Introduction to Machine Learning.ipynb

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   },
   "source": [
    "# Introduction to machine learning"
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {
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     "text": [
      "['predictions_2017_804.xlsx', 'Introduction to Machine Learning.ipynb', 'tf_model', '.ipynb_checkpoints', 'toy_sales.xlsx', 'Dockerfile']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt # plotting figures\n",
    "\n",
    "# drawing decision tree\n",
    "from sklearn.externals.six import StringIO  \n",
    "from IPython.display import Image  \n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus\n",
    "\n",
    "from sklearn.model_selection import train_test_split # split dataset into train and test data\n",
    "# A bunch of standard machine learning models \n",
    "from sklearn import tree\n",
    "from sklearn import linear_model\n",
    "from sklearn.neural_network import MLPClassifier, MLPRegressor\n",
    "from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\n",
    "from sklearn.svm import SVC, SVR\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier, GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingRegressor, AdaBoostRegressor\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "from sklearn import metrics #used to plot the ROC AUC\n",
    "\n",
    "import tensorflow as tf # tensorflow is googles library for deep learning and other graph-processing tasks\n",
    "\n",
    "\n",
    "# Input data files are available in the \"../work/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "import datetime\n",
    "import os\n",
    "print(os.listdir(\"../work\"))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b8484b37-a79a-4e9d-8462-3b7c5ba6cb99",
    "_uuid": "6cb24a57def211cf9be8a7f3c6e9d31e96fa9bf8"
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   "source": [
    "# Machine Learning\n",
    "\n",
    "*Machine learning is the art and science of making machines learn from data to give answers to questions. A good machine learning application has accurate results, generalizes well across the problem domain and limits the need for human action to a minimum.*\n",
    "\n",
    "*Some important terms in machine learning are:*\n",
    "* Model\n",
    ">A model processes input data x to produce an output y. It's the core of any machine learning application\n",
    "* training\n",
    ">Training is the process of feeding examples of input data into a model, comparing the results with the desired behaviour and adjusting the model's internal state incrementally so that it produces more favourable output\n",
    "* features and labels\n",
    "> In machine mearning we try to find a method to learn from examples (X) to generate a desirable output (y) generally we try to find f(X) --> y . Now features are the elements of X, while labels are the elents of y e.g. age and income ; f((age, income)) --> sales .\n",
    "* examples\n",
    "> examples are instances of (X[ ,y]) that can be fed into the model for training or prediction e.g. (35, 2000) --> 42\n",
    "\n",
    "* Supervised Learning\n",
    "> In supervised learning the training-examples are labelled and we try to predict the label correctly. Examples of applications are predicting house-prices, sales or stock-values, classifying images or detecting spam-mails\n",
    "* Semi-Supervised Learning\n",
    "> In semi-supervised learning or reinforcement learning the examples don't have a distinct label. However we have a function or method to reward the model if it produces desirable output. examples are autonomous driving or robotics in general\n",
    "* Unsupervised Learning\n",
    "> In unsupervised learning the model does not receive any labels on the data. instead the goal is to somehow structure the data in a meaningful way. On example is clustering / segmentation\n",
    "\n",
    "We'll focus on supervised learning in this introduction.\n",
    "\n",
    "## Supervised Learning\n",
    "> Supervised learning is concerned with two main tasks: classification and regression.\n",
    "\n",
    "* Classification\n",
    "> In classification we deal with discrete labels like black/white, good/bad, red/green/blue/yellow, cat/dog/bird . The models often calculate a probability or score for each class and we can use the class with the highest score as the prediction of the model.\n",
    "* Regression\n",
    "> In regression we try to predict a (more or less) continuous variable like price, age, time. \n",
    "\n",
    "The typical machine learning workflow consists of 7 steps:\n",
    "1.  Gather and explore data\n",
    "2. Prepare data\n",
    "3. Choose a model\n",
    "4. Train the model\n",
    "5. Evaluate the model\n",
    "6. Tune model parameters\n",
    "7. Move model into production / predict\n"
   ]
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   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## 1. Gather and explore data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [],
   "source": [
    "df = pd.read_excel('../work/toy_sales.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "ccf45646-1603-4f1f-b3f2-17921646d81e",
    "_uuid": "8c38f1cf85822928feebebc286ba0cd2ea3ab4e1"
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer</th>\n",
       "      <th>date</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>707</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>707</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>706</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>509.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>545</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>539.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>913</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>509.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   customer       date  value\n",
       "0       707 2015-01-01  489.0\n",
       "1       707 2015-01-01  438.0\n",
       "2       706 2015-01-01  509.0\n",
       "3       545 2015-01-01  539.0\n",
       "4       913 2015-01-02  509.0"
      ]
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     "metadata": {},
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   "source": [
    "df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "c921069f-6747-4848-bf16-107d3f3d8ae3",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4352.000000</td>\n",
       "      <td>4352.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>496.520680</td>\n",
       "      <td>1524.165097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>284.561043</td>\n",
       "      <td>1234.585512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>251.750000</td>\n",
       "      <td>658.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>499.000000</td>\n",
       "      <td>1198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>738.250000</td>\n",
       "      <td>1978.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>12530.000000</td>\n",
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      "text/plain": [
       "          customer         value\n",
       "count  4352.000000   4352.000000\n",
       "mean    496.520680   1524.165097\n",
       "std     284.561043   1234.585512\n",
       "min       1.000000      0.000000\n",
       "25%     251.750000    658.750000\n",
       "50%     499.000000   1198.000000\n",
       "75%     738.250000   1978.000000\n",
       "max    1000.000000  12530.000000"
      ]
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   "source": [
    "df.describe()"
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   "source": [
    "## 2. Prepare data"
   ]
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   "source": []
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   "execution_count": 5,
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>y_frequency</th>\n",
       "      <th>y_recency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>407</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>314</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>158</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>306</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>152.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          recency  frequency  y_frequency  y_recency\n",
       "customer                                            \n",
       "1             407          1          1.0      215.0\n",
       "2              43          3          0.0      366.0\n",
       "3             314          2          1.0      143.0\n",
       "4             158          5          0.0      366.0\n",
       "5             306          4          1.0      152.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T0 = datetime.date(2017, 1, 1)\n",
    "df['days'] = (T0 - df['date']).dt.days\n",
    "future = df[df['days']<0]\n",
    "past = df[df['days']>=0]\n",
    "past_group = past.groupby(by=['customer'])\n",
    "x = past_group['days'].min()\n",
    "x.name = 'recency'\n",
    "x = pd.DataFrame(x)\n",
    "x['frequency'] = past_group['days'].count()\n",
    "\n",
    "future_group = future.groupby(by=['customer'])\n",
    "y = future_group['days'].count()\n",
    "y.name = 'y_frequency'\n",
    "y = pd.DataFrame(y)\n",
    "y['y_recency'] = future_group['days'].max()*(-1)\n",
    "\n",
    "data=x.join(y)\n",
    "data['y_recency'].fillna(value=366, inplace=True)\n",
    "data['y_frequency'].fillna(value=0, inplace=True)\n",
    "data['y_frequency'][data['y_frequency']>0] = 1\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x0, y0 = data[data['y_frequency']==0]['recency'],data[data['y_frequency']==0]['frequency']\n",
    "x1, y1 = data[data['y_frequency']>0]['recency'],data[data['y_frequency']>0]['frequency']\n",
    "\n",
    "plt.scatter(x1, y1, label='Buyer', color='b')\n",
    "plt.scatter(x0, y0, label='No-buy', color='r')\n",
    "\n",
    "plt.xlabel('recency', fontsize=16)\n",
    "plt.ylabel('frequency', fontsize=16)\n",
    "plt.title('scatter plot - recency vs frequency',fontsize=20)\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "010898d4-ffea-4630-9702-7ed3a8fc3ffc",
    "_uuid": "b414f6bf590793afab8a62889e3dd07a9ff99395"
   },
   "source": [
    "#### Train/Test split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "d6cd65ab-7a09-4621-b2d8-b54de2c9e7fb",
    "_uuid": "ff60b95f9aeb6dee62cefc050fb06d9b2d45a7da"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x = data[['recency', 'frequency']]\n",
    "y_class = data['y_frequency']\n",
    "y_regress = data['y_recency']\n",
    "\n",
    "x_train, x_test, yc_train, yc_test = train_test_split(x, y_class, test_size=0.25)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "90047a84-1a8a-4997-93f2-557420d71982",
    "_uuid": "61bc9afa327a2dffb34beaaa947ec06270816185"
   },
   "source": [
    "## 3. Model selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc_auc(preds, y_true):\n",
    "    # calculate the fpr and tpr for all thresholds of the classification\n",
    "    fpr, tpr, threshold = metrics.roc_curve(y_true, preds)\n",
    "    roc_auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "    plt.title('Receiver Operating Characteristic')\n",
    "    plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "    plt.legend(loc = 'lower right')\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0, 1])\n",
    "    plt.ylim([0, 1])\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "360e3540-50c4-4e1e-b236-560ac615985f",
    "_kg_hide-input": false,
    "_kg_hide-output": false,
    "_uuid": "0016ab028409f30db95e3db98195fc14e2bd5bc7"
   },
   "outputs": [],
   "source": [
    "\n",
    "predictions, scores = {}, {}\n",
    "predictions['true_values'] = yc_test.values\n",
    "\n",
    "classifiers = [\n",
    "    {'name': 'tree', 'clf': tree.DecisionTreeClassifier()},\n",
    "    {'name': 'forest', 'clf': RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1)},\n",
    "    {'name': 'logistic', 'clf': linear_model.LogisticRegression()},\n",
    "    {'name': 'kneighbors', 'clf': KNeighborsClassifier()},\n",
    "    {'name': 'svc', 'clf':  SVC(gamma=2, C=1)},\n",
    "    {'name': 'gaussian', 'clf': GaussianProcessClassifier(1.0 * RBF(1.0))},\n",
    "    {'name': 'neural', 'clf':  MLPClassifier(solver='lbfgs', learning_rate='adaptive', max_iter=3000)},\n",
    "    {'name': 'adaboost', 'clf':  AdaBoostClassifier()},\n",
    "     ]\n",
    "\n",
    "for c in classifiers:\n",
    "    clf = c['clf']\n",
    "    clf.fit(x_train, yc_train)\n",
    "    predictions[c['name']] = clf.predict(x_test)\n",
    "    scores[c['name']] = [clf.score(x_test, yc_test)]\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "ba863e8e-7b3e-45f8-8789-32e39453f183",
    "_uuid": "fcd7928bcc41c9154f01431fded785bf46f01989"
   },
   "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>adaboost</th>\n",
       "      <th>forest</th>\n",
       "      <th>gaussian</th>\n",
       "      <th>kneighbors</th>\n",
       "      <th>logistic</th>\n",
       "      <th>neural</th>\n",
       "      <th>svc</th>\n",
       "      <th>tree</th>\n",
       "      <th>true_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   adaboost  forest  gaussian  kneighbors  logistic  neural  svc  tree  \\\n",
       "0       0.0     1.0       1.0         1.0       1.0     1.0  1.0   1.0   \n",
       "1       1.0     1.0       1.0         1.0       1.0     1.0  1.0   1.0   \n",
       "2       1.0     1.0       1.0         1.0       1.0     1.0  1.0   1.0   \n",
       "3       1.0     1.0       1.0         1.0       1.0     1.0  1.0   1.0   \n",
       "4       1.0     1.0       1.0         1.0       1.0     1.0  1.0   1.0   \n",
       "\n",
       "   true_values  \n",
       "0          1.0  \n",
       "1          1.0  \n",
       "2          1.0  \n",
       "3          1.0  \n",
       "4          1.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(predictions).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "36e1a11f-cadf-41c6-8ce5-034f3e49765d",
    "_uuid": "8848209e2900a0efa4d989dc24322adfc6da4f6b"
   },
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>adaboost</th>\n",
       "      <td>0.762097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forest</th>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaussian</th>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kneighbors</th>\n",
       "      <td>0.709677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logistic</th>\n",
       "      <td>0.766129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neural</th>\n",
       "      <td>0.758065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>svc</th>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tree</th>\n",
       "      <td>0.717742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0\n",
       "adaboost    0.762097\n",
       "forest      0.774194\n",
       "gaussian    0.774194\n",
       "kneighbors  0.709677\n",
       "logistic    0.766129\n",
       "neural      0.758065\n",
       "svc         0.750000\n",
       "tree        0.717742"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(scores).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Azp3hwgsts/bkyUkeJNauhZtugho1bKUfWOW5Qh4kwK8onHNJ6K+/oG1b+OEHq/T57ruwzz6JblUuBgywPrFVq+DRRxOexC/WPFA455LKmDFw1VWwZg28954VdEvqZQY33QQffgh168L//gf16iW6RTHngcI5lxRU4dVX4ZFHrObD+PH22ZuUIpP4NWpk07A6doRSpRLbrpB4oHDOJdy6dbZAedAgy83Usyfsv3+iW5WDP/6wnCHXXGP9Yu3bJ7pFofPBbOdcQk2ZYr01//sfvP469O+fpEEiPR26drVKRmPG2MKOYsIDhXMuIVShWzerB7F9u+XHu//+JB2PmDfP1kTcfbc1eOZMGzwpJrzryTkXdxs2WO9Nnz42/bV3bzjkkES3Khfz5lkZvF69rLspKaNZeDxQOOfiavZsaNXKPnv/7//gP/+BEsnYtzF1qiXxu/FGuOwyS+J3wAGJblVCJOOfxzlXRPXubUsM1qyxFdePP56EQWLLFotep5xiK/4ykvgV0yABHiicc3GwZYt1NV13nWV+nToVzj030a3KxtixNif3hResi2natEKZxC/WvOvJOReqQlM7YtkyOOccy9E0bBhccEGiW5Q0kvHP5ZwrIgYOzCzgNmiQdfUnndmzLT9TxYrw5ZcWLPbdN9GtSire9eSci7nI2hHVq1vtiKQLEn//bVGsZk2bmwtW4MKDxG78isI5F1PLllmupqSuHfHll3DXXTaq/thj0KBBoluU1DxQOOdiJrJ2RJ8+cPXViW5RNtq1g48+suXg336bxAmlkocHCufcHtuxwwapn37auvv794cTTkh0qyJEJvE77TQrLPTgg0k6qp58Qh2jEJGmIjJPRBaIyCPZPF5FREaIyFQRmSEiF4fZHudc7K1aZVU+O3eGa6+FCROSLEgsXmwzmD7+2G63b2/TrzxIRC20QCEiJYGuwEVADeBqEamRZbPHgc9V9WSgDfBuWO1xzsXe2LFw8sk2Ftyjh/XoJE2BoR074K23LInf+PGZVxUu38K8omgALFDVRaq6DegHNM+yjQL7Bb/vDywPsT3OuRjJqB1x1lm2Hm3cOLj11iRKgTRnDjRuDPfdZ42cNcvGJlyBhHntVRFYGnE7FWiYZZvOwHcicg+wD9Akux2JSHugPUCVKlVi3lDnXPTWrbP0RwMH2vTXDz9MwrTgCxZYMqlPPrGaqkkTwQqnMK8osvvLZL32uxropaqVgIuBT0Rktzapag9VTVHVlAoVKoTQVOdcNH75xVJwfP21TXv98sskChJTpljFI7D1EIsX26CJB4k9FmagSAUqR9yuxO5dSzcDnwOo6jigLJDMyYadK5ZUoXt3mzC0bRv89BN06JAkn8GbN1v91IYNLR1tRhK//fbL/XkuamEGiknAsSJSTURKY4PVg7NsswQ4D0BETsQCxaoQ2+Scy6cNGyyZ3+23w9lnW86m005LdKsCo0bBSSfBSy/ZGMTUqZ7ELwShjVGoapqI3A0MA0oCPVV1log8A0xW1cHAg8B7ItIB65Zqp+pTE5xLFhm1I+bOhWeesUXMSZMWfNkyOO88qFwZfvjBfnehCHUisaoOBYZmue/JiN9nA6eH2QbnXMF8+qktOdh3X1txnTSfw7/+CrVrWxK/AQMsiV/SzMktmpLlu4FzLkls2WLdTNdem1k7IimCxOrV1gdWp05mEr9mzTxIxIEHCufcTosWwemn28D1Qw/B8OFw5JEJbpQqfP655Qbp1w+eesoGrl3c+Bp25xyQxLUjbrjB1kOkpMCPP1q3k4srDxTOFXPbt8Ojj9pK6/r14YsvoFq1BDcqMonfWWdZd9P993t+pgTxrifnirGM6p+vvmq1I8aOTYIgsWgRNGkCvXrZ7ZtvtipIHiQSxgOFc8XU999bKYZp06x2RNeuCS4wtGMHvPGGdS1NmpRE83Cd/yWcK2Z27LC6ERdeCIceap/JCS8wNHu2jaJ36GCXOLNn29iESwp+LedcMbJqlU17/e47m2n63/8myezSxYth4UK7tGnTJklyg7gMHiicKybGjrVa1qtXW+2IW25J8OfxpEnW73XrrXDJJTY2Ub58AhvkcuJdT84VcaqW6fXss5OkdsSmTTY43agRvPBCZhI/DxJJywOFc0XYunXQsqWVh770UsvEffLJCWzQyJE21fXVVy1aeRK/QsG7npwroqZOtYR+S5bY53LC04KnpsL558NRR9mS73POSWBjXH74FYVzRYyqjUGceips3Wq1Ix54IIFBYvp0+7dSJVvyPWOGB4lCxgOFc0XIxo1w/fVw2222oDmhtSNWrYJrrrHFGj/9ZPddfDHsvXeCGuQKygOFc0XEnDnQoIGlB3/6aRg6FBJSOVgV+va1JH79+1tjTj01AQ1xsRLVGEVQoa6Kqi4IuT3OuQLo08dqR+y9t62RaNIkgY257jqLVg0bwgcfQM2aCWyMi4U8ryhE5BLgV+D74HZdERkQdsOcc3nbsgXuuAPatrXZTFOnJihIpKdnJvI75xybjzt2rAeJIiKarqdngIbAOgBVnQZUD7NRzrm8ZdSO6NYNOnWyiUQVKyagIQsWWGWjDz+02zffbFOsSpZMQGNcGKIJFNtVdV2W+7yutXMJNGgQ1KtnwWLQIOjSBUqVinMj0tLglVcsid/UqVC6dJwb4OIlmkAxR0RaAyVEpJqIvAGMD7ldzrlsbN9uVw8tWkD16vDLLwkqMDRzpg1Qd+pk2QVnz7YkUq5IiiZQ3A3UB9KBr4AtwH1hNso5t7tly+Dcc+1L/B13wJgxCawdsWQJ/PGHlSYdMCAJ6qW6MEUz6+lCVX0YeDjjDhG5Agsazrk4+OEHW5KwaZNNKLrmmgQ0YsIEWzzXvr2th1i0CPbdNwENcfEWzRXF49nc91isG+Kc211G7YgLLrA1EZMmJSBIbNxoS7tPPdUGQ7Zutfs9SBQbOV5RiMiFQFOgooi8FvHQflg3lHMuRElRO2L4cEvet2iR9Xe9+GKCy+C5RMit6+kvYCY2JjEr4v71wCNhNsq54i4pakekptpAdbVqloLjzDPj3ACXLHIMFKo6FZgqIp+q6pY4tsm5YksVXn8dHn7YkqyOG5eAtOBTp9pBK1WCIUMsaVS5cnFuhEsm0YxRVBSRfiIyQ0TmZ/yE3jLnipnI2hHNmsHkyXEOEn/+aZcx9eplJvFr2tSDhIsqUPQCPgQEuAj4HOgXYpucK3amToX69e0L/KuvwldfwQEHxOngqtC7tyXxGzgQnn02gSlnXTKKJlDsrarDAFR1oao+DngyeediIGvtiJEjE1A74pprbLT8+OOthvVjjyVgmbdLZtGso9gqIgIsFJHbgWXAoeE2y7mib+NGm0j0ySc2/bV37zimBU9Pt2gkYgc/9VS46y7Pz+SyFc0VRQdgX+Be4HTgVuCmMBvlXFE3Z45l4e7dOwG1I+bPtwyvPXva7RtvhHvv9SDhcpTnFYWqTgh+XQ9cByAilcJslHNFWd++tjRh771h2DArIx0XaWmW/vupp6BsWR+kdlHL9YpCRE4RkRYickhwu6aIfIwnBXQu37ZuhTvvzKwOOnVqHIPEjBnQqJHNu73oIkvil5A8IK4wyjFQiMgLwKdAW+BbEXkMGAFMB46LT/OcKxoWL7baEf/9L3TsCCNGxLl2RGoqLF0KX3wBX34JRxwRx4O7wi63rqfmwEmqullEDgKWB7fnRbtzEWkKvAmUBN5X1Rez2aY10BmrcTFdVf1rjitSBg+GG26wGU4DB0Lz5nE68M8/25XE7bdnJvGLew4QVxTk1vW0RVU3A6jq38DcfAaJkkBXbO1FDeBqEamRZZtjgUeB01W1JnB/PtvvXNLavh0eesgCw9FHW+2IuASJDRvgvvvgjDNsUUZGEj8PEq6AcruiOFpEMlKJC1A14jaqekUe+24ALFDVRQAi0g+7Spkdsc2tQFdVXRvs8698tt+5pLRsGbRpYzUjbr/d0nKULRuHA3/3naUBX7LEprs+/7wn8XN7LLdA0TLL7Xfyue+KwNKI26lY7e1IxwGIyFise6qzqn6bdUci0h5oD1ClSpV8NsO5+EpY7YilS+GSS+CYY2DUKLuicC4GcksK+OMe7ju7taVZa23vBRwLnA1UAkaLSK2sNbpVtQfQAyAlJcXrdbuklJ5u2S86d4YTT4T+/e3f0E2ZYvk/Kle2BRmNG8fp8sUVF9EsuCuoVKByxO1K2IB41m0Gqep2VV0MzMMCh3OFyqpVNuv0qaegbVuYODEOQWLlSrjySkhJyUzid/75HiRczIUZKCYBx4pINREpDbQBBmfZZiBB3qhgrcZxwKIQ2+RczP38s2V5/ekn6N4dPv445HFjVfjoI0viN2SIjUN4Ej8XoqgDhYjka0RMVdOAu4FhwBzgc1WdJSLPiMhlwWbDgDUiMhtbo9FJVdfk5zjOJYqqLXQ+6ywbL/75ZxtHDj2hX5s20K6dBYpp0+DRRz2JnwuVqObe5S8iDYAPgP1VtYqInATcoqr3xKOBWaWkpOjkyZMTcWjndlq3Dm66CQYMgBYt4MMPQ04LHpnE76OPYP16W+ZdIsxOAVeUiMgUVU0pyHOjeZe9BTQD1gCo6nQ8zbgrxuJeO2LuXCtD+sEHdvuGG+Duuz1IuLiJ5p1WQlX/yHLfjjAa41wyi3vtiO3bbfzhpJMsN9O++4Z0IOdyF009iqVB95MGq63vAbwUqitW4l47Yto0S/89bRq0agVvvw2HHx7iAZ3LWTSB4g6s+6kK8CfwQ3Cfc8XC3Ln2WT17tq2RePzxOJRuWLnSfr78Eq7IKwmCc+GKJlCkqWqb0FviXBLKqB1RrlwcakeMGWNJ/O68E5o2hYULrWiFcwkWzRjFJBEZKiI3iEj50FvkXBLIWjti2rQQg8T69TY43bgxvPFGZhI/DxIuSeQZKFT1GOBZoD7wq4gMFBG/wnBF1uLFliYpLrUjhg2DWrXg3Xct4+svv3gSP5d0oppfp6o/q+q9QD3gX6ygkXNFzpAhUK8e/PabrZF4+eUQ17ItXQrNmtmVw5gxdjXhM5tcEsozUIjIviLSVkSGABOBVYDnC3BFSlqaVQm97DKoVs2+2LdoEcKBVC0RFFgSv2++sYUZnoLDJbForihmAo2ALqpaXVUfVNUJIbfLubhZvhzOPRe6dIHbbrNUHEcfHcKBVqyAli2hYcPMJH5NmngSP5f0opn1dLSqpofeEucS4McfbcB6wwZbG9G2bQgHUYVevWx13pYt8NJLVkDbuUIix0AhIq+q6oPAlyKyW0KoKCrcOZe00tPhuecsLfgJJ9iAdY0aeT+vQFq3tuIUjRvD++/DcceFdCDnwpHbFcVnwb/5rWznXFJbvRquvdYmHLVtC926hTCGvGOH5fYoUQIuvdT6tm67zfMzuUIpx3etqgYjbpyoqj9G/gDxqNvlXMyNG2e1I0aMsADxySchBIk5c+zqISOJ3/XXW/4PDxKukIrmnXtTNvfdHOuGOBcmVXj9dUvCWqqUBYzbbotxQr/t260Wat26MG8e7L9/DHfuXOLkNkZxFVaVrpqIfBXxUHlgXfbPci75/POP1Y746qsQa0dMnWrFhGbMgKuugrfegkMPjfFBnEuM3MYoJmI1KCoBXSPuXw9MDbNRzsVKRvLV33+HV14JMS34n3/a4MfAgdC8eQgHcC5xcgwUqroYWIxli3WuUFG1CUb33AOHHGLLFmI+I3XUKPj1V7jrLkvit2CBZQ90rojJcYxCRH4K/l0rIn9H/KwVkb/j10Tn8mfjRisC1769jUlMnRrjIPHvv5Yx8KyzrIspI4mfBwlXROU2mJ1R7vQQoELET8Zt55LO3Lm28Ll3b6sd8c03MS4wNHQo1KwJ3btbP5Yn8XPFQG5dTxmrsSsDy1V1m4icAdQBemPJAZ1LGqHXjli61MYfjj/eFtA1bBjjAziXnKKZHjsQK4N6DPAxtoaiT6itci4fQq0doQrjx9vvlSvDd9/ZVYQHCVeMRBMo0lV1O3AF8Iaq3gOElZ3fuXz5/fcQa0csX27zaU89NTOJ3znnQOnSMTqAc4VDVKVQReRK4DogI/FyWBn6nYvakCG26FnVakfELC24qq2q7tjRLldeecWT+LliLdqV2ec57ch1AAAdvUlEQVRgacYXiUg1oG+4zXIuZ1lrR0yZEuPaEa1a2WBH3bo2/fXBB2GvaL5TOVc05fnuV9WZInIvUF1ETgAWqOpz4TfNud0tXw5t2sDo0ZaC4403YlTOITKJX4sWcMEFFiw8P5NzUVW4awwsAD4AegLzRcSvw13cDR9uCf2mTLFkft26xShIzJxpXUsZSfyuu84zvToXIZr/Ca8DF6vq6ap6GnAJ8Ga4zXIuU3q65do7/3w4+GCYNMnShO+xbdvg6aetSPbChXDggTHYqXNFTzQdr6VVdXbGDVWdIyI+7cPFxerV9gX/229t+mv37jFKCz5liiXxmznTdvzGGzFemedc0RFNoPhFRLoDnwS32+JJAV0cjBtnxeH++sumv8Y0LfiaNbBunU2datYsRjt1rmiKJlDcDtwLPAQIMAp4O8xGueJNFd58Ezp1sjVuP/8M9evHYMcjRtgspnvvtcHq336L0SCHc0VbroFCRGoDxwADVLVLfJrkirN//oGbb4Yvv7RsGR9+GIOhg3/+gYcegh49rED2bbdZfiYPEs5FJbfssf/B0ne0Bb4Xkewq3TkXM9OmQUqKlXR4+WVbRLfHQWLIEKhRw3KOd+xoYxOexM+5fMntiqItUEdVN4pIBWAoNj3WuZjKWAh99902q2nkSEvLsceWLoWWLe0qYuBAOOWUGOzUueInt+mxW1V1I4CqrspjW+cKZONGm3x0663QuLHVjtijIKFqgxqQmcRv8mQPEs7tgdw+/I8Wka+CnwHAMRG3v8rleTuJSFMRmSciC0TkkVy2ayUiKiIp+X0BrvDKqB3xySfw1FM2BXaPykynplpej9NPz0zid/bZnsTPuT2UW9dTyyy338nPjkWkJFZr+3wgFZgkIoMj12QE25XHZlVNyM/+XeHWr59dRZQtawHiggv2YGfp6fDeezZNKi0NXnstRn1XzjnIvXDRj3u47wZYXqhFACLSD2gOzM6y3f8BXYCOe3g8Vwhs3WqF4d59F047DT77DCpV2sOdtmxpYxDnnmsB4+ijY9JW55wJc9yhIrA04nYqWepYiMjJQGVV/Tq3HYlIexGZLCKTV61aFfuWurjIqB3x7ruWkHXkyD0IEmlpdiUBFijeew9++MGDhHMhCDNQZLeGVnc+KFICyyP1YF47UtUeqpqiqikVPM1CoTRkiCX0++03m/b6yitQqqBVTWbMsGJC771nt6+9Fm65JYbLtp1zkaIOFCKS38nnqVi97QyVgOURt8sDtYCRIvI70AgY7APaRUtMa0ds3Wqj3vXrwx9/eG4m5+IkmjTjDUTkV+C34PZJIhJNCo9JwLEiUi1IItgGGJzxoKr+o6qHqGpVVa0KjAcuU9XJBXkhLvksX27DBl262GLon3+GY44p4M4mTbIsr888A1dfDXPmwBVXxLS9zrnsRZPr6S2gGbZKG1WdLiLn5PUkVU0TkbuBYUBJoKeqzhKRZ4DJqjo49z24wmz4cPs837DBpr/ucVrwtWttZ0OHwkUXxaSNzrnoRBMoSqjqH7Jr/++OaHauqkOxFd2R9z2Zw7ZnR7NPl9zS0+H5562H6PjjLQ9fjRoF3Nnw4ZbE7777bP7s/PmefsO5BIhmjGKpiDQAVERKisj9wPyQ2+UKodWr4ZJL4IknrFzpxIkFDBLr1tkii/POswIUW7fa/R4knEuIaALFHcADQBXgT2zQ+Y4wG+UKn/HjbQhh+HCrHdG7dwELDA0aZNGlZ0/L+OpJ/JxLuDy7nlT1L2wg2rndqMJbb1li1j2uHbFkCVx5JZx4IgwebKlknXMJl2egEJH3iFj/kEFV24fSIldoRNaOuOwy6NWrAGnBVWHMGMsIWKWKLZpr1MjzMzmXRKLpevoB+DH4GQscCmwNs1Eu+U2fnlk7oksX+zffQWLJEhvUOPPMzCR+Z57pQcK5JBNN19NnkbdF5BPg+9Ba5JKaqg0f3H03HHSQzWpq3DifO0lPh27dbCVeRt+VJ/FzLmlFMz02q2rAUbFuiEt+mzbBnXfCRx/ZhKQ+fQqYFvyKK2zQ+vzzrTxp1aqxbqpzLoaiGaNYS+YYRQngbyDH2hKuaJo3D1q1glmz4Mkn7adkyXzsIC0NSpSwn6uusoLY7dp5fibnCoFcA4XYKruTgGXBXemqutvAtivaPvvMcu4VuHbE9Olw0022NuL2223JtnOu0Mh1MDsICgNUdUfw40GiGNm61cYi2rSBOnWsTGm+gsSWLfD44zbqnZoKhx8eWludc+GJZtbTRBGpF3pLXFL5/XcbpO7a1QoN5bt2xMSJllf8ueegbVtL4lfgtLHOuUTKsetJRPZS1TTgDOBWEVkIbMTqTKiqevAoor7+Gq6/HnbsgK++gssvL8BO/v0XNm+2vqoLL4x5G51z8ZPbGMVEoB7gXwOLibQ06yl66SW7GPjii3ymBf/uOxvt7tABmjSxEXBPv+FcoZdboBAAVV0Yp7a4BFqxwsYiRo2C9u3hzTdt8Doqa9da/1SvXlCzps2hLVPGg4RzRURugaKCiDyQ04Oq+loI7XEJsEe1I776Cu66C1atgkcftXmzHiCcK1JyCxQlgX3Jvva1KwIia0ccd5wFjJo187GDJUvsMqRWLSsodPLJobXVOZc4uQWKFar6TNxa4uJq9Wq47joba77mGiv7EFVacFXrnzrrLEviN3w4NGwIpUqF3mbnXGLkNj3WrySKqALXjvjjDytDevbZmUn8zjjDg4RzRVxugeK8uLXCxYWqDVI3bgx77WW1I26/PYosGunp8M471i81Zgy8/XYBMgE65wqrHLueVPXveDbEhevff612RP/+Bagd0aIFDBli6yG6d4ejPCekc8VJQbLHukJm+nRL6Ld4sdWO6NgxiquI7dst61+JEjYlqlUrG9TwJH7OFTvRpPBwhVjPnlYwbuNGqx3RqVMUn/W//AINGljNCLBAcf31HiScK6Y8UBRRmzbBjTdad9Ppp8O0aVEMK2zebGshGjSAlSutCLZzrtjzrqciaN48uPJKmDkTnnjC1knkWTti/Hi44QaYP99Sgr/ySgFqmzrniiIPFEXM55/bVUSZMvDNN/nIx7dxo41LfP+95WlyzrmAdz0VEVu3wj33WPG42rWtdkSeQeLbb+HVV+33886DuXM9SDjnduOBogj44w8bf3jnHUvc+tNPeQwvrFlj3UwXXWQFsLdts/tLl45Le51zhYsHikLuf/+zFEvz5sGXX8Jrr+WyUFrVFlLUqAF9+lhO8UmTPEA453LlgaKQSkuzCUrNmtn6tylT4Ior8njSkiWW2KlyZZg8Gf7v/zzTq3MuTx4oCqEVK2wo4cUX4dZbLRVH9eo5bKxqSZ3AIsrIkTbD6aST4tVc51wh54GikBkxwrqaJk2Cjz+GHj2gXLkcNl68GC64wAaqM5L4nXaaJXpyzrkoeaAoJNLT4bnn7EriwANh4kTLqJGtHTss+1+tWjBhgqWI9SR+zrkC8q+WhcCaNRYUvvnGsmn06JFHWvDmzW2U++KLLQ2Hr7B2zu0BDxRJbvx4aN0a/vwT3n03l7TgkUn8rrvOIso113h+JufcHgu160lEmorIPBFZICKPZPP4AyIyW0RmiMiPIuL5qwMZtSPOPNM+/8eOhTvuyOFzf/JkSEmxLiawVXdt23qQcM7FRGiBQkRKAl2Bi4AawNUiUiPLZlOBFFWtA/QHuoTVnsLk33/tKuL++21N3C+/WBzYzebN8PDDVop01SqvE+GcC0WYVxQNgAWqukhVtwH9gOaRG6jqCFXdFNwcD1QKsT2FwvTpUL8+DBhgtSMGDswhN9+4cTbFtUsXS+I3e7YtqnDOuRgLc4yiIrA04nYq0DCX7W8GvsnuARFpD7QHqFKlSqzal3R69oS77rLAMGJEHhOVNm+2qVA//GDTX51zLiRhXlFk10Gu2W4oci2QAryc3eOq2kNVU1Q1pUKFCjFsYnKIunbE0KHwcnCKzj0X5szxIOGcC12YgSIViJyXWQlYnnUjEWkCPAZcpqpbQ2xPUpo/3yrQffSR1Y4YNgwOPTTLRqtXw7XXwiWXwKefZibxyzGpk3POxU6YgWIScKyIVBOR0kAbYHDkBiJyMtAdCxJ/hdiWpPT55zYesXy5rZF45pksBYZUoV8/OPFE2/ipp2ylnSfxc87FUWiBQlXTgLuBYcAc4HNVnSUiz4jIZcFmLwP7Al+IyDQRGZzD7oqUbdvg3nujqB2xZImlA69WzbL+de7sQcI5F3eimu2wQdJKSUnRyZMnJ7oZBfbHHzb1deJEqx3x4otZPvtV4ccfMwsIjR8Pp5wSRS1T55zLmYhMUdXsJtrnyXM9xVFG7Yi5c60sxGuvZQkSCxfa4PT552cm8WvUyIOEcy6hPFDEQVoa/Oc/tsyhShXrRWrZMmKDHTssatSubQ927+5J/JxzScNzPYVsxQpLu/TTT3DLLfDWW9mkBb/0UhvNbtbM0nBUKvbrDp1zScQDRYhGjoQ2bSwlx0cfwfXXRzy4bZvVhShRAtq1s0R+bdp4fibnXNLxrqcQpKfD88/bcMMBB9jA9S5BYuJEmxf77rt2u3Vru+zwIOGcS0IeKGJszRrrSXrsMfv8nzTJ6gcBtgT7wQfh1FNh7Vo45piEttU556LhXU8xNGGCBYeVK6Fr1yxpwceMsTURixbBbbfBSy/B/vsntL3OORcNDxQxoApvvw0dO0LFilY7Yre04BmFhUaMgLPPTkQznXOuQDxQ7KF//7XZTF98YV1OH30UkRZ8yBBL3PfQQ3DOOZYKfC8/5c65wsXHKPbAjBl25fDVV9aTtLN2xKpVVob0ssugb9/MJH4eJJxzhZAHigLq2dMKy23YAMOH20VDCVHo08eS+PXvb1n+Jkzw/EzOuULNv+Lm06ZNVlyoVy8rCdGnDxx2WPDgkiVWWOLkk+GDD6BmzUQ21TnnYsKvKPIha+2I776DwyqkWxEJsJrVo0fbaLYHCedcEeGBIkqRtSOGDg1qRyz6zS4rmjaFUaNswwYNPImfc65I8UCRh+xqRzRtkmYlSevUsbqlH3zgSfycc0WWj1HkIsfaEU2bWXdT8+aWhuPIIxPdVOeS0vbt20lNTWXLli2JbkqxUbZsWSpVqkSpGJZK9kCRg6FDLU9fWppNYGrZbCvsVQooYQsnbroJrrzS8zM5l4vU1FTKly9P1apVEf+/EjpVZc2aNaSmplKtWrWY7de7nrLIqB1xySVQuXJQO6LieKhXz/JyALRqZZca/sZ3Lldbtmzh4IMP9iARJyLCwQcfHPMrOA8UEVautOJyL7xgFw3jfthI9a4d4LTTYP16OPbYRDfRuULHg0R8hXG+vespMHKkZfr+5x9bI3HD0aOhwQ2weDHceadFj/32S3QznXMu7or9FUV6usWA886zZK4TJliSV9LSoFQpK03XtasHCecKsQEDBiAizJ07d+d9I0eOpFmzZrts165dO/r37w/YQPwjjzzCscceS61atWjQoAHffPPNHrflhRdeoHr16hx//PEMy1iDlUW7du2oVq0adevWpW7dukybNg2Al19+eed9tWrVomTJkvz999973Ka8FOsrijVrrKDQ0KE2/fXD5gMp9/UcqP2oJfGbNcvzMzlXBPTt25czzjiDfv360blz56ie88QTT7BixQpmzpxJmTJl+PPPP/npp5/2qB2zZ8+mX79+zJo1i+XLl9OkSRPmz59PyWzWXr388su0atVql/s6depEp06dABgyZAivv/46Bx100B61KRrF9lNw4kSbtLRiBXzw/J/cOPUe5JovbND6wQdtHqwHCedi5v77bdlRLNWtC2+8kfs2GzZsYOzYsYwYMYLLLrssqkCxadMm3nvvPRYvXkyZMmUAOOyww2jduvUetXfQoEG0adOGMmXKUK1aNapXr87EiRM59dRT872vvn37cvXVV+9Re6JV7LqeMmpHnHEGCMrcxz7hpldqIIMGwXPPwfjxnsTPuSJk4MCBNG3alOOOO46DDjqIX375Jc/nLFiwgCpVqrBfFF3OHTp02NkdFPnz4osv7rbtsmXLqFy58s7blSpVYtmyZdnu97HHHqNOnTp06NCBrVu37vLYpk2b+Pbbb2nZsmWe7YuFYvWVObJ2RLNm8MmzSzigwS2WK/yDD+CEExLdROeKrLy++Yelb9++3H///QC0adOGvn37Uq9evRxnB+V31tDrr78e9baqGtXxXnjhBQ4//HC2bdtG+/bteemll3jyySd3Pj5kyBBOP/30uHQ7QTEKFDNm2PKHxQvT+fzGYbR8/yJKlDjKEvidfLLnZ3KuCFqzZg3Dhw9n5syZiAg7duxAROjSpQsHH3wwa9eu3WX7v//+m0MOOYTq1auzZMkS1q9fT/ny5XM9RocOHRgxYsRu97dp04ZHHnlkl/sqVarE0qVLd95OTU3lyGwyOxxxxBEAlClThhtvvJFXXnlll8f79esXt24nwCJcYfqpX7++5lfPnqply6qeXmGerqvTWBVUR47M936cc/kze/bshB6/W7du2r59+13uO/PMM3XUqFG6ZcsWrVq16s42/v7771qlShVdt26dqqp26tRJ27Vrp1u3blVV1eXLl+snn3yyR+2ZOXOm1qlTR7ds2aKLFi3SatWqaVpa2m7bLV++XFVV09PT9b777tOHH35452Pr1q3TAw88UDds2JDjcbI778BkLeDnbpEeo9i0yTJt3HpTGm9VfInR/9Zh/yW/wocfwplnJrp5zrmQ9e3bl8svv3yX+1q2bEmfPn0oU6YMvXv35sYbb6Ru3bq0atWK999/n/333x+AZ599lgoVKlCjRg1q1apFixYtqFChwh61p2bNmrRu3ZoaNWrQtGlTunbtunPG08UXX8zy5csBaNu2LbVr16Z27dqsXr2axx9/fOc+BgwYwAUXXMA+++yzR23JD9Fs+sySWUpKik6ePDnP7ebPt66mX3+F+UdfyLGLvoMrrrA1EYcfHoeWOufmzJnDiSeemOhmFDvZnXcRmaKqKQXZX5Eco/jiC7jzpi1I6VJ8801Jjt3YHmgPcZoh4JxzRUmRChTbtkHHjjDl7bFMLnsz+3W8kwOb3gt4gHDOuYIqMmMUf/wBF5y2gepv38toGlPlsC0ceJpf8jqXaIWte7uwC+N8F4kriqFD4b9tfuKjDTdQRZYgd98Nzz8P++6b6KY5V6yVLVuWNWvWeKrxONGgHkXZsmVjut9CHSjS0uDJJy2p303HwBGH7o18NBpOPz3RTXPOYesGUlNTWbVqVaKbUmxkVLiLpUIbKFauhHebfAWz5nLLLf/hrbfOonTpX33hnHNJpFSpUjGttOYSI9QxChFpKiLzRGSBiDySzeNlROSz4PEJIlI1mv3+/NVKJldtxTOzWvJAtQG813Ub5crhQcI550IQWqAQkZJAV+AioAZwtYjUyLLZzcBaVa0OvA68lNd+/1m0hhNbnsj5275m5X0vcMi8nz2Jn3POhSjMK4oGwAJVXaSq24B+QPMs2zQHPgp+7w+cJ3mMeO239g/+PKQW2ydP5/A3HrHiQs4550IT5hhFRWBpxO1UoGFO26hqmoj8AxwMrI7cSESCFXMAbD1x9ZiZ1PdMr8AhZDlXxZifi0x+LjL5uch0fEGfGGagyO7KIOsE32i2QVV7AD0ARGRyQZehFzV+LjL5ucjk5yKTn4tMIpJ37qMchNn1lApUjrhdCVie0zYishewPxB+AVjnnHNRCzNQTAKOFZFqIlIaaAMMzrLNYOCG4PdWwHD1ZZzOOZdUQut6CsYc7gaGASWBnqo6S0SewfKiDwY+AD4RkQXYlUSbKHbdI6w2F0J+LjL5ucjk5yKTn4tMBT4XhS7NuHPOufgqMkkBnXPOhcMDhXPOuVwlbaAIK/1HYRTFuXhARGaLyAwR+VFEjkpEO+Mhr3MRsV0rEVERKbJTI6M5FyLSOnhvzBKRPvFuY7xE8X+kioiMEJGpwf+TixPRzrCJSE8R+UtEZubwuIjIW8F5miEi9aLacUGLbYf5gw1+LwSOBkoD04EaWba5E+gW/N4G+CzR7U7guTgH2Dv4/Y7ifC6C7coDo4DxQEqi253A98WxwFTgwOD2oYludwLPRQ/gjuD3GsDviW53SOfiTKAeMDOHxy8GvsHWsDUCJkSz32S9oggl/Uchlee5UNURqropuDkeW7NSFEXzvgD4P6ALsCWejYuzaM7FrUBXVV0LoKp/xbmN8RLNuVBgv+D3/dl9TVeRoKqjyH0tWnPgYzXjgQNE5Ii89pusgSK79B8Vc9pGVdOAjPQfRU005yLSzdg3hqIoz3MhIicDlVX163g2LAGieV8cBxwnImNFZLyINI1b6+IrmnPRGbhWRFKBocA98Wla0snv5wmQvPUoYpb+owiI+nWKyLVACnBWqC1KnFzPhYiUwLIQt4tXgxIomvfFXlj309nYVeZoEamlqutCblu8RXMurgZ6qeqrInIqtn6rlqqmh9+8pFKgz81kvaLw9B+ZojkXiEgT4DHgMlXdGqe2xVte56I8UAsYKSK/Y32wg4vogHa0/0cGqep2VV0MzMMCR1ETzbm4GfgcQFXHAWWxhIHFTVSfJ1kla6Dw9B+Z8jwXQXdLdyxIFNV+aMjjXKjqP6p6iKpWVdWq2HjNZapa4GRoSSya/yMDsYkOiMghWFfUori2Mj6iORdLgPMARORELFAUx/qsg4Hrg9lPjYB/VHVFXk9Kyq4nDS/9R6ET5bl4GdgX+CIYz1+iqpclrNEhifJcFAtRnothwAUiMhvYAXRS1TWJa3U4ojwXDwLviUgHrKulXVH8YikifbGuxkOC8ZingFIAqtoNG5+5GFgAbAJujGq/RfBcOeeci6Fk7XpyzjmXJDxQOOecy5UHCuecc7nyQOGccy5XHiicc87lygOFSzoiskNEpkX8VM1l26o5ZcrM5zFHBtlHpwcpL44vwD5uF5Hrg9/biciREY+9LyI1YtzOSSJSN4rn3C8ie+/psV3x5YHCJaPNqlo34uf3OB23raqehCWbfDm/T1bVbqr6cXCzHXBkxGO3qOrsmLQys53vEl077wc8ULgC80DhCoXgymG0iPwS/JyWzTY1RWRicBUyQ0SODe6/NuL+7iJSMo/DjQKqB889L6hh8GuQ679McP+LklkD5JXgvs4i0lFEWmE5tz4NjlkuuBJIEZE7RKRLRJvbicjbBWznOCISuonIf0VksljtiaeD++7FAtYIERkR3HeBiIwLzuMXIrJvHsdxxZwHCpeMykV0Ow0I7vsLOF9V6wFXAW9l87zbgTdVtS72QZ0apGu4Cjg9uH8H0DaP418K/CoiZYFewFWqWhvLZHCHiBwEXA7UVNU6wLORT1bV/sBk7Jt/XVXdHPFwf+CKiNtXAZ8VsJ1NsTQdGR5T1RSgDnCWiNRR1bewXD7nqOo5QSqPx4EmwbmcDDyQx3FcMZeUKTxcsbc5+LCMVAp4J+iT34HlLcpqHPCYiFQCvlLV30TkPKA+MClIb1IOCzrZ+VRENgO/Y2mojwcWq+r84PGPgLuAd7BaF++LyP+AqFOaq+oqEVkU5Nn5LTjG2GC/+WnnPli6isgKZa1FpD32//oIrEDPjCzPbRTcPzY4TmnsvDmXIw8UrrDoAPwJnIRdCe9WlEhV+4jIBOASYJiI3IKlVf5IVR+N4hhtIxMIiki29U2C3EINsCRzbYC7gXPz8Vo+A1oDc4EBqqpin9pRtxOr4vYi0BW4QkSqAR2BU1R1rYj0whLfZSXA96p6dT7a64o573pyhcX+wIqgfsB12LfpXYjI0cCioLtlMNYF8yPQSkQODbY5SKKvKT4XqCoi1YPb1wE/BX36+6vqUGygOLuZR+uxtOfZ+QpogdVI+Cy4L1/tVNXtWBdSo6Dbaj9gI/CPiBwGXJRDW8YDp2e8JhHZW0SyuzpzbicPFK6weBe4QUTGY91OG7PZ5ipgpohMA07ASj7Oxj5QvxORGcD3WLdMnlR1C5Zd8wsR+RVIB7phH7pfB/v7CbvayaoX0C1jMDvLftcCs4GjVHVicF++2xmMfbwKdFTV6Vh97FlAT6w7K0MP4BsRGaGqq7AZWX2D44zHzpVzOfLssc4553LlVxTOOedy5YHCOedcrjxQOOecy5UHCuecc7nyQOGccy5XHiicc87lygOFc865XP0/22YXQIETPbcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = predictions['adaboost']\n",
    "y_true = predictions['true_values']\n",
    "plot_roc_auc(preds, y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = predictions['forest']\n",
    "y_true = predictions['true_values']\n",
    "plot_roc_auc(preds, y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gaussian Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = predictions['gaussian']\n",
    "y_true = predictions['true_values']\n",
    "plot_roc_auc(preds, y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### K-Nearest Neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = predictions['kneighbors']\n",
    "y_true = predictions['true_values']\n",
    "plot_roc_auc(preds, y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = predictions['logistic']\n",
    "y_true = predictions['true_values']\n",
    "plot_roc_auc(preds, y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multi-layer perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = predictions['neural']\n",
    "y_true = predictions['true_values']\n",
    "plot_roc_auc(preds, y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = predictions['svc']\n",
    "y_true = predictions['true_values']\n",
    "plot_roc_auc(preds, y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = predictions['tree']\n",
    "y_true = predictions['true_values']\n",
    "plot_roc_auc(preds, y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "xr_train, xr_test, yr_train, yr_test = train_test_split(x, y_class, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "398a36a5-764a-4e73-a3e4-cf17de1026b9",
    "_uuid": "d8c25f62f9e1c5e1d32911a82aaeb5969e1ddff1"
   },
   "outputs": [],
   "source": [
    "predictions, scores = {}, {}\n",
    "predictions['true_values'] = yr_test.values\n",
    "\n",
    "regressors = [\n",
    "    {'name': 'tree', 'clf': tree.DecisionTreeRegressor()},\n",
    "    {'name': 'gradientboost', 'clf': GradientBoostingRegressor(n_estimators=100, learning_rate=0.5, max_depth=1, random_state=0, loss='ls')},\n",
    "    {'name': 'linear', 'clf': linear_model.LinearRegression()},\n",
    "    {'name': 'kneighbors', 'clf': KNeighborsRegressor()},\n",
    "    {'name': 'svr', 'clf':  SVR()},\n",
    "    {'name': 'gaussian', 'clf': GaussianProcessRegressor(1.0 * RBF(1.0))},\n",
    "    {'name': 'neural', 'clf':  MLPRegressor(alpha=1, max_iter=1000)},\n",
    "    {'name': 'adaboost', 'clf':  AdaBoostRegressor()},\n",
    "     ]\n",
    "\n",
    "for r in regressors:\n",
    "    clf = r['clf']\n",
    "    clf.fit(xr_train, yr_train)\n",
    "    predictions[r['name']] = clf.predict(xr_test)\n",
    "    scores[r['name']] = [clf.score(xr_test, yr_test)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "b237e3c8-07e8-4be0-95df-0ab73d11a51a",
    "_uuid": "952778529a33b8fe483abe236b8e4e85eeefa938"
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>adaboost</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.572917e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.761468</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaussian</th>\n",
       "      <td>0.487924</td>\n",
       "      <td>7.697982e-148</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001523</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gradientboost</th>\n",
       "      <td>0.963597</td>\n",
       "      <td>6.045191e-01</td>\n",
       "      <td>0.977819</td>\n",
       "      <td>0.795032</td>\n",
       "      <td>0.977497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kneighbors</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>8.000000e-01</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear</th>\n",
       "      <td>0.840072</td>\n",
       "      <td>7.923201e-01</td>\n",
       "      <td>0.921777</td>\n",
       "      <td>0.519738</td>\n",
       "      <td>0.861983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neural</th>\n",
       "      <td>0.515180</td>\n",
       "      <td>1.131642e+00</td>\n",
       "      <td>3.765912</td>\n",
       "      <td>0.560937</td>\n",
       "      <td>2.982590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>svr</th>\n",
       "      <td>0.831137</td>\n",
       "      <td>5.479128e-01</td>\n",
       "      <td>0.900017</td>\n",
       "      <td>0.779158</td>\n",
       "      <td>0.899724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tree</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>true_values</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      0              1         2         3         4\n",
       "adaboost       1.000000   5.572917e-01  1.000000  0.761468  1.000000\n",
       "gaussian       0.487924  7.697982e-148  1.000000 -0.001523  1.000000\n",
       "gradientboost  0.963597   6.045191e-01  0.977819  0.795032  0.977497\n",
       "kneighbors     0.600000   8.000000e-01  0.800000  0.600000  1.000000\n",
       "linear         0.840072   7.923201e-01  0.921777  0.519738  0.861983\n",
       "neural         0.515180   1.131642e+00  3.765912  0.560937  2.982590\n",
       "svr            0.831137   5.479128e-01  0.900017  0.779158  0.899724\n",
       "tree           1.000000   0.000000e+00  1.000000  0.000000  1.000000\n",
       "true_values    1.000000   1.000000e+00  1.000000  1.000000  1.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(predictions).head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "f4ee3737-680a-488b-8e67-6b57fb6d8c50",
    "_uuid": "b616f6f9b6540dc5b8e3003186d48b9c5abc0cf1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>adaboost</th>\n",
       "      <td>0.234506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaussian</th>\n",
       "      <td>-1.124414</td>\n",
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       "    <tr>\n",
       "      <th>gradientboost</th>\n",
       "      <td>0.221171</td>\n",
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       "    <tr>\n",
       "      <th>kneighbors</th>\n",
       "      <td>-0.224988</td>\n",
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       "      <th>linear</th>\n",
       "      <td>0.034786</td>\n",
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       "      <th>neural</th>\n",
       "      <td>-9.966374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>svr</th>\n",
       "      <td>-0.310401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tree</th>\n",
       "      <td>-0.656842</td>\n",
       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "                      0\n",
       "adaboost       0.234506\n",
       "gaussian      -1.124414\n",
       "gradientboost  0.221171\n",
       "kneighbors    -0.224988\n",
       "linear         0.034786\n",
       "neural        -9.966374\n",
       "svr           -0.310401\n",
       "tree          -0.656842"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(scores).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neuronal Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* <http://neuralnetworksanddeeplearning.com/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perceptron\n",
    "\n",
    "![perceptron](http://neuralnetworksanddeeplearning.com/images/tikz0.png)\n",
    "\n",
    "1. `output = activation( w1*x1 + w2*x2 + w3*x3 + b)`\n",
    "1. ` foreach(example):`\n",
    "  1. `loss = (true_value - output)`\n",
    "  1. `w1 = w1 + (learning_rate * loss * x1)`\n",
    "  1. `w2 = w2 + (learning_rate * loss * x2)`\n",
    "  1. `w3 = w3 + (learning_rate * loss * x3)`\n",
    "\n",
    "### Activations\n",
    "\n",
    "#### Linear\n",
    "![linear](https://cdn-images-1.medium.com/max/1600/1*tldIgyDQWqm-sMwP7m3Bww.png)\n",
    "\n",
    "#### Sigmoid\n",
    "![sigmoid](https://ml4a.github.io/images/figures/sigmoid.png)\n",
    "\n",
    "#### RelU\n",
    "![relu](https://ml4a.github.io/images/figures/relu.png)\n",
    "\n",
    "### Multi-Layer-Perceptron\n",
    "![mlp](http://neuralnetworksanddeeplearning.com/images/tikz1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1a604aaf-75ea-4c3d-9d30-75ef2eb069bf",
    "_uuid": "f67992564a7adc036c54235c5214dc43a89ec147"
   },
   "source": [
    "##  Deep Learning and Tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* <http://playground.tensorflow.org>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_cell_guid": "50aa0002-7089-478e-9dcc-08ed8e916fe4",
    "_uuid": "f7c917b75fe32f2f799d77730f6151e80daf4a4f"
   },
   "outputs": [],
   "source": [
    "def build_model_columns(cols):\n",
    "    \"\"\"Builds a set of wide and deep feature columns.\"\"\"\n",
    "    assert isinstance(cols, pd.Index)\n",
    "    feats = list()\n",
    "    for c in cols:\n",
    "        feats.append(tf.feature_column.numeric_column(c))\n",
    "\n",
    "    # return wide_columns, deep_columns, weight_column\n",
    "    return feats, feats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "ed18e956-deb7-4d31-ae84-af8bf6e1c683",
    "_uuid": "d3ab2199bd6e45e42964a859ac604c8ed993dd67"
   },
   "outputs": [],
   "source": [
    "def input_fn(features, labels, num_epochs, shuffle, batch_size):\n",
    "    \"\"\"Generate an input function for the Estimator.\"\"\"\n",
    "\n",
    "    # Convert the inputs to a Dataset.\n",
    "    ds = tf.data.Dataset.from_tensor_slices((dict(features), labels))\n",
    "\n",
    "    if shuffle:\n",
    "        ds = ds.shuffle(1000)\n",
    "\n",
    "    # We call repeat after shuffling, rather than before, to prevent separate\n",
    "    # epochs from blending together.\n",
    "    ds = ds.repeat(num_epochs)\n",
    "    ds = ds.batch(batch_size)\n",
    "    return ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "a738eee2-8db6-42fa-b283-0b85af298f04",
    "_uuid": "7f25df50f13509204bfabf9ca5188507c483c749"
   },
   "outputs": [],
   "source": [
    "def eval_input_fn(features, labels, batch_size):\n",
    "    \"\"\"An input function for evaluation or prediction\"\"\"\n",
    "    features=dict(features)\n",
    "    if labels is None:\n",
    "        # No labels, use only features.\n",
    "        inputs = features\n",
    "    else:\n",
    "        inputs = (features, labels)\n",
    "\n",
    "    # Convert the inputs to a Dataset.\n",
    "    dataset = tf.data.Dataset.from_tensor_slices(inputs)\n",
    "\n",
    "    # Batch the examples\n",
    "    assert batch_size is not None, \"batch_size must not be None\"\n",
    "    dataset = dataset.batch(batch_size)\n",
    "\n",
    "    # Return the dataset.\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "5d016323-7295-43eb-aaa8-b47390ec3bd9",
    "_uuid": "4498495b5f512b633c8e0227fd1aad364fdf4aae"
   },
   "outputs": [],
   "source": [
    "def build_estimator(cols):\n",
    "    \"\"\"Build an estimator appropriate for the given model type.\"\"\"\n",
    "    wide_columns, deep_columns = build_model_columns(cols)\n",
    "\n",
    "    #hidden_units = [100, 75, 50, 25]\n",
    "    a = len(wide_columns) #365\n",
    "    hidden_units = [5, 3, 2]\n",
    "\n",
    "    # Create a tf.estimator.RunConfig to ensure the model is run on CPU, which\n",
    "    # trains faster than GPU for this model.\n",
    "    run_config = tf.estimator.RunConfig().replace(session_config=tf.ConfigProto())\n",
    "\n",
    "    return tf.estimator.DNNLinearCombinedClassifier(\n",
    "        model_dir='../work/tf_model/',\n",
    "        linear_feature_columns=wide_columns,\n",
    "        dnn_feature_columns=deep_columns,\n",
    "        dnn_hidden_units=hidden_units,\n",
    "        config=run_config,\n",
    "        dnn_optimizer=tf.train.AdamOptimizer()\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "151549bc-41bb-4773-bd2b-80991bf9d213",
    "_uuid": "2d3504ef2219c3ac43c44b43857013b05502056c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using config: {'_model_dir': '../work/tf_model/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': , '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f85c3e6d438>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
     ]
    }
   ],
   "source": [
    "clf = build_estimator(x_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "1dea883c-e930-400b-8484-5e985c7aafad",
    "_uuid": "739526269a67a97abb92994de2a5a786d0e2cda8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from ../work/tf_model/model.ckpt-24767\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Saving checkpoints for 24768 into ../work/tf_model/model.ckpt.\n",
      "INFO:tensorflow:loss = 0.81010973, step = 24768\n",
      "INFO:tensorflow:global_step/sec: 845.399\n",
      "INFO:tensorflow:loss = 1.8280398, step = 24868 (0.121 sec)\n",
      "INFO:tensorflow:global_step/sec: 1615.81\n",
      "INFO:tensorflow:loss = 1.9486759, step = 24968 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1597.7\n",
      "INFO:tensorflow:loss = 2.0057576, step = 25068 (0.063 sec)\n",
      "INFO:tensorflow:global_step/sec: 1598.67\n",
      "INFO:tensorflow:loss = 2.289481, step = 25168 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1600.9\n",
      "INFO:tensorflow:loss = 0.8026135, step = 25268 (0.063 sec)\n",
      "INFO:tensorflow:global_step/sec: 1613.65\n",
      "INFO:tensorflow:loss = 3.26292, step = 25368 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1569.44\n",
      "INFO:tensorflow:loss = 1.927176, step = 25468 (0.064 sec)\n",
      "INFO:tensorflow:global_step/sec: 1588.91\n",
      "INFO:tensorflow:loss = 1.8805186, step = 25568 (0.063 sec)\n",
      "INFO:tensorflow:global_step/sec: 1615.85\n",
      "INFO:tensorflow:loss = 0.6077782, step = 25668 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1589.41\n",
      "INFO:tensorflow:loss = 2.2748537, step = 25768 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1609.81\n",
      "INFO:tensorflow:loss = 0.677801, step = 25868 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1581.76\n",
      "INFO:tensorflow:loss = 1.9226396, step = 25968 (0.063 sec)\n",
      "INFO:tensorflow:global_step/sec: 1591.82\n",
      "INFO:tensorflow:loss = 2.3181818, step = 26068 (0.063 sec)\n",
      "INFO:tensorflow:global_step/sec: 1611.08\n",
      "INFO:tensorflow:loss = 1.9651427, step = 26168 (0.061 sec)\n",
      "INFO:tensorflow:global_step/sec: 1565.87\n",
      "INFO:tensorflow:loss = 2.470365, step = 26268 (0.064 sec)\n",
      "INFO:tensorflow:global_step/sec: 1545.86\n",
      "INFO:tensorflow:loss = 1.6793921, step = 26368 (0.064 sec)\n",
      "INFO:tensorflow:global_step/sec: 1550.79\n",
      "INFO:tensorflow:loss = 0.86389065, step = 26468 (0.064 sec)\n",
      "INFO:tensorflow:global_step/sec: 1588.92\n",
      "INFO:tensorflow:loss = 0.7472993, step = 26568 (0.063 sec)\n",
      "INFO:tensorflow:global_step/sec: 1581.5\n",
      "INFO:tensorflow:loss = 0.7280574, step = 26668 (0.064 sec)\n",
      "INFO:tensorflow:global_step/sec: 1539.12\n",
      "INFO:tensorflow:loss = 2.6340911, step = 26768 (0.065 sec)\n",
      "INFO:tensorflow:global_step/sec: 1595.08\n",
      "INFO:tensorflow:loss = 1.9078785, step = 26868 (0.063 sec)\n",
      "INFO:tensorflow:global_step/sec: 1612.63\n",
      "INFO:tensorflow:loss = 0.9107147, step = 26968 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1569.71\n",
      "INFO:tensorflow:loss = 1.8462789, step = 27068 (0.064 sec)\n",
      "INFO:tensorflow:global_step/sec: 1614\n",
      "INFO:tensorflow:loss = 1.5971746, step = 27168 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1607.72\n",
      "INFO:tensorflow:loss = 0.6465327, step = 27268 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1626.12\n",
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      "INFO:tensorflow:global_step/sec: 1623.72\n",
      "INFO:tensorflow:loss = 2.753375, step = 27468 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1591.36\n",
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      "INFO:tensorflow:global_step/sec: 1624.08\n",
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      "INFO:tensorflow:global_step/sec: 1606.22\n",
      "INFO:tensorflow:loss = 1.9573067, step = 27768 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1614.86\n",
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      "INFO:tensorflow:global_step/sec: 1619.24\n",
      "INFO:tensorflow:loss = 0.8406079, step = 27968 (0.061 sec)\n",
      "INFO:tensorflow:global_step/sec: 1616.88\n",
      "INFO:tensorflow:loss = 2.2971444, step = 28068 (0.062 sec)\n",
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      "INFO:tensorflow:loss = 0.6146864, step = 28168 (0.061 sec)\n",
      "INFO:tensorflow:global_step/sec: 1611.83\n",
      "INFO:tensorflow:loss = 2.1751668, step = 28268 (0.063 sec)\n",
      "INFO:tensorflow:global_step/sec: 1587.25\n",
      "INFO:tensorflow:loss = 2.9430137, step = 28368 (0.063 sec)\n",
      "INFO:tensorflow:global_step/sec: 1595.6\n",
      "INFO:tensorflow:loss = 0.88962376, step = 28468 (0.063 sec)\n",
      "INFO:tensorflow:global_step/sec: 1576.72\n",
      "INFO:tensorflow:loss = 2.3761258, step = 28568 (0.063 sec)\n",
      "INFO:tensorflow:global_step/sec: 1607.39\n",
      "INFO:tensorflow:loss = 1.8072408, step = 28668 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1593.48\n",
      "INFO:tensorflow:loss = 0.7863963, step = 28768 (0.062 sec)\n",
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      "INFO:tensorflow:loss = 2.8470283, step = 29368 (0.062 sec)\n",
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      "INFO:tensorflow:loss = 1.579719, step = 29468 (0.064 sec)\n",
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      "INFO:tensorflow:loss = 1.88723, step = 29568 (0.062 sec)\n",
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      "INFO:tensorflow:loss = 2.006325, step = 29668 (0.061 sec)\n",
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      "INFO:tensorflow:loss = 0.7644969, step = 29768 (0.062 sec)\n",
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      "INFO:tensorflow:global_step/sec: 1605\n",
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      "INFO:tensorflow:loss = 3.0471573, step = 30568 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1610.31\n",
      "INFO:tensorflow:loss = 3.2994156, step = 30668 (0.062 sec)\n",
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      "INFO:tensorflow:loss = 0.8865637, step = 30768 (0.064 sec)\n",
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      "INFO:tensorflow:loss = 2.7463489, step = 30868 (0.065 sec)\n",
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      "INFO:tensorflow:loss = 1.8999603, step = 30968 (0.064 sec)\n",
      "INFO:tensorflow:global_step/sec: 1593.15\n",
      "INFO:tensorflow:loss = 0.7085695, step = 31068 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1615.37\n",
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      "INFO:tensorflow:global_step/sec: 1587.03\n",
      "INFO:tensorflow:loss = 1.9942871, step = 31568 (0.063 sec)\n",
      "INFO:tensorflow:global_step/sec: 1589.16\n",
      "INFO:tensorflow:loss = 0.71096647, step = 31668 (0.063 sec)\n",
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      "INFO:tensorflow:loss = 2.2044272, step = 31768 (0.064 sec)\n",
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      "INFO:tensorflow:loss = 4.545562, step = 32168 (0.063 sec)\n",
      "INFO:tensorflow:global_step/sec: 1596.64\n",
      "INFO:tensorflow:loss = 1.5260081, step = 32268 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1621.05\n",
      "INFO:tensorflow:loss = 0.7055426, step = 32368 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1607.17\n",
      "INFO:tensorflow:loss = 0.6361065, step = 32468 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1616.09\n",
      "INFO:tensorflow:loss = 0.9024489, step = 32568 (0.061 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 1630.33\n",
      "INFO:tensorflow:loss = 0.84570813, step = 32668 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1604.97\n",
      "INFO:tensorflow:loss = 0.5478512, step = 32768 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1621.01\n",
      "INFO:tensorflow:loss = 1.9647647, step = 32868 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1604.89\n",
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      "INFO:tensorflow:global_step/sec: 1617.89\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 1623.07\n",
      "INFO:tensorflow:loss = 0.7594193, step = 49068 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1617.36\n",
      "INFO:tensorflow:loss = 1.750082, step = 49168 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1626.54\n",
      "INFO:tensorflow:loss = 2.0667531, step = 49268 (0.062 sec)\n",
      "INFO:tensorflow:global_step/sec: 1570.15\n",
      "INFO:tensorflow:loss = 3.2280693, step = 49368 (0.064 sec)\n",
      "INFO:tensorflow:global_step/sec: 1599.37\n",
      "INFO:tensorflow:loss = 2.6777718, step = 49468 (0.063 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 49534 into ../work/tf_model/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 0.38640112.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.estimator.canned.dnn_linear_combined.DNNLinearCombinedClassifier at 0x7f85c3e6d1d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.train(input_fn=lambda: input_fn(x_train.reset_index().drop('customer', axis=1), yc_train, num_epochs=100, shuffle=True, batch_size=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_cell_guid": "d6281c2b-2cd7-46f4-a03a-e54d3948067c",
    "_uuid": "d36476b3a6b30193626437cf3c186d625ff27961"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from ../work/tf_model/model.ckpt-49534\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    }
   ],
   "source": [
    "predictions = list(clf.predict(input_fn=lambda: eval_input_fn(x_test, labels=None, batch_size=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "c6615d94-16e2-4cbe-aabb-9d097f5e8df3",
    "_uuid": "102434682b71018db028f8a1be776881a72d8ea6"
   },
   "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>Proba</th>\n",
       "      <th>y_frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>0.793621</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>0.853266</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>0.775237</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>0.644106</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.858086</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Proba  y_frequency\n",
       "customer                       \n",
       "389       0.793621          1.0\n",
       "546       0.853266          1.0\n",
       "562       0.775237          1.0\n",
       "443       0.644106          1.0\n",
       "55        0.858086          1.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unpack probabilities\n",
    "t = list()\n",
    "for i in range(len(predictions)):\n",
    "    t.append(predictions[i]['probabilities'][1])\n",
    "pred_df = pd.DataFrame(data=t, index=x_test.index, columns=['Proba'])\n",
    "pred_df = pred_df.join(yc_test)\n",
    "pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_cell_guid": "5a7afaf1-b13d-4db1-a405-46f626588bf1",
    "_uuid": "098613dd82c063860d67a3a9372ab62268f945c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Starting evaluation at 2018-03-26-08:50:05\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from ../work/tf_model/model.ckpt-49534\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Finished evaluation at 2018-03-26-08:50:05\n",
      "INFO:tensorflow:Saving dict for global step 49534: accuracy = 0.766129, accuracy_baseline = 0.7741935, auc = 0.64941406, auc_precision_recall = 0.8742449, average_loss = 0.5313023, global_step = 49534, label/mean = 0.7741935, loss = 0.5313023, prediction/mean = 0.7912591\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.766129,\n",
       " 'accuracy_baseline': 0.7741935,\n",
       " 'auc': 0.64941406,\n",
       " 'auc_precision_recall': 0.8742449,\n",
       " 'average_loss': 0.5313023,\n",
       " 'global_step': 49534,\n",
       " 'label/mean': 0.7741935,\n",
       " 'loss': 0.5313023,\n",
       " 'prediction/mean': 0.7912591}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.evaluate(input_fn=lambda: eval_input_fn(x_test, labels=yc_test, batch_size=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_cell_guid": "4d6f9d1d-2d1f-430d-97e7-6ef5d8d683a7",
    "_uuid": "ccea3479c2c8e1f43e270c66d918612b7493e6fb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs = pred_df['Proba'] # predicted probabilities generated by classifier\n",
    "preds = (probs>0.8) * 1 # predictions \n",
    "y_true = pred_df['y_frequency']\n",
    "\n",
    "plot_roc_auc(preds, y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "722a130f-2b69-4fee-9f12-1ea39e9fcccc",
    "_uuid": "d50971a32df12e40caf7f75c69fe8e5c1d9ad2c7"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate and draw decision tree to explain a model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=6,\n",
       "            max_features=None, max_leaf_nodes=30,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=10, min_samples_split=5,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "dtree = tree.DecisionTreeClassifier(criterion='gini', splitter='best'\n",
    "        , max_depth=6, min_samples_split=5, min_samples_leaf=10, min_weight_fraction_leaf=0.0\n",
    "        , max_features=None, random_state=None, max_leaf_nodes=30, min_impurity_decrease=0.0\n",
    "        , min_impurity_split=None, class_weight=None, presort=False)\n",
    "dtree.fit(x_test, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = StringIO()\n",
    "\n",
    "export_graphviz(dtree, out_file=dot_data,  \n",
    "                filled=True, rounded=True,\n",
    "                special_characters=True, feature_names=x_train.columns)\n",
    "\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_auc(dtree.predict(x_test), preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}