3_timeseries_feature_extraction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.util import ts_preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tsfresh import extract_features, select_features, extract_relevant_features\n",
    "from tsfresh.utilities.dataframe_functions import impute\n",
    "from tsfresh.feature_extraction.settings import MinimalFCParameters, ComprehensiveFCParameters\n",
    "from tsfresh.transformers import RelevantFeatureAugmenter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "stu_vle = pd.read_csv('data/studentVle.csv')\n",
    "stu_reg = pd.read_csv('data/studentRegistration.csv')\n",
    "stu_info = pd.read_csv('data/studentInfo.csv')\n",
    "ass = pd.read_csv('data/assessments.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "module = 'EEE'\n",
    "pres = '2014J'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "//anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n",
      "//anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "stvl_ = stu_vle[stu_vle.code_module.isin([module])][stu_vle.code_presentation.isin([pres])]\n",
    "stre_ = stu_reg[stu_reg.code_module.isin([module])][stu_reg.code_presentation.isin([pres])]\n",
    "stin_ = stu_info[stu_info.code_module.isin([module])][stu_info.code_presentation.isin([pres])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "ass_ = ass[ass.code_module.isin([module])][ass.code_presentation.isin([pres])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This is a demonstration of how to use ts-fresh for time series feature extraction and/or selection ONLY. (separate from pipeline) To be used in the future with more feature curating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>code_module</th>\n",
       "      <th>code_presentation</th>\n",
       "      <th>id_assessment</th>\n",
       "      <th>assessment_type</th>\n",
       "      <th>date</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>EEE</td>\n",
       "      <td>2014J</td>\n",
       "      <td>30719</td>\n",
       "      <td>TMA</td>\n",
       "      <td>33.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>EEE</td>\n",
       "      <td>2014J</td>\n",
       "      <td>30720</td>\n",
       "      <td>TMA</td>\n",
       "      <td>68.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>EEE</td>\n",
       "      <td>2014J</td>\n",
       "      <td>30721</td>\n",
       "      <td>TMA</td>\n",
       "      <td>131.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>EEE</td>\n",
       "      <td>2014J</td>\n",
       "      <td>30722</td>\n",
       "      <td>TMA</td>\n",
       "      <td>166.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>EEE</td>\n",
       "      <td>2014J</td>\n",
       "      <td>30723</td>\n",
       "      <td>Exam</td>\n",
       "      <td>235.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    code_module code_presentation  id_assessment assessment_type   date  \\\n",
       "119         EEE             2014J          30719             TMA   33.0   \n",
       "120         EEE             2014J          30720             TMA   68.0   \n",
       "121         EEE             2014J          30721             TMA  131.0   \n",
       "122         EEE             2014J          30722             TMA  166.0   \n",
       "123         EEE             2014J          30723            Exam  235.0   \n",
       "\n",
       "     weight  \n",
       "119    16.0  \n",
       "120    28.0  \n",
       "121    28.0  \n",
       "122    28.0  \n",
       "123   100.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ass_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test, df_train, df_test = ts_preprocess(68, stre_, stin_, stvl_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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>id_student</th>\n",
       "      <th>date</th>\n",
       "      <th>sum_click</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60809</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60809</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60809</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60809</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60809</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_student  date  sum_click\n",
       "0       60809     0         10\n",
       "1       60809     3          4\n",
       "2       60809     4          1\n",
       "3       60809     7          1\n",
       "4       60809     8          1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ppl = Pipeline([('fresh', RelevantFeatureAugmenter(column_id='id_student', column_sort='date', \n",
    "                                                   default_fc_parameters=ComprehensiveFCParameters())),\n",
    "                ('clf', RandomForestClassifier(n_estimators = 200,\n",
    "                                max_depth=5,\n",
    "                                random_state = 21))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|██████████| 20/20 [00:31<00:00,  1.15s/it]\n",
      "WARNING:tsfresh.feature_selection.relevance:Infered classification as machine learning task\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.2 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.2 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.2 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.6__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.8__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.2 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__fft_coefficient__coeff_0__attr_\"angle\" is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.6__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__fft_coefficient__coeff_0__attr_\"imag\" is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.8__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.2__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.8__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__number_crossing_m__m_-1 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__large_standard_deviation__r_0.5 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__number_crossing_m__m_0 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__large_standard_deviation__r_0.55 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__large_standard_deviation__r_0.6000000000000001 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__symmetry_looking__r_0.0 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__partial_autocorrelation__lag_0 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__large_standard_deviation__r_0.65 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__large_standard_deviation__r_0.7000000000000001 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__large_standard_deviation__r_0.75 is constant\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__value_count__value_-1 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__value_count__value_0 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__range_count__max_0__min_1000000000000.0 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__large_standard_deviation__r_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__large_standard_deviation__r_0.8500000000000001 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__range_count__max_1__min_-1 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__large_standard_deviation__r_0.9 is constant\n",
      "WARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature sum_click__large_standard_deviation__r_0.9500000000000001 is constant\n",
      "Feature Extraction: 100%|██████████| 20/20 [00:14<00:00,  1.36it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('fresh',\n",
       "                 RelevantFeatureAugmenter(chunksize=None,\n",
       "                                          column_id='id_student',\n",
       "                                          column_kind=None, column_sort='date',\n",
       "                                          column_value=None,\n",
       "                                          default_fc_parameters={'abs_energy': None,\n",
       "                                                                 'absolute_sum_of_changes': None,\n",
       "                                                                 'agg_autocorrelation': [{'f_agg': 'mean',\n",
       "                                                                                          'maxlag': 40},\n",
       "                                                                                         {'f_agg': 'median',\n",
       "                                                                                          'maxlag': 40},\n",
       "                                                                                         {'f_agg': 'var',\n",
       "                                                                                          'maxlag': 40}],\n",
       "                                                                 'agg_linear_...\n",
       "                ('clf',\n",
       "                 RandomForestClassifier(bootstrap=True, class_weight=None,\n",
       "                                        criterion='gini', max_depth=5,\n",
       "                                        max_features='auto',\n",
       "                                        max_leaf_nodes=None,\n",
       "                                        min_impurity_decrease=0.0,\n",
       "                                        min_impurity_split=None,\n",
       "                                        min_samples_leaf=1, min_samples_split=2,\n",
       "                                        min_weight_fraction_leaf=0.0,\n",
       "                                        n_estimators=200, n_jobs=None,\n",
       "                                        oob_score=False, random_state=21,\n",
       "                                        verbose=0, warm_start=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for the fit on the train test set, we set the fresh__timeseries_container to `df_train`\n",
    "ppl.set_params(fresh__timeseries_container=df_train)\n",
    "ppl.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|██████████| 20/20 [00:03<00:00,  6.10it/s]\n"
     ]
    }
   ],
   "source": [
    "# for the predict on the test test set, we set the fresh__timeseries_container to `df_test`\n",
    "ppl.set_params(fresh__timeseries_container=df_test)\n",
    "y_pred = ppl.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.72      0.78        53\n",
      "           1       0.90      0.95      0.92       142\n",
      "\n",
      "    accuracy                           0.89       195\n",
      "   macro avg       0.87      0.83      0.85       195\n",
      "weighted avg       0.88      0.89      0.88       195\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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