\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": 124,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:06:24.039034Z",
      "start_time": "2018-10-31T22:06:24.033920Z"
     }
    },
    "outputs": [],
    "source": [
     "def regression_benchmark():\n",
     "    rmse = []\n",
     "    for train_idx, test_idx in cv.split(X):\n",
     "        mean = y.iloc[train_idx].mean()\n",
     "        data = y.iloc[test_idx].to_frame('y_test').assign(y_pred=mean)\n",
     "        rmse.append(np.sqrt(mean_squared_error(data.y_test, data.y_pred))) \n",
     "    return np.mean(rmse)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 125,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:06:24.280282Z",
      "start_time": "2018-10-31T22:06:24.277278Z"
     }
    },
    "outputs": [],
    "source": [
     "def classification_benchmark():\n",
     "    auc = []\n",
     "    for train_idx, test_idx in cv.split(X):\n",
     "        mean = y_binary.iloc[train_idx].mean()\n",
     "        data = y_binary.iloc[test_idx].to_frame('y_test').assign(y_pred=mean)\n",
     "        auc.append(roc_auc_score(data.y_test, data.y_pred))\n",
     "    return np.mean(auc)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Simple Regression Tree"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Regression trees make predictions based on the mean outcome value for the training samples assigned to a given node and typically rely on the mean-squared error to select optimal rules during recursive binary splitting.\n",
     "\n",
     "Given a training set, the algorithm iterates over the predictors, $X_1, X_2, ..., X_p$, and possible cutpoints, $s_1, s_2, ..., s_N$, to find an optimal combination. The optimal rule splits the feature space into two regions, $\\{X\\mid X_i < s_j\\}$ and $\\{X\\mid X_i > s_j\\}$, with values for the $X_i$ feature either below or above the $s_j$ threshold so that predictions based on the training subsets maximize the reduction of the squared residuals relative to the current node."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Configure Tree"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Let's start with a simplified example to facilitate visualization and only use two months of lagged returns to predict the following month, in the vein of an AR(2) model from the last chapter:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 126,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:06:29.430468Z",
      "start_time": "2018-10-31T22:06:29.423601Z"
     }
    },
    "outputs": [],
    "source": [
     "reg_tree_t2 = DecisionTreeRegressor(criterion='mse',\n",
     "                                    splitter='best',\n",
     "                                    max_depth=4,\n",
     "                                    min_samples_split=2,\n",
     "                                    min_samples_leaf=1,\n",
     "                                    min_weight_fraction_leaf=0.0,\n",
     "                                    max_features=None,\n",
     "                                    random_state=42,\n",
     "                                    max_leaf_nodes=None,\n",
     "                                    min_impurity_decrease=0.0,\n",
     "                                    min_impurity_split=None,\n",
     "                                    presort=False)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Train Decision Tree"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 127,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:06:35.627350Z",
      "start_time": "2018-10-31T22:06:35.081496Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "DecisionTreeRegressor(criterion='mse', max_depth=4, max_features=None,\n",
        "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
        "           min_impurity_split=None, min_samples_leaf=1,\n",
        "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
        "           presort=False, random_state=42, splitter='best')"
       ]
      },
      "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "reg_tree_t2.fit(X=X2, y=y)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Visualize Tree"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "You can visualize the tree using the graphviz library (see GitHub for installation instructions) because sklearn can output a description of the tree using the .dot language used by that library. \n",
     "\n",
     "You can configure the output to include feature and class labels and limit the number of levels to keep the chart readable, as follows:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 128,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:06:38.863228Z",
      "start_time": "2018-10-31T22:06:38.755940Z"
     }
    },
    "outputs": [
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        "<graphviz.files.Source at 0x7fce01743c50>"
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      },
      "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
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    ],
    "source": [
     "out_file = 'figures/reg_tree_t2.dot'\n",
     "dot_data = export_graphviz(reg_tree_t2,\n",
     "                          out_file=out_file,\n",
     "                          feature_names=X2.columns,\n",
     "                          max_depth=2,\n",
     "                          filled=True,\n",
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     "                          special_characters=True)\n",
     "if out_file is not None:\n",
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     "\n",
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   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Compare with Linear Regression"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The OLS summary below and a visualization of the first two levels of the decision tree above reveal the striking differences between the models. The OLS model provides three parameters for the intercepts and the two features in line with the linear assumption.\n",
     "\n",
     "In contrast, the regression tree chart above displays for each node of the first two levels the feature and threshold used to split the data (note that features can be used repeatedly), as well as the current value of the mean-squared error (MSE), the number of samples, and predicted value based on these training samples."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The tree chart also highlights the uneven distribution of samples across the nodes as the numbers vary between 31,000 and 65,000 samples after only two splits."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### statsmodels OLS"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 129,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:06:45.762964Z",
      "start_time": "2018-10-31T22:06:45.561094Z"
     }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                returns   R-squared:                       0.001\n",
       "Model:                            OLS   Adj. R-squared:                  0.001\n",
       "Method:                 Least Squares   F-statistic:                     113.2\n",
       "Date:                Fri, 19 Apr 2019   Prob (F-statistic):           7.64e-50\n",
       "Time:                        20:38:49   Log-Likelihood:             2.1652e+05\n",
       "No. Observations:              171162   AIC:                        -4.330e+05\n",
       "Df Residuals:                  171159   BIC:                        -4.330e+05\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.0101      0.000     59.627      0.000       0.010       0.010\n",
       "t-1           -0.0348      0.002    -14.404      0.000      -0.040      -0.030\n",
       "t-2            0.0092      0.002      3.809      0.000       0.004       0.014\n",
       "==============================================================================\n",
       "Omnibus:                      778.433   Durbin-Watson:                   2.003\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              604.711\n",
       "Skew:                           0.058   Prob(JB):                    4.88e-132\n",
       "Kurtosis:                       2.733   Cond. No.                         14.9\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
      ]
     }
    ],
    "source": [
     "ols_model = sm.OLS(endog=y, exog=sm.add_constant(X2)).fit()\n",
     "print(ols_model.summary())"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Compare with Linear Time Series Models"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### statsmodels AR(2) Model"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Coefficients slighty different because AR model treats returns as a single time series instead creating groups by ticker."
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   },
   {
    "cell_type": "code",
    "execution_count": 95,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:07:32.482806Z",
      "start_time": "2018-10-31T22:07:27.792548Z"
     }
    },
    "outputs": [],
    "source": [
     "ar_model = sm.tsa.ARMA(endog=y, order=(2,0)).fit()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 96,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:07:32.490833Z",
      "start_time": "2018-10-31T22:07:32.484038Z"
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    },
    "outputs": [
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      "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>AR(2)</th>\n",
        "      <th>OLS</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>const</th>\n",
        "      <td>0.009827</td>\n",
        "      <td>0.010077</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>t-1</th>\n",
        "      <td>-0.035791</td>\n",
        "      <td>-0.034772</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>t-2</th>\n",
        "      <td>0.007291</td>\n",
        "      <td>0.009200</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "          AR(2)       OLS\n",
        "const  0.009827  0.010077\n",
        "t-1   -0.035791 -0.034772\n",
        "t-2    0.007291  0.009200"
       ]
      },
      "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "pd.DataFrame({'AR(2)': ar_model.params.values, \n",
     "              'OLS': ols_model.params.values}, \n",
     "             index=ols_model.params.index)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 97,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:07:32.604890Z",
      "start_time": "2018-10-31T22:07:32.492041Z"
     }
    },
    "outputs": [],
    "source": [
     "ar_preds = ar_model.predict()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### ARMA(2,2)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.066091Z",
      "start_time": "2018-10-31T22:06:38.840Z"
     }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "                              ARMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                returns   No. Observations:               431221\n",
       "Model:                     ARMA(2, 2)   Log Likelihood              310290.628\n",
       "Method:                       css-mle   S.D. of innovations              0.118\n",
       "Date:                Fri, 19 Apr 2019   AIC                        -620569.256\n",
       "Time:                        01:09:52   BIC                        -620503.410\n",
       "Sample:                             0   HQIC                       -620550.501\n",
       "                                                                              \n",
       "=================================================================================\n",
       "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "const             0.0117      0.000     64.348      0.000       0.011       0.012\n",
       "ar.L1.returns    -0.4107      0.073     -5.617      0.000      -0.554      -0.267\n",
       "ar.L2.returns     0.5148      0.069      7.460      0.000       0.380       0.650\n",
       "ma.L1.returns     0.4201      0.073      5.732      0.000       0.276       0.564\n",
       "ma.L2.returns    -0.5140      0.070     -7.382      0.000      -0.650      -0.378\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1           -1.0508           +0.0000j            1.0508            0.5000\n",
       "AR.2            1.8486           +0.0000j            1.8486            0.0000\n",
       "MA.1           -1.0448           +0.0000j            1.0448            0.5000\n",
       "MA.2            1.8621           +0.0000j            1.8621            0.0000\n",
       "-----------------------------------------------------------------------------\n"
      ]
     }
    ],
    "source": [
     "arma_model = sm.tsa.ARMA(endog=y, order=(2, 2)).fit()\n",
     "print(arma_model.summary())"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.068811Z",
      "start_time": "2018-10-31T22:06:38.854Z"
     }
    },
    "outputs": [],
    "source": [
     "arma_preds = arma_model.predict()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.074680Z",
      "start_time": "2018-10-31T22:06:38.862Z"
     }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
       "MultiIndex: 43122 entries, (PL, 2009-03-31 00:00:00) to (FTK, 2011-01-31 00:00:00)\n",
       "Data columns (total 4 columns):\n",
       "t-1     43122 non-null float64\n",
       "t-2     43122 non-null float64\n",
       "arma    43122 non-null float64\n",
       "ar      43122 non-null float64\n",
       "dtypes: float64(4)\n",
       "memory usage: 1.5+ MB\n"
      ]
     }
    ],
    "source": [
     "preds = X2.assign(arma=arma_preds, ar=ar_preds).sample(frac=.1).sort_values(['t-1', 't-2'])\n",
     "preds.info()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.076706Z",
      "start_time": "2018-10-31T22:06:38.865Z"
     }
    },
    "outputs": [],
    "source": [
     "q = 20\n",
     "preds['t-1q'] = pd.qcut(preds['t-1'], q=q, labels=list(range(1, q+1))).astype(int)\n",
     "preds['t-2q'] = pd.qcut(preds['t-2'], q=q, labels=list(range(1, q+1))).astype(int)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Decision Surfaces"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The plot of the decision surface for both time series models illustrates how the ARMA model is capable of representing a more complex dynamic relationship."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.077708Z",
      "start_time": "2018-10-31T22:06:38.875Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 1008x432 with 4 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig, axes = plt.subplots(ncols=2, figsize=(14, 6))\n",
     "sns.heatmap(preds.groupby(['t-1q', 't-2q']).ar.median().unstack(), ax=axes[0], cmap='BuPu_r')\n",
     "axes[0].set_title('AR(2) Model')\n",
     "sns.heatmap(preds.groupby(['t-1q', 't-2q']).arma.median().unstack(), ax=axes[1], cmap='BuPu_r')\n",
     "axes[1].set_title('ARMA(2,2) Model')\n",
     "fig.tight_layout();"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### sklearn Linear Regression"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 130,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.078692Z",
      "start_time": "2018-10-31T22:06:38.882Z"
     }
    },
    "outputs": [],
    "source": [
     "lin_reg = LinearRegression()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 131,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.079858Z",
      "start_time": "2018-10-31T22:06:38.885Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
        "         normalize=False)"
       ]
      },
      "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# %%timeit\n",
     "lin_reg.fit(X=X2,y=y)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 132,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.084325Z",
      "start_time": "2018-10-31T22:06:38.897Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "0.010076949882654875"
       ]
      },
      "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "lin_reg.intercept_"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 133,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.085484Z",
      "start_time": "2018-10-31T22:06:38.901Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "array([-0.0347715 ,  0.00920031])"
       ]
      },
      "execution_count": 133,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "lin_reg.coef_"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Linear Regression vs Regressin Tree Decision Surfaces"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "To further illustrate the different assumptions about the functional form of the relationships between the input variables and the output, we can visualize current return predictions as a function of the feature space, that is, as a function of the range of values for the lagged returns. The following figure shows the current period return as a function of returns one and two periods ago for linear regression and the regression tree:\n",
     "\n",
     "The linear-regression model result on the right side underlines the linearity of the relationship between lagged and current returns, whereas the regression tree chart on the left illustrates the non-linear relationship encoded in the recursive partitioning of the feature space."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 150,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.087092Z",
      "start_time": "2018-10-31T22:06:38.904Z"
     }
    },
    "outputs": [],
    "source": [
     "t1, t2 = np.meshgrid(np.linspace(X2['t-1'].quantile(.01), X2['t-1'].quantile(.99), 100),\n",
     "                     np.linspace(X2['t-2'].quantile(.01), X2['t-2'].quantile(.99), 100))\n",
     "X_data = np.c_[t1.ravel(), t2.ravel()]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 151,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.089090Z",
      "start_time": "2018-10-31T22:06:38.922Z"
     },
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x360 with 4 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig, axes = plt.subplots(ncols=2, figsize=(12,5))\n",
     "\n",
     "# Linear Regression\n",
     "ret1 = lin_reg.predict(X_data).reshape(t1.shape)\n",
     "surface1 = axes[0].contourf(t1, t2, ret1, cmap='Blues')\n",
     "plt.colorbar(mappable=surface1, ax=axes[0])\n",
     "\n",
     "# Regression Tree\n",
     "ret2 = reg_tree_t2.predict(X_data).reshape(t1.shape)\n",
     "surface2 = axes[1].contourf(t1, t2, ret2, cmap='Blues')\n",
     "# surface2 = axes[1].contourf(t1, t2, np.clip(ret2, a_min=-.05, a_max=.05), cmap='Blues')\n",
     "plt.colorbar(mappable=surface2, ax=axes[1])\n",
     "\n",
     "# Format plots\n",
     "titles = ['Linear Regression', 'Regression Tree']\n",
     "for i, ax in enumerate(axes):\n",
     "    ax.set_xlabel('t-1')\n",
     "    ax.set_ylabel('t-2')\n",
     "    ax.set_title(titles[i])\n",
     "\n",
     "fig.suptitle('Decision Surfaces', fontsize=20)\n",
     "fig.tight_layout()\n",
     "fig.subplots_adjust(top=.9);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Simple Classification Tree"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "A classification tree works just like the regression version, except that categorical nature of the outcome requires a different approach to making predictions and measuring the loss. While a regression tree predicts the response for an observation assigned to a leaf node using the mean outcome of the associated training samples, a classification tree instead uses the mode, that is, the most common class among the training samples in the relevant region. A classification tree can also generate probabilistic predictions based on relative class frequencies."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Loss Functions"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "When growing a classification tree, we also use recursive binary splitting but, instead of evaluating the quality of a decision rule using the reduction of the mean-squared error, we can use the classification error rate, which is simply the fraction of the training samples in a given (leave) node that do not belong to the most common class."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "However, the alternative measures, Gini Index or Cross-Entropy, are preferred because they are more sensitive to node purity than the classification error rate. Node purity refers to the extent of the preponderance of a single class in a node. A node that only contains samples with outcomes belonging to a single class is pure and imply successful classification for this particular region of the feature space. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 49,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.095170Z",
      "start_time": "2018-10-31T22:06:38.934Z"
     }
    },
    "outputs": [],
    "source": [
     "def entropy(f):\n",
     "    return (-f*np.log2(f) - (1-f)*np.log2(1-f))/2"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 50,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.096711Z",
      "start_time": "2018-10-31T22:06:38.943Z"
     }
    },
    "outputs": [],
    "source": [
     "def gini(f):\n",
     "    return 2*f*(1-f)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 51,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.098059Z",
      "start_time": "2018-10-31T22:06:38.952Z"
     }
    },
    "outputs": [],
    "source": [
     "def misclassification_rate(f):\n",
     "    return np.where(f<=.5, f, 1-f)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Both the Gini Impurity and the Cross-Entropy measure take on smaller values when the class proportions approach zero or one, that is, when the child nodes become pure as a result of the split and are highest when the class proportions are even or 0.5 in the binary case. \n",
     "\n",
     "The chart below visualizes the values assumed by these two measures and the misclassification error rates across the [0, 1] interval of proportions."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 52,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.100377Z",
      "start_time": "2018-10-31T22:06:38.969Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "x = np.linspace(0, 1, 10000)\n",
     "(pd.DataFrame({'Gini': gini(x), \n",
     "              'Entropy': entropy(x),\n",
     "             'Misclassification Rate': misclassification_rate(x)}, index=x)\n",
     " .plot(title='Classification Loss Functions', lw=2));"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Compare computation time"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Gini is often preferred over entropy because it computes faster:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 53,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.103157Z",
      "start_time": "2018-10-31T22:06:38.978Z"
     }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "14.5 µs ± 458 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
      ]
     }
    ],
    "source": [
     "%%timeit\n",
     "misclassification_rate(x)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 42,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.105649Z",
      "start_time": "2018-10-31T22:06:38.986Z"
     }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "11.4 µs ± 458 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
      ]
     }
    ],
    "source": [
     "%%timeit\n",
     "gini(x)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 43,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.106843Z",
      "start_time": "2018-10-31T22:06:38.994Z"
     }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "211 µs ± 11.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
      ]
     }
    ],
    "source": [
     "%%timeit\n",
     "entropy(x)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Configure Tree"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 152,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.109952Z",
      "start_time": "2018-10-31T22:06:39.005Z"
     }
    },
    "outputs": [],
    "source": [
     "clf_tree_t2 = DecisionTreeClassifier(criterion='gini',\n",
     "                                     splitter='best',\n",
     "                                     max_depth=4,\n",
     "                                     min_samples_split=2,\n",
     "                                     min_samples_leaf=1,\n",
     "                                     min_weight_fraction_leaf=0.0,\n",
     "                                     max_features=None,\n",
     "                                     random_state=42,\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": [
     "### Train Tree"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 153,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.111002Z",
      "start_time": "2018-10-31T22:06:39.017Z"
     },
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=4,\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, presort=False, random_state=42,\n",
        "            splitter='best')"
       ]
      },
      "execution_count": 153,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# %%timeit\n",
     "clf_tree_t2.fit(X=X2, y=y_binary)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Visualize Tree"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 56,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.115952Z",
      "start_time": "2018-10-31T22:06:39.049Z"
     }
    },
    "outputs": [
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        "<text text-anchor=\"start\" x=\"527\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 95313</text>\n",
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        "<!-- 24&#45;&gt;28 -->\n",
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        "<title>24&#45;&gt;28</title>\n",
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        "</g>\n",
        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
        "<graphviz.files.Source at 0x7fce01a57470>"
       ]
      },
      "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "out_file = 'figures/clf_tree_t2.dot'\n",
     "dot_data = export_graphviz(clf_tree_t2,\n",
     "                           out_file=out_file,\n",
     "                           feature_names=X2.columns,\n",
     "                           class_names=['Down', 'Up'],\n",
     "                           max_depth=2,\n",
     "                           filled=True,\n",
     "                           rounded=True,\n",
     "                           special_characters=True)\n",
     "if out_file is not None:\n",
     "    dot_data = Path(out_file).read_text()\n",
     "\n",
     "graphviz.Source(dot_data)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Compare with Logistic Regression"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Statsmodels"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 57,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.120856Z",
      "start_time": "2018-10-31T22:06:39.068Z"
     }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Optimization terminated successfully.\n",
       "         Current function value: 0.686503\n",
       "         Iterations 4\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                returns   No. Observations:               171162\n",
       "Model:                          Logit   Df Residuals:                   171159\n",
       "Method:                           MLE   Df Model:                            2\n",
       "Date:                Fri, 19 Apr 2019   Pseudo R-squ.:               0.0006944\n",
       "Time:                        01:41:26   Log-Likelihood:            -1.1750e+05\n",
       "converged:                       True   LL-Null:                   -1.1758e+05\n",
       "                                        LLR p-value:                 3.478e-36\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.2246      0.005     45.067      0.000       0.215       0.234\n",
       "t-1           -0.7208      0.071    -10.126      0.000      -0.860      -0.581\n",
       "t-2            0.5279      0.071      7.410      0.000       0.388       0.667\n",
       "==============================================================================\n"
      ]
     }
    ],
    "source": [
     "model = sm.Logit(endog=y_binary, exog=sm.add_constant(X2)).fit()\n",
     "print(model.summary())"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### sklearn"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 58,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.124316Z",
      "start_time": "2018-10-31T22:06:39.078Z"
     }
    },
    "outputs": [],
    "source": [
     "logistic_reg = LogisticRegression()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 59,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.125951Z",
      "start_time": "2018-10-31T22:06:39.086Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
        "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
        "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
        "          tol=0.0001, verbose=0, warm_start=False)"
       ]
      },
      "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# %%timeit\n",
     "logistic_reg.fit(X=X2, y=y_binary)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 60,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.129350Z",
      "start_time": "2018-10-31T22:06:39.094Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "array([[-0.71721119,  0.52531984]])"
       ]
      },
      "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "logistic_reg.coef_"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Plot Decision Surfaces"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 154,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.132367Z",
      "start_time": "2018-10-31T22:06:39.105Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x360 with 4 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig, axes = plt.subplots(ncols=2, figsize=(12,5))\n",
     "\n",
     "# Linear Regression\n",
     "ret1 = logistic_reg.predict_proba(X_data)[:, 1].reshape(t1.shape)\n",
     "surface1 = axes[0].contourf(t1, t2, ret1, cmap='Blues')\n",
     "plt.colorbar(mappable=surface1, ax=axes[0])\n",
     "\n",
     "# Regression Tree\n",
     "ret2 = clf_tree_t2.predict_proba(X_data)[:, 1].reshape(t1.shape)\n",
     "surface2 = axes[1].contourf(t1, t2, ret2, cmap='Blues')\n",
     "plt.colorbar(mappable=surface2, ax=axes[1])\n",
     "\n",
     "# Format plots\n",
     "titles = ['Logistic Regression', 'Classification Tree']\n",
     "for i, ax in enumerate(axes):\n",
     "    ax.set_xlabel('t-1')\n",
     "    ax.set_ylabel('t-2')\n",
     "    ax.set_title(titles[i])\n",
     "\n",
     "fig.suptitle('Decision Surfaces', fontsize=20)\n",
     "fig.tight_layout()\n",
     "fig.subplots_adjust(top=.9);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Regression Tree with all Features"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We now train, visualize, and evaluate a regression tree with up to 5 consecutive splits using 80% of the samples for training to predict the remaining 20%.\n",
     "\n",
     "We are taking a shortcut here to simplify the illustration and use the built-in train_test_split, which does not protect against lookahead bias, as our custom iterator. The tree configuration implies up to $2^5=32$ leaf nodes that, on average in the balanced case, would contain over 4,300 of the training samples."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Train-Test Split"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 63,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.162314Z",
      "start_time": "2018-10-31T22:06:39.234Z"
     }
    },
    "outputs": [],
    "source": [
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Configure Tree"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The output after training the model displays all the DecisionTreeClassifier parameters that we will address in more detail in the next section when we discuss parameter-tuning. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 64,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.163307Z",
      "start_time": "2018-10-31T22:06:39.243Z"
     }
    },
    "outputs": [],
    "source": [
     "regression_tree = DecisionTreeRegressor(criterion='mse',\n",
     "                                        splitter='best',\n",
     "                                        max_depth=5,\n",
     "                                        min_samples_split=2,\n",
     "                                        min_samples_leaf=1,\n",
     "                                        min_weight_fraction_leaf=0.0,\n",
     "                                        max_features=None,\n",
     "                                        random_state=42,\n",
     "                                        max_leaf_nodes=None,\n",
     "                                        min_impurity_decrease=0.0,\n",
     "                                        min_impurity_split=None,\n",
     "                                        presort=False)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Train Model"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 65,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.164295Z",
      "start_time": "2018-10-31T22:06:39.252Z"
     },
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "DecisionTreeRegressor(criterion='mse', max_depth=5, max_features=None,\n",
        "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
        "           min_impurity_split=None, min_samples_leaf=1,\n",
        "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
        "           presort=False, random_state=42, splitter='best')"
       ]
      },
      "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "regression_tree.fit(X=X_train, y=y_train)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Visualize Tree"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The result shows that the model uses a variety of different features and indicates the split rules for both continuous and categorical (dummy) variables. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 66,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.165329Z",
      "start_time": "2018-10-31T22:06:39.261Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/svg+xml": [
        "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
        "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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        "<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
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        "<!-- Title: Tree Pages: 1 -->\n",
        "<svg width=\"1163pt\" height=\"460pt\"\n",
        " viewBox=\"0.00 0.00 1163.00 460.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
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        "<title>Tree</title>\n",
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        "<g id=\"node1\" class=\"node\">\n",
        "<title>0</title>\n",
        "<path fill=\"#e58139\" fill-opacity=\"0.349020\" stroke=\"#000000\" d=\"M620,-452C620,-452 516,-452 516,-452 510,-452 504,-446 504,-440 504,-440 504,-396 504,-396 504,-390 510,-384 516,-384 516,-384 620,-384 620,-384 626,-384 632,-390 632,-396 632,-396 632,-440 632,-440 632,-446 626,-452 620,-452\"/>\n",
        "<text text-anchor=\"start\" x=\"518.5\" y=\"-436.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">year_2013 ≤ 0.5</text>\n",
        "<text text-anchor=\"start\" x=\"530\" y=\"-421.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.005</text>\n",
        "<text text-anchor=\"start\" x=\"512\" y=\"-406.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 136929</text>\n",
        "<text text-anchor=\"start\" x=\"530.5\" y=\"-391.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.01</text>\n",
        "</g>\n",
        "<!-- 1 -->\n",
        "<g id=\"node2\" class=\"node\">\n",
        "<title>1</title>\n",
        "<path fill=\"#e58139\" fill-opacity=\"0.337255\" stroke=\"#000000\" d=\"M489,-348C489,-348 385,-348 385,-348 379,-348 373,-342 373,-336 373,-336 373,-292 373,-292 373,-286 379,-280 385,-280 385,-280 489,-280 489,-280 495,-280 501,-286 501,-292 501,-292 501,-336 501,-336 501,-342 495,-348 489,-348\"/>\n",
        "<text text-anchor=\"start\" x=\"389\" y=\"-332.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">month_10 ≤ 0.5</text>\n",
        "<text text-anchor=\"start\" x=\"399\" y=\"-317.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.005</text>\n",
        "<text text-anchor=\"start\" x=\"381\" y=\"-302.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 117661</text>\n",
        "<text text-anchor=\"start\" x=\"396\" y=\"-287.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.007</text>\n",
        "</g>\n",
        "<!-- 0&#45;&gt;1 -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
        "<title>0&#45;&gt;1</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M525.1057,-383.9465C513.2307,-374.519 500.2259,-364.1946 487.9407,-354.4415\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"489.9989,-351.6066 479.9907,-348.13 485.6464,-357.089 489.9989,-351.6066\"/>\n",
        "<text text-anchor=\"middle\" x=\"482.8442\" y=\"-369.2666\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">True</text>\n",
        "</g>\n",
        "<!-- 32 -->\n",
        "<g id=\"node17\" class=\"node\">\n",
        "<title>32</title>\n",
        "<path fill=\"#e58139\" fill-opacity=\"0.407843\" stroke=\"#000000\" d=\"M764.5,-348C764.5,-348 667.5,-348 667.5,-348 661.5,-348 655.5,-342 655.5,-336 655.5,-336 655.5,-292 655.5,-292 655.5,-286 661.5,-280 667.5,-280 667.5,-280 764.5,-280 764.5,-280 770.5,-280 776.5,-286 776.5,-292 776.5,-292 776.5,-336 776.5,-336 776.5,-342 770.5,-348 764.5,-348\"/>\n",
        "<text text-anchor=\"start\" x=\"672\" y=\"-332.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">month_8 ≤ 0.5</text>\n",
        "<text text-anchor=\"start\" x=\"678\" y=\"-317.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.004</text>\n",
        "<text text-anchor=\"start\" x=\"663.5\" y=\"-302.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 19268</text>\n",
        "<text text-anchor=\"start\" x=\"675\" y=\"-287.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.024</text>\n",
        "</g>\n",
        "<!-- 0&#45;&gt;32 -->\n",
        "<g id=\"edge16\" class=\"edge\">\n",
        "<title>0&#45;&gt;32</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M616.4607,-383.9465C630.1323,-374.3395 645.1294,-363.8009 659.2409,-353.8848\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"661.2608,-356.7432 667.4304,-348.13 657.2361,-351.0158 661.2608,-356.7432\"/>\n",
        "<text text-anchor=\"middle\" x=\"663.1304\" y=\"-369.0574\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">False</text>\n",
        "</g>\n",
        "<!-- 2 -->\n",
        "<g id=\"node3\" class=\"node\">\n",
        "<title>2</title>\n",
        "<path fill=\"#e58139\" fill-opacity=\"0.333333\" stroke=\"#000000\" d=\"M289,-244C289,-244 185,-244 185,-244 179,-244 173,-238 173,-232 173,-232 173,-188 173,-188 173,-182 179,-176 185,-176 185,-176 289,-176 289,-176 295,-176 301,-182 301,-188 301,-188 301,-232 301,-232 301,-238 295,-244 289,-244\"/>\n",
        "<text text-anchor=\"start\" x=\"187.5\" y=\"-228.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">year_2011 ≤ 0.5</text>\n",
        "<text text-anchor=\"start\" x=\"199\" y=\"-213.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.005</text>\n",
        "<text text-anchor=\"start\" x=\"181\" y=\"-198.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 108377</text>\n",
        "<text text-anchor=\"start\" x=\"196\" y=\"-183.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.006</text>\n",
        "</g>\n",
        "<!-- 1&#45;&gt;2 -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
        "<title>1&#45;&gt;2</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M372.8935,-280.6646C352.8884,-270.262 330.6736,-258.7103 310.0999,-248.012\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"311.5735,-244.8333 301.0866,-243.325 308.344,-251.0439 311.5735,-244.8333\"/>\n",
        "</g>\n",
        "<!-- 17 -->\n",
        "<g id=\"node10\" class=\"node\">\n",
        "<title>17</title>\n",
        "<path fill=\"#e58139\" fill-opacity=\"0.400000\" stroke=\"#000000\" d=\"M482.5,-244C482.5,-244 391.5,-244 391.5,-244 385.5,-244 379.5,-238 379.5,-232 379.5,-232 379.5,-188 379.5,-188 379.5,-182 385.5,-176 391.5,-176 391.5,-176 482.5,-176 482.5,-176 488.5,-176 494.5,-182 494.5,-188 494.5,-188 494.5,-232 494.5,-232 494.5,-238 488.5,-244 482.5,-244\"/>\n",
        "<text text-anchor=\"start\" x=\"387.5\" y=\"-228.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">year_2011 ≤ 0.5</text>\n",
        "<text text-anchor=\"start\" x=\"399\" y=\"-213.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.006</text>\n",
        "<text text-anchor=\"start\" x=\"388.5\" y=\"-198.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 9284</text>\n",
        "<text text-anchor=\"start\" x=\"396\" y=\"-183.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.023</text>\n",
        "</g>\n",
        "<!-- 1&#45;&gt;17 -->\n",
        "<g id=\"edge9\" class=\"edge\">\n",
        "<title>1&#45;&gt;17</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M437,-279.9465C437,-271.776 437,-262.9318 437,-254.3697\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"440.5001,-254.13 437,-244.13 433.5001,-254.13 440.5001,-254.13\"/>\n",
        "</g>\n",
        "<!-- 3 -->\n",
        "<g id=\"node4\" class=\"node\">\n",
        "<title>3</title>\n",
        "<path fill=\"#e58139\" fill-opacity=\"0.341176\" stroke=\"#000000\" d=\"M146.5,-140C146.5,-140 49.5,-140 49.5,-140 43.5,-140 37.5,-134 37.5,-128 37.5,-128 37.5,-84 37.5,-84 37.5,-78 43.5,-72 49.5,-72 49.5,-72 146.5,-72 146.5,-72 152.5,-72 158.5,-78 158.5,-84 158.5,-84 158.5,-128 158.5,-128 158.5,-134 152.5,-140 146.5,-140\"/>\n",
        "<text text-anchor=\"start\" x=\"48.5\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">year_2015 ≤ 0.5</text>\n",
        "<text text-anchor=\"start\" x=\"60\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.005</text>\n",
        "<text text-anchor=\"start\" x=\"45.5\" y=\"-94.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 96296</text>\n",
        "<text text-anchor=\"start\" x=\"57\" y=\"-79.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.008</text>\n",
        "</g>\n",
        "<!-- 2&#45;&gt;3 -->\n",
        "<g id=\"edge3\" class=\"edge\">\n",
        "<title>2&#45;&gt;3</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M191.4862,-175.9465C178.766,-166.4293 164.824,-155.9978 151.6794,-146.163\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"153.7198,-143.3184 143.6161,-140.13 149.5262,-148.9232 153.7198,-143.3184\"/>\n",
        "</g>\n",
        "<!-- 10 -->\n",
        "<g id=\"node7\" class=\"node\">\n",
        "<title>10</title>\n",
        "<path fill=\"#e58139\" fill-opacity=\"0.266667\" stroke=\"#000000\" d=\"M285.5,-140C285.5,-140 188.5,-140 188.5,-140 182.5,-140 176.5,-134 176.5,-128 176.5,-128 176.5,-84 176.5,-84 176.5,-78 182.5,-72 188.5,-72 188.5,-72 285.5,-72 285.5,-72 291.5,-72 297.5,-78 297.5,-84 297.5,-84 297.5,-128 297.5,-128 297.5,-134 291.5,-140 285.5,-140\"/>\n",
        "<text text-anchor=\"start\" x=\"193\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">month_9 ≤ 0.5</text>\n",
        "<text text-anchor=\"start\" x=\"199\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.004</text>\n",
        "<text text-anchor=\"start\" x=\"184.5\" y=\"-94.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 12081</text>\n",
        "<text text-anchor=\"start\" x=\"197.5\" y=\"-79.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = &#45;0.01</text>\n",
        "</g>\n",
        "<!-- 2&#45;&gt;10 -->\n",
        "<g id=\"edge6\" class=\"edge\">\n",
        "<title>2&#45;&gt;10</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M237,-175.9465C237,-167.776 237,-158.9318 237,-150.3697\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"240.5001,-150.13 237,-140.13 233.5001,-150.13 240.5001,-150.13\"/>\n",
        "</g>\n",
        "<!-- 4 -->\n",
        "<g id=\"node5\" class=\"node\">\n",
        "<title>4</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M42,-36C42,-36 12,-36 12,-36 6,-36 0,-30 0,-24 0,-24 0,-12 0,-12 0,-6 6,0 12,0 12,0 42,0 42,0 48,0 54,-6 54,-12 54,-12 54,-24 54,-24 54,-30 48,-36 42,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"27\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 3&#45;&gt;4 -->\n",
        "<g id=\"edge4\" class=\"edge\">\n",
        "<title>3&#45;&gt;4</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M70.5495,-71.9769C63.0975,-62.7406 55.1459,-52.8851 48.1042,-44.1573\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"50.6809,-41.777 41.6776,-36.192 45.2329,-46.1725 50.6809,-41.777\"/>\n",
        "</g>\n",
        "<!-- 7 -->\n",
        "<g id=\"node6\" class=\"node\">\n",
        "<title>7</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M114,-36C114,-36 84,-36 84,-36 78,-36 72,-30 72,-24 72,-24 72,-12 72,-12 72,-6 78,0 84,0 84,0 114,0 114,0 120,0 126,-6 126,-12 126,-12 126,-24 126,-24 126,-30 120,-36 114,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"99\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 3&#45;&gt;7 -->\n",
        "<g id=\"edge5\" class=\"edge\">\n",
        "<title>3&#45;&gt;7</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M98.3866,-71.9769C98.4829,-63.5023 98.5852,-54.5065 98.678,-46.3388\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"102.1793,-46.2311 98.7933,-36.192 95.1798,-46.1515 102.1793,-46.2311\"/>\n",
        "</g>\n",
        "<!-- 11 -->\n",
        "<g id=\"node8\" class=\"node\">\n",
        "<title>11</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M197,-36C197,-36 167,-36 167,-36 161,-36 155,-30 155,-24 155,-24 155,-12 155,-12 155,-6 161,0 167,0 167,0 197,0 197,0 203,0 209,-6 209,-12 209,-12 209,-24 209,-24 209,-30 203,-36 197,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"182\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 10&#45;&gt;11 -->\n",
        "<g id=\"edge7\" class=\"edge\">\n",
        "<title>10&#45;&gt;11</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M215.7356,-71.9769C210.0819,-62.931 204.057,-53.2913 198.6866,-44.6986\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"201.638,-42.8169 193.37,-36.192 195.702,-46.527 201.638,-42.8169\"/>\n",
        "</g>\n",
        "<!-- 14 -->\n",
        "<g id=\"node9\" class=\"node\">\n",
        "<title>14</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M269,-36C269,-36 239,-36 239,-36 233,-36 227,-30 227,-24 227,-24 227,-12 227,-12 227,-6 233,0 239,0 239,0 269,0 269,0 275,0 281,-6 281,-12 281,-12 281,-24 281,-24 281,-30 275,-36 269,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"254\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 10&#45;&gt;14 -->\n",
        "<g id=\"edge8\" class=\"edge\">\n",
        "<title>10&#45;&gt;14</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M243.5726,-71.9769C245.2282,-63.4071 246.9867,-54.3043 248.5786,-46.0638\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"252.0253,-46.6743 250.4856,-36.192 245.1524,-45.3465 252.0253,-46.6743\"/>\n",
        "</g>\n",
        "<!-- 18 -->\n",
        "<g id=\"node11\" class=\"node\">\n",
        "<title>18</title>\n",
        "<path fill=\"#e58139\" fill-opacity=\"0.368627\" stroke=\"#000000\" d=\"M418.5,-140C418.5,-140 327.5,-140 327.5,-140 321.5,-140 315.5,-134 315.5,-128 315.5,-128 315.5,-84 315.5,-84 315.5,-78 321.5,-72 327.5,-72 327.5,-72 418.5,-72 418.5,-72 424.5,-72 430.5,-78 430.5,-84 430.5,-84 430.5,-128 430.5,-128 430.5,-134 424.5,-140 418.5,-140\"/>\n",
        "<text text-anchor=\"start\" x=\"323.5\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">year_2016 ≤ 0.5</text>\n",
        "<text text-anchor=\"start\" x=\"335\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.005</text>\n",
        "<text text-anchor=\"start\" x=\"324.5\" y=\"-94.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 8225</text>\n",
        "<text text-anchor=\"start\" x=\"332\" y=\"-79.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.015</text>\n",
        "</g>\n",
        "<!-- 17&#45;&gt;18 -->\n",
        "<g id=\"edge10\" class=\"edge\">\n",
        "<title>17&#45;&gt;18</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M416.044,-175.9465C410.6845,-167.2373 404.8539,-157.7626 399.2647,-148.6801\"/>\n",
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        "<!-- 25 -->\n",
        "<g id=\"node14\" class=\"node\">\n",
        "<title>25</title>\n",
        "<path fill=\"#e58139\" fill-opacity=\"0.674510\" stroke=\"#000000\" d=\"M549.5,-140C549.5,-140 460.5,-140 460.5,-140 454.5,-140 448.5,-134 448.5,-128 448.5,-128 448.5,-84 448.5,-84 448.5,-78 454.5,-72 460.5,-72 460.5,-72 549.5,-72 549.5,-72 555.5,-72 561.5,-78 561.5,-84 561.5,-84 561.5,-128 561.5,-128 561.5,-134 555.5,-140 549.5,-140\"/>\n",
        "<text text-anchor=\"start\" x=\"470\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">t&#45;1 ≤ &#45;0.014</text>\n",
        "<text text-anchor=\"start\" x=\"467\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.004</text>\n",
        "<text text-anchor=\"start\" x=\"456.5\" y=\"-94.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1059</text>\n",
        "<text text-anchor=\"start\" x=\"464\" y=\"-79.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.089</text>\n",
        "</g>\n",
        "<!-- 17&#45;&gt;25 -->\n",
        "<g id=\"edge13\" class=\"edge\">\n",
        "<title>17&#45;&gt;25</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M459.2657,-175.9465C464.9602,-167.2373 471.1552,-157.7626 477.0938,-148.6801\"/>\n",
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        "<g id=\"node12\" class=\"node\">\n",
        "<title>19</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M346,-36C346,-36 316,-36 316,-36 310,-36 304,-30 304,-24 304,-24 304,-12 304,-12 304,-6 310,0 316,0 316,0 346,0 346,0 352,0 358,-6 358,-12 358,-12 358,-24 358,-24 358,-30 352,-36 346,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"331\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 18&#45;&gt;19 -->\n",
        "<g id=\"edge11\" class=\"edge\">\n",
        "<title>18&#45;&gt;19</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M356.7617,-71.9769C352.5352,-63.1215 348.0371,-53.6969 344.0022,-45.2427\"/>\n",
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        "<g id=\"node13\" class=\"node\">\n",
        "<title>22</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M418,-36C418,-36 388,-36 388,-36 382,-36 376,-30 376,-24 376,-24 376,-12 376,-12 376,-6 382,0 388,0 388,0 418,0 418,0 424,0 430,-6 430,-12 430,-12 430,-24 430,-24 430,-30 424,-36 418,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"403\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 18&#45;&gt;22 -->\n",
        "<g id=\"edge12\" class=\"edge\">\n",
        "<title>18&#45;&gt;22</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M384.5988,-71.9769C387.5528,-63.3119 390.6925,-54.102 393.5263,-45.7894\"/>\n",
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        "<!-- 26 -->\n",
        "<g id=\"node15\" class=\"node\">\n",
        "<title>26</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M493,-36C493,-36 463,-36 463,-36 457,-36 451,-30 451,-24 451,-24 451,-12 451,-12 451,-6 457,0 463,0 463,0 493,0 493,0 499,0 505,-6 505,-12 505,-12 505,-24 505,-24 505,-30 499,-36 493,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"478\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 25&#45;&gt;26 -->\n",
        "<g id=\"edge14\" class=\"edge\">\n",
        "<title>25&#45;&gt;26</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M494.5611,-71.9769C491.9025,-63.3119 489.0767,-54.102 486.5263,-45.7894\"/>\n",
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        "</g>\n",
        "<!-- 29 -->\n",
        "<g id=\"node16\" class=\"node\">\n",
        "<title>29</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M565,-36C565,-36 535,-36 535,-36 529,-36 523,-30 523,-24 523,-24 523,-12 523,-12 523,-6 529,0 535,0 535,0 565,0 565,0 571,0 577,-6 577,-12 577,-12 577,-24 577,-24 577,-30 571,-36 565,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"550\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 25&#45;&gt;29 -->\n",
        "<g id=\"edge15\" class=\"edge\">\n",
        "<title>25&#45;&gt;29</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M522.3982,-71.9769C526.9265,-63.1215 531.7459,-53.6969 536.0691,-45.2427\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"539.2606,-46.6889 540.6973,-36.192 533.0281,-43.5019 539.2606,-46.6889\"/>\n",
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        "<!-- 33 -->\n",
        "<g id=\"node18\" class=\"node\">\n",
        "<title>33</title>\n",
        "<path fill=\"#e58139\" fill-opacity=\"0.427451\" stroke=\"#000000\" d=\"M764.5,-244C764.5,-244 667.5,-244 667.5,-244 661.5,-244 655.5,-238 655.5,-232 655.5,-232 655.5,-188 655.5,-188 655.5,-182 661.5,-176 667.5,-176 667.5,-176 764.5,-176 764.5,-176 770.5,-176 776.5,-182 776.5,-188 776.5,-188 776.5,-232 776.5,-232 776.5,-238 770.5,-244 764.5,-244\"/>\n",
        "<text text-anchor=\"start\" x=\"672\" y=\"-228.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">month_6 ≤ 0.5</text>\n",
        "<text text-anchor=\"start\" x=\"678\" y=\"-213.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.004</text>\n",
        "<text text-anchor=\"start\" x=\"663.5\" y=\"-198.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 17670</text>\n",
        "<text text-anchor=\"start\" x=\"675\" y=\"-183.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.029</text>\n",
        "</g>\n",
        "<!-- 32&#45;&gt;33 -->\n",
        "<g id=\"edge17\" class=\"edge\">\n",
        "<title>32&#45;&gt;33</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M716,-279.9465C716,-271.776 716,-262.9318 716,-254.3697\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"719.5001,-254.13 716,-244.13 712.5001,-254.13 719.5001,-254.13\"/>\n",
        "</g>\n",
        "<!-- 48 -->\n",
        "<g id=\"node25\" class=\"node\">\n",
        "<title>48</title>\n",
        "<path fill=\"#e58139\" fill-opacity=\"0.188235\" stroke=\"#000000\" d=\"M976.5,-244C976.5,-244 887.5,-244 887.5,-244 881.5,-244 875.5,-238 875.5,-232 875.5,-232 875.5,-188 875.5,-188 875.5,-182 881.5,-176 887.5,-176 887.5,-176 976.5,-176 976.5,-176 982.5,-176 988.5,-182 988.5,-188 988.5,-188 988.5,-232 988.5,-232 988.5,-238 982.5,-244 976.5,-244\"/>\n",
        "<text text-anchor=\"start\" x=\"897\" y=\"-228.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">t&#45;7 ≤ &#45;0.089</text>\n",
        "<text text-anchor=\"start\" x=\"894\" y=\"-213.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.004</text>\n",
        "<text text-anchor=\"start\" x=\"883.5\" y=\"-198.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1598</text>\n",
        "<text text-anchor=\"start\" x=\"888.5\" y=\"-183.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = &#45;0.028</text>\n",
        "</g>\n",
        "<!-- 32&#45;&gt;48 -->\n",
        "<g id=\"edge24\" class=\"edge\">\n",
        "<title>32&#45;&gt;48</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M776.7429,-284.7534C804.6663,-271.3088 837.8398,-255.3364 866.3397,-241.6142\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"867.9245,-244.7358 875.4161,-237.2441 864.8877,-238.4288 867.9245,-244.7358\"/>\n",
        "</g>\n",
        "<!-- 34 -->\n",
        "<g id=\"node19\" class=\"node\">\n",
        "<title>34</title>\n",
        "<path fill=\"#e58139\" fill-opacity=\"0.439216\" stroke=\"#000000\" d=\"M688.5,-140C688.5,-140 591.5,-140 591.5,-140 585.5,-140 579.5,-134 579.5,-128 579.5,-128 579.5,-84 579.5,-84 579.5,-78 585.5,-72 591.5,-72 591.5,-72 688.5,-72 688.5,-72 694.5,-72 700.5,-78 700.5,-84 700.5,-84 700.5,-128 700.5,-128 700.5,-134 694.5,-140 688.5,-140\"/>\n",
        "<text text-anchor=\"start\" x=\"596\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">month_4 ≤ 0.5</text>\n",
        "<text text-anchor=\"start\" x=\"602\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.004</text>\n",
        "<text text-anchor=\"start\" x=\"587.5\" y=\"-94.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 16029</text>\n",
        "<text text-anchor=\"start\" x=\"599\" y=\"-79.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.032</text>\n",
        "</g>\n",
        "<!-- 33&#45;&gt;34 -->\n",
        "<g id=\"edge18\" class=\"edge\">\n",
        "<title>33&#45;&gt;34</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M691.1148,-175.9465C684.6847,-167.1475 677.6837,-157.5672 670.9841,-148.3993\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"673.6672,-146.1389 664.9412,-140.13 668.0155,-150.269 673.6672,-146.1389\"/>\n",
        "</g>\n",
        "<!-- 41 -->\n",
        "<g id=\"node22\" class=\"node\">\n",
        "<title>41</title>\n",
        "<path fill=\"#e58139\" fill-opacity=\"0.294118\" stroke=\"#000000\" d=\"M853,-140C853,-140 731,-140 731,-140 725,-140 719,-134 719,-128 719,-128 719,-84 719,-84 719,-78 725,-72 731,-72 731,-72 853,-72 853,-72 859,-72 865,-78 865,-84 865,-84 865,-128 865,-128 865,-134 859,-140 853,-140\"/>\n",
        "<text text-anchor=\"start\" x=\"727\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Basic Industries ≤ 0.5</text>\n",
        "<text text-anchor=\"start\" x=\"754\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.004</text>\n",
        "<text text-anchor=\"start\" x=\"743.5\" y=\"-94.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1641</text>\n",
        "<text text-anchor=\"start\" x=\"748.5\" y=\"-79.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = &#45;0.003</text>\n",
        "</g>\n",
        "<!-- 33&#45;&gt;41 -->\n",
        "<g id=\"edge21\" class=\"edge\">\n",
        "<title>33&#45;&gt;41</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M740.8852,-175.9465C747.3153,-167.1475 754.3163,-157.5672 761.0159,-148.3993\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"763.9845,-150.269 767.0588,-140.13 758.3328,-146.1389 763.9845,-150.269\"/>\n",
        "</g>\n",
        "<!-- 35 -->\n",
        "<g id=\"node20\" class=\"node\">\n",
        "<title>35</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M638,-36C638,-36 608,-36 608,-36 602,-36 596,-30 596,-24 596,-24 596,-12 596,-12 596,-6 602,0 608,0 608,0 638,0 638,0 644,0 650,-6 650,-12 650,-12 650,-24 650,-24 650,-30 644,-36 638,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"623\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 34&#45;&gt;35 -->\n",
        "<g id=\"edge19\" class=\"edge\">\n",
        "<title>34&#45;&gt;35</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M633.4274,-71.9769C631.7718,-63.4071 630.0133,-54.3043 628.4214,-46.0638\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"631.8476,-45.3465 626.5144,-36.192 624.9747,-46.6743 631.8476,-45.3465\"/>\n",
        "</g>\n",
        "<!-- 38 -->\n",
        "<g id=\"node21\" class=\"node\">\n",
        "<title>38</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M710,-36C710,-36 680,-36 680,-36 674,-36 668,-30 668,-24 668,-24 668,-12 668,-12 668,-6 674,0 680,0 680,0 710,0 710,0 716,0 722,-6 722,-12 722,-12 722,-24 722,-24 722,-30 716,-36 710,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"695\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 34&#45;&gt;38 -->\n",
        "<g id=\"edge20\" class=\"edge\">\n",
        "<title>34&#45;&gt;38</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M661.2644,-71.9769C666.9181,-62.931 672.943,-53.2913 678.3134,-44.6986\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"681.298,-46.527 683.63,-36.192 675.362,-42.8169 681.298,-46.527\"/>\n",
        "</g>\n",
        "<!-- 42 -->\n",
        "<g id=\"node23\" class=\"node\">\n",
        "<title>42</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M783,-36C783,-36 753,-36 753,-36 747,-36 741,-30 741,-24 741,-24 741,-12 741,-12 741,-6 747,0 753,0 753,0 783,0 783,0 789,0 795,-6 795,-12 795,-12 795,-24 795,-24 795,-30 789,-36 783,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"768\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 41&#45;&gt;42 -->\n",
        "<g id=\"edge22\" class=\"edge\">\n",
        "<title>41&#45;&gt;42</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M782.721,-71.9769C780.3838,-63.4071 777.9012,-54.3043 775.6538,-46.0638\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"778.9694,-44.9187 772.9614,-36.192 772.216,-46.7605 778.9694,-44.9187\"/>\n",
        "</g>\n",
        "<!-- 45 -->\n",
        "<g id=\"node24\" class=\"node\">\n",
        "<title>45</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M855,-36C855,-36 825,-36 825,-36 819,-36 813,-30 813,-24 813,-24 813,-12 813,-12 813,-6 819,0 825,0 825,0 855,0 855,0 861,0 867,-6 867,-12 867,-12 867,-24 867,-24 867,-30 861,-36 855,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"840\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 41&#45;&gt;45 -->\n",
        "<g id=\"edge23\" class=\"edge\">\n",
        "<title>41&#45;&gt;45</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M810.5581,-71.9769C815.3883,-63.1215 820.529,-53.6969 825.1404,-45.2427\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"828.3612,-46.6469 830.0771,-36.192 822.2159,-43.2949 828.3612,-46.6469\"/>\n",
        "</g>\n",
        "<!-- 49 -->\n",
        "<g id=\"node26\" class=\"node\">\n",
        "<title>49</title>\n",
        "<path fill=\"#e58139\" fill-opacity=\"0.403922\" stroke=\"#000000\" d=\"M969,-140C969,-140 895,-140 895,-140 889,-140 883,-134 883,-128 883,-128 883,-84 883,-84 883,-78 889,-72 895,-72 895,-72 969,-72 969,-72 975,-72 981,-78 981,-84 981,-84 981,-128 981,-128 981,-134 975,-140 969,-140\"/>\n",
        "<text text-anchor=\"start\" x=\"899\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">t&#45;5 ≤ 0.152</text>\n",
        "<text text-anchor=\"start\" x=\"894\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.005</text>\n",
        "<text text-anchor=\"start\" x=\"891\" y=\"-94.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 40</text>\n",
        "<text text-anchor=\"start\" x=\"891\" y=\"-79.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.024</text>\n",
        "</g>\n",
        "<!-- 48&#45;&gt;49 -->\n",
        "<g id=\"edge25\" class=\"edge\">\n",
        "<title>48&#45;&gt;49</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M932,-175.9465C932,-167.776 932,-158.9318 932,-150.3697\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"935.5001,-150.13 932,-140.13 928.5001,-150.13 935.5001,-150.13\"/>\n",
        "</g>\n",
        "<!-- 56 -->\n",
        "<g id=\"node29\" class=\"node\">\n",
        "<title>56</title>\n",
        "<path fill=\"#e58139\" fill-opacity=\"0.184314\" stroke=\"#000000\" d=\"M1100.5,-140C1100.5,-140 1011.5,-140 1011.5,-140 1005.5,-140 999.5,-134 999.5,-128 999.5,-128 999.5,-84 999.5,-84 999.5,-78 1005.5,-72 1011.5,-72 1011.5,-72 1100.5,-72 1100.5,-72 1106.5,-72 1112.5,-78 1112.5,-84 1112.5,-84 1112.5,-128 1112.5,-128 1112.5,-134 1106.5,-140 1100.5,-140\"/>\n",
        "<text text-anchor=\"start\" x=\"1014.5\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Finance ≤ 0.5</text>\n",
        "<text text-anchor=\"start\" x=\"1018\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.003</text>\n",
        "<text text-anchor=\"start\" x=\"1007.5\" y=\"-94.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1558</text>\n",
        "<text text-anchor=\"start\" x=\"1016.5\" y=\"-79.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = &#45;0.03</text>\n",
        "</g>\n",
        "<!-- 48&#45;&gt;56 -->\n",
        "<g id=\"edge28\" class=\"edge\">\n",
        "<title>48&#45;&gt;56</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M972.6022,-175.9465C983.7356,-166.6088 995.9183,-156.3911 1007.4489,-146.7203\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1009.8938,-149.2378 1015.3065,-140.13 1005.3954,-143.8745 1009.8938,-149.2378\"/>\n",
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        "<!-- 50 -->\n",
        "<g id=\"node27\" class=\"node\">\n",
        "<title>50</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M927,-36C927,-36 897,-36 897,-36 891,-36 885,-30 885,-24 885,-24 885,-12 885,-12 885,-6 891,0 897,0 897,0 927,0 927,0 933,0 939,-6 939,-12 939,-12 939,-24 939,-24 939,-30 933,-36 927,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"912\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 49&#45;&gt;50 -->\n",
        "<g id=\"edge26\" class=\"edge\">\n",
        "<title>49&#45;&gt;50</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M924.2675,-71.9769C922.3198,-63.4071 920.251,-54.3043 918.3781,-46.0638\"/>\n",
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        "<!-- 53 -->\n",
        "<g id=\"node28\" class=\"node\">\n",
        "<title>53</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M999,-36C999,-36 969,-36 969,-36 963,-36 957,-30 957,-24 957,-24 957,-12 957,-12 957,-6 963,0 969,0 969,0 999,0 999,0 1005,0 1011,-6 1011,-12 1011,-12 1011,-24 1011,-24 1011,-30 1005,-36 999,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"984\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 49&#45;&gt;53 -->\n",
        "<g id=\"edge27\" class=\"edge\">\n",
        "<title>49&#45;&gt;53</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M952.1046,-71.9769C957.3936,-63.0262 963.0262,-53.4941 968.063,-44.9703\"/>\n",
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        "<!-- 57 -->\n",
        "<g id=\"node30\" class=\"node\">\n",
        "<title>57</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M1071,-36C1071,-36 1041,-36 1041,-36 1035,-36 1029,-30 1029,-24 1029,-24 1029,-12 1029,-12 1029,-6 1035,0 1041,0 1041,0 1071,0 1071,0 1077,0 1083,-6 1083,-12 1083,-12 1083,-24 1083,-24 1083,-30 1077,-36 1071,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"1056\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 56&#45;&gt;57 -->\n",
        "<g id=\"edge29\" class=\"edge\">\n",
        "<title>56&#45;&gt;57</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M1056,-71.9769C1056,-63.5023 1056,-54.5065 1056,-46.3388\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1059.5001,-46.1919 1056,-36.192 1052.5001,-46.192 1059.5001,-46.1919\"/>\n",
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        "<!-- 60 -->\n",
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        "<title>60</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M1143,-36C1143,-36 1113,-36 1113,-36 1107,-36 1101,-30 1101,-24 1101,-24 1101,-12 1101,-12 1101,-6 1107,0 1113,0 1113,0 1143,0 1143,0 1149,0 1155,-6 1155,-12 1155,-12 1155,-24 1155,-24 1155,-30 1149,-36 1143,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"1128\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 56&#45;&gt;60 -->\n",
        "<g id=\"edge30\" class=\"edge\">\n",
        "<title>56&#45;&gt;60</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M1083.8371,-71.9769C1091.3941,-62.7406 1099.4576,-52.8851 1106.5986,-44.1573\"/>\n",
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        "</g>\n",
        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
        "<graphviz.files.Source at 0x7fce013212b0>"
       ]
      },
      "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "out_file = 'figures/reg_tree.dot'\n",
     "dot_data = export_graphviz(regression_tree,\n",
     "                           out_file=out_file,\n",
     "                           feature_names=X_train.columns,\n",
     "                           max_depth=3,\n",
     "                           filled=True,\n",
     "                           rounded=True,\n",
     "                           special_characters=True)\n",
     "if out_file is not None:\n",
     "    dot_data = Path(out_file).read_text()\n",
     "\n",
     "graphviz.Source(dot_data)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Evaluate Test Set"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 67,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.166403Z",
      "start_time": "2018-10-31T22:06:39.270Z"
     }
    },
    "outputs": [],
    "source": [
     "y_pred = regression_tree.predict(X_test)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 68,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.169601Z",
      "start_time": "2018-10-31T22:06:39.277Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "0.06600785966046621"
       ]
      },
      "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "np.sqrt(mean_squared_error(y_pred=y_pred, y_true=y_test))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Classification Tree with all Features"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We will now train, visualize, and evaluate a classification tree with up to 5 consecutive splits using 80% of the samples for training to predict the remaining 20%. We are taking a shortcut here to simplify the illustration and use the built-in train_test_split, which does not protect against lookahead bias, as our custom iterator. The tree configuration implies up to $2^5=32$ leaf nodes that, on average in the balanced case, would contain over 4,300 of the training samples."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Train-Test Split"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 69,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.143123Z",
      "start_time": "2018-10-31T22:06:39.172Z"
     }
    },
    "outputs": [],
    "source": [
     "X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.2, random_state=42)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 70,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.144442Z",
      "start_time": "2018-10-31T22:06:39.181Z"
     }
    },
    "outputs": [],
    "source": [
     "clf = DecisionTreeClassifier(criterion='gini',\n",
     "                             max_depth=5,\n",
     "                             random_state=42)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 71,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.148241Z",
      "start_time": "2018-10-31T22:06:39.190Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,\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, presort=False, random_state=42,\n",
        "            splitter='best')"
       ]
      },
      "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "clf.fit(X=X_train, y=y_train)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "To evaluate the predictive accuracy of our first classification tree, we will use our test set to generate predicted class probabilities. \n",
     "\n",
     "The `.predict_proba()` method produces one probability for each class. In the binary class, these probabilities are complementary and sum to 1, so we only need the value for the positive class. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 72,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.150012Z",
      "start_time": "2018-10-31T22:06:39.198Z"
     }
    },
    "outputs": [],
    "source": [
     "y_score = clf.predict_proba(X=X_test)[:, 1]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "To evaluate the generalization error, we will use the area under the curve based on the receiver-operating characteristic that we introduced in Chapter 6, The Machine Learning Process. The result indicates a significant improvement above and beyond the baseline value of 0.5 for a random prediction:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "0.6139767794050386"
       ]
      },
      "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "roc_auc_score(y_score=y_score, y_true=y_test)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Plot Tree"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 74,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.151746Z",
      "start_time": "2018-10-31T22:06:39.207Z"
     }
    },
    "outputs": [
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        "<title>10&#45;&gt;11</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M224.7147,-71.8901C219.9728,-62.8331 215.0741,-53.4765 210.7161,-45.1528\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"213.802,-43.5009 206.0629,-36.2651 207.6005,-46.7478 213.802,-43.5009\"/>\n",
        "</g>\n",
        "<!-- 14 -->\n",
        "<g id=\"node9\" class=\"node\">\n",
        "<title>14</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M283.5,-36C283.5,-36 253.5,-36 253.5,-36 247.5,-36 241.5,-30 241.5,-24 241.5,-24 241.5,-12 241.5,-12 241.5,-6 247.5,0 253.5,0 253.5,0 283.5,0 283.5,0 289.5,0 295.5,-6 295.5,-12 295.5,-12 295.5,-24 295.5,-24 295.5,-30 289.5,-36 283.5,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"268.5\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 10&#45;&gt;14 -->\n",
        "<g id=\"edge8\" class=\"edge\">\n",
        "<title>10&#45;&gt;14</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M256.0855,-71.8901C258.0822,-63.2227 260.1421,-54.2808 261.9962,-46.2325\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"265.4581,-46.7956 264.2923,-36.2651 258.6367,-45.2241 265.4581,-46.7956\"/>\n",
        "</g>\n",
        "<!-- 18 -->\n",
        "<g id=\"node11\" class=\"node\">\n",
        "<title>18</title>\n",
        "<path fill=\"#399de5\" fill-opacity=\"0.349020\" stroke=\"#000000\" d=\"M466.5,-155C466.5,-155 350.5,-155 350.5,-155 344.5,-155 338.5,-149 338.5,-143 338.5,-143 338.5,-84 338.5,-84 338.5,-78 344.5,-72 350.5,-72 350.5,-72 466.5,-72 466.5,-72 472.5,-72 478.5,-78 478.5,-84 478.5,-84 478.5,-143 478.5,-143 478.5,-149 472.5,-155 466.5,-155\"/>\n",
        "<text text-anchor=\"start\" x=\"373.5\" y=\"-139.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">t&#45;2 ≤ &#45;0.046</text>\n",
        "<text text-anchor=\"start\" x=\"373\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.478</text>\n",
        "<text text-anchor=\"start\" x=\"360\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 8026</text>\n",
        "<text text-anchor=\"start\" x=\"346.5\" y=\"-94.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [3165, 4861]</text>\n",
        "<text text-anchor=\"start\" x=\"376.5\" y=\"-79.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Up</text>\n",
        "</g>\n",
        "<!-- 17&#45;&gt;18 -->\n",
        "<g id=\"edge10\" class=\"edge\">\n",
        "<title>17&#45;&gt;18</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M455.9681,-190.8796C450.609,-182.1434 444.9021,-172.8404 439.3718,-163.8253\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"442.3533,-161.9919 434.1408,-155.2981 436.3865,-165.6522 442.3533,-161.9919\"/>\n",
        "</g>\n",
        "<!-- 25 -->\n",
        "<g id=\"node14\" class=\"node\">\n",
        "<title>25</title>\n",
        "<path fill=\"#399de5\" fill-opacity=\"0.650980\" stroke=\"#000000\" d=\"M616.5,-155C616.5,-155 508.5,-155 508.5,-155 502.5,-155 496.5,-149 496.5,-143 496.5,-143 496.5,-84 496.5,-84 496.5,-78 502.5,-72 508.5,-72 508.5,-72 616.5,-72 616.5,-72 622.5,-72 628.5,-78 628.5,-84 628.5,-84 628.5,-143 628.5,-143 628.5,-149 622.5,-155 616.5,-155\"/>\n",
        "<text text-anchor=\"start\" x=\"529.5\" y=\"-139.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">t&#45;3 ≤ 0.001</text>\n",
        "<text text-anchor=\"start\" x=\"527\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.384</text>\n",
        "<text text-anchor=\"start\" x=\"514\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1457</text>\n",
        "<text text-anchor=\"start\" x=\"504.5\" y=\"-94.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [377, 1080]</text>\n",
        "<text text-anchor=\"start\" x=\"530.5\" y=\"-79.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Up</text>\n",
        "</g>\n",
        "<!-- 17&#45;&gt;25 -->\n",
        "<g id=\"edge13\" class=\"edge\">\n",
        "<title>17&#45;&gt;25</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M509.8299,-190.8796C515.7763,-182.1434 522.1086,-172.8404 528.245,-163.8253\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"531.3156,-165.5342 534.0492,-155.2981 525.5289,-161.5953 531.3156,-165.5342\"/>\n",
        "</g>\n",
        "<!-- 19 -->\n",
        "<g id=\"node12\" class=\"node\">\n",
        "<title>19</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M375.5,-36C375.5,-36 345.5,-36 345.5,-36 339.5,-36 333.5,-30 333.5,-24 333.5,-24 333.5,-12 333.5,-12 333.5,-6 339.5,0 345.5,0 345.5,0 375.5,0 375.5,0 381.5,0 387.5,-6 387.5,-12 387.5,-12 387.5,-24 387.5,-24 387.5,-30 381.5,-36 375.5,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"360.5\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 18&#45;&gt;19 -->\n",
        "<g id=\"edge11\" class=\"edge\">\n",
        "<title>18&#45;&gt;19</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M387.5861,-71.8901C383.0829,-62.9305 378.4322,-53.6777 374.2826,-45.4217\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"377.2985,-43.6282 369.6804,-36.2651 371.044,-46.7718 377.2985,-43.6282\"/>\n",
        "</g>\n",
        "<!-- 22 -->\n",
        "<g id=\"node13\" class=\"node\">\n",
        "<title>22</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M447.5,-36C447.5,-36 417.5,-36 417.5,-36 411.5,-36 405.5,-30 405.5,-24 405.5,-24 405.5,-12 405.5,-12 405.5,-6 411.5,0 417.5,0 417.5,0 447.5,0 447.5,0 453.5,0 459.5,-6 459.5,-12 459.5,-12 459.5,-24 459.5,-24 459.5,-30 453.5,-36 447.5,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"432.5\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 18&#45;&gt;22 -->\n",
        "<g id=\"edge12\" class=\"edge\">\n",
        "<title>18&#45;&gt;22</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M418.9569,-71.8901C421.1351,-63.2227 423.3823,-54.2808 425.4049,-46.2325\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"428.8669,-46.8166 427.9098,-36.2651 422.078,-45.1105 428.8669,-46.8166\"/>\n",
        "</g>\n",
        "<!-- 26 -->\n",
        "<g id=\"node15\" class=\"node\">\n",
        "<title>26</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M529.5,-36C529.5,-36 499.5,-36 499.5,-36 493.5,-36 487.5,-30 487.5,-24 487.5,-24 487.5,-12 487.5,-12 487.5,-6 493.5,0 499.5,0 499.5,0 529.5,0 529.5,0 535.5,0 541.5,-6 541.5,-12 541.5,-12 541.5,-24 541.5,-24 541.5,-30 535.5,-36 529.5,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"514.5\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 25&#45;&gt;26 -->\n",
        "<g id=\"edge14\" class=\"edge\">\n",
        "<title>25&#45;&gt;26</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M541.5861,-71.8901C537.0829,-62.9305 532.4322,-53.6777 528.2826,-45.4217\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"531.2985,-43.6282 523.6804,-36.2651 525.044,-46.7718 531.2985,-43.6282\"/>\n",
        "</g>\n",
        "<!-- 29 -->\n",
        "<g id=\"node16\" class=\"node\">\n",
        "<title>29</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M601.5,-36C601.5,-36 571.5,-36 571.5,-36 565.5,-36 559.5,-30 559.5,-24 559.5,-24 559.5,-12 559.5,-12 559.5,-6 565.5,0 571.5,0 571.5,0 601.5,0 601.5,0 607.5,0 613.5,-6 613.5,-12 613.5,-12 613.5,-24 613.5,-24 613.5,-30 607.5,-36 601.5,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"586.5\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 25&#45;&gt;29 -->\n",
        "<g id=\"edge15\" class=\"edge\">\n",
        "<title>25&#45;&gt;29</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M572.9569,-71.8901C575.1351,-63.2227 577.3823,-54.2808 579.4049,-46.2325\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"582.8669,-46.8166 581.9098,-36.2651 576.078,-45.1105 582.8669,-46.8166\"/>\n",
        "</g>\n",
        "<!-- 33 -->\n",
        "<g id=\"node18\" class=\"node\">\n",
        "<title>33</title>\n",
        "<path fill=\"#399de5\" fill-opacity=\"0.552941\" stroke=\"#000000\" d=\"M851,-274C851,-274 728,-274 728,-274 722,-274 716,-268 716,-262 716,-262 716,-203 716,-203 716,-197 722,-191 728,-191 728,-191 851,-191 851,-191 857,-191 863,-197 863,-203 863,-203 863,-262 863,-262 863,-268 857,-274 851,-274\"/>\n",
        "<text text-anchor=\"start\" x=\"745.5\" y=\"-258.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">month_6 ≤ 0.5</text>\n",
        "<text text-anchor=\"start\" x=\"754\" y=\"-243.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.427</text>\n",
        "<text text-anchor=\"start\" x=\"737\" y=\"-228.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 17670</text>\n",
        "<text text-anchor=\"start\" x=\"724\" y=\"-213.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [5461, 12209]</text>\n",
        "<text text-anchor=\"start\" x=\"757.5\" y=\"-198.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Up</text>\n",
        "</g>\n",
        "<!-- 32&#45;&gt;33 -->\n",
        "<g id=\"edge17\" class=\"edge\">\n",
        "<title>32&#45;&gt;33</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M789.5,-309.8796C789.5,-301.6838 789.5,-292.9891 789.5,-284.5013\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"793.0001,-284.298 789.5,-274.2981 786.0001,-284.2981 793.0001,-284.298\"/>\n",
        "</g>\n",
        "<!-- 48 -->\n",
        "<g id=\"node25\" class=\"node\">\n",
        "<title>48</title>\n",
        "<path fill=\"#e58139\" fill-opacity=\"0.674510\" stroke=\"#000000\" d=\"M1071.5,-274C1071.5,-274 963.5,-274 963.5,-274 957.5,-274 951.5,-268 951.5,-262 951.5,-262 951.5,-203 951.5,-203 951.5,-197 957.5,-191 963.5,-191 963.5,-191 1071.5,-191 1071.5,-191 1077.5,-191 1083.5,-197 1083.5,-203 1083.5,-203 1083.5,-262 1083.5,-262 1083.5,-268 1077.5,-274 1071.5,-274\"/>\n",
        "<text text-anchor=\"start\" x=\"976\" y=\"-258.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Finance ≤ 0.5</text>\n",
        "<text text-anchor=\"start\" x=\"982\" y=\"-243.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.372</text>\n",
        "<text text-anchor=\"start\" x=\"969\" y=\"-228.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1598</text>\n",
        "<text text-anchor=\"start\" x=\"959.5\" y=\"-213.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [1204, 394]</text>\n",
        "<text text-anchor=\"start\" x=\"976.5\" y=\"-198.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Down</text>\n",
        "</g>\n",
        "<!-- 32&#45;&gt;48 -->\n",
        "<g id=\"edge24\" class=\"edge\">\n",
        "<title>32&#45;&gt;48</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M863.2102,-313.0284C888.5816,-299.7864 917.0085,-284.9495 942.4828,-271.6537\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"944.1147,-274.7501 951.3604,-267.0202 940.8758,-268.5445 944.1147,-274.7501\"/>\n",
        "</g>\n",
        "<!-- 34 -->\n",
        "<g id=\"node19\" class=\"node\">\n",
        "<title>34</title>\n",
        "<path fill=\"#399de5\" fill-opacity=\"0.596078\" stroke=\"#000000\" d=\"M782,-155C782,-155 659,-155 659,-155 653,-155 647,-149 647,-143 647,-143 647,-84 647,-84 647,-78 653,-72 659,-72 659,-72 782,-72 782,-72 788,-72 794,-78 794,-84 794,-84 794,-143 794,-143 794,-149 788,-155 782,-155\"/>\n",
        "<text text-anchor=\"start\" x=\"676.5\" y=\"-139.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">month_4 ≤ 0.5</text>\n",
        "<text text-anchor=\"start\" x=\"685\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.409</text>\n",
        "<text text-anchor=\"start\" x=\"668\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 16029</text>\n",
        "<text text-anchor=\"start\" x=\"655\" y=\"-94.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [4598, 11431]</text>\n",
        "<text text-anchor=\"start\" x=\"688.5\" y=\"-79.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Up</text>\n",
        "</g>\n",
        "<!-- 33&#45;&gt;34 -->\n",
        "<g id=\"edge18\" class=\"edge\">\n",
        "<title>33&#45;&gt;34</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M765.3671,-190.8796C760.3539,-182.2335 755.0187,-173.0322 749.8419,-164.1042\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"752.7798,-162.1934 744.7359,-155.2981 746.7242,-165.7047 752.7798,-162.1934\"/>\n",
        "</g>\n",
        "<!-- 41 -->\n",
        "<g id=\"node22\" class=\"node\">\n",
        "<title>41</title>\n",
        "<path fill=\"#e58139\" fill-opacity=\"0.098039\" stroke=\"#000000\" d=\"M925,-155C925,-155 824,-155 824,-155 818,-155 812,-149 812,-143 812,-143 812,-84 812,-84 812,-78 818,-72 824,-72 824,-72 925,-72 925,-72 931,-72 937,-78 937,-84 937,-84 937,-143 937,-143 937,-149 931,-155 925,-155\"/>\n",
        "<text text-anchor=\"start\" x=\"833\" y=\"-139.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Finance ≤ 0.5</text>\n",
        "<text text-anchor=\"start\" x=\"839\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.499</text>\n",
        "<text text-anchor=\"start\" x=\"826\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1641</text>\n",
        "<text text-anchor=\"start\" x=\"820\" y=\"-94.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [863, 778]</text>\n",
        "<text text-anchor=\"start\" x=\"833.5\" y=\"-79.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Down</text>\n",
        "</g>\n",
        "<!-- 33&#45;&gt;41 -->\n",
        "<g id=\"edge21\" class=\"edge\">\n",
        "<title>33&#45;&gt;41</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M819.2289,-190.8796C825.5333,-182.0534 832.251,-172.6485 838.7524,-163.5466\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"841.6799,-165.4698 844.6442,-155.2981 835.9837,-161.4011 841.6799,-165.4698\"/>\n",
        "</g>\n",
        "<!-- 35 -->\n",
        "<g id=\"node20\" class=\"node\">\n",
        "<title>35</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M678.5,-36C678.5,-36 648.5,-36 648.5,-36 642.5,-36 636.5,-30 636.5,-24 636.5,-24 636.5,-12 636.5,-12 636.5,-6 642.5,0 648.5,0 648.5,0 678.5,0 678.5,0 684.5,0 690.5,-6 690.5,-12 690.5,-12 690.5,-24 690.5,-24 690.5,-30 684.5,-36 678.5,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"663.5\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 34&#45;&gt;35 -->\n",
        "<g id=\"edge19\" class=\"edge\">\n",
        "<title>34&#45;&gt;35</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M695.6648,-71.8901C690.2009,-62.7357 684.5543,-53.2752 679.5464,-44.8847\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"682.5322,-43.0581 674.4017,-36.2651 676.5215,-46.6457 682.5322,-43.0581\"/>\n",
        "</g>\n",
        "<!-- 38 -->\n",
        "<g id=\"node21\" class=\"node\">\n",
        "<title>38</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M750.5,-36C750.5,-36 720.5,-36 720.5,-36 714.5,-36 708.5,-30 708.5,-24 708.5,-24 708.5,-12 708.5,-12 708.5,-6 714.5,0 720.5,0 720.5,0 750.5,0 750.5,0 756.5,0 762.5,-6 762.5,-12 762.5,-12 762.5,-24 762.5,-24 762.5,-30 756.5,-36 750.5,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"735.5\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 34&#45;&gt;38 -->\n",
        "<g id=\"edge20\" class=\"edge\">\n",
        "<title>34&#45;&gt;38</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M727.0356,-71.8901C728.397,-63.2227 729.8014,-54.2808 731.0656,-46.2325\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"734.537,-46.6871 732.6311,-36.2651 727.6218,-45.6009 734.537,-46.6871\"/>\n",
        "</g>\n",
        "<!-- 42 -->\n",
        "<g id=\"node23\" class=\"node\">\n",
        "<title>42</title>\n",
        "<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M825.5,-36C825.5,-36 795.5,-36 795.5,-36 789.5,-36 783.5,-30 783.5,-24 783.5,-24 783.5,-12 783.5,-12 783.5,-6 789.5,0 795.5,0 795.5,0 825.5,0 825.5,0 831.5,0 837.5,-6 837.5,-12 837.5,-12 837.5,-24 837.5,-24 837.5,-30 831.5,-36 825.5,-36\"/>\n",
        "<text text-anchor=\"middle\" x=\"810.5\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n",
        "</g>\n",
        "<!-- 41&#45;&gt;42 -->\n",
        "<g id=\"edge22\" class=\"edge\">\n",
        "<title>41&#45;&gt;42</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M846.6148,-71.8901C840.4147,-62.6384 834.005,-53.0739 828.3377,-44.6173\"/>\n",
        "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"831.2151,-42.6237 822.7405,-36.2651 825.4001,-46.5207 831.2151,-42.6237\"/>\n",
        "</g>\n",
        "<!-- 45 -->\n",
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        "<title>45</title>\n",
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        "<text text-anchor=\"start\" x=\"1111.5\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 286</text>\n",
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        "</svg>\n"
       ],
       "text/plain": [
        "<graphviz.files.Source at 0x7fce016c4128>"
       ]
      },
      "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "out_file = 'figures/clf_tree.dot'\n",
     "dot_data = export_graphviz(clf,\n",
     "                           out_file=out_file,\n",
     "                           feature_names=X.columns,\n",
     "                           class_names=['Down', 'Up'],\n",
     "                           max_depth=3,\n",
     "                           filled=True,\n",
     "                           rounded=True,\n",
     "                           special_characters=True)\n",
     "if out_file is not None:\n",
     "    dot_data = Path(out_file).read_text()\n",
     "\n",
     "graphviz.Source(dot_data)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Evaluate Test Set"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 77,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.160193Z",
      "start_time": "2018-10-31T22:06:39.216Z"
     }
    },
    "outputs": [],
    "source": [
     "y_pred = clf.predict_proba(X_test)[:, 1]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 78,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.161309Z",
      "start_time": "2018-10-31T22:06:39.223Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "0.6139767794050386"
       ]
      },
      "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "roc_auc_score(y_true=y_test, y_score=y_pred)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Print Decision Path"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Inspired by https://stackoverflow.com/questions/20224526/how-to-extract-the-decision-rules-from-scikit-learn-decision-tree"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 79,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.134173Z",
      "start_time": "2018-10-31T22:06:39.125Z"
     }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Help on class Tree in module sklearn.tree._tree:\n",
       "\n",
       "class Tree(builtins.object)\n",
       " |  Array-based representation of a binary decision tree.\n",
       " |  \n",
       " |  The binary tree is represented as a number of parallel arrays. The i-th\n",
       " |  element of each array holds information about the node `i`. Node 0 is the\n",
       " |  tree's root. You can find a detailed description of all arrays in\n",
       " |  `_tree.pxd`. NOTE: Some of the arrays only apply to either leaves or split\n",
       " |  nodes, resp. In this case the values of nodes of the other type are\n",
       " |  arbitrary!\n",
       " |  \n",
       " |  Attributes\n",
       " |  ----------\n",
       " |  node_count : int\n",
       " |      The number of nodes (internal nodes + leaves) in the tree.\n",
       " |  \n",
       " |  capacity : int\n",
       " |      The current capacity (i.e., size) of the arrays, which is at least as\n",
       " |      great as `node_count`.\n",
       " |  \n",
       " |  max_depth : int\n",
       " |      The maximal depth of the tree.\n",
       " |  \n",
       " |  children_left : array of int, shape [node_count]\n",
       " |      children_left[i] holds the node id of the left child of node i.\n",
       " |      For leaves, children_left[i] == TREE_LEAF. Otherwise,\n",
       " |      children_left[i] > i. This child handles the case where\n",
       " |      X[:, feature[i]] <= threshold[i].\n",
       " |  \n",
       " |  children_right : array of int, shape [node_count]\n",
       " |      children_right[i] holds the node id of the right child of node i.\n",
       " |      For leaves, children_right[i] == TREE_LEAF. Otherwise,\n",
       " |      children_right[i] > i. This child handles the case where\n",
       " |      X[:, feature[i]] > threshold[i].\n",
       " |  \n",
       " |  feature : array of int, shape [node_count]\n",
       " |      feature[i] holds the feature to split on, for the internal node i.\n",
       " |  \n",
       " |  threshold : array of double, shape [node_count]\n",
       " |      threshold[i] holds the threshold for the internal node i.\n",
       " |  \n",
       " |  value : array of double, shape [node_count, n_outputs, max_n_classes]\n",
       " |      Contains the constant prediction value of each node.\n",
       " |  \n",
       " |  impurity : array of double, shape [node_count]\n",
       " |      impurity[i] holds the impurity (i.e., the value of the splitting\n",
       " |      criterion) at node i.\n",
       " |  \n",
       " |  n_node_samples : array of int, shape [node_count]\n",
       " |      n_node_samples[i] holds the number of training samples reaching node i.\n",
       " |  \n",
       " |  weighted_n_node_samples : array of int, shape [node_count]\n",
       " |      weighted_n_node_samples[i] holds the weighted number of training samples\n",
       " |      reaching node i.\n",
       " |  \n",
       " |  Methods defined here:\n",
       " |  \n",
       " |  __getstate__(...)\n",
       " |      Getstate re-implementation, for pickling.\n",
       " |  \n",
       " |  __reduce__(...)\n",
       " |      Reduce re-implementation, for pickling.\n",
       " |  \n",
       " |  __setstate__(...)\n",
       " |      Setstate re-implementation, for unpickling.\n",
       " |  \n",
       " |  apply(...)\n",
       " |      Finds the terminal region (=leaf node) for each sample in X.\n",
       " |  \n",
       " |  compute_feature_importances(...)\n",
       " |      Computes the importance of each feature (aka variable).\n",
       " |  \n",
       " |  decision_path(...)\n",
       " |      Finds the decision path (=node) for each sample in X.\n",
       " |  \n",
       " |  predict(...)\n",
       " |      Predict target for X.\n",
       " |  \n",
       " |  ----------------------------------------------------------------------\n",
       " |  Static methods defined here:\n",
       " |  \n",
       " |  __new__(*args, **kwargs) from builtins.type\n",
       " |      Create and return a new object.  See help(type) for accurate signature.\n",
       " |  \n",
       " |  ----------------------------------------------------------------------\n",
       " |  Data descriptors defined here:\n",
       " |  \n",
       " |  capacity\n",
       " |  \n",
       " |  children_left\n",
       " |  \n",
       " |  children_right\n",
       " |  \n",
       " |  feature\n",
       " |  \n",
       " |  impurity\n",
       " |  \n",
       " |  max_depth\n",
       " |  \n",
       " |  max_n_classes\n",
       " |  \n",
       " |  n_classes\n",
       " |  \n",
       " |  n_features\n",
       " |  \n",
       " |  n_node_samples\n",
       " |  \n",
       " |  n_outputs\n",
       " |  \n",
       " |  node_count\n",
       " |  \n",
       " |  threshold\n",
       " |  \n",
       " |  value\n",
       " |  \n",
       " |  weighted_n_node_samples\n",
       " |  \n",
       " |  ----------------------------------------------------------------------\n",
       " |  Data and other attributes defined here:\n",
       " |  \n",
       " |  __pyx_vtable__ = <capsule object NULL>\n",
       "\n"
      ]
     }
    ],
    "source": [
     "from sklearn.tree._tree import Tree\n",
     "help(Tree)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 80,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.140502Z",
      "start_time": "2018-10-31T22:06:39.134Z"
     }
    },
    "outputs": [],
    "source": [
     "def tree_to_code(tree, feature_names):\n",
     "    if isinstance(tree, DecisionTreeClassifier):\n",
     "        model = 'clf'\n",
     "    elif isinstance(tree, DecisionTreeRegressor):\n",
     "        model = 'reg'\n",
     "    else:\n",
     "        raise ValueError('Need Regression or Classification Tree')\n",
     "        \n",
     "    tree_ = tree.tree_\n",
     "    feature_name = [\n",
     "        feature_names[i] if i != _tree.TREE_UNDEFINED else \"undefined!\"\n",
     "        for i in tree_.feature\n",
     "    ]\n",
     "    print(\"def tree({}):\".format(\", \".join(feature_names)))\n",
     "\n",
     "    def recurse(node, depth):\n",
     "        indent = \"  \" * depth\n",
     "        if tree_.feature[node] != _tree.TREE_UNDEFINED:\n",
     "            name = feature_name[node]\n",
     "            threshold = tree_.threshold[node]\n",
     "            print(indent, f'if {name} <= {threshold:.2%}')\n",
     "            recurse(tree_.children_left[node], depth + 1)\n",
     "            print(indent, f'else:  # if {name} > {threshold:.2%}')\n",
     "            recurse(tree_.children_right[node], depth + 1)\n",
     "        else:\n",
     "            pred = tree_.value[node][0]\n",
     "            val = pred[1]/sum(pred) if model == 'clf' else pred[0]\n",
     "            print(indent, f'return {val:.2%}')\n",
     "    recurse(0, 1)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 82,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.142001Z",
      "start_time": "2018-10-31T22:06:39.142Z"
     }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "def tree(t-1, t-2):\n",
       "   if t-1 <= -0.01%\n",
       "     if t-2 <= -0.80%\n",
       "       if t-2 <= -4.34%\n",
       "         if t-1 <= -0.06%\n",
       "           return 54.06%\n",
       "         else:  # if t-1 > -0.06%\n",
       "           return 76.60%\n",
       "       else:  # if t-2 > -4.34%\n",
       "         if t-2 <= -4.27%\n",
       "           return 68.55%\n",
       "         else:  # if t-2 > -4.27%\n",
       "           return 56.10%\n",
       "     else:  # if t-2 > -0.80%\n",
       "       if t-2 <= 12.29%\n",
       "         if t-2 <= 3.49%\n",
       "           return 57.98%\n",
       "         else:  # if t-2 > 3.49%\n",
       "           return 59.71%\n",
       "       else:  # if t-2 > 12.29%\n",
       "         if t-2 <= 12.98%\n",
       "           return 50.12%\n",
       "         else:  # if t-2 > 12.98%\n",
       "           return 55.72%\n",
       "   else:  # if t-1 > -0.01%\n",
       "     if t-1 <= 0.00%\n",
       "       if t-2 <= 0.29%\n",
       "         if t-2 <= -0.46%\n",
       "           return 45.24%\n",
       "         else:  # if t-2 > -0.46%\n",
       "           return 3.21%\n",
       "       else:  # if t-2 > 0.29%\n",
       "         if t-2 <= 8.22%\n",
       "           return 43.55%\n",
       "         else:  # if t-2 > 8.22%\n",
       "           return 57.01%\n",
       "     else:  # if t-1 > 0.00%\n",
       "       if t-1 <= 3.64%\n",
       "         if t-2 <= 0.31%\n",
       "           return 54.89%\n",
       "         else:  # if t-2 > 0.31%\n",
       "           return 57.30%\n",
       "       else:  # if t-1 > 3.64%\n",
       "         if t-2 <= -15.16%\n",
       "           return 25.00%\n",
       "         else:  # if t-2 > -15.16%\n",
       "           return 53.69%\n"
      ]
     }
    ],
    "source": [
     "tree_to_code(clf_tree_t2, X2.columns)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Overfitting, Regularization & Parameter Tuning"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Decision trees have a strong tendency to overfit, especially when a dataset has a large number of features relative to the number of samples. As discussed in previous chapters, overfitting increases the prediction error because the model does not only learn the signal contained in the training data, but also the noise.\n",
     "There are several ways to address the risk of overfitting."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Decision trees provide several regularization hyperparameters to limit the growth of a tree and the associated complexity. While every split increases the number of nodes, it also reduces the number of samples available per node to support a prediction. For each additional level, twice the number of samples is needed to populate the new nodes with the same sample density. "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Decision Tree Parameters"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The following table lists key parameters available for this purpose in the sklearn decision tree implementation. After introducing the most important parameters, we will illustrate how to use cross-validation to optimize the hyperparameter settings with respect to the bias-variance tradeoff and lower prediction errors:"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "| Parameter                | Default | Options                                                                                                                                                               | Description                                                                                                                                                                                                             |\n",
     "|--------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
     "| criterion                | gini    | Regression: MSE, MAE Classification: Gini impurity, Cross Entropy                                                                                                     | Metric to evaluate split quality.                                                                                                                                                                                       |\n",
     "| splitter                 | best    | best, random                                                                                                                                                          | How to choose the split at each node. Supported strategies are “best” to choose the best split and “random” to choose the best random split.                                                                            |\n",
     "| max_depth                | None    | int                                                                                                                                                                   | Max # of levels in tree. Split nodes until max_depth is reached or all leaves are pure or all leaves contain less than min_samples_split samples.                                                                       |\n",
     "| max_features             | None    | None: max_features=n_features; int; float (fraction): int(max_features * n_features)  auto, sqrt: max_features=sqrt(n_features). log2: max_features=log2(n_features). | # of features to consider when evaluating split                                                                                                                                                                         |\n",
     "| max_leaf_nodes           | None    | None: unlimited # of leaf nodes int                                                                                                                                   | Continue to split nodes that reduce relative impurity the most until reaching max_leaf_nodes.                                                                                                                           |\n",
     "| min_impurity_decrease    | 0       | float                                                                                                                                                                 | Split node if impurity decreases by at least this value.                                                                                                                                                                |\n",
     "| min_samples_leaf         | 1       | int; float (as percentage of N)                                                                                                                                       | Minimum # of samples to be at a leaf node. A split will only be considered if there are at least min_samples_leaf training samples in each of the left and right branches. May smoothen the model, esp. for regression. |\n",
     "| min_samples_split        | 2       | int; float (as percentage of N)                                                                                                                                       | The minimum number of samples required to split an internal node:                                                                                                                                                       |\n",
     "| min_weight_fraction_leaf | 0       | NA                                                                                                                                                                    | The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided (in fit method).                       |\n",
     "| presort                  | False   | NA                                                                                                                                                                    | Sort the data to speed up the search for best splits during training. Can slow down training on 'large' datasets but may speed up training on small data or with small max_depth setting.                               |\n",
     "| class_weight             | None    | balanced: inversely proportional to class frequencies dict: {class_label: weight} list of dicts (for multi-output)                                                    | Weights associated with classes                                                                                                                                                                                         |"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The `max_depth` parameter imposes a hard limit on the number of consecutive splits and represents the most straightforward way to cap the growth of a tree.\n",
     "\n",
     "The m`in_samples_split` and `min_samples_leaf` parameters are alternative, data-driven ways to limit the growth of a tree. Rather than imposing a hard limit on the number of consecutive splits, these parameters control the minimum number of samples required to further split the data. The latter guarantees a certain number of samples per leaf, while the former can create very small leaves if a split results in a very uneven distribution. Small parameter values facilitate overfitting, while a high number may prevent the tree from learning the signal in the data. \n",
     "\n",
     "The default values are often quite low, and you should use cross-validation to explore a range of potential values. You can also use a float to indicate a percentage as opposed to an absolute number. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 83,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.173622Z",
      "start_time": "2018-10-31T22:06:39.288Z"
     }
    },
    "outputs": [],
    "source": [
     "def plot_cv_results(cv_scores, metric='AUC', parameter='Max. Depth'):\n",
     "    fig, ax = plt.subplots(figsize=(12,6))\n",
     "    df = pd.DataFrame(cv_scores)\n",
     "    sns.tsplot(df.values, time=df.columns, ax=ax, lw=2)\n",
     "    ax.set_title(f'{len(df)}-Fold Cross-Validation Result')\n",
     "    ax.set_xlabel(parameter)\n",
     "    ax.set_ylabel(metric)\n",
     "    if metric == 'AUC':\n",
     "        ax.axvline(df.mean().idxmax(), ls='--', c='k', lw=1);\n",
     "        ax.axhline(classification_benchmark(), c='red', lw=1, ls='--') \n",
     "    else:\n",
     "        ax.axvline(df.mean().idxmin(), ls='--', c='k', lw=1);\n",
     "        ax.axhline(regression_benchmark(), c='red', lw=1, ls='--')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Cross-Validation Score"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Cross-validation is the most important tool to obtain an unbiased estimate of the generalization error, which in turn permits an informed choice among the various configuration options. sklearn offers several tools to facilitate the process of cross-validating numerous parameter settings, namely the GridSearchCV convenience class that we will illustrate in the next section. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 84,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.175010Z",
      "start_time": "2018-10-31T22:06:39.302Z"
     }
    },
    "outputs": [],
    "source": [
     "cv = OneStepTimeSeriesSplit(n_splits=10)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 85,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.176416Z",
      "start_time": "2018-10-31T22:06:39.309Z"
     }
    },
    "outputs": [],
    "source": [
     "clf_results = {}\n",
     "for max_depth in range(1, 26):\n",
     "    clf_tree = DecisionTreeClassifier(criterion='gini',\n",
     "                                      max_depth=max_depth,\n",
     "                                      min_samples_leaf=5,\n",
     "                                      random_state=42)\n",
     "    clf_results[max_depth] = cross_val_score(clf_tree,\n",
     "                                             X=X,\n",
     "                                             y=y_binary,\n",
     "                                             scoring='roc_auc',\n",
     "                                             n_jobs=-1,\n",
     "                                             cv=cv)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 86,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.188450Z",
      "start_time": "2018-10-31T22:06:39.316Z"
     }
    },
    "outputs": [
     {
      "data": {
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\n",
       "text/plain": [
        "<Figure size 864x432 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plot_cv_results(clf_results)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Train-Test Result"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 87,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.191896Z",
      "start_time": "2018-10-31T22:06:39.324Z"
     }
    },
    "outputs": [],
    "source": [
     "max_depths = range(1, 26)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### How to inspect the tree structure"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The following code illustrates how to run cross-validation more manually to obtain custom tree attributes, such as the total number of nodes or leaf nodes associated with certain hyperparameter settings. \n",
     "\n",
     "The following function accesses the internal `.tree_` attribute to retrieve information about the total node count, and how many of these nodes are leaf nodes:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 88,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.193266Z",
      "start_time": "2018-10-31T22:06:39.330Z"
     }
    },
    "outputs": [],
    "source": [
     "def get_leaves_count(tree):\n",
     "    t = tree.tree_\n",
     "    n = t.node_count\n",
     "    leaves = len([i for i in range(t.node_count) if t.children_left[i]== -1])\n",
     "    return leaves"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We can combine this information with the train and test scores to gain detailed knowledge about the model behavior throughout the cross-validation process, as follows:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 89,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.196039Z",
      "start_time": "2018-10-31T22:06:39.339Z"
     }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 "
      ]
     }
    ],
    "source": [
     "train_scores, val_scores, leaves = {}, {}, {}\n",
     "for max_depth in max_depths:\n",
     "    print(max_depth, end=' ', flush=True)\n",
     "    clf = DecisionTreeClassifier(criterion='gini', \n",
     "                                 max_depth=max_depth,\n",
     "                                 min_samples_leaf=500,\n",
     "                                 max_features='auto',\n",
     "                                 random_state=42)\n",
     "    train_scores[max_depth], val_scores[max_depth], leaves[max_depth] = [], [], []\n",
     "    for train_idx, test_idx in cv.split(X):\n",
     "        X_train, y_train,  = X.iloc[train_idx], y_binary.iloc[train_idx]\n",
     "        X_test, y_test = X.iloc[test_idx], y_binary.iloc[test_idx]\n",
     "        clf.fit(X=X_train, y=y_train)\n",
     "\n",
     "        train_pred = clf.predict_proba(X=X_train)[:, 1]\n",
     "        train_score = roc_auc_score(y_score=train_pred, y_true=y_train)\n",
     "        train_scores[max_depth].append(train_score)\n",
     "\n",
     "        test_pred = clf.predict_proba(X=X_test)[:, 1]\n",
     "        val_score = roc_auc_score(y_score=test_pred, y_true=y_test)\n",
     "        val_scores[max_depth].append(val_score)    \n",
     "        leaves[max_depth].append(get_leaves_count(clf))\n",
     "        \n",
     "clf_train_scores = pd.DataFrame(train_scores)\n",
     "clf_valid_scores = pd.DataFrame(val_scores)\n",
     "clf_leaves = pd.DataFrame(leaves)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Regression Tree"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Cross-Validation Scores"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 90,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.199204Z",
      "start_time": "2018-10-31T22:06:39.351Z"
     }
    },
    "outputs": [],
    "source": [
     "reg_results = {}\n",
     "for max_depth in range(1, 26):\n",
     "    reg_tree = DecisionTreeRegressor(criterion='mse',\n",
     "                                     max_depth=max_depth,\n",
     "                                     min_samples_leaf=500,\n",
     "                                     random_state=42)\n",
     "    reg_results[max_depth] = np.sqrt(-cross_val_score(reg_tree,\n",
     "                                             X=X,\n",
     "                                             y=y,\n",
     "                                             scoring='neg_mean_squared_error',\n",
     "                                             n_jobs=-1,\n",
     "                                             cv=cv))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 91,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.202490Z",
      "start_time": "2018-10-31T22:06:39.358Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x432 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plot_cv_results(reg_results, metric='RMSE')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 92,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.207248Z",
      "start_time": "2018-10-31T22:06:39.365Z"
     }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 "
      ]
     }
    ],
    "source": [
     "train_scores, val_scores, leaves = {}, {}, {}\n",
     "for max_depth in max_depths:\n",
     "    print(max_depth, end=' ', flush=True)\n",
     "    reg_tree = DecisionTreeRegressor(max_depth=max_depth,\n",
     "                                     min_samples_leaf=500,\n",
     "                                     max_features= 'sqrt',\n",
     "                                     random_state=42)\n",
     "    train_scores[max_depth], val_scores[max_depth], leaves[max_depth] = [], [], []\n",
     "    for train_idx, test_idx in cv.split(X):\n",
     "        X_train, y_train,  = X.iloc[train_idx], y.iloc[train_idx]\n",
     "        X_test, y_test = X.iloc[test_idx], y.iloc[test_idx]\n",
     "        reg_tree.fit(X=X_train, y=y_train)\n",
     "\n",
     "        train_pred = reg_tree.predict(X=X_train)\n",
     "        train_score = np.sqrt(mean_squared_error(\n",
     "            y_pred=train_pred, y_true=y_train))\n",
     "        train_scores[max_depth].append(train_score)\n",
     "\n",
     "        test_pred = reg_tree.predict(X=X_test)\n",
     "        val_score = np.sqrt(mean_squared_error(\n",
     "            y_pred=test_pred, y_true=y_test))\n",
     "        val_scores[max_depth].append(val_score)\n",
     "        leaves[max_depth].append(get_leaves_count(reg_tree))\n",
     "\n",
     "reg_train_scores = pd.DataFrame(train_scores)\n",
     "reg_valid_scores = pd.DataFrame(val_scores)\n",
     "reg_leaves = pd.DataFrame(leaves)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Plot Results"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 93,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.210055Z",
      "start_time": "2018-10-31T22:06:39.371Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 1008x360 with 4 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig, axes = plt.subplots(ncols=2, figsize=(14, 5))\n",
     "time = pd.Series(max_depths, name='Max. Depth')\n",
     "sns.tsplot(data=np.dstack((reg_train_scores, reg_valid_scores)),\n",
     "           time=time,\n",
     "           condition=['Train', 'Valid'],\n",
     "           ci=95,\n",
     "           ax=axes[0],\n",
     "           lw=2)\n",
     "ax0 = axes[0].twinx()\n",
     "sns.tsplot(data=reg_leaves.values, time=time, ax=ax0, ls='--', lw=1, color='k')\n",
     "ax0.set_yscale('log', basey=2)\n",
     "ax0.grid(None)\n",
     "ax0.set_ylabel('# Leaf Nodes')\n",
     "axes[0].set_title('Regression Tree')\n",
     "axes[0].set_ylabel('RMSE')\n",
     "axes[0].yaxis.set_major_formatter(\n",
     "    FuncFormatter(lambda y, _: '{:.2%}'.format(y)))\n",
     "axes[0].axvline(x=reg_valid_scores.mean().idxmin(), ls='--', c='k', lw=1)\n",
     "axes[0].axhline(y=regression_benchmark(), ls='-', c='k', lw=1)\n",
     "\n",
     "\n",
     "sns.tsplot(data=np.dstack((clf_train_scores, clf_valid_scores)),\n",
     "           time=pd.Series(max_depths, name='Max. Depth'),\n",
     "           condition=['Train', 'Valid'],\n",
     "           ci=95,\n",
     "           ax=axes[1],\n",
     "           lw=2)\n",
     "ax1 = axes[1].twinx()\n",
     "sns.tsplot(data=clf_leaves.values, time=time, ax=ax1, ls='--', lw=1, color='k')\n",
     "ax1.set_yscale('log', basey=2)\n",
     "ax1.grid(None)\n",
     "axes[1].set_title('Classification Tree')\n",
     "axes[1].set_ylabel('ROC AUC')\n",
     "axes[1].axvline(x=clf_valid_scores.mean().idxmax(), ls='--', c='k', lw=1)\n",
     "axes[1].axhline(y=classification_benchmark(), ls='-', c='k', lw=1)\n",
     "\n",
     "fig.suptitle(f'Train-Validation Scores', fontsize=18)\n",
     "fig.tight_layout()\n",
     "fig.subplots_adjust(top=.9)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### GridSearch"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "sklearn provides a method to define ranges of values for multiple hyperparameters. It automates the process of cross-validating the various combinations of these parameter values to identify the optimal configuration. Let's walk through the process of automatically tuning your model."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Classification Tree"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 94,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.211483Z",
      "start_time": "2018-10-31T22:06:39.378Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "__main__.OneStepTimeSeriesSplit"
       ]
      },
      "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "OneStepTimeSeriesSplit"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The first step is to instantiate a model object and define a dictionary where the keywords name the hyperparameters, and the values list the parameter settings to be tested:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 95,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.214633Z",
      "start_time": "2018-10-31T22:06:39.382Z"
     }
    },
    "outputs": [],
    "source": [
     "clf = DecisionTreeClassifier(random_state=42)\n",
     "param_grid = {'max_depth': range(10, 20),\n",
     "              'min_samples_leaf': [250, 500, 750],\n",
     "              'max_features': ['sqrt', 'auto']\n",
     "              }"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Then, instantiate the GridSearchCV object, providing the estimator object and parameter grid, as well as a scoring method and cross-validation choice to the initialization method. We'll use an object of our custom OneStepTimeSeriesSplit class, initialized to use ten folds for the cv parameter, and set the scoring to the roc_auc metric. We can parallelize the search using the n_jobs parameter and automatically obtain a trained model that uses the optimal hyperparameters by setting `refit=True`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 96,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.219459Z",
      "start_time": "2018-10-31T22:06:39.389Z"
     }
    },
    "outputs": [],
    "source": [
     "gridsearch_clf = GridSearchCV(estimator=clf,\n",
     "                          param_grid=param_grid,\n",
     "                          scoring='roc_auc',\n",
     "                          n_jobs=-1,\n",
     "                          cv=cv,\n",
     "                          refit=True,\n",
     "                          return_train_score=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "With all settings in place, we can fit GridSearchCV just like any other model:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 97,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.223221Z",
      "start_time": "2018-10-31T22:06:39.392Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "GridSearchCV(cv=<__main__.OneStepTimeSeriesSplit object at 0x7fce013c9240>,\n",
        "       error_score='raise-deprecating',\n",
        "       estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\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, presort=False, random_state=42,\n",
        "            splitter='best'),\n",
        "       fit_params=None, iid='warn', n_jobs=-1,\n",
        "       param_grid={'max_depth': range(10, 20), 'min_samples_leaf': [250, 500, 750], 'max_features': ['sqrt', 'auto']},\n",
        "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
        "       scoring='roc_auc', verbose=0)"
       ]
      },
      "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "gridsearch_clf.fit(X=X, y=y_binary)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The training process produces some new attributes for our GridSearchCV object, most importantly the information about the optimal settings and the best cross-validation score (now using the proper setup that avoids lookahead bias).\n",
     "\n",
     "Setting `max_depth` to 16, `min_samples_leaf` to 750, and randomly selecting only a number corresponding to the square root of the total number of features when deciding on a split, produces the best results, with an AUC of 0.529:\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 98,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.229918Z",
      "start_time": "2018-10-31T22:06:39.397Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'max_depth': 16, 'max_features': 'sqrt', 'min_samples_leaf': 750}"
       ]
      },
      "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "gridsearch_clf.best_params_"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 99,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.231570Z",
      "start_time": "2018-10-31T22:06:39.402Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "0.5287748989846169"
       ]
      },
      "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "gridsearch_clf.best_score_"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Regression Tree"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 100,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.232834Z",
      "start_time": "2018-10-31T22:06:39.408Z"
     }
    },
    "outputs": [],
    "source": [
     "reg_tree = DecisionTreeRegressor(random_state=42)\n",
     "\n",
     "param_grid = {'max_depth': [1,2],\n",
     "              'min_samples_leaf': [10],\n",
     "              'max_features': [None, 'sqrt']\n",
     "              }"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 101,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.233894Z",
      "start_time": "2018-10-31T22:06:39.412Z"
     }
    },
    "outputs": [],
    "source": [
     "gridsearch_reg = GridSearchCV(estimator=reg_tree,\n",
     "                              param_grid=param_grid,\n",
     "                              scoring='neg_mean_squared_error',\n",
     "                              n_jobs=-1,\n",
     "                              cv=cv,\n",
     "                              refit=True,\n",
     "                              return_train_score=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 102,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.234974Z",
      "start_time": "2018-10-31T22:06:39.417Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "GridSearchCV(cv=<__main__.OneStepTimeSeriesSplit object at 0x7fce013c9240>,\n",
        "       error_score='raise-deprecating',\n",
        "       estimator=DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
        "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
        "           min_impurity_split=None, min_samples_leaf=1,\n",
        "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
        "           presort=False, random_state=42, splitter='best'),\n",
        "       fit_params=None, iid='warn', n_jobs=-1,\n",
        "       param_grid={'max_depth': [1, 2], 'min_samples_leaf': [10], 'max_features': [None, 'sqrt']},\n",
        "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
        "       scoring='neg_mean_squared_error', verbose=0)"
       ]
      },
      "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "gridsearch_reg.fit(X=X, y=y)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 103,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.240647Z",
      "start_time": "2018-10-31T22:06:39.427Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'max_depth': 1, 'max_features': None, 'min_samples_leaf': 10}"
       ]
      },
      "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "gridsearch_reg.best_params_"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 104,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.242586Z",
      "start_time": "2018-10-31T22:06:39.441Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "0.06595263400245509"
       ]
      },
      "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "np.sqrt(-gridsearch_reg.best_score_)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 105,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.244244Z",
      "start_time": "2018-10-31T22:06:39.446Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "0.06583066067442996"
       ]
      },
      "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "regression_benchmark()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Learning Curves"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "A learning curve is a useful tool that displays how the validation and training score evolve as the number of training samples evolves.\n",
     "\n",
     "The purpose of the learning curve is to find out whether and how much the model would benefit from using more data during training. It is also useful to diagnose whether the model's generalization error is more likely driven by bias or variance.\n",
     "\n",
     "If, for example, both the validation score and the training score converge to a similarly low value despite an increasing training set size, the error is more likely due to bias, and additional training data is unlikely to help."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Classifier"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 106,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.247244Z",
      "start_time": "2018-10-31T22:06:39.453Z"
     }
    },
    "outputs": [],
    "source": [
     "sizes = np.arange(.1, 1.01, .1)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 107,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.256493Z",
      "start_time": "2018-10-31T22:06:39.459Z"
     }
    },
    "outputs": [],
    "source": [
     "train_sizes, train_scores, valid_scores = learning_curve(gridsearch_clf.best_estimator_,\n",
     "                                                          X,\n",
     "                                                          y_binary,\n",
     "                                                          train_sizes=sizes,\n",
     "                                                          cv=cv,\n",
     "                                                          scoring='roc_auc',\n",
     "                                                          n_jobs=-1,\n",
     "                                                          shuffle=True,\n",
     "                                                          random_state=42)\n",
     "clf_data = np.dstack((train_scores.T, valid_scores.T))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 108,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.261153Z",
      "start_time": "2018-10-31T22:06:39.464Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 1008x360 with 3 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig, axes = plt.subplots(ncols=2, figsize=(14, 5))\n",
     "condition = ['Training', 'Validation']\n",
     "sns.tsplot(data=np.dstack((clf_train_scores, clf_valid_scores)),\n",
     "           time=pd.Series(max_depths, name='Max. Depth'),\n",
     "           condition=condition,\n",
     "           ci=95,\n",
     "           ax=axes[0],\n",
     "           lw=2)\n",
     "ax1 = axes[0].twinx()\n",
     "sns.tsplot(data=clf_leaves.values, time=time, ax=ax1, ls='--', lw=1, color='k')\n",
     "ax1.set_yscale('log', basey=2)\n",
     "ax1.grid(None)\n",
     "axes[0].set_title('Cross-Validation Results')\n",
     "axes[0].set_ylabel('ROC AUC')\n",
     "axes[0].axvline(x=clf_valid_scores.mean().idxmax(), ls='--', c='k', lw=1)\n",
     "\n",
     "\n",
     "sns.tsplot(data=clf_data, \n",
     "           time=pd.Series(train_sizes, name='Train Size'), \n",
     "           condition=condition, \n",
     "           ci=95, \n",
     "           ax=axes[1],\n",
     "          lw=2)\n",
     "axes[1].set_title('Learning Curve')\n",
     "axes[1].set_ylabel('ROC AUC')\n",
     "axes[1].xaxis.set_major_formatter(FuncFormatter(lambda x, _: '{:,.0f}'.format(x)))\n",
     "\n",
     "\n",
     "# fig.suptitle(f'Train-Validation Scores', fontsize=18)\n",
     "fig.tight_layout()\n",
     "# fig.subplots_adjust(top=.9)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Regression Tree"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 109,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.262507Z",
      "start_time": "2018-10-31T22:06:39.470Z"
     }
    },
    "outputs": [],
    "source": [
     "train_sizes, train_scores, valid_scores = learning_curve(gridsearch_reg.best_estimator_,\n",
     "                                                          X, y,\n",
     "                                                          train_sizes=sizes,\n",
     "                                                          cv=cv,\n",
     "                                                          scoring='neg_mean_squared_error',\n",
     "                                                          n_jobs=-1,\n",
     "                                                          shuffle=True,\n",
     "                                                          random_state=42)\n",
     "reg_data = np.dstack((train_scores.T, valid_scores.T))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Plot Result"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 110,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.263636Z",
      "start_time": "2018-10-31T22:06:39.475Z"
     }
    },
    "outputs": [],
    "source": [
     "time = pd.Series(train_sizes, name='Train Size')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 111,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.266924Z",
      "start_time": "2018-10-31T22:06:39.480Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 1008x360 with 2 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig, axes = plt.subplots(ncols=2, figsize=(14,5))\n",
     "sns.tsplot(data=clf_data, \n",
     "           time=time, \n",
     "           condition=['Train', 'Valid'], \n",
     "           ci=95, \n",
     "           ax=axes[0],\n",
     "          lw=2)\n",
     "axes[0].set_title('Best Classification Tree')\n",
     "axes[0].set_ylabel('ROC AUC')\n",
     "axes[0].xaxis.set_major_formatter(FuncFormatter(lambda x, _: '{:,.0f}'.format(x)))\n",
     "\n",
     "sns.tsplot(data=np.sqrt(-reg_data), \n",
     "           time=time, \n",
     "           condition=['Train', 'Valid'], \n",
     "           ci=95, \n",
     "           ax=axes[1],\n",
     "           lw=2)\n",
     "axes[1].set_title('Best Regression Tree')\n",
     "axes[1].set_ylabel('RMSE')\n",
     "axes[1].yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.2%}'.format(y)))\n",
     "axes[1].xaxis.set_major_formatter(FuncFormatter(lambda x, _: '{:,.0f}'.format(x)))\n",
     "fig.suptitle('Learning Curves', fontsize=20)\n",
     "fig.tight_layout()\n",
     "fig.subplots_adjust(top=.9);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Feature Importance"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Decision trees can not only be visualized to inspect the decision path for a given feature, but also provide a summary measure of the contribution of each feature to the model fit to the training data. \n",
     "\n",
     "The feature importance captures how much the splits produced by the feature helped to optimize the model's metric used to evaluate the split quality, which in our case is the Gini Impurity index. \n",
     "\n",
     "A feature's importance is computed as the (normalized) total reduction of this metric and takes into account the number of samples affected by a split. Hence, features used earlier in the tree where the nodes tend to contain more samples typically are considered of higher importance."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 112,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.268283Z",
      "start_time": "2018-10-31T22:06:39.485Z"
     }
    },
    "outputs": [],
    "source": [
     "top_n = 15\n",
     "labels = X.columns.str.replace('_', ' ').str.capitalize()\n",
     "fi_clf = (pd.Series(gridsearch_clf.best_estimator_.feature_importances_, \n",
     "                    index=labels).sort_values(ascending=False).iloc[:top_n])\n",
     "fi_reg = (pd.Series(gridsearch_reg.best_estimator_.feature_importances_, \n",
     "                    index=labels).sort_values(ascending=False).iloc[:top_n])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 113,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.276682Z",
      "start_time": "2018-10-31T22:06:39.489Z"
     }
    },
    "outputs": [
     {
      "data": {
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\n",
       "text/plain": [
        "<Figure size 864x432 with 2 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig, axes= plt.subplots(ncols=2, figsize=(12,6))\n",
     "color = cm.Blues(np.linspace(.4,.9, top_n))\n",
     "fi_clf.sort_values().plot.barh(ax=axes[0], title='Classification Tree', color=color)\n",
     "fi_reg.sort_values().plot.barh(ax=axes[1], title='Regression Tree', color=color)\n",
     "fig.suptitle(f'Top {top_n} Feature Importances', fontsize=18)\n",
     "fig.tight_layout()\n",
     "fig.subplots_adjust(top=.9);"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 114,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.278296Z",
      "start_time": "2018-10-31T22:06:39.493Z"
     }
    },
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
        "<Figure size 1008x360 with 2 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig, axes = plt.subplots(ncols=2, figsize=(14,5))\n",
     "(pd.DataFrame({'Gini': gini(x), \n",
     "              'Entropy': entropy(x),\n",
     "             'Misclassification Rate': misclassification_rate(x)}, index=x)\n",
     " .plot(title='Classification Loss Functions', lw=2, ax=axes[0], ylim=(0, .55)))\n",
     "\n",
     "top_n = 15\n",
     "labels = X.columns.str.replace('_', ' ').str.capitalize()\n",
     "fi_clf = (pd.Series(gridsearch_clf.best_estimator_.feature_importances_, \n",
     "                    index=labels).sort_values(ascending=False).iloc[:top_n])\n",
     "color = cm.Blues(np.linspace(.4,.9, top_n))\n",
     "fi_clf.sort_values().plot.barh(ax=axes[1], title='Feature Importances', color=color)\n",
     "\n",
     "\n",
     "# fig.suptitle('Best Classification Tree', fontsize=20)\n",
     "fig.tight_layout()\n",
     "# fig.subplots_adjust(top=.9);"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 115,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2018-10-31T22:34:16.279759Z",
      "start_time": "2018-10-31T22:06:39.499Z"
     }
    },
    "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>y</th>\n",
        "      <th>x</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1</td>\n",
        "      <td>2.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>2</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>3</td>\n",
        "      <td>3.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "   y    x\n",
        "0  1  2.0\n",
        "1  2  NaN\n",
        "2  3  3.0"
       ]
      },
      "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "dt = pd.DataFrame({'y': [1,2,3], 'x': [2, np.nan, 3]})\n",
     "dt"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
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    "codemirror_mode": {
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     "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.7.0"
   },
   "toc": {
    "base_numbering": 1,
    "nav_menu": {},
    "number_sections": true,
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    "skip_h1_title": true,
    "title_cell": "Table of Contents",
    "title_sidebar": "Contents",
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     "left": "10px",
     "top": "150px",
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    },
    "toc_section_display": true,
    "toc_window_display": true
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }