ComRun_MLv2.5_wkly.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s7n20Yz89dwe",
    "tags": []
   },
   "source": [
    "# Importing and Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1662732225454,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "_01TAJ-GjI0y"
   },
   "outputs": [],
   "source": [
    "# !pip install h2o \n",
    "# #we need to run this at every instance if we are using google colab, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1662732225455,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "WgcpKC5LKx0n",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import h2o\n",
    "from h2o.estimators.deeplearning import H2OAutoEncoderEstimator, H2ODeepLearningEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "executionInfo": {
     "elapsed": 298,
     "status": "ok",
     "timestamp": 1662732667396,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "paYvI9iy5izh",
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# importing relevant libraries\n",
    "\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt # plotting graphs\n",
    "import seaborn as sns\n",
    "\n",
    "from pylab import rcParams\n",
    "\n",
    "import sys\n",
    "import shap\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "np.set_printoptions(threshold=sys.maxsize) # view max elements of numpy array\n",
    "\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "# pd.set_option('display.max_columns', None) #pandas shows maximum columns\n",
    "# pd.set_option('display.max_rows', 146) #pandas shows 146 rows\n",
    "\n",
    "import os # for file directories\n",
    "\n",
    "from sklearn.metrics import (confusion_matrix, precision_recall_curve, auc,\n",
    "                             roc_curve, recall_score, classification_report, f1_score,\n",
    "                             precision_recall_fscore_support, make_scorer, mean_squared_error, accuracy_score)\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn import model_selection\n",
    "\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.svm import OneClassSVM\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1662732226714,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "pyC1C5CMiiQB",
    "outputId": "9915d28f-0188-4661-9cc6-77c4bfdfefc7"
   },
   "outputs": [],
   "source": [
    "# ls # shows our directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1662732226715,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "7crl5r-O5qRi"
   },
   "outputs": [],
   "source": [
    "# os.chdir('/content/drive/MyDrive/Colab Files/MercJbs') # change directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 338,
     "status": "ok",
     "timestamp": 1662732227048,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "mOJ6ZDRL-zVD",
    "outputId": "ed2f1ed1-9dc1-4e6c-8de5-0a5faad693d2"
   },
   "outputs": [],
   "source": [
    "# ls # show current files/folders in directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "executionInfo": {
     "elapsed": 860,
     "status": "ok",
     "timestamp": 1662732227906,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "QGgz2H94ZFT8"
   },
   "outputs": [],
   "source": [
    "# import dataset\n",
    "df_week = pd.read_csv(r'data/week_approach_maskedID_timeseries_v2.0.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 179
    },
    "executionInfo": {
     "elapsed": 338,
     "status": "ok",
     "timestamp": 1662732228240,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "YNCTAcN7wVKO",
    "outputId": "4ccd4988-ea93-455b-e674-59001600bc25"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>nr_sessions</th>\n",
       "      <th>nr_rest_days</th>\n",
       "      <th>total_kms</th>\n",
       "      <th>max_km_one_day</th>\n",
       "      <th>total_km_z3-z4-z5-t1-t2</th>\n",
       "      <th>nr_tough_sessions_(effort_in_z5,_t1_or_t2)</th>\n",
       "      <th>nr_days_with_interval_session</th>\n",
       "      <th>total_km_z3-4</th>\n",
       "      <th>max_km_z3-4_one_day</th>\n",
       "      <th>...</th>\n",
       "      <th>max_training_success_2</th>\n",
       "      <th>avg_recovery_2</th>\n",
       "      <th>min_recovery_2</th>\n",
       "      <th>max_recovery_2</th>\n",
       "      <th>athlete_id</th>\n",
       "      <th>injury</th>\n",
       "      <th>rel_total_kms_week_0_1</th>\n",
       "      <th>rel_total_kms_week_0_2</th>\n",
       "      <th>rel_total_kms_week_1_2</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>22.2</td>\n",
       "      <td>16.4</td>\n",
       "      <td>11.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.718447</td>\n",
       "      <td>1.378882</td>\n",
       "      <td>1.919255</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  nr_sessions  nr_rest_days  total_kms  max_km_one_day  \\\n",
       "0           0            5             2       22.2            16.4   \n",
       "\n",
       "   total_km_z3-z4-z5-t1-t2  nr_tough_sessions_(effort_in_z5,_t1_or_t2)  \\\n",
       "0                     11.8                                           1   \n",
       "\n",
       "   nr_days_with_interval_session  total_km_z3-4  max_km_z3-4_one_day  ...  \\\n",
       "0                              2           10.0                 10.0  ...   \n",
       "\n",
       "   max_training_success_2  avg_recovery_2  min_recovery_2  max_recovery_2  \\\n",
       "0                     0.0            0.18            0.16             0.2   \n",
       "\n",
       "   athlete_id  injury  rel_total_kms_week_0_1  rel_total_kms_week_0_2  \\\n",
       "0           0       0                0.718447                1.378882   \n",
       "\n",
       "   rel_total_kms_week_1_2  date  \n",
       "0                1.919255     0  \n",
       "\n",
       "[1 rows x 73 columns]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_week.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1662732228241,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "MH3yW-pcDx3C"
   },
   "outputs": [],
   "source": [
    "df_week.drop(['Unnamed: 0'], axis = 1, inplace = True)\n",
    "df_week['date'] = df_week['date'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1662732228242,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "Wowekz941-d6"
   },
   "outputs": [],
   "source": [
    "# df_week['injury'] = df_week['injury'].apply(lambda x: 'yes' if x == 1 else 'no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 393,
     "status": "ok",
     "timestamp": 1662732228627,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "XB12N9N82dHt",
    "outputId": "1b44ff1a-ba23-4255-b725-d70dc5e0d8b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1]\n",
       "Categories (2, int64): [0, 1]"
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     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_week[df_week.nunique()[df_week.nunique() < 30].index] = df_week[df_week.nunique()[df_week.nunique() < 30].index].astype('category')\n",
    "df_week['injury'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 305
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
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     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "VJlFVtqGDhb-",
    "outputId": "afcc131a-9e63-42bd-c90c-1198f076d8d3"
   },
   "outputs": [
    {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nr_sessions</th>\n",
       "      <th>nr_rest_days</th>\n",
       "      <th>total_kms</th>\n",
       "      <th>max_km_one_day</th>\n",
       "      <th>total_km_z3-z4-z5-t1-t2</th>\n",
       "      <th>nr_tough_sessions_(effort_in_z5,_t1_or_t2)</th>\n",
       "      <th>nr_days_with_interval_session</th>\n",
       "      <th>total_km_z3-4</th>\n",
       "      <th>max_km_z3-4_one_day</th>\n",
       "      <th>total_km_z5-t1-t2</th>\n",
       "      <th>...</th>\n",
       "      <th>max_training_success_2</th>\n",
       "      <th>avg_recovery_2</th>\n",
       "      <th>min_recovery_2</th>\n",
       "      <th>max_recovery_2</th>\n",
       "      <th>athlete_id</th>\n",
       "      <th>injury</th>\n",
       "      <th>rel_total_kms_week_0_1</th>\n",
       "      <th>rel_total_kms_week_0_2</th>\n",
       "      <th>rel_total_kms_week_1_2</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>22.2</td>\n",
       "      <td>16.4</td>\n",
       "      <td>11.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.6</td>\n",
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       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>21.6</td>\n",
       "      <td>16.4</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.683544</td>\n",
       "      <td>1.018868</td>\n",
       "      <td>1.490566</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>21.6</td>\n",
       "      <td>16.4</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.683544</td>\n",
       "      <td>1.018868</td>\n",
       "      <td>1.490566</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>21.6</td>\n",
       "      <td>16.4</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.683544</td>\n",
       "      <td>1.018868</td>\n",
       "      <td>1.490566</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>39.2</td>\n",
       "      <td>17.6</td>\n",
       "      <td>18.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.202247</td>\n",
       "      <td>1.361111</td>\n",
       "      <td>0.618056</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 72 columns</p>\n",
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      ],
      "text/plain": [
       "  nr_sessions nr_rest_days  total_kms  max_km_one_day  \\\n",
       "0           5            2       22.2            16.4   \n",
       "1           5            2       21.6            16.4   \n",
       "2           5            2       21.6            16.4   \n",
       "3           5            2       21.6            16.4   \n",
       "4           6            1       39.2            17.6   \n",
       "\n",
       "   total_km_z3-z4-z5-t1-t2 nr_tough_sessions_(effort_in_z5,_t1_or_t2)  \\\n",
       "0                     11.8                                          1   \n",
       "1                     11.7                                          1   \n",
       "2                     11.7                                          1   \n",
       "3                     11.7                                          1   \n",
       "4                     18.9                                          1   \n",
       "\n",
       "  nr_days_with_interval_session  total_km_z3-4  max_km_z3-4_one_day  \\\n",
       "0                             2           10.0                 10.0   \n",
       "1                             2           10.0                 10.0   \n",
       "2                             2           10.0                 10.0   \n",
       "3                             2           10.0                 10.0   \n",
       "4                             3           17.2                 10.0   \n",
       "\n",
       "   total_km_z5-t1-t2  ...  max_training_success_2  avg_recovery_2  \\\n",
       "0                0.6  ...                     0.0            0.18   \n",
       "1                0.5  ...                     0.0            0.18   \n",
       "2                0.5  ...                     0.0            0.17   \n",
       "3                0.5  ...                     0.0            0.18   \n",
       "4                0.5  ...                     0.0            0.17   \n",
       "\n",
       "  min_recovery_2  max_recovery_2  athlete_id  injury  rel_total_kms_week_0_1  \\\n",
       "0           0.16            0.20           0       0                0.718447   \n",
       "1           0.16            0.20           0       0                0.683544   \n",
       "2           0.16            0.18           0       0                0.683544   \n",
       "3           0.16            0.18           0       0                0.683544   \n",
       "4           0.16            0.18           0       0                2.202247   \n",
       "\n",
       "   rel_total_kms_week_0_2  rel_total_kms_week_1_2  date  \n",
       "0                1.378882                1.919255     0  \n",
       "1                1.018868                1.490566     1  \n",
       "2                1.018868                1.490566     2  \n",
       "3                1.018868                1.490566     3  \n",
       "4                1.361111                0.618056     4  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_week.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1662732228629,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "nmUf6Fhz69V4",
    "outputId": "32fed26a-479e-46d4-af18-4896e7e9446a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 42798 entries, 0 to 42797\n",
      "Data columns (total 72 columns):\n",
      " #   Column                                        Non-Null Count  Dtype   \n",
      "---  ------                                        --------------  -----   \n",
      " 0   nr_sessions                                   42798 non-null  category\n",
      " 1   nr_rest_days                                  42798 non-null  category\n",
      " 2   total_kms                                     42798 non-null  float64 \n",
      " 3   max_km_one_day                                42798 non-null  float64 \n",
      " 4   total_km_z3-z4-z5-t1-t2                       42798 non-null  float64 \n",
      " 5   nr_tough_sessions_(effort_in_z5,_t1_or_t2)    42798 non-null  category\n",
      " 6   nr_days_with_interval_session                 42798 non-null  category\n",
      " 7   total_km_z3-4                                 42798 non-null  float64 \n",
      " 8   max_km_z3-4_one_day                           42798 non-null  float64 \n",
      " 9   total_km_z5-t1-t2                             42798 non-null  float64 \n",
      " 10  max_km_z5-t1-t2_one_day                       42798 non-null  float64 \n",
      " 11  total_hours_alternative_training              42798 non-null  float64 \n",
      " 12  nr_strength_trainings                         42798 non-null  category\n",
      " 13  avg_exertion                                  42798 non-null  float64 \n",
      " 14  min_exertion                                  42798 non-null  float64 \n",
      " 15  max_exertion                                  42798 non-null  float64 \n",
      " 16  avg_training_success                          42798 non-null  float64 \n",
      " 17  min_training_success                          42798 non-null  float64 \n",
      " 18  max_training_success                          42798 non-null  float64 \n",
      " 19  avg_recovery                                  42798 non-null  float64 \n",
      " 20  min_recovery                                  42798 non-null  float64 \n",
      " 21  max_recovery                                  42798 non-null  float64 \n",
      " 22  nr_sessions_1                                 42798 non-null  category\n",
      " 23  nr_rest_days_1                                42798 non-null  category\n",
      " 24  total_kms_1                                   42798 non-null  float64 \n",
      " 25  max_km_one_day_1                              42798 non-null  float64 \n",
      " 26  total_km_z3-z4-z5-t1-t2_1                     42798 non-null  float64 \n",
      " 27  nr_tough_sessions_(effort_in_z5,_t1_or_t2)_1  42798 non-null  category\n",
      " 28  nr_days_with_interval_session_1               42798 non-null  category\n",
      " 29  total_km_z3-4_1                               42798 non-null  float64 \n",
      " 30  max_km_z3-4_one_day_1                         42798 non-null  float64 \n",
      " 31  total_km_z5-t1-t2_1                           42798 non-null  float64 \n",
      " 32  max_km_z5-t1-t2_one_day_1                     42798 non-null  float64 \n",
      " 33  total_hours_alternative_training_1            42798 non-null  float64 \n",
      " 34  nr_strength_trainings_1                       42798 non-null  category\n",
      " 35  avg_exertion_1                                42798 non-null  float64 \n",
      " 36  min_exertion_1                                42798 non-null  float64 \n",
      " 37  max_exertion_1                                42798 non-null  float64 \n",
      " 38  avg_training_success_1                        42798 non-null  float64 \n",
      " 39  min_training_success_1                        42798 non-null  float64 \n",
      " 40  max_training_success_1                        42798 non-null  float64 \n",
      " 41  avg_recovery_1                                42798 non-null  float64 \n",
      " 42  min_recovery_1                                42798 non-null  float64 \n",
      " 43  max_recovery_1                                42798 non-null  float64 \n",
      " 44  nr_sessions_2                                 42798 non-null  category\n",
      " 45  nr_rest_days_2                                42798 non-null  category\n",
      " 46  total_kms_2                                   42798 non-null  float64 \n",
      " 47  max_km_one_day_2                              42798 non-null  float64 \n",
      " 48  total_km_z3-z4-z5-t1-t2_2                     42798 non-null  float64 \n",
      " 49  nr_tough_sessions_(effort_in_z5,_t1_or_t2)_2  42798 non-null  category\n",
      " 50  nr_days_with_interval_session_2               42798 non-null  category\n",
      " 51  total_km_z3-4_2                               42798 non-null  float64 \n",
      " 52  max_km_z3-4_one_day_2                         42798 non-null  float64 \n",
      " 53  total_km_z5-t1-t2_2                           42798 non-null  float64 \n",
      " 54  max_km_z5-t1-t2_one_day_2                     42798 non-null  float64 \n",
      " 55  total_hours_alternative_training_2            42798 non-null  float64 \n",
      " 56  nr_strength_trainings_2                       42798 non-null  category\n",
      " 57  avg_exertion_2                                42798 non-null  float64 \n",
      " 58  min_exertion_2                                42798 non-null  float64 \n",
      " 59  max_exertion_2                                42798 non-null  float64 \n",
      " 60  avg_training_success_2                        42798 non-null  float64 \n",
      " 61  min_training_success_2                        42798 non-null  float64 \n",
      " 62  max_training_success_2                        42798 non-null  float64 \n",
      " 63  avg_recovery_2                                42798 non-null  float64 \n",
      " 64  min_recovery_2                                42798 non-null  float64 \n",
      " 65  max_recovery_2                                42798 non-null  float64 \n",
      " 66  athlete_id                                    42798 non-null  int64   \n",
      " 67  injury                                        42798 non-null  category\n",
      " 68  rel_total_kms_week_0_1                        42798 non-null  float64 \n",
      " 69  rel_total_kms_week_0_2                        42798 non-null  float64 \n",
      " 70  rel_total_kms_week_1_2                        42798 non-null  float64 \n",
      " 71  date                                          42798 non-null  category\n",
      "dtypes: category(17), float64(54), int64(1)\n",
      "memory usage: 18.8 MB\n"
     ]
    }
   ],
   "source": [
    "df_week.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "executionInfo": {
     "elapsed": 1845,
     "status": "ok",
     "timestamp": 1662705831978,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "-NrcE8oR3DWM"
   },
   "outputs": [],
   "source": [
    "df_week.to_csv('data/week_approach_maskedID_timeseries_v3.0.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CPiXjFJ1YEGP"
   },
   "source": [
    "# Introduction\n",
    "\n",
    "In this notebook, we run some advance classification ML algorithms on our datasets. As a result of our dataset having many rows/entries (42766) and columns (73), we use a dimensionality reduction technique (PCA) to see the variability on the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mrMcJotaSJyE"
   },
   "source": [
    "<!-- ## PCA -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ovll_ZxqQ0TF"
   },
   "source": [
    "<!-- from sklearn.model_selection import train_test_split\n",
    "from sklearn.decomposition import PCA -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XYly1zejVFcA"
   },
   "source": [
    "<!-- Xd = df_week.drop(columns = ['injury'])\n",
    "yd = df_week['injury'] -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "006qxTRhSTm3"
   },
   "source": [
    "<!-- pca = make_pipeline(StandardScaler(), PCA(.95))\n",
    "# pca = PCA()\n",
    "pca.fit_transform(Xd)\n",
    "pca -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3ZW-PDn-udze"
   },
   "source": [
    "<!-- **Explained variance** is the amount of variance explained by each of the selected components.  -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fe7WxdrpVbc4"
   },
   "source": [
    "<!-- # pca[1].n_components_ -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_qDdDcPEUn1k"
   },
   "source": [
    "<!-- explained = pca[1].explained_variance_ratio_\n",
    "explained -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LiLqc2RrnTs2"
   },
   "source": [
    "<!-- cumulative_explained = explained.cumsum()\n",
    "\n",
    "cumulative_explained -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "thW8N6EhoeE9"
   },
   "source": [
    "<!-- pca[1].components_.shape -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LTAq_wQsowSW"
   },
   "source": [
    "<!-- plt.figure(figsize=(15,7.5))\n",
    "plt.plot(range(0, 53), explained, label='Explained Variance')\n",
    "plt.plot(range(0, 53), cumulative_explained, label='Cumulative Explained Variance')\n",
    "plt.xlabel('Principal Components')\n",
    "plt.vlines(x=42, ymin= -0.1, ymax=1.1, colors= 'k', label = \"42nd Component\")\n",
    "plt.hlines(y=0.90, xmin=-0.5, xmax=72, colors= 'g', linestyles='--', label = \"90% Explained\")\n",
    "plt.title('Explained Variance Ratio')\n",
    "plt.legend()\n",
    "plt.show() -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Kof3dEI9YB99"
   },
   "source": [
    "# Model 1 : Autoencoder\n",
    "**What is Autoencoder?**\n",
    "\n",
    "Autoencoder is an unsupervised artificial neural network that learns how to efficiently compress and encode data then learns how to reconstruct the data back from the reduced encoded representation to a representation that is as close to the original input as possible. Autoencoder, by design, reduces data dimensions by learning how to ignore the noise in the data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5zuZXUOEpg-P"
   },
   "source": [
    "**Why Autoencoder works for Anomaly Detection?**\n",
    "\n",
    "Autoencoders are great in learning what is normal. I can identify the normal region ina distribution then anything that is considered an outlier will return a high reconstruction error.\n",
    "\n",
    "Let's start by installing some pre-requisite libraries, h20. \n",
    "\n",
    "**H2O** is an open-source library that can be used to do a number of machine learning tasks. It has a function for the autoencoders. Let's have a look"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wRqCXGxmZhAr"
   },
   "source": [
    "## Starting and initializing H2O server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 491
    },
    "executionInfo": {
     "elapsed": 12449,
     "status": "ok",
     "timestamp": 1661545719192,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "wQFBKEG0ZjQ3",
    "outputId": "b9ee3732-e2bb-473d-9bfe-df0bac427685"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321 . connected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>4 hours 51 mins</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>Europe/Berlin</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.36.1.4</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>1 month and 11 days </td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_USER_61dy3j</td></tr>\n",
       "<tr><td>H2O_cluster_total_nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_free_memory:</td>\n",
       "<td>1.549 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>4</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>4</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>http://localhost:54321</td></tr>\n",
       "<tr><td>H2O_connection_proxy:</td>\n",
       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.9.13 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  -----------------------------\n",
       "H2O_cluster_uptime:         4 hours 51 mins\n",
       "H2O_cluster_timezone:       Europe/Berlin\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.36.1.4\n",
       "H2O_cluster_version_age:    1 month and 11 days\n",
       "H2O_cluster_name:           H2O_from_python_USER_61dy3j\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    1.549 Gb\n",
       "H2O_cluster_total_cores:    4\n",
       "H2O_cluster_allowed_cores:  4\n",
       "H2O_cluster_status:         locked, healthy\n",
       "H2O_connection_url:         http://localhost:54321\n",
       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
       "H2O_internal_security:      False\n",
       "Python_version:             3.9.13 final\n",
       "--------------------------  -----------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2o.init() # initializing h2o server\n",
    "# h2o.remove_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wI2O7uOQZsZU"
   },
   "source": [
    "We now load the dataset into a H2O dataframe and splitting the DS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6950,
     "status": "ok",
     "timestamp": 1661545778080,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "KOf-6XZracj9",
    "outputId": "07f7059d-cbf1-419c-fff1-f52daa0c0f12"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"
     ]
    }
   ],
   "source": [
    "hdf_week = h2o.import_file(r\"data/week_approach_maskedID_timeseries_v3.0.csv\") #import the file as a h2o file\n",
    "hdf_week[\"date\"] = hdf_week[\"date\"].asfactor() # coonvert date to factor i.e categorical\n",
    "hdf_week[\"injury\"] = hdf_week[\"injury\"].asfactor()\n",
    "features_week  = hdf_week.drop(['athlete_id', 'C1'], axis=1) # we do not need the athlete id again alonside the 'C1' column which is an automatic addition by h2o import\n",
    "train_week , test_week = features_week.split_frame([0.8]) # 80% of data for training and 20% for testing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e3qmuar_rKP-"
   },
   "source": [
    "We used a 80-20 split between the training and testing because of the imbalance in the dataset. This spit enables us to get enough of the undersampled class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 137
    },
    "executionInfo": {
     "elapsed": 1539,
     "status": "ok",
     "timestamp": 1661545805988,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "KbNDIY3GGQ3x",
    "outputId": "1d9f0ed5-ad70-43c9-997e-6fdee67a3839"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  nr_sessions</th><th style=\"text-align: right;\">  nr_rest_days</th><th style=\"text-align: right;\">  total_kms</th><th style=\"text-align: right;\">  max_km_one_day</th><th style=\"text-align: right;\">  total_km_z3-z4-z5-t1-t2</th><th style=\"text-align: right;\">  nr_tough_sessions_(effort_in_z5,_t1_or_t2)</th><th style=\"text-align: right;\">  nr_days_with_interval_session</th><th style=\"text-align: right;\">  total_km_z3-4</th><th style=\"text-align: right;\">  max_km_z3-4_one_day</th><th style=\"text-align: right;\">  total_km_z5-t1-t2</th><th style=\"text-align: right;\">  max_km_z5-t1-t2_one_day</th><th style=\"text-align: right;\">  total_hours_alternative_training</th><th style=\"text-align: right;\">  nr_strength_trainings</th><th style=\"text-align: right;\">  avg_exertion</th><th style=\"text-align: right;\">  min_exertion</th><th style=\"text-align: right;\">  max_exertion</th><th style=\"text-align: right;\">  avg_training_success</th><th style=\"text-align: right;\">  min_training_success</th><th style=\"text-align: right;\">  max_training_success</th><th style=\"text-align: right;\">  avg_recovery</th><th style=\"text-align: right;\">  min_recovery</th><th style=\"text-align: right;\">  max_recovery</th><th style=\"text-align: right;\">  nr_sessions_1</th><th style=\"text-align: right;\">  nr_rest_days_1</th><th style=\"text-align: right;\">  total_kms_1</th><th style=\"text-align: right;\">  max_km_one_day_1</th><th style=\"text-align: right;\">  total_km_z3-z4-z5-t1-t2_1</th><th style=\"text-align: right;\">  nr_tough_sessions_(effort_in_z5,_t1_or_t2)_1</th><th style=\"text-align: right;\">  nr_days_with_interval_session_1</th><th style=\"text-align: right;\">  total_km_z3-4_1</th><th style=\"text-align: right;\">  max_km_z3-4_one_day_1</th><th style=\"text-align: right;\">  total_km_z5-t1-t2_1</th><th style=\"text-align: right;\">  max_km_z5-t1-t2_one_day_1</th><th style=\"text-align: right;\">  total_hours_alternative_training_1</th><th style=\"text-align: right;\">  nr_strength_trainings_1</th><th style=\"text-align: right;\">  avg_exertion_1</th><th style=\"text-align: right;\">  min_exertion_1</th><th style=\"text-align: right;\">  max_exertion_1</th><th style=\"text-align: right;\">  avg_training_success_1</th><th style=\"text-align: right;\">  min_training_success_1</th><th style=\"text-align: right;\">  max_training_success_1</th><th style=\"text-align: right;\">  avg_recovery_1</th><th style=\"text-align: right;\">  min_recovery_1</th><th style=\"text-align: right;\">  max_recovery_1</th><th style=\"text-align: right;\">  nr_sessions_2</th><th style=\"text-align: right;\">  nr_rest_days_2</th><th style=\"text-align: right;\">  total_kms_2</th><th style=\"text-align: right;\">  max_km_one_day_2</th><th style=\"text-align: right;\">  total_km_z3-z4-z5-t1-t2_2</th><th style=\"text-align: right;\">  nr_tough_sessions_(effort_in_z5,_t1_or_t2)_2</th><th style=\"text-align: right;\">  nr_days_with_interval_session_2</th><th style=\"text-align: right;\">  total_km_z3-4_2</th><th style=\"text-align: right;\">  max_km_z3-4_one_day_2</th><th style=\"text-align: right;\">  total_km_z5-t1-t2_2</th><th style=\"text-align: right;\">  max_km_z5-t1-t2_one_day_2</th><th style=\"text-align: right;\">  total_hours_alternative_training_2</th><th style=\"text-align: right;\">  nr_strength_trainings_2</th><th style=\"text-align: right;\">  avg_exertion_2</th><th style=\"text-align: right;\">  min_exertion_2</th><th style=\"text-align: right;\">  max_exertion_2</th><th style=\"text-align: right;\">  avg_training_success_2</th><th style=\"text-align: right;\">  min_training_success_2</th><th style=\"text-align: right;\">  max_training_success_2</th><th style=\"text-align: right;\">  avg_recovery_2</th><th style=\"text-align: right;\">  min_recovery_2</th><th style=\"text-align: right;\">  max_recovery_2</th><th style=\"text-align: right;\">  injury</th><th style=\"text-align: right;\">  rel_total_kms_week_0_1</th><th style=\"text-align: right;\">  rel_total_kms_week_0_2</th><th style=\"text-align: right;\">  rel_total_kms_week_1_2</th><th style=\"text-align: right;\">  date</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">            5</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">       22.2</td><td style=\"text-align: right;\">            16.4</td><td style=\"text-align: right;\">                     11.8</td><td style=\"text-align: right;\">                                           1</td><td style=\"text-align: right;\">                              2</td><td style=\"text-align: right;\">             10</td><td style=\"text-align: right;\">                   10</td><td style=\"text-align: right;\">                0.6</td><td style=\"text-align: right;\">                      0.6</td><td style=\"text-align: right;\">                              2.08</td><td style=\"text-align: right;\">                      2</td><td style=\"text-align: right;\">           0.1</td><td style=\"text-align: right;\">          0.08</td><td style=\"text-align: right;\">          0.11</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.15</td><td style=\"text-align: right;\">          0.18</td><td style=\"text-align: right;\">              6</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">         30.9</td><td style=\"text-align: right;\">              13.8</td><td style=\"text-align: right;\">                       13.5</td><td style=\"text-align: right;\">                                             0</td><td style=\"text-align: right;\">                                2</td><td style=\"text-align: right;\">             12.4</td><td style=\"text-align: right;\">                    6.4</td><td style=\"text-align: right;\">                  0  </td><td style=\"text-align: right;\">                        0  </td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.12</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">              3</td><td style=\"text-align: right;\">               4</td><td style=\"text-align: right;\">         16.1</td><td style=\"text-align: right;\">               9.9</td><td style=\"text-align: right;\">                        3.9</td><td style=\"text-align: right;\">                                             0</td><td style=\"text-align: right;\">                                1</td><td style=\"text-align: right;\">              3.2</td><td style=\"text-align: right;\">                    3.2</td><td style=\"text-align: right;\">                    0</td><td style=\"text-align: right;\">                          0</td><td style=\"text-align: right;\">                                   0</td><td style=\"text-align: right;\">                        1</td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.08</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.2 </td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">                0.718447</td><td style=\"text-align: right;\">                 1.37888</td><td style=\"text-align: right;\">                 1.91925</td><td style=\"text-align: right;\">     0</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            5</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">       21.6</td><td style=\"text-align: right;\">            16.4</td><td style=\"text-align: right;\">                     11.7</td><td style=\"text-align: right;\">                                           1</td><td style=\"text-align: right;\">                              2</td><td style=\"text-align: right;\">             10</td><td style=\"text-align: right;\">                   10</td><td style=\"text-align: right;\">                0.5</td><td style=\"text-align: right;\">                      0.5</td><td style=\"text-align: right;\">                              2.08</td><td style=\"text-align: right;\">                      2</td><td style=\"text-align: right;\">           0.1</td><td style=\"text-align: right;\">          0.08</td><td style=\"text-align: right;\">          0.11</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.15</td><td style=\"text-align: right;\">          0.18</td><td style=\"text-align: right;\">              6</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">         31.6</td><td style=\"text-align: right;\">              13.8</td><td style=\"text-align: right;\">                       14.2</td><td style=\"text-align: right;\">                                             1</td><td style=\"text-align: right;\">                                3</td><td style=\"text-align: right;\">             12.4</td><td style=\"text-align: right;\">                    6.4</td><td style=\"text-align: right;\">                  0.6</td><td style=\"text-align: right;\">                        0.6</td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.12</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">              4</td><td style=\"text-align: right;\">               3</td><td style=\"text-align: right;\">         21.2</td><td style=\"text-align: right;\">               9.9</td><td style=\"text-align: right;\">                        5  </td><td style=\"text-align: right;\">                                             0</td><td style=\"text-align: right;\">                                1</td><td style=\"text-align: right;\">              3.2</td><td style=\"text-align: right;\">                    3.2</td><td style=\"text-align: right;\">                    0</td><td style=\"text-align: right;\">                          0</td><td style=\"text-align: right;\">                                   0</td><td style=\"text-align: right;\">                        1</td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">            0.08</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.2 </td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">                0.683544</td><td style=\"text-align: right;\">                 1.01887</td><td style=\"text-align: right;\">                 1.49057</td><td style=\"text-align: right;\">     1</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            5</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">       21.6</td><td style=\"text-align: right;\">            16.4</td><td style=\"text-align: right;\">                     11.7</td><td style=\"text-align: right;\">                                           1</td><td style=\"text-align: right;\">                              2</td><td style=\"text-align: right;\">             10</td><td style=\"text-align: right;\">                   10</td><td style=\"text-align: right;\">                0.5</td><td style=\"text-align: right;\">                      0.5</td><td style=\"text-align: right;\">                              2.08</td><td style=\"text-align: right;\">                      2</td><td style=\"text-align: right;\">           0.1</td><td style=\"text-align: right;\">          0.08</td><td style=\"text-align: right;\">          0.11</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.15</td><td style=\"text-align: right;\">          0.18</td><td style=\"text-align: right;\">              6</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">         31.6</td><td style=\"text-align: right;\">              13.8</td><td style=\"text-align: right;\">                       14.2</td><td style=\"text-align: right;\">                                             1</td><td style=\"text-align: right;\">                                3</td><td style=\"text-align: right;\">             12.4</td><td style=\"text-align: right;\">                    6.4</td><td style=\"text-align: right;\">                  0.6</td><td style=\"text-align: right;\">                        0.6</td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.12</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">              3</td><td style=\"text-align: right;\">               4</td><td style=\"text-align: right;\">         21.2</td><td style=\"text-align: right;\">               9.9</td><td style=\"text-align: right;\">                        5  </td><td style=\"text-align: right;\">                                             0</td><td style=\"text-align: right;\">                                1</td><td style=\"text-align: right;\">              3.2</td><td style=\"text-align: right;\">                    3.2</td><td style=\"text-align: right;\">                    0</td><td style=\"text-align: right;\">                          0</td><td style=\"text-align: right;\">                                   0</td><td style=\"text-align: right;\">                        0</td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">                0.683544</td><td style=\"text-align: right;\">                 1.01887</td><td style=\"text-align: right;\">                 1.49057</td><td style=\"text-align: right;\">     2</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_week.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 466,
     "status": "ok",
     "timestamp": 1661545813800,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "QSSl9ZHW-uwg",
    "outputId": "4c22aa90-b431-4ed4-dac5-b57c3cbd59e3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'nr_sessions': 'int',\n",
       " 'nr_rest_days': 'int',\n",
       " 'total_kms': 'real',\n",
       " 'max_km_one_day': 'real',\n",
       " 'total_km_z3-z4-z5-t1-t2': 'real',\n",
       " 'nr_tough_sessions_(effort_in_z5,_t1_or_t2)': 'int',\n",
       " 'nr_days_with_interval_session': 'int',\n",
       " 'total_km_z3-4': 'real',\n",
       " 'max_km_z3-4_one_day': 'real',\n",
       " 'total_km_z5-t1-t2': 'real',\n",
       " 'max_km_z5-t1-t2_one_day': 'real',\n",
       " 'total_hours_alternative_training': 'real',\n",
       " 'nr_strength_trainings': 'int',\n",
       " 'avg_exertion': 'real',\n",
       " 'min_exertion': 'real',\n",
       " 'max_exertion': 'real',\n",
       " 'avg_training_success': 'real',\n",
       " 'min_training_success': 'real',\n",
       " 'max_training_success': 'real',\n",
       " 'avg_recovery': 'real',\n",
       " 'min_recovery': 'real',\n",
       " 'max_recovery': 'real',\n",
       " 'nr_sessions_1': 'int',\n",
       " 'nr_rest_days_1': 'int',\n",
       " 'total_kms_1': 'real',\n",
       " 'max_km_one_day_1': 'real',\n",
       " 'total_km_z3-z4-z5-t1-t2_1': 'real',\n",
       " 'nr_tough_sessions_(effort_in_z5,_t1_or_t2)_1': 'int',\n",
       " 'nr_days_with_interval_session_1': 'int',\n",
       " 'total_km_z3-4_1': 'real',\n",
       " 'max_km_z3-4_one_day_1': 'real',\n",
       " 'total_km_z5-t1-t2_1': 'real',\n",
       " 'max_km_z5-t1-t2_one_day_1': 'real',\n",
       " 'total_hours_alternative_training_1': 'real',\n",
       " 'nr_strength_trainings_1': 'int',\n",
       " 'avg_exertion_1': 'real',\n",
       " 'min_exertion_1': 'real',\n",
       " 'max_exertion_1': 'real',\n",
       " 'avg_training_success_1': 'real',\n",
       " 'min_training_success_1': 'real',\n",
       " 'max_training_success_1': 'real',\n",
       " 'avg_recovery_1': 'real',\n",
       " 'min_recovery_1': 'real',\n",
       " 'max_recovery_1': 'real',\n",
       " 'nr_sessions_2': 'int',\n",
       " 'nr_rest_days_2': 'int',\n",
       " 'total_kms_2': 'real',\n",
       " 'max_km_one_day_2': 'real',\n",
       " 'total_km_z3-z4-z5-t1-t2_2': 'real',\n",
       " 'nr_tough_sessions_(effort_in_z5,_t1_or_t2)_2': 'int',\n",
       " 'nr_days_with_interval_session_2': 'int',\n",
       " 'total_km_z3-4_2': 'real',\n",
       " 'max_km_z3-4_one_day_2': 'real',\n",
       " 'total_km_z5-t1-t2_2': 'real',\n",
       " 'max_km_z5-t1-t2_one_day_2': 'real',\n",
       " 'total_hours_alternative_training_2': 'real',\n",
       " 'nr_strength_trainings_2': 'int',\n",
       " 'avg_exertion_2': 'real',\n",
       " 'min_exertion_2': 'real',\n",
       " 'max_exertion_2': 'real',\n",
       " 'avg_training_success_2': 'real',\n",
       " 'min_training_success_2': 'real',\n",
       " 'max_training_success_2': 'real',\n",
       " 'avg_recovery_2': 'real',\n",
       " 'min_recovery_2': 'real',\n",
       " 'max_recovery_2': 'real',\n",
       " 'injury': 'enum',\n",
       " 'rel_total_kms_week_0_1': 'real',\n",
       " 'rel_total_kms_week_0_2': 'real',\n",
       " 'rel_total_kms_week_1_2': 'real',\n",
       " 'date': 'enum'}"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_week.types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "17tYnFWfcV9M"
   },
   "source": [
    "We now preprocess the dataset by seperating training and test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "id": "ByKA-DPJdcHG"
   },
   "outputs": [],
   "source": [
    "# converting to pandas dataframe\n",
    "train_week_df = train_week.as_data_frame()\n",
    "test_week_df = test_week.as_data_frame()\n",
    "\n",
    "# noinjury_df_tr = train_week_df[train_week_df['injury'] == 0]\n",
    "# injury_df_tr = train_week_df[train_week_df['injury'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "id": "aaIfZzwecTMK"
   },
   "outputs": [],
   "source": [
    "# drop the Class variable\n",
    "\n",
    "Y_test_df = test_week_df['injury'] # true labels of the testing set\n",
    "Y_test_df_ = pd.DataFrame(Y_test_df) # convert to pandas dataframe from pandas series\n",
    "test_df = test_week_df.drop(['injury'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4376,
     "status": "ok",
     "timestamp": 1661545963462,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "DGbyLdAVfP44",
    "outputId": "24720f8e-f68f-4ea1-c468-ca32e7001253"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"
     ]
    }
   ],
   "source": [
    "# converting to h2o frame\n",
    "Y_test_h2o = h2o.H2OFrame(Y_test_df_) \n",
    "train_h2o = h2o.H2OFrame(train_week_df) \n",
    "test_h2o = h2o.H2OFrame(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "id": "ddpFQeSPgvDW"
   },
   "outputs": [],
   "source": [
    "cols = train_h2o.columns\n",
    "cols.remove('injury')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1661192382378,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "Z78C7aMEClx0",
    "outputId": "876eb936-d137-492d-a7a8-88cd3706c556"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nr_sessions',\n",
       " 'nr_rest_days',\n",
       " 'total_kms',\n",
       " 'max_km_one_day',\n",
       " 'total_km_z3-z4-z5-t1-t2',\n",
       " 'nr_tough_sessions_(effort_in_z5,_t1_or_t2)',\n",
       " 'nr_days_with_interval_session',\n",
       " 'total_km_z3-4',\n",
       " 'max_km_z3-4_one_day',\n",
       " 'total_km_z5-t1-t2',\n",
       " 'max_km_z5-t1-t2_one_day',\n",
       " 'total_hours_alternative_training',\n",
       " 'nr_strength_trainings',\n",
       " 'avg_exertion',\n",
       " 'min_exertion',\n",
       " 'max_exertion',\n",
       " 'avg_training_success',\n",
       " 'min_training_success',\n",
       " 'max_training_success',\n",
       " 'avg_recovery',\n",
       " 'min_recovery',\n",
       " 'max_recovery',\n",
       " 'nr_sessions_1',\n",
       " 'nr_rest_days_1',\n",
       " 'total_kms_1',\n",
       " 'max_km_one_day_1',\n",
       " 'total_km_z3-z4-z5-t1-t2_1',\n",
       " 'nr_tough_sessions_(effort_in_z5,_t1_or_t2)_1',\n",
       " 'nr_days_with_interval_session_1',\n",
       " 'total_km_z3-4_1',\n",
       " 'max_km_z3-4_one_day_1',\n",
       " 'total_km_z5-t1-t2_1',\n",
       " 'max_km_z5-t1-t2_one_day_1',\n",
       " 'total_hours_alternative_training_1',\n",
       " 'nr_strength_trainings_1',\n",
       " 'avg_exertion_1',\n",
       " 'min_exertion_1',\n",
       " 'max_exertion_1',\n",
       " 'avg_training_success_1',\n",
       " 'min_training_success_1',\n",
       " 'max_training_success_1',\n",
       " 'avg_recovery_1',\n",
       " 'min_recovery_1',\n",
       " 'max_recovery_1',\n",
       " 'nr_sessions_2',\n",
       " 'nr_rest_days_2',\n",
       " 'total_kms_2',\n",
       " 'max_km_one_day_2',\n",
       " 'total_km_z3-z4-z5-t1-t2_2',\n",
       " 'nr_tough_sessions_(effort_in_z5,_t1_or_t2)_2',\n",
       " 'nr_days_with_interval_session_2',\n",
       " 'total_km_z3-4_2',\n",
       " 'max_km_z3-4_one_day_2',\n",
       " 'total_km_z5-t1-t2_2',\n",
       " 'max_km_z5-t1-t2_one_day_2',\n",
       " 'total_hours_alternative_training_2',\n",
       " 'nr_strength_trainings_2',\n",
       " 'avg_exertion_2',\n",
       " 'min_exertion_2',\n",
       " 'max_exertion_2',\n",
       " 'avg_training_success_2',\n",
       " 'min_training_success_2',\n",
       " 'max_training_success_2',\n",
       " 'avg_recovery_2',\n",
       " 'min_recovery_2',\n",
       " 'max_recovery_2',\n",
       " 'rel_total_kms_week_0_1',\n",
       " 'rel_total_kms_week_0_2',\n",
       " 'rel_total_kms_week_1_2',\n",
       " 'date']"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-uyGkZF6cH4U"
   },
   "source": [
    "We now create a local estimator and start the training process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vIRuF-xaqPta"
   },
   "source": [
    "In the deep learning model below, we included the h2o library for anomaly detection i.e autoencoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "id": "EZvOc8B2i-bH"
   },
   "outputs": [],
   "source": [
    "# our model with its hyper parameters\n",
    "\n",
    "deepl_model = H2ODeepLearningEstimator(activation = \"Tanh\",\n",
    "                               hidden = [15,7,7,7,15],\n",
    "                               epochs = 100,\n",
    "                               standardize = True,\n",
    "                                stopping_metric = 'MSE',\n",
    "                                loss = 'automatic',\n",
    "                                train_samples_per_iteration = 32,\n",
    "                                shuffle_training_data = True,     \n",
    "                               autoencoder = True,\n",
    "                               l1 = 10e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2972793,
     "status": "ok",
     "timestamp": 1661549483129,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "ysiog36JjAY3",
    "outputId": "e91cd1c6-966f-4036-e71c-800eba6cda44"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n",
      "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n",
      "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n",
      "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n",
      "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n",
      "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n",
      "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n",
      "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n",
      "2min 31s ± 35 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit deepl_model.train(x = cols , y = 'injury', training_frame = train_h2o) # this takes a while at least an hour "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 473,
     "status": "ok",
     "timestamp": 1661549953220,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "5Ken-3NPo4Dq",
    "outputId": "64826733-332a-43f4-e562-b57020411837"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ModelMetricsAutoEncoder: deeplearning\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 0.005657444115943373\n",
      "RMSE: 0.0752159831149163\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate performance:\n",
    "\n",
    "perf = deepl_model.model_performance()\n",
    "perf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ic2BV2d-qlE2"
   },
   "source": [
    "The MSE & RMSE helps us to understand how far off our prediction is on the training data as opposed to the actual results. The small number above which almost equals zero tells us that our model performs well on the training set.\n",
    "\n",
    "We also note that $RMSE = \\sqrt{MSE}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cxuafel6kRAt"
   },
   "source": [
    "Show the model feature importance in a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 464,
     "status": "ok",
     "timestamp": 1661549956349,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "oA54wpE9jJDG",
    "outputId": "9abef579-3d10-475e-e3a6-2237898c04ba"
   },
   "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>variable</th>\n",
       "      <th>relative_importance</th>\n",
       "      <th>scaled_importance</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>date</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nr_rest_days_1</td>\n",
       "      <td>0.796457</td>\n",
       "      <td>0.796457</td>\n",
       "      <td>0.027072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nr_rest_days</td>\n",
       "      <td>0.794395</td>\n",
       "      <td>0.794395</td>\n",
       "      <td>0.027002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>min_training_success_1</td>\n",
       "      <td>0.781789</td>\n",
       "      <td>0.781789</td>\n",
       "      <td>0.026574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>max_training_success_2</td>\n",
       "      <td>0.774766</td>\n",
       "      <td>0.774766</td>\n",
       "      <td>0.026335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>max_km_z5-t1-t2_one_day_1</td>\n",
       "      <td>0.017313</td>\n",
       "      <td>0.017313</td>\n",
       "      <td>0.000588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>rel_total_kms_week_1_2</td>\n",
       "      <td>0.015652</td>\n",
       "      <td>0.015652</td>\n",
       "      <td>0.000532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>rel_total_kms_week_0_2</td>\n",
       "      <td>0.014960</td>\n",
       "      <td>0.014960</td>\n",
       "      <td>0.000509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>injury</td>\n",
       "      <td>0.014096</td>\n",
       "      <td>0.014096</td>\n",
       "      <td>0.000479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>rel_total_kms_week_0_1</td>\n",
       "      <td>0.005141</td>\n",
       "      <td>0.005141</td>\n",
       "      <td>0.000175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     variable  relative_importance  scaled_importance  \\\n",
       "0                        date             1.000000           1.000000   \n",
       "1              nr_rest_days_1             0.796457           0.796457   \n",
       "2                nr_rest_days             0.794395           0.794395   \n",
       "3      min_training_success_1             0.781789           0.781789   \n",
       "4      max_training_success_2             0.774766           0.774766   \n",
       "..                        ...                  ...                ...   \n",
       "66  max_km_z5-t1-t2_one_day_1             0.017313           0.017313   \n",
       "67     rel_total_kms_week_1_2             0.015652           0.015652   \n",
       "68     rel_total_kms_week_0_2             0.014960           0.014960   \n",
       "69                     injury             0.014096           0.014096   \n",
       "70     rel_total_kms_week_0_1             0.005141           0.005141   \n",
       "\n",
       "    percentage  \n",
       "0     0.033991  \n",
       "1     0.027072  \n",
       "2     0.027002  \n",
       "3     0.026574  \n",
       "4     0.026335  \n",
       "..         ...  \n",
       "66    0.000588  \n",
       "67    0.000532  \n",
       "68    0.000509  \n",
       "69    0.000479  \n",
       "70    0.000175  \n",
       "\n",
       "[71 rows x 4 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deepl_model._model_json['output']['variable_importances'].as_data_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0ctJxD89kgAw"
   },
   "source": [
    "Visualization of the feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513
    },
    "executionInfo": {
     "elapsed": 851,
     "status": "ok",
     "timestamp": 1661549965239,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "73vZUHmmkfhI",
    "outputId": "1f6f7393-0d14-49a9-b3f9-29cda6aba5a5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the variable importance\n",
    "rcParams['figure.figsize'] = 14, 8\n",
    "#plt.rcdefaults()\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "variables = deepl_model._model_json['output']['variable_importances']['variable']\n",
    "var = variables[0:10]\n",
    "y_pos = np.arange(len(var))\n",
    "\n",
    "scaled_importance = deepl_model._model_json['output']['variable_importances']['scaled_importance']\n",
    "sc = scaled_importance[0:10]\n",
    "\n",
    "ax.barh(y_pos, sc, align='center', color='green', ecolor='black')\n",
    "ax.set_yticks(y_pos)\n",
    "# ax.set_yticklabels(variables)\n",
    "ax.set_yticklabels(var)\n",
    "ax.invert_yaxis()\n",
    "ax.set_xlabel('Scaled Importance')\n",
    "ax.set_title('Variable Importance of Top 10 Features - Weekly')\n",
    "plt.savefig('feat_imp_weeklydata.png' ,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ir90Ed2_krem"
   },
   "source": [
    "Visualize the model loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 531
    },
    "executionInfo": {
     "elapsed": 558,
     "status": "ok",
     "timestamp": 1661549977060,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "G7ZoE668kfdh",
    "outputId": "186e2a3e-d233-488b-a088-02f8ca814870",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'epoch')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the loss\n",
    "\n",
    "scoring_history = deepl_model.score_history()\n",
    "\n",
    "rcParams['figure.figsize'] = 14, 8\n",
    "plt.plot(scoring_history['training_mse'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2SFWIkEUkyrQ"
   },
   "source": [
    "Visualize the scatter plot across the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "executionInfo": {
     "elapsed": 1284,
     "status": "ok",
     "timestamp": 1661550047053,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "BQFsmBYqkedk",
    "outputId": "45a6bc5f-7683-40d8-f615-3d58497b7a7b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_rec_error = deepl_model.anomaly(test_h2o) \n",
    "\n",
    "# deepl is a H2O function which calculates the error for the dataset\n",
    "\n",
    "# converting to pandas dataframe\n",
    "test_rec_error_df = test_rec_error.as_data_frame()\n",
    "\n",
    "# plotting the testing dataset against the error\n",
    "test_rec_error_df['id'] = test_rec_error_df.index\n",
    "rcParams['figure.figsize'] = 14, 8\n",
    "test_rec_error_df.plot(kind=\"scatter\", x='id', y=\"Reconstruction.MSE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 162
    },
    "executionInfo": {
     "elapsed": 923,
     "status": "ok",
     "timestamp": 1661550105410,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "bh5vqpwrk-6f",
    "outputId": "025423f6-730d-45cb-f405-43343b980dee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning prediction progress: |██████████████████████████████████████████████| (done) 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reconstruction_error</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>true_class</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            reconstruction_error\n",
       "true_class                      \n",
       "0                           8405\n",
       "1                            120"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predicting the class for the testing dataset\n",
    "predictions = deepl_model.predict(test_h2o)\n",
    "error_df = pd.DataFrame({'reconstruction_error': test_rec_error_df['Reconstruction.MSE'],\n",
    "                        'true_class': Y_test_df})\n",
    "error_df.groupby('true_class').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1w7rTflOmqaR"
   },
   "source": [
    "Calculate the threshold score/ cutoff score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1053,
     "status": "ok",
     "timestamp": 1661550124839,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "Eh8uvN88lJCG",
    "outputId": "7e0c71d8-88a3-4e6c-cf73-14ed3e3d65db"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.022119428856661474"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_mean = error_df['reconstruction_error'].mean()\n",
    "score_std = error_df['reconstruction_error'].std()\n",
    "score_cutoff = score_mean + 4*score_std\n",
    "score_cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 923,
     "status": "ok",
     "timestamp": 1661550129468,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "QXGWE7GEmuZV",
    "outputId": "52ab71a1-a420-40b3-df20-85a100072836"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reconstruction_error    120\n",
       "true_class              120\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_df[(error_df['true_class'] == 1) & error_df['reconstruction_error'] > 0.0095].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R3ostMdJm446"
   },
   "source": [
    "Print out the confusion matrix and adjust and tune the confusion matrix from the threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "ysr4PhagVgjL"
   },
   "outputs": [],
   "source": [
    "# len(y_pred), len(error_df.true_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49
    },
    "executionInfo": {
     "elapsed": 420,
     "status": "ok",
     "timestamp": 1661550139734,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "Qv9BWEtgXSA4",
    "outputId": "212b4761-b96d-4326-f3f7-bea7151125ab"
   },
   "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>reconstruction_error</th>\n",
       "      <th>true_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [reconstruction_error, true_class]\n",
       "Index: []"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_df[error_df['true_class'] == 'NaN' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 730
    },
    "executionInfo": {
     "elapsed": 957,
     "status": "ok",
     "timestamp": 1661550570050,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "9zhQqaJ5mw6h",
    "outputId": "33383ec3-8f68-49da-f984-4625be177fd9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold = 0.0085 # we can equally choose to use different values for our threshold [0.009]\n",
    "LABELS = ['Injury', 'No Injury']\n",
    "y_pred = [1 if e > threshold else 0 for e in error_df.reconstruction_error.values]\n",
    "conf_matrix = confusion_matrix(error_df.true_class, y_pred)\n",
    "plt.figure(figsize=(12, 12))\n",
    "sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E7WigCeargv-"
   },
   "source": [
    "The confusion matrix above helps us to understand our accuracy on the test data. \n",
    "\n",
    "We note that:\n",
    "- 6963: The model predicts this case to be actually injury where the true case is injury too.\n",
    "- 24: The model predicts no-injury with the true case being no injury.\n",
    "- 96: The model predicts injury where the actual class in no-injury\n",
    "- 1442: The model predicts no-injury where the actual class is injury.\n",
    "\n",
    "The above explanation is given in the classification report below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 604,
     "status": "ok",
     "timestamp": 1661550572900,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "1r58H_V9HjyY",
    "outputId": "de25a40b-0a02-41d3-f297-c44167817cd1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.83      0.90      8405\n",
      "           1       0.02      0.20      0.03       120\n",
      "\n",
      "    accuracy                           0.82      8525\n",
      "   macro avg       0.50      0.51      0.47      8525\n",
      "weighted avg       0.97      0.82      0.89      8525\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(error_df.true_class, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.030264817150063048"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(error_df.true_class, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8195894428152493"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(error_df.true_class, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18041055718475074"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(error_df.true_class, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MNXhdwY0ZtSb"
   },
   "source": [
    "# Model 2: Isolation Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "executionInfo": {
     "elapsed": 292,
     "status": "ok",
     "timestamp": 1662705368957,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "ZPYa1f_8B7hk"
   },
   "outputs": [],
   "source": [
    "def drop_a_column(df, cols):\n",
    "    \"\"\"\n",
    "    This functiion drops a column in a dataframe and returns the new df without\n",
    "    the dropped column.\n",
    "\n",
    "    cols is a list\n",
    "    df is a dataframe\n",
    "    \"\"\"\n",
    "\n",
    "    df_dr = df.drop(cols, axis = 1)\n",
    "\n",
    "    return df_dr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1662705370145,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "I4VBDJdW5-SS"
   },
   "outputs": [],
   "source": [
    "# inliers = df_week[df_week.injury == 'no']\n",
    "inliers = df_week[df_week.injury == 0]\n",
    "inliers_y = inliers.injury\n",
    "inliers = inliers.drop(['injury', 'athlete_id'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1662705371351,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "o7cvpMDt6nPF"
   },
   "outputs": [],
   "source": [
    "# outliers = df_week[df_week.injury == 'yes']\n",
    "outliers = df_week[df_week.injury == 1]\n",
    "outliers_y = outliers.injury\n",
    "outliers = outliers.drop(['injury', 'athlete_id'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 211
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1662705372845,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "bnIQ2LkvJe38",
    "outputId": "0aff445a-e326-4ee5-eb42-9e5e22bfbdd5"
   },
   "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>nr_sessions</th>\n",
       "      <th>nr_rest_days</th>\n",
       "      <th>total_kms</th>\n",
       "      <th>max_km_one_day</th>\n",
       "      <th>total_km_z3-z4-z5-t1-t2</th>\n",
       "      <th>nr_tough_sessions_(effort_in_z5,_t1_or_t2)</th>\n",
       "      <th>nr_days_with_interval_session</th>\n",
       "      <th>total_km_z3-4</th>\n",
       "      <th>max_km_z3-4_one_day</th>\n",
       "      <th>total_km_z5-t1-t2</th>\n",
       "      <th>...</th>\n",
       "      <th>avg_training_success_2</th>\n",
       "      <th>min_training_success_2</th>\n",
       "      <th>max_training_success_2</th>\n",
       "      <th>avg_recovery_2</th>\n",
       "      <th>min_recovery_2</th>\n",
       "      <th>max_recovery_2</th>\n",
       "      <th>rel_total_kms_week_0_1</th>\n",
       "      <th>rel_total_kms_week_0_2</th>\n",
       "      <th>rel_total_kms_week_1_2</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>22.2</td>\n",
       "      <td>16.4</td>\n",
       "      <td>11.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.718447</td>\n",
       "      <td>1.378882</td>\n",
       "      <td>1.919255</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>21.6</td>\n",
       "      <td>16.4</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.683544</td>\n",
       "      <td>1.018868</td>\n",
       "      <td>1.490566</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  nr_sessions nr_rest_days  total_kms  max_km_one_day  \\\n",
       "0           5            2       22.2            16.4   \n",
       "1           5            2       21.6            16.4   \n",
       "\n",
       "   total_km_z3-z4-z5-t1-t2 nr_tough_sessions_(effort_in_z5,_t1_or_t2)  \\\n",
       "0                     11.8                                          1   \n",
       "1                     11.7                                          1   \n",
       "\n",
       "  nr_days_with_interval_session  total_km_z3-4  max_km_z3-4_one_day  \\\n",
       "0                             2           10.0                 10.0   \n",
       "1                             2           10.0                 10.0   \n",
       "\n",
       "   total_km_z5-t1-t2  ...  avg_training_success_2  min_training_success_2  \\\n",
       "0                0.6  ...                     0.0                     0.0   \n",
       "1                0.5  ...                     0.0                     0.0   \n",
       "\n",
       "  max_training_success_2  avg_recovery_2  min_recovery_2  max_recovery_2  \\\n",
       "0                    0.0            0.18            0.16             0.2   \n",
       "1                    0.0            0.18            0.16             0.2   \n",
       "\n",
       "   rel_total_kms_week_0_1  rel_total_kms_week_0_2  rel_total_kms_week_1_2  \\\n",
       "0                0.718447                1.378882                1.919255   \n",
       "1                0.683544                1.018868                1.490566   \n",
       "\n",
       "   date  \n",
       "0     0  \n",
       "1     1  \n",
       "\n",
       "[2 rows x 70 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inliers.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 318,
     "status": "ok",
     "timestamp": 1662705378663,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "Ga9WMzXt89Y3",
    "outputId": "dbd4712d-ffb2-4611-fe09-1085cc70d206"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((42223, 70), (575, 70))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inliers.shape, outliers.shape # this is the respective dimension of our no-injury and injury data in our dataset respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "executionInfo": {
     "elapsed": 305,
     "status": "ok",
     "timestamp": 1662705380062,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "56HuFunU66Lg"
   },
   "outputs": [],
   "source": [
    "inliers_train, inliers_test = train_test_split(inliers, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hzQwUMU57MB6"
   },
   "source": [
    "## Train the isolation Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "executionInfo": {
     "elapsed": 471,
     "status": "ok",
     "timestamp": 1662705382282,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "YkeH4DA37UjD"
   },
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1662704175406,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "vNrHZsT3DPmG"
   },
   "outputs": [],
   "source": [
    "# inliers = df_week[df_week.injury == 'no']\n",
    "# inliers = df_week[df_week.injury == 1]\n",
    "# inliers_y = inliers.injury\n",
    "# inliers = inliers.drop(['injury'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "executionInfo": {
     "elapsed": 311,
     "status": "ok",
     "timestamp": 1662705384534,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "B7Tq24fuDai1"
   },
   "outputs": [],
   "source": [
    "inliers_train, inliers_test = train_test_split(inliers, test_size=0.20, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "executionInfo": {
     "elapsed": 334,
     "status": "ok",
     "timestamp": 1662704553965,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "cAAUapyNDb7Y"
   },
   "outputs": [],
   "source": [
    "# inliers_tr_y = inliers_train.injury\n",
    "# inliers_train = drop_a_column(inliers_train, ['injury', 'athlete_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1662704555307,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "FL7pBN7KKX7z"
   },
   "outputs": [],
   "source": [
    "# inliers_test = drop_a_column(inliers_test, ['injury', 'athlete_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "executionInfo": {
     "elapsed": 1302,
     "status": "ok",
     "timestamp": 1662705392224,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "DWwb9Dev66B-"
   },
   "outputs": [],
   "source": [
    "model_iso = IsolationForest( max_samples=150, random_state=rng, contamination='auto')\n",
    "# model_iso = IsolationForest( max_samples=150, random_state=rng, contamination=0.01)\n",
    "model_iso.fit(inliers_train)\n",
    "inlier_pred_test = model_iso.predict(inliers_test)\n",
    "outlier_pred = model_iso.predict(outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 313,
     "status": "ok",
     "timestamp": 1662705396129,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "_7qKk8wi7eHF",
    "outputId": "5048b19b-dee9-44ba-a50e-d23a6b73a5c4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy in Detecting no-injury Cases: 0.8467732386027235\n",
      "Accuracy in Detecting injury Cases: 0.17565217391304347\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy in Detecting no-injury Cases:\", list(inlier_pred_test).count(1)/inlier_pred_test.shape[0])\n",
    "print(\"Accuracy in Detecting injury Cases:\", list(outlier_pred).count(-1)/outlier_pred.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 331,
     "status": "ok",
     "timestamp": 1662705400078,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "jfwEOG8m7AHu",
    "outputId": "0314db4d-644c-4c64-aeda-9486f3b5d334"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  1])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(outlier_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1662705400976,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "eGqbEW5Q6qlA",
    "outputId": "038bbf07-db59-487d-e2bd-e5433157d762"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.299\n"
     ]
    }
   ],
   "source": [
    "# calculate score\n",
    "\n",
    "# mark inliers 1, outliers -1\n",
    "\n",
    "outliers_y = outliers_y.replace(1, -1)\n",
    "\n",
    "outliers_y = outliers_y.replace(0, 1)\n",
    "\n",
    "score = f1_score(outliers_y, outlier_pred, pos_label=-1)\n",
    "print('F1 Score: %.3f' % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17565217391304347"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(outliers_y, outlier_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.2973913043478262"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(outliers_y, outlier_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RFEBUpz2ANF6"
   },
   "source": [
    "We can then compare the predictions from the model to the expected target values and calculate a score. Given that we have crisp class labels, we might use a score like precision, recall, or a combination of both, such as the F-measure (F1-score).\n",
    "\n",
    "In this case, we will use F-measure score, which is the harmonic mean of precision and recall. We can calculate the F-measure using the f1_score() function and specify the label of the minority class as -1 via the “pos_label” argument."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "66hA5N6humEZ"
   },
   "source": [
    "# Model 3: One Class SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wVp9xkgfXGfS"
   },
   "source": [
    "One Class Classification (OCC) aims to differentiate samples of one particular class by learning from single class samples during training. It is one of the most commonly used approaches to solve Anamoly Detection (AD), a subfield of machine learning that deals with identifying anomalous cases which the model has never seen before. OCC is also called unary classification, class-modelling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "executionInfo": {
     "elapsed": 307,
     "status": "ok",
     "timestamp": 1662700785733,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "Ap3-8zUrZiGm"
   },
   "outputs": [],
   "source": [
    "X = df_week.drop(columns = ['injury', 'athlete_id'])\n",
    "y = df_week['injury']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1662700786809,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "n69G-RJ7ZrtH"
   },
   "outputs": [],
   "source": [
    "# split into train/test sets\n",
    "trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.80, random_state=2, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kJ9kaP2hdjUT"
   },
   "source": [
    "<!-- train = trainX.copy()\n",
    "train['injury'] = trainy\n",
    "# train -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xP3naJcadIGl"
   },
   "source": [
    "<!-- # standardize the dataset\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit on training set only.\n",
    "scaler.fit(trainX)\n",
    "\n",
    "# Apply transform to both the training set and the test set.\n",
    "trainX = scaler.transform(trainX)\n",
    "testX = scaler.transform(testX) -->\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "executionInfo": {
     "elapsed": 403,
     "status": "ok",
     "timestamp": 1662701473047,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "Kj1I0Rem3loL"
   },
   "outputs": [],
   "source": [
    "model_onec_svm = OneClassSVM() #one class svm model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1662701474659,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "At88wiRHZ2BZ"
   },
   "outputs": [],
   "source": [
    "# kernel = ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed']\n",
    "kernel = ['linear', 'poly', 'rbf', 'sigmoid']\n",
    "degree = np.arange(2,5)\n",
    "gamma = ['scale', 'auto']\n",
    "shrinking = [True, False]\n",
    "\n",
    "tune_parameters = [{\"kernel\": kernel, 'degree':degree, 'gamma':gamma, 'shrinking':shrinking}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1662701477906,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "0swzQvyKcaDE",
    "outputId": "215b9c5f-4c13-42bf-dfcd-97b1920e5015"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n",
       "  'degree': array([2, 3, 4]),\n",
       "  'gamma': ['scale', 'auto'],\n",
       "  'shrinking': [True, False]}]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tune_parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "executionInfo": {
     "elapsed": 309,
     "status": "ok",
     "timestamp": 1662701481928,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "fGRFjLbZ3p60"
   },
   "outputs": [],
   "source": [
    "# Instantiate the grid search model\n",
    "grid_search = GridSearchCV(estimator = model_onec_svm,\n",
    "                           param_grid = tune_parameters,\n",
    "                           cv = 10,\n",
    "                           n_jobs=-1, verbose=1, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "executionInfo": {
     "elapsed": 454,
     "status": "ok",
     "timestamp": 1662701485485,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "JweW30iWfwcv"
   },
   "outputs": [],
   "source": [
    "# we fit on majority class\n",
    "trainX_copy = trainX.copy()\n",
    "trainX_copy['injury'] = trainy\n",
    "trainX_copy = trainX_copy[trainX_copy.injury == 0]\n",
    "\n",
    "trainy_copy = trainy[trainy ==0]\n",
    "trainX_copy = trainX_copy.drop(columns = 'injury')\n",
    "# trainy_copy = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 384,
     "status": "ok",
     "timestamp": 1662701495705,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "h_H4OHsztflE",
    "outputId": "689f597b-c9da-43e9-ea5c-1e17b8257bfa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8444,), (8444, 70))"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainy_copy.shape, trainX_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 111252,
     "status": "ok",
     "timestamp": 1662703380413,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "SCp87OvPcC6s",
    "outputId": "082d0902-4565-424d-e7b4-0320c8b274ea"
   },
   "outputs": [],
   "source": [
    "# %%time\n",
    "# grid_search.fit(trainX_copy, trainy_copy)  # hyper parameter tuning to avoid over fitting\n",
    "# # grid_search.fit(trainX_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 890,
     "status": "ok",
     "timestamp": 1662703388978,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "ntSV7X71cHQ0",
    "outputId": "81eaf22c-21b6-4577-a8cc-2106f1f2b5eb"
   },
   "outputs": [],
   "source": [
    "# grid_search.best_score_, grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "id": "10rqKYqcv09R"
   },
   "outputs": [],
   "source": [
    "# oc_svm_best = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "executionInfo": {
     "elapsed": 305,
     "status": "ok",
     "timestamp": 1662703540106,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "CDtazhBR2Nnj"
   },
   "outputs": [],
   "source": [
    "# define outlier detection model\n",
    "model = OneClassSVM(gamma='auto', nu=0.55)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 382,
     "status": "ok",
     "timestamp": 1662703554499,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "xKg76-B92bbV",
    "outputId": "4c731607-88ac-4d9e-ea6d-d2ab82962442"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneClassSVM(gamma=&#x27;auto&#x27;, nu=0.55)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneClassSVM</label><div class=\"sk-toggleable__content\"><pre>OneClassSVM(gamma=&#x27;auto&#x27;, nu=0.55)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "OneClassSVM(gamma='auto', nu=0.55)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit on majority class\n",
    "\n",
    "model.fit(trainX_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "executionInfo": {
     "elapsed": 847,
     "status": "ok",
     "timestamp": 1662703578422,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "XwJVzYjw2u0w"
   },
   "outputs": [],
   "source": [
    "# detect outliers in the test set\n",
    "yhat = model.predict(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "executionInfo": {
     "elapsed": 267,
     "status": "ok",
     "timestamp": 1662703843149,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "edst2O5T2zOp"
   },
   "outputs": [],
   "source": [
    "# mark inliers 1, outliers -1\n",
    "\n",
    "testy = testy.replace(1, -1)\n",
    "\n",
    "testy = testy.replace(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.01      1.00      0.03       460\n",
      "           1       1.00      0.03      0.06     33779\n",
      "\n",
      "    accuracy                           0.04     34239\n",
      "   macro avg       0.51      0.51      0.04     34239\n",
      "weighted avg       0.98      0.04      0.06     34239\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report( testy, yhat ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 446,
     "status": "ok",
     "timestamp": 1662703846632,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "PBWkZVnF2045",
    "outputId": "2c457d10-1c27-47dc-80be-1205dcfb5dee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.027\n"
     ]
    }
   ],
   "source": [
    "# calculate score\n",
    "score = f1_score(testy, yhat, pos_label= -1)\n",
    "print('F1 Score: %.3f' % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0436344519407693"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(testy, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.8254621922369227"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(testy, yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yiZHyrZb8jKp"
   },
   "source": [
    "# A -  Repeating Models with Standardized data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "executionInfo": {
     "elapsed": 299,
     "status": "ok",
     "timestamp": 1662732551796,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "eoJ39hYs3SLG"
   },
   "outputs": [],
   "source": [
    "df_week2 = df_week.drop(columns = 'athlete_id')\n",
    "\n",
    "std_scaler = StandardScaler()\n",
    "\n",
    "df_week_scaled = std_scaler.fit_transform(df_week2.drop(columns = 'injury').to_numpy())\n",
    "df_week_scaled = pd.DataFrame(df_week_scaled, columns=list(df_week2.drop(columns = 'injury')))\n",
    "df_week_scaled['injury'] = df_week.injury"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C-Rza9gW-weF"
   },
   "source": [
    "## Model 2: Isolation Forest II"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "executionInfo": {
     "elapsed": 279,
     "status": "ok",
     "timestamp": 1662705839095,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "8QcX_fhm-weG"
   },
   "outputs": [],
   "source": [
    "def drop_a_column(df, cols):\n",
    "    \"\"\"\n",
    "    This functiion drops a column in a dataframe and returns the new df without\n",
    "    the dropped column.\n",
    "\n",
    "    cols is a list\n",
    "    df is a dataframe\n",
    "    \"\"\"\n",
    "\n",
    "    df_dr = df.drop(cols, axis = 1)\n",
    "\n",
    "    return df_dr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "executionInfo": {
     "elapsed": 311,
     "status": "ok",
     "timestamp": 1662705849287,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "BiOrbWwx-weG"
   },
   "outputs": [],
   "source": [
    "# inliers = df_week_scaled[df_week_scaled.injury == 'no']\n",
    "inliers = df_week_scaled[df_week_scaled.injury == 0] #  no injury data\n",
    "inliers_y = inliers.injury #  no injury reponse data\n",
    "inliers = inliers.drop(['injury'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "executionInfo": {
     "elapsed": 293,
     "status": "ok",
     "timestamp": 1662705857779,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "BH4eQBtB-weG"
   },
   "outputs": [],
   "source": [
    "# outliers = df_week_scaled[df_week_scaled.injury == 'yes']\n",
    "outliers = df_week_scaled[df_week_scaled.injury == 1] #  injury data\n",
    "outliers_y = outliers.injury #  injury reponse data\n",
    "outliers = outliers.drop(['injury'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 193
    },
    "executionInfo": {
     "elapsed": 291,
     "status": "ok",
     "timestamp": 1662705859794,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "9Mx3h7u9-weG",
    "outputId": "1ea2a73b-989e-4152-a3d8-20618813bc2c"
   },
   "outputs": [
    {
     "data": {
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       "<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>nr_sessions</th>\n",
       "      <th>nr_rest_days</th>\n",
       "      <th>total_kms</th>\n",
       "      <th>max_km_one_day</th>\n",
       "      <th>total_km_z3-z4-z5-t1-t2</th>\n",
       "      <th>nr_tough_sessions_(effort_in_z5,_t1_or_t2)</th>\n",
       "      <th>nr_days_with_interval_session</th>\n",
       "      <th>total_km_z3-4</th>\n",
       "      <th>max_km_z3-4_one_day</th>\n",
       "      <th>total_km_z5-t1-t2</th>\n",
       "      <th>...</th>\n",
       "      <th>avg_training_success_2</th>\n",
       "      <th>min_training_success_2</th>\n",
       "      <th>max_training_success_2</th>\n",
       "      <th>avg_recovery_2</th>\n",
       "      <th>min_recovery_2</th>\n",
       "      <th>max_recovery_2</th>\n",
       "      <th>rel_total_kms_week_0_1</th>\n",
       "      <th>rel_total_kms_week_0_2</th>\n",
       "      <th>rel_total_kms_week_1_2</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.325793</td>\n",
       "      <td>0.067628</td>\n",
       "      <td>-0.744770</td>\n",
       "      <td>0.263543</td>\n",
       "      <td>0.266274</td>\n",
       "      <td>0.067091</td>\n",
       "      <td>0.259173</td>\n",
       "      <td>0.735992</td>\n",
       "      <td>1.429448</td>\n",
       "      <td>-0.613609</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.316877</td>\n",
       "      <td>-1.138703</td>\n",
       "      <td>-1.345902</td>\n",
       "      <td>-0.490097</td>\n",
       "      <td>-0.194751</td>\n",
       "      <td>-0.653759</td>\n",
       "      <td>-0.101858</td>\n",
       "      <td>-0.129793</td>\n",
       "      <td>-0.103154</td>\n",
       "      <td>-1.522325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.325793</td>\n",
       "      <td>0.067628</td>\n",
       "      <td>-0.761112</td>\n",
       "      <td>0.263543</td>\n",
       "      <td>0.255021</td>\n",
       "      <td>0.067091</td>\n",
       "      <td>0.259173</td>\n",
       "      <td>0.735992</td>\n",
       "      <td>1.429448</td>\n",
       "      <td>-0.631323</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.316877</td>\n",
       "      <td>-1.138703</td>\n",
       "      <td>-1.345902</td>\n",
       "      <td>-0.490097</td>\n",
       "      <td>-0.194751</td>\n",
       "      <td>-0.653759</td>\n",
       "      <td>-0.101858</td>\n",
       "      <td>-0.129793</td>\n",
       "      <td>-0.103154</td>\n",
       "      <td>-1.521085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   nr_sessions  nr_rest_days  total_kms  max_km_one_day  \\\n",
       "0    -0.325793      0.067628  -0.744770        0.263543   \n",
       "1    -0.325793      0.067628  -0.761112        0.263543   \n",
       "\n",
       "   total_km_z3-z4-z5-t1-t2  nr_tough_sessions_(effort_in_z5,_t1_or_t2)  \\\n",
       "0                 0.266274                                    0.067091   \n",
       "1                 0.255021                                    0.067091   \n",
       "\n",
       "   nr_days_with_interval_session  total_km_z3-4  max_km_z3-4_one_day  \\\n",
       "0                       0.259173       0.735992             1.429448   \n",
       "1                       0.259173       0.735992             1.429448   \n",
       "\n",
       "   total_km_z5-t1-t2  ...  avg_training_success_2  min_training_success_2  \\\n",
       "0          -0.613609  ...               -1.316877               -1.138703   \n",
       "1          -0.631323  ...               -1.316877               -1.138703   \n",
       "\n",
       "   max_training_success_2  avg_recovery_2  min_recovery_2  max_recovery_2  \\\n",
       "0               -1.345902       -0.490097       -0.194751       -0.653759   \n",
       "1               -1.345902       -0.490097       -0.194751       -0.653759   \n",
       "\n",
       "   rel_total_kms_week_0_1  rel_total_kms_week_0_2  rel_total_kms_week_1_2  \\\n",
       "0               -0.101858               -0.129793               -0.103154   \n",
       "1               -0.101858               -0.129793               -0.103154   \n",
       "\n",
       "       date  \n",
       "0 -1.522325  \n",
       "1 -1.521085  \n",
       "\n",
       "[2 rows x 70 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inliers.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 385,
     "status": "ok",
     "timestamp": 1662705867676,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "dOJq2kRL-weH",
    "outputId": "03bad760-ca33-4faf-d355-8d176df1a544"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((42223, 70), (575, 70))"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inliers.shape, outliers.shape # this is the respective dimension of our no-injury and injury data in our dataset respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "executionInfo": {
     "elapsed": 288,
     "status": "ok",
     "timestamp": 1662705869320,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "k64Rg5d7-weH"
   },
   "outputs": [],
   "source": [
    "inliers_train, inliers_test = train_test_split(inliers, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w0GS4lCM-weH"
   },
   "source": [
    "### Train the isolation Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "executionInfo": {
     "elapsed": 314,
     "status": "ok",
     "timestamp": 1662705871314,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "K1WoHNoV-weH"
   },
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "executionInfo": {
     "elapsed": 277,
     "status": "ok",
     "timestamp": 1662705874740,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "JVkQCRha-weH"
   },
   "outputs": [],
   "source": [
    "inliers_train, inliers_test = train_test_split(inliers, test_size=0.20, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "executionInfo": {
     "elapsed": 1095,
     "status": "ok",
     "timestamp": 1662705898921,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "UCpF9D5u-weH"
   },
   "outputs": [],
   "source": [
    "model_iso = IsolationForest( max_samples=150, random_state=rng, contamination='auto')\n",
    "# model_iso = IsolationForest( max_samples=150, random_state=rng, contamination=0.01)\n",
    "model_iso.fit(inliers_train)\n",
    "inlier_pred_test = model_iso.predict(inliers_test)\n",
    "outlier_pred = model_iso.predict(outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1662705899652,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "F5byreA--weH",
    "outputId": "769d198c-ddc3-4b89-a14d-a4b970410e1f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy in Detecting injury Cases: 0.846299585553582\n",
      "Accuracy in Detecting no-injury Cases: 0.16521739130434782\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy in Detecting injury Cases:\", list(inlier_pred_test).count(1)/inlier_pred_test.shape[0])\n",
    "print(\"Accuracy in Detecting no-injury Cases:\", list(outlier_pred).count(-1)/outlier_pred.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 355,
     "status": "ok",
     "timestamp": 1662705902252,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "ws2irXbr-weI",
    "outputId": "cac7a440-9195-428e-ceda-8dbe0ad088d5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  1])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(outlier_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 296,
     "status": "ok",
     "timestamp": 1662705887500,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "ax8tQfK7-weI",
    "outputId": "acdfd657-2ace-44af-c2b4-a2a2dfc03aff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.284\n"
     ]
    }
   ],
   "source": [
    "# calculate score\n",
    "\n",
    "# mark inliers 1, outliers -1\n",
    "\n",
    "outliers_y = outliers_y.replace(1, -1)\n",
    "\n",
    "outliers_y = outliers_y.replace(0, 1)\n",
    "\n",
    "score = f1_score(outliers_y, outlier_pred, pos_label=-1)\n",
    "print('F1 Score: %.3f' % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16521739130434782"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(outliers_y, outlier_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3391304347826085"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(outliers_y, outlier_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iA1bvJMa__Uu"
   },
   "source": [
    "## Model 3: One Class SVM II"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "executionInfo": {
     "elapsed": 386,
     "status": "ok",
     "timestamp": 1662706082865,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "VdT1OFGo__Uv"
   },
   "outputs": [],
   "source": [
    "X = df_week_scaled.drop(columns = ['injury'])\n",
    "y = df_week_scaled['injury']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "id": "iBbO-M2M__Uv"
   },
   "outputs": [],
   "source": [
    "# split into train/test sets\n",
    "trainX, testX, trainy, testy = train_test_split(X, y, test_size = 0.80, random_state=2, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "executionInfo": {
     "elapsed": 277,
     "status": "ok",
     "timestamp": 1662706094209,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "jVUR7ant__Uv"
   },
   "outputs": [],
   "source": [
    "# we fit on majority class\n",
    "trainX_copy = trainX.copy()\n",
    "trainX_copy['injury'] = trainy\n",
    "trainX_copy = trainX_copy[trainX_copy.injury == 0]\n",
    "\n",
    "trainy_copy = trainy[trainy ==0]\n",
    "\n",
    "trainX_copy = trainX_copy.drop(columns = 'injury')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 393,
     "status": "ok",
     "timestamp": 1662706096889,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "P2j-Xifw__Uv",
    "outputId": "8c550058-f7ae-4dda-cb45-a7c3697c6422"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8444,), (8444, 70))"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainy_copy.shape, trainX_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "executionInfo": {
     "elapsed": 289,
     "status": "ok",
     "timestamp": 1662706099936,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "R0xBBRmv__Uw"
   },
   "outputs": [],
   "source": [
    "# define outlier detection model\n",
    "model = OneClassSVM(gamma='auto', nu=.55)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 271,
     "status": "ok",
     "timestamp": 1662706102023,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "3aBB3iYK__Uw",
    "outputId": "314370a4-e056-470c-fc3f-d07c3daabbee"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneClassSVM(gamma=&#x27;auto&#x27;, nu=0.55)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneClassSVM</label><div class=\"sk-toggleable__content\"><pre>OneClassSVM(gamma=&#x27;auto&#x27;, nu=0.55)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "OneClassSVM(gamma='auto', nu=0.55)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit on majority class\n",
    "\n",
    "model.fit(trainX_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "executionInfo": {
     "elapsed": 697,
     "status": "ok",
     "timestamp": 1662706105616,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "KlSvZcXU__Uw"
   },
   "outputs": [],
   "source": [
    "# detect outliers in the test set\n",
    "yhat = model.predict(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "executionInfo": {
     "elapsed": 282,
     "status": "ok",
     "timestamp": 1662706107851,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "4L87a1fx__Uw"
   },
   "outputs": [],
   "source": [
    "# mark inliers 1, outliers -1\n",
    "\n",
    "testy = testy.replace(1, -1)\n",
    "\n",
    "testy = testy.replace(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.01      0.52      0.02       460\n",
      "           1       0.99      0.45      0.62     33779\n",
      "\n",
      "    accuracy                           0.45     34239\n",
      "   macro avg       0.50      0.48      0.32     34239\n",
      "weighted avg       0.97      0.45      0.61     34239\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report( testy, yhat ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4509769561026899"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(testy, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4509769561026899"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(testy, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.19609217558924"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(testy, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 276,
     "status": "ok",
     "timestamp": 1662706109967,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "N1lZ7udQ__Uw",
    "outputId": "676c2bae-49cd-41f5-f9a6-39625272ccfe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.025\n"
     ]
    }
   ],
   "source": [
    "# calculate score\n",
    "score = f1_score(testy, yhat, pos_label= -1)\n",
    "print('F1 Score: %.3f' % score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "79sMx1_oAreC"
   },
   "source": [
    "The outcome for both scaled and unscaled data, the results are the same in terms of f1 score for IF, but for the SVM, there is an improvement with scaling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kvoYYIXVi6o5"
   },
   "source": [
    "# B - Repeating Models with PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ciigzlJ0lMzO"
   },
   "source": [
    "## Model 2: Isolation Forest III"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1662733034893,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "JglPp2sble3E"
   },
   "outputs": [],
   "source": [
    "# Make an instance of the Model\n",
    "pca_if = PCA(.95) # retain 95% variance in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "executionInfo": {
     "elapsed": 242,
     "status": "ok",
     "timestamp": 1662733038129,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "w78q11XFlMzP"
   },
   "outputs": [],
   "source": [
    "# inliers = df_week_scaled[df_week_scaled.injury == 'no']\n",
    "inliers = df_week_scaled[df_week_scaled.injury == 0] #  no injury data\n",
    "\n",
    "\n",
    "####---------may comment-------------###\n",
    "# inliers_y = inliers.injury #  no injury reponse data \n",
    "# inliers = inliers.drop(['injury'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "executionInfo": {
     "elapsed": 280,
     "status": "ok",
     "timestamp": 1662733110171,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "w0F1XLDglMzP"
   },
   "outputs": [],
   "source": [
    "# outliers = df_week_scaled[df_week_scaled.injury == 'yes']\n",
    "outliers = df_week_scaled[df_week_scaled.injury == 1] #  injury data\n",
    "outliers_y = outliers.injury #  injury reponse data\n",
    "outliers = outliers.drop(['injury'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 193
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1662733111890,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "dXwGIGsulMzP",
    "outputId": "73ec0452-7e47-49e2-f4fc-2be3c57fe515",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nr_sessions</th>\n",
       "      <th>nr_rest_days</th>\n",
       "      <th>total_kms</th>\n",
       "      <th>max_km_one_day</th>\n",
       "      <th>total_km_z3-z4-z5-t1-t2</th>\n",
       "      <th>nr_tough_sessions_(effort_in_z5,_t1_or_t2)</th>\n",
       "      <th>nr_days_with_interval_session</th>\n",
       "      <th>total_km_z3-4</th>\n",
       "      <th>max_km_z3-4_one_day</th>\n",
       "      <th>total_km_z5-t1-t2</th>\n",
       "      <th>...</th>\n",
       "      <th>min_training_success_2</th>\n",
       "      <th>max_training_success_2</th>\n",
       "      <th>avg_recovery_2</th>\n",
       "      <th>min_recovery_2</th>\n",
       "      <th>max_recovery_2</th>\n",
       "      <th>rel_total_kms_week_0_1</th>\n",
       "      <th>rel_total_kms_week_0_2</th>\n",
       "      <th>rel_total_kms_week_1_2</th>\n",
       "      <th>date</th>\n",
       "      <th>injury</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0.266274</td>\n",
       "      <td>0.067091</td>\n",
       "      <td>0.259173</td>\n",
       "      <td>0.735992</td>\n",
       "      <td>1.429448</td>\n",
       "      <td>-0.613609</td>\n",
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       "      <td>-1.138703</td>\n",
       "      <td>-1.345902</td>\n",
       "      <td>-0.490097</td>\n",
       "      <td>-0.194751</td>\n",
       "      <td>-0.653759</td>\n",
       "      <td>-0.101858</td>\n",
       "      <td>-0.129793</td>\n",
       "      <td>-0.103154</td>\n",
       "      <td>-1.522325</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.325793</td>\n",
       "      <td>0.067628</td>\n",
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       "      <td>0.263543</td>\n",
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       "      <td>0.067091</td>\n",
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       "      <td>0.735992</td>\n",
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       "      <td>-0.631323</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.490097</td>\n",
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       "      <td>-0.129793</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   nr_sessions  nr_rest_days  total_kms  max_km_one_day  \\\n",
       "0    -0.325793      0.067628  -0.744770        0.263543   \n",
       "1    -0.325793      0.067628  -0.761112        0.263543   \n",
       "\n",
       "   total_km_z3-z4-z5-t1-t2  nr_tough_sessions_(effort_in_z5,_t1_or_t2)  \\\n",
       "0                 0.266274                                    0.067091   \n",
       "1                 0.255021                                    0.067091   \n",
       "\n",
       "   nr_days_with_interval_session  total_km_z3-4  max_km_z3-4_one_day  \\\n",
       "0                       0.259173       0.735992             1.429448   \n",
       "1                       0.259173       0.735992             1.429448   \n",
       "\n",
       "   total_km_z5-t1-t2  ...  min_training_success_2  max_training_success_2  \\\n",
       "0          -0.613609  ...               -1.138703               -1.345902   \n",
       "1          -0.631323  ...               -1.138703               -1.345902   \n",
       "\n",
       "   avg_recovery_2  min_recovery_2  max_recovery_2  rel_total_kms_week_0_1  \\\n",
       "0       -0.490097       -0.194751       -0.653759               -0.101858   \n",
       "1       -0.490097       -0.194751       -0.653759               -0.101858   \n",
       "\n",
       "   rel_total_kms_week_0_2  rel_total_kms_week_1_2      date  injury  \n",
       "0               -0.129793               -0.103154 -1.522325       0  \n",
       "1               -0.129793               -0.103154 -1.521085       0  \n",
       "\n",
       "[2 rows x 71 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inliers.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 379,
     "status": "ok",
     "timestamp": 1662733129658,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "C-4Nc5qAlMzP",
    "outputId": "931b5a77-eb30-4b3b-ef1c-040093a79082"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((42223, 71), (575, 70))"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inliers.shape, outliers.shape # this is the respective dimension of our no-injury and injury data in our dataset respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "executionInfo": {
     "elapsed": 339,
     "status": "ok",
     "timestamp": 1662733148108,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "ea-dzE_XlMzQ"
   },
   "outputs": [],
   "source": [
    "inliers_train, inliers_test = train_test_split(inliers, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "executionInfo": {
     "elapsed": 300,
     "status": "ok",
     "timestamp": 1662733151029,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "O-nFHph8mq9m"
   },
   "outputs": [],
   "source": [
    "inliers_train_y = inliers_train.injury #  no injury reponse data\n",
    "inliers_train = inliers_train.drop(['injury'], axis = 1)\n",
    "\n",
    "inliers_test_y = inliers_test.injury\n",
    "inliers_test = inliers_test.drop(['injury'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 505
    },
    "executionInfo": {
     "elapsed": 480,
     "status": "ok",
     "timestamp": 1662733153768,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "qtionQ6TmTID",
    "outputId": "9d47ac11-f89b-444e-91c5-1094b4f9238e",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# inliers_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RJ27gfNKl624"
   },
   "source": [
    "### Apply PCA on data splits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kE_kzAEnmIHq"
   },
   "source": [
    "Fit PCA on training set on training set alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 276,
     "status": "ok",
     "timestamp": 1662733392134,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "dc7UMXelmJdl",
    "outputId": "803095be-1c8c-4c8a-f1a2-6d8addb97d15",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=0.95)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=0.95)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "PCA(n_components=0.95)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_if.fit(inliers_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NkNC1c-ynzww"
   },
   "source": [
    "Apply the mapping (transform) to both the training set and the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "background_save": true,
     "output_embedded_package_id": "1zH0UOo5HYgO6vNJw3Jy0p32CsYf3haEu"
    },
    "id": "w3M5BnW2o0kt",
    "outputId": "3a70fd94-7020-44f6-be89-4edcaec7ac91",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33778, 70)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inliers_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 399
    },
    "executionInfo": {
     "elapsed": 598,
     "status": "error",
     "timestamp": 1662733394315,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "EtyJb4SRmAf6",
    "outputId": "b497c651-cfea-4929-b2cd-d7399fc06d10"
   },
   "outputs": [],
   "source": [
    "inliers_train = pca_if.transform(inliers_train)\n",
    "inliers_test = pca_if.transform(inliers_test)\n",
    "outliers = pca_if.transform(outliers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dNGH17ctlMzQ"
   },
   "source": [
    "### Train the isolation Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "executionInfo": {
     "elapsed": 375,
     "status": "ok",
     "timestamp": 1662733299118,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "1E32wNCKlMzQ",
    "tags": []
   },
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "id": "lb8go5aolMzQ"
   },
   "outputs": [],
   "source": [
    "# inliers_train, inliers_test = train_test_split(inliers, test_size=0.20, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "executionInfo": {
     "elapsed": 1856,
     "status": "error",
     "timestamp": 1662733331512,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "BJm-swXZlMzQ",
    "outputId": "26807a54-b4f6-4677-9e61-c0e4285277ab"
   },
   "outputs": [],
   "source": [
    "model_iso = IsolationForest( max_samples=150, random_state=rng, contamination='auto')\n",
    "# model_iso = IsolationForest( max_samples=150, random_state=rng, contamination=0.01)\n",
    "model_iso.fit(inliers_train)\n",
    "inlier_pred_test = model_iso.predict(inliers_test)\n",
    "outlier_pred = model_iso.predict(outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1662705899652,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "yFPTyb5ClMzQ",
    "outputId": "769d198c-ddc3-4b89-a14d-a4b970410e1f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy in Detecting injury Cases: 0.959976317347543\n",
      "Accuracy in Detecting no-injury Cases: 0.03130434782608696\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy in Detecting injury Cases:\", list(inlier_pred_test).count(1)/inlier_pred_test.shape[0])\n",
    "print(\"Accuracy in Detecting no-injury Cases:\", list(outlier_pred).count(-1)/outlier_pred.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 355,
     "status": "ok",
     "timestamp": 1662705902252,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "IqQfrEx2lMzR",
    "outputId": "cac7a440-9195-428e-ceda-8dbe0ad088d5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  1])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(outlier_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 296,
     "status": "ok",
     "timestamp": 1662705887500,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "cUGyDlH1lMzR",
    "outputId": "acdfd657-2ace-44af-c2b4-a2a2dfc03aff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.061\n"
     ]
    }
   ],
   "source": [
    "# calculate score\n",
    "\n",
    "# mark inliers 1, outliers -1\n",
    "\n",
    "outliers_y = outliers_y.replace(1, -1)\n",
    "\n",
    "outliers_y = outliers_y.replace(0, 1)\n",
    "\n",
    "score = f1_score(outliers_y, outlier_pred, pos_label=-1)\n",
    "print('F1 Score: %.3f' % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03130434782608696"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(outliers_y, outlier_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.8747826086956523"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(outliers_y, outlier_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The use of PCA reduced our prediction accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0-UxUllslMzR"
   },
   "source": [
    "## Model 3: One Class SVM III"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "id": "Ql06Rjd0lMzR"
   },
   "outputs": [],
   "source": [
    "X = df_week_scaled.drop(columns = ['injury'])\n",
    "y = df_week_scaled['injury']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "id": "hbLgHrunlMzR",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# split into train/test sets\n",
    "trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.80, random_state=2, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "id": "boDYbiTwlMzR",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# we fit on majority class\n",
    "trainX_copy = trainX.copy()\n",
    "trainX_copy['injury'] = trainy\n",
    "trainX_copy = trainX_copy[trainX_copy.injury == 0]\n",
    "\n",
    "trainy_copy = trainy[trainy ==0]\n",
    "trainX_copy = trainX_copy.drop(columns = 'injury')\n",
    "# trainy_copy = "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RJ27gfNKl624"
   },
   "source": [
    "### Apply PCA on data splits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1662733034893,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "JglPp2sble3E"
   },
   "outputs": [],
   "source": [
    "# Make an instance of the Model\n",
    "pca_oc_svm = PCA(.95) # retain 95% variance in the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kE_kzAEnmIHq"
   },
   "source": [
    "Fit PCA on training set on training set alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 276,
     "status": "ok",
     "timestamp": 1662733392134,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "dc7UMXelmJdl",
    "outputId": "803095be-1c8c-4c8a-f1a2-6d8addb97d15",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=0.95)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=0.95)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "PCA(n_components=0.95)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_oc_svm.fit(trainX_copy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NkNC1c-ynzww"
   },
   "source": [
    "Apply the mapping (transform) to both the training set and the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 399
    },
    "executionInfo": {
     "elapsed": 598,
     "status": "error",
     "timestamp": 1662733394315,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "EtyJb4SRmAf6",
    "outputId": "b497c651-cfea-4929-b2cd-d7399fc06d10"
   },
   "outputs": [],
   "source": [
    "trainX_copy = pca_oc_svm.transform(trainX_copy)\n",
    "testX = pca_oc_svm.transform(testX)\n",
    "# outliers = pca_oc_svm.transform(outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 393,
     "status": "ok",
     "timestamp": 1662706096889,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "PAxeTFj-lMzR",
    "outputId": "8c550058-f7ae-4dda-cb45-a7c3697c6422"
   },
   "outputs": [],
   "source": [
    "# trainy_copy.shape, trainX_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "id": "73iYZqdrlMzS"
   },
   "outputs": [],
   "source": [
    "# define outlier detection model\n",
    "model_one_class_svm_pca = OneClassSVM(gamma='auto', nu=0.55)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 271,
     "status": "ok",
     "timestamp": 1662706102023,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "aQrGnofilMzS",
    "outputId": "314370a4-e056-470c-fc3f-d07c3daabbee"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneClassSVM(gamma=&#x27;auto&#x27;, nu=0.55)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneClassSVM</label><div class=\"sk-toggleable__content\"><pre>OneClassSVM(gamma=&#x27;auto&#x27;, nu=0.55)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "OneClassSVM(gamma='auto', nu=0.55)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit on majority class\n",
    "\n",
    "model_one_class_svm_pca.fit(trainX_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "id": "PDVXWyunlMzS"
   },
   "outputs": [],
   "source": [
    "# detect outliers in the test set\n",
    "yhat = model_one_class_svm_pca.predict(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "id": "PbSMi-v7lMzS"
   },
   "outputs": [],
   "source": [
    "# mark inliers 1, outliers -1\n",
    "\n",
    "testy = testy.replace(1, -1)\n",
    "\n",
    "testy = testy.replace(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.01      0.53      0.03       460\n",
      "           1       0.99      0.45      0.61     33779\n",
      "\n",
      "    accuracy                           0.45     34239\n",
      "   macro avg       0.50      0.49      0.32     34239\n",
      "weighted avg       0.97      0.45      0.61     34239\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report( testy, yhat ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 276,
     "status": "ok",
     "timestamp": 1662706109967,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "siy27vfclMzW",
    "outputId": "676c2bae-49cd-41f5-f9a6-39625272ccfe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.025\n"
     ]
    }
   ],
   "source": [
    "# calculate score\n",
    "score = f1_score(testy, yhat, pos_label= -1)\n",
    "print('F1 Score: %.3f' % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.44744297438593417"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(testy, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.2102281024562633"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(testy, yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UmiKJ1PKjalu"
   },
   "source": [
    "The accuracy further decreased  by 0.002 when we use PCA in One Class SVM."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# C - Dim Red + Unscaled Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ciigzlJ0lMzO"
   },
   "source": [
    "## Model 3: Isolation Forest IV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1662733034893,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "JglPp2sble3E"
   },
   "outputs": [],
   "source": [
    "# Make an instance of the Model\n",
    "pca_if = PCA(.95) # retain 95% variance in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "executionInfo": {
     "elapsed": 242,
     "status": "ok",
     "timestamp": 1662733038129,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "w78q11XFlMzP"
   },
   "outputs": [],
   "source": [
    "# inliers = df_week_scaled[df_week_scaled.injury == 'no']\n",
    "inliers = df_week[df_week.injury == 0] #  no injury data\n",
    "\n",
    "\n",
    "####---------may comment-------------###\n",
    "# inliers_y = inliers.injury #  no injury reponse data \n",
    "# inliers = inliers.drop(['injury'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "executionInfo": {
     "elapsed": 280,
     "status": "ok",
     "timestamp": 1662733110171,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "w0F1XLDglMzP"
   },
   "outputs": [],
   "source": [
    "# outliers = df_week_scaled[df_week_scaled.injury == 'yes']\n",
    "outliers = df_week[df_week.injury == 1] #  injury data\n",
    "outliers_y = outliers.injury #  injury reponse data\n",
    "outliers = outliers.drop(['injury'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 193
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1662733111890,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "dXwGIGsulMzP",
    "outputId": "73ec0452-7e47-49e2-f4fc-2be3c57fe515",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nr_sessions</th>\n",
       "      <th>nr_rest_days</th>\n",
       "      <th>total_kms</th>\n",
       "      <th>max_km_one_day</th>\n",
       "      <th>total_km_z3-z4-z5-t1-t2</th>\n",
       "      <th>nr_tough_sessions_(effort_in_z5,_t1_or_t2)</th>\n",
       "      <th>nr_days_with_interval_session</th>\n",
       "      <th>total_km_z3-4</th>\n",
       "      <th>max_km_z3-4_one_day</th>\n",
       "      <th>total_km_z5-t1-t2</th>\n",
       "      <th>...</th>\n",
       "      <th>max_training_success_2</th>\n",
       "      <th>avg_recovery_2</th>\n",
       "      <th>min_recovery_2</th>\n",
       "      <th>max_recovery_2</th>\n",
       "      <th>athlete_id</th>\n",
       "      <th>injury</th>\n",
       "      <th>rel_total_kms_week_0_1</th>\n",
       "      <th>rel_total_kms_week_0_2</th>\n",
       "      <th>rel_total_kms_week_1_2</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>22.2</td>\n",
       "      <td>16.4</td>\n",
       "      <td>11.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.718447</td>\n",
       "      <td>1.378882</td>\n",
       "      <td>1.919255</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>21.6</td>\n",
       "      <td>16.4</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.683544</td>\n",
       "      <td>1.018868</td>\n",
       "      <td>1.490566</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  nr_sessions nr_rest_days  total_kms  max_km_one_day  \\\n",
       "0           5            2       22.2            16.4   \n",
       "1           5            2       21.6            16.4   \n",
       "\n",
       "   total_km_z3-z4-z5-t1-t2 nr_tough_sessions_(effort_in_z5,_t1_or_t2)  \\\n",
       "0                     11.8                                          1   \n",
       "1                     11.7                                          1   \n",
       "\n",
       "  nr_days_with_interval_session  total_km_z3-4  max_km_z3-4_one_day  \\\n",
       "0                             2           10.0                 10.0   \n",
       "1                             2           10.0                 10.0   \n",
       "\n",
       "   total_km_z5-t1-t2  ...  max_training_success_2  avg_recovery_2  \\\n",
       "0                0.6  ...                     0.0            0.18   \n",
       "1                0.5  ...                     0.0            0.18   \n",
       "\n",
       "  min_recovery_2  max_recovery_2  athlete_id  injury  rel_total_kms_week_0_1  \\\n",
       "0           0.16             0.2           0       0                0.718447   \n",
       "1           0.16             0.2           0       0                0.683544   \n",
       "\n",
       "   rel_total_kms_week_0_2  rel_total_kms_week_1_2  date  \n",
       "0                1.378882                1.919255     0  \n",
       "1                1.018868                1.490566     1  \n",
       "\n",
       "[2 rows x 72 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inliers.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 379,
     "status": "ok",
     "timestamp": 1662733129658,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "C-4Nc5qAlMzP",
    "outputId": "931b5a77-eb30-4b3b-ef1c-040093a79082"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((42223, 72), (575, 71))"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inliers.shape, outliers.shape # this is the respective dimension of our no-injury and injury data in our dataset respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "executionInfo": {
     "elapsed": 339,
     "status": "ok",
     "timestamp": 1662733148108,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "ea-dzE_XlMzQ"
   },
   "outputs": [],
   "source": [
    "inliers_train, inliers_test = train_test_split(inliers, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "executionInfo": {
     "elapsed": 300,
     "status": "ok",
     "timestamp": 1662733151029,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "O-nFHph8mq9m"
   },
   "outputs": [],
   "source": [
    "inliers_train_y = inliers_train.injury #  no injury reponse data\n",
    "inliers_train = inliers_train.drop(['injury'], axis = 1)\n",
    "\n",
    "inliers_test_y = inliers_test.injury\n",
    "inliers_test = inliers_test.drop(['injury'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 505
    },
    "executionInfo": {
     "elapsed": 480,
     "status": "ok",
     "timestamp": 1662733153768,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "qtionQ6TmTID",
    "outputId": "9d47ac11-f89b-444e-91c5-1094b4f9238e",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# inliers_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RJ27gfNKl624"
   },
   "source": [
    "### Apply PCA on data splits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kE_kzAEnmIHq"
   },
   "source": [
    "Fit PCA on training set on training set alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 276,
     "status": "ok",
     "timestamp": 1662733392134,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "dc7UMXelmJdl",
    "outputId": "803095be-1c8c-4c8a-f1a2-6d8addb97d15",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=0.95)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=0.95)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "PCA(n_components=0.95)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_if.fit(inliers_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NkNC1c-ynzww"
   },
   "source": [
    "Apply the mapping (transform) to both the training set and the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 399
    },
    "executionInfo": {
     "elapsed": 598,
     "status": "error",
     "timestamp": 1662733394315,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "EtyJb4SRmAf6",
    "outputId": "b497c651-cfea-4929-b2cd-d7399fc06d10"
   },
   "outputs": [],
   "source": [
    "inliers_train = pca_if.transform(inliers_train)\n",
    "inliers_test = pca_if.transform(inliers_test)\n",
    "outliers = pca_if.transform(outliers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dNGH17ctlMzQ",
    "tags": []
   },
   "source": [
    "### Train the isolation Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "executionInfo": {
     "elapsed": 375,
     "status": "ok",
     "timestamp": 1662733299118,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "1E32wNCKlMzQ",
    "tags": []
   },
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "id": "lb8go5aolMzQ"
   },
   "outputs": [],
   "source": [
    "# inliers_train, inliers_test = train_test_split(inliers, test_size=0.20, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "executionInfo": {
     "elapsed": 1856,
     "status": "error",
     "timestamp": 1662733331512,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "BJm-swXZlMzQ",
    "outputId": "26807a54-b4f6-4677-9e61-c0e4285277ab"
   },
   "outputs": [],
   "source": [
    "model_iso = IsolationForest( max_samples=150, random_state=rng, contamination='auto')\n",
    "# model_iso = IsolationForest( max_samples=150, random_state=rng, contamination=0.01)\n",
    "model_iso.fit(inliers_train)\n",
    "inlier_pred_test = model_iso.predict(inliers_test)\n",
    "outlier_pred = model_iso.predict(outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1662705899652,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "yFPTyb5ClMzQ",
    "outputId": "769d198c-ddc3-4b89-a14d-a4b970410e1f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy in Detecting injury Cases: 0.9416222616933096\n",
      "Accuracy in Detecting no-injury Cases: 0.05217391304347826\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy in Detecting injury Cases:\", list(inlier_pred_test).count(1)/inlier_pred_test.shape[0])\n",
    "print(\"Accuracy in Detecting no-injury Cases:\", list(outlier_pred).count(-1)/outlier_pred.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 355,
     "status": "ok",
     "timestamp": 1662705902252,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "IqQfrEx2lMzR",
    "outputId": "cac7a440-9195-428e-ceda-8dbe0ad088d5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  1])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(outlier_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 296,
     "status": "ok",
     "timestamp": 1662705887500,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "cUGyDlH1lMzR",
    "outputId": "acdfd657-2ace-44af-c2b4-a2a2dfc03aff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.099\n"
     ]
    }
   ],
   "source": [
    "# calculate score\n",
    "\n",
    "# mark inliers 1, outliers -1\n",
    "\n",
    "outliers_y = outliers_y.replace(1, -1)\n",
    "\n",
    "outliers_y = outliers_y.replace(0, 1)\n",
    "\n",
    "score = f1_score(outliers_y, outlier_pred, pos_label=-1)\n",
    "print('F1 Score: %.3f' % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05217391304347826"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(outliers_y, outlier_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.791304347826087"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(outliers_y, outlier_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The use of PCA reduced our prediction accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0-UxUllslMzR"
   },
   "source": [
    "## Model 3: One Class SVM IV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "id": "Ql06Rjd0lMzR"
   },
   "outputs": [],
   "source": [
    "X = df_week.drop(columns = ['injury'])\n",
    "y = df_week['injury']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "id": "hbLgHrunlMzR",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# split into train/test sets\n",
    "trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.80, random_state=2, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "id": "boDYbiTwlMzR",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# we fit on majority class\n",
    "trainX_copy = trainX.copy()\n",
    "trainX_copy['injury'] = trainy\n",
    "trainX_copy = trainX_copy[trainX_copy.injury == 0]\n",
    "\n",
    "trainy_copy = trainy[trainy ==0]\n",
    "trainX_copy = trainX_copy.drop(columns = 'injury')\n",
    "# trainy_copy = "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RJ27gfNKl624"
   },
   "source": [
    "### Apply PCA on data splits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1662733034893,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "JglPp2sble3E"
   },
   "outputs": [],
   "source": [
    "# Make an instance of the Model\n",
    "pca_oc_svm = PCA(.95) # retain 95% variance in the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kE_kzAEnmIHq"
   },
   "source": [
    "Fit PCA on training set on training set alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 276,
     "status": "ok",
     "timestamp": 1662733392134,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "dc7UMXelmJdl",
    "outputId": "803095be-1c8c-4c8a-f1a2-6d8addb97d15",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=0.95)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=0.95)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "PCA(n_components=0.95)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_oc_svm.fit(trainX_copy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NkNC1c-ynzww"
   },
   "source": [
    "Apply the mapping (transform) to both the training set and the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 399
    },
    "executionInfo": {
     "elapsed": 598,
     "status": "error",
     "timestamp": 1662733394315,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "EtyJb4SRmAf6",
    "outputId": "b497c651-cfea-4929-b2cd-d7399fc06d10"
   },
   "outputs": [],
   "source": [
    "trainX_copy = pca_oc_svm.transform(trainX_copy)\n",
    "testX = pca_oc_svm.transform(testX)\n",
    "# outliers = pca_oc_svm.transform(outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 393,
     "status": "ok",
     "timestamp": 1662706096889,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "PAxeTFj-lMzR",
    "outputId": "8c550058-f7ae-4dda-cb45-a7c3697c6422"
   },
   "outputs": [],
   "source": [
    "# trainy_copy.shape, trainX_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "id": "73iYZqdrlMzS"
   },
   "outputs": [],
   "source": [
    "# define outlier detection model\n",
    "model_one_class_svm_pca = OneClassSVM(gamma='auto', nu=0.55)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 271,
     "status": "ok",
     "timestamp": 1662706102023,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "aQrGnofilMzS",
    "outputId": "314370a4-e056-470c-fc3f-d07c3daabbee"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneClassSVM(gamma=&#x27;auto&#x27;, nu=0.55)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneClassSVM</label><div class=\"sk-toggleable__content\"><pre>OneClassSVM(gamma=&#x27;auto&#x27;, nu=0.55)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "OneClassSVM(gamma='auto', nu=0.55)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit on majority class\n",
    "\n",
    "model_one_class_svm_pca.fit(trainX_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "id": "PDVXWyunlMzS"
   },
   "outputs": [],
   "source": [
    "# detect outliers in the test set\n",
    "yhat = model_one_class_svm_pca.predict(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "id": "PbSMi-v7lMzS"
   },
   "outputs": [],
   "source": [
    "# mark inliers 1, outliers -1\n",
    "\n",
    "testy = testy.replace(1, -1)\n",
    "\n",
    "testy = testy.replace(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.01      0.53      0.03       460\n",
      "           1       0.99      0.45      0.62     33779\n",
      "\n",
      "    accuracy                           0.45     34239\n",
      "   macro avg       0.50      0.49      0.32     34239\n",
      "weighted avg       0.97      0.45      0.61     34239\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report( testy, yhat ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 276,
     "status": "ok",
     "timestamp": 1662706109967,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "siy27vfclMzW",
    "outputId": "676c2bae-49cd-41f5-f9a6-39625272ccfe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.025\n"
     ]
    }
   ],
   "source": [
    "# calculate score\n",
    "score = f1_score(testy, yhat, pos_label= -1)\n",
    "print('F1 Score: %.3f' % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.44869885218610356"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(testy, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.2052045912555855"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(testy, yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UmiKJ1PKjalu"
   },
   "source": [
    "The accuracy further decreased  by 0.002 when we use PCA in One Class SVM."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Auto Encoder II"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wRqCXGxmZhAr"
   },
   "source": [
    "## Starting and initializing H2O server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 491
    },
    "executionInfo": {
     "elapsed": 12449,
     "status": "ok",
     "timestamp": 1661545719192,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "wQFBKEG0ZjQ3",
    "outputId": "b9ee3732-e2bb-473d-9bfe-df0bac427685"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321 . connected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>49 mins 47 secs</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>Europe/Berlin</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.36.1.4</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>1 month and 11 days </td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_USER_61dy3j</td></tr>\n",
       "<tr><td>H2O_cluster_total_nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_free_memory:</td>\n",
       "<td>1.567 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>4</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>4</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>http://localhost:54321</td></tr>\n",
       "<tr><td>H2O_connection_proxy:</td>\n",
       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.9.13 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  -----------------------------\n",
       "H2O_cluster_uptime:         49 mins 47 secs\n",
       "H2O_cluster_timezone:       Europe/Berlin\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.36.1.4\n",
       "H2O_cluster_version_age:    1 month and 11 days\n",
       "H2O_cluster_name:           H2O_from_python_USER_61dy3j\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    1.567 Gb\n",
       "H2O_cluster_total_cores:    4\n",
       "H2O_cluster_allowed_cores:  4\n",
       "H2O_cluster_status:         locked, healthy\n",
       "H2O_connection_url:         http://localhost:54321\n",
       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
       "H2O_internal_security:      False\n",
       "Python_version:             3.9.13 final\n",
       "--------------------------  -----------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2o.init() # initializing h2o server\n",
    "# h2o.remove_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6950,
     "status": "ok",
     "timestamp": 1661545778080,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "KOf-6XZracj9",
    "outputId": "07f7059d-cbf1-419c-fff1-f52daa0c0f12"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"
     ]
    }
   ],
   "source": [
    "hdf_week = h2o.import_file(r\"data/week_approach_maskedID_timeseries_v3.0.csv\") #import the file as a h2o file\n",
    "hdf_week[\"date\"] = hdf_week[\"date\"].asfactor() # coonvert date to factor i.e categorical\n",
    "hdf_week[\"injury\"] = hdf_week[\"injury\"].asfactor()\n",
    "features_week  = hdf_week.drop(['athlete_id', 'C1'], axis=1) # we do not need the athlete id again alonside the 'C1' column which is an automatic addition by h2o import\n",
    "train_week , test_week = features_week.split_frame([0.8]) # 80% of data for training and 20% for testing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e3qmuar_rKP-"
   },
   "source": [
    "We used a 80-20 split between the training and testing because of the imbalance in the dataset. This spit enables us to get enough of the undersampled class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 137
    },
    "executionInfo": {
     "elapsed": 1539,
     "status": "ok",
     "timestamp": 1661545805988,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "KbNDIY3GGQ3x",
    "outputId": "1d9f0ed5-ad70-43c9-997e-6fdee67a3839"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  nr_sessions</th><th style=\"text-align: right;\">  nr_rest_days</th><th style=\"text-align: right;\">  total_kms</th><th style=\"text-align: right;\">  max_km_one_day</th><th style=\"text-align: right;\">  total_km_z3-z4-z5-t1-t2</th><th style=\"text-align: right;\">  nr_tough_sessions_(effort_in_z5,_t1_or_t2)</th><th style=\"text-align: right;\">  nr_days_with_interval_session</th><th style=\"text-align: right;\">  total_km_z3-4</th><th style=\"text-align: right;\">  max_km_z3-4_one_day</th><th style=\"text-align: right;\">  total_km_z5-t1-t2</th><th style=\"text-align: right;\">  max_km_z5-t1-t2_one_day</th><th style=\"text-align: right;\">  total_hours_alternative_training</th><th style=\"text-align: right;\">  nr_strength_trainings</th><th style=\"text-align: right;\">  avg_exertion</th><th style=\"text-align: right;\">  min_exertion</th><th style=\"text-align: right;\">  max_exertion</th><th style=\"text-align: right;\">  avg_training_success</th><th style=\"text-align: right;\">  min_training_success</th><th style=\"text-align: right;\">  max_training_success</th><th style=\"text-align: right;\">  avg_recovery</th><th style=\"text-align: right;\">  min_recovery</th><th style=\"text-align: right;\">  max_recovery</th><th style=\"text-align: right;\">  nr_sessions_1</th><th style=\"text-align: right;\">  nr_rest_days_1</th><th style=\"text-align: right;\">  total_kms_1</th><th style=\"text-align: right;\">  max_km_one_day_1</th><th style=\"text-align: right;\">  total_km_z3-z4-z5-t1-t2_1</th><th style=\"text-align: right;\">  nr_tough_sessions_(effort_in_z5,_t1_or_t2)_1</th><th style=\"text-align: right;\">  nr_days_with_interval_session_1</th><th style=\"text-align: right;\">  total_km_z3-4_1</th><th style=\"text-align: right;\">  max_km_z3-4_one_day_1</th><th style=\"text-align: right;\">  total_km_z5-t1-t2_1</th><th style=\"text-align: right;\">  max_km_z5-t1-t2_one_day_1</th><th style=\"text-align: right;\">  total_hours_alternative_training_1</th><th style=\"text-align: right;\">  nr_strength_trainings_1</th><th style=\"text-align: right;\">  avg_exertion_1</th><th style=\"text-align: right;\">  min_exertion_1</th><th style=\"text-align: right;\">  max_exertion_1</th><th style=\"text-align: right;\">  avg_training_success_1</th><th style=\"text-align: right;\">  min_training_success_1</th><th style=\"text-align: right;\">  max_training_success_1</th><th style=\"text-align: right;\">  avg_recovery_1</th><th style=\"text-align: right;\">  min_recovery_1</th><th style=\"text-align: right;\">  max_recovery_1</th><th style=\"text-align: right;\">  nr_sessions_2</th><th style=\"text-align: right;\">  nr_rest_days_2</th><th style=\"text-align: right;\">  total_kms_2</th><th style=\"text-align: right;\">  max_km_one_day_2</th><th style=\"text-align: right;\">  total_km_z3-z4-z5-t1-t2_2</th><th style=\"text-align: right;\">  nr_tough_sessions_(effort_in_z5,_t1_or_t2)_2</th><th style=\"text-align: right;\">  nr_days_with_interval_session_2</th><th style=\"text-align: right;\">  total_km_z3-4_2</th><th style=\"text-align: right;\">  max_km_z3-4_one_day_2</th><th style=\"text-align: right;\">  total_km_z5-t1-t2_2</th><th style=\"text-align: right;\">  max_km_z5-t1-t2_one_day_2</th><th style=\"text-align: right;\">  total_hours_alternative_training_2</th><th style=\"text-align: right;\">  nr_strength_trainings_2</th><th style=\"text-align: right;\">  avg_exertion_2</th><th style=\"text-align: right;\">  min_exertion_2</th><th style=\"text-align: right;\">  max_exertion_2</th><th style=\"text-align: right;\">  avg_training_success_2</th><th style=\"text-align: right;\">  min_training_success_2</th><th style=\"text-align: right;\">  max_training_success_2</th><th style=\"text-align: right;\">  avg_recovery_2</th><th style=\"text-align: right;\">  min_recovery_2</th><th style=\"text-align: right;\">  max_recovery_2</th><th style=\"text-align: right;\">  injury</th><th style=\"text-align: right;\">  rel_total_kms_week_0_1</th><th style=\"text-align: right;\">  rel_total_kms_week_0_2</th><th style=\"text-align: right;\">  rel_total_kms_week_1_2</th><th style=\"text-align: right;\">  date</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">            5</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">       22.2</td><td style=\"text-align: right;\">            16.4</td><td style=\"text-align: right;\">                     11.8</td><td style=\"text-align: right;\">                                           1</td><td style=\"text-align: right;\">                              2</td><td style=\"text-align: right;\">             10</td><td style=\"text-align: right;\">                   10</td><td style=\"text-align: right;\">                0.6</td><td style=\"text-align: right;\">                      0.6</td><td style=\"text-align: right;\">                              2.08</td><td style=\"text-align: right;\">                      2</td><td style=\"text-align: right;\">           0.1</td><td style=\"text-align: right;\">          0.08</td><td style=\"text-align: right;\">          0.11</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.15</td><td style=\"text-align: right;\">          0.18</td><td style=\"text-align: right;\">              6</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">         30.9</td><td style=\"text-align: right;\">              13.8</td><td style=\"text-align: right;\">                       13.5</td><td style=\"text-align: right;\">                                             0</td><td style=\"text-align: right;\">                                2</td><td style=\"text-align: right;\">             12.4</td><td style=\"text-align: right;\">                    6.4</td><td style=\"text-align: right;\">                  0  </td><td style=\"text-align: right;\">                        0  </td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.12</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">              3</td><td style=\"text-align: right;\">               4</td><td style=\"text-align: right;\">         16.1</td><td style=\"text-align: right;\">               9.9</td><td style=\"text-align: right;\">                        3.9</td><td style=\"text-align: right;\">                                             0</td><td style=\"text-align: right;\">                                1</td><td style=\"text-align: right;\">              3.2</td><td style=\"text-align: right;\">                    3.2</td><td style=\"text-align: right;\">                    0</td><td style=\"text-align: right;\">                          0</td><td style=\"text-align: right;\">                                   0</td><td style=\"text-align: right;\">                        1</td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.08</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.2 </td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">                0.718447</td><td style=\"text-align: right;\">                 1.37888</td><td style=\"text-align: right;\">                 1.91925</td><td style=\"text-align: right;\">     0</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            5</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">       21.6</td><td style=\"text-align: right;\">            16.4</td><td style=\"text-align: right;\">                     11.7</td><td style=\"text-align: right;\">                                           1</td><td style=\"text-align: right;\">                              2</td><td style=\"text-align: right;\">             10</td><td style=\"text-align: right;\">                   10</td><td style=\"text-align: right;\">                0.5</td><td style=\"text-align: right;\">                      0.5</td><td style=\"text-align: right;\">                              2.08</td><td style=\"text-align: right;\">                      2</td><td style=\"text-align: right;\">           0.1</td><td style=\"text-align: right;\">          0.08</td><td style=\"text-align: right;\">          0.11</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.15</td><td style=\"text-align: right;\">          0.18</td><td style=\"text-align: right;\">              6</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">         31.6</td><td style=\"text-align: right;\">              13.8</td><td style=\"text-align: right;\">                       14.2</td><td style=\"text-align: right;\">                                             1</td><td style=\"text-align: right;\">                                3</td><td style=\"text-align: right;\">             12.4</td><td style=\"text-align: right;\">                    6.4</td><td style=\"text-align: right;\">                  0.6</td><td style=\"text-align: right;\">                        0.6</td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.12</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">              4</td><td style=\"text-align: right;\">               3</td><td style=\"text-align: right;\">         21.2</td><td style=\"text-align: right;\">               9.9</td><td style=\"text-align: right;\">                        5  </td><td style=\"text-align: right;\">                                             0</td><td style=\"text-align: right;\">                                1</td><td style=\"text-align: right;\">              3.2</td><td style=\"text-align: right;\">                    3.2</td><td style=\"text-align: right;\">                    0</td><td style=\"text-align: right;\">                          0</td><td style=\"text-align: right;\">                                   0</td><td style=\"text-align: right;\">                        1</td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">            0.08</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.2 </td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">                0.683544</td><td style=\"text-align: right;\">                 1.01887</td><td style=\"text-align: right;\">                 1.49057</td><td style=\"text-align: right;\">     1</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            5</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">       21.6</td><td style=\"text-align: right;\">            16.4</td><td style=\"text-align: right;\">                     11.7</td><td style=\"text-align: right;\">                                           1</td><td style=\"text-align: right;\">                              2</td><td style=\"text-align: right;\">             10</td><td style=\"text-align: right;\">                   10</td><td style=\"text-align: right;\">                0.5</td><td style=\"text-align: right;\">                      0.5</td><td style=\"text-align: right;\">                              2.08</td><td style=\"text-align: right;\">                      2</td><td style=\"text-align: right;\">           0.1</td><td style=\"text-align: right;\">          0.08</td><td style=\"text-align: right;\">          0.11</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.15</td><td style=\"text-align: right;\">          0.18</td><td style=\"text-align: right;\">              6</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">         31.6</td><td style=\"text-align: right;\">              13.8</td><td style=\"text-align: right;\">                       14.2</td><td style=\"text-align: right;\">                                             1</td><td style=\"text-align: right;\">                                3</td><td style=\"text-align: right;\">             12.4</td><td style=\"text-align: right;\">                    6.4</td><td style=\"text-align: right;\">                  0.6</td><td style=\"text-align: right;\">                        0.6</td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.12</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">              3</td><td style=\"text-align: right;\">               4</td><td style=\"text-align: right;\">         21.2</td><td style=\"text-align: right;\">               9.9</td><td style=\"text-align: right;\">                        5  </td><td style=\"text-align: right;\">                                             0</td><td style=\"text-align: right;\">                                1</td><td style=\"text-align: right;\">              3.2</td><td style=\"text-align: right;\">                    3.2</td><td style=\"text-align: right;\">                    0</td><td style=\"text-align: right;\">                          0</td><td style=\"text-align: right;\">                                   0</td><td style=\"text-align: right;\">                        0</td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">                0.683544</td><td style=\"text-align: right;\">                 1.01887</td><td style=\"text-align: right;\">                 1.49057</td><td style=\"text-align: right;\">     2</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_week.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 466,
     "status": "ok",
     "timestamp": 1661545813800,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "QSSl9ZHW-uwg",
    "outputId": "4c22aa90-b431-4ed4-dac5-b57c3cbd59e3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'nr_sessions': 'int',\n",
       " 'nr_rest_days': 'int',\n",
       " 'total_kms': 'real',\n",
       " 'max_km_one_day': 'real',\n",
       " 'total_km_z3-z4-z5-t1-t2': 'real',\n",
       " 'nr_tough_sessions_(effort_in_z5,_t1_or_t2)': 'int',\n",
       " 'nr_days_with_interval_session': 'int',\n",
       " 'total_km_z3-4': 'real',\n",
       " 'max_km_z3-4_one_day': 'real',\n",
       " 'total_km_z5-t1-t2': 'real',\n",
       " 'max_km_z5-t1-t2_one_day': 'real',\n",
       " 'total_hours_alternative_training': 'real',\n",
       " 'nr_strength_trainings': 'int',\n",
       " 'avg_exertion': 'real',\n",
       " 'min_exertion': 'real',\n",
       " 'max_exertion': 'real',\n",
       " 'avg_training_success': 'real',\n",
       " 'min_training_success': 'real',\n",
       " 'max_training_success': 'real',\n",
       " 'avg_recovery': 'real',\n",
       " 'min_recovery': 'real',\n",
       " 'max_recovery': 'real',\n",
       " 'nr_sessions_1': 'int',\n",
       " 'nr_rest_days_1': 'int',\n",
       " 'total_kms_1': 'real',\n",
       " 'max_km_one_day_1': 'real',\n",
       " 'total_km_z3-z4-z5-t1-t2_1': 'real',\n",
       " 'nr_tough_sessions_(effort_in_z5,_t1_or_t2)_1': 'int',\n",
       " 'nr_days_with_interval_session_1': 'int',\n",
       " 'total_km_z3-4_1': 'real',\n",
       " 'max_km_z3-4_one_day_1': 'real',\n",
       " 'total_km_z5-t1-t2_1': 'real',\n",
       " 'max_km_z5-t1-t2_one_day_1': 'real',\n",
       " 'total_hours_alternative_training_1': 'real',\n",
       " 'nr_strength_trainings_1': 'int',\n",
       " 'avg_exertion_1': 'real',\n",
       " 'min_exertion_1': 'real',\n",
       " 'max_exertion_1': 'real',\n",
       " 'avg_training_success_1': 'real',\n",
       " 'min_training_success_1': 'real',\n",
       " 'max_training_success_1': 'real',\n",
       " 'avg_recovery_1': 'real',\n",
       " 'min_recovery_1': 'real',\n",
       " 'max_recovery_1': 'real',\n",
       " 'nr_sessions_2': 'int',\n",
       " 'nr_rest_days_2': 'int',\n",
       " 'total_kms_2': 'real',\n",
       " 'max_km_one_day_2': 'real',\n",
       " 'total_km_z3-z4-z5-t1-t2_2': 'real',\n",
       " 'nr_tough_sessions_(effort_in_z5,_t1_or_t2)_2': 'int',\n",
       " 'nr_days_with_interval_session_2': 'int',\n",
       " 'total_km_z3-4_2': 'real',\n",
       " 'max_km_z3-4_one_day_2': 'real',\n",
       " 'total_km_z5-t1-t2_2': 'real',\n",
       " 'max_km_z5-t1-t2_one_day_2': 'real',\n",
       " 'total_hours_alternative_training_2': 'real',\n",
       " 'nr_strength_trainings_2': 'int',\n",
       " 'avg_exertion_2': 'real',\n",
       " 'min_exertion_2': 'real',\n",
       " 'max_exertion_2': 'real',\n",
       " 'avg_training_success_2': 'real',\n",
       " 'min_training_success_2': 'real',\n",
       " 'max_training_success_2': 'real',\n",
       " 'avg_recovery_2': 'real',\n",
       " 'min_recovery_2': 'real',\n",
       " 'max_recovery_2': 'real',\n",
       " 'injury': 'enum',\n",
       " 'rel_total_kms_week_0_1': 'real',\n",
       " 'rel_total_kms_week_0_2': 'real',\n",
       " 'rel_total_kms_week_1_2': 'real',\n",
       " 'date': 'enum'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_week.types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "17tYnFWfcV9M"
   },
   "source": [
    "We now preprocess the dataset by seperating training and test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "ByKA-DPJdcHG"
   },
   "outputs": [],
   "source": [
    "# converting to pandas dataframe\n",
    "train_week_df = train_week.as_data_frame()\n",
    "test_week_df = test_week.as_data_frame()\n",
    "\n",
    "# noinjury_df_tr = train_week_df[train_week_df['injury'] == 0]\n",
    "# injury_df_tr = train_week_df[train_week_df['injury'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "aaIfZzwecTMK"
   },
   "outputs": [],
   "source": [
    "# drop the Class variable\n",
    "\n",
    "Y_test_df = test_week_df['injury'] # true labels of the testing set\n",
    "Y_test_df_ = pd.DataFrame(Y_test_df) # convert to pandas dataframe from pandas series\n",
    "test_df = test_week_df.drop(['injury'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4376,
     "status": "ok",
     "timestamp": 1661545963462,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "DGbyLdAVfP44",
    "outputId": "24720f8e-f68f-4ea1-c468-ca32e7001253"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"
     ]
    }
   ],
   "source": [
    "# converting to h2o frame\n",
    "Y_test_h2o = h2o.H2OFrame(Y_test_df_) \n",
    "train_h2o = h2o.H2OFrame(train_week_df) \n",
    "test_h2o = h2o.H2OFrame(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "ddpFQeSPgvDW"
   },
   "outputs": [],
   "source": [
    "cols = train_h2o.columns\n",
    "cols.remove('injury')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1661192382378,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "Z78C7aMEClx0",
    "outputId": "876eb936-d137-492d-a7a8-88cd3706c556"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nr_sessions',\n",
       " 'nr_rest_days',\n",
       " 'total_kms',\n",
       " 'max_km_one_day',\n",
       " 'total_km_z3-z4-z5-t1-t2',\n",
       " 'nr_tough_sessions_(effort_in_z5,_t1_or_t2)',\n",
       " 'nr_days_with_interval_session',\n",
       " 'total_km_z3-4',\n",
       " 'max_km_z3-4_one_day',\n",
       " 'total_km_z5-t1-t2',\n",
       " 'max_km_z5-t1-t2_one_day',\n",
       " 'total_hours_alternative_training',\n",
       " 'nr_strength_trainings',\n",
       " 'avg_exertion',\n",
       " 'min_exertion',\n",
       " 'max_exertion',\n",
       " 'avg_training_success',\n",
       " 'min_training_success',\n",
       " 'max_training_success',\n",
       " 'avg_recovery',\n",
       " 'min_recovery',\n",
       " 'max_recovery',\n",
       " 'nr_sessions_1',\n",
       " 'nr_rest_days_1',\n",
       " 'total_kms_1',\n",
       " 'max_km_one_day_1',\n",
       " 'total_km_z3-z4-z5-t1-t2_1',\n",
       " 'nr_tough_sessions_(effort_in_z5,_t1_or_t2)_1',\n",
       " 'nr_days_with_interval_session_1',\n",
       " 'total_km_z3-4_1',\n",
       " 'max_km_z3-4_one_day_1',\n",
       " 'total_km_z5-t1-t2_1',\n",
       " 'max_km_z5-t1-t2_one_day_1',\n",
       " 'total_hours_alternative_training_1',\n",
       " 'nr_strength_trainings_1',\n",
       " 'avg_exertion_1',\n",
       " 'min_exertion_1',\n",
       " 'max_exertion_1',\n",
       " 'avg_training_success_1',\n",
       " 'min_training_success_1',\n",
       " 'max_training_success_1',\n",
       " 'avg_recovery_1',\n",
       " 'min_recovery_1',\n",
       " 'max_recovery_1',\n",
       " 'nr_sessions_2',\n",
       " 'nr_rest_days_2',\n",
       " 'total_kms_2',\n",
       " 'max_km_one_day_2',\n",
       " 'total_km_z3-z4-z5-t1-t2_2',\n",
       " 'nr_tough_sessions_(effort_in_z5,_t1_or_t2)_2',\n",
       " 'nr_days_with_interval_session_2',\n",
       " 'total_km_z3-4_2',\n",
       " 'max_km_z3-4_one_day_2',\n",
       " 'total_km_z5-t1-t2_2',\n",
       " 'max_km_z5-t1-t2_one_day_2',\n",
       " 'total_hours_alternative_training_2',\n",
       " 'nr_strength_trainings_2',\n",
       " 'avg_exertion_2',\n",
       " 'min_exertion_2',\n",
       " 'max_exertion_2',\n",
       " 'avg_training_success_2',\n",
       " 'min_training_success_2',\n",
       " 'max_training_success_2',\n",
       " 'avg_recovery_2',\n",
       " 'min_recovery_2',\n",
       " 'max_recovery_2',\n",
       " 'rel_total_kms_week_0_1',\n",
       " 'rel_total_kms_week_0_2',\n",
       " 'rel_total_kms_week_1_2',\n",
       " 'date']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-uyGkZF6cH4U"
   },
   "source": [
    "We now create a local estimator and start the training process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vIRuF-xaqPta"
   },
   "source": [
    "In the deep learning model below, we included the h2o library for anomaly detection i.e autoencoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  nr_sessions</th><th style=\"text-align: right;\">  nr_rest_days</th><th style=\"text-align: right;\">  total_kms</th><th style=\"text-align: right;\">  max_km_one_day</th><th style=\"text-align: right;\">  total_km_z3-z4-z5-t1-t2</th><th style=\"text-align: right;\">  nr_tough_sessions_(effort_in_z5,_t1_or_t2)</th><th style=\"text-align: right;\">  nr_days_with_interval_session</th><th style=\"text-align: right;\">  total_km_z3-4</th><th style=\"text-align: right;\">  max_km_z3-4_one_day</th><th style=\"text-align: right;\">  total_km_z5-t1-t2</th><th style=\"text-align: right;\">  max_km_z5-t1-t2_one_day</th><th style=\"text-align: right;\">  total_hours_alternative_training</th><th style=\"text-align: right;\">  nr_strength_trainings</th><th style=\"text-align: right;\">  avg_exertion</th><th style=\"text-align: right;\">  min_exertion</th><th style=\"text-align: right;\">  max_exertion</th><th style=\"text-align: right;\">  avg_training_success</th><th style=\"text-align: right;\">  min_training_success</th><th style=\"text-align: right;\">  max_training_success</th><th style=\"text-align: right;\">  avg_recovery</th><th style=\"text-align: right;\">  min_recovery</th><th style=\"text-align: right;\">  max_recovery</th><th style=\"text-align: right;\">  nr_sessions_1</th><th style=\"text-align: right;\">  nr_rest_days_1</th><th style=\"text-align: right;\">  total_kms_1</th><th style=\"text-align: right;\">  max_km_one_day_1</th><th style=\"text-align: right;\">  total_km_z3-z4-z5-t1-t2_1</th><th style=\"text-align: right;\">  nr_tough_sessions_(effort_in_z5,_t1_or_t2)_1</th><th style=\"text-align: right;\">  nr_days_with_interval_session_1</th><th style=\"text-align: right;\">  total_km_z3-4_1</th><th style=\"text-align: right;\">  max_km_z3-4_one_day_1</th><th style=\"text-align: right;\">  total_km_z5-t1-t2_1</th><th style=\"text-align: right;\">  max_km_z5-t1-t2_one_day_1</th><th style=\"text-align: right;\">  total_hours_alternative_training_1</th><th style=\"text-align: right;\">  nr_strength_trainings_1</th><th style=\"text-align: right;\">  avg_exertion_1</th><th style=\"text-align: right;\">  min_exertion_1</th><th style=\"text-align: right;\">  max_exertion_1</th><th style=\"text-align: right;\">  avg_training_success_1</th><th style=\"text-align: right;\">  min_training_success_1</th><th style=\"text-align: right;\">  max_training_success_1</th><th style=\"text-align: right;\">  avg_recovery_1</th><th style=\"text-align: right;\">  min_recovery_1</th><th style=\"text-align: right;\">  max_recovery_1</th><th style=\"text-align: right;\">  nr_sessions_2</th><th style=\"text-align: right;\">  nr_rest_days_2</th><th style=\"text-align: right;\">  total_kms_2</th><th style=\"text-align: right;\">  max_km_one_day_2</th><th style=\"text-align: right;\">  total_km_z3-z4-z5-t1-t2_2</th><th style=\"text-align: right;\">  nr_tough_sessions_(effort_in_z5,_t1_or_t2)_2</th><th style=\"text-align: right;\">  nr_days_with_interval_session_2</th><th style=\"text-align: right;\">  total_km_z3-4_2</th><th style=\"text-align: right;\">  max_km_z3-4_one_day_2</th><th style=\"text-align: right;\">  total_km_z5-t1-t2_2</th><th style=\"text-align: right;\">  max_km_z5-t1-t2_one_day_2</th><th style=\"text-align: right;\">  total_hours_alternative_training_2</th><th style=\"text-align: right;\">  nr_strength_trainings_2</th><th style=\"text-align: right;\">  avg_exertion_2</th><th style=\"text-align: right;\">  min_exertion_2</th><th style=\"text-align: right;\">  max_exertion_2</th><th style=\"text-align: right;\">  avg_training_success_2</th><th style=\"text-align: right;\">  min_training_success_2</th><th style=\"text-align: right;\">  max_training_success_2</th><th style=\"text-align: right;\">  avg_recovery_2</th><th style=\"text-align: right;\">  min_recovery_2</th><th style=\"text-align: right;\">  max_recovery_2</th><th style=\"text-align: right;\">  injury</th><th style=\"text-align: right;\">  rel_total_kms_week_0_1</th><th style=\"text-align: right;\">  rel_total_kms_week_0_2</th><th style=\"text-align: right;\">  rel_total_kms_week_1_2</th><th style=\"text-align: right;\">  date</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">            5</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">       22.2</td><td style=\"text-align: right;\">            16.4</td><td style=\"text-align: right;\">                     11.8</td><td style=\"text-align: right;\">                                           1</td><td style=\"text-align: right;\">                              2</td><td style=\"text-align: right;\">           10  </td><td style=\"text-align: right;\">                 10  </td><td style=\"text-align: right;\">                0.6</td><td style=\"text-align: right;\">                      0.6</td><td style=\"text-align: right;\">                              2.08</td><td style=\"text-align: right;\">                      2</td><td style=\"text-align: right;\">           0.1</td><td style=\"text-align: right;\">          0.08</td><td style=\"text-align: right;\">          0.11</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.15</td><td style=\"text-align: right;\">          0.18</td><td style=\"text-align: right;\">              6</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">         30.9</td><td style=\"text-align: right;\">              13.8</td><td style=\"text-align: right;\">                       13.5</td><td style=\"text-align: right;\">                                             0</td><td style=\"text-align: right;\">                                2</td><td style=\"text-align: right;\">             12.4</td><td style=\"text-align: right;\">                    6.4</td><td style=\"text-align: right;\">                  0  </td><td style=\"text-align: right;\">                        0  </td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.12</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">              3</td><td style=\"text-align: right;\">               4</td><td style=\"text-align: right;\">         16.1</td><td style=\"text-align: right;\">               9.9</td><td style=\"text-align: right;\">                        3.9</td><td style=\"text-align: right;\">                                             0</td><td style=\"text-align: right;\">                                1</td><td style=\"text-align: right;\">              3.2</td><td style=\"text-align: right;\">                    3.2</td><td style=\"text-align: right;\">                  0  </td><td style=\"text-align: right;\">                        0  </td><td style=\"text-align: right;\">                                0   </td><td style=\"text-align: right;\">                        1</td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.08</td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.2 </td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">                0.718447</td><td style=\"text-align: right;\">                 1.37888</td><td style=\"text-align: right;\">                1.91925 </td><td style=\"text-align: right;\">     0</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            5</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">       21.6</td><td style=\"text-align: right;\">            16.4</td><td style=\"text-align: right;\">                     11.7</td><td style=\"text-align: right;\">                                           1</td><td style=\"text-align: right;\">                              2</td><td style=\"text-align: right;\">           10  </td><td style=\"text-align: right;\">                 10  </td><td style=\"text-align: right;\">                0.5</td><td style=\"text-align: right;\">                      0.5</td><td style=\"text-align: right;\">                              2.08</td><td style=\"text-align: right;\">                      2</td><td style=\"text-align: right;\">           0.1</td><td style=\"text-align: right;\">          0.08</td><td style=\"text-align: right;\">          0.11</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.15</td><td style=\"text-align: right;\">          0.18</td><td style=\"text-align: right;\">              6</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">         31.6</td><td style=\"text-align: right;\">              13.8</td><td style=\"text-align: right;\">                       14.2</td><td style=\"text-align: right;\">                                             1</td><td style=\"text-align: right;\">                                3</td><td style=\"text-align: right;\">             12.4</td><td style=\"text-align: right;\">                    6.4</td><td style=\"text-align: right;\">                  0.6</td><td style=\"text-align: right;\">                        0.6</td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.12</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">              4</td><td style=\"text-align: right;\">               3</td><td style=\"text-align: right;\">         21.2</td><td style=\"text-align: right;\">               9.9</td><td style=\"text-align: right;\">                        5  </td><td style=\"text-align: right;\">                                             0</td><td style=\"text-align: right;\">                                1</td><td style=\"text-align: right;\">              3.2</td><td style=\"text-align: right;\">                    3.2</td><td style=\"text-align: right;\">                  0  </td><td style=\"text-align: right;\">                        0  </td><td style=\"text-align: right;\">                                0   </td><td style=\"text-align: right;\">                        1</td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">            0.08</td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.2 </td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">                0.683544</td><td style=\"text-align: right;\">                 1.01887</td><td style=\"text-align: right;\">                1.49057 </td><td style=\"text-align: right;\">     1</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            5</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">       21.6</td><td style=\"text-align: right;\">            16.4</td><td style=\"text-align: right;\">                     11.7</td><td style=\"text-align: right;\">                                           1</td><td style=\"text-align: right;\">                              2</td><td style=\"text-align: right;\">           10  </td><td style=\"text-align: right;\">                 10  </td><td style=\"text-align: right;\">                0.5</td><td style=\"text-align: right;\">                      0.5</td><td style=\"text-align: right;\">                              2.08</td><td style=\"text-align: right;\">                      2</td><td style=\"text-align: right;\">           0.1</td><td style=\"text-align: right;\">          0.08</td><td style=\"text-align: right;\">          0.11</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.15</td><td style=\"text-align: right;\">          0.18</td><td style=\"text-align: right;\">              6</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">         31.6</td><td style=\"text-align: right;\">              13.8</td><td style=\"text-align: right;\">                       14.2</td><td style=\"text-align: right;\">                                             1</td><td style=\"text-align: right;\">                                3</td><td style=\"text-align: right;\">             12.4</td><td style=\"text-align: right;\">                    6.4</td><td style=\"text-align: right;\">                  0.6</td><td style=\"text-align: right;\">                        0.6</td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.12</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">              3</td><td style=\"text-align: right;\">               4</td><td style=\"text-align: right;\">         21.2</td><td style=\"text-align: right;\">               9.9</td><td style=\"text-align: right;\">                        5  </td><td style=\"text-align: right;\">                                             0</td><td style=\"text-align: right;\">                                1</td><td style=\"text-align: right;\">              3.2</td><td style=\"text-align: right;\">                    3.2</td><td style=\"text-align: right;\">                  0  </td><td style=\"text-align: right;\">                        0  </td><td style=\"text-align: right;\">                                0   </td><td style=\"text-align: right;\">                        0</td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">                0.683544</td><td style=\"text-align: right;\">                 1.01887</td><td style=\"text-align: right;\">                1.49057 </td><td style=\"text-align: right;\">     2</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            5</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">       21.6</td><td style=\"text-align: right;\">            16.4</td><td style=\"text-align: right;\">                     11.7</td><td style=\"text-align: right;\">                                           1</td><td style=\"text-align: right;\">                              2</td><td style=\"text-align: right;\">           10  </td><td style=\"text-align: right;\">                 10  </td><td style=\"text-align: right;\">                0.5</td><td style=\"text-align: right;\">                      0.5</td><td style=\"text-align: right;\">                              2.08</td><td style=\"text-align: right;\">                      2</td><td style=\"text-align: right;\">           0.1</td><td style=\"text-align: right;\">          0.08</td><td style=\"text-align: right;\">          0.11</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.15</td><td style=\"text-align: right;\">          0.18</td><td style=\"text-align: right;\">              6</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">         31.6</td><td style=\"text-align: right;\">              13.8</td><td style=\"text-align: right;\">                       14.2</td><td style=\"text-align: right;\">                                             1</td><td style=\"text-align: right;\">                                3</td><td style=\"text-align: right;\">             12.4</td><td style=\"text-align: right;\">                    6.4</td><td style=\"text-align: right;\">                  0.6</td><td style=\"text-align: right;\">                        0.6</td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.11</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">              4</td><td style=\"text-align: right;\">               3</td><td style=\"text-align: right;\">         21.2</td><td style=\"text-align: right;\">               9.9</td><td style=\"text-align: right;\">                        5  </td><td style=\"text-align: right;\">                                             0</td><td style=\"text-align: right;\">                                1</td><td style=\"text-align: right;\">              3.2</td><td style=\"text-align: right;\">                    3.2</td><td style=\"text-align: right;\">                  0  </td><td style=\"text-align: right;\">                        0  </td><td style=\"text-align: right;\">                                0   </td><td style=\"text-align: right;\">                        1</td><td style=\"text-align: right;\">            0.11</td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">            0.12</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">                0.683544</td><td style=\"text-align: right;\">                 1.01887</td><td style=\"text-align: right;\">                1.49057 </td><td style=\"text-align: right;\">     3</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            6</td><td style=\"text-align: right;\">             1</td><td style=\"text-align: right;\">       39.2</td><td style=\"text-align: right;\">            17.6</td><td style=\"text-align: right;\">                     18.9</td><td style=\"text-align: right;\">                                           1</td><td style=\"text-align: right;\">                              3</td><td style=\"text-align: right;\">           17.2</td><td style=\"text-align: right;\">                 10  </td><td style=\"text-align: right;\">                0.5</td><td style=\"text-align: right;\">                      0.5</td><td style=\"text-align: right;\">                              2.08</td><td style=\"text-align: right;\">                      2</td><td style=\"text-align: right;\">           0.1</td><td style=\"text-align: right;\">          0.08</td><td style=\"text-align: right;\">          0.11</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.15</td><td style=\"text-align: right;\">          0.18</td><td style=\"text-align: right;\">              5</td><td style=\"text-align: right;\">               2</td><td style=\"text-align: right;\">         17.8</td><td style=\"text-align: right;\">              12  </td><td style=\"text-align: right;\">                        7.8</td><td style=\"text-align: right;\">                                             1</td><td style=\"text-align: right;\">                                2</td><td style=\"text-align: right;\">              6  </td><td style=\"text-align: right;\">                    6  </td><td style=\"text-align: right;\">                  0.6</td><td style=\"text-align: right;\">                        0.6</td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.11</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">              4</td><td style=\"text-align: right;\">               3</td><td style=\"text-align: right;\">         28.8</td><td style=\"text-align: right;\">              13.8</td><td style=\"text-align: right;\">                       10.7</td><td style=\"text-align: right;\">                                             0</td><td style=\"text-align: right;\">                                2</td><td style=\"text-align: right;\">              9.6</td><td style=\"text-align: right;\">                    6.4</td><td style=\"text-align: right;\">                  0  </td><td style=\"text-align: right;\">                        0  </td><td style=\"text-align: right;\">                                0   </td><td style=\"text-align: right;\">                        1</td><td style=\"text-align: right;\">            0.11</td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">            0.12</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">                2.20225 </td><td style=\"text-align: right;\">                 1.36111</td><td style=\"text-align: right;\">                0.618056</td><td style=\"text-align: right;\">     4</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            6</td><td style=\"text-align: right;\">             1</td><td style=\"text-align: right;\">       39.2</td><td style=\"text-align: right;\">            17.6</td><td style=\"text-align: right;\">                     18.9</td><td style=\"text-align: right;\">                                           1</td><td style=\"text-align: right;\">                              3</td><td style=\"text-align: right;\">           17.2</td><td style=\"text-align: right;\">                 10  </td><td style=\"text-align: right;\">                0.5</td><td style=\"text-align: right;\">                      0.5</td><td style=\"text-align: right;\">                              2   </td><td style=\"text-align: right;\">                      2</td><td style=\"text-align: right;\">           0.1</td><td style=\"text-align: right;\">          0.08</td><td style=\"text-align: right;\">          0.11</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.15</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">              5</td><td style=\"text-align: right;\">               2</td><td style=\"text-align: right;\">         17.8</td><td style=\"text-align: right;\">              12  </td><td style=\"text-align: right;\">                        7.8</td><td style=\"text-align: right;\">                                             1</td><td style=\"text-align: right;\">                                2</td><td style=\"text-align: right;\">              6  </td><td style=\"text-align: right;\">                    6  </td><td style=\"text-align: right;\">                  0.6</td><td style=\"text-align: right;\">                        0.6</td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">            0.08</td><td style=\"text-align: right;\">            0.11</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">              5</td><td style=\"text-align: right;\">               2</td><td style=\"text-align: right;\">         28.8</td><td style=\"text-align: right;\">              13.8</td><td style=\"text-align: right;\">                       10.7</td><td style=\"text-align: right;\">                                             0</td><td style=\"text-align: right;\">                                2</td><td style=\"text-align: right;\">              9.6</td><td style=\"text-align: right;\">                    6.4</td><td style=\"text-align: right;\">                  0  </td><td style=\"text-align: right;\">                        0  </td><td style=\"text-align: right;\">                                1.08</td><td style=\"text-align: right;\">                        1</td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.12</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">                2.20225 </td><td style=\"text-align: right;\">                 1.36111</td><td style=\"text-align: right;\">                0.618056</td><td style=\"text-align: right;\">     5</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            5</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">       33.3</td><td style=\"text-align: right;\">            17.6</td><td style=\"text-align: right;\">                     15.4</td><td style=\"text-align: right;\">                                           1</td><td style=\"text-align: right;\">                              3</td><td style=\"text-align: right;\">           13.7</td><td style=\"text-align: right;\">                  7.2</td><td style=\"text-align: right;\">                0.5</td><td style=\"text-align: right;\">                      0.5</td><td style=\"text-align: right;\">                              1   </td><td style=\"text-align: right;\">                      1</td><td style=\"text-align: right;\">           0.1</td><td style=\"text-align: right;\">          0.08</td><td style=\"text-align: right;\">          0.11</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.18</td><td style=\"text-align: right;\">              5</td><td style=\"text-align: right;\">               2</td><td style=\"text-align: right;\">         22.2</td><td style=\"text-align: right;\">              16.4</td><td style=\"text-align: right;\">                       11.8</td><td style=\"text-align: right;\">                                             1</td><td style=\"text-align: right;\">                                2</td><td style=\"text-align: right;\">             10  </td><td style=\"text-align: right;\">                   10  </td><td style=\"text-align: right;\">                  0.6</td><td style=\"text-align: right;\">                        0.6</td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">            0.08</td><td style=\"text-align: right;\">            0.11</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.15</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">              6</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">         30.9</td><td style=\"text-align: right;\">              13.8</td><td style=\"text-align: right;\">                       13.5</td><td style=\"text-align: right;\">                                             0</td><td style=\"text-align: right;\">                                2</td><td style=\"text-align: right;\">             12.4</td><td style=\"text-align: right;\">                    6.4</td><td style=\"text-align: right;\">                  0  </td><td style=\"text-align: right;\">                        0  </td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.12</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">                1.5     </td><td style=\"text-align: right;\">                 1.07767</td><td style=\"text-align: right;\">                0.718447</td><td style=\"text-align: right;\">     7</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            5</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">       32.9</td><td style=\"text-align: right;\">            17.6</td><td style=\"text-align: right;\">                     15.5</td><td style=\"text-align: right;\">                                           0</td><td style=\"text-align: right;\">                              3</td><td style=\"text-align: right;\">           14.3</td><td style=\"text-align: right;\">                  7.2</td><td style=\"text-align: right;\">                0  </td><td style=\"text-align: right;\">                      0  </td><td style=\"text-align: right;\">                              1   </td><td style=\"text-align: right;\">                      1</td><td style=\"text-align: right;\">           0.1</td><td style=\"text-align: right;\">          0.08</td><td style=\"text-align: right;\">          0.11</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.18</td><td style=\"text-align: right;\">              5</td><td style=\"text-align: right;\">               2</td><td style=\"text-align: right;\">         21.6</td><td style=\"text-align: right;\">              16.4</td><td style=\"text-align: right;\">                       11.7</td><td style=\"text-align: right;\">                                             1</td><td style=\"text-align: right;\">                                2</td><td style=\"text-align: right;\">             10  </td><td style=\"text-align: right;\">                   10  </td><td style=\"text-align: right;\">                  0.5</td><td style=\"text-align: right;\">                        0.5</td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">            0.08</td><td style=\"text-align: right;\">            0.11</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.15</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">              6</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">         31.6</td><td style=\"text-align: right;\">              13.8</td><td style=\"text-align: right;\">                       14.2</td><td style=\"text-align: right;\">                                             1</td><td style=\"text-align: right;\">                                3</td><td style=\"text-align: right;\">             12.4</td><td style=\"text-align: right;\">                    6.4</td><td style=\"text-align: right;\">                  0.6</td><td style=\"text-align: right;\">                        0.6</td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.12</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">                1.52315 </td><td style=\"text-align: right;\">                 1.04114</td><td style=\"text-align: right;\">                0.683544</td><td style=\"text-align: right;\">     8</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            5</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">       32.9</td><td style=\"text-align: right;\">            17.6</td><td style=\"text-align: right;\">                     15.5</td><td style=\"text-align: right;\">                                           0</td><td style=\"text-align: right;\">                              3</td><td style=\"text-align: right;\">           14.3</td><td style=\"text-align: right;\">                  7.2</td><td style=\"text-align: right;\">                0  </td><td style=\"text-align: right;\">                      0  </td><td style=\"text-align: right;\">                              1   </td><td style=\"text-align: right;\">                      1</td><td style=\"text-align: right;\">           0.1</td><td style=\"text-align: right;\">          0.08</td><td style=\"text-align: right;\">          0.11</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.18</td><td style=\"text-align: right;\">              5</td><td style=\"text-align: right;\">               2</td><td style=\"text-align: right;\">         21.6</td><td style=\"text-align: right;\">              16.4</td><td style=\"text-align: right;\">                       11.7</td><td style=\"text-align: right;\">                                             1</td><td style=\"text-align: right;\">                                2</td><td style=\"text-align: right;\">             10  </td><td style=\"text-align: right;\">                   10  </td><td style=\"text-align: right;\">                  0.5</td><td style=\"text-align: right;\">                        0.5</td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">            0.08</td><td style=\"text-align: right;\">            0.11</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.15</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">              6</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">         31.6</td><td style=\"text-align: right;\">              13.8</td><td style=\"text-align: right;\">                       14.2</td><td style=\"text-align: right;\">                                             1</td><td style=\"text-align: right;\">                                3</td><td style=\"text-align: right;\">             12.4</td><td style=\"text-align: right;\">                    6.4</td><td style=\"text-align: right;\">                  0.6</td><td style=\"text-align: right;\">                        0.6</td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.12</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">                1.52315 </td><td style=\"text-align: right;\">                 1.04114</td><td style=\"text-align: right;\">                0.683544</td><td style=\"text-align: right;\">     9</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            5</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">       32.9</td><td style=\"text-align: right;\">            17.6</td><td style=\"text-align: right;\">                     15.5</td><td style=\"text-align: right;\">                                           0</td><td style=\"text-align: right;\">                              3</td><td style=\"text-align: right;\">           14.3</td><td style=\"text-align: right;\">                  7.2</td><td style=\"text-align: right;\">                0  </td><td style=\"text-align: right;\">                      0  </td><td style=\"text-align: right;\">                              1   </td><td style=\"text-align: right;\">                      1</td><td style=\"text-align: right;\">           0.1</td><td style=\"text-align: right;\">          0.08</td><td style=\"text-align: right;\">          0.11</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.17</td><td style=\"text-align: right;\">          0.18</td><td style=\"text-align: right;\">              5</td><td style=\"text-align: right;\">               2</td><td style=\"text-align: right;\">         21.6</td><td style=\"text-align: right;\">              16.4</td><td style=\"text-align: right;\">                       11.7</td><td style=\"text-align: right;\">                                             1</td><td style=\"text-align: right;\">                                2</td><td style=\"text-align: right;\">             10  </td><td style=\"text-align: right;\">                   10  </td><td style=\"text-align: right;\">                  0.5</td><td style=\"text-align: right;\">                        0.5</td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">             0.1</td><td style=\"text-align: right;\">            0.08</td><td style=\"text-align: right;\">            0.11</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.15</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">              6</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">         31.6</td><td style=\"text-align: right;\">              13.8</td><td style=\"text-align: right;\">                       14.2</td><td style=\"text-align: right;\">                                             1</td><td style=\"text-align: right;\">                                3</td><td style=\"text-align: right;\">             12.4</td><td style=\"text-align: right;\">                    6.4</td><td style=\"text-align: right;\">                  0.6</td><td style=\"text-align: right;\">                        0.6</td><td style=\"text-align: right;\">                                2.08</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">            0.1 </td><td style=\"text-align: right;\">            0.09</td><td style=\"text-align: right;\">            0.11</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">            0.17</td><td style=\"text-align: right;\">            0.16</td><td style=\"text-align: right;\">            0.18</td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">                1.52315 </td><td style=\"text-align: right;\">                 1.04114</td><td style=\"text-align: right;\">                0.683544</td><td style=\"text-align: right;\">    10</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_h2o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "EZvOc8B2i-bH"
   },
   "outputs": [],
   "source": [
    "# our model with its hyper parameters\n",
    "\n",
    "deepl_model = H2ODeepLearningEstimator(activation = \"Tanh\",\n",
    "                               hidden = [15,7,7,7,15],\n",
    "                               epochs = 100,\n",
    "                               standardize = False,\n",
    "                                stopping_metric = 'MSE',\n",
    "                                loss = 'automatic',\n",
    "                                train_samples_per_iteration = 32,\n",
    "                                shuffle_training_data = True,     \n",
    "                               autoencoder = True,\n",
    "                               l1 = 10e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2972793,
     "status": "ok",
     "timestamp": 1661549483129,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "ysiog36JjAY3",
    "outputId": "e91cd1c6-966f-4036-e71c-800eba6cda44"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n",
      "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n",
      "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n",
      "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n",
      "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n",
      "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n",
      "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n",
      "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n",
      "1min 1s ± 8.43 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit deepl_model.train(x = cols , y = 'injury', training_frame = train_h2o) # this takes a while at least an hour "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 473,
     "status": "ok",
     "timestamp": 1661549953220,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "5Ken-3NPo4Dq",
    "outputId": "64826733-332a-43f4-e562-b57020411837"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ModelMetricsAutoEncoder: deeplearning\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 1232303716189.851\n",
      "RMSE: 1110091.7602567147\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate performance:\n",
    "\n",
    "perf = deepl_model.model_performance()\n",
    "perf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ic2BV2d-qlE2"
   },
   "source": [
    "The MSE & RMSE helps us to understand how far off our prediction is on the training data as opposed to the actual results. The small number above which almost equals zero tells us that our model performs well on the training set.\n",
    "\n",
    "We also note that $RMSE = \\sqrt{MSE}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cxuafel6kRAt"
   },
   "source": [
    "Show the model feature importance in a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 464,
     "status": "ok",
     "timestamp": 1661549956349,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "oA54wpE9jJDG",
    "outputId": "9abef579-3d10-475e-e3a6-2237898c04ba"
   },
   "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>variable</th>\n",
       "      <th>relative_importance</th>\n",
       "      <th>scaled_importance</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>injury</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nr_tough_sessions_(effort_in_z5,_t1_or_t2)_1</td>\n",
       "      <td>0.191991</td>\n",
       "      <td>0.191991</td>\n",
       "      <td>0.017166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>min_training_success_2</td>\n",
       "      <td>0.188565</td>\n",
       "      <td>0.188565</td>\n",
       "      <td>0.016859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nr_rest_days_2</td>\n",
       "      <td>0.186311</td>\n",
       "      <td>0.186311</td>\n",
       "      <td>0.016658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>total_hours_alternative_training</td>\n",
       "      <td>0.181459</td>\n",
       "      <td>0.181459</td>\n",
       "      <td>0.016224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>min_training_success_1</td>\n",
       "      <td>0.103075</td>\n",
       "      <td>0.103075</td>\n",
       "      <td>0.009216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>date</td>\n",
       "      <td>0.102763</td>\n",
       "      <td>0.102763</td>\n",
       "      <td>0.009188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>total_km_z5-t1-t2</td>\n",
       "      <td>0.101149</td>\n",
       "      <td>0.101149</td>\n",
       "      <td>0.009044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>total_hours_alternative_training_1</td>\n",
       "      <td>0.097870</td>\n",
       "      <td>0.097870</td>\n",
       "      <td>0.008750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>total_km_z3-z4-z5-t1-t2</td>\n",
       "      <td>0.096745</td>\n",
       "      <td>0.096745</td>\n",
       "      <td>0.008650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        variable  relative_importance  \\\n",
       "0                                         injury             1.000000   \n",
       "1   nr_tough_sessions_(effort_in_z5,_t1_or_t2)_1             0.191991   \n",
       "2                         min_training_success_2             0.188565   \n",
       "3                                 nr_rest_days_2             0.186311   \n",
       "4               total_hours_alternative_training             0.181459   \n",
       "..                                           ...                  ...   \n",
       "66                        min_training_success_1             0.103075   \n",
       "67                                          date             0.102763   \n",
       "68                             total_km_z5-t1-t2             0.101149   \n",
       "69            total_hours_alternative_training_1             0.097870   \n",
       "70                       total_km_z3-z4-z5-t1-t2             0.096745   \n",
       "\n",
       "    scaled_importance  percentage  \n",
       "0            1.000000    0.089409  \n",
       "1            0.191991    0.017166  \n",
       "2            0.188565    0.016859  \n",
       "3            0.186311    0.016658  \n",
       "4            0.181459    0.016224  \n",
       "..                ...         ...  \n",
       "66           0.103075    0.009216  \n",
       "67           0.102763    0.009188  \n",
       "68           0.101149    0.009044  \n",
       "69           0.097870    0.008750  \n",
       "70           0.096745    0.008650  \n",
       "\n",
       "[71 rows x 4 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deepl_model._model_json['output']['variable_importances'].as_data_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0ctJxD89kgAw"
   },
   "source": [
    "Visualization of the feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513
    },
    "executionInfo": {
     "elapsed": 851,
     "status": "ok",
     "timestamp": 1661549965239,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "73vZUHmmkfhI",
    "outputId": "1f6f7393-0d14-49a9-b3f9-29cda6aba5a5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the variable importance\n",
    "rcParams['figure.figsize'] = 14, 8\n",
    "#plt.rcdefaults()\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "variables = deepl_model._model_json['output']['variable_importances']['variable']\n",
    "var = variables[0:10]\n",
    "y_pos = np.arange(len(var))\n",
    "\n",
    "scaled_importance = deepl_model._model_json['output']['variable_importances']['scaled_importance']\n",
    "sc = scaled_importance[0:10]\n",
    "\n",
    "ax.barh(y_pos, sc, align='center', color='green', ecolor='black')\n",
    "ax.set_yticks(y_pos)\n",
    "# ax.set_yticklabels(variables)\n",
    "ax.set_yticklabels(var)\n",
    "ax.invert_yaxis()\n",
    "ax.set_xlabel('Scaled Importance')\n",
    "ax.set_title('Variable Importance of Top 10 Features ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ir90Ed2_krem"
   },
   "source": [
    "Visualize the model loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 531
    },
    "executionInfo": {
     "elapsed": 558,
     "status": "ok",
     "timestamp": 1661549977060,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "G7ZoE668kfdh",
    "outputId": "186e2a3e-d233-488b-a088-02f8ca814870",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'epoch')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the loss\n",
    "\n",
    "scoring_history = deepl_model.score_history()\n",
    "\n",
    "rcParams['figure.figsize'] = 14, 8\n",
    "plt.plot(scoring_history['training_mse'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2SFWIkEUkyrQ"
   },
   "source": [
    "Visualize the scatter plot across the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "executionInfo": {
     "elapsed": 1284,
     "status": "ok",
     "timestamp": 1661550047053,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "BQFsmBYqkedk",
    "outputId": "45a6bc5f-7683-40d8-f615-3d58497b7a7b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_rec_error = deepl_model.anomaly(test_h2o) \n",
    "\n",
    "# deepl is a H2O function which calculates the error for the dataset\n",
    "\n",
    "# converting to pandas dataframe\n",
    "test_rec_error_df = test_rec_error.as_data_frame()\n",
    "\n",
    "# plotting the testing dataset against the error\n",
    "test_rec_error_df['id'] = test_rec_error_df.index\n",
    "rcParams['figure.figsize'] = 14, 8\n",
    "test_rec_error_df.plot(kind=\"scatter\", x='id', y=\"Reconstruction.MSE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 162
    },
    "executionInfo": {
     "elapsed": 923,
     "status": "ok",
     "timestamp": 1661550105410,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "bh5vqpwrk-6f",
    "outputId": "025423f6-730d-45cb-f405-43343b980dee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning prediction progress: |██████████████████████████████████████████████| (done) 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reconstruction_error</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>true_class</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            reconstruction_error\n",
       "true_class                      \n",
       "0                           8441\n",
       "1                            121"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predicting the class for the testing dataset\n",
    "predictions = deepl_model.predict(test_h2o)\n",
    "error_df = pd.DataFrame({'reconstruction_error': test_rec_error_df['Reconstruction.MSE'],\n",
    "                        'true_class': Y_test_df})\n",
    "error_df.groupby('true_class').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1w7rTflOmqaR"
   },
   "source": [
    "Calculate the threshold score/ cutoff score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1053,
     "status": "ok",
     "timestamp": 1661550124839,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "Eh8uvN88lJCG",
    "outputId": "7e0c71d8-88a3-4e6c-cf73-14ed3e3d65db"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61742376521536.88"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_mean = error_df['reconstruction_error'].mean()\n",
    "score_std = error_df['reconstruction_error'].std()\n",
    "score_cutoff = score_mean + 4*score_std\n",
    "score_cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 923,
     "status": "ok",
     "timestamp": 1661550129468,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "QXGWE7GEmuZV",
    "outputId": "52ab71a1-a420-40b3-df20-85a100072836"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reconstruction_error    121\n",
       "true_class              121\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_df[(error_df['true_class'] == 1) & error_df['reconstruction_error'] > 0.0095].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R3ostMdJm446"
   },
   "source": [
    "Print out the confusion matrix and adjust and tune the confusion matrix from the threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "ysr4PhagVgjL"
   },
   "outputs": [],
   "source": [
    "# len(y_pred), len(error_df.true_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49
    },
    "executionInfo": {
     "elapsed": 420,
     "status": "ok",
     "timestamp": 1661550139734,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "Qv9BWEtgXSA4",
    "outputId": "212b4761-b96d-4326-f3f7-bea7151125ab"
   },
   "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>reconstruction_error</th>\n",
       "      <th>true_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [reconstruction_error, true_class]\n",
       "Index: []"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_df[error_df['true_class'] == 'NaN' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 730
    },
    "executionInfo": {
     "elapsed": 957,
     "status": "ok",
     "timestamp": 1661550570050,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "9zhQqaJ5mw6h",
    "outputId": "33383ec3-8f68-49da-f984-4625be177fd9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold = 85000 # we can equally choose to use different values for our threshold [0.009]\n",
    "LABELS = ['Injury', 'No Injury']\n",
    "y_pred = [1 if e > threshold else 0 for e in error_df.reconstruction_error.values]\n",
    "conf_matrix = confusion_matrix(error_df.true_class, y_pred)\n",
    "plt.figure(figsize=(12, 12))\n",
    "sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E7WigCeargv-"
   },
   "source": [
    "The confusion matrix above helps us to understand our accuracy on the test data. \n",
    "\n",
    "We note that:\n",
    "- 6963: The model predicts this case to be actually injury where the true case is injury too.\n",
    "- 24: The model predicts no-injury with the true case being no injury.\n",
    "- 96: The model predicts injury where the actual class in no-injury\n",
    "- 1442: The model predicts no-injury where the actual class is injury.\n",
    "\n",
    "The above explanation is given in the classification report below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 604,
     "status": "ok",
     "timestamp": 1661550572900,
     "user": {
      "displayName": "Olusegun Ajibola",
      "userId": "14016280963587867119"
     },
     "user_tz": -120
    },
    "id": "1r58H_V9HjyY",
    "outputId": "de25a40b-0a02-41d3-f297-c44167817cd1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.88      0.93      8441\n",
      "           1       0.01      0.12      0.03       121\n",
      "\n",
      "    accuracy                           0.87      8562\n",
      "   macro avg       0.50      0.50      0.48      8562\n",
      "weighted avg       0.97      0.87      0.92      8562\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(error_df.true_class, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02617801047120419"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(error_df.true_class, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.869656622284513"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(error_df.true_class, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13034337771548704"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(error_df.true_class, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LHDzPS1PAyAy"
   },
   "source": [
    "# Conclusion I\n",
    "\n",
    "- All models i.e Autoenconders, Isolation Forest and One Class SVM have a high accuracy.  \n",
    "- Isolation Forest has the best f1 score and we conclude it to be our best model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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