Cleaning and classification.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Copia di DA.ipynb","provenance":[{"file_id":"https://github.com/arpit-shukla1/Extrasensory/blob/master/unknot.ipynb","timestamp":1632221409977}],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"g6ngfMPuTmOk"},"source":["# DATASET PREPARATION\n","\n","Our intention is to classify the users' behaviours in 2 different classes: STATIC and MOVEMENT.\n","\n","In the analysis conducted before, we selected just a subset of labels with respect to the numerous labels that we can found in the ExtraSensory dataset. The selected labels were splitted in 2 different macro-groups: STATIC and MOVEMENT. \n","\n","So first of all let's load the dataset, then let's remove unnecessary labels"]},{"cell_type":"markdown","metadata":{"id":"XVXrJNGvzhVG"},"source":["# Nuova sezione"]},{"cell_type":"code","metadata":{"id":"_4ldazB-V1YB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1636062268673,"user_tz":-60,"elapsed":21725,"user":{"displayName":"GIGI JR DEL MONACO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01639860855077365833"}},"outputId":"103698c3-8636-43ec-cc5e-44d5aef7639b"},"source":["from google.colab import drive\n","drive.mount('/content/drive', force_remount=True)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","metadata":{"id":"vyJgUHoBVMwn","colab":{"base_uri":"https://localhost:8080/","height":966},"executionInfo":{"status":"ok","timestamp":1636062313145,"user_tz":-60,"elapsed":42149,"user":{"displayName":"GIGI JR DEL MONACO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01639860855077365833"}},"outputId":"e09d9599-1098-47f6-d5c9-68a314f5e021"},"source":["import pandas as pd\n","import os\n","import gzip\n","\n","datasetZipFolder = \"drive/MyDrive/IoT DA/Behavioral-Context-Recognition-main/datasetExtrasensory\"\n","\n","df_list=[]\n","for gzFile in os.listdir(datasetZipFolder):\n","  gzPath = datasetZipFolder+\"//\"+gzFile\n","  with gzip.open(gzPath) as f:\n","      df = pd.read_csv(f, header=0)\n","      df.insert(0,'uuid',gzFile[:-23])\n","      df_list.append(df)\n","\n","df_dset = pd.concat(df_list, axis=0, ignore_index=True)\n","df_dset"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>uuid</th>\n","      <th>timestamp</th>\n","      <th>raw_acc:magnitude_stats:mean</th>\n","      <th>raw_acc:magnitude_stats:std</th>\n","      <th>raw_acc:magnitude_stats:moment3</th>\n","      <th>raw_acc:magnitude_stats:moment4</th>\n","      <th>raw_acc:magnitude_stats:percentile25</th>\n","      <th>raw_acc:magnitude_stats:percentile50</th>\n","      <th>raw_acc:magnitude_stats:percentile75</th>\n","      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      2\n","2       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","3       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","4       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","...                                      ...  ...           ...\n","377341  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377342  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377343  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377344  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377345  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","\n","[377346 rows x 279 columns]"]},"metadata":{},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"eLIbj2uiXwVi"},"source":["def get_label_pretty_name(label):\n","    if label == 'FIX_walking':\n","        return 'Walking';\n","    if label == 'FIX_running':\n","        return 'Running';\n","    if label == 'LOC_main_workplace':\n","        return 'At main workplace';\n","    if label == 'OR_indoors':\n","        return 'Indoors';\n","    if label == 'OR_outside':\n","        return 'Outside';\n","    if label == 'LOC_home':\n","        return 'At home';\n","    if label == 'FIX_restaurant':\n","        return 'At a restaurant';\n","    if label == 'OR_exercise':\n","        return 'Exercise';\n","    if label == 'LOC_beach':\n","        return 'At the beach';\n","    if label == 'OR_standing':\n","        return 'Standing';\n","    if label == 'WATCHING_TV':\n","        return 'Watching TV'\n","    \n","    if label.endswith('_'):\n","        label = label[:-1] + ')';\n","        pass;\n","    \n","    label = label.replace('__',' (').replace('_',' ');\n","    label = label[0] + label[1:].lower();\n","    label = label.replace('i m','I\\'m');\n","    return label;"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"OoD4dewCXzFr"},"source":["movement_labels = ['Walking', 'Cooking', 'Cleaning', 'Shopping', 'Running', 'Bicycling', 'Drive - I\\'m the driver', 'Strolling', \n","                   'Doing laundry', 'At the gym', 'Stairs - going up', 'Stairs - going down']\n","static_labels = ['Sitting', 'Lying down', 'Sleeping', 'In a meeting', 'In class', 'Computer work', 'Watching TV', 'Drive - I\\'m a passenger']"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ANObfI3QUwCw"},"source":["user_data = []\n","\n","# for label in static_labels:\n","#   user_data += df_dset.loc[df_dset[\"label:\"+label]==1]\n","\n","for col in df_dset.columns:\n","  if(col.startswith(\"label:\") ):\n","    temp_label = col.replace(\"label:\", \"\")\n","    # print(temp_label)\n","    temp_label = get_label_pretty_name(temp_label)       \n","    # print(temp_label)\n","    if(temp_label in static_labels or temp_label in movement_labels):\n","      # print(temp_label)\n","      # print(col, temp_label)\n","      df_dset = df_dset.rename(columns = {col: temp_label})\n","    else:\n","      # print(temp_label)\n","      df_dset = df_dset.drop(columns=[col])\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Vng-m-NoY2sO","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1636062344327,"user_tz":-60,"elapsed":595,"user":{"displayName":"GIGI JR DEL MONACO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01639860855077365833"}},"outputId":"14e2748f-8b08-40ac-bc95-9a79387d8d35"},"source":["df_dset"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>uuid</th>\n","      <th>timestamp</th>\n","      <th>raw_acc:magnitude_stats:mean</th>\n","      <th>raw_acc:magnitude_stats:std</th>\n","      <th>raw_acc:magnitude_stats:moment3</th>\n","      <th>raw_acc:magnitude_stats:moment4</th>\n","      <th>raw_acc:magnitude_stats:percentile25</th>\n","      <th>raw_acc:magnitude_stats:percentile50</th>\n","      <th>raw_acc:magnitude_stats:percentile75</th>\n","      <th>raw_acc:magnitude_stats:value_entropy</th>\n","      <th>raw_acc:magnitude_stats:time_entropy</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band0</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band1</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band2</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band3</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band4</th>\n","      <th>raw_acc:magnitude_spectrum:spectral_entropy</th>\n","      <th>raw_acc:magnitude_autocorrelation:period</th>\n","      <th>raw_acc:magnitude_autocorrelation:normalized_ac</th>\n","      <th>raw_acc:3d:mean_x</th>\n","      <th>raw_acc:3d:mean_y</th>\n","      <th>raw_acc:3d:mean_z</th>\n","      <th>raw_acc:3d:std_x</th>\n","      <th>raw_acc:3d:std_y</th>\n","      <th>raw_acc:3d:std_z</th>\n","      <th>raw_acc:3d:ro_xy</th>\n","      <th>raw_acc:3d:ro_xz</th>\n","      <th>raw_acc:3d:ro_yz</th>\n","      <th>proc_gyro:magnitude_stats:mean</th>\n","      <th>proc_gyro:magnitude_stats:std</th>\n","      <th>proc_gyro:magnitude_stats:moment3</th>\n","      <th>proc_gyro:magnitude_stats:moment4</th>\n","      <th>proc_gyro:magnitude_stats:percentile25</th>\n","      <th>proc_gyro:magnitude_stats:percentile50</th>\n","      <th>proc_gyro:magnitude_stats:percentile75</th>\n","      <th>proc_gyro:magnitude_stats:value_entropy</th>\n","      <th>proc_gyro:magnitude_stats:time_entropy</th>\n","      <th>proc_gyro:magnitude_spectrum:log_energy_band0</th>\n","      <th>proc_gyro:magnitude_spectrum:log_energy_band1</th>\n","      <th>proc_gyro:magnitude_spectrum:log_energy_band2</th>\n","      <th>...</th>\n","      <th>discrete:wifi_status:is_reachable_via_wifi</th>\n","      <th>discrete:wifi_status:is_reachable_via_wwan</th>\n","      <th>discrete:wifi_status:missing</th>\n","      <th>lf_measurements:light</th>\n","      <th>lf_measurements:pressure</th>\n","      <th>lf_measurements:proximity_cm</th>\n","      <th>lf_measurements:proximity</th>\n","      <th>lf_measurements:relative_humidity</th>\n","      <th>lf_measurements:battery_level</th>\n","      <th>lf_measurements:screen_brightness</th>\n","      <th>lf_measurements:temperature_ambient</th>\n","      <th>discrete:time_of_day:between0and6</th>\n","      <th>discrete:time_of_day:between3and9</th>\n","      <th>discrete:time_of_day:between6and12</th>\n","      <th>discrete:time_of_day:between9and15</th>\n","      <th>discrete:time_of_day:between12and18</th>\n","      <th>discrete:time_of_day:between15and21</th>\n","      <th>discrete:time_of_day:between18and24</th>\n","      <th>discrete:time_of_day:between21and3</th>\n","      <th>Lying down</th>\n","      <th>Sitting</th>\n","      <th>Walking</th>\n","      <th>Running</th>\n","      <th>Bicycling</th>\n","      <th>Sleeping</th>\n","      <th>In class</th>\n","      <th>In a meeting</th>\n","      <th>Drive - I'm the driver</th>\n","      <th>Drive - I'm a passenger</th>\n","      <th>Cooking</th>\n","      <th>Shopping</th>\n","      <th>Strolling</th>\n","      <th>Cleaning</th>\n","      <th>Doing laundry</th>\n","      <th>Watching TV</th>\n","      <th>Computer work</th>\n","      <th>At the gym</th>\n","      <th>Stairs - going up</th>\n","      <th>Stairs - going down</th>\n","      <th>label_source</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      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<td>0.003230</td>\n","      <td>2.236059</td>\n","      <td>6.532865</td>\n","      <td>5.149616</td>\n","      <td>2.818909</td>\n","      <td>3.757026</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>0.46</td>\n","      <td>0.381436</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","     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00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","...                                      ...  ...           ...\n","377341  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377342  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377343  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377344  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377345  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","\n","[377346 rows x 248 columns]"]},"metadata":{},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"M7ibAVEJZOD0"},"source":["Now, let's define a method that allow to select just a subset of features that we want to include in the final dataset that we will use for the classifcation.\n"]},{"cell_type":"code","metadata":{"id":"KD_EaKIiPdUM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1636062350672,"user_tz":-60,"elapsed":264,"user":{"displayName":"GIGI JR DEL MONACO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01639860855077365833"}},"outputId":"fb37f703-7629-46a1-ce95-5b05dd016754"},"source":["for col in df_dset.columns:\n","  print(col)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["uuid\n","timestamp\n","raw_acc:magnitude_stats:mean\n","raw_acc:magnitude_stats:std\n","raw_acc:magnitude_stats:moment3\n","raw_acc:magnitude_stats:moment4\n","raw_acc:magnitude_stats:percentile25\n","raw_acc:magnitude_stats:percentile50\n","raw_acc:magnitude_stats:percentile75\n","raw_acc:magnitude_stats:value_entropy\n","raw_acc:magnitude_stats:time_entropy\n","raw_acc:magnitude_spectrum:log_energy_band0\n","raw_acc:magnitude_spectrum:log_energy_band1\n","raw_acc:magnitude_spectrum:log_energy_band2\n","raw_acc:magnitude_spectrum:log_energy_band3\n","raw_acc:magnitude_spectrum:log_energy_band4\n","raw_acc:magnitude_spectrum:spectral_entropy\n","raw_acc:magnitude_autocorrelation:period\n","raw_acc:magnitude_autocorrelation:normalized_ac\n","raw_acc:3d:mean_x\n","raw_acc:3d:mean_y\n","raw_acc:3d:mean_z\n","raw_acc:3d:std_x\n","raw_acc:3d:std_y\n","raw_acc:3d:std_z\n","raw_acc:3d:ro_xy\n","raw_acc:3d:ro_xz\n","raw_acc:3d:ro_yz\n","proc_gyro:magnitude_stats:mean\n","proc_gyro:magnitude_stats:std\n","proc_gyro:magnitude_stats:moment3\n","proc_gyro:magnitude_stats:moment4\n","proc_gyro:magnitude_stats:percentile25\n","proc_gyro:magnitude_stats:percentile50\n","proc_gyro:magnitude_stats:percentile75\n","proc_gyro:magnitude_stats:value_entropy\n","proc_gyro:magnitude_stats:time_entropy\n","proc_gyro:magnitude_spectrum:log_energy_band0\n","proc_gyro:magnitude_spectrum:log_energy_band1\n","proc_gyro:magnitude_spectrum:log_energy_band2\n","proc_gyro:magnitude_spectrum:log_energy_band3\n","proc_gyro:magnitude_spectrum:log_energy_band4\n","proc_gyro:magnitude_spectrum:spectral_entropy\n","proc_gyro:magnitude_autocorrelation:period\n","proc_gyro:magnitude_autocorrelation:normalized_ac\n","proc_gyro:3d:mean_x\n","proc_gyro:3d:mean_y\n","proc_gyro:3d:mean_z\n","proc_gyro:3d:std_x\n","proc_gyro:3d:std_y\n","proc_gyro:3d:std_z\n","proc_gyro:3d:ro_xy\n","proc_gyro:3d:ro_xz\n","proc_gyro:3d:ro_yz\n","raw_magnet:magnitude_stats:mean\n","raw_magnet:magnitude_stats:std\n","raw_magnet:magnitude_stats:moment3\n","raw_magnet:magnitude_stats:moment4\n","raw_magnet:magnitude_stats:percentile25\n","raw_magnet:magnitude_stats:percentile50\n","raw_magnet:magnitude_stats:percentile75\n","raw_magnet:magnitude_stats:value_entropy\n","raw_magnet:magnitude_stats:time_entropy\n","raw_magnet:magnitude_spectrum:log_energy_band0\n","raw_magnet:magnitude_spectrum:log_energy_band1\n","raw_magnet:magnitude_spectrum:log_energy_band2\n","raw_magnet:magnitude_spectrum:log_energy_band3\n","raw_magnet:magnitude_spectrum:log_energy_band4\n","raw_magnet:magnitude_spectrum:spectral_entropy\n","raw_magnet:magnitude_autocorrelation:period\n","raw_magnet:magnitude_autocorrelation:normalized_ac\n","raw_magnet:3d:mean_x\n","raw_magnet:3d:mean_y\n","raw_magnet:3d:mean_z\n","raw_magnet:3d:std_x\n","raw_magnet:3d:std_y\n","raw_magnet:3d:std_z\n","raw_magnet:3d:ro_xy\n","raw_magnet:3d:ro_xz\n","raw_magnet:3d:ro_yz\n","raw_magnet:avr_cosine_similarity_lag_range0\n","raw_magnet:avr_cosine_similarity_lag_range1\n","raw_magnet:avr_cosine_similarity_lag_range2\n","raw_magnet:avr_cosine_similarity_lag_range3\n","raw_magnet:avr_cosine_similarity_lag_range4\n","watch_acceleration:magnitude_stats:mean\n","watch_acceleration:magnitude_stats:std\n","watch_acceleration:magnitude_stats:moment3\n","watch_acceleration:magnitude_stats:moment4\n","watch_acceleration:magnitude_stats:percentile25\n","watch_acceleration:magnitude_stats:percentile50\n","watch_acceleration:magnitude_stats:percentile75\n","watch_acceleration:magnitude_stats:value_entropy\n","watch_acceleration:magnitude_stats:time_entropy\n","watch_acceleration:magnitude_spectrum:log_energy_band0\n","watch_acceleration:magnitude_spectrum:log_energy_band1\n","watch_acceleration:magnitude_spectrum:log_energy_band2\n","watch_acceleration:magnitude_spectrum:log_energy_band3\n","watch_acceleration:magnitude_spectrum:log_energy_band4\n","watch_acceleration:magnitude_spectrum:spectral_entropy\n","watch_acceleration:magnitude_autocorrelation:period\n","watch_acceleration:magnitude_autocorrelation:normalized_ac\n","watch_acceleration:3d:mean_x\n","watch_acceleration:3d:mean_y\n","watch_acceleration:3d:mean_z\n","watch_acceleration:3d:std_x\n","watch_acceleration:3d:std_y\n","watch_acceleration:3d:std_z\n","watch_acceleration:3d:ro_xy\n","watch_acceleration:3d:ro_xz\n","watch_acceleration:3d:ro_yz\n","watch_acceleration:spectrum:x_log_energy_band0\n","watch_acceleration:spectrum:x_log_energy_band1\n","watch_acceleration:spectrum:x_log_energy_band2\n","watch_acceleration:spectrum:x_log_energy_band3\n","watch_acceleration:spectrum:x_log_energy_band4\n","watch_acceleration:spectrum:y_log_energy_band0\n","watch_acceleration:spectrum:y_log_energy_band1\n","watch_acceleration:spectrum:y_log_energy_band2\n","watch_acceleration:spectrum:y_log_energy_band3\n","watch_acceleration:spectrum:y_log_energy_band4\n","watch_acceleration:spectrum:z_log_energy_band0\n","watch_acceleration:spectrum:z_log_energy_band1\n","watch_acceleration:spectrum:z_log_energy_band2\n","watch_acceleration:spectrum:z_log_energy_band3\n","watch_acceleration:spectrum:z_log_energy_band4\n","watch_acceleration:relative_directions:avr_cosine_similarity_lag_range0\n","watch_acceleration:relative_directions:avr_cosine_similarity_lag_range1\n","watch_acceleration:relative_directions:avr_cosine_similarity_lag_range2\n","watch_acceleration:relative_directions:avr_cosine_similarity_lag_range3\n","watch_acceleration:relative_directions:avr_cosine_similarity_lag_range4\n","watch_heading:mean_cos\n","watch_heading:std_cos\n","watch_heading:mom3_cos\n","watch_heading:mom4_cos\n","watch_heading:mean_sin\n","watch_heading:std_sin\n","watch_heading:mom3_sin\n","watch_heading:mom4_sin\n","watch_heading:entropy_8bins\n","location:num_valid_updates\n","location:log_latitude_range\n","location:log_longitude_range\n","location:min_altitude\n","location:max_altitude\n","location:min_speed\n","location:max_speed\n","location:best_horizontal_accuracy\n","location:best_vertical_accuracy\n","location:diameter\n","location:log_diameter\n","location_quick_features:std_lat\n","location_quick_features:std_long\n","location_quick_features:lat_change\n","location_quick_features:long_change\n","location_quick_features:mean_abs_lat_deriv\n","location_quick_features:mean_abs_long_deriv\n","audio_naive:mfcc0:mean\n","audio_naive:mfcc1:mean\n","audio_naive:mfcc2:mean\n","audio_naive:mfcc3:mean\n","audio_naive:mfcc4:mean\n","audio_naive:mfcc5:mean\n","audio_naive:mfcc6:mean\n","audio_naive:mfcc7:mean\n","audio_naive:mfcc8:mean\n","audio_naive:mfcc9:mean\n","audio_naive:mfcc10:mean\n","audio_naive:mfcc11:mean\n","audio_naive:mfcc12:mean\n","audio_naive:mfcc0:std\n","audio_naive:mfcc1:std\n","audio_naive:mfcc2:std\n","audio_naive:mfcc3:std\n","audio_naive:mfcc4:std\n","audio_naive:mfcc5:std\n","audio_naive:mfcc6:std\n","audio_naive:mfcc7:std\n","audio_naive:mfcc8:std\n","audio_naive:mfcc9:std\n","audio_naive:mfcc10:std\n","audio_naive:mfcc11:std\n","audio_naive:mfcc12:std\n","audio_properties:max_abs_value\n","audio_properties:normalization_multiplier\n","discrete:app_state:is_active\n","discrete:app_state:is_inactive\n","discrete:app_state:is_background\n","discrete:app_state:missing\n","discrete:battery_plugged:is_ac\n","discrete:battery_plugged:is_usb\n","discrete:battery_plugged:is_wireless\n","discrete:battery_plugged:missing\n","discrete:battery_state:is_unknown\n","discrete:battery_state:is_unplugged\n","discrete:battery_state:is_not_charging\n","discrete:battery_state:is_discharging\n","discrete:battery_state:is_charging\n","discrete:battery_state:is_full\n","discrete:battery_state:missing\n","discrete:on_the_phone:is_False\n","discrete:on_the_phone:is_True\n","discrete:on_the_phone:missing\n","discrete:ringer_mode:is_normal\n","discrete:ringer_mode:is_silent_no_vibrate\n","discrete:ringer_mode:is_silent_with_vibrate\n","discrete:ringer_mode:missing\n","discrete:wifi_status:is_not_reachable\n","discrete:wifi_status:is_reachable_via_wifi\n","discrete:wifi_status:is_reachable_via_wwan\n","discrete:wifi_status:missing\n","lf_measurements:light\n","lf_measurements:pressure\n","lf_measurements:proximity_cm\n","lf_measurements:proximity\n","lf_measurements:relative_humidity\n","lf_measurements:battery_level\n","lf_measurements:screen_brightness\n","lf_measurements:temperature_ambient\n","discrete:time_of_day:between0and6\n","discrete:time_of_day:between3and9\n","discrete:time_of_day:between6and12\n","discrete:time_of_day:between9and15\n","discrete:time_of_day:between12and18\n","discrete:time_of_day:between15and21\n","discrete:time_of_day:between18and24\n","discrete:time_of_day:between21and3\n","Lying down\n","Sitting\n","Walking\n","Running\n","Bicycling\n","Sleeping\n","In class\n","In a meeting\n","Drive - I'm the driver\n","Drive - I'm a passenger\n","Cooking\n","Shopping\n","Strolling\n","Cleaning\n","Doing laundry\n","Watching TV\n","Computer work\n","At the gym\n","Stairs - going up\n","Stairs - going down\n","label_source\n"]}]},{"cell_type":"code","metadata":{"id":"UjHkd_C4ZWB-"},"source":["features_to_consider = ['raw_acc', 'watch_acceleration', 'location', 'audio_naive', 'lf_measurements']\n","\n","final_feature_names = []\n","columns_to_delete = []\n","to_delete = True\n","\n","for col in df_dset.columns:\n","  if(col == 'Lying down'):\n","    #it's the first label column: starting from here, all columns must not be discarded\n","    break;\n","  to_delete = True\n","  for feature in features_to_consider:\n","    # print(\"CHECK\",col, feature)\n","    if (col.startswith(feature) or col == 'uuid' or col == 'timestamp'):\n","      # print(\"MATCH\", col)\n","      to_delete = False\n","      final_feature_names.append(col)\n","      break;\n","\n","  if(to_delete):\n","    # print(\"DELETE\", col)\n","    columns_to_delete.append(col)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"9Cl_rLWHAsrt","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1636062359533,"user_tz":-60,"elapsed":763,"user":{"displayName":"GIGI JR DEL MONACO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"01639860855077365833"}},"outputId":"8af5df07-3453-4ec6-e224-b0e1eabfcbe2"},"source":["final_dataset = df_dset.drop(columns= columns_to_delete)\n","final_dataset"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>uuid</th>\n","      <th>timestamp</th>\n","      <th>raw_acc:magnitude_stats:mean</th>\n","      <th>raw_acc:magnitude_stats:std</th>\n","      <th>raw_acc:magnitude_stats:moment3</th>\n","      <th>raw_acc:magnitude_stats:moment4</th>\n","      <th>raw_acc:magnitude_stats:percentile25</th>\n","      <th>raw_acc:magnitude_stats:percentile50</th>\n","      <th>raw_acc:magnitude_stats:percentile75</th>\n","      <th>raw_acc:magnitude_stats:value_entropy</th>\n","      <th>raw_acc:magnitude_stats:time_entropy</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band0</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band1</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band2</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band3</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band4</th>\n","      <th>raw_acc:magnitude_spectrum:spectral_entropy</th>\n","      <th>raw_acc:magnitude_autocorrelation:period</th>\n","      <th>raw_acc:magnitude_autocorrelation:normalized_ac</th>\n","      <th>raw_acc:3d:mean_x</th>\n","      <th>raw_acc:3d:mean_y</th>\n","      <th>raw_acc:3d:mean_z</th>\n","      <th>raw_acc:3d:std_x</th>\n","      <th>raw_acc:3d:std_y</th>\n","      <th>raw_acc:3d:std_z</th>\n","      <th>raw_acc:3d:ro_xy</th>\n","      <th>raw_acc:3d:ro_xz</th>\n","      <th>raw_acc:3d:ro_yz</th>\n","      <th>watch_acceleration:magnitude_stats:mean</th>\n","      <th>watch_acceleration:magnitude_stats:std</th>\n","      <th>watch_acceleration:magnitude_stats:moment3</th>\n","      <th>watch_acceleration:magnitude_stats:moment4</th>\n","      <th>watch_acceleration:magnitude_stats:percentile25</th>\n","      <th>watch_acceleration:magnitude_stats:percentile50</th>\n","      <th>watch_acceleration:magnitude_stats:percentile75</th>\n","      <th>watch_acceleration:magnitude_stats:value_entropy</th>\n","      <th>watch_acceleration:magnitude_stats:time_entropy</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band0</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band1</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band2</th>\n","      <th>...</th>\n","      <th>audio_naive:mfcc2:std</th>\n","      <th>audio_naive:mfcc3:std</th>\n","      <th>audio_naive:mfcc4:std</th>\n","      <th>audio_naive:mfcc5:std</th>\n","      <th>audio_naive:mfcc6:std</th>\n","      <th>audio_naive:mfcc7:std</th>\n","      <th>audio_naive:mfcc8:std</th>\n","      <th>audio_naive:mfcc9:std</th>\n","      <th>audio_naive:mfcc10:std</th>\n","      <th>audio_naive:mfcc11:std</th>\n","      <th>audio_naive:mfcc12:std</th>\n","      <th>lf_measurements:light</th>\n","      <th>lf_measurements:pressure</th>\n","      <th>lf_measurements:proximity_cm</th>\n","      <th>lf_measurements:proximity</th>\n","      <th>lf_measurements:relative_humidity</th>\n","      <th>lf_measurements:battery_level</th>\n","      <th>lf_measurements:screen_brightness</th>\n","      <th>lf_measurements:temperature_ambient</th>\n","      <th>Lying down</th>\n","      <th>Sitting</th>\n","      <th>Walking</th>\n","      <th>Running</th>\n","      <th>Bicycling</th>\n","      <th>Sleeping</th>\n","      <th>In class</th>\n","      <th>In a meeting</th>\n","      <th>Drive - I'm the driver</th>\n","      <th>Drive - I'm a passenger</th>\n","      <th>Cooking</th>\n","      <th>Shopping</th>\n","      <th>Strolling</th>\n","      <th>Cleaning</th>\n","      <th>Doing laundry</th>\n","      <th>Watching TV</th>\n","      <th>Computer work</th>\n","      <th>At the gym</th>\n","      <th>Stairs - going up</th>\n","      <th>Stairs - going down</th>\n","      <th>label_source</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>00EABED2-271D-49D8-B599-1D4A09240601</td>\n","      <td>1444079161</td>\n","      <td>0.996815</td>\n","      <td>0.003529</td>\n","      <td>-0.002786</td>\n","      <td>0.006496</td>\n","      <td>0.995203</td>\n","      <td>0.996825</td>\n","      <td>0.998502</td>\n","      <td>1.748756</td>\n","      <td>6.684605</td>\n","      <td>5.043970</td>\n","      <td>0.000042</td>\n","      <td>0.000364</td>\n","      <td>0.000761</td>\n","      <td>0.005429</td>\n","      <td>0.429853</td>\n","      <td>0.173685</td>\n","      <td>0.148988</td>\n","      <td>0.002331</td>\n","      <td>0.004614</td>\n","      <td>-0.996790</td>\n","      <td>0.003269</td>\n","      <td>0.003521</td>\n","      <td>0.003539</td>\n","      <td>0.106920</td>\n","      <td>0.516842</td>\n","      <td>0.255494</td>\n","      <td>1032.508157</td>\n","      <td>17.195149</td>\n","      <td>12.121621</td>\n","      <td>25.390972</td>\n","      <td>1024.812178</td>\n","      <td>1033.146650</td>\n","      <td>1041.537325</td>\n","      <td>2.220749</td>\n","      <td>6.214470</td>\n","      <td>5.045673</td>\n","      <td>0.004896</td>\n","      <td>0.023827</td>\n","      <td>...</td>\n","      <td>1.177478</td>\n","      <td>0.670053</td>\n","      <td>0.389200</td>\n","      <td>0.535904</td>\n","      <td>0.468701</td>\n","      <td>0.358315</td>\n","      <td>0.401412</td>\n","      <td>0.408432</td>\n","      <td>0.277176</td>\n","      <td>0.416881</td>\n","      <td>0.263832</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>0.46</td>\n","      <td>0.381436</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","     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<td>0.406084</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>377344</th>\n","      <td>FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF</td>\n","      <td>1444234959</td>\n","      <td>1.123513</td>\n","      <td>0.273450</td>\n","      <td>0.312818</td>\n","      <td>0.480787</td>\n","      <td>0.966631</td>\n","      <td>1.051232</td>\n","      <td>1.229708</td>\n","      <td>1.808524</td>\n","      <td>6.656521</td>\n","      <td>5.039056</td>\n","      <td>1.906018</td>\n","      <td>2.991313</td>\n","      <td>1.275461</td>\n","      <td>1.965808</td>\n","      <td>0.755036</td>\n","      <td>1.064535</td>\n","      <td>0.274951</td>\n","      <td>-0.308654</td>\n","      <td>0.845493</td>\n","      <td>-0.173434</td>\n","      <td>0.459076</td>\n","      <td>0.439645</td>\n","      <td>0.304650</td>\n","      <td>-0.260843</td>\n","      <td>-0.208532</td>\n","      <td>0.587430</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>0.748026</td>\n","      <td>0.560401</td>\n","      <td>0.640854</td>\n","      <td>0.557653</td>\n","      <td>0.358110</td>\n","      <td>0.323353</td>\n","      <td>0.270569</td>\n","      <td>0.299399</td>\n","      <td>0.255175</td>\n","      <td>0.251885</td>\n","      <td>0.272702</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>0.87</td>\n","      <td>0.406084</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>377345</th>\n","      <td>FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF</td>\n","      <td>1444235019</td>\n","      <td>1.020624</td>\n","      <td>0.135672</td>\n","      <td>0.104091</td>\n","      <td>0.185868</td>\n","      <td>0.915936</td>\n","      <td>1.018161</td>\n","      <td>1.115126</td>\n","      <td>2.402200</td>\n","      <td>6.675872</td>\n","      <td>5.036107</td>\n","      <td>0.185628</td>\n","      <td>2.014092</td>\n","      <td>0.341746</td>\n","      <td>0.909975</td>\n","      <td>0.502315</td>\n","      <td>1.067688</td>\n","      <td>0.587929</td>\n","      <td>-0.032776</td>\n","      <td>-0.476483</td>\n","      <td>-0.889445</td>\n","      <td>0.115057</td>\n","      <td>0.083429</td>\n","      <td>0.143732</td>\n","      <td>-0.131437</td>\n","      <td>-0.004327</td>\n","      <td>-0.005321</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>0.416582</td>\n","      <td>0.315639</td>\n","      <td>0.273821</td>\n","      <td>0.351644</td>\n","      <td>0.270480</td>\n","      <td>0.277546</td>\n","      <td>0.227045</td>\n","      <td>0.243238</td>\n","      <td>0.200503</td>\n","      <td>0.227202</td>\n","      <td>0.212655</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>0.86</td>\n","      <td>0.945178</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>-1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>377346 rows × 146 columns</p>\n","</div>"],"text/plain":["                                        uuid  ...  label_source\n","0       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","1       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","2       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","3       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","4       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","...                                      ...  ...           ...\n","377341  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377342  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377343  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377344  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377345  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","\n","[377346 rows x 146 columns]"]},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","metadata":{"id":"ZDOH7bImYT6D"},"source":["This is the final dataset that we are going to consider for our classification: now we have to clean it because there are some missing values"]},{"cell_type":"code","metadata":{"id":"cS-ElswLclHG","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635928423157,"user_tz":-60,"elapsed":37,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"6bf1b9bd-85ce-403c-bb73-70942d3a8bff"},"source":["print(\"Number of NaN values in the dataset: \",final_dataset.isna().sum().sum())\n","noNaN_each_row = (final_dataset.count())\n","total_noNaN = 0\n","for value in noNaN_each_row:\n","  total_noNaN+=value\n","\n","print(\"Number of no-NaN values in the dataset: \",total_noNaN)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of NaN values in the dataset:  13955657\n","Number of no-NaN values in the dataset:  41136859\n"]}]},{"cell_type":"markdown","metadata":{"id":"ClSKT-px8WOd"},"source":["Since there are many missing values, we can start cleaning the dataset removing all those columns where the number of no-NaN values is less than 10% of the rows."]},{"cell_type":"code","metadata":{"id":"Z1bgHBfVrlVi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635928423158,"user_tz":-60,"elapsed":30,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"a8b781cf-3e72-40ee-8a24-2c07920bd4ea"},"source":["n_values_each_column = final_dataset.count(axis = 0)\n","n_values_each_column"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["uuid                               377346\n","timestamp                          377346\n","raw_acc:magnitude_stats:mean       377056\n","raw_acc:magnitude_stats:std        377056\n","raw_acc:magnitude_stats:moment3    377056\n","                                    ...  \n","Computer work                      237559\n","At the gym                          44393\n","Stairs - going up                   94629\n","Stairs - going down                 94415\n","label_source                       377346\n","Length: 146, dtype: int64"]},"metadata":{},"execution_count":13}]},{"cell_type":"markdown","metadata":{"id":"Z1Pgpca428I6"},"source":["Let's find the columns' index that we have to delete"]},{"cell_type":"code","metadata":{"id":"qW427m-8t5I6","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635928423160,"user_tz":-60,"elapsed":26,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"9d08ac59-440d-446d-cf72-661c77492905"},"source":["index_toDel = []\n","\n","for column_index,n_values in enumerate(n_values_each_column):\n","  if((float(n_values)/final_dataset.shape[0]*100)<10):\n","    print(float(n_values)/final_dataset.shape[0]*100)\n","    index_toDel.append(column_index)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["6.874062531469792\n","7.009482013854658\n"]}]},{"cell_type":"code","metadata":{"id":"6lNq9NrThGNE","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1635928423610,"user_tz":-60,"elapsed":470,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"31834b70-de92-4c70-a663-ed3486c86ff1"},"source":["feature_to_del = []\n","print(index_toDel)\n","for i in index_toDel:\n","  feature_to_del.append(final_dataset.columns[i])\n","\n","final_dataset = final_dataset.drop(feature_to_del, axis = 1)\n","final_dataset"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[121, 124]\n"]},{"output_type":"execute_result","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>uuid</th>\n","      <th>timestamp</th>\n","      <th>raw_acc:magnitude_stats:mean</th>\n","      <th>raw_acc:magnitude_stats:std</th>\n","      <th>raw_acc:magnitude_stats:moment3</th>\n","      <th>raw_acc:magnitude_stats:moment4</th>\n","      <th>raw_acc:magnitude_stats:percentile25</th>\n","      <th>raw_acc:magnitude_stats:percentile50</th>\n","      <th>raw_acc:magnitude_stats:percentile75</th>\n","      <th>raw_acc:magnitude_stats:value_entropy</th>\n","      <th>raw_acc:magnitude_stats:time_entropy</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band0</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band1</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band2</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band3</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band4</th>\n","      <th>raw_acc:magnitude_spectrum:spectral_entropy</th>\n","      <th>raw_acc:magnitude_autocorrelation:period</th>\n","      <th>raw_acc:magnitude_autocorrelation:normalized_ac</th>\n","      <th>raw_acc:3d:mean_x</th>\n","      <th>raw_acc:3d:mean_y</th>\n","      <th>raw_acc:3d:mean_z</th>\n","      <th>raw_acc:3d:std_x</th>\n","      <th>raw_acc:3d:std_y</th>\n","      <th>raw_acc:3d:std_z</th>\n","      <th>raw_acc:3d:ro_xy</th>\n","      <th>raw_acc:3d:ro_xz</th>\n","      <th>raw_acc:3d:ro_yz</th>\n","      <th>watch_acceleration:magnitude_stats:mean</th>\n","      <th>watch_acceleration:magnitude_stats:std</th>\n","      <th>watch_acceleration:magnitude_stats:moment3</th>\n","      <th>watch_acceleration:magnitude_stats:moment4</th>\n","      <th>watch_acceleration:magnitude_stats:percentile25</th>\n","      <th>watch_acceleration:magnitude_stats:percentile50</th>\n","      <th>watch_acceleration:magnitude_stats:percentile75</th>\n","      <th>watch_acceleration:magnitude_stats:value_entropy</th>\n","      <th>watch_acceleration:magnitude_stats:time_entropy</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band0</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band1</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band2</th>\n","      <th>...</th>\n","      <th>audio_naive:mfcc0:std</th>\n","      <th>audio_naive:mfcc1:std</th>\n","      <th>audio_naive:mfcc2:std</th>\n","      <th>audio_naive:mfcc3:std</th>\n","      <th>audio_naive:mfcc4:std</th>\n","      <th>audio_naive:mfcc5:std</th>\n","      <th>audio_naive:mfcc6:std</th>\n","      <th>audio_naive:mfcc7:std</th>\n","      <th>audio_naive:mfcc8:std</th>\n","      <th>audio_naive:mfcc9:std</th>\n","      <th>audio_naive:mfcc10:std</th>\n","      <th>audio_naive:mfcc11:std</th>\n","      <th>audio_naive:mfcc12:std</th>\n","      <th>lf_measurements:light</th>\n","      <th>lf_measurements:pressure</th>\n","      <th>lf_measurements:proximity_cm</th>\n","      <th>lf_measurements:proximity</th>\n","      <th>lf_measurements:battery_level</th>\n","      <th>lf_measurements:screen_brightness</th>\n","      <th>Lying down</th>\n","      <th>Sitting</th>\n","      <th>Walking</th>\n","      <th>Running</th>\n","      <th>Bicycling</th>\n","      <th>Sleeping</th>\n","      <th>In class</th>\n","      <th>In a meeting</th>\n","      <th>Drive - I'm the driver</th>\n","      <th>Drive - I'm a passenger</th>\n","      <th>Cooking</th>\n","      <th>Shopping</th>\n","      <th>Strolling</th>\n","      <th>Cleaning</th>\n","      <th>Doing laundry</th>\n","      <th>Watching TV</th>\n","      <th>Computer work</th>\n","      <th>At the gym</th>\n","      <th>Stairs - going up</th>\n","      <th>Stairs - going down</th>\n","      <th>label_source</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>00EABED2-271D-49D8-B599-1D4A09240601</td>\n","      <td>1444079161</td>\n","      <td>0.996815</td>\n","      <td>0.003529</td>\n","      <td>-0.002786</td>\n","      <td>0.006496</td>\n","      <td>0.995203</td>\n","      <td>0.996825</td>\n","      <td>0.998502</td>\n","      <td>1.748756</td>\n","      <td>6.684605</td>\n","      <td>5.043970</td>\n","      <td>0.000042</td>\n","      <td>0.000364</td>\n","      <td>0.000761</td>\n","      <td>0.005429</td>\n","      <td>0.429853</td>\n","      <td>0.173685</td>\n","      <td>0.148988</td>\n","      <td>0.002331</td>\n","      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<td>1.026223</td>\n","      <td>0.195112</td>\n","      <td>0.255240</td>\n","      <td>0.379733</td>\n","      <td>0.983684</td>\n","      <td>1.000173</td>\n","      <td>1.030232</td>\n","      <td>1.484173</td>\n","      <td>6.668006</td>\n","      <td>5.031912</td>\n","      <td>1.109431</td>\n","      <td>2.380978</td>\n","      <td>1.761493</td>\n","      <td>2.263843</td>\n","      <td>0.688024</td>\n","      <td>0.852263</td>\n","      <td>0.223209</td>\n","      <td>0.192250</td>\n","      <td>0.723133</td>\n","      <td>-0.199525</td>\n","      <td>0.294796</td>\n","      <td>0.438057</td>\n","      <td>0.461207</td>\n","      <td>-0.219706</td>\n","      <td>0.350897</td>\n","      <td>0.097009</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>4.160774</td>\n","      <td>1.870759</td>\n","      <td>1.166230</td>\n","      <td>0.798008</td>\n","      <td>0.685471</td>\n","      <td>0.639430</td>\n","      <td>0.580977</td>\n","      <td>0.442967</td>\n","      <td>0.573607</td>\n","      <td>0.438202</td>\n","      <td>0.414396</td>\n","      <td>0.353584</td>\n","      <td>0.370632</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.87</td>\n","      <td>0.406084</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>377344</th>\n","      <td>FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF</td>\n","      <td>1444234959</td>\n","      <td>1.123513</td>\n","      <td>0.273450</td>\n","      <td>0.312818</td>\n","      <td>0.480787</td>\n","      <td>0.966631</td>\n","      <td>1.051232</td>\n","      <td>1.229708</td>\n","      <td>1.808524</td>\n","      <td>6.656521</td>\n","      <td>5.039056</td>\n","      <td>1.906018</td>\n","      <td>2.991313</td>\n","      <td>1.275461</td>\n","      <td>1.965808</td>\n","      <td>0.755036</td>\n","      <td>1.064535</td>\n","      <td>0.274951</td>\n","      <td>-0.308654</td>\n","      <td>0.845493</td>\n","      <td>-0.173434</td>\n","      <td>0.459076</td>\n","      <td>0.439645</td>\n","      <td>0.304650</td>\n","      <td>-0.260843</td>\n","      <td>-0.208532</td>\n","      <td>0.587430</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>4.797643</td>\n","      <td>1.308541</td>\n","      <td>0.748026</td>\n","      <td>0.560401</td>\n","      <td>0.640854</td>\n","      <td>0.557653</td>\n","      <td>0.358110</td>\n","      <td>0.323353</td>\n","      <td>0.270569</td>\n","      <td>0.299399</td>\n","      <td>0.255175</td>\n","      <td>0.251885</td>\n","      <td>0.272702</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.87</td>\n","      <td>0.406084</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>377345</th>\n","      <td>FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF</td>\n","      <td>1444235019</td>\n","      <td>1.020624</td>\n","      <td>0.135672</td>\n","      <td>0.104091</td>\n","      <td>0.185868</td>\n","      <td>0.915936</td>\n","      <td>1.018161</td>\n","      <td>1.115126</td>\n","      <td>2.402200</td>\n","      <td>6.675872</td>\n","      <td>5.036107</td>\n","      <td>0.185628</td>\n","      <td>2.014092</td>\n","      <td>0.341746</td>\n","      <td>0.909975</td>\n","      <td>0.502315</td>\n","      <td>1.067688</td>\n","      <td>0.587929</td>\n","      <td>-0.032776</td>\n","      <td>-0.476483</td>\n","      <td>-0.889445</td>\n","      <td>0.115057</td>\n","      <td>0.083429</td>\n","      <td>0.143732</td>\n","      <td>-0.131437</td>\n","      <td>-0.004327</td>\n","      <td>-0.005321</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>1.599810</td>\n","      <td>1.072016</td>\n","      <td>0.416582</td>\n","      <td>0.315639</td>\n","      <td>0.273821</td>\n","      <td>0.351644</td>\n","      <td>0.270480</td>\n","      <td>0.277546</td>\n","      <td>0.227045</td>\n","      <td>0.243238</td>\n","      <td>0.200503</td>\n","      <td>0.227202</td>\n","      <td>0.212655</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.86</td>\n","      <td>0.945178</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>-1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>377346 rows × 144 columns</p>\n","</div>"],"text/plain":["                                        uuid  ...  label_source\n","0       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","1       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","2       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","3       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","4       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","...                                      ...  ...           ...\n","377341  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377342  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377343  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377344  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377345  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","\n","[377346 rows x 144 columns]"]},"metadata":{},"execution_count":15}]},{"cell_type":"markdown","metadata":{"id":"Neb_KEKUlKdc"},"source":["Let's see how many nan values are still in the dataset"]},{"cell_type":"code","metadata":{"id":"p1-SQ5K5klHm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635928424250,"user_tz":-60,"elapsed":648,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"b86c7069-2bcc-414e-af00-374806b8d95e"},"source":["print(\"Number of NaN values in the dataset: \",final_dataset.isna().sum().sum())\n","noNaN_each_row = (final_dataset.count())\n","total_noNaN = 0\n","for value in noNaN_each_row:\n","  total_noNaN+=value\n","\n","print(\"Number of no-NaN values in the dataset: \",total_noNaN)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of NaN values in the dataset:  13253354\n","Number of no-NaN values in the dataset:  41084470\n"]}]},{"cell_type":"markdown","metadata":{"id":"Gd7U51Jq3E7p"},"source":["Since there are still many nan values, let's remove columns with more than 50% of values that are nan"]},{"cell_type":"code","metadata":{"id":"zkrbTyjjlVdl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635928424252,"user_tz":-60,"elapsed":18,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"f03c18f3-4517-4d7f-f489-085f58240e2f"},"source":["n_values_each_column = final_dataset.count(axis = 0)\n","n_values_each_column"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["uuid                               377346\n","timestamp                          377346\n","raw_acc:magnitude_stats:mean       377056\n","raw_acc:magnitude_stats:std        377056\n","raw_acc:magnitude_stats:moment3    377056\n","                                    ...  \n","Computer work                      237559\n","At the gym                          44393\n","Stairs - going up                   94629\n","Stairs - going down                 94415\n","label_source                       377346\n","Length: 144, dtype: int64"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","metadata":{"id":"TYsz8ry_mtpJ"},"source":["index_toDel = []\n","\n","for column_index,n_values in enumerate(n_values_each_column):\n","  if((float(n_values)/final_dataset.shape[0]*100)<50):\n","    # print(final_dataset.columns[column_index], float(n_values)/final_dataset.shape[0]*100)\n","    if(not(final_dataset.columns[column_index] in static_labels or final_dataset.columns[column_index] in movement_labels)): #because we don't want to delete also label columns\n","      index_toDel.append(column_index)\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"UI9J9NVgrPzy","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1635928424761,"user_tz":-60,"elapsed":519,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"3ead1b4a-7796-4abd-d612-02e7414c2429"},"source":["feature_to_del = []\n","print(index_toDel)\n","for i in index_toDel:\n","  feature_to_del.append(final_dataset.columns[i])\n","\n","final_dataset = final_dataset.drop(feature_to_del, axis = 1)\n","final_dataset"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[79, 80, 117, 118, 119]\n"]},{"output_type":"execute_result","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>uuid</th>\n","      <th>timestamp</th>\n","      <th>raw_acc:magnitude_stats:mean</th>\n","      <th>raw_acc:magnitude_stats:std</th>\n","      <th>raw_acc:magnitude_stats:moment3</th>\n","      <th>raw_acc:magnitude_stats:moment4</th>\n","      <th>raw_acc:magnitude_stats:percentile25</th>\n","      <th>raw_acc:magnitude_stats:percentile50</th>\n","      <th>raw_acc:magnitude_stats:percentile75</th>\n","      <th>raw_acc:magnitude_stats:value_entropy</th>\n","      <th>raw_acc:magnitude_stats:time_entropy</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band0</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band1</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band2</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band3</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band4</th>\n","      <th>raw_acc:magnitude_spectrum:spectral_entropy</th>\n","      <th>raw_acc:magnitude_autocorrelation:period</th>\n","      <th>raw_acc:magnitude_autocorrelation:normalized_ac</th>\n","      <th>raw_acc:3d:mean_x</th>\n","      <th>raw_acc:3d:mean_y</th>\n","      <th>raw_acc:3d:mean_z</th>\n","      <th>raw_acc:3d:std_x</th>\n","      <th>raw_acc:3d:std_y</th>\n","      <th>raw_acc:3d:std_z</th>\n","      <th>raw_acc:3d:ro_xy</th>\n","      <th>raw_acc:3d:ro_xz</th>\n","      <th>raw_acc:3d:ro_yz</th>\n","      <th>watch_acceleration:magnitude_stats:mean</th>\n","      <th>watch_acceleration:magnitude_stats:std</th>\n","      <th>watch_acceleration:magnitude_stats:moment3</th>\n","      <th>watch_acceleration:magnitude_stats:moment4</th>\n","      <th>watch_acceleration:magnitude_stats:percentile25</th>\n","      <th>watch_acceleration:magnitude_stats:percentile50</th>\n","      <th>watch_acceleration:magnitude_stats:percentile75</th>\n","      <th>watch_acceleration:magnitude_stats:value_entropy</th>\n","      <th>watch_acceleration:magnitude_stats:time_entropy</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band0</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band1</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band2</th>\n","      <th>...</th>\n","      <th>audio_naive:mfcc10:mean</th>\n","      <th>audio_naive:mfcc11:mean</th>\n","      <th>audio_naive:mfcc12:mean</th>\n","      <th>audio_naive:mfcc0:std</th>\n","      <th>audio_naive:mfcc1:std</th>\n","      <th>audio_naive:mfcc2:std</th>\n","      <th>audio_naive:mfcc3:std</th>\n","      <th>audio_naive:mfcc4:std</th>\n","      <th>audio_naive:mfcc5:std</th>\n","      <th>audio_naive:mfcc6:std</th>\n","      <th>audio_naive:mfcc7:std</th>\n","      <th>audio_naive:mfcc8:std</th>\n","      <th>audio_naive:mfcc9:std</th>\n","      <th>audio_naive:mfcc10:std</th>\n","      <th>audio_naive:mfcc11:std</th>\n","      <th>audio_naive:mfcc12:std</th>\n","      <th>lf_measurements:proximity</th>\n","      <th>lf_measurements:battery_level</th>\n","      <th>lf_measurements:screen_brightness</th>\n","      <th>Lying down</th>\n","      <th>Sitting</th>\n","      <th>Walking</th>\n","      <th>Running</th>\n","      <th>Bicycling</th>\n","      <th>Sleeping</th>\n","      <th>In class</th>\n","      <th>In a meeting</th>\n","      <th>Drive - I'm the driver</th>\n","      <th>Drive - I'm a passenger</th>\n","      <th>Cooking</th>\n","      <th>Shopping</th>\n","      <th>Strolling</th>\n","      <th>Cleaning</th>\n","      <th>Doing laundry</th>\n","      <th>Watching TV</th>\n","      <th>Computer work</th>\n","      <th>At the gym</th>\n","      <th>Stairs - going up</th>\n","      <th>Stairs - going down</th>\n","      <th>label_source</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>00EABED2-271D-49D8-B599-1D4A09240601</td>\n","      <td>1444079161</td>\n","      <td>0.996815</td>\n","      <td>0.003529</td>\n","      <td>-0.002786</td>\n","      <td>0.006496</td>\n","      <td>0.995203</td>\n","      <td>0.996825</td>\n","      <td>0.998502</td>\n","      <td>1.748756</td>\n","      <td>6.684605</td>\n","      <td>5.043970</td>\n","      <td>0.000042</td>\n","      <td>0.000364</td>\n","      <td>0.000761</td>\n","      <td>0.005429</td>\n","      <td>0.429853</td>\n","      <td>0.173685</td>\n","      <td>0.148988</td>\n","      <td>0.002331</td>\n","      <td>0.004614</td>\n","      <td>-0.996790</td>\n","      <td>0.003269</td>\n","      <td>0.003521</td>\n","      <td>0.003539</td>\n","      <td>0.106920</td>\n","      <td>0.516842</td>\n","      <td>0.255494</td>\n","      <td>1032.508157</td>\n","      <td>17.195149</td>\n","      <td>12.121621</td>\n","      <td>25.390972</td>\n","      <td>1024.812178</td>\n","      <td>1033.146650</td>\n","      <td>1041.537325</td>\n","      <td>2.220749</td>\n","      <td>6.214470</td>\n","      <td>5.045673</td>\n","      <td>0.004896</td>\n","      <td>0.023827</td>\n","      <td>...</td>\n","      <td>-0.907876</td>\n","      <td>-0.681724</td>\n","      <td>-0.683515</td>\n","      <td>2.276349</td>\n","      <td>1.271966</td>\n","      <td>1.177478</td>\n","      <td>0.670053</td>\n","      <td>0.389200</td>\n","      <td>0.535904</td>\n","      <td>0.468701</td>\n","      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    <th>2</th>\n","      <td>00EABED2-271D-49D8-B599-1D4A09240601</td>\n","      <td>1444079281</td>\n","      <td>0.996825</td>\n","      <td>0.003667</td>\n","      <td>0.003094</td>\n","      <td>0.006076</td>\n","      <td>0.994797</td>\n","      <td>0.996614</td>\n","      <td>0.998704</td>\n","      <td>2.031780</td>\n","      <td>6.684605</td>\n","      <td>5.043599</td>\n","      <td>0.000052</td>\n","      <td>0.000251</td>\n","      <td>0.000730</td>\n","      <td>0.008223</td>\n","      <td>0.429799</td>\n","      <td>0.173440</td>\n","      <td>0.186961</td>\n","      <td>0.004180</td>\n","      <td>0.005776</td>\n","      <td>-0.996785</td>\n","      <td>0.003567</td>\n","      <td>0.004051</td>\n","      <td>0.003681</td>\n","      <td>0.122432</td>\n","      <td>0.464881</td>\n","      <td>0.222375</td>\n","      <td>1031.643125</td>\n","      <td>17.119299</td>\n","      <td>10.606363</td>\n","      <td>24.535996</td>\n","      <td>1025.124383</td>\n","      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<td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>-1.020632</td>\n","      <td>-0.870663</td>\n","      <td>-0.468204</td>\n","      <td>2.790744</td>\n","      <td>1.595842</td>\n","      <td>0.854285</td>\n","      <td>0.561068</td>\n","      <td>0.432008</td>\n","      <td>0.446733</td>\n","      <td>0.390386</td>\n","      <td>0.406569</td>\n","      <td>0.363570</td>\n","      <td>0.366800</td>\n","      <td>0.324302</td>\n","      <td>0.297043</td>\n","      <td>0.276778</td>\n","      <td>0.0</td>\n","      <td>0.87</td>\n","      <td>0.406084</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>377343</th>\n","      <td>FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF</td>\n","      <td>1444234899</td>\n","      <td>1.026223</td>\n","      <td>0.195112</td>\n","      <td>0.255240</td>\n","      <td>0.379733</td>\n","      <td>0.983684</td>\n","      <td>1.000173</td>\n","      <td>1.030232</td>\n","      <td>1.484173</td>\n","      <td>6.668006</td>\n","      <td>5.031912</td>\n","      <td>1.109431</td>\n","      <td>2.380978</td>\n","      <td>1.761493</td>\n","      <td>2.263843</td>\n","      <td>0.688024</td>\n","      <td>0.852263</td>\n","      <td>0.223209</td>\n","      <td>0.192250</td>\n","      <td>0.723133</td>\n","      <td>-0.199525</td>\n","      <td>0.294796</td>\n","      <td>0.438057</td>\n","      <td>0.461207</td>\n","      <td>-0.219706</td>\n","      <td>0.350897</td>\n","      <td>0.097009</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>-0.583909</td>\n","      <td>-0.642581</td>\n","      <td>-0.703723</td>\n","      <td>4.160774</td>\n","      <td>1.870759</td>\n","      <td>1.166230</td>\n","      <td>0.798008</td>\n","      <td>0.685471</td>\n","      <td>0.639430</td>\n","      <td>0.580977</td>\n","      <td>0.442967</td>\n","      <td>0.573607</td>\n","      <td>0.438202</td>\n","      <td>0.414396</td>\n","      <td>0.353584</td>\n","      <td>0.370632</td>\n","      <td>0.0</td>\n","      <td>0.87</td>\n","      <td>0.406084</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>377344</th>\n","      <td>FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF</td>\n","      <td>1444234959</td>\n","      <td>1.123513</td>\n","      <td>0.273450</td>\n","      <td>0.312818</td>\n","      <td>0.480787</td>\n","      <td>0.966631</td>\n","      <td>1.051232</td>\n","      <td>1.229708</td>\n","      <td>1.808524</td>\n","      <td>6.656521</td>\n","      <td>5.039056</td>\n","      <td>1.906018</td>\n","      <td>2.991313</td>\n","      <td>1.275461</td>\n","      <td>1.965808</td>\n","      <td>0.755036</td>\n","      <td>1.064535</td>\n","      <td>0.274951</td>\n","      <td>-0.308654</td>\n","      <td>0.845493</td>\n","      <td>-0.173434</td>\n","      <td>0.459076</td>\n","      <td>0.439645</td>\n","      <td>0.304650</td>\n","      <td>-0.260843</td>\n","      <td>-0.208532</td>\n","      <td>0.587430</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>-0.463523</td>\n","      <td>-0.502507</td>\n","      <td>-0.437339</td>\n","      <td>4.797643</td>\n","      <td>1.308541</td>\n","      <td>0.748026</td>\n","      <td>0.560401</td>\n","      <td>0.640854</td>\n","      <td>0.557653</td>\n","      <td>0.358110</td>\n","      <td>0.323353</td>\n","      <td>0.270569</td>\n","      <td>0.299399</td>\n","      <td>0.255175</td>\n","      <td>0.251885</td>\n","      <td>0.272702</td>\n","      <td>0.0</td>\n","      <td>0.87</td>\n","      <td>0.406084</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>377345</th>\n","      <td>FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF</td>\n","      <td>1444235019</td>\n","      <td>1.020624</td>\n","      <td>0.135672</td>\n","      <td>0.104091</td>\n","      <td>0.185868</td>\n","      <td>0.915936</td>\n","      <td>1.018161</td>\n","      <td>1.115126</td>\n","      <td>2.402200</td>\n","      <td>6.675872</td>\n","      <td>5.036107</td>\n","      <td>0.185628</td>\n","      <td>2.014092</td>\n","      <td>0.341746</td>\n","      <td>0.909975</td>\n","      <td>0.502315</td>\n","      <td>1.067688</td>\n","      <td>0.587929</td>\n","      <td>-0.032776</td>\n","      <td>-0.476483</td>\n","      <td>-0.889445</td>\n","      <td>0.115057</td>\n","      <td>0.083429</td>\n","      <td>0.143732</td>\n","      <td>-0.131437</td>\n","      <td>-0.004327</td>\n","      <td>-0.005321</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>-0.559879</td>\n","      <td>-0.722400</td>\n","      <td>-0.686517</td>\n","      <td>1.599810</td>\n","      <td>1.072016</td>\n","      <td>0.416582</td>\n","      <td>0.315639</td>\n","      <td>0.273821</td>\n","      <td>0.351644</td>\n","      <td>0.270480</td>\n","      <td>0.277546</td>\n","      <td>0.227045</td>\n","      <td>0.243238</td>\n","      <td>0.200503</td>\n","      <td>0.227202</td>\n","      <td>0.212655</td>\n","      <td>0.0</td>\n","      <td>0.86</td>\n","      <td>0.945178</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>-1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>377346 rows × 139 columns</p>\n","</div>"],"text/plain":["                                        uuid  ...  label_source\n","0       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","1       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","2       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","3       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","4       00EABED2-271D-49D8-B599-1D4A09240601  ...             2\n","...                                      ...  ...           ...\n","377341  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377342  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377343  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377344  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377345  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","\n","[377346 rows x 139 columns]"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","metadata":{"id":"S0wazWY2rciM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635928425580,"user_tz":-60,"elapsed":827,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"0cffac58-648f-4361-8111-6a7cbb1a7591"},"source":["print(\"Number of NaN values in the dataset: \",final_dataset.isna().sum().sum())\n","noNaN_each_row = (final_dataset.count())\n","total_noNaN = 0\n","for value in noNaN_each_row:\n","  total_noNaN+=value\n","\n","print(\"Number of no-NaN values in the dataset: \",total_noNaN)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of NaN values in the dataset:  11978529\n","Number of no-NaN values in the dataset:  40472565\n"]}]},{"cell_type":"markdown","metadata":{"id":"o0kHg4jwrfjc"},"source":["There are still many nan values. But we can notice that many nan values are in the column labels (Lying down, Sitting ,....): for example there are rows with Sitting = 1.0, and all the other labels nan. \n","\n","We simply have to substitute all these label columns with a single column 'CLASS' that is 1 for static labels(those rows where Sitting, Lying down or other static labels =1), 0 for movement ones(those rows were Cookin, shopping, cleaning or other movement labels = 1)."]},{"cell_type":"code","metadata":{"id":"oOyBNAL4rtwy","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1635928425582,"user_tz":-60,"elapsed":16,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"3fdf685c-ecc8-47ee-8d11-1f25d90c40e1"},"source":["final_dataset"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>uuid</th>\n","      <th>timestamp</th>\n","      <th>raw_acc:magnitude_stats:mean</th>\n","      <th>raw_acc:magnitude_stats:std</th>\n","      <th>raw_acc:magnitude_stats:moment3</th>\n","      <th>raw_acc:magnitude_stats:moment4</th>\n","      <th>raw_acc:magnitude_stats:percentile25</th>\n","      <th>raw_acc:magnitude_stats:percentile50</th>\n","      <th>raw_acc:magnitude_stats:percentile75</th>\n","      <th>raw_acc:magnitude_stats:value_entropy</th>\n","      <th>raw_acc:magnitude_stats:time_entropy</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band0</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band1</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band2</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band3</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band4</th>\n","      <th>raw_acc:magnitude_spectrum:spectral_entropy</th>\n","      <th>raw_acc:magnitude_autocorrelation:period</th>\n","      <th>raw_acc:magnitude_autocorrelation:normalized_ac</th>\n","      <th>raw_acc:3d:mean_x</th>\n","      <th>raw_acc:3d:mean_y</th>\n","      <th>raw_acc:3d:mean_z</th>\n","      <th>raw_acc:3d:std_x</th>\n","      <th>raw_acc:3d:std_y</th>\n","      <th>raw_acc:3d:std_z</th>\n","      <th>raw_acc:3d:ro_xy</th>\n","      <th>raw_acc:3d:ro_xz</th>\n","      <th>raw_acc:3d:ro_yz</th>\n","      <th>watch_acceleration:magnitude_stats:mean</th>\n","      <th>watch_acceleration:magnitude_stats:std</th>\n","      <th>watch_acceleration:magnitude_stats:moment3</th>\n","      <th>watch_acceleration:magnitude_stats:moment4</th>\n","      <th>watch_acceleration:magnitude_stats:percentile25</th>\n","      <th>watch_acceleration:magnitude_stats:percentile50</th>\n","      <th>watch_acceleration:magnitude_stats:percentile75</th>\n","      <th>watch_acceleration:magnitude_stats:value_entropy</th>\n","      <th>watch_acceleration:magnitude_stats:time_entropy</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band0</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band1</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band2</th>\n","      <th>...</th>\n","      <th>audio_naive:mfcc10:mean</th>\n","      <th>audio_naive:mfcc11:mean</th>\n","      <th>audio_naive:mfcc12:mean</th>\n","      <th>audio_naive:mfcc0:std</th>\n","      <th>audio_naive:mfcc1:std</th>\n","      <th>audio_naive:mfcc2:std</th>\n","      <th>audio_naive:mfcc3:std</th>\n","      <th>audio_naive:mfcc4:std</th>\n","      <th>audio_naive:mfcc5:std</th>\n","      <th>audio_naive:mfcc6:std</th>\n","      <th>audio_naive:mfcc7:std</th>\n","      <th>audio_naive:mfcc8:std</th>\n","      <th>audio_naive:mfcc9:std</th>\n","      <th>audio_naive:mfcc10:std</th>\n","      <th>audio_naive:mfcc11:std</th>\n","      <th>audio_naive:mfcc12:std</th>\n","      <th>lf_measurements:proximity</th>\n","      <th>lf_measurements:battery_level</th>\n","      <th>lf_measurements:screen_brightness</th>\n","      <th>Lying down</th>\n","      <th>Sitting</th>\n","      <th>Walking</th>\n","      <th>Running</th>\n","      <th>Bicycling</th>\n","      <th>Sleeping</th>\n","      <th>In class</th>\n","      <th>In a meeting</th>\n","      <th>Drive - I'm the driver</th>\n","      <th>Drive - I'm a passenger</th>\n","      <th>Cooking</th>\n","      <th>Shopping</th>\n","      <th>Strolling</th>\n","      <th>Cleaning</th>\n","      <th>Doing laundry</th>\n","      <th>Watching TV</th>\n","      <th>Computer work</th>\n","      <th>At the gym</th>\n","      <th>Stairs - going up</th>\n","      <th>Stairs - going down</th>\n","      <th>label_source</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>00EABED2-271D-49D8-B599-1D4A09240601</td>\n","      <td>1444079161</td>\n","      <td>0.996815</td>\n","      <td>0.003529</td>\n","      <td>-0.002786</td>\n","      <td>0.006496</td>\n","      <td>0.995203</td>\n","      <td>0.996825</td>\n","      <td>0.998502</td>\n","      <td>1.748756</td>\n","      <td>6.684605</td>\n","      <td>5.043970</td>\n","      <td>0.000042</td>\n","      <td>0.000364</td>\n","      <td>0.000761</td>\n","      <td>0.005429</td>\n","      <td>0.429853</td>\n","      <td>0.173685</td>\n","      <td>0.148988</td>\n","      <td>0.002331</td>\n","      <td>0.004614</td>\n","      <td>-0.996790</td>\n","      <td>0.003269</td>\n","      <td>0.003521</td>\n","      <td>0.003539</td>\n","      <td>0.106920</td>\n","      <td>0.516842</td>\n","    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<tr>\n","      <th>377344</th>\n","      <td>FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF</td>\n","      <td>1444234959</td>\n","      <td>1.123513</td>\n","      <td>0.273450</td>\n","      <td>0.312818</td>\n","      <td>0.480787</td>\n","      <td>0.966631</td>\n","      <td>1.051232</td>\n","      <td>1.229708</td>\n","      <td>1.808524</td>\n","      <td>6.656521</td>\n","      <td>5.039056</td>\n","      <td>1.906018</td>\n","      <td>2.991313</td>\n","      <td>1.275461</td>\n","      <td>1.965808</td>\n","      <td>0.755036</td>\n","      <td>1.064535</td>\n","      <td>0.274951</td>\n","      <td>-0.308654</td>\n","      <td>0.845493</td>\n","      <td>-0.173434</td>\n","      <td>0.459076</td>\n","      <td>0.439645</td>\n","      <td>0.304650</td>\n","      <td>-0.260843</td>\n","      <td>-0.208532</td>\n","      <td>0.587430</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      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-1\n","377342  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377343  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377344  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","377345  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...            -1\n","\n","[377346 rows x 139 columns]"]},"metadata":{},"execution_count":21}]},{"cell_type":"markdown","metadata":{"id":"0vb-8-U24FgH"},"source":["Let's set CLASS= 1 for static, CLASS=0 for movement "]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"IN7CENt3toWm","executionInfo":{"status":"ok","timestamp":1635928499521,"user_tz":-60,"elapsed":73951,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"cfd3e85d-fc9c-4c22-b47a-955792c64023"},"source":["final_dataset['CLASS'] = 0 #default\n","\n","for index, row in final_dataset.iterrows():\n","  for label in static_labels:\n","    if(row[label]==1.0):\n","      final_dataset.at[index, 'CLASS'] = 1\n","\n","\n","final_dataset\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>uuid</th>\n","      <th>timestamp</th>\n","      <th>raw_acc:magnitude_stats:mean</th>\n","      <th>raw_acc:magnitude_stats:std</th>\n","      <th>raw_acc:magnitude_stats:moment3</th>\n","      <th>raw_acc:magnitude_stats:moment4</th>\n","      <th>raw_acc:magnitude_stats:percentile25</th>\n","      <th>raw_acc:magnitude_stats:percentile50</th>\n","      <th>raw_acc:magnitude_stats:percentile75</th>\n","      <th>raw_acc:magnitude_stats:value_entropy</th>\n","      <th>raw_acc:magnitude_stats:time_entropy</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band0</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band1</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band2</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band3</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band4</th>\n","      <th>raw_acc:magnitude_spectrum:spectral_entropy</th>\n","      <th>raw_acc:magnitude_autocorrelation:period</th>\n","      <th>raw_acc:magnitude_autocorrelation:normalized_ac</th>\n","      <th>raw_acc:3d:mean_x</th>\n","      <th>raw_acc:3d:mean_y</th>\n","      <th>raw_acc:3d:mean_z</th>\n","      <th>raw_acc:3d:std_x</th>\n","      <th>raw_acc:3d:std_y</th>\n","      <th>raw_acc:3d:std_z</th>\n","      <th>raw_acc:3d:ro_xy</th>\n","      <th>raw_acc:3d:ro_xz</th>\n","      <th>raw_acc:3d:ro_yz</th>\n","      <th>watch_acceleration:magnitude_stats:mean</th>\n","      <th>watch_acceleration:magnitude_stats:std</th>\n","      <th>watch_acceleration:magnitude_stats:moment3</th>\n","      <th>watch_acceleration:magnitude_stats:moment4</th>\n","      <th>watch_acceleration:magnitude_stats:percentile25</th>\n","      <th>watch_acceleration:magnitude_stats:percentile50</th>\n","      <th>watch_acceleration:magnitude_stats:percentile75</th>\n","      <th>watch_acceleration:magnitude_stats:value_entropy</th>\n","      <th>watch_acceleration:magnitude_stats:time_entropy</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band0</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band1</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band2</th>\n","      <th>...</th>\n","      <th>audio_naive:mfcc11:mean</th>\n","      <th>audio_naive:mfcc12:mean</th>\n","      <th>audio_naive:mfcc0:std</th>\n","      <th>audio_naive:mfcc1:std</th>\n","      <th>audio_naive:mfcc2:std</th>\n","      <th>audio_naive:mfcc3:std</th>\n","      <th>audio_naive:mfcc4:std</th>\n","      <th>audio_naive:mfcc5:std</th>\n","      <th>audio_naive:mfcc6:std</th>\n","      <th>audio_naive:mfcc7:std</th>\n","      <th>audio_naive:mfcc8:std</th>\n","      <th>audio_naive:mfcc9:std</th>\n","      <th>audio_naive:mfcc10:std</th>\n","      <th>audio_naive:mfcc11:std</th>\n","      <th>audio_naive:mfcc12:std</th>\n","      <th>lf_measurements:proximity</th>\n","      <th>lf_measurements:battery_level</th>\n","      <th>lf_measurements:screen_brightness</th>\n","      <th>Lying down</th>\n","      <th>Sitting</th>\n","      <th>Walking</th>\n","      <th>Running</th>\n","      <th>Bicycling</th>\n","      <th>Sleeping</th>\n","      <th>In class</th>\n","      <th>In a meeting</th>\n","      <th>Drive - I'm the driver</th>\n","      <th>Drive - I'm a passenger</th>\n","      <th>Cooking</th>\n","      <th>Shopping</th>\n","      <th>Strolling</th>\n","      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<td>1444234899</td>\n","      <td>1.026223</td>\n","      <td>0.195112</td>\n","      <td>0.255240</td>\n","      <td>0.379733</td>\n","      <td>0.983684</td>\n","      <td>1.000173</td>\n","      <td>1.030232</td>\n","      <td>1.484173</td>\n","      <td>6.668006</td>\n","      <td>5.031912</td>\n","      <td>1.109431</td>\n","      <td>2.380978</td>\n","      <td>1.761493</td>\n","      <td>2.263843</td>\n","      <td>0.688024</td>\n","      <td>0.852263</td>\n","      <td>0.223209</td>\n","      <td>0.192250</td>\n","      <td>0.723133</td>\n","      <td>-0.199525</td>\n","      <td>0.294796</td>\n","      <td>0.438057</td>\n","      <td>0.461207</td>\n","      <td>-0.219706</td>\n","      <td>0.350897</td>\n","      <td>0.097009</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>-0.642581</td>\n","      <td>-0.703723</td>\n","      <td>4.160774</td>\n","      <td>1.870759</td>\n","      <td>1.166230</td>\n","      <td>0.798008</td>\n","      <td>0.685471</td>\n","      <td>0.639430</td>\n","      <td>0.580977</td>\n","      <td>0.442967</td>\n","      <td>0.573607</td>\n","      <td>0.438202</td>\n","      <td>0.414396</td>\n","      <td>0.353584</td>\n","      <td>0.370632</td>\n","      <td>0.0</td>\n","      <td>0.87</td>\n","      <td>0.406084</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>-1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>377344</th>\n","      <td>FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF</td>\n","      <td>1444234959</td>\n","      <td>1.123513</td>\n","      <td>0.273450</td>\n","      <td>0.312818</td>\n","      <td>0.480787</td>\n","      <td>0.966631</td>\n","      <td>1.051232</td>\n","      <td>1.229708</td>\n","      <td>1.808524</td>\n","      <td>6.656521</td>\n","      <td>5.039056</td>\n","      <td>1.906018</td>\n","      <td>2.991313</td>\n","      <td>1.275461</td>\n","      <td>1.965808</td>\n","      <td>0.755036</td>\n","      <td>1.064535</td>\n","      <td>0.274951</td>\n","      <td>-0.308654</td>\n","      <td>0.845493</td>\n","      <td>-0.173434</td>\n","      <td>0.459076</td>\n","      <td>0.439645</td>\n","      <td>0.304650</td>\n","      <td>-0.260843</td>\n","      <td>-0.208532</td>\n","      <td>0.587430</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>-0.502507</td>\n","      <td>-0.437339</td>\n","      <td>4.797643</td>\n","      <td>1.308541</td>\n","      <td>0.748026</td>\n","      <td>0.560401</td>\n","      <td>0.640854</td>\n","      <td>0.557653</td>\n","      <td>0.358110</td>\n","      <td>0.323353</td>\n","      <td>0.270569</td>\n","      <td>0.299399</td>\n","      <td>0.255175</td>\n","      <td>0.251885</td>\n","      <td>0.272702</td>\n","      <td>0.0</td>\n","      <td>0.87</td>\n","      <td>0.406084</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>-1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>377345</th>\n","      <td>FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF</td>\n","      <td>1444235019</td>\n","      <td>1.020624</td>\n","      <td>0.135672</td>\n","      <td>0.104091</td>\n","      <td>0.185868</td>\n","      <td>0.915936</td>\n","      <td>1.018161</td>\n","      <td>1.115126</td>\n","      <td>2.402200</td>\n","      <td>6.675872</td>\n","      <td>5.036107</td>\n","      <td>0.185628</td>\n","      <td>2.014092</td>\n","      <td>0.341746</td>\n","      <td>0.909975</td>\n","      <td>0.502315</td>\n","      <td>1.067688</td>\n","      <td>0.587929</td>\n","      <td>-0.032776</td>\n","      <td>-0.476483</td>\n","      <td>-0.889445</td>\n","      <td>0.115057</td>\n","      <td>0.083429</td>\n","      <td>0.143732</td>\n","      <td>-0.131437</td>\n","      <td>-0.004327</td>\n","      <td>-0.005321</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>-0.722400</td>\n","      <td>-0.686517</td>\n","      <td>1.599810</td>\n","      <td>1.072016</td>\n","      <td>0.416582</td>\n","      <td>0.315639</td>\n","      <td>0.273821</td>\n","      <td>0.351644</td>\n","      <td>0.270480</td>\n","      <td>0.277546</td>\n","      <td>0.227045</td>\n","      <td>0.243238</td>\n","      <td>0.200503</td>\n","      <td>0.227202</td>\n","      <td>0.212655</td>\n","      <td>0.0</td>\n","      <td>0.86</td>\n","      <td>0.945178</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>-1</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>377346 rows × 140 columns</p>\n","</div>"],"text/plain":["                                        uuid   timestamp  ...  label_source  CLASS\n","0       00EABED2-271D-49D8-B599-1D4A09240601  1444079161  ...             2      1\n","1       00EABED2-271D-49D8-B599-1D4A09240601  1444079221  ...             2      1\n","2       00EABED2-271D-49D8-B599-1D4A09240601  1444079281  ...             2      1\n","3       00EABED2-271D-49D8-B599-1D4A09240601  1444079341  ...             2      1\n","4       00EABED2-271D-49D8-B599-1D4A09240601  1444079431  ...             2      1\n","...                                      ...         ...  ...           ...    ...\n","377341  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  1444234779  ...            -1      0\n","377342  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  1444234839  ...            -1      0\n","377343  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  1444234899  ...            -1      0\n","377344  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  1444234959  ...            -1      0\n","377345  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  1444235019  ...            -1      0\n","\n","[377346 rows x 140 columns]"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","metadata":{"id":"S5kxjkCX0PWf"},"source":["# for index, row in final_dataset.iterrows():\n","#   for label in static_labels+movement_labels:\n","#     if(row[label]=='NaN'):\n","#       print(\"OH\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qIPq4CqDwH2H"},"source":["# label_indexs = []\n","\n","# for label in static_labels+movement_labels:\n","#   label_indexs.append(final_dataset.columns.get_loc(label))\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"mWRgW5rJ2yLJ"},"source":["# final_dataset[final_dataset.columns[118:]]"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"fffmhF325P1P"},"source":["Now let's see if there are rows with all nan values in labels' columns: this means that if there are rows with Sitting = nan, Lying down = nan, Cooking = nan ... and all the other labels = nan, we have to delete these rows.\n"]},{"cell_type":"code","metadata":{"id":"rkJJe1g4ol-Q"},"source":["import math\n","\n","row_with_allNan = True\n","rowIndex_toDel = []\n","\n","for index, row in final_dataset.iterrows():\n","  row_with_allNan = True\n","  # print(\"Riga \",index)\n","  for i in range (118, len(row)-2):\n","    # print(final_dataset.columns[i], row[i])\n","    if(math.isnan(row[i])):\n","      ;\n","    else:\n","      row_with_allNan = False\n","      # print(\"No-nan value: cambia riga\")\n","      break\n","  if(row_with_allNan):\n","    rowIndex_toDel.append(index)\n","    \n","  "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"6plQJXLqyAMx","executionInfo":{"status":"ok","timestamp":1635928556252,"user_tz":-60,"elapsed":546,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"c9485148-07de-4422-85a9-04c81caca172"},"source":["final_dataset_copy = final_dataset.copy()\n","\n","final_dataset_copy.drop(labels = rowIndex_toDel, axis = 0, inplace=True)\n","\n","final_dataset_copy"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>uuid</th>\n","      <th>timestamp</th>\n","      <th>raw_acc:magnitude_stats:mean</th>\n","      <th>raw_acc:magnitude_stats:std</th>\n","      <th>raw_acc:magnitude_stats:moment3</th>\n","      <th>raw_acc:magnitude_stats:moment4</th>\n","      <th>raw_acc:magnitude_stats:percentile25</th>\n","      <th>raw_acc:magnitude_stats:percentile50</th>\n","      <th>raw_acc:magnitude_stats:percentile75</th>\n","      <th>raw_acc:magnitude_stats:value_entropy</th>\n","      <th>raw_acc:magnitude_stats:time_entropy</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band0</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band1</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band2</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band3</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band4</th>\n","      <th>raw_acc:magnitude_spectrum:spectral_entropy</th>\n","      <th>raw_acc:magnitude_autocorrelation:period</th>\n","      <th>raw_acc:magnitude_autocorrelation:normalized_ac</th>\n","      <th>raw_acc:3d:mean_x</th>\n","      <th>raw_acc:3d:mean_y</th>\n","      <th>raw_acc:3d:mean_z</th>\n","      <th>raw_acc:3d:std_x</th>\n","      <th>raw_acc:3d:std_y</th>\n","      <th>raw_acc:3d:std_z</th>\n","      <th>raw_acc:3d:ro_xy</th>\n","      <th>raw_acc:3d:ro_xz</th>\n","      <th>raw_acc:3d:ro_yz</th>\n","      <th>watch_acceleration:magnitude_stats:mean</th>\n","      <th>watch_acceleration:magnitude_stats:std</th>\n","      <th>watch_acceleration:magnitude_stats:moment3</th>\n","      <th>watch_acceleration:magnitude_stats:moment4</th>\n","      <th>watch_acceleration:magnitude_stats:percentile25</th>\n","      <th>watch_acceleration:magnitude_stats:percentile50</th>\n","      <th>watch_acceleration:magnitude_stats:percentile75</th>\n","      <th>watch_acceleration:magnitude_stats:value_entropy</th>\n","      <th>watch_acceleration:magnitude_stats:time_entropy</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band0</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band1</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band2</th>\n","      <th>...</th>\n","      <th>audio_naive:mfcc11:mean</th>\n","      <th>audio_naive:mfcc12:mean</th>\n","      <th>audio_naive:mfcc0:std</th>\n","      <th>audio_naive:mfcc1:std</th>\n","      <th>audio_naive:mfcc2:std</th>\n","      <th>audio_naive:mfcc3:std</th>\n","      <th>audio_naive:mfcc4:std</th>\n","      <th>audio_naive:mfcc5:std</th>\n","      <th>audio_naive:mfcc6:std</th>\n","      <th>audio_naive:mfcc7:std</th>\n","      <th>audio_naive:mfcc8:std</th>\n","      <th>audio_naive:mfcc9:std</th>\n","      <th>audio_naive:mfcc10:std</th>\n","      <th>audio_naive:mfcc11:std</th>\n","      <th>audio_naive:mfcc12:std</th>\n","      <th>lf_measurements:proximity</th>\n","      <th>lf_measurements:battery_level</th>\n","      <th>lf_measurements:screen_brightness</th>\n","      <th>Lying down</th>\n","      <th>Sitting</th>\n","      <th>Walking</th>\n","      <th>Running</th>\n","      <th>Bicycling</th>\n","      <th>Sleeping</th>\n","      <th>In class</th>\n","      <th>In a meeting</th>\n","      <th>Drive - I'm the driver</th>\n","      <th>Drive - I'm a passenger</th>\n","      <th>Cooking</th>\n","      <th>Shopping</th>\n","      <th>Strolling</th>\n","      <th>Cleaning</th>\n","      <th>Doing laundry</th>\n","      <th>Watching TV</th>\n","      <th>Computer work</th>\n","      <th>At the gym</th>\n","      <th>Stairs - going up</th>\n","      <th>Stairs - going down</th>\n","      <th>label_source</th>\n","      <th>CLASS</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>00EABED2-271D-49D8-B599-1D4A09240601</td>\n","      <td>1444079161</td>\n","      <td>0.996815</td>\n","      <td>0.003529</td>\n","      <td>-0.002786</td>\n","      <td>0.006496</td>\n","      <td>0.995203</td>\n","      <td>0.996825</td>\n","      <td>0.998502</td>\n","      <td>1.748756</td>\n","      <td>6.684605</td>\n","      <td>5.043970</td>\n","      <td>0.000042</td>\n","      <td>0.000364</td>\n","      <td>0.000761</td>\n","      <td>0.005429</td>\n","      <td>0.429853</td>\n","      <td>0.173685</td>\n","      <td>0.148988</td>\n","      <td>0.002331</td>\n","      <td>0.004614</td>\n","      <td>-0.996790</td>\n","      <td>0.003269</td>\n","      <td>0.003521</td>\n","      <td>0.003539</td>\n","      <td>0.106920</td>\n","      <td>0.516842</td>\n","      <td>0.255494</td>\n","      <td>1032.508157</td>\n","      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<td>0.233415</td>\n","      <td>0.200681</td>\n","      <td>0.183018</td>\n","      <td>0.178549</td>\n","      <td>0.0</td>\n","      <td>0.93</td>\n","      <td>0.000015</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>377323</th>\n","      <td>FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF</td>\n","      <td>1444233688</td>\n","      <td>1.341859</td>\n","      <td>0.773106</td>\n","      <td>0.776163</td>\n","      <td>1.095507</td>\n","      <td>0.827742</td>\n","      <td>1.173246</td>\n","      <td>1.754705</td>\n","      <td>2.574067</td>\n","      <td>6.524614</td>\n","      <td>4.819947</td>\n","      <td>0.880762</td>\n","      <td>4.445558</td>\n","      <td>2.492145</td>\n","      <td>4.152807</td>\n","      <td>1.469691</td>\n","      <td>1.036381</td>\n","      <td>0.434195</td>\n","      <td>-0.069125</td>\n","      <td>1.045107</td>\n","      <td>-0.084946</td>\n","      <td>0.481218</td>\n","      <td>0.896641</td>\n","      <td>0.508428</td>\n","      <td>-0.014607</td>\n","      <td>-0.129393</td>\n","      <td>0.179742</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>-0.621415</td>\n","      <td>-0.570843</td>\n","      <td>2.756219</td>\n","      <td>1.205948</td>\n","      <td>0.795810</td>\n","      <td>0.465592</td>\n","      <td>0.366454</td>\n","      <td>0.303812</td>\n","      <td>0.252889</td>\n","      <td>0.291147</td>\n","      <td>0.238335</td>\n","      <td>0.213589</td>\n","      <td>0.198586</td>\n","      <td>0.198836</td>\n","      <td>0.207751</td>\n","      <td>0.0</td>\n","      <td>0.92</td>\n","      <td>0.000015</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>377324</th>\n","      <td>FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF</td>\n","      <td>1444233749</td>\n","      <td>1.164082</td>\n","      <td>0.333725</td>\n","      <td>0.180477</td>\n","      <td>0.447232</td>\n","      <td>0.916853</td>\n","      <td>1.162816</td>\n","      <td>1.415372</td>\n","      <td>2.351249</td>\n","      <td>6.642348</td>\n","      <td>5.002014</td>\n","      <td>1.578506</td>\n","      <td>3.533138</td>\n","      <td>1.892941</td>\n","      <td>1.379731</td>\n","      <td>0.754874</td>\n","      <td>0.989973</td>\n","      <td>0.479110</td>\n","      <td>0.722619</td>\n","      <td>0.666697</td>\n","      <td>0.330850</td>\n","      <td>0.406958</td>\n","      <td>0.390675</td>\n","      <td>0.268498</td>\n","      <td>0.283204</td>\n","      <td>0.039222</td>\n","      <td>-0.468622</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>-0.627151</td>\n","      <td>-0.528442</td>\n","      <td>1.180174</td>\n","      <td>0.691122</td>\n","      <td>0.425398</td>\n","      <td>0.369699</td>\n","      <td>0.289125</td>\n","      <td>0.310188</td>\n","      <td>0.238018</td>\n","      <td>0.234267</td>\n","      <td>0.234472</td>\n","      <td>0.235194</td>\n","      <td>0.193000</td>\n","      <td>0.175390</td>\n","      <td>0.179356</td>\n","      <td>0.0</td>\n","      <td>0.92</td>\n","      <td>0.456982</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>377325</th>\n","      <td>FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF</td>\n","      <td>1444233808</td>\n","      <td>1.136058</td>\n","      <td>0.332312</td>\n","      <td>0.296363</td>\n","      <td>0.492753</td>\n","      <td>0.922363</td>\n","      <td>1.085739</td>\n","      <td>1.346369</td>\n","      <td>2.312827</td>\n","      <td>6.642416</td>\n","      <td>5.015416</td>\n","      <td>1.984600</td>\n","      <td>3.662844</td>\n","      <td>1.338516</td>\n","      <td>1.410716</td>\n","      <td>0.847237</td>\n","      <td>1.004818</td>\n","      <td>0.448941</td>\n","      <td>0.488478</td>\n","      <td>0.071944</td>\n","      <td>-0.304263</td>\n","      <td>0.513951</td>\n","      <td>0.634718</td>\n","      <td>0.630621</td>\n","      <td>0.703853</td>\n","      <td>0.706985</td>\n","      <td>0.828545</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>-0.764358</td>\n","      <td>-0.532948</td>\n","      <td>1.164243</td>\n","      <td>0.654368</td>\n","      <td>0.539415</td>\n","      <td>0.381460</td>\n","      <td>0.321242</td>\n","      <td>0.243568</td>\n","      <td>0.250475</td>\n","      <td>0.225360</td>\n","      <td>0.221822</td>\n","      <td>0.212152</td>\n","      <td>0.211043</td>\n","      <td>0.207095</td>\n","      <td>0.196992</td>\n","      <td>0.0</td>\n","      <td>0.92</td>\n","      <td>0.987446</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>308320 rows × 140 columns</p>\n","</div>"],"text/plain":["                                        uuid   timestamp  ...  label_source  CLASS\n","0       00EABED2-271D-49D8-B599-1D4A09240601  1444079161  ...             2      1\n","1       00EABED2-271D-49D8-B599-1D4A09240601  1444079221  ...             2      1\n","2       00EABED2-271D-49D8-B599-1D4A09240601  1444079281  ...             2      1\n","3       00EABED2-271D-49D8-B599-1D4A09240601  1444079341  ...             2      1\n","4       00EABED2-271D-49D8-B599-1D4A09240601  1444079431  ...             2      1\n","...                                      ...         ...  ...           ...    ...\n","377321  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  1444233568  ...             2      0\n","377322  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  1444233628  ...             2      0\n","377323  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  1444233688  ...             2      0\n","377324  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  1444233749  ...             2      0\n","377325  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  1444233808  ...             2      0\n","\n","[308320 rows x 140 columns]"]},"metadata":{},"execution_count":27}]},{"cell_type":"markdown","metadata":{"id":"YUSMrGRg6TIe"},"source":["After deleting those rows, now we can remove all the labels' columns since we successfully added the CLASS column that has been constructed as explained before."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"oJXKi1V70ZDT","executionInfo":{"status":"ok","timestamp":1635928556789,"user_tz":-60,"elapsed":550,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"a51902a8-680c-49da-aa7e-75744d30d729"},"source":["print(final_dataset_copy.columns[118:-1])\n","\n","final_dataset_copy.drop(labels = final_dataset_copy.columns[118:-1], axis = 1, inplace= True)\n","\n","final_dataset_copy\n"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Index(['Lying down', 'Sitting', 'Walking', 'Running', 'Bicycling', 'Sleeping',\n","       'In class', 'In a meeting', 'Drive - I'm the driver',\n","       'Drive - I'm a passenger', 'Cooking', 'Shopping', 'Strolling',\n","       'Cleaning', 'Doing laundry', 'Watching TV', 'Computer work',\n","       'At the gym', 'Stairs - going up', 'Stairs - going down',\n","       'label_source'],\n","      dtype='object')\n"]},{"output_type":"execute_result","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>uuid</th>\n","      <th>timestamp</th>\n","      <th>raw_acc:magnitude_stats:mean</th>\n","      <th>raw_acc:magnitude_stats:std</th>\n","      <th>raw_acc:magnitude_stats:moment3</th>\n","      <th>raw_acc:magnitude_stats:moment4</th>\n","      <th>raw_acc:magnitude_stats:percentile25</th>\n","      <th>raw_acc:magnitude_stats:percentile50</th>\n","      <th>raw_acc:magnitude_stats:percentile75</th>\n","      <th>raw_acc:magnitude_stats:value_entropy</th>\n","      <th>raw_acc:magnitude_stats:time_entropy</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band0</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band1</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band2</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band3</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band4</th>\n","      <th>raw_acc:magnitude_spectrum:spectral_entropy</th>\n","      <th>raw_acc:magnitude_autocorrelation:period</th>\n","      <th>raw_acc:magnitude_autocorrelation:normalized_ac</th>\n","      <th>raw_acc:3d:mean_x</th>\n","      <th>raw_acc:3d:mean_y</th>\n","      <th>raw_acc:3d:mean_z</th>\n","      <th>raw_acc:3d:std_x</th>\n","      <th>raw_acc:3d:std_y</th>\n","      <th>raw_acc:3d:std_z</th>\n","      <th>raw_acc:3d:ro_xy</th>\n","      <th>raw_acc:3d:ro_xz</th>\n","      <th>raw_acc:3d:ro_yz</th>\n","      <th>watch_acceleration:magnitude_stats:mean</th>\n","      <th>watch_acceleration:magnitude_stats:std</th>\n","      <th>watch_acceleration:magnitude_stats:moment3</th>\n","      <th>watch_acceleration:magnitude_stats:moment4</th>\n","      <th>watch_acceleration:magnitude_stats:percentile25</th>\n","      <th>watch_acceleration:magnitude_stats:percentile50</th>\n","      <th>watch_acceleration:magnitude_stats:percentile75</th>\n","      <th>watch_acceleration:magnitude_stats:value_entropy</th>\n","      <th>watch_acceleration:magnitude_stats:time_entropy</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band0</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band1</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band2</th>\n","      <th>...</th>\n","      <th>location:best_horizontal_accuracy</th>\n","      <th>location:best_vertical_accuracy</th>\n","      <th>location:diameter</th>\n","      <th>location:log_diameter</th>\n","      <th>location_quick_features:std_lat</th>\n","      <th>location_quick_features:std_long</th>\n","      <th>location_quick_features:lat_change</th>\n","      <th>location_quick_features:long_change</th>\n","      <th>location_quick_features:mean_abs_lat_deriv</th>\n","      <th>location_quick_features:mean_abs_long_deriv</th>\n","      <th>audio_naive:mfcc0:mean</th>\n","      <th>audio_naive:mfcc1:mean</th>\n","      <th>audio_naive:mfcc2:mean</th>\n","      <th>audio_naive:mfcc3:mean</th>\n","      <th>audio_naive:mfcc4:mean</th>\n","      <th>audio_naive:mfcc5:mean</th>\n","      <th>audio_naive:mfcc6:mean</th>\n","      <th>audio_naive:mfcc7:mean</th>\n","      <th>audio_naive:mfcc8:mean</th>\n","      <th>audio_naive:mfcc9:mean</th>\n","      <th>audio_naive:mfcc10:mean</th>\n","      <th>audio_naive:mfcc11:mean</th>\n","      <th>audio_naive:mfcc12:mean</th>\n","      <th>audio_naive:mfcc0:std</th>\n","      <th>audio_naive:mfcc1:std</th>\n","      <th>audio_naive:mfcc2:std</th>\n","      <th>audio_naive:mfcc3:std</th>\n","      <th>audio_naive:mfcc4:std</th>\n","      <th>audio_naive:mfcc5:std</th>\n","      <th>audio_naive:mfcc6:std</th>\n","      <th>audio_naive:mfcc7:std</th>\n","      <th>audio_naive:mfcc8:std</th>\n","      <th>audio_naive:mfcc9:std</th>\n","      <th>audio_naive:mfcc10:std</th>\n","      <th>audio_naive:mfcc11:std</th>\n","      <th>audio_naive:mfcc12:std</th>\n","      <th>lf_measurements:proximity</th>\n","      <th>lf_measurements:battery_level</th>\n","      <th>lf_measurements:screen_brightness</th>\n","      <th>CLASS</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>00EABED2-271D-49D8-B599-1D4A09240601</td>\n","      <td>1444079161</td>\n","      <td>0.996815</td>\n","      <td>0.003529</td>\n","      <td>-0.002786</td>\n","      <td>0.006496</td>\n","      <td>0.995203</td>\n","      <td>0.996825</td>\n","      <td>0.998502</td>\n","      <td>1.748756</td>\n","      <td>6.684605</td>\n","      <td>5.043970</td>\n","      <td>0.000042</td>\n","      <td>0.000364</td>\n","      <td>0.000761</td>\n","      <td>0.005429</td>\n","      <td>0.429853</td>\n","      <td>0.173685</td>\n","      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<td>1.018056</td>\n","      <td>0.468301</td>\n","      <td>-0.067906</td>\n","      <td>1.052425</td>\n","      <td>-0.067219</td>\n","      <td>0.478224</td>\n","      <td>0.882011</td>\n","      <td>0.492160</td>\n","      <td>0.036155</td>\n","      <td>-0.113936</td>\n","      <td>0.226918</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>81.662434</td>\n","      <td>4.402594</td>\n","      <td>0.000251</td>\n","      <td>0.000164</td>\n","      <td>0.000638</td>\n","      <td>0.000792</td>\n","      <td>0.000095</td>\n","      <td>0.000032</td>\n","      <td>-7.774111</td>\n","      <td>1.690882</td>\n","      <td>-1.753544</td>\n","      <td>-0.433618</td>\n","      <td>-1.909998</td>\n","      <td>-0.514209</td>\n","      <td>-1.054091</td>\n","      <td>-0.692147</td>\n","      <td>-0.650268</td>\n","      <td>-0.745260</td>\n","      <td>-0.399710</td>\n","      <td>-0.611605</td>\n","      <td>-0.541675</td>\n","      <td>2.915772</td>\n","      <td>1.184854</td>\n","      <td>0.733604</td>\n","      <td>0.500369</td>\n","      <td>0.424783</td>\n","      <td>0.346504</td>\n","      <td>0.271555</td>\n","      <td>0.259373</td>\n","      <td>0.255782</td>\n","      <td>0.233415</td>\n","      <td>0.200681</td>\n","      <td>0.183018</td>\n","      <td>0.178549</td>\n","      <td>0.0</td>\n","      <td>0.93</td>\n","      <td>0.000015</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>377323</th>\n","      <td>FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF</td>\n","      <td>1444233688</td>\n","      <td>1.341859</td>\n","      <td>0.773106</td>\n","      <td>0.776163</td>\n","      <td>1.095507</td>\n","      <td>0.827742</td>\n","      <td>1.173246</td>\n","      <td>1.754705</td>\n","      <td>2.574067</td>\n","      <td>6.524614</td>\n","      <td>4.819947</td>\n","      <td>0.880762</td>\n","      <td>4.445558</td>\n","      <td>2.492145</td>\n","      <td>4.152807</td>\n","      <td>1.469691</td>\n","      <td>1.036381</td>\n","      <td>0.434195</td>\n","      <td>-0.069125</td>\n","      <td>1.045107</td>\n","      <td>-0.084946</td>\n","      <td>0.481218</td>\n","      <td>0.896641</td>\n","      <td>0.508428</td>\n","      <td>-0.014607</td>\n","      <td>-0.129393</td>\n","      <td>0.179742</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>31.987068</td>\n","      <td>3.465332</td>\n","      <td>0.000095</td>\n","      <td>0.000089</td>\n","      <td>0.000251</td>\n","      <td>0.000146</td>\n","      <td>0.000017</td>\n","      <td>0.000045</td>\n","      <td>-9.083226</td>\n","      <td>1.928444</td>\n","      <td>-1.830741</td>\n","      <td>-0.423809</td>\n","      <td>-1.787859</td>\n","      <td>-0.396832</td>\n","      <td>-1.023771</td>\n","      <td>-0.694875</td>\n","      <td>-0.671679</td>\n","      <td>-0.762406</td>\n","      <td>-0.444701</td>\n","      <td>-0.621415</td>\n","      <td>-0.570843</td>\n","      <td>2.756219</td>\n","      <td>1.205948</td>\n","      <td>0.795810</td>\n","      <td>0.465592</td>\n","      <td>0.366454</td>\n","      <td>0.303812</td>\n","      <td>0.252889</td>\n","      <td>0.291147</td>\n","      <td>0.238335</td>\n","      <td>0.213589</td>\n","      <td>0.198586</td>\n","      <td>0.198836</td>\n","      <td>0.207751</td>\n","      <td>0.0</td>\n","      <td>0.92</td>\n","      <td>0.000015</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>377324</th>\n","      <td>FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF</td>\n","      <td>1444233749</td>\n","      <td>1.164082</td>\n","      <td>0.333725</td>\n","      <td>0.180477</td>\n","      <td>0.447232</td>\n","      <td>0.916853</td>\n","      <td>1.162816</td>\n","      <td>1.415372</td>\n","      <td>2.351249</td>\n","      <td>6.642348</td>\n","      <td>5.002014</td>\n","      <td>1.578506</td>\n","      <td>3.533138</td>\n","      <td>1.892941</td>\n","      <td>1.379731</td>\n","      <td>0.754874</td>\n","      <td>0.989973</td>\n","      <td>0.479110</td>\n","      <td>0.722619</td>\n","      <td>0.666697</td>\n","      <td>0.330850</td>\n","      <td>0.406958</td>\n","      <td>0.390675</td>\n","      <td>0.268498</td>\n","      <td>0.283204</td>\n","      <td>0.039222</td>\n","      <td>-0.468622</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>63.526585</td>\n","      <td>4.151458</td>\n","      <td>0.000165</td>\n","      <td>0.000144</td>\n","      <td>0.000584</td>\n","      <td>-0.000315</td>\n","      <td>0.000033</td>\n","      <td>0.000060</td>\n","      <td>-3.504425</td>\n","      <td>-0.155248</td>\n","      <td>-3.252557</td>\n","      <td>0.613563</td>\n","      <td>-1.700879</td>\n","      <td>-0.305183</td>\n","      <td>-1.432183</td>\n","      <td>-0.825732</td>\n","      <td>-0.511022</td>\n","      <td>-1.020042</td>\n","      <td>-0.671250</td>\n","      <td>-0.627151</td>\n","      <td>-0.528442</td>\n","      <td>1.180174</td>\n","      <td>0.691122</td>\n","      <td>0.425398</td>\n","      <td>0.369699</td>\n","      <td>0.289125</td>\n","      <td>0.310188</td>\n","      <td>0.238018</td>\n","      <td>0.234267</td>\n","      <td>0.234472</td>\n","      <td>0.235194</td>\n","      <td>0.193000</td>\n","      <td>0.175390</td>\n","      <td>0.179356</td>\n","      <td>0.0</td>\n","      <td>0.92</td>\n","      <td>0.456982</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>377325</th>\n","      <td>FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF</td>\n","      <td>1444233808</td>\n","      <td>1.136058</td>\n","      <td>0.332312</td>\n","      <td>0.296363</td>\n","      <td>0.492753</td>\n","      <td>0.922363</td>\n","      <td>1.085739</td>\n","      <td>1.346369</td>\n","      <td>2.312827</td>\n","      <td>6.642416</td>\n","      <td>5.015416</td>\n","      <td>1.984600</td>\n","      <td>3.662844</td>\n","      <td>1.338516</td>\n","      <td>1.410716</td>\n","      <td>0.847237</td>\n","      <td>1.004818</td>\n","      <td>0.448941</td>\n","      <td>0.488478</td>\n","      <td>0.071944</td>\n","      <td>-0.304263</td>\n","      <td>0.513951</td>\n","      <td>0.634718</td>\n","      <td>0.630621</td>\n","      <td>0.703853</td>\n","      <td>0.706985</td>\n","      <td>0.828545</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>62.430883</td>\n","      <td>4.134060</td>\n","      <td>0.000092</td>\n","      <td>0.000208</td>\n","      <td>0.000151</td>\n","      <td>0.000280</td>\n","      <td>0.000076</td>\n","      <td>0.000137</td>\n","      <td>-9.893793</td>\n","      <td>0.923950</td>\n","      <td>-2.176131</td>\n","      <td>-0.030906</td>\n","      <td>-1.619127</td>\n","      <td>-0.274928</td>\n","      <td>-1.214098</td>\n","      <td>-0.622256</td>\n","      <td>-0.715429</td>\n","      <td>-1.013833</td>\n","      <td>-0.658552</td>\n","      <td>-0.764358</td>\n","      <td>-0.532948</td>\n","      <td>1.164243</td>\n","      <td>0.654368</td>\n","      <td>0.539415</td>\n","      <td>0.381460</td>\n","      <td>0.321242</td>\n","      <td>0.243568</td>\n","      <td>0.250475</td>\n","      <td>0.225360</td>\n","      <td>0.221822</td>\n","      <td>0.212152</td>\n","      <td>0.211043</td>\n","      <td>0.207095</td>\n","      <td>0.196992</td>\n","      <td>0.0</td>\n","      <td>0.92</td>\n","      <td>0.987446</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>308320 rows × 119 columns</p>\n","</div>"],"text/plain":["                                        uuid  ...  CLASS\n","0       00EABED2-271D-49D8-B599-1D4A09240601  ...      1\n","1       00EABED2-271D-49D8-B599-1D4A09240601  ...      1\n","2       00EABED2-271D-49D8-B599-1D4A09240601  ...      1\n","3       00EABED2-271D-49D8-B599-1D4A09240601  ...      1\n","4       00EABED2-271D-49D8-B599-1D4A09240601  ...      1\n","...                                      ...  ...    ...\n","377321  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...      0\n","377322  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...      0\n","377323  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...      0\n","377324  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...      0\n","377325  FDAA70A1-42A3-4E3F-9AE3-3FDA412E03BF  ...      0\n","\n","[308320 rows x 119 columns]"]},"metadata":{},"execution_count":28}]},{"cell_type":"markdown","metadata":{"id":"krTJzNig6q4B"},"source":["Now let's count all the nan and nonan values in the overall dataset"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"q7U94X3h6pnl","executionInfo":{"status":"ok","timestamp":1635928556791,"user_tz":-60,"elapsed":15,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"114386a2-da52-425a-b0a1-574726774b5e"},"source":["print(\"Number of NaN values in the dataset: \",final_dataset_copy.isna().sum().sum())\n","noNaN_each_row = (final_dataset_copy.count())\n","total_noNaN = 0\n","for value in noNaN_each_row:\n","  total_noNaN+=value\n","\n","print(\"Number of no-NaN values in the dataset: \",total_noNaN)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of NaN values in the dataset:  5851131\n","Number of no-NaN values in the dataset:  30838949\n"]}]},{"cell_type":"markdown","metadata":{"id":"Q6M7bu9N6ETb"},"source":["Let's build a dataset without uuid and timestamp columns, without substituting the NaN values:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":439},"id":"4a8HBSzx6Dpf","executionInfo":{"status":"ok","timestamp":1635961756223,"user_tz":-60,"elapsed":912,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"29cde082-b3dd-4250-f91e-14a882f143d2"},"source":["# del final_dataset_copy['uuid']\n","del final_dataset_copy['timestamp']\n","final_dataset_copy"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>raw_acc:magnitude_stats:mean</th>\n","      <th>raw_acc:magnitude_stats:std</th>\n","      <th>raw_acc:magnitude_stats:moment3</th>\n","      <th>raw_acc:magnitude_stats:moment4</th>\n","      <th>raw_acc:magnitude_stats:percentile25</th>\n","      <th>raw_acc:magnitude_stats:percentile50</th>\n","      <th>raw_acc:magnitude_stats:percentile75</th>\n","      <th>raw_acc:magnitude_stats:value_entropy</th>\n","      <th>raw_acc:magnitude_stats:time_entropy</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band0</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band1</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band2</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band3</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band4</th>\n","      <th>raw_acc:magnitude_spectrum:spectral_entropy</th>\n","      <th>raw_acc:magnitude_autocorrelation:period</th>\n","      <th>raw_acc:magnitude_autocorrelation:normalized_ac</th>\n","      <th>raw_acc:3d:mean_x</th>\n","      <th>raw_acc:3d:mean_y</th>\n","      <th>raw_acc:3d:mean_z</th>\n","      <th>raw_acc:3d:std_x</th>\n","      <th>raw_acc:3d:std_y</th>\n","      <th>raw_acc:3d:std_z</th>\n","      <th>raw_acc:3d:ro_xy</th>\n","      <th>raw_acc:3d:ro_xz</th>\n","      <th>raw_acc:3d:ro_yz</th>\n","      <th>watch_acceleration:magnitude_stats:mean</th>\n","      <th>watch_acceleration:magnitude_stats:std</th>\n","      <th>watch_acceleration:magnitude_stats:moment3</th>\n","      <th>watch_acceleration:magnitude_stats:moment4</th>\n","      <th>watch_acceleration:magnitude_stats:percentile25</th>\n","      <th>watch_acceleration:magnitude_stats:percentile50</th>\n","      <th>watch_acceleration:magnitude_stats:percentile75</th>\n","      <th>watch_acceleration:magnitude_stats:value_entropy</th>\n","      <th>watch_acceleration:magnitude_stats:time_entropy</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band0</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band1</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band2</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band3</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band4</th>\n","      <th>...</th>\n","      <th>location:best_horizontal_accuracy</th>\n","      <th>location:best_vertical_accuracy</th>\n","      <th>location:diameter</th>\n","      <th>location:log_diameter</th>\n","      <th>location_quick_features:std_lat</th>\n","      <th>location_quick_features:std_long</th>\n","      <th>location_quick_features:lat_change</th>\n","      <th>location_quick_features:long_change</th>\n","      <th>location_quick_features:mean_abs_lat_deriv</th>\n","      <th>location_quick_features:mean_abs_long_deriv</th>\n","      <th>audio_naive:mfcc0:mean</th>\n","      <th>audio_naive:mfcc1:mean</th>\n","      <th>audio_naive:mfcc2:mean</th>\n","      <th>audio_naive:mfcc3:mean</th>\n","      <th>audio_naive:mfcc4:mean</th>\n","      <th>audio_naive:mfcc5:mean</th>\n","      <th>audio_naive:mfcc6:mean</th>\n","      <th>audio_naive:mfcc7:mean</th>\n","      <th>audio_naive:mfcc8:mean</th>\n","      <th>audio_naive:mfcc9:mean</th>\n","      <th>audio_naive:mfcc10:mean</th>\n","      <th>audio_naive:mfcc11:mean</th>\n","      <th>audio_naive:mfcc12:mean</th>\n","      <th>audio_naive:mfcc0:std</th>\n","      <th>audio_naive:mfcc1:std</th>\n","      <th>audio_naive:mfcc2:std</th>\n","      <th>audio_naive:mfcc3:std</th>\n","      <th>audio_naive:mfcc4:std</th>\n","      <th>audio_naive:mfcc5:std</th>\n","      <th>audio_naive:mfcc6:std</th>\n","      <th>audio_naive:mfcc7:std</th>\n","      <th>audio_naive:mfcc8:std</th>\n","      <th>audio_naive:mfcc9:std</th>\n","      <th>audio_naive:mfcc10:std</th>\n","      <th>audio_naive:mfcc11:std</th>\n","      <th>audio_naive:mfcc12:std</th>\n","      <th>lf_measurements:proximity</th>\n","      <th>lf_measurements:battery_level</th>\n","      <th>lf_measurements:screen_brightness</th>\n","      <th>CLASS</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.996815</td>\n","      <td>0.003529</td>\n","      <td>-0.002786</td>\n","      <td>0.006496</td>\n","      <td>0.995203</td>\n","      <td>0.996825</td>\n","      <td>0.998502</td>\n","      <td>1.748756</td>\n","      <td>6.684605</td>\n","      <td>5.043970</td>\n","      <td>0.000042</td>\n","      <td>0.000364</td>\n","      <td>0.000761</td>\n","      <td>0.005429</td>\n","      <td>0.429853</td>\n","      <td>0.173685</td>\n","      <td>0.148988</td>\n","      <td>0.002331</td>\n","      <td>0.004614</td>\n","      <td>-0.996790</td>\n","      <td>0.003269</td>\n","      <td>0.003521</td>\n","      <td>0.003539</td>\n","      <td>0.106920</td>\n","      <td>0.516842</td>\n","      <td>0.255494</td>\n","      <td>1032.508157</td>\n","      <td>17.195149</td>\n","      <td>12.121621</td>\n","      <td>25.390972</td>\n","      <td>1024.812178</td>\n","      <td>1033.146650</td>\n","      <td>1041.537325</td>\n","      <td>2.220749</td>\n","      <td>6.214470</td>\n","      <td>5.045673</td>\n","      <td>0.004896</td>\n","      <td>0.023827</td>\n","      <td>0.022606</td>\n","      <td>0.083165</td>\n","      <td>...</td>\n","      <td>65.0</td>\n","      <td>10.0</td>\n","      <td>10.102999</td>\n","      <td>2.312832</td>\n","      <td>0.000029</td>\n","      <td>0.000032</td>\n","      <td>-0.000067</td>\n","      <td>-0.000073</td>\n","      <td>0.000111</td>\n","      <td>0.000120</td>\n","      <td>-4.219657</td>\n","      <td>-0.012806</td>\n","      <td>-1.298291</td>\n","      <td>0.094373</td>\n","      <td>-1.220977</td>\n","      <td>-0.851300</td>\n","      <td>-1.656516</td>\n","      <td>-0.898563</td>\n","      <td>-0.503573</td>\n","      <td>-0.518072</td>\n","      <td>-0.907876</td>\n","      <td>-0.681724</td>\n","      <td>-0.683515</td>\n","      <td>2.276349</td>\n","      <td>1.271966</td>\n","      <td>1.177478</td>\n","      <td>0.670053</td>\n","      <td>0.389200</td>\n","      <td>0.535904</td>\n","      <td>0.468701</td>\n","      <td>0.358315</td>\n","      <td>0.401412</td>\n","      <td>0.408432</td>\n","      <td>0.277176</td>\n","      <td>0.416881</td>\n","      <td>0.263832</td>\n","      <td>0.0</td>\n","      <td>0.46</td>\n","      <td>0.381436</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.996864</td>\n","      <td>0.004172</td>\n","      <td>-0.003110</td>\n","      <td>0.007050</td>\n","      <td>0.994957</td>\n","      <td>0.996981</td>\n","      <td>0.998766</td>\n","      <td>1.935573</td>\n","      <td>6.684603</td>\n","      <td>5.043367</td>\n","      <td>0.000071</td>\n","      <td>0.000468</td>\n","      <td>0.000630</td>\n","      <td>0.007756</td>\n","      <td>0.429733</td>\n","      <td>0.057819</td>\n","      <td>0.207046</td>\n","      <td>0.003557</td>\n","      <td>0.005495</td>\n","      <td>-0.996832</td>\n","      <td>0.002489</td>\n","      <td>0.003772</td>\n","      <td>0.004177</td>\n","      <td>-0.079483</td>\n","      <td>0.357748</td>\n","      <td>0.036252</td>\n","      <td>1031.283026</td>\n","      <td>18.901671</td>\n","      <td>5.132795</td>\n","      <td>27.607064</td>\n","      <td>1018.115906</td>\n","      <td>1033.115676</td>\n","      <td>1041.537325</td>\n","      <td>2.355872</td>\n","      <td>6.214440</td>\n","      <td>5.042166</td>\n","      <td>0.003692</td>\n","      <td>0.025719</td>\n","      <td>0.022919</td>\n","      <td>0.128473</td>\n","      <td>...</td>\n","      <td>65.0</td>\n","      <td>10.0</td>\n","      <td>9.611984</td>\n","      <td>2.263011</td>\n","      <td>0.000030</td>\n","      <td>0.000025</td>\n","      <td>-0.000021</td>\n","      <td>0.000013</td>\n","      <td>0.000044</td>\n","      <td>0.000033</td>\n","      <td>-7.505606</td>\n","      <td>1.294372</td>\n","      <td>-0.745550</td>\n","      <td>-0.052791</td>\n","      <td>-1.220453</td>\n","      <td>-0.822848</td>\n","      <td>-1.559542</td>\n","      <td>-0.835154</td>\n","      <td>-0.665443</td>\n","      <td>-0.580521</td>\n","      <td>-0.865482</td>\n","      <td>-0.703950</td>\n","      <td>-0.718397</td>\n","      <td>2.231302</td>\n","      <td>1.232031</td>\n","      <td>1.098946</td>\n","      <td>0.669467</td>\n","      <td>0.499003</td>\n","      <td>0.584068</td>\n","      <td>0.525900</td>\n","      <td>0.435339</td>\n","      <td>0.459064</td>\n","      <td>0.391962</td>\n","      <td>0.341445</td>\n","      <td>0.362252</td>\n","      <td>0.298252</td>\n","      <td>0.0</td>\n","      <td>0.46</td>\n","      <td>0.381436</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.996825</td>\n","      <td>0.003667</td>\n","      <td>0.003094</td>\n","      <td>0.006076</td>\n","      <td>0.994797</td>\n","      <td>0.996614</td>\n","      <td>0.998704</td>\n","      <td>2.031780</td>\n","      <td>6.684605</td>\n","      <td>5.043599</td>\n","      <td>0.000052</td>\n","      <td>0.000251</td>\n","      <td>0.000730</td>\n","      <td>0.008223</td>\n","      <td>0.429799</td>\n","      <td>0.173440</td>\n","      <td>0.186961</td>\n","      <td>0.004180</td>\n","      <td>0.005776</td>\n","      <td>-0.996785</td>\n","      <td>0.003567</td>\n","      <td>0.004051</td>\n","      <td>0.003681</td>\n","      <td>0.122432</td>\n","      <td>0.464881</td>\n","      <td>0.222375</td>\n","      <td>1031.643125</td>\n","      <td>17.119299</td>\n","      <td>10.606363</td>\n","      <td>24.535996</td>\n","      <td>1025.124383</td>\n","      <td>1033.518263</td>\n","      <td>1041.506601</td>\n","      <td>2.322894</td>\n","      <td>6.214471</td>\n","      <td>5.046789</td>\n","      <td>0.007309</td>\n","      <td>0.020632</td>\n","      <td>0.026453</td>\n","      <td>0.079950</td>\n","      <td>...</td>\n","      <td>65.0</td>\n","      <td>10.0</td>\n","      <td>0.568155</td>\n","      <td>-0.565360</td>\n","      <td>0.000002</td>\n","      <td>0.000002</td>\n","      <td>-0.000005</td>\n","      <td>0.000003</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>-7.966769</td>\n","      <td>1.309082</td>\n","      <td>-0.770455</td>\n","      <td>0.080046</td>\n","      <td>-1.238489</td>\n","      <td>-0.922404</td>\n","      <td>-1.695809</td>\n","      <td>-0.864904</td>\n","      <td>-0.670674</td>\n","      <td>-0.580025</td>\n","      <td>-0.710762</td>\n","      <td>-0.694606</td>\n","      <td>-0.720886</td>\n","      <td>2.537650</td>\n","      <td>1.186965</td>\n","      <td>1.251340</td>\n","      <td>0.803958</td>\n","      <td>0.546688</td>\n","      <td>0.562475</td>\n","      <td>0.559488</td>\n","      <td>0.445827</td>\n","      <td>0.472388</td>\n","      <td>0.462703</td>\n","      <td>0.359176</td>\n","      <td>0.430171</td>\n","      <td>0.330055</td>\n","      <td>0.0</td>\n","      <td>0.46</td>\n","      <td>0.381436</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.996874</td>\n","      <td>0.003541</td>\n","      <td>0.000626</td>\n","      <td>0.006059</td>\n","      <td>0.995050</td>\n","      <td>0.996907</td>\n","      <td>0.998690</td>\n","      <td>1.865318</td>\n","      <td>6.684605</td>\n","      <td>5.043263</td>\n","      <td>0.000019</td>\n","      <td>0.000173</td>\n","      <td>0.000623</td>\n","      <td>0.003763</td>\n","      <td>0.429628</td>\n","      <td>0.173474</td>\n","      <td>0.479430</td>\n","      <td>0.004179</td>\n","      <td>0.005551</td>\n","      <td>-0.996836</td>\n","      <td>0.004202</td>\n","      <td>0.002919</td>\n","      <td>0.003559</td>\n","      <td>0.308841</td>\n","      <td>0.773514</td>\n","      <td>0.296194</td>\n","      <td>1032.123385</td>\n","      <td>17.796377</td>\n","      <td>10.444489</td>\n","      <td>27.338486</td>\n","      <td>1025.124383</td>\n","      <td>1033.518263</td>\n","      <td>1041.537325</td>\n","      <td>2.139388</td>\n","      <td>6.214460</td>\n","      <td>5.042475</td>\n","      <td>0.004308</td>\n","      <td>0.034187</td>\n","      <td>0.035234</td>\n","      <td>0.089009</td>\n","      <td>...</td>\n","      <td>65.0</td>\n","      <td>10.0</td>\n","      <td>2.097436</td>\n","      <td>0.740716</td>\n","      <td>0.000006</td>\n","      <td>0.000003</td>\n","      <td>0.000007</td>\n","      <td>0.000004</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>-5.367933</td>\n","      <td>0.182373</td>\n","      <td>-0.891874</td>\n","      <td>0.112003</td>\n","      <td>-1.437741</td>\n","      <td>-0.770266</td>\n","      <td>-1.646552</td>\n","      <td>-0.922945</td>\n","      <td>-0.614741</td>\n","      <td>-0.531608</td>\n","      <td>-0.836680</td>\n","      <td>-0.704615</td>\n","      <td>-0.719910</td>\n","      <td>2.179684</td>\n","      <td>1.410698</td>\n","      <td>1.111916</td>\n","      <td>0.821571</td>\n","      <td>0.519879</td>\n","      <td>0.572094</td>\n","      <td>0.591143</td>\n","      <td>0.467151</td>\n","      <td>0.453082</td>\n","      <td>0.459266</td>\n","      <td>0.325971</td>\n","      <td>0.379887</td>\n","      <td>0.389737</td>\n","      <td>0.0</td>\n","      <td>0.46</td>\n","      <td>0.381436</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.997371</td>\n","      <td>0.037653</td>\n","      <td>0.043389</td>\n","      <td>0.102332</td>\n","      <td>0.995548</td>\n","      <td>0.996860</td>\n","      <td>0.998205</td>\n","      <td>0.460806</td>\n","      <td>6.683904</td>\n","      <td>5.042779</td>\n","      <td>0.000858</td>\n","      <td>0.006035</td>\n","      <td>0.014974</td>\n","      <td>0.023291</td>\n","      <td>0.430164</td>\n","      <td>0.260603</td>\n","      <td>0.202305</td>\n","      <td>-0.008383</td>\n","      <td>-0.000009</td>\n","      <td>-0.994184</td>\n","      <td>0.074612</td>\n","      <td>0.023784</td>\n","      <td>0.039512</td>\n","      <td>-0.187758</td>\n","      <td>-0.204381</td>\n","      <td>0.062696</td>\n","      <td>1042.973796</td>\n","      <td>121.591905</td>\n","      <td>292.986177</td>\n","      <td>487.849018</td>\n","      <td>1022.436306</td>\n","      <td>1034.261089</td>\n","      <td>1047.938926</td>\n","      <td>0.589813</td>\n","      <td>6.158404</td>\n","      <td>5.061130</td>\n","      <td>0.442844</td>\n","      <td>1.154677</td>\n","      <td>1.265153</td>\n","      <td>2.547586</td>\n","      <td>...</td>\n","      <td>65.0</td>\n","      <td>10.0</td>\n","      <td>5.015308</td>\n","      <td>1.612495</td>\n","      <td>0.000011</td>\n","      <td>0.000011</td>\n","      <td>0.000002</td>\n","      <td>-0.000008</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>-13.415772</td>\n","      <td>0.908387</td>\n","      <td>-1.442258</td>\n","      <td>-0.294636</td>\n","      <td>-1.760813</td>\n","      <td>-1.104186</td>\n","      <td>-1.523959</td>\n","      <td>-1.005763</td>\n","      <td>-0.577766</td>\n","      <td>-0.579184</td>\n","      <td>-0.746564</td>\n","      <td>-0.592549</td>\n","      <td>-0.506650</td>\n","      <td>3.267976</td>\n","      <td>2.147232</td>\n","      <td>1.251696</td>\n","      <td>0.902605</td>\n","      <td>0.777665</td>\n","      <td>0.678277</td>\n","      <td>0.652441</td>\n","      <td>0.492996</td>\n","      <td>0.455644</td>\n","      <td>0.466388</td>\n","      <td>0.368922</td>\n","      <td>0.393817</td>\n","      <td>0.331128</td>\n","      <td>0.0</td>\n","      <td>0.47</td>\n","      <td>0.381436</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","     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<td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>377321</th>\n","      <td>1.331421</td>\n","      <td>0.718740</td>\n","      <td>0.687265</td>\n","      <td>1.025471</td>\n","      <td>0.837166</td>\n","      <td>1.205505</td>\n","      <td>1.740379</td>\n","      <td>2.491773</td>\n","      <td>6.540563</td>\n","      <td>4.901628</td>\n","      <td>0.385474</td>\n","      <td>4.349721</td>\n","      <td>2.611766</td>\n","      <td>3.976680</td>\n","      <td>1.385175</td>\n","      <td>1.020477</td>\n","      <td>0.475464</td>\n","      <td>-0.072138</td>\n","      <td>1.048724</td>\n","      <td>-0.064042</td>\n","      <td>0.481198</td>\n","      <td>0.850998</td>\n","      <td>0.473700</td>\n","      <td>0.065288</td>\n","      <td>-0.113568</td>\n","      <td>0.237155</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>65.287252</td>\n","      <td>4.178797</td>\n","      <td>0.000059</td>\n","      <td>0.000196</td>\n","      <td>0.000016</td>\n","      <td>0.000935</td>\n","      <td>0.000300</td>\n","      <td>0.000525</td>\n","      <td>-7.392128</td>\n","      <td>1.603140</td>\n","      <td>-1.813540</td>\n","      <td>-0.402678</td>\n","      <td>-1.860173</td>\n","      <td>-0.456350</td>\n","      <td>-1.072378</td>\n","      <td>-0.733506</td>\n","      <td>-0.740843</td>\n","      <td>-0.787012</td>\n","      <td>-0.455363</td>\n","      <td>-0.634024</td>\n","      <td>-0.540541</td>\n","      <td>2.807642</td>\n","      <td>1.125063</td>\n","      <td>0.728020</td>\n","      <td>0.482000</td>\n","      <td>0.373640</td>\n","      <td>0.286663</td>\n","      <td>0.242171</td>\n","      <td>0.244087</td>\n","      <td>0.258124</td>\n","      <td>0.235640</td>\n","      <td>0.213171</td>\n","      <td>0.213801</td>\n","      <td>0.186613</td>\n","      <td>0.0</td>\n","      <td>0.93</td>\n","      <td>0.000015</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>377322</th>\n","      <td>1.348864</td>\n","      <td>0.739024</td>\n","      <td>0.696439</td>\n","      <td>1.021943</td>\n","      <td>0.852239</td>\n","      <td>1.209750</td>\n","      <td>1.776921</td>\n","      <td>2.613384</td>\n","      <td>6.536557</td>\n","      <td>4.899425</td>\n","      <td>0.670574</td>\n","      <td>4.304233</td>\n","      <td>2.588150</td>\n","      <td>4.103140</td>\n","      <td>1.419140</td>\n","      <td>1.018056</td>\n","      <td>0.468301</td>\n","      <td>-0.067906</td>\n","      <td>1.052425</td>\n","      <td>-0.067219</td>\n","      <td>0.478224</td>\n","      <td>0.882011</td>\n","      <td>0.492160</td>\n","      <td>0.036155</td>\n","      <td>-0.113936</td>\n","      <td>0.226918</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>81.662434</td>\n","      <td>4.402594</td>\n","      <td>0.000251</td>\n","      <td>0.000164</td>\n","      <td>0.000638</td>\n","      <td>0.000792</td>\n","      <td>0.000095</td>\n","      <td>0.000032</td>\n","      <td>-7.774111</td>\n","      <td>1.690882</td>\n","      <td>-1.753544</td>\n","      <td>-0.433618</td>\n","      <td>-1.909998</td>\n","      <td>-0.514209</td>\n","      <td>-1.054091</td>\n","      <td>-0.692147</td>\n","      <td>-0.650268</td>\n","      <td>-0.745260</td>\n","      <td>-0.399710</td>\n","      <td>-0.611605</td>\n","      <td>-0.541675</td>\n","      <td>2.915772</td>\n","      <td>1.184854</td>\n","      <td>0.733604</td>\n","      <td>0.500369</td>\n","      <td>0.424783</td>\n","      <td>0.346504</td>\n","      <td>0.271555</td>\n","      <td>0.259373</td>\n","      <td>0.255782</td>\n","      <td>0.233415</td>\n","      <td>0.200681</td>\n","      <td>0.183018</td>\n","      <td>0.178549</td>\n","      <td>0.0</td>\n","      <td>0.93</td>\n","      <td>0.000015</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>377323</th>\n","      <td>1.341859</td>\n","      <td>0.773106</td>\n","      <td>0.776163</td>\n","      <td>1.095507</td>\n","      <td>0.827742</td>\n","      <td>1.173246</td>\n","      <td>1.754705</td>\n","      <td>2.574067</td>\n","      <td>6.524614</td>\n","      <td>4.819947</td>\n","      <td>0.880762</td>\n","      <td>4.445558</td>\n","      <td>2.492145</td>\n","      <td>4.152807</td>\n","      <td>1.469691</td>\n","      <td>1.036381</td>\n","      <td>0.434195</td>\n","      <td>-0.069125</td>\n","      <td>1.045107</td>\n","      <td>-0.084946</td>\n","      <td>0.481218</td>\n","      <td>0.896641</td>\n","      <td>0.508428</td>\n","      <td>-0.014607</td>\n","      <td>-0.129393</td>\n","      <td>0.179742</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>31.987068</td>\n","      <td>3.465332</td>\n","      <td>0.000095</td>\n","      <td>0.000089</td>\n","      <td>0.000251</td>\n","      <td>0.000146</td>\n","      <td>0.000017</td>\n","      <td>0.000045</td>\n","      <td>-9.083226</td>\n","      <td>1.928444</td>\n","      <td>-1.830741</td>\n","      <td>-0.423809</td>\n","      <td>-1.787859</td>\n","      <td>-0.396832</td>\n","      <td>-1.023771</td>\n","      <td>-0.694875</td>\n","      <td>-0.671679</td>\n","      <td>-0.762406</td>\n","      <td>-0.444701</td>\n","      <td>-0.621415</td>\n","      <td>-0.570843</td>\n","      <td>2.756219</td>\n","      <td>1.205948</td>\n","      <td>0.795810</td>\n","      <td>0.465592</td>\n","      <td>0.366454</td>\n","      <td>0.303812</td>\n","      <td>0.252889</td>\n","      <td>0.291147</td>\n","      <td>0.238335</td>\n","      <td>0.213589</td>\n","      <td>0.198586</td>\n","      <td>0.198836</td>\n","      <td>0.207751</td>\n","      <td>0.0</td>\n","      <td>0.92</td>\n","      <td>0.000015</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>377324</th>\n","      <td>1.164082</td>\n","      <td>0.333725</td>\n","      <td>0.180477</td>\n","      <td>0.447232</td>\n","      <td>0.916853</td>\n","      <td>1.162816</td>\n","      <td>1.415372</td>\n","      <td>2.351249</td>\n","      <td>6.642348</td>\n","      <td>5.002014</td>\n","      <td>1.578506</td>\n","      <td>3.533138</td>\n","      <td>1.892941</td>\n","      <td>1.379731</td>\n","      <td>0.754874</td>\n","      <td>0.989973</td>\n","      <td>0.479110</td>\n","      <td>0.722619</td>\n","      <td>0.666697</td>\n","      <td>0.330850</td>\n","      <td>0.406958</td>\n","      <td>0.390675</td>\n","      <td>0.268498</td>\n","      <td>0.283204</td>\n","      <td>0.039222</td>\n","      <td>-0.468622</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>63.526585</td>\n","      <td>4.151458</td>\n","      <td>0.000165</td>\n","      <td>0.000144</td>\n","      <td>0.000584</td>\n","      <td>-0.000315</td>\n","      <td>0.000033</td>\n","      <td>0.000060</td>\n","      <td>-3.504425</td>\n","      <td>-0.155248</td>\n","      <td>-3.252557</td>\n","      <td>0.613563</td>\n","      <td>-1.700879</td>\n","      <td>-0.305183</td>\n","      <td>-1.432183</td>\n","      <td>-0.825732</td>\n","      <td>-0.511022</td>\n","      <td>-1.020042</td>\n","      <td>-0.671250</td>\n","      <td>-0.627151</td>\n","      <td>-0.528442</td>\n","      <td>1.180174</td>\n","      <td>0.691122</td>\n","      <td>0.425398</td>\n","      <td>0.369699</td>\n","      <td>0.289125</td>\n","      <td>0.310188</td>\n","      <td>0.238018</td>\n","      <td>0.234267</td>\n","      <td>0.234472</td>\n","      <td>0.235194</td>\n","      <td>0.193000</td>\n","      <td>0.175390</td>\n","      <td>0.179356</td>\n","      <td>0.0</td>\n","      <td>0.92</td>\n","      <td>0.456982</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>377325</th>\n","      <td>1.136058</td>\n","      <td>0.332312</td>\n","      <td>0.296363</td>\n","      <td>0.492753</td>\n","      <td>0.922363</td>\n","      <td>1.085739</td>\n","      <td>1.346369</td>\n","      <td>2.312827</td>\n","      <td>6.642416</td>\n","      <td>5.015416</td>\n","      <td>1.984600</td>\n","      <td>3.662844</td>\n","      <td>1.338516</td>\n","      <td>1.410716</td>\n","      <td>0.847237</td>\n","      <td>1.004818</td>\n","      <td>0.448941</td>\n","      <td>0.488478</td>\n","      <td>0.071944</td>\n","      <td>-0.304263</td>\n","      <td>0.513951</td>\n","      <td>0.634718</td>\n","      <td>0.630621</td>\n","      <td>0.703853</td>\n","      <td>0.706985</td>\n","      <td>0.828545</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>62.430883</td>\n","      <td>4.134060</td>\n","      <td>0.000092</td>\n","      <td>0.000208</td>\n","      <td>0.000151</td>\n","      <td>0.000280</td>\n","      <td>0.000076</td>\n","      <td>0.000137</td>\n","      <td>-9.893793</td>\n","      <td>0.923950</td>\n","      <td>-2.176131</td>\n","      <td>-0.030906</td>\n","      <td>-1.619127</td>\n","      <td>-0.274928</td>\n","      <td>-1.214098</td>\n","      <td>-0.622256</td>\n","      <td>-0.715429</td>\n","      <td>-1.013833</td>\n","      <td>-0.658552</td>\n","      <td>-0.764358</td>\n","      <td>-0.532948</td>\n","      <td>1.164243</td>\n","      <td>0.654368</td>\n","      <td>0.539415</td>\n","      <td>0.381460</td>\n","      <td>0.321242</td>\n","      <td>0.243568</td>\n","      <td>0.250475</td>\n","      <td>0.225360</td>\n","      <td>0.221822</td>\n","      <td>0.212152</td>\n","      <td>0.211043</td>\n","      <td>0.207095</td>\n","      <td>0.196992</td>\n","      <td>0.0</td>\n","      <td>0.92</td>\n","      <td>0.987446</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>308320 rows × 117 columns</p>\n","</div>"],"text/plain":["        raw_acc:magnitude_stats:mean  ...  CLASS\n","0                           0.996815  ...      1\n","1                           0.996864  ...      1\n","2                           0.996825  ...      1\n","3                           0.996874  ...      1\n","4                           0.997371  ...      1\n","...                              ...  ...    ...\n","377321                      1.331421  ...      0\n","377322                      1.348864  ...      0\n","377323                      1.341859  ...      0\n","377324                      1.164082  ...      0\n","377325                      1.136058  ...      0\n","\n","[308320 rows x 117 columns]"]},"metadata":{},"execution_count":104}]},{"cell_type":"markdown","metadata":{"id":"5fASHFFU7GTR"},"source":["Let's store this dataset, that contains numerous values, in a csv file: we'll give it as input for our classifier and to MOA later"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3Bt6j9Lk65WN","executionInfo":{"status":"ok","timestamp":1635961887879,"user_tz":-60,"elapsed":49642,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"cad082f5-126f-4857-dd46-e8055ed7b216"},"source":["# Store the dataset in CSV files\n","%cd \"/content/drive/MyDrive/IoT DA\"\n","final_dataset_copy.to_csv('df_uncleaned.csv', index=False)\n","print(\"CSV Created\")"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/IoT DA\n","CSV Created\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"G5_jCK0yug0G","executionInfo":{"status":"ok","timestamp":1635928558896,"user_tz":-60,"elapsed":2115,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"a7f0dddd-379a-4bbd-ec7c-8bd9a82f48e3"},"source":["import numpy as np\n","import pandas as pd\n","from sklearn.impute import SimpleImputer\n","\n","imp = SimpleImputer(missing_values = np.nan, strategy = 'mean')\n","\n","copy_of_final = final_dataset_copy.copy()\n","\n","imp = imp.fit(copy_of_final)\n","copy_of_final = imp.transform(copy_of_final)\n","\n","copy_of_final"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[1.44407916e+09, 9.96815000e-01, 3.52900000e-03, ...,\n","        4.60000000e-01, 3.81436000e-01, 1.00000000e+00],\n","       [1.44407922e+09, 9.96864000e-01, 4.17200000e-03, ...,\n","        4.60000000e-01, 3.81436000e-01, 1.00000000e+00],\n","       [1.44407928e+09, 9.96825000e-01, 3.66700000e-03, ...,\n","        4.60000000e-01, 3.81436000e-01, 1.00000000e+00],\n","       ...,\n","       [1.44423369e+09, 1.34185900e+00, 7.73106000e-01, ...,\n","        9.20000000e-01, 1.50000000e-05, 0.00000000e+00],\n","       [1.44423375e+09, 1.16408200e+00, 3.33725000e-01, ...,\n","        9.20000000e-01, 4.56982000e-01, 0.00000000e+00],\n","       [1.44423381e+09, 1.13605800e+00, 3.32312000e-01, ...,\n","        9.20000000e-01, 9.87446000e-01, 0.00000000e+00]])"]},"metadata":{},"execution_count":30}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":439},"id":"VlXtnHjL3Uhz","executionInfo":{"status":"ok","timestamp":1635928558898,"user_tz":-60,"elapsed":15,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"1115a8af-fdfb-42c9-d1e4-4cb3339bb780"},"source":[" df_imputed = pd.DataFrame(copy_of_final)\n","\n"," #print(len(df_imputed.columns)) \n","\n"," df_imputed.columns = final_dataset_copy.columns\n","\n","\n"," df_imputed"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>raw_acc:magnitude_stats:mean</th>\n","      <th>raw_acc:magnitude_stats:std</th>\n","      <th>raw_acc:magnitude_stats:moment3</th>\n","      <th>raw_acc:magnitude_stats:moment4</th>\n","      <th>raw_acc:magnitude_stats:percentile25</th>\n","      <th>raw_acc:magnitude_stats:percentile50</th>\n","      <th>raw_acc:magnitude_stats:percentile75</th>\n","      <th>raw_acc:magnitude_stats:value_entropy</th>\n","      <th>raw_acc:magnitude_stats:time_entropy</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band0</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band1</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band2</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band3</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band4</th>\n","      <th>raw_acc:magnitude_spectrum:spectral_entropy</th>\n","      <th>raw_acc:magnitude_autocorrelation:period</th>\n","      <th>raw_acc:magnitude_autocorrelation:normalized_ac</th>\n","      <th>raw_acc:3d:mean_x</th>\n","      <th>raw_acc:3d:mean_y</th>\n","      <th>raw_acc:3d:mean_z</th>\n","      <th>raw_acc:3d:std_x</th>\n","      <th>raw_acc:3d:std_y</th>\n","      <th>raw_acc:3d:std_z</th>\n","      <th>raw_acc:3d:ro_xy</th>\n","      <th>raw_acc:3d:ro_xz</th>\n","      <th>raw_acc:3d:ro_yz</th>\n","      <th>watch_acceleration:magnitude_stats:mean</th>\n","      <th>watch_acceleration:magnitude_stats:std</th>\n","      <th>watch_acceleration:magnitude_stats:moment3</th>\n","      <th>watch_acceleration:magnitude_stats:moment4</th>\n","      <th>watch_acceleration:magnitude_stats:percentile25</th>\n","      <th>watch_acceleration:magnitude_stats:percentile50</th>\n","      <th>watch_acceleration:magnitude_stats:percentile75</th>\n","      <th>watch_acceleration:magnitude_stats:value_entropy</th>\n","      <th>watch_acceleration:magnitude_stats:time_entropy</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band0</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band1</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band2</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band3</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band4</th>\n","      <th>...</th>\n","      <th>location:best_horizontal_accuracy</th>\n","      <th>location:best_vertical_accuracy</th>\n","      <th>location:diameter</th>\n","      <th>location:log_diameter</th>\n","      <th>location_quick_features:std_lat</th>\n","      <th>location_quick_features:std_long</th>\n","      <th>location_quick_features:lat_change</th>\n","      <th>location_quick_features:long_change</th>\n","      <th>location_quick_features:mean_abs_lat_deriv</th>\n","      <th>location_quick_features:mean_abs_long_deriv</th>\n","      <th>audio_naive:mfcc0:mean</th>\n","      <th>audio_naive:mfcc1:mean</th>\n","      <th>audio_naive:mfcc2:mean</th>\n","      <th>audio_naive:mfcc3:mean</th>\n","      <th>audio_naive:mfcc4:mean</th>\n","      <th>audio_naive:mfcc5:mean</th>\n","      <th>audio_naive:mfcc6:mean</th>\n","      <th>audio_naive:mfcc7:mean</th>\n","      <th>audio_naive:mfcc8:mean</th>\n","      <th>audio_naive:mfcc9:mean</th>\n","      <th>audio_naive:mfcc10:mean</th>\n","      <th>audio_naive:mfcc11:mean</th>\n","      <th>audio_naive:mfcc12:mean</th>\n","      <th>audio_naive:mfcc0:std</th>\n","      <th>audio_naive:mfcc1:std</th>\n","      <th>audio_naive:mfcc2:std</th>\n","      <th>audio_naive:mfcc3:std</th>\n","      <th>audio_naive:mfcc4:std</th>\n","      <th>audio_naive:mfcc5:std</th>\n","      <th>audio_naive:mfcc6:std</th>\n","      <th>audio_naive:mfcc7:std</th>\n","      <th>audio_naive:mfcc8:std</th>\n","      <th>audio_naive:mfcc9:std</th>\n","      <th>audio_naive:mfcc10:std</th>\n","      <th>audio_naive:mfcc11:std</th>\n","      <th>audio_naive:mfcc12:std</th>\n","      <th>lf_measurements:proximity</th>\n","      <th>lf_measurements:battery_level</th>\n","      <th>lf_measurements:screen_brightness</th>\n","      <th>CLASS</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.996815</td>\n","      <td>0.003529</td>\n","      <td>-0.002786</td>\n","      <td>0.006496</td>\n","      <td>0.995203</td>\n","      <td>0.996825</td>\n","      <td>0.998502</td>\n","      <td>1.748756</td>\n","      <td>6.684605</td>\n","      <td>5.043970</td>\n","      <td>0.000042</td>\n","      <td>0.000364</td>\n","      <td>0.000761</td>\n","      <td>0.005429</td>\n","      <td>0.429853</td>\n","      <td>0.173685</td>\n","      <td>0.148988</td>\n","      <td>0.002331</td>\n","      <td>0.004614</td>\n","      <td>-0.996790</td>\n","      <td>0.003269</td>\n","      <td>0.003521</td>\n","      <td>0.003539</td>\n","      <td>0.106920</td>\n","      <td>0.516842</td>\n","      <td>0.255494</td>\n","      <td>1032.508157</td>\n","      <td>17.195149</td>\n","      <td>12.121621</td>\n","      <td>25.390972</td>\n","      <td>1024.812178</td>\n","      <td>1033.146650</td>\n","      <td>1041.537325</td>\n","      <td>2.220749</td>\n","      <td>6.214470</td>\n","      <td>5.045673</td>\n","      <td>0.004896</td>\n","      <td>0.023827</td>\n","      <td>0.022606</td>\n","      <td>0.083165</td>\n","      <td>...</td>\n","      <td>65.0</td>\n","      <td>10.0</td>\n","      <td>10.102999</td>\n","      <td>2.312832</td>\n","      <td>0.000029</td>\n","      <td>0.000032</td>\n","      <td>-0.000067</td>\n","      <td>-0.000073</td>\n","      <td>0.000111</td>\n","      <td>0.000120</td>\n","      <td>-4.219657</td>\n","      <td>-0.012806</td>\n","      <td>-1.298291</td>\n","      <td>0.094373</td>\n","      <td>-1.220977</td>\n","      <td>-0.851300</td>\n","      <td>-1.656516</td>\n","      <td>-0.898563</td>\n","      <td>-0.503573</td>\n","      <td>-0.518072</td>\n","      <td>-0.907876</td>\n","      <td>-0.681724</td>\n","      <td>-0.683515</td>\n","      <td>2.276349</td>\n","      <td>1.271966</td>\n","      <td>1.177478</td>\n","      <td>0.670053</td>\n","      <td>0.389200</td>\n","      <td>0.535904</td>\n","      <td>0.468701</td>\n","      <td>0.358315</td>\n","      <td>0.401412</td>\n","      <td>0.408432</td>\n","      <td>0.277176</td>\n","      <td>0.416881</td>\n","      <td>0.263832</td>\n","      <td>0.0</td>\n","      <td>0.46</td>\n","      <td>0.381436</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.996864</td>\n","      <td>0.004172</td>\n","      <td>-0.003110</td>\n","      <td>0.007050</td>\n","      <td>0.994957</td>\n","      <td>0.996981</td>\n","      <td>0.998766</td>\n","      <td>1.935573</td>\n","      <td>6.684603</td>\n","      <td>5.043367</td>\n","      <td>0.000071</td>\n","      <td>0.000468</td>\n","      <td>0.000630</td>\n","      <td>0.007756</td>\n","      <td>0.429733</td>\n","      <td>0.057819</td>\n","      <td>0.207046</td>\n","      <td>0.003557</td>\n","      <td>0.005495</td>\n","      <td>-0.996832</td>\n","      <td>0.002489</td>\n","      <td>0.003772</td>\n","      <td>0.004177</td>\n","      <td>-0.079483</td>\n","      <td>0.357748</td>\n","      <td>0.036252</td>\n","      <td>1031.283026</td>\n","      <td>18.901671</td>\n","      <td>5.132795</td>\n","      <td>27.607064</td>\n","      <td>1018.115906</td>\n","      <td>1033.115676</td>\n","      <td>1041.537325</td>\n","      <td>2.355872</td>\n","      <td>6.214440</td>\n","      <td>5.042166</td>\n","      <td>0.003692</td>\n","      <td>0.025719</td>\n","      <td>0.022919</td>\n","      <td>0.128473</td>\n","      <td>...</td>\n","      <td>65.0</td>\n","      <td>10.0</td>\n","      <td>9.611984</td>\n","      <td>2.263011</td>\n","      <td>0.000030</td>\n","      <td>0.000025</td>\n","      <td>-0.000021</td>\n","      <td>0.000013</td>\n","      <td>0.000044</td>\n","      <td>0.000033</td>\n","      <td>-7.505606</td>\n","      <td>1.294372</td>\n","      <td>-0.745550</td>\n","      <td>-0.052791</td>\n","      <td>-1.220453</td>\n","      <td>-0.822848</td>\n","      <td>-1.559542</td>\n","      <td>-0.835154</td>\n","      <td>-0.665443</td>\n","      <td>-0.580521</td>\n","      <td>-0.865482</td>\n","      <td>-0.703950</td>\n","      <td>-0.718397</td>\n","      <td>2.231302</td>\n","      <td>1.232031</td>\n","      <td>1.098946</td>\n","      <td>0.669467</td>\n","      <td>0.499003</td>\n","      <td>0.584068</td>\n","      <td>0.525900</td>\n","      <td>0.435339</td>\n","      <td>0.459064</td>\n","      <td>0.391962</td>\n","      <td>0.341445</td>\n","      <td>0.362252</td>\n","      <td>0.298252</td>\n","      <td>0.0</td>\n","      <td>0.46</td>\n","      <td>0.381436</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.996825</td>\n","      <td>0.003667</td>\n","      <td>0.003094</td>\n","      <td>0.006076</td>\n","      <td>0.994797</td>\n","      <td>0.996614</td>\n","      <td>0.998704</td>\n","      <td>2.031780</td>\n","      <td>6.684605</td>\n","      <td>5.043599</td>\n","      <td>0.000052</td>\n","      <td>0.000251</td>\n","      <td>0.000730</td>\n","      <td>0.008223</td>\n","      <td>0.429799</td>\n","      <td>0.173440</td>\n","      <td>0.186961</td>\n","      <td>0.004180</td>\n","      <td>0.005776</td>\n","      <td>-0.996785</td>\n","      <td>0.003567</td>\n","      <td>0.004051</td>\n","      <td>0.003681</td>\n","      <td>0.122432</td>\n","      <td>0.464881</td>\n","      <td>0.222375</td>\n","      <td>1031.643125</td>\n","      <td>17.119299</td>\n","      <td>10.606363</td>\n","      <td>24.535996</td>\n","      <td>1025.124383</td>\n","      <td>1033.518263</td>\n","      <td>1041.506601</td>\n","      <td>2.322894</td>\n","      <td>6.214471</td>\n","      <td>5.046789</td>\n","      <td>0.007309</td>\n","      <td>0.020632</td>\n","      <td>0.026453</td>\n","      <td>0.079950</td>\n","      <td>...</td>\n","      <td>65.0</td>\n","      <td>10.0</td>\n","      <td>0.568155</td>\n","      <td>-0.565360</td>\n","      <td>0.000002</td>\n","      <td>0.000002</td>\n","      <td>-0.000005</td>\n","      <td>0.000003</td>\n","      <td>0.000610</td>\n","      <td>0.000757</td>\n","      <td>-7.966769</td>\n","      <td>1.309082</td>\n","      <td>-0.770455</td>\n","      <td>0.080046</td>\n","      <td>-1.238489</td>\n","      <td>-0.922404</td>\n","      <td>-1.695809</td>\n","      <td>-0.864904</td>\n","      <td>-0.670674</td>\n","      <td>-0.580025</td>\n","      <td>-0.710762</td>\n","      <td>-0.694606</td>\n","      <td>-0.720886</td>\n","      <td>2.537650</td>\n","      <td>1.186965</td>\n","      <td>1.251340</td>\n","      <td>0.803958</td>\n","      <td>0.546688</td>\n","      <td>0.562475</td>\n","      <td>0.559488</td>\n","      <td>0.445827</td>\n","      <td>0.472388</td>\n","      <td>0.462703</td>\n","      <td>0.359176</td>\n","      <td>0.430171</td>\n","      <td>0.330055</td>\n","      <td>0.0</td>\n","      <td>0.46</td>\n","      <td>0.381436</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.996874</td>\n","      <td>0.003541</td>\n","      <td>0.000626</td>\n","      <td>0.006059</td>\n","      <td>0.995050</td>\n","      <td>0.996907</td>\n","      <td>0.998690</td>\n","      <td>1.865318</td>\n","      <td>6.684605</td>\n","      <td>5.043263</td>\n","      <td>0.000019</td>\n","      <td>0.000173</td>\n","      <td>0.000623</td>\n","      <td>0.003763</td>\n","      <td>0.429628</td>\n","      <td>0.173474</td>\n","      <td>0.479430</td>\n","      <td>0.004179</td>\n","      <td>0.005551</td>\n","      <td>-0.996836</td>\n","      <td>0.004202</td>\n","      <td>0.002919</td>\n","      <td>0.003559</td>\n","      <td>0.308841</td>\n","      <td>0.773514</td>\n","      <td>0.296194</td>\n","      <td>1032.123385</td>\n","      <td>17.796377</td>\n","      <td>10.444489</td>\n","      <td>27.338486</td>\n","      <td>1025.124383</td>\n","      <td>1033.518263</td>\n","      <td>1041.537325</td>\n","      <td>2.139388</td>\n","      <td>6.214460</td>\n","      <td>5.042475</td>\n","      <td>0.004308</td>\n","      <td>0.034187</td>\n","      <td>0.035234</td>\n","      <td>0.089009</td>\n","      <td>...</td>\n","      <td>65.0</td>\n","      <td>10.0</td>\n","      <td>2.097436</td>\n","      <td>0.740716</td>\n","      <td>0.000006</td>\n","      <td>0.000003</td>\n","      <td>0.000007</td>\n","      <td>0.000004</td>\n","      <td>0.000610</td>\n","      <td>0.000757</td>\n","      <td>-5.367933</td>\n","      <td>0.182373</td>\n","      <td>-0.891874</td>\n","      <td>0.112003</td>\n","      <td>-1.437741</td>\n","      <td>-0.770266</td>\n","      <td>-1.646552</td>\n","      <td>-0.922945</td>\n","      <td>-0.614741</td>\n","      <td>-0.531608</td>\n","      <td>-0.836680</td>\n","      <td>-0.704615</td>\n","      <td>-0.719910</td>\n","      <td>2.179684</td>\n","      <td>1.410698</td>\n","      <td>1.111916</td>\n","      <td>0.821571</td>\n","      <td>0.519879</td>\n","      <td>0.572094</td>\n","      <td>0.591143</td>\n","      <td>0.467151</td>\n","      <td>0.453082</td>\n","      <td>0.459266</td>\n","      <td>0.325971</td>\n","      <td>0.379887</td>\n","      <td>0.389737</td>\n","      <td>0.0</td>\n","      <td>0.46</td>\n","      <td>0.381436</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.997371</td>\n","      <td>0.037653</td>\n","      <td>0.043389</td>\n","      <td>0.102332</td>\n","      <td>0.995548</td>\n","      <td>0.996860</td>\n","      <td>0.998205</td>\n","      <td>0.460806</td>\n","      <td>6.683904</td>\n","      <td>5.042779</td>\n","      <td>0.000858</td>\n","      <td>0.006035</td>\n","      <td>0.014974</td>\n","      <td>0.023291</td>\n","      <td>0.430164</td>\n","      <td>0.260603</td>\n","      <td>0.202305</td>\n","      <td>-0.008383</td>\n","      <td>-0.000009</td>\n","      <td>-0.994184</td>\n","      <td>0.074612</td>\n","      <td>0.023784</td>\n","      <td>0.039512</td>\n","      <td>-0.187758</td>\n","      <td>-0.204381</td>\n","      <td>0.062696</td>\n","      <td>1042.973796</td>\n","      <td>121.591905</td>\n","      <td>292.986177</td>\n","      <td>487.849018</td>\n","      <td>1022.436306</td>\n","      <td>1034.261089</td>\n","      <td>1047.938926</td>\n","      <td>0.589813</td>\n","      <td>6.158404</td>\n","      <td>5.061130</td>\n","      <td>0.442844</td>\n","      <td>1.154677</td>\n","      <td>1.265153</td>\n","      <td>2.547586</td>\n","      <td>...</td>\n","      <td>65.0</td>\n","      <td>10.0</td>\n","      <td>5.015308</td>\n","      <td>1.612495</td>\n","      <td>0.000011</td>\n","      <td>0.000011</td>\n","      <td>0.000002</td>\n","      <td>-0.000008</td>\n","      <td>0.000610</td>\n","      <td>0.000757</td>\n","      <td>-13.415772</td>\n","      <td>0.908387</td>\n","      <td>-1.442258</td>\n","      <td>-0.294636</td>\n","      <td>-1.760813</td>\n","      <td>-1.104186</td>\n","      <td>-1.523959</td>\n","      <td>-1.005763</td>\n","      <td>-0.577766</td>\n","      <td>-0.579184</td>\n","      <td>-0.746564</td>\n","      <td>-0.592549</td>\n","      <td>-0.506650</td>\n","      <td>3.267976</td>\n","      <td>2.147232</td>\n","      <td>1.251696</td>\n","      <td>0.902605</td>\n","      <td>0.777665</td>\n","      <td>0.678277</td>\n","      <td>0.652441</td>\n","      <td>0.492996</td>\n","      <td>0.455644</td>\n","      <td>0.466388</td>\n","      <td>0.368922</td>\n","      <td>0.393817</td>\n","      <td>0.331128</td>\n","      <td>0.0</td>\n","      <td>0.47</td>\n","      <td>0.381436</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>308315</th>\n","      <td>1.331421</td>\n","      <td>0.718740</td>\n","      <td>0.687265</td>\n","      <td>1.025471</td>\n","      <td>0.837166</td>\n","      <td>1.205505</td>\n","      <td>1.740379</td>\n","      <td>2.491773</td>\n","      <td>6.540563</td>\n","      <td>4.901628</td>\n","      <td>0.385474</td>\n","      <td>4.349721</td>\n","      <td>2.611766</td>\n","      <td>3.976680</td>\n","      <td>1.385175</td>\n","      <td>1.020477</td>\n","      <td>0.475464</td>\n","      <td>-0.072138</td>\n","      <td>1.048724</td>\n","      <td>-0.064042</td>\n","      <td>0.481198</td>\n","      <td>0.850998</td>\n","      <td>0.473700</td>\n","      <td>0.065288</td>\n","      <td>-0.113568</td>\n","      <td>0.237155</td>\n","      <td>1029.004922</td>\n","      <td>64.896102</td>\n","      <td>54.068766</td>\n","      <td>121.289399</td>\n","      <td>999.204221</td>\n","      <td>1023.837335</td>\n","      <td>1052.323882</td>\n","      <td>1.994675</td>\n","      <td>5.931323</td>\n","      <td>5.024812</td>\n","      <td>0.262276</td>\n","      <td>0.554810</td>\n","      <td>0.262479</td>\n","      <td>0.302496</td>\n","      <td>...</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>65.287252</td>\n","      <td>4.178797</td>\n","      <td>0.000059</td>\n","      <td>0.000196</td>\n","      <td>0.000016</td>\n","      <td>0.000935</td>\n","      <td>0.000300</td>\n","      <td>0.000525</td>\n","      <td>-7.392128</td>\n","      <td>1.603140</td>\n","      <td>-1.813540</td>\n","      <td>-0.402678</td>\n","      <td>-1.860173</td>\n","      <td>-0.456350</td>\n","      <td>-1.072378</td>\n","      <td>-0.733506</td>\n","      <td>-0.740843</td>\n","      <td>-0.787012</td>\n","      <td>-0.455363</td>\n","      <td>-0.634024</td>\n","      <td>-0.540541</td>\n","      <td>2.807642</td>\n","      <td>1.125063</td>\n","      <td>0.728020</td>\n","      <td>0.482000</td>\n","      <td>0.373640</td>\n","      <td>0.286663</td>\n","      <td>0.242171</td>\n","      <td>0.244087</td>\n","      <td>0.258124</td>\n","      <td>0.235640</td>\n","      <td>0.213171</td>\n","      <td>0.213801</td>\n","      <td>0.186613</td>\n","      <td>0.0</td>\n","      <td>0.93</td>\n","      <td>0.000015</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>308316</th>\n","      <td>1.348864</td>\n","      <td>0.739024</td>\n","      <td>0.696439</td>\n","      <td>1.021943</td>\n","      <td>0.852239</td>\n","      <td>1.209750</td>\n","      <td>1.776921</td>\n","      <td>2.613384</td>\n","      <td>6.536557</td>\n","      <td>4.899425</td>\n","      <td>0.670574</td>\n","      <td>4.304233</td>\n","      <td>2.588150</td>\n","      <td>4.103140</td>\n","      <td>1.419140</td>\n","      <td>1.018056</td>\n","      <td>0.468301</td>\n","      <td>-0.067906</td>\n","      <td>1.052425</td>\n","      <td>-0.067219</td>\n","      <td>0.478224</td>\n","      <td>0.882011</td>\n","      <td>0.492160</td>\n","      <td>0.036155</td>\n","      <td>-0.113936</td>\n","      <td>0.226918</td>\n","      <td>1029.004922</td>\n","      <td>64.896102</td>\n","      <td>54.068766</td>\n","      <td>121.289399</td>\n","      <td>999.204221</td>\n","      <td>1023.837335</td>\n","      <td>1052.323882</td>\n","      <td>1.994675</td>\n","      <td>5.931323</td>\n","      <td>5.024812</td>\n","      <td>0.262276</td>\n","      <td>0.554810</td>\n","      <td>0.262479</td>\n","      <td>0.302496</td>\n","      <td>...</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>81.662434</td>\n","      <td>4.402594</td>\n","      <td>0.000251</td>\n","      <td>0.000164</td>\n","      <td>0.000638</td>\n","      <td>0.000792</td>\n","      <td>0.000095</td>\n","      <td>0.000032</td>\n","      <td>-7.774111</td>\n","      <td>1.690882</td>\n","      <td>-1.753544</td>\n","      <td>-0.433618</td>\n","      <td>-1.909998</td>\n","      <td>-0.514209</td>\n","      <td>-1.054091</td>\n","      <td>-0.692147</td>\n","      <td>-0.650268</td>\n","      <td>-0.745260</td>\n","      <td>-0.399710</td>\n","      <td>-0.611605</td>\n","      <td>-0.541675</td>\n","      <td>2.915772</td>\n","      <td>1.184854</td>\n","      <td>0.733604</td>\n","      <td>0.500369</td>\n","      <td>0.424783</td>\n","      <td>0.346504</td>\n","      <td>0.271555</td>\n","      <td>0.259373</td>\n","      <td>0.255782</td>\n","      <td>0.233415</td>\n","      <td>0.200681</td>\n","      <td>0.183018</td>\n","      <td>0.178549</td>\n","      <td>0.0</td>\n","      <td>0.93</td>\n","      <td>0.000015</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>308317</th>\n","      <td>1.341859</td>\n","      <td>0.773106</td>\n","      <td>0.776163</td>\n","      <td>1.095507</td>\n","      <td>0.827742</td>\n","      <td>1.173246</td>\n","      <td>1.754705</td>\n","      <td>2.574067</td>\n","      <td>6.524614</td>\n","      <td>4.819947</td>\n","      <td>0.880762</td>\n","      <td>4.445558</td>\n","      <td>2.492145</td>\n","      <td>4.152807</td>\n","      <td>1.469691</td>\n","      <td>1.036381</td>\n","      <td>0.434195</td>\n","      <td>-0.069125</td>\n","      <td>1.045107</td>\n","      <td>-0.084946</td>\n","      <td>0.481218</td>\n","      <td>0.896641</td>\n","      <td>0.508428</td>\n","      <td>-0.014607</td>\n","      <td>-0.129393</td>\n","      <td>0.179742</td>\n","      <td>1029.004922</td>\n","      <td>64.896102</td>\n","      <td>54.068766</td>\n","      <td>121.289399</td>\n","      <td>999.204221</td>\n","      <td>1023.837335</td>\n","      <td>1052.323882</td>\n","      <td>1.994675</td>\n","      <td>5.931323</td>\n","      <td>5.024812</td>\n","      <td>0.262276</td>\n","      <td>0.554810</td>\n","      <td>0.262479</td>\n","      <td>0.302496</td>\n","      <td>...</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>31.987068</td>\n","      <td>3.465332</td>\n","      <td>0.000095</td>\n","      <td>0.000089</td>\n","      <td>0.000251</td>\n","      <td>0.000146</td>\n","      <td>0.000017</td>\n","      <td>0.000045</td>\n","      <td>-9.083226</td>\n","      <td>1.928444</td>\n","      <td>-1.830741</td>\n","      <td>-0.423809</td>\n","      <td>-1.787859</td>\n","      <td>-0.396832</td>\n","      <td>-1.023771</td>\n","      <td>-0.694875</td>\n","      <td>-0.671679</td>\n","      <td>-0.762406</td>\n","      <td>-0.444701</td>\n","      <td>-0.621415</td>\n","      <td>-0.570843</td>\n","      <td>2.756219</td>\n","      <td>1.205948</td>\n","      <td>0.795810</td>\n","      <td>0.465592</td>\n","      <td>0.366454</td>\n","      <td>0.303812</td>\n","      <td>0.252889</td>\n","      <td>0.291147</td>\n","      <td>0.238335</td>\n","      <td>0.213589</td>\n","      <td>0.198586</td>\n","      <td>0.198836</td>\n","      <td>0.207751</td>\n","      <td>0.0</td>\n","      <td>0.92</td>\n","      <td>0.000015</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>308318</th>\n","      <td>1.164082</td>\n","      <td>0.333725</td>\n","      <td>0.180477</td>\n","      <td>0.447232</td>\n","      <td>0.916853</td>\n","      <td>1.162816</td>\n","      <td>1.415372</td>\n","      <td>2.351249</td>\n","      <td>6.642348</td>\n","      <td>5.002014</td>\n","      <td>1.578506</td>\n","      <td>3.533138</td>\n","      <td>1.892941</td>\n","      <td>1.379731</td>\n","      <td>0.754874</td>\n","      <td>0.989973</td>\n","      <td>0.479110</td>\n","      <td>0.722619</td>\n","      <td>0.666697</td>\n","      <td>0.330850</td>\n","      <td>0.406958</td>\n","      <td>0.390675</td>\n","      <td>0.268498</td>\n","      <td>0.283204</td>\n","      <td>0.039222</td>\n","      <td>-0.468622</td>\n","      <td>1029.004922</td>\n","      <td>64.896102</td>\n","      <td>54.068766</td>\n","      <td>121.289399</td>\n","      <td>999.204221</td>\n","      <td>1023.837335</td>\n","      <td>1052.323882</td>\n","      <td>1.994675</td>\n","      <td>5.931323</td>\n","      <td>5.024812</td>\n","      <td>0.262276</td>\n","      <td>0.554810</td>\n","      <td>0.262479</td>\n","      <td>0.302496</td>\n","      <td>...</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>63.526585</td>\n","      <td>4.151458</td>\n","      <td>0.000165</td>\n","      <td>0.000144</td>\n","      <td>0.000584</td>\n","      <td>-0.000315</td>\n","      <td>0.000033</td>\n","      <td>0.000060</td>\n","      <td>-3.504425</td>\n","      <td>-0.155248</td>\n","      <td>-3.252557</td>\n","      <td>0.613563</td>\n","      <td>-1.700879</td>\n","      <td>-0.305183</td>\n","      <td>-1.432183</td>\n","      <td>-0.825732</td>\n","      <td>-0.511022</td>\n","      <td>-1.020042</td>\n","      <td>-0.671250</td>\n","      <td>-0.627151</td>\n","      <td>-0.528442</td>\n","      <td>1.180174</td>\n","      <td>0.691122</td>\n","      <td>0.425398</td>\n","      <td>0.369699</td>\n","      <td>0.289125</td>\n","      <td>0.310188</td>\n","      <td>0.238018</td>\n","      <td>0.234267</td>\n","      <td>0.234472</td>\n","      <td>0.235194</td>\n","      <td>0.193000</td>\n","      <td>0.175390</td>\n","      <td>0.179356</td>\n","      <td>0.0</td>\n","      <td>0.92</td>\n","      <td>0.456982</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>308319</th>\n","      <td>1.136058</td>\n","      <td>0.332312</td>\n","      <td>0.296363</td>\n","      <td>0.492753</td>\n","      <td>0.922363</td>\n","      <td>1.085739</td>\n","      <td>1.346369</td>\n","      <td>2.312827</td>\n","      <td>6.642416</td>\n","      <td>5.015416</td>\n","      <td>1.984600</td>\n","      <td>3.662844</td>\n","      <td>1.338516</td>\n","      <td>1.410716</td>\n","      <td>0.847237</td>\n","      <td>1.004818</td>\n","      <td>0.448941</td>\n","      <td>0.488478</td>\n","      <td>0.071944</td>\n","      <td>-0.304263</td>\n","      <td>0.513951</td>\n","      <td>0.634718</td>\n","      <td>0.630621</td>\n","      <td>0.703853</td>\n","      <td>0.706985</td>\n","      <td>0.828545</td>\n","      <td>1029.004922</td>\n","      <td>64.896102</td>\n","      <td>54.068766</td>\n","      <td>121.289399</td>\n","      <td>999.204221</td>\n","      <td>1023.837335</td>\n","      <td>1052.323882</td>\n","      <td>1.994675</td>\n","      <td>5.931323</td>\n","      <td>5.024812</td>\n","      <td>0.262276</td>\n","      <td>0.554810</td>\n","      <td>0.262479</td>\n","      <td>0.302496</td>\n","      <td>...</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>62.430883</td>\n","      <td>4.134060</td>\n","      <td>0.000092</td>\n","      <td>0.000208</td>\n","      <td>0.000151</td>\n","      <td>0.000280</td>\n","      <td>0.000076</td>\n","      <td>0.000137</td>\n","      <td>-9.893793</td>\n","      <td>0.923950</td>\n","      <td>-2.176131</td>\n","      <td>-0.030906</td>\n","      <td>-1.619127</td>\n","      <td>-0.274928</td>\n","      <td>-1.214098</td>\n","      <td>-0.622256</td>\n","      <td>-0.715429</td>\n","      <td>-1.013833</td>\n","      <td>-0.658552</td>\n","      <td>-0.764358</td>\n","      <td>-0.532948</td>\n","      <td>1.164243</td>\n","      <td>0.654368</td>\n","      <td>0.539415</td>\n","      <td>0.381460</td>\n","      <td>0.321242</td>\n","      <td>0.243568</td>\n","      <td>0.250475</td>\n","      <td>0.225360</td>\n","      <td>0.221822</td>\n","      <td>0.212152</td>\n","      <td>0.211043</td>\n","      <td>0.207095</td>\n","      <td>0.196992</td>\n","      <td>0.0</td>\n","      <td>0.92</td>\n","      <td>0.987446</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>308320 rows × 117 columns</p>\n","</div>"],"text/plain":["        raw_acc:magnitude_stats:mean  ...  CLASS\n","0                           0.996815  ...    1.0\n","1                           0.996864  ...    1.0\n","2                           0.996825  ...    1.0\n","3                           0.996874  ...    1.0\n","4                           0.997371  ...    1.0\n","...                              ...  ...    ...\n","308315                      1.331421  ...    0.0\n","308316                      1.348864  ...    0.0\n","308317                      1.341859  ...    0.0\n","308318                      1.164082  ...    0.0\n","308319                      1.136058  ...    0.0\n","\n","[308320 rows x 117 columns]"]},"metadata":{},"execution_count":31}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H8337mzu4lXE","executionInfo":{"status":"ok","timestamp":1635928559414,"user_tz":-60,"elapsed":527,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"05839c89-ee32-44ed-ec09-9e907453c935"},"source":["print(\"Number of NaN values in the dataset: \",df_imputed.isna().sum().sum())\n","noNaN_each_row = (df_imputed.count())\n","total_noNaN = 0\n","for value in noNaN_each_row:\n","  total_noNaN+=value\n","\n","print(\"Number of no-NaN values in the dataset: \",total_noNaN)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of NaN values in the dataset:  0\n","Number of no-NaN values in the dataset:  36073440\n"]}]},{"cell_type":"code","metadata":{"id":"kvdPE1XaIJRA","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635928617682,"user_tz":-60,"elapsed":58277,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"47838fd7-e593-4fac-cc00-9bb8409e38b5"},"source":["# Store the dataset in CSV files\n","%cd \"/content/drive/MyDrive/IoT DA\"\n","df_imputed.to_csv('df_imputed.csv', index=False)\n","print(\"CSV Created\")"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/IoT DA\n","CSV Created\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"elTbVTlu8YED","executionInfo":{"status":"ok","timestamp":1635940946110,"user_tz":-60,"elapsed":12973,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"e52e65d9-8721-4600-b478-7029a943f29f"},"source":["import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from sklearn import preprocessing\n","\n","%cd \"/content/drive/MyDrive/IoT DA\"\n","\n","dataset = pd.read_csv('df_imputed.csv')\n","X = dataset.iloc[:, :-1].values\n","y = dataset.iloc[:, -1].values\n","validation_size = 0.3\n","seed = 7\n","\n","X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size = validation_size, random_state = seed) # random_state = 7 because maybe better splitting data\n","\n","print(X_train.shape)\n","print(Y_test.shape)\n"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/IoT DA\n","(215824, 116)\n","(92496,)\n"]}]},{"cell_type":"markdown","metadata":{"id":"TadS_-93nrdi"},"source":["# Is the dataset umbalanced?"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7gpAR6iQnvCY","executionInfo":{"status":"ok","timestamp":1635940166636,"user_tz":-60,"elapsed":533,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"5be5a42a-7fa8-4157-a6c9-ad27a77acf18"},"source":["samples_eachclass = df_imputed['CLASS'].value_counts()\n","samples_eachclass"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.0    244456\n","0.0     63864\n","Name: CLASS, dtype: int64"]},"metadata":{},"execution_count":58}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pfMlNT31ofHJ","executionInfo":{"status":"ok","timestamp":1635940382549,"user_tz":-60,"elapsed":478,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"b0166fa0-8d55-4c7e-fe58-d3b9ceee6e5c"},"source":["print(samples_eachclass[0], samples_eachclass[1])\n","samples_eachclass[0]/(samples_eachclass[0]+samples_eachclass[1])*100"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["63864 244456\n"]},{"output_type":"execute_result","data":{"text/plain":["20.713544369486247"]},"metadata":{},"execution_count":62}]},{"cell_type":"markdown","metadata":{"id":"XShnOk0Gpmcm"},"source":["The second class, labeled with 0, is the 20% out of the total: we can oversample the minority class using SMOTE"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z5Mhdgx9prtP","executionInfo":{"status":"ok","timestamp":1635940987453,"user_tz":-60,"elapsed":644,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"530f8940-b83f-4504-c3b2-671a405af959"},"source":["from imblearn.over_sampling import SMOTE \n","from collections import Counter\n","\n","print('Original dataset shape %s' % Counter(y))\n"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Original dataset shape Counter({1.0: 244456, 0.0: 63864})\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":298},"id":"l1jT47pzZJkW","executionInfo":{"status":"ok","timestamp":1635970943102,"user_tz":-60,"elapsed":5391,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"83b0c3e7-2aa1-4f62-e83d-13bc0e59cdf5"},"source":["from numpy import where\n","\n","counter = Counter(y)\n","\n","\n","for label, _ in counter.items():\n","  print(_)\n","  row_ix = where(y == label)[0]\n","  pyplot.scatter(X[row_ix, 0], X[row_ix, 1], label=str(label))\n","  pyplot.legend()\n"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["244456\n","63864\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TLXn3hI8rtaM","executionInfo":{"status":"ok","timestamp":1635971052840,"user_tz":-60,"elapsed":106679,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"360cac5d-6891-42ff-d971-0f9fb966ae8b"},"source":["sm = SMOTE(random_state=42)\n","X_res, y_res = sm.fit_resample(X, y)\n","print('Resampled dataset shape %s' % Counter(y_res))\n"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Resampled dataset shape Counter({1.0: 244456, 0.0: 244456})\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":298},"id":"pA_qn4D_dLXy","executionInfo":{"status":"ok","timestamp":1635971095731,"user_tz":-60,"elapsed":7991,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"522bee68-3e68-4b0d-c9dc-bbb2b1b2edab"},"source":["counter_res = Counter(y_res)\n","\n","\n","for label, _ in counter_res.items():\n","  print(_)\n","  row_ix_res = where(y_res == label)[0]\n","  pyplot.scatter(X_res[row_ix_res, 0], X_res[row_ix_res, 1], label=str(label))\n","  pyplot.legend()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["244456\n","244456\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":745},"id":"pcEEv2LdOPCs","executionInfo":{"status":"ok","timestamp":1635951519809,"user_tz":-60,"elapsed":2737,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"e6a82560-a592-4861-86e6-f9059d3664f1"},"source":["print(X_res)\n","print(\"\\n\")\n","print(y_res)\n","print(\"\\n\")\n","\n","balanced_values = (np.insert(X_res, X_res.shape[1], y_res, axis = 1))\n","\n","balanced_dataset = pd.DataFrame(data=balanced_values, columns=df_imputed.columns)\n","balanced_dataset"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[ 9.96815000e-01  3.52900000e-03 -2.78600000e-03 ...  0.00000000e+00\n","   4.60000000e-01  3.81436000e-01]\n"," [ 9.96864000e-01  4.17200000e-03 -3.11000000e-03 ...  0.00000000e+00\n","   4.60000000e-01  3.81436000e-01]\n"," [ 9.96825000e-01  3.66700000e-03  3.09400000e-03 ...  0.00000000e+00\n","   4.60000000e-01  3.81436000e-01]\n"," ...\n"," [ 9.26978532e-01  2.17202953e-03  8.00827098e-04 ...  0.00000000e+00\n","   6.00000000e-02  3.12412037e-01]\n"," [ 1.09540227e+00  3.02157862e-01  2.03916663e-01 ...  0.00000000e+00\n","   6.19362665e-01  8.17788439e-01]\n"," [ 1.00210724e+00  2.73443625e-02  4.77570604e-02 ...  0.00000000e+00\n","   4.53309371e-01  3.86829019e-01]]\n","\n","\n","[1. 1. 1. ... 0. 0. 0.]\n","\n","\n"]},{"output_type":"execute_result","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>raw_acc:magnitude_stats:mean</th>\n","      <th>raw_acc:magnitude_stats:std</th>\n","      <th>raw_acc:magnitude_stats:moment3</th>\n","      <th>raw_acc:magnitude_stats:moment4</th>\n","      <th>raw_acc:magnitude_stats:percentile25</th>\n","      <th>raw_acc:magnitude_stats:percentile50</th>\n","      <th>raw_acc:magnitude_stats:percentile75</th>\n","      <th>raw_acc:magnitude_stats:value_entropy</th>\n","      <th>raw_acc:magnitude_stats:time_entropy</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band0</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band1</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band2</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band3</th>\n","      <th>raw_acc:magnitude_spectrum:log_energy_band4</th>\n","      <th>raw_acc:magnitude_spectrum:spectral_entropy</th>\n","      <th>raw_acc:magnitude_autocorrelation:period</th>\n","      <th>raw_acc:magnitude_autocorrelation:normalized_ac</th>\n","      <th>raw_acc:3d:mean_x</th>\n","      <th>raw_acc:3d:mean_y</th>\n","      <th>raw_acc:3d:mean_z</th>\n","      <th>raw_acc:3d:std_x</th>\n","      <th>raw_acc:3d:std_y</th>\n","      <th>raw_acc:3d:std_z</th>\n","      <th>raw_acc:3d:ro_xy</th>\n","      <th>raw_acc:3d:ro_xz</th>\n","      <th>raw_acc:3d:ro_yz</th>\n","      <th>watch_acceleration:magnitude_stats:mean</th>\n","      <th>watch_acceleration:magnitude_stats:std</th>\n","      <th>watch_acceleration:magnitude_stats:moment3</th>\n","      <th>watch_acceleration:magnitude_stats:moment4</th>\n","      <th>watch_acceleration:magnitude_stats:percentile25</th>\n","      <th>watch_acceleration:magnitude_stats:percentile50</th>\n","      <th>watch_acceleration:magnitude_stats:percentile75</th>\n","      <th>watch_acceleration:magnitude_stats:value_entropy</th>\n","      <th>watch_acceleration:magnitude_stats:time_entropy</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band0</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band1</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band2</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band3</th>\n","      <th>watch_acceleration:magnitude_spectrum:log_energy_band4</th>\n","      <th>...</th>\n","      <th>location:best_horizontal_accuracy</th>\n","      <th>location:best_vertical_accuracy</th>\n","      <th>location:diameter</th>\n","      <th>location:log_diameter</th>\n","      <th>location_quick_features:std_lat</th>\n","      <th>location_quick_features:std_long</th>\n","      <th>location_quick_features:lat_change</th>\n","      <th>location_quick_features:long_change</th>\n","      <th>location_quick_features:mean_abs_lat_deriv</th>\n","      <th>location_quick_features:mean_abs_long_deriv</th>\n","      <th>audio_naive:mfcc0:mean</th>\n","      <th>audio_naive:mfcc1:mean</th>\n","      <th>audio_naive:mfcc2:mean</th>\n","      <th>audio_naive:mfcc3:mean</th>\n","      <th>audio_naive:mfcc4:mean</th>\n","      <th>audio_naive:mfcc5:mean</th>\n","      <th>audio_naive:mfcc6:mean</th>\n","      <th>audio_naive:mfcc7:mean</th>\n","      <th>audio_naive:mfcc8:mean</th>\n","      <th>audio_naive:mfcc9:mean</th>\n","      <th>audio_naive:mfcc10:mean</th>\n","      <th>audio_naive:mfcc11:mean</th>\n","      <th>audio_naive:mfcc12:mean</th>\n","      <th>audio_naive:mfcc0:std</th>\n","      <th>audio_naive:mfcc1:std</th>\n","      <th>audio_naive:mfcc2:std</th>\n","      <th>audio_naive:mfcc3:std</th>\n","      <th>audio_naive:mfcc4:std</th>\n","      <th>audio_naive:mfcc5:std</th>\n","      <th>audio_naive:mfcc6:std</th>\n","      <th>audio_naive:mfcc7:std</th>\n","      <th>audio_naive:mfcc8:std</th>\n","      <th>audio_naive:mfcc9:std</th>\n","      <th>audio_naive:mfcc10:std</th>\n","      <th>audio_naive:mfcc11:std</th>\n","      <th>audio_naive:mfcc12:std</th>\n","      <th>lf_measurements:proximity</th>\n","      <th>lf_measurements:battery_level</th>\n","      <th>lf_measurements:screen_brightness</th>\n","      <th>CLASS</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.996815</td>\n","      <td>0.003529</td>\n","      <td>-0.002786</td>\n","      <td>0.006496</td>\n","      <td>0.995203</td>\n","      <td>0.996825</td>\n","      <td>0.998502</td>\n","      <td>1.748756</td>\n","      <td>6.684605</td>\n","      <td>5.043970</td>\n","      <td>0.000042</td>\n","      <td>0.000364</td>\n","      <td>0.000761</td>\n","      <td>0.005429</td>\n","      <td>0.429853</td>\n","      <td>0.173685</td>\n","      <td>0.148988</td>\n","      <td>0.002331</td>\n","      <td>0.004614</td>\n","      <td>-0.996790</td>\n","      <td>0.003269</td>\n","      <td>0.003521</td>\n","      <td>0.003539</td>\n","      <td>0.106920</td>\n","      <td>0.516842</td>\n","      <td>0.255494</td>\n","      <td>1032.508157</td>\n","      <td>17.195149</td>\n","      <td>12.121621</td>\n","      <td>25.390972</td>\n","      <td>1024.812178</td>\n","      <td>1033.146650</td>\n","      <td>1041.537325</td>\n","      <td>2.220749</td>\n","      <td>6.214470</td>\n","      <td>5.045673</td>\n","      <td>0.004896</td>\n","      <td>0.023827</td>\n","      <td>0.022606</td>\n","      <td>0.083165</td>\n","      <td>...</td>\n","      <td>65.000000</td>\n","      <td>10.000000</td>\n","      <td>10.102999</td>\n","      <td>2.312832</td>\n","      <td>0.000029</td>\n","      <td>0.000032</td>\n","      <td>-0.000067</td>\n","      <td>-0.000073</td>\n","      <td>0.000111</td>\n","      <td>0.000120</td>\n","      <td>-4.219657</td>\n","      <td>-0.012806</td>\n","      <td>-1.298291</td>\n","      <td>0.094373</td>\n","      <td>-1.220977</td>\n","      <td>-0.851300</td>\n","      <td>-1.656516</td>\n","      <td>-0.898563</td>\n","      <td>-0.503573</td>\n","      <td>-0.518072</td>\n","      <td>-0.907876</td>\n","      <td>-0.681724</td>\n","      <td>-0.683515</td>\n","      <td>2.276349</td>\n","      <td>1.271966</td>\n","      <td>1.177478</td>\n","      <td>0.670053</td>\n","      <td>0.389200</td>\n","      <td>0.535904</td>\n","      <td>0.468701</td>\n","      <td>0.358315</td>\n","      <td>0.401412</td>\n","      <td>0.408432</td>\n","      <td>0.277176</td>\n","      <td>0.416881</td>\n","      <td>0.263832</td>\n","      <td>0.0</td>\n","      <td>0.460000</td>\n","      <td>0.381436</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.996864</td>\n","      <td>0.004172</td>\n","      <td>-0.003110</td>\n","      <td>0.007050</td>\n","      <td>0.994957</td>\n","      <td>0.996981</td>\n","      <td>0.998766</td>\n","      <td>1.935573</td>\n","      <td>6.684603</td>\n","      <td>5.043367</td>\n","      <td>0.000071</td>\n","      <td>0.000468</td>\n","      <td>0.000630</td>\n","      <td>0.007756</td>\n","      <td>0.429733</td>\n","      <td>0.057819</td>\n","      <td>0.207046</td>\n","      <td>0.003557</td>\n","      <td>0.005495</td>\n","      <td>-0.996832</td>\n","      <td>0.002489</td>\n","      <td>0.003772</td>\n","      <td>0.004177</td>\n","      <td>-0.079483</td>\n","      <td>0.357748</td>\n","      <td>0.036252</td>\n","      <td>1031.283026</td>\n","      <td>18.901671</td>\n","      <td>5.132795</td>\n","      <td>27.607064</td>\n","      <td>1018.115906</td>\n","      <td>1033.115676</td>\n","      <td>1041.537325</td>\n","      <td>2.355872</td>\n","      <td>6.214440</td>\n","      <td>5.042166</td>\n","      <td>0.003692</td>\n","      <td>0.025719</td>\n","      <td>0.022919</td>\n","      <td>0.128473</td>\n","      <td>...</td>\n","      <td>65.000000</td>\n","      <td>10.000000</td>\n","      <td>9.611984</td>\n","      <td>2.263011</td>\n","      <td>0.000030</td>\n","      <td>0.000025</td>\n","      <td>-0.000021</td>\n","      <td>0.000013</td>\n","      <td>0.000044</td>\n","      <td>0.000033</td>\n","      <td>-7.505606</td>\n","      <td>1.294372</td>\n","      <td>-0.745550</td>\n","      <td>-0.052791</td>\n","      <td>-1.220453</td>\n","      <td>-0.822848</td>\n","      <td>-1.559542</td>\n","      <td>-0.835154</td>\n","      <td>-0.665443</td>\n","      <td>-0.580521</td>\n","      <td>-0.865482</td>\n","      <td>-0.703950</td>\n","      <td>-0.718397</td>\n","      <td>2.231302</td>\n","      <td>1.232031</td>\n","      <td>1.098946</td>\n","      <td>0.669467</td>\n","      <td>0.499003</td>\n","      <td>0.584068</td>\n","      <td>0.525900</td>\n","      <td>0.435339</td>\n","      <td>0.459064</td>\n","      <td>0.391962</td>\n","      <td>0.341445</td>\n","      <td>0.362252</td>\n","      <td>0.298252</td>\n","      <td>0.0</td>\n","      <td>0.460000</td>\n","      <td>0.381436</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.996825</td>\n","      <td>0.003667</td>\n","     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<td>0.020632</td>\n","      <td>0.026453</td>\n","      <td>0.079950</td>\n","      <td>...</td>\n","      <td>65.000000</td>\n","      <td>10.000000</td>\n","      <td>0.568155</td>\n","      <td>-0.565360</td>\n","      <td>0.000002</td>\n","      <td>0.000002</td>\n","      <td>-0.000005</td>\n","      <td>0.000003</td>\n","      <td>0.000610</td>\n","      <td>0.000757</td>\n","      <td>-7.966769</td>\n","      <td>1.309082</td>\n","      <td>-0.770455</td>\n","      <td>0.080046</td>\n","      <td>-1.238489</td>\n","      <td>-0.922404</td>\n","      <td>-1.695809</td>\n","      <td>-0.864904</td>\n","      <td>-0.670674</td>\n","      <td>-0.580025</td>\n","      <td>-0.710762</td>\n","      <td>-0.694606</td>\n","      <td>-0.720886</td>\n","      <td>2.537650</td>\n","      <td>1.186965</td>\n","      <td>1.251340</td>\n","      <td>0.803958</td>\n","      <td>0.546688</td>\n","      <td>0.562475</td>\n","      <td>0.559488</td>\n","      <td>0.445827</td>\n","      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<td>0.296194</td>\n","      <td>1032.123385</td>\n","      <td>17.796377</td>\n","      <td>10.444489</td>\n","      <td>27.338486</td>\n","      <td>1025.124383</td>\n","      <td>1033.518263</td>\n","      <td>1041.537325</td>\n","      <td>2.139388</td>\n","      <td>6.214460</td>\n","      <td>5.042475</td>\n","      <td>0.004308</td>\n","      <td>0.034187</td>\n","      <td>0.035234</td>\n","      <td>0.089009</td>\n","      <td>...</td>\n","      <td>65.000000</td>\n","      <td>10.000000</td>\n","      <td>2.097436</td>\n","      <td>0.740716</td>\n","      <td>0.000006</td>\n","      <td>0.000003</td>\n","      <td>0.000007</td>\n","      <td>0.000004</td>\n","      <td>0.000610</td>\n","      <td>0.000757</td>\n","      <td>-5.367933</td>\n","      <td>0.182373</td>\n","      <td>-0.891874</td>\n","      <td>0.112003</td>\n","      <td>-1.437741</td>\n","      <td>-0.770266</td>\n","      <td>-1.646552</td>\n","      <td>-0.922945</td>\n","      <td>-0.614741</td>\n","      <td>-0.531608</td>\n","      <td>-0.836680</td>\n","      <td>-0.704615</td>\n","      <td>-0.719910</td>\n","      <td>2.179684</td>\n","      <td>1.410698</td>\n","      <td>1.111916</td>\n","      <td>0.821571</td>\n","      <td>0.519879</td>\n","      <td>0.572094</td>\n","      <td>0.591143</td>\n","      <td>0.467151</td>\n","      <td>0.453082</td>\n","      <td>0.459266</td>\n","      <td>0.325971</td>\n","      <td>0.379887</td>\n","      <td>0.389737</td>\n","      <td>0.0</td>\n","      <td>0.460000</td>\n","      <td>0.381436</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.997371</td>\n","      <td>0.037653</td>\n","      <td>0.043389</td>\n","      <td>0.102332</td>\n","      <td>0.995548</td>\n","      <td>0.996860</td>\n","      <td>0.998205</td>\n","      <td>0.460806</td>\n","      <td>6.683904</td>\n","      <td>5.042779</td>\n","      <td>0.000858</td>\n","      <td>0.006035</td>\n","      <td>0.014974</td>\n","      <td>0.023291</td>\n","      <td>0.430164</td>\n","      <td>0.260603</td>\n","      <td>0.202305</td>\n","      <td>-0.008383</td>\n","      <td>-0.000009</td>\n","      <td>-0.994184</td>\n","      <td>0.074612</td>\n","      <td>0.023784</td>\n","      <td>0.039512</td>\n","      <td>-0.187758</td>\n","      <td>-0.204381</td>\n","      <td>0.062696</td>\n","      <td>1042.973796</td>\n","      <td>121.591905</td>\n","      <td>292.986177</td>\n","      <td>487.849018</td>\n","      <td>1022.436306</td>\n","      <td>1034.261089</td>\n","      <td>1047.938926</td>\n","      <td>0.589813</td>\n","      <td>6.158404</td>\n","      <td>5.061130</td>\n","      <td>0.442844</td>\n","      <td>1.154677</td>\n","      <td>1.265153</td>\n","      <td>2.547586</td>\n","      <td>...</td>\n","      <td>65.000000</td>\n","      <td>10.000000</td>\n","      <td>5.015308</td>\n","      <td>1.612495</td>\n","      <td>0.000011</td>\n","      <td>0.000011</td>\n","      <td>0.000002</td>\n","      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<td>0.308034</td>\n","      <td>0.282412</td>\n","      <td>0.293662</td>\n","      <td>0.286113</td>\n","      <td>0.0</td>\n","      <td>0.826611</td>\n","      <td>0.257653</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>488908</th>\n","      <td>1.001128</td>\n","      <td>0.266208</td>\n","      <td>-0.021175</td>\n","      <td>0.332435</td>\n","      <td>0.780602</td>\n","      <td>0.999714</td>\n","      <td>1.202316</td>\n","      <td>2.734743</td>\n","      <td>6.648152</td>\n","      <td>5.002609</td>\n","      <td>0.854890</td>\n","      <td>3.549029</td>\n","      <td>1.322197</td>\n","      <td>0.816043</td>\n","      <td>0.680516</td>\n","      <td>0.911055</td>\n","      <td>0.695917</td>\n","      <td>0.230665</td>\n","      <td>-0.244989</td>\n","      <td>-0.662497</td>\n","      <td>0.164162</td>\n","      <td>0.152451</td>\n","      <td>0.258976</td>\n","      <td>-0.174100</td>\n","      <td>-0.282626</td>\n","      <td>0.085193</td>\n","      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    <td>-0.682656</td>\n","      <td>-0.218682</td>\n","      <td>-0.307144</td>\n","      <td>1.668809</td>\n","      <td>0.639561</td>\n","      <td>0.494611</td>\n","      <td>0.419309</td>\n","      <td>0.435305</td>\n","      <td>0.313965</td>\n","      <td>0.375149</td>\n","      <td>0.299137</td>\n","      <td>0.315982</td>\n","      <td>0.255973</td>\n","      <td>0.267164</td>\n","      <td>0.235524</td>\n","      <td>0.219306</td>\n","      <td>0.0</td>\n","      <td>0.469663</td>\n","      <td>0.896217</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>488909</th>\n","      <td>0.926979</td>\n","      <td>0.002172</td>\n","      <td>0.000801</td>\n","      <td>0.002829</td>\n","      <td>0.925441</td>\n","      <td>0.926958</td>\n","      <td>0.928505</td>\n","      <td>2.596578</td>\n","      <td>6.684609</td>\n","      <td>5.043658</td>\n","      <td>0.000110</td>\n","      <td>0.000265</td>\n","      <td>0.000225</td>\n","      <td>0.002630</td>\n","      <td>0.429712</td>\n","      <td>2.381937</td>\n","      <td>0.113431</td>\n","      <td>0.034947</td>\n","      <td>-0.009084</td>\n","      <td>0.926272</td>\n","      <td>0.001449</td>\n","      <td>0.001539</td>\n","      <td>0.002168</td>\n","      <td>-0.000321</td>\n","      <td>0.098672</td>\n","      <td>0.031745</td>\n","      <td>1003.870016</td>\n","      <td>18.142330</td>\n","      <td>10.460184</td>\n","      <td>28.647109</td>\n","      <td>994.185061</td>\n","      <td>1001.307175</td>\n","      <td>1009.304682</td>\n","      <td>2.152039</td>\n","      <td>6.214445</td>\n","      <td>5.047371</td>\n","      <td>0.008378</td>\n","      <td>0.035571</td>\n","      <td>0.038558</td>\n","      <td>0.112553</td>\n","      <td>...</td>\n","      <td>4.124077</td>\n","      <td>6.389393</td>\n","      <td>0.011174</td>\n","      <td>-4.555751</td>\n","      <td>0.000126</td>\n","      <td>0.000146</td>\n","      <td>0.000012</td>\n","      <td>-0.000021</td>\n","      <td>0.000610</td>\n","      <td>0.000757</td>\n","      <td>8.938255</td>\n","      <td>-0.619216</td>\n","      <td>-0.502659</td>\n","      <td>-0.292469</td>\n","      <td>-0.066106</td>\n","      <td>-1.114814</td>\n","      <td>-0.295341</td>\n","      <td>-0.407445</td>\n","      <td>-0.413443</td>\n","      <td>-0.170915</td>\n","      <td>-0.020711</td>\n","      <td>-0.144267</td>\n","      <td>-0.045541</td>\n","      <td>2.810937</td>\n","      <td>1.005073</td>\n","      <td>1.019036</td>\n","      <td>0.630051</td>\n","      <td>0.571995</td>\n","      <td>0.507248</td>\n","      <td>0.417708</td>\n","      <td>0.378539</td>\n","      <td>0.352836</td>\n","      <td>0.306870</td>\n","      <td>0.252689</td>\n","      <td>0.220463</td>\n","      <td>0.268815</td>\n","      <td>0.0</td>\n","      <td>0.060000</td>\n","      <td>0.312412</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>488910</th>\n","      <td>1.095402</td>\n","      <td>0.302158</td>\n","      <td>0.203917</td>\n","      <td>0.404368</td>\n","      <td>0.874065</td>\n","      <td>1.070770</td>\n","      <td>1.299008</td>\n","      <td>2.523934</td>\n","      <td>6.645088</td>\n","      <td>5.036886</td>\n","      <td>0.976737</td>\n","      <td>3.348205</td>\n","      <td>1.481230</td>\n","      <td>1.604837</td>\n","      <td>0.706533</td>\n","      <td>0.865760</td>\n","      <td>0.597070</td>\n","      <td>-0.291024</td>\n","      <td>-0.035392</td>\n","      <td>-0.537794</td>\n","      <td>0.271081</td>\n","      <td>0.305249</td>\n","      <td>0.277076</td>\n","      <td>-0.185402</td>\n","      <td>0.078760</td>\n","      <td>-0.314958</td>\n","      <td>1029.004922</td>\n","      <td>64.896102</td>\n","      <td>54.068766</td>\n","      <td>121.289399</td>\n","      <td>999.204221</td>\n","      <td>1023.837335</td>\n","      <td>1052.323882</td>\n","      <td>1.994675</td>\n","      <td>5.931323</td>\n","      <td>5.024812</td>\n","      <td>0.262276</td>\n","      <td>0.554810</td>\n","      <td>0.262479</td>\n","      <td>0.302496</td>\n","      <td>...</td>\n","      <td>5.000000</td>\n","      <td>3.000000</td>\n","      <td>44.793952</td>\n","      <td>3.802068</td>\n","      <td>0.000083</td>\n","      <td>0.000114</td>\n","      <td>-0.000324</td>\n","      <td>-0.000028</td>\n","      <td>0.000205</td>\n","      <td>0.000155</td>\n","      <td>-1.645656</td>\n","      <td>0.210199</td>\n","      <td>-1.109487</td>\n","      <td>-0.323939</td>\n","      <td>-0.780386</td>\n","      <td>-0.414783</td>\n","      <td>-0.680746</td>\n","      <td>-0.493851</td>\n","      <td>-0.574699</td>\n","      <td>-0.373475</td>\n","      <td>-0.532141</td>\n","      <td>-0.314221</td>\n","      <td>-0.420830</td>\n","      <td>1.980672</td>\n","      <td>0.739469</td>\n","      <td>0.508324</td>\n","      <td>0.362144</td>\n","      <td>0.312545</td>\n","      <td>0.291211</td>\n","      <td>0.272528</td>\n","      <td>0.248237</td>\n","      <td>0.225603</td>\n","      <td>0.226544</td>\n","      <td>0.211211</td>\n","      <td>0.200464</td>\n","      <td>0.197953</td>\n","      <td>0.0</td>\n","      <td>0.619363</td>\n","      <td>0.817788</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>488911</th>\n","      <td>1.002107</td>\n","      <td>0.027344</td>\n","      <td>0.047757</td>\n","      <td>0.079401</td>\n","      <td>0.999024</td>\n","      <td>1.000926</td>\n","      <td>1.002899</td>\n","      <td>0.460303</td>\n","      <td>6.684245</td>\n","      <td>5.042789</td>\n","      <td>0.000440</td>\n","      <td>0.004314</td>\n","      <td>0.005174</td>\n","      <td>0.007708</td>\n","      <td>0.429732</td>\n","      <td>0.483347</td>\n","      <td>0.435352</td>\n","      <td>0.370523</td>\n","      <td>0.357854</td>\n","      <td>0.770941</td>\n","      <td>0.136502</td>\n","      <td>0.108344</td>\n","      <td>0.094237</td>\n","      <td>-0.859929</td>\n","      <td>-0.817742</td>\n","      <td>0.777329</td>\n","      <td>956.447520</td>\n","      <td>31.775989</td>\n","      <td>28.905865</td>\n","      <td>61.033325</td>\n","      <td>944.414429</td>\n","      <td>954.735181</td>\n","      <td>966.706847</td>\n","      <td>1.634351</td>\n","      <td>6.212516</td>\n","      <td>5.047290</td>\n","      <td>0.032703</td>\n","      <td>0.315465</td>\n","      <td>0.216920</td>\n","      <td>0.227423</td>\n","      <td>...</td>\n","      <td>65.000000</td>\n","      <td>14.551793</td>\n","      <td>24.227602</td>\n","      <td>3.156874</td>\n","      <td>0.000058</td>\n","      <td>0.000083</td>\n","      <td>0.000101</td>\n","      <td>0.000123</td>\n","      <td>0.000093</td>\n","      <td>0.000132</td>\n","      <td>-11.966331</td>\n","      <td>2.250640</td>\n","      <td>-2.132060</td>\n","      <td>-0.640428</td>\n","      <td>-2.489443</td>\n","      <td>-1.589056</td>\n","      <td>-1.618910</td>\n","      <td>-1.052891</td>\n","      <td>-0.723198</td>\n","      <td>-0.395877</td>\n","      <td>-0.585016</td>\n","      <td>-0.629720</td>\n","      <td>-0.924316</td>\n","      <td>3.055672</td>\n","      <td>1.612968</td>\n","      <td>1.619956</td>\n","      <td>0.974417</td>\n","      <td>0.751693</td>\n","      <td>0.734507</td>\n","      <td>0.693998</td>\n","      <td>0.604658</td>\n","      <td>0.495488</td>\n","      <td>0.415434</td>\n","      <td>0.512774</td>\n","      <td>0.594825</td>\n","      <td>0.535096</td>\n","      <td>0.0</td>\n","      <td>0.453309</td>\n","      <td>0.386829</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>488912 rows × 117 columns</p>\n","</div>"],"text/plain":["        raw_acc:magnitude_stats:mean  ...  CLASS\n","0                           0.996815  ...    1.0\n","1                           0.996864  ...    1.0\n","2                           0.996825  ...    1.0\n","3                           0.996874  ...    1.0\n","4                           0.997371  ...    1.0\n","...                              ...  ...    ...\n","488907                      1.006492  ...    0.0\n","488908                      1.001128  ...    0.0\n","488909                      0.926979  ...    0.0\n","488910                      1.095402  ...    0.0\n","488911                      1.002107  ...    0.0\n","\n","[488912 rows x 117 columns]"]},"metadata":{},"execution_count":98}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0ECef79dT7zk","executionInfo":{"status":"ok","timestamp":1635951670083,"user_tz":-60,"elapsed":92570,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"b642bb69-a38c-48d9-ae67-c41b732a95e0"},"source":["# Store the dataset in CSV files\n","%cd \"/content/drive/MyDrive/IoT DA\"\n","balanced_dataset.to_csv('df_balanced.csv', index=False)\n","print(\"CSV Created\")"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/IoT DA\n","CSV Created\n"]}]},{"cell_type":"markdown","metadata":{"id":"k5JL_WmYsjXY"},"source":["So, let's split again the dataset:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b-_39vUlspka","executionInfo":{"status":"ok","timestamp":1635941810397,"user_tz":-60,"elapsed":756,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"8c514daf-fbc4-46c9-c4e4-2b3225dfa678"},"source":["X_train, X_test, Y_train, Y_test = train_test_split(X_res, y_res, test_size = validation_size, random_state = seed) # random_state = 7 because maybe better splitting data\n","print(X_train.shape)\n","print(Y_test.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(342238, 116)\n","(146674,)\n"]}]},{"cell_type":"markdown","metadata":{"id":"uBKjPSpIo5Ad"},"source":["# Identification and comparison of the classification model\n","Loading of the dataset and than we split the original dataset into two parts (train and test) to evaluate the performance of a machine learning algorithm."]},{"cell_type":"markdown","metadata":{"id":"v3p2ossN8Y_6"},"source":["Standardization and Normalization of Dataset Befor training"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_vwcPg3znLqZ","executionInfo":{"status":"ok","timestamp":1635941810842,"user_tz":-60,"elapsed":11,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"5653aa3a-447f-4dd0-d3ea-46f0624180bb"},"source":["normalized = preprocessing.normalize(X_train)\n","normalized"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 4.25164039e-04,  1.56399355e-06,  7.13256909e-07, ...,\n","         0.00000000e+00,  9.05038978e-05,  1.34640510e-04],\n","       [ 4.70473923e-04,  3.84409686e-05,  4.78771784e-05, ...,\n","         0.00000000e+00,  1.84061310e-04,  1.47443510e-04],\n","       [ 4.74480165e-04,  2.27377376e-06, -3.81750171e-06, ...,\n","         0.00000000e+00,  4.72521563e-04,  7.08782345e-09],\n","       ...,\n","       [ 4.73118538e-04,  5.79657512e-07, -2.83220283e-07, ...,\n","         0.00000000e+00,  4.48432114e-04,  1.68402780e-04],\n","       [ 4.37540883e-04,  2.72383817e-06, -2.33207386e-06, ...,\n","         0.00000000e+00,  2.54001030e-04,  0.00000000e+00],\n","       [ 4.68491761e-04,  2.13168100e-06,  1.18688902e-06, ...,\n","         0.00000000e+00,  4.48327862e-04,  1.47434759e-04]])"]},"metadata":{},"execution_count":71}]},{"cell_type":"code","metadata":{"id":"lgmbasmYrEhL"},"source":["# #Preprocessing on data\n","# #We then preprocessing the data to standardize it, so that all have the same scale useful \n","# #for the optimization algorithms so that the classifiers can best interpret the values.\n","\n","# from sklearn import preprocessing\n","\n","# min_max_scaler = preprocessing.StandardScaler()\n","# X_train_scaled = min_max_scaler.fit_transform(X_train)\n","# X_test_scaled = min_max_scaler.transform(X_test)\n","# pd.DataFrame(X_train_scaled)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"240Bb0oJqJVp"},"source":["#Behaviour and testing of Classifiers\n"]},{"cell_type":"markdown","metadata":{"id":"4W_eIKyyiDuZ"},"source":["## LOGISTIC REGRESSION"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MB9iRqT9HS5e","executionInfo":{"status":"ok","timestamp":1635942837531,"user_tz":-60,"elapsed":1026693,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"221e425d-023d-4bc2-d128-cac5930cb485"},"source":["results = []\n","names = []\n","\n","\n","datas = {\n","    'name': 'LC',\n","    'model': LogisticRegression(),\n","    'params': {\n","       'penalty': ['l2'],\n","       'C':  [1.0, 3.0, 6.0, 7.0, 10.0],\n","       'solver': ['sag', 'saga']\n","    }}\n","\n","predictionsLC, results, names = training_and_test(datas, results, names)\n"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["### using LC ###\n","Fitting 5 folds for each of 10 candidates, totalling 50 fits\n","[CV] C=1.0, penalty=l2, solver=sag ...................................\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"]},{"output_type":"stream","name":"stdout","text":["[CV] ....... C=1.0, penalty=l2, solver=sag, score=0.698, total=  13.2s\n","[CV] C=1.0, penalty=l2, solver=sag ...................................\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   13.2s remaining:    0.0s\n"]},{"output_type":"stream","name":"stdout","text":["[CV] ....... C=1.0, penalty=l2, solver=sag, score=0.694, total=  12.6s\n","[CV] C=1.0, penalty=l2, solver=sag ...................................\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   25.8s remaining:    0.0s\n"]},{"output_type":"stream","name":"stdout","text":["[CV] ....... C=1.0, penalty=l2, solver=sag, score=0.698, total=  11.1s\n","[CV] C=1.0, penalty=l2, solver=sag ...................................\n","[CV] ....... C=1.0, penalty=l2, solver=sag, score=0.696, total=  11.5s\n","[CV] C=1.0, penalty=l2, solver=sag ...................................\n","[CV] ....... C=1.0, penalty=l2, solver=sag, score=0.700, total=  12.0s\n","[CV] C=1.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=1.0, penalty=l2, solver=saga, score=0.698, total=   9.7s\n","[CV] C=1.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=1.0, penalty=l2, solver=saga, score=0.694, total=  10.3s\n","[CV] C=1.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=1.0, penalty=l2, solver=saga, score=0.698, total=   9.1s\n","[CV] C=1.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=1.0, penalty=l2, solver=saga, score=0.696, total=   9.7s\n","[CV] C=1.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=1.0, penalty=l2, solver=saga, score=0.700, total=   9.7s\n","[CV] C=3.0, penalty=l2, solver=sag ...................................\n","[CV] ....... C=3.0, penalty=l2, solver=sag, score=0.711, total=  17.1s\n","[CV] C=3.0, penalty=l2, solver=sag ...................................\n","[CV] ....... C=3.0, penalty=l2, solver=sag, score=0.709, total=  14.5s\n","[CV] C=3.0, penalty=l2, solver=sag ...................................\n","[CV] ....... C=3.0, penalty=l2, solver=sag, score=0.710, total=  13.1s\n","[CV] C=3.0, penalty=l2, solver=sag ...................................\n","[CV] ....... C=3.0, penalty=l2, solver=sag, score=0.711, total=  12.9s\n","[CV] C=3.0, penalty=l2, solver=sag ...................................\n","[CV] ....... C=3.0, penalty=l2, solver=sag, score=0.713, total=  13.9s\n","[CV] C=3.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=3.0, penalty=l2, solver=saga, score=0.711, total=  17.9s\n","[CV] C=3.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=3.0, penalty=l2, solver=saga, score=0.709, total=  18.1s\n","[CV] C=3.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=3.0, penalty=l2, solver=saga, score=0.710, total=  17.9s\n","[CV] C=3.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=3.0, penalty=l2, solver=saga, score=0.711, total=  18.0s\n","[CV] C=3.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=3.0, penalty=l2, solver=saga, score=0.713, total=  18.1s\n","[CV] C=6.0, penalty=l2, solver=sag ...................................\n","[CV] ....... C=6.0, penalty=l2, solver=sag, score=0.718, total=  16.1s\n","[CV] C=6.0, penalty=l2, solver=sag ...................................\n","[CV] ....... C=6.0, penalty=l2, solver=sag, score=0.718, total=  15.8s\n","[CV] C=6.0, penalty=l2, solver=sag ...................................\n","[CV] ....... C=6.0, penalty=l2, solver=sag, score=0.717, total=  13.0s\n","[CV] C=6.0, penalty=l2, solver=sag ...................................\n","[CV] ....... C=6.0, penalty=l2, solver=sag, score=0.718, total=  15.1s\n","[CV] C=6.0, penalty=l2, solver=sag ...................................\n","[CV] ....... C=6.0, penalty=l2, solver=sag, score=0.721, total=  14.7s\n","[CV] C=6.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=6.0, penalty=l2, solver=saga, score=0.718, total=  29.1s\n","[CV] C=6.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=6.0, penalty=l2, solver=saga, score=0.717, total=  29.0s\n","[CV] C=6.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=6.0, penalty=l2, solver=saga, score=0.717, total=  28.8s\n","[CV] C=6.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=6.0, penalty=l2, solver=saga, score=0.719, total=  28.4s\n","[CV] C=6.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=6.0, penalty=l2, solver=saga, score=0.721, total=  29.0s\n","[CV] C=7.0, penalty=l2, solver=sag ...................................\n","[CV] ....... C=7.0, penalty=l2, solver=sag, score=0.720, total=  15.0s\n","[CV] C=7.0, penalty=l2, solver=sag ...................................\n","[CV] ....... C=7.0, penalty=l2, solver=sag, score=0.719, total=  15.4s\n","[CV] C=7.0, penalty=l2, solver=sag ...................................\n","[CV] ....... C=7.0, penalty=l2, solver=sag, score=0.719, total=  18.5s\n","[CV] C=7.0, penalty=l2, solver=sag ...................................\n","[CV] ....... C=7.0, penalty=l2, solver=sag, score=0.720, total=  14.0s\n","[CV] C=7.0, penalty=l2, solver=sag ...................................\n","[CV] ....... C=7.0, penalty=l2, solver=sag, score=0.722, total=  14.0s\n","[CV] C=7.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=7.0, penalty=l2, solver=saga, score=0.720, total=  31.2s\n","[CV] C=7.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=7.0, penalty=l2, solver=saga, score=0.719, total=  31.2s\n","[CV] C=7.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=7.0, penalty=l2, solver=saga, score=0.719, total=  31.4s\n","[CV] C=7.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=7.0, penalty=l2, solver=saga, score=0.720, total=  31.5s\n","[CV] C=7.0, penalty=l2, solver=saga ..................................\n","[CV] ...... C=7.0, penalty=l2, solver=saga, score=0.722, total=  31.4s\n","[CV] C=10.0, penalty=l2, solver=sag ..................................\n","[CV] ...... C=10.0, penalty=l2, solver=sag, score=0.724, total=  17.6s\n","[CV] C=10.0, penalty=l2, solver=sag ..................................\n","[CV] ...... C=10.0, penalty=l2, solver=sag, score=0.722, total=  17.1s\n","[CV] C=10.0, penalty=l2, solver=sag ..................................\n","[CV] ...... C=10.0, penalty=l2, solver=sag, score=0.722, total=  16.5s\n","[CV] C=10.0, penalty=l2, solver=sag ..................................\n","[CV] ...... C=10.0, penalty=l2, solver=sag, score=0.723, total=  19.1s\n","[CV] C=10.0, penalty=l2, solver=sag ..................................\n","[CV] ...... C=10.0, penalty=l2, solver=sag, score=0.726, total=  17.1s\n","[CV] C=10.0, penalty=l2, solver=saga .................................\n","[CV] ..... C=10.0, penalty=l2, solver=saga, score=0.724, total=  39.1s\n","[CV] C=10.0, penalty=l2, solver=saga .................................\n","[CV] ..... C=10.0, penalty=l2, solver=saga, score=0.722, total=  38.9s\n","[CV] C=10.0, penalty=l2, solver=saga .................................\n","[CV] ..... C=10.0, penalty=l2, solver=saga, score=0.722, total=  39.4s\n","[CV] C=10.0, penalty=l2, solver=saga .................................\n","[CV] ..... C=10.0, penalty=l2, solver=saga, score=0.723, total=  39.5s\n","[CV] C=10.0, penalty=l2, solver=saga .................................\n","[CV] ..... C=10.0, penalty=l2, solver=saga, score=0.726, total=  39.3s\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed: 16.8min finished\n"]},{"output_type":"stream","name":"stdout","text":["Best: 0.723277 using {'C': 10.0, 'penalty': 'l2', 'solver': 'sag'}\n","accuracy:  0.7244228697656027\n","              precision    recall  f1-score   support\n","\n","         0.0       0.76      0.65      0.70     73085\n","         1.0       0.70      0.80      0.74     73589\n","\n","    accuracy                           0.72    146674\n","   macro avg       0.73      0.72      0.72    146674\n","weighted avg       0.73      0.72      0.72    146674\n","\n"]}]},{"cell_type":"markdown","metadata":{"id":"Z86QU5ZsiHIs"},"source":["## GAUSSIAN NAIVE BAYES"]},{"cell_type":"code","metadata":{"id":"tsZgQXKwlY9B"},"source":["import numpy as np\n","\n","datas = {\n","    'name': 'GaussianNB',\n","    'model': GaussianNB(),\n","    'params': {\n","             'var_smoothing' : np.logspace(0,-9, num=100) \n","    }}\n","    "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zVPQAT-7l_oA","executionInfo":{"status":"ok","timestamp":1635943183285,"user_tz":-60,"elapsed":345757,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"63a2895a-e715-4ee3-8688-e5247796536e"},"source":["\n","predictionsNB, results, names = training_and_test(datas, results, names)\n","\n"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["### using GaussianNB ###\n","Fitting 5 folds for each of 100 candidates, totalling 500 fits\n","[CV] var_smoothing=1.0 ...............................................\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"]},{"output_type":"stream","name":"stdout","text":["[CV] ................... var_smoothing=1.0, score=0.644, total=   0.7s\n","[CV] var_smoothing=1.0 ...............................................\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.7s remaining:    0.0s\n"]},{"output_type":"stream","name":"stdout","text":["[CV] ................... var_smoothing=1.0, score=0.644, total=   0.7s\n","[CV] var_smoothing=1.0 ...............................................\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    1.4s remaining:    0.0s\n"]},{"output_type":"stream","name":"stdout","text":["[CV] ................... var_smoothing=1.0, score=0.647, total=   0.7s\n","[CV] var_smoothing=1.0 ...............................................\n","[CV] ................... var_smoothing=1.0, score=0.647, total=   0.7s\n","[CV] var_smoothing=1.0 ...............................................\n","[CV] ................... var_smoothing=1.0, score=0.649, total=   0.7s\n","[CV] var_smoothing=0.8111308307896871 ................................\n","[CV] .... var_smoothing=0.8111308307896871, score=0.646, total=   0.7s\n","[CV] var_smoothing=0.8111308307896871 ................................\n","[CV] .... var_smoothing=0.8111308307896871, score=0.645, total=   0.7s\n","[CV] var_smoothing=0.8111308307896871 ................................\n","[CV] .... var_smoothing=0.8111308307896871, score=0.648, total=   0.7s\n","[CV] var_smoothing=0.8111308307896871 ................................\n","[CV] .... var_smoothing=0.8111308307896871, score=0.647, total=   0.7s\n","[CV] var_smoothing=0.8111308307896871 ................................\n","[CV] .... var_smoothing=0.8111308307896871, score=0.649, total=   0.7s\n","[CV] var_smoothing=0.657933224657568 .................................\n","[CV] ..... var_smoothing=0.657933224657568, score=0.646, total=   0.7s\n","[CV] var_smoothing=0.657933224657568 .................................\n","[CV] ..... var_smoothing=0.657933224657568, score=0.645, total=   0.7s\n","[CV] var_smoothing=0.657933224657568 .................................\n","[CV] ..... var_smoothing=0.657933224657568, score=0.648, total=   0.7s\n","[CV] var_smoothing=0.657933224657568 .................................\n","[CV] ..... var_smoothing=0.657933224657568, score=0.647, total=   0.7s\n","[CV] var_smoothing=0.657933224657568 .................................\n","[CV] ..... var_smoothing=0.657933224657568, score=0.649, total=   0.7s\n","[CV] var_smoothing=0.533669923120631 .................................\n","[CV] ..... var_smoothing=0.533669923120631, score=0.646, total=   0.7s\n","[CV] var_smoothing=0.533669923120631 .................................\n","[CV] ..... var_smoothing=0.533669923120631, score=0.645, total=   0.7s\n","[CV] var_smoothing=0.533669923120631 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total=   0.7s\n","[CV] var_smoothing=0.43287612810830584 ...............................\n","[CV] ... var_smoothing=0.43287612810830584, score=0.649, total=   0.7s\n","[CV] var_smoothing=0.3511191734215131 ................................\n","[CV] .... var_smoothing=0.3511191734215131, score=0.648, total=   0.7s\n","[CV] var_smoothing=0.3511191734215131 ................................\n","[CV] .... var_smoothing=0.3511191734215131, score=0.647, total=   0.7s\n","[CV] var_smoothing=0.3511191734215131 ................................\n","[CV] .... var_smoothing=0.3511191734215131, score=0.649, total=   0.7s\n","[CV] var_smoothing=0.3511191734215131 ................................\n","[CV] .... var_smoothing=0.3511191734215131, score=0.648, total=   0.7s\n","[CV] var_smoothing=0.3511191734215131 ................................\n","[CV] .... var_smoothing=0.3511191734215131, score=0.651, total=   0.7s\n","[CV] var_smoothing=0.2848035868435802 ................................\n","[CV] .... var_smoothing=0.2848035868435802, score=0.654, total=   0.7s\n","[CV] var_smoothing=0.2848035868435802 ................................\n","[CV] .... var_smoothing=0.2848035868435802, score=0.653, total=   0.7s\n","[CV] var_smoothing=0.2848035868435802 ................................\n","[CV] .... var_smoothing=0.2848035868435802, score=0.656, total=   0.7s\n","[CV] var_smoothing=0.2848035868435802 ................................\n","[CV] .... var_smoothing=0.2848035868435802, score=0.654, total=   0.7s\n","[CV] var_smoothing=0.2848035868435802 ................................\n","[CV] .... var_smoothing=0.2848035868435802, score=0.657, total=   0.7s\n","[CV] var_smoothing=0.23101297000831597 ...............................\n","[CV] ... var_smoothing=0.23101297000831597, score=0.655, total=   0.7s\n","[CV] var_smoothing=0.23101297000831597 ...............................\n","[CV] ... var_smoothing=0.23101297000831597, score=0.653, total=   0.7s\n","[CV] var_smoothing=0.23101297000831597 ...............................\n","[CV] ... var_smoothing=0.23101297000831597, score=0.658, total=   0.7s\n","[CV] var_smoothing=0.23101297000831597 ...............................\n","[CV] ... var_smoothing=0.23101297000831597, score=0.656, total=   0.7s\n","[CV] var_smoothing=0.23101297000831597 ...............................\n","[CV] ... var_smoothing=0.23101297000831597, score=0.657, total=   0.7s\n","[CV] var_smoothing=0.1873817422860384 ................................\n","[CV] .... var_smoothing=0.1873817422860384, score=0.654, total=   0.7s\n","[CV] var_smoothing=0.1873817422860384 ................................\n","[CV] .... var_smoothing=0.1873817422860384, score=0.652, total=   0.7s\n","[CV] var_smoothing=0.1873817422860384 ................................\n","[CV] .... var_smoothing=0.1873817422860384, score=0.657, total=   0.7s\n","[CV] var_smoothing=0.1873817422860384 ................................\n","[CV] .... var_smoothing=0.1873817422860384, score=0.656, total=   0.7s\n","[CV] var_smoothing=0.1873817422860384 ................................\n","[CV] .... var_smoothing=0.1873817422860384, score=0.656, total=   0.7s\n","[CV] var_smoothing=0.15199110829529336 ...............................\n","[CV] ... var_smoothing=0.15199110829529336, score=0.653, total=   0.7s\n","[CV] var_smoothing=0.15199110829529336 ...............................\n","[CV] ... var_smoothing=0.15199110829529336, score=0.650, total=   0.7s\n","[CV] var_smoothing=0.15199110829529336 ...............................\n","[CV] ... var_smoothing=0.15199110829529336, score=0.655, total=   0.7s\n","[CV] var_smoothing=0.15199110829529336 ...............................\n","[CV] ... var_smoothing=0.15199110829529336, score=0.653, total=   0.7s\n","[CV] var_smoothing=0.15199110829529336 ...............................\n","[CV] ... var_smoothing=0.15199110829529336, score=0.654, total=   0.7s\n","[CV] var_smoothing=0.12328467394420659 ...............................\n","[CV] ... var_smoothing=0.12328467394420659, score=0.651, total=   0.7s\n","[CV] var_smoothing=0.12328467394420659 ...............................\n","[CV] ... var_smoothing=0.12328467394420659, score=0.648, total=   0.7s\n","[CV] var_smoothing=0.12328467394420659 ...............................\n","[CV] ... var_smoothing=0.12328467394420659, score=0.653, total=   0.7s\n","[CV] var_smoothing=0.12328467394420659 ...............................\n","[CV] ... var_smoothing=0.12328467394420659, score=0.652, total=   0.7s\n","[CV] var_smoothing=0.12328467394420659 ...............................\n","[CV] ... var_smoothing=0.12328467394420659, score=0.653, total=   0.7s\n","[CV] var_smoothing=0.1 ...............................................\n","[CV] ................... var_smoothing=0.1, score=0.650, total=   0.7s\n","[CV] var_smoothing=0.1 ...............................................\n","[CV] ................... var_smoothing=0.1, score=0.647, total=   0.7s\n","[CV] var_smoothing=0.1 ...............................................\n","[CV] ................... var_smoothing=0.1, score=0.652, total=   0.7s\n","[CV] var_smoothing=0.1 ...............................................\n","[CV] ................... var_smoothing=0.1, score=0.651, total=   0.7s\n","[CV] var_smoothing=0.1 ...............................................\n","[CV] ................... var_smoothing=0.1, score=0.652, total=   0.7s\n","[CV] var_smoothing=0.08111308307896872 ...............................\n","[CV] ... var_smoothing=0.08111308307896872, score=0.649, total=   0.7s\n","[CV] var_smoothing=0.08111308307896872 ...............................\n","[CV] ... var_smoothing=0.08111308307896872, score=0.646, total=   0.7s\n","[CV] var_smoothing=0.08111308307896872 ...............................\n","[CV] ... var_smoothing=0.08111308307896872, score=0.651, total=   0.7s\n","[CV] var_smoothing=0.08111308307896872 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var_smoothing=0.002848035868435802, score=0.646, total=   0.7s\n","[CV] var_smoothing=0.002848035868435802 ..............................\n","[CV] .. var_smoothing=0.002848035868435802, score=0.650, total=   0.7s\n","[CV] var_smoothing=0.002848035868435802 ..............................\n","[CV] .. var_smoothing=0.002848035868435802, score=0.650, total=   0.7s\n","[CV] var_smoothing=0.002848035868435802 ..............................\n","[CV] .. var_smoothing=0.002848035868435802, score=0.650, total=   0.7s\n","[CV] var_smoothing=0.0023101297000831605 .............................\n","[CV] . var_smoothing=0.0023101297000831605, score=0.649, total=   0.7s\n","[CV] var_smoothing=0.0023101297000831605 .............................\n","[CV] . var_smoothing=0.0023101297000831605, score=0.646, total=   0.7s\n","[CV] var_smoothing=0.0023101297000831605 .............................\n","[CV] . var_smoothing=0.0023101297000831605, score=0.650, total=   0.7s\n","[CV] 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var_smoothing=0.0004328761281083057, score=0.651, total=   0.7s\n","[CV] var_smoothing=0.0004328761281083057 .............................\n","[CV] . var_smoothing=0.0004328761281083057, score=0.652, total=   0.7s\n","[CV] var_smoothing=0.0004328761281083057 .............................\n","[CV] . var_smoothing=0.0004328761281083057, score=0.651, total=   0.7s\n","[CV] var_smoothing=0.0003511191734215131 .............................\n","[CV] . var_smoothing=0.0003511191734215131, score=0.650, total=   0.7s\n","[CV] var_smoothing=0.0003511191734215131 .............................\n","[CV] . var_smoothing=0.0003511191734215131, score=0.647, total=   0.7s\n","[CV] var_smoothing=0.0003511191734215131 .............................\n","[CV] . var_smoothing=0.0003511191734215131, score=0.651, total=   0.7s\n","[CV] var_smoothing=0.0003511191734215131 .............................\n","[CV] . var_smoothing=0.0003511191734215131, score=0.652, total=   0.7s\n","[CV] 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var_smoothing=0.0002310129700083158, score=0.651, total=   0.7s\n","[CV] var_smoothing=0.0002310129700083158 .............................\n","[CV] . var_smoothing=0.0002310129700083158, score=0.647, total=   0.7s\n","[CV] var_smoothing=0.0002310129700083158 .............................\n","[CV] . var_smoothing=0.0002310129700083158, score=0.651, total=   0.7s\n","[CV] var_smoothing=0.0002310129700083158 .............................\n","[CV] . var_smoothing=0.0002310129700083158, score=0.652, total=   0.7s\n","[CV] var_smoothing=0.0002310129700083158 .............................\n","[CV] . var_smoothing=0.0002310129700083158, score=0.652, total=   0.7s\n","[CV] var_smoothing=0.0001873817422860383 .............................\n","[CV] . var_smoothing=0.0001873817422860383, score=0.651, total=   0.7s\n","[CV] var_smoothing=0.0001873817422860383 .............................\n","[CV] . var_smoothing=0.0001873817422860383, score=0.648, total=   0.7s\n","[CV] 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var_smoothing=0.0001519911082952933, score=0.653, total=   0.7s\n","[CV] var_smoothing=0.0001519911082952933 .............................\n","[CV] . var_smoothing=0.0001519911082952933, score=0.653, total=   0.7s\n","[CV] var_smoothing=0.0001232846739442066 .............................\n","[CV] . var_smoothing=0.0001232846739442066, score=0.652, total=   0.7s\n","[CV] var_smoothing=0.0001232846739442066 .............................\n","[CV] . var_smoothing=0.0001232846739442066, score=0.648, total=   0.7s\n","[CV] var_smoothing=0.0001232846739442066 .............................\n","[CV] . var_smoothing=0.0001232846739442066, score=0.652, total=   0.7s\n","[CV] var_smoothing=0.0001232846739442066 .............................\n","[CV] . var_smoothing=0.0001232846739442066, score=0.653, total=   0.7s\n","[CV] var_smoothing=0.0001232846739442066 .............................\n","[CV] . var_smoothing=0.0001232846739442066, score=0.653, total=   0.7s\n","[CV] var_smoothing=0.0001 ............................................\n","[CV] ................ var_smoothing=0.0001, score=0.652, total=   0.7s\n","[CV] var_smoothing=0.0001 ............................................\n","[CV] ................ var_smoothing=0.0001, score=0.648, total=   0.7s\n","[CV] var_smoothing=0.0001 ............................................\n","[CV] ................ var_smoothing=0.0001, score=0.653, total=   0.7s\n","[CV] var_smoothing=0.0001 ............................................\n","[CV] ................ var_smoothing=0.0001, score=0.653, total=   0.7s\n","[CV] var_smoothing=0.0001 ............................................\n","[CV] ................ var_smoothing=0.0001, score=0.653, total=   0.7s\n","[CV] var_smoothing=8.111308307896872e-05 .............................\n","[CV] . var_smoothing=8.111308307896872e-05, score=0.652, total=   0.7s\n","[CV] var_smoothing=8.111308307896872e-05 .............................\n","[CV] . var_smoothing=8.111308307896872e-05, score=0.649, total=   0.7s\n","[CV] var_smoothing=8.111308307896872e-05 .............................\n","[CV] . var_smoothing=8.111308307896872e-05, score=0.653, total=   0.7s\n","[CV] var_smoothing=8.111308307896872e-05 .............................\n","[CV] . var_smoothing=8.111308307896872e-05, score=0.654, total=   0.7s\n","[CV] var_smoothing=8.111308307896872e-05 .............................\n","[CV] . var_smoothing=8.111308307896872e-05, score=0.653, total=   0.7s\n","[CV] var_smoothing=6.579332246575683e-05 .............................\n","[CV] . var_smoothing=6.579332246575683e-05, score=0.653, total=   0.7s\n","[CV] var_smoothing=6.579332246575683e-05 .............................\n","[CV] . var_smoothing=6.579332246575683e-05, score=0.649, total=   0.7s\n","[CV] var_smoothing=6.579332246575683e-05 .............................\n","[CV] . var_smoothing=6.579332246575683e-05, score=0.654, total=   0.7s\n","[CV] var_smoothing=6.579332246575683e-05 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0.7s\n","[CV] var_smoothing=4.328761281083062e-05 .............................\n","[CV] . var_smoothing=4.328761281083062e-05, score=0.654, total=   0.7s\n","[CV] var_smoothing=4.328761281083062e-05 .............................\n","[CV] . var_smoothing=4.328761281083062e-05, score=0.650, total=   0.7s\n","[CV] var_smoothing=4.328761281083062e-05 .............................\n","[CV] . var_smoothing=4.328761281083062e-05, score=0.655, total=   0.7s\n","[CV] var_smoothing=4.328761281083062e-05 .............................\n","[CV] . var_smoothing=4.328761281083062e-05, score=0.655, total=   0.7s\n","[CV] var_smoothing=4.328761281083062e-05 .............................\n","[CV] . var_smoothing=4.328761281083062e-05, score=0.655, total=   0.7s\n","[CV] var_smoothing=3.511191734215127e-05 .............................\n","[CV] . var_smoothing=3.511191734215127e-05, score=0.655, total=   0.7s\n","[CV] var_smoothing=3.511191734215127e-05 .............................\n","[CV] . var_smoothing=3.511191734215127e-05, score=0.651, total=   0.7s\n","[CV] var_smoothing=3.511191734215127e-05 .............................\n","[CV] . var_smoothing=3.511191734215127e-05, score=0.656, total=   0.7s\n","[CV] var_smoothing=3.511191734215127e-05 .............................\n","[CV] . var_smoothing=3.511191734215127e-05, score=0.657, total=   0.7s\n","[CV] var_smoothing=3.511191734215127e-05 .............................\n","[CV] . var_smoothing=3.511191734215127e-05, score=0.656, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-05 .............................\n","[CV] . var_smoothing=2.848035868435799e-05, score=0.659, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-05 .............................\n","[CV] . var_smoothing=2.848035868435799e-05, score=0.654, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-05 .............................\n","[CV] . var_smoothing=2.848035868435799e-05, score=0.660, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-05 .............................\n","[CV] . var_smoothing=2.848035868435799e-05, score=0.660, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-05 .............................\n","[CV] . var_smoothing=2.848035868435799e-05, score=0.660, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-05 .............................\n","[CV] . var_smoothing=2.310129700083158e-05, score=0.663, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-05 .............................\n","[CV] . var_smoothing=2.310129700083158e-05, score=0.658, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-05 .............................\n","[CV] . var_smoothing=2.310129700083158e-05, score=0.665, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-05 .............................\n","[CV] . var_smoothing=2.310129700083158e-05, score=0.665, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-05 .............................\n","[CV] . var_smoothing=2.310129700083158e-05, score=0.665, total=   0.7s\n","[CV] var_smoothing=1.873817422860383e-05 .............................\n","[CV] . var_smoothing=1.873817422860383e-05, score=0.669, total=   0.7s\n","[CV] var_smoothing=1.873817422860383e-05 .............................\n","[CV] . var_smoothing=1.873817422860383e-05, score=0.663, total=   0.7s\n","[CV] var_smoothing=1.873817422860383e-05 .............................\n","[CV] . var_smoothing=1.873817422860383e-05, score=0.670, total=   0.7s\n","[CV] var_smoothing=1.873817422860383e-05 .............................\n","[CV] . var_smoothing=1.873817422860383e-05, score=0.670, total=   0.7s\n","[CV] var_smoothing=1.873817422860383e-05 .............................\n","[CV] . var_smoothing=1.873817422860383e-05, score=0.671, total=   0.7s\n","[CV] var_smoothing=1.5199110829529332e-05 ............................\n","[CV]  var_smoothing=1.5199110829529332e-05, score=0.673, total=   0.7s\n","[CV] var_smoothing=1.5199110829529332e-05 ............................\n","[CV]  var_smoothing=1.5199110829529332e-05, score=0.668, total=   0.7s\n","[CV] var_smoothing=1.5199110829529332e-05 ............................\n","[CV]  var_smoothing=1.5199110829529332e-05, score=0.674, total=   0.7s\n","[CV] var_smoothing=1.5199110829529332e-05 ............................\n","[CV]  var_smoothing=1.5199110829529332e-05, score=0.674, total=   0.7s\n","[CV] var_smoothing=1.5199110829529332e-05 ............................\n","[CV]  var_smoothing=1.5199110829529332e-05, score=0.675, total=   0.7s\n","[CV] var_smoothing=1.2328467394420658e-05 ............................\n","[CV]  var_smoothing=1.2328467394420658e-05, score=0.677, total=   0.7s\n","[CV] var_smoothing=1.2328467394420658e-05 ............................\n","[CV]  var_smoothing=1.2328467394420658e-05, score=0.672, total=   0.7s\n","[CV] var_smoothing=1.2328467394420658e-05 ............................\n","[CV]  var_smoothing=1.2328467394420658e-05, score=0.678, total=   0.7s\n","[CV] var_smoothing=1.2328467394420658e-05 ............................\n","[CV]  var_smoothing=1.2328467394420658e-05, score=0.678, total=   0.7s\n","[CV] var_smoothing=1.2328467394420658e-05 ............................\n","[CV]  var_smoothing=1.2328467394420658e-05, score=0.679, total=   0.7s\n","[CV] var_smoothing=1e-05 .............................................\n","[CV] ................. var_smoothing=1e-05, score=0.680, total=   0.7s\n","[CV] var_smoothing=1e-05 .............................................\n","[CV] ................. var_smoothing=1e-05, score=0.675, total=   0.7s\n","[CV] var_smoothing=1e-05 .............................................\n","[CV] ................. var_smoothing=1e-05, score=0.681, total=   0.7s\n","[CV] var_smoothing=1e-05 .............................................\n","[CV] ................. var_smoothing=1e-05, score=0.681, total=   0.7s\n","[CV] var_smoothing=1e-05 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score=0.685, total=   0.7s\n","[CV] var_smoothing=6.579332246575683e-06 .............................\n","[CV] . var_smoothing=6.579332246575683e-06, score=0.680, total=   0.7s\n","[CV] var_smoothing=6.579332246575683e-06 .............................\n","[CV] . var_smoothing=6.579332246575683e-06, score=0.685, total=   0.7s\n","[CV] var_smoothing=6.579332246575683e-06 .............................\n","[CV] . var_smoothing=6.579332246575683e-06, score=0.685, total=   0.7s\n","[CV] var_smoothing=6.579332246575683e-06 .............................\n","[CV] . var_smoothing=6.579332246575683e-06, score=0.686, total=   0.7s\n","[CV] var_smoothing=5.336699231206313e-06 .............................\n","[CV] . var_smoothing=5.336699231206313e-06, score=0.686, total=   0.7s\n","[CV] var_smoothing=5.336699231206313e-06 .............................\n","[CV] . var_smoothing=5.336699231206313e-06, score=0.681, total=   0.7s\n","[CV] var_smoothing=5.336699231206313e-06 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0.7s\n","[CV] var_smoothing=4.328761281083053e-06 .............................\n","[CV] . var_smoothing=4.328761281083053e-06, score=0.690, total=   0.7s\n","[CV] var_smoothing=3.5111917342151275e-06 ............................\n","[CV]  var_smoothing=3.5111917342151275e-06, score=0.690, total=   0.7s\n","[CV] var_smoothing=3.5111917342151275e-06 ............................\n","[CV]  var_smoothing=3.5111917342151275e-06, score=0.685, total=   0.7s\n","[CV] var_smoothing=3.5111917342151275e-06 ............................\n","[CV]  var_smoothing=3.5111917342151275e-06, score=0.690, total=   0.7s\n","[CV] var_smoothing=3.5111917342151275e-06 ............................\n","[CV]  var_smoothing=3.5111917342151275e-06, score=0.690, total=   0.7s\n","[CV] var_smoothing=3.5111917342151275e-06 ............................\n","[CV]  var_smoothing=3.5111917342151275e-06, score=0.691, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-06 .............................\n","[CV] . var_smoothing=2.848035868435799e-06, score=0.692, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-06 .............................\n","[CV] . var_smoothing=2.848035868435799e-06, score=0.686, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-06 .............................\n","[CV] . var_smoothing=2.848035868435799e-06, score=0.691, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-06 .............................\n","[CV] . var_smoothing=2.848035868435799e-06, score=0.691, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-06 .............................\n","[CV] . var_smoothing=2.848035868435799e-06, score=0.693, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-06 .............................\n","[CV] . var_smoothing=2.310129700083158e-06, score=0.692, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-06 .............................\n","[CV] . var_smoothing=2.310129700083158e-06, score=0.688, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-06 .............................\n","[CV] . var_smoothing=2.310129700083158e-06, score=0.692, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-06 .............................\n","[CV] . var_smoothing=2.310129700083158e-06, score=0.693, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-06 .............................\n","[CV] . var_smoothing=2.310129700083158e-06, score=0.693, total=   0.7s\n","[CV] var_smoothing=1.873817422860383e-06 .............................\n","[CV] . var_smoothing=1.873817422860383e-06, score=0.694, total=   0.7s\n","[CV] var_smoothing=1.873817422860383e-06 .............................\n","[CV] . var_smoothing=1.873817422860383e-06, score=0.689, total=   0.7s\n","[CV] var_smoothing=1.873817422860383e-06 .............................\n","[CV] . var_smoothing=1.873817422860383e-06, score=0.693, total=   0.7s\n","[CV] var_smoothing=1.873817422860383e-06 .............................\n","[CV] . var_smoothing=1.873817422860383e-06, score=0.694, total=   0.7s\n","[CV] var_smoothing=1.873817422860383e-06 .............................\n","[CV] . var_smoothing=1.873817422860383e-06, score=0.694, total=   0.7s\n","[CV] var_smoothing=1.519911082952933e-06 .............................\n","[CV] . var_smoothing=1.519911082952933e-06, score=0.695, total=   0.7s\n","[CV] var_smoothing=1.519911082952933e-06 .............................\n","[CV] . var_smoothing=1.519911082952933e-06, score=0.690, total=   0.7s\n","[CV] var_smoothing=1.519911082952933e-06 .............................\n","[CV] . var_smoothing=1.519911082952933e-06, score=0.694, total=   0.7s\n","[CV] var_smoothing=1.519911082952933e-06 .............................\n","[CV] . var_smoothing=1.519911082952933e-06, score=0.695, total=   0.7s\n","[CV] var_smoothing=1.519911082952933e-06 .............................\n","[CV] . var_smoothing=1.519911082952933e-06, score=0.696, total=   0.7s\n","[CV] 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var_smoothing=1e-06, score=0.692, total=   0.7s\n","[CV] var_smoothing=1e-06 .............................................\n","[CV] ................. var_smoothing=1e-06, score=0.696, total=   0.7s\n","[CV] var_smoothing=1e-06 .............................................\n","[CV] ................. var_smoothing=1e-06, score=0.696, total=   0.7s\n","[CV] var_smoothing=1e-06 .............................................\n","[CV] ................. var_smoothing=1e-06, score=0.698, total=   0.7s\n","[CV] var_smoothing=8.111308307896872e-07 .............................\n","[CV] . var_smoothing=8.111308307896872e-07, score=0.697, total=   0.7s\n","[CV] var_smoothing=8.111308307896872e-07 .............................\n","[CV] . var_smoothing=8.111308307896872e-07, score=0.692, total=   0.7s\n","[CV] var_smoothing=8.111308307896872e-07 .............................\n","[CV] . var_smoothing=8.111308307896872e-07, score=0.696, total=   0.7s\n","[CV] var_smoothing=8.111308307896872e-07 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0.7s\n","[CV] var_smoothing=5.336699231206313e-07 .............................\n","[CV] . var_smoothing=5.336699231206313e-07, score=0.698, total=   0.7s\n","[CV] var_smoothing=5.336699231206313e-07 .............................\n","[CV] . var_smoothing=5.336699231206313e-07, score=0.693, total=   0.7s\n","[CV] var_smoothing=5.336699231206313e-07 .............................\n","[CV] . var_smoothing=5.336699231206313e-07, score=0.697, total=   0.7s\n","[CV] var_smoothing=5.336699231206313e-07 .............................\n","[CV] . var_smoothing=5.336699231206313e-07, score=0.698, total=   0.7s\n","[CV] var_smoothing=5.336699231206313e-07 .............................\n","[CV] . var_smoothing=5.336699231206313e-07, score=0.699, total=   0.7s\n","[CV] var_smoothing=4.3287612810830526e-07 ............................\n","[CV]  var_smoothing=4.3287612810830526e-07, score=0.698, total=   0.7s\n","[CV] var_smoothing=4.3287612810830526e-07 ............................\n","[CV]  var_smoothing=4.3287612810830526e-07, score=0.694, total=   0.7s\n","[CV] var_smoothing=4.3287612810830526e-07 ............................\n","[CV]  var_smoothing=4.3287612810830526e-07, score=0.697, total=   0.7s\n","[CV] var_smoothing=4.3287612810830526e-07 ............................\n","[CV]  var_smoothing=4.3287612810830526e-07, score=0.699, total=   0.7s\n","[CV] var_smoothing=4.3287612810830526e-07 ............................\n","[CV]  var_smoothing=4.3287612810830526e-07, score=0.699, total=   0.7s\n","[CV] var_smoothing=3.5111917342151277e-07 ............................\n","[CV]  var_smoothing=3.5111917342151277e-07, score=0.698, total=   0.7s\n","[CV] var_smoothing=3.5111917342151277e-07 ............................\n","[CV]  var_smoothing=3.5111917342151277e-07, score=0.695, total=   0.7s\n","[CV] var_smoothing=3.5111917342151277e-07 ............................\n","[CV]  var_smoothing=3.5111917342151277e-07, score=0.698, total=   0.7s\n","[CV] var_smoothing=3.5111917342151277e-07 ............................\n","[CV]  var_smoothing=3.5111917342151277e-07, score=0.699, total=   0.7s\n","[CV] var_smoothing=3.5111917342151277e-07 ............................\n","[CV]  var_smoothing=3.5111917342151277e-07, score=0.699, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-07 .............................\n","[CV] . var_smoothing=2.848035868435799e-07, score=0.698, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-07 .............................\n","[CV] . var_smoothing=2.848035868435799e-07, score=0.695, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-07 .............................\n","[CV] . var_smoothing=2.848035868435799e-07, score=0.698, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-07 .............................\n","[CV] . var_smoothing=2.848035868435799e-07, score=0.699, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-07 .............................\n","[CV] . var_smoothing=2.848035868435799e-07, score=0.699, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-07 .............................\n","[CV] . var_smoothing=2.310129700083158e-07, score=0.699, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-07 .............................\n","[CV] . var_smoothing=2.310129700083158e-07, score=0.695, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-07 .............................\n","[CV] . var_smoothing=2.310129700083158e-07, score=0.698, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-07 .............................\n","[CV] . var_smoothing=2.310129700083158e-07, score=0.699, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-07 .............................\n","[CV] . var_smoothing=2.310129700083158e-07, score=0.699, total=   0.7s\n","[CV] var_smoothing=1.873817422860383e-07 .............................\n","[CV] . var_smoothing=1.873817422860383e-07, score=0.698, total=   0.7s\n","[CV] 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var_smoothing=1.519911082952933e-07, score=0.698, total=   0.7s\n","[CV] var_smoothing=1.519911082952933e-07 .............................\n","[CV] . var_smoothing=1.519911082952933e-07, score=0.699, total=   0.7s\n","[CV] var_smoothing=1.519911082952933e-07 .............................\n","[CV] . var_smoothing=1.519911082952933e-07, score=0.699, total=   0.7s\n","[CV] var_smoothing=1.232846739442066e-07 .............................\n","[CV] . var_smoothing=1.232846739442066e-07, score=0.698, total=   0.7s\n","[CV] var_smoothing=1.232846739442066e-07 .............................\n","[CV] . var_smoothing=1.232846739442066e-07, score=0.695, total=   0.7s\n","[CV] var_smoothing=1.232846739442066e-07 .............................\n","[CV] . var_smoothing=1.232846739442066e-07, score=0.698, total=   0.7s\n","[CV] var_smoothing=1.232846739442066e-07 .............................\n","[CV] . var_smoothing=1.232846739442066e-07, score=0.699, total=   0.7s\n","[CV] var_smoothing=1.232846739442066e-07 .............................\n","[CV] . var_smoothing=1.232846739442066e-07, score=0.698, total=   0.7s\n","[CV] var_smoothing=1e-07 .............................................\n","[CV] ................. var_smoothing=1e-07, score=0.698, total=   0.7s\n","[CV] var_smoothing=1e-07 .............................................\n","[CV] ................. var_smoothing=1e-07, score=0.695, total=   0.7s\n","[CV] var_smoothing=1e-07 .............................................\n","[CV] ................. var_smoothing=1e-07, score=0.698, total=   0.7s\n","[CV] var_smoothing=1e-07 .............................................\n","[CV] ................. var_smoothing=1e-07, score=0.699, total=   0.7s\n","[CV] var_smoothing=1e-07 .............................................\n","[CV] ................. var_smoothing=1e-07, score=0.698, total=   0.7s\n","[CV] var_smoothing=8.111308307896873e-08 .............................\n","[CV] . var_smoothing=8.111308307896873e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=8.111308307896873e-08 .............................\n","[CV] . var_smoothing=8.111308307896873e-08, score=0.695, total=   0.7s\n","[CV] var_smoothing=8.111308307896873e-08 .............................\n","[CV] . var_smoothing=8.111308307896873e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=8.111308307896873e-08 .............................\n","[CV] . var_smoothing=8.111308307896873e-08, score=0.699, total=   0.7s\n","[CV] var_smoothing=8.111308307896873e-08 .............................\n","[CV] . var_smoothing=8.111308307896873e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=6.579332246575682e-08 .............................\n","[CV] . var_smoothing=6.579332246575682e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=6.579332246575682e-08 .............................\n","[CV] . var_smoothing=6.579332246575682e-08, score=0.695, total=   0.7s\n","[CV] var_smoothing=6.579332246575682e-08 .............................\n","[CV] . var_smoothing=6.579332246575682e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=6.579332246575682e-08 .............................\n","[CV] . var_smoothing=6.579332246575682e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=6.579332246575682e-08 .............................\n","[CV] . var_smoothing=6.579332246575682e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=5.336699231206302e-08 .............................\n","[CV] . var_smoothing=5.336699231206302e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=5.336699231206302e-08 .............................\n","[CV] . var_smoothing=5.336699231206302e-08, score=0.695, total=   0.7s\n","[CV] var_smoothing=5.336699231206302e-08 .............................\n","[CV] . var_smoothing=5.336699231206302e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=5.336699231206302e-08 .............................\n","[CV] . var_smoothing=5.336699231206302e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=5.336699231206302e-08 .............................\n","[CV] . var_smoothing=5.336699231206302e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=4.3287612810830526e-08 ............................\n","[CV]  var_smoothing=4.3287612810830526e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=4.3287612810830526e-08 ............................\n","[CV]  var_smoothing=4.3287612810830526e-08, score=0.695, total=   0.7s\n","[CV] var_smoothing=4.3287612810830526e-08 ............................\n","[CV]  var_smoothing=4.3287612810830526e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=4.3287612810830526e-08 ............................\n","[CV]  var_smoothing=4.3287612810830526e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=4.3287612810830526e-08 ............................\n","[CV]  var_smoothing=4.3287612810830526e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=3.5111917342151277e-08 ............................\n","[CV]  var_smoothing=3.5111917342151277e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=3.5111917342151277e-08 ............................\n","[CV]  var_smoothing=3.5111917342151277e-08, score=0.694, total=   0.7s\n","[CV] var_smoothing=3.5111917342151277e-08 ............................\n","[CV]  var_smoothing=3.5111917342151277e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=3.5111917342151277e-08 ............................\n","[CV]  var_smoothing=3.5111917342151277e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=3.5111917342151277e-08 ............................\n","[CV]  var_smoothing=3.5111917342151277e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-08 .............................\n","[CV] . var_smoothing=2.848035868435799e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-08 .............................\n","[CV] . var_smoothing=2.848035868435799e-08, score=0.694, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-08 .............................\n","[CV] . var_smoothing=2.848035868435799e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-08 .............................\n","[CV] . var_smoothing=2.848035868435799e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=2.848035868435799e-08 .............................\n","[CV] . var_smoothing=2.848035868435799e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-08 .............................\n","[CV] . var_smoothing=2.310129700083158e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-08 .............................\n","[CV] . var_smoothing=2.310129700083158e-08, score=0.694, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-08 .............................\n","[CV] . var_smoothing=2.310129700083158e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-08 .............................\n","[CV] . var_smoothing=2.310129700083158e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-08 .............................\n","[CV] . var_smoothing=2.310129700083158e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=1.873817422860383e-08 .............................\n","[CV] . var_smoothing=1.873817422860383e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=1.873817422860383e-08 .............................\n","[CV] . var_smoothing=1.873817422860383e-08, score=0.694, total=   0.7s\n","[CV] var_smoothing=1.873817422860383e-08 .............................\n","[CV] . var_smoothing=1.873817422860383e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=1.873817422860383e-08 .............................\n","[CV] . var_smoothing=1.873817422860383e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=1.873817422860383e-08 .............................\n","[CV] . var_smoothing=1.873817422860383e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=1.519911082952933e-08 .............................\n","[CV] . var_smoothing=1.519911082952933e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=1.519911082952933e-08 .............................\n","[CV] . var_smoothing=1.519911082952933e-08, score=0.694, total=   0.7s\n","[CV] var_smoothing=1.519911082952933e-08 .............................\n","[CV] . var_smoothing=1.519911082952933e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=1.519911082952933e-08 .............................\n","[CV] . var_smoothing=1.519911082952933e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=1.519911082952933e-08 .............................\n","[CV] . var_smoothing=1.519911082952933e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=1.232846739442066e-08 .............................\n","[CV] . var_smoothing=1.232846739442066e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=1.232846739442066e-08 .............................\n","[CV] . var_smoothing=1.232846739442066e-08, score=0.695, total=   0.7s\n","[CV] var_smoothing=1.232846739442066e-08 .............................\n","[CV] . var_smoothing=1.232846739442066e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=1.232846739442066e-08 .............................\n","[CV] . var_smoothing=1.232846739442066e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=1.232846739442066e-08 .............................\n","[CV] . var_smoothing=1.232846739442066e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=1e-08 .............................................\n","[CV] ................. var_smoothing=1e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=1e-08 .............................................\n","[CV] ................. var_smoothing=1e-08, score=0.695, total=   0.7s\n","[CV] var_smoothing=1e-08 .............................................\n","[CV] ................. var_smoothing=1e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=1e-08 .............................................\n","[CV] ................. var_smoothing=1e-08, score=0.698, total=   0.7s\n","[CV] var_smoothing=1e-08 .............................................\n","[CV] ................. var_smoothing=1e-08, score=0.697, total=   0.7s\n","[CV] var_smoothing=8.111308307896856e-09 .............................\n","[CV] . var_smoothing=8.111308307896856e-09, score=0.697, total=   0.7s\n","[CV] var_smoothing=8.111308307896856e-09 .............................\n","[CV] . var_smoothing=8.111308307896856e-09, score=0.695, total=   0.7s\n","[CV] var_smoothing=8.111308307896856e-09 .............................\n","[CV] . var_smoothing=8.111308307896856e-09, score=0.697, total=   0.7s\n","[CV] var_smoothing=8.111308307896856e-09 .............................\n","[CV] . var_smoothing=8.111308307896856e-09, score=0.699, total=   0.7s\n","[CV] var_smoothing=8.111308307896856e-09 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0.7s\n","[CV] var_smoothing=5.336699231206302e-09 .............................\n","[CV] . var_smoothing=5.336699231206302e-09, score=0.695, total=   0.7s\n","[CV] var_smoothing=5.336699231206302e-09 .............................\n","[CV] . var_smoothing=5.336699231206302e-09, score=0.697, total=   0.7s\n","[CV] var_smoothing=5.336699231206302e-09 .............................\n","[CV] . var_smoothing=5.336699231206302e-09, score=0.698, total=   0.7s\n","[CV] var_smoothing=5.336699231206302e-09 .............................\n","[CV] . var_smoothing=5.336699231206302e-09, score=0.697, total=   0.7s\n","[CV] var_smoothing=4.328761281083061e-09 .............................\n","[CV] . var_smoothing=4.328761281083061e-09, score=0.697, total=   0.7s\n","[CV] var_smoothing=4.328761281083061e-09 .............................\n","[CV] . var_smoothing=4.328761281083061e-09, score=0.695, total=   0.7s\n","[CV] var_smoothing=4.328761281083061e-09 .............................\n","[CV] . var_smoothing=4.328761281083061e-09, score=0.697, total=   0.7s\n","[CV] var_smoothing=4.328761281083061e-09 .............................\n","[CV] . var_smoothing=4.328761281083061e-09, score=0.698, total=   0.7s\n","[CV] var_smoothing=4.328761281083061e-09 .............................\n","[CV] . var_smoothing=4.328761281083061e-09, score=0.698, total=   0.7s\n","[CV] var_smoothing=3.5111917342151273e-09 ............................\n","[CV]  var_smoothing=3.5111917342151273e-09, score=0.697, total=   0.7s\n","[CV] var_smoothing=3.5111917342151273e-09 ............................\n","[CV]  var_smoothing=3.5111917342151273e-09, score=0.695, total=   0.7s\n","[CV] var_smoothing=3.5111917342151273e-09 ............................\n","[CV]  var_smoothing=3.5111917342151273e-09, score=0.697, total=   0.7s\n","[CV] var_smoothing=3.5111917342151273e-09 ............................\n","[CV]  var_smoothing=3.5111917342151273e-09, score=0.699, total=   0.7s\n","[CV] var_smoothing=3.5111917342151273e-09 ............................\n","[CV]  var_smoothing=3.5111917342151273e-09, score=0.698, total=   0.7s\n","[CV] var_smoothing=2.848035868435805e-09 .............................\n","[CV] . var_smoothing=2.848035868435805e-09, score=0.697, total=   0.7s\n","[CV] var_smoothing=2.848035868435805e-09 .............................\n","[CV] . var_smoothing=2.848035868435805e-09, score=0.695, total=   0.7s\n","[CV] var_smoothing=2.848035868435805e-09 .............................\n","[CV] . var_smoothing=2.848035868435805e-09, score=0.697, total=   0.7s\n","[CV] var_smoothing=2.848035868435805e-09 .............................\n","[CV] . var_smoothing=2.848035868435805e-09, score=0.699, total=   0.7s\n","[CV] var_smoothing=2.848035868435805e-09 .............................\n","[CV] . var_smoothing=2.848035868435805e-09, score=0.698, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-09 .............................\n","[CV] . var_smoothing=2.310129700083158e-09, score=0.698, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-09 .............................\n","[CV] . var_smoothing=2.310129700083158e-09, score=0.695, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-09 .............................\n","[CV] . var_smoothing=2.310129700083158e-09, score=0.697, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-09 .............................\n","[CV] . var_smoothing=2.310129700083158e-09, score=0.699, total=   0.7s\n","[CV] var_smoothing=2.310129700083158e-09 .............................\n","[CV] . var_smoothing=2.310129700083158e-09, score=0.698, total=   0.7s\n","[CV] var_smoothing=1.873817422860387e-09 .............................\n","[CV] . var_smoothing=1.873817422860387e-09, score=0.698, total=   0.7s\n","[CV] var_smoothing=1.873817422860387e-09 .............................\n","[CV] . var_smoothing=1.873817422860387e-09, score=0.695, total=   0.7s\n","[CV] var_smoothing=1.873817422860387e-09 .............................\n","[CV] . var_smoothing=1.873817422860387e-09, score=0.697, total=   0.7s\n","[CV] var_smoothing=1.873817422860387e-09 .............................\n","[CV] . var_smoothing=1.873817422860387e-09, score=0.699, total=   0.7s\n","[CV] var_smoothing=1.873817422860387e-09 .............................\n","[CV] . var_smoothing=1.873817422860387e-09, score=0.698, total=   0.7s\n","[CV] var_smoothing=1.519911082952933e-09 .............................\n","[CV] . var_smoothing=1.519911082952933e-09, score=0.698, total=   0.7s\n","[CV] var_smoothing=1.519911082952933e-09 .............................\n","[CV] . var_smoothing=1.519911082952933e-09, score=0.695, total=   0.7s\n","[CV] var_smoothing=1.519911082952933e-09 .............................\n","[CV] . var_smoothing=1.519911082952933e-09, score=0.697, total=   0.7s\n","[CV] var_smoothing=1.519911082952933e-09 .............................\n","[CV] . var_smoothing=1.519911082952933e-09, score=0.699, total=   0.7s\n","[CV] var_smoothing=1.519911082952933e-09 .............................\n","[CV] . var_smoothing=1.519911082952933e-09, score=0.698, total=   0.7s\n","[CV] var_smoothing=1.2328467394420635e-09 ............................\n","[CV]  var_smoothing=1.2328467394420635e-09, score=0.698, total=   0.7s\n","[CV] var_smoothing=1.2328467394420635e-09 ............................\n","[CV]  var_smoothing=1.2328467394420635e-09, score=0.695, total=   0.7s\n","[CV] var_smoothing=1.2328467394420635e-09 ............................\n","[CV]  var_smoothing=1.2328467394420635e-09, score=0.697, total=   0.7s\n","[CV] var_smoothing=1.2328467394420635e-09 ............................\n","[CV]  var_smoothing=1.2328467394420635e-09, score=0.699, total=   0.7s\n","[CV] var_smoothing=1.2328467394420635e-09 ............................\n","[CV]  var_smoothing=1.2328467394420635e-09, score=0.698, total=   0.7s\n","[CV] var_smoothing=1e-09 .............................................\n","[CV] ................. var_smoothing=1e-09, score=0.698, total=   0.7s\n","[CV] var_smoothing=1e-09 .............................................\n","[CV] ................. var_smoothing=1e-09, score=0.695, total=   0.7s\n","[CV] var_smoothing=1e-09 .............................................\n","[CV] ................. var_smoothing=1e-09, score=0.697, total=   0.7s\n","[CV] var_smoothing=1e-09 .............................................\n","[CV] ................. var_smoothing=1e-09, score=0.699, total=   0.7s\n","[CV] var_smoothing=1e-09 .............................................\n","[CV] ................. var_smoothing=1e-09, score=0.698, total=   0.7s\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=1)]: Done 500 out of 500 | elapsed:  5.7min finished\n"]},{"output_type":"stream","name":"stdout","text":["Best: 0.697944 using {'var_smoothing': 2.310129700083158e-07}\n","accuracy:  0.6978741971992309\n","              precision    recall  f1-score   support\n","\n","         0.0       0.82      0.51      0.63     73085\n","         1.0       0.64      0.89      0.75     73589\n","\n","    accuracy                           0.70    146674\n","   macro avg       0.73      0.70      0.69    146674\n","weighted avg       0.73      0.70      0.69    146674\n","\n"]}]},{"cell_type":"markdown","metadata":{"id":"m7oTJEvWnoN-"},"source":["## KNN"]},{"cell_type":"code","metadata":{"id":"xpJG2EY-nne3"},"source":["datas = {\n","    'name': 'KNN',\n","    'model': KNeighborsClassifier(),\n","    'params': {\n","    'n_neighbors': [7, 9, 11, 13, 15]\n","    }}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"s4zD_cdxn-ED","executionInfo":{"status":"ok","timestamp":1635947302672,"user_tz":-60,"elapsed":4119392,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"97b084c2-2373-4281-f8b7-fa14021c29f9"},"source":["predictionsKNN, results, names = training_and_test(datas, results, names)\n"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["### using KNN ###\n","Fitting 5 folds for each of 5 candidates, totalling 25 fits\n","[CV] n_neighbors=7 ...................................................\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"]},{"output_type":"stream","name":"stdout","text":["[CV] ....................... n_neighbors=7, score=0.849, total= 2.3min\n","[CV] n_neighbors=7 ...................................................\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  2.3min remaining:    0.0s\n"]},{"output_type":"stream","name":"stdout","text":["[CV] ....................... n_neighbors=7, score=0.849, total= 2.3min\n","[CV] n_neighbors=7 ...................................................\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:  4.6min remaining:    0.0s\n"]},{"output_type":"stream","name":"stdout","text":["[CV] ....................... n_neighbors=7, score=0.846, total= 2.3min\n","[CV] n_neighbors=7 ...................................................\n","[CV] ....................... n_neighbors=7, score=0.847, total= 2.3min\n","[CV] n_neighbors=7 ...................................................\n","[CV] ....................... n_neighbors=7, score=0.851, total= 2.3min\n","[CV] n_neighbors=9 ...................................................\n","[CV] ....................... n_neighbors=9, score=0.842, total= 2.4min\n","[CV] n_neighbors=9 ...................................................\n","[CV] ....................... n_neighbors=9, score=0.842, total= 2.4min\n","[CV] n_neighbors=9 ...................................................\n","[CV] ....................... n_neighbors=9, score=0.840, total= 2.5min\n","[CV] n_neighbors=9 ...................................................\n","[CV] ....................... n_neighbors=9, score=0.841, total= 2.4min\n","[CV] n_neighbors=9 ...................................................\n","[CV] ....................... n_neighbors=9, score=0.844, total= 2.4min\n","[CV] n_neighbors=11 ..................................................\n","[CV] ...................... n_neighbors=11, score=0.837, total= 2.6min\n","[CV] n_neighbors=11 ..................................................\n","[CV] ...................... n_neighbors=11, score=0.836, total= 2.6min\n","[CV] n_neighbors=11 ..................................................\n","[CV] ...................... n_neighbors=11, score=0.835, total= 2.6min\n","[CV] n_neighbors=11 ..................................................\n","[CV] ...................... n_neighbors=11, score=0.837, total= 2.6min\n","[CV] n_neighbors=11 ..................................................\n","[CV] ...................... n_neighbors=11, score=0.838, total= 2.5min\n","[CV] n_neighbors=13 ..................................................\n","[CV] ...................... n_neighbors=13, score=0.833, total= 2.7min\n","[CV] n_neighbors=13 ..................................................\n","[CV] ...................... n_neighbors=13, score=0.832, total= 2.7min\n","[CV] n_neighbors=13 ..................................................\n","[CV] ...................... n_neighbors=13, score=0.831, total= 2.7min\n","[CV] n_neighbors=13 ..................................................\n","[CV] ...................... n_neighbors=13, score=0.832, total= 2.7min\n","[CV] n_neighbors=13 ..................................................\n","[CV] ...................... n_neighbors=13, score=0.834, total= 2.7min\n","[CV] n_neighbors=15 ..................................................\n","[CV] ...................... n_neighbors=15, score=0.828, total= 2.8min\n","[CV] n_neighbors=15 ..................................................\n","[CV] ...................... n_neighbors=15, score=0.828, total= 2.8min\n","[CV] n_neighbors=15 ..................................................\n","[CV] ...................... n_neighbors=15, score=0.826, total= 2.8min\n","[CV] n_neighbors=15 ..................................................\n","[CV] ...................... n_neighbors=15, score=0.828, total= 2.8min\n","[CV] n_neighbors=15 ..................................................\n","[CV] ...................... n_neighbors=15, score=0.830, total= 2.8min\n"]},{"output_type":"stream","name":"stderr","text":["[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed: 64.0min finished\n"]},{"output_type":"stream","name":"stdout","text":["Best: 0.848553 using {'n_neighbors': 7}\n","accuracy:  0.8566889837326316\n","              precision    recall  f1-score   support\n","\n","         0.0       0.81      0.94      0.87     73085\n","         1.0       0.93      0.78      0.84     73589\n","\n","    accuracy                           0.86    146674\n","   macro avg       0.87      0.86      0.86    146674\n","weighted avg       0.87      0.86      0.86    146674\n","\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":717},"id":"iNSRhrIy3uVI","executionInfo":{"status":"ok","timestamp":1635947303113,"user_tz":-60,"elapsed":461,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"f0d941cf-9d9b-4b7b-8a15-9608ade446af"},"source":["import matplotlib.pyplot as pyplot\n","\n","# Plot the results\n","fig = pyplot.figure(figsize=(20,10))\n","fig.suptitle('Final Algorithm Comparison')\n","ax = fig.add_subplot(111)\n","pyplot.boxplot(results)\n","ax.set_xticklabels(names)\n","pyplot.show()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n","  return array(a, dtype, copy=False, order=order)\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1440x720 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"GRTtr-1Ol_az"},"source":["# CONFUSION MATRIX"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":296},"id":"WNJj8jFWl-9V","executionInfo":{"status":"ok","timestamp":1635947303114,"user_tz":-60,"elapsed":6,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"c48396c2-9497-4ed6-ac63-c3ee934d0c0c"},"source":["from sklearn.metrics import confusion_matrix\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","\n","cf_matrix = confusion_matrix(Y_test, predictionsKNN)\n","tn, fp, fn, tp = cf_matrix.ravel()\n","labels = ['TN=%d'%tn,\"FP=%d\"%fp,\"FN=%d\"%fn,\"TP=%d\"%tp]\n","print(labels)\n","labels = np.asarray(labels).reshape(2,2)\n","sns.heatmap(cf_matrix, annot=labels, fmt='', cmap='Greens')\n","plt.ylabel('True label')\n","plt.xlabel('Predicted label')\n","axis_labels = ['0', '1']\n","plt.xticks([0.5,1.5], axis_labels)\n","plt.yticks([0.5,1.5], axis_labels)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["['TN=68456', 'FP=4629', 'FN=16391', 'TP=57198']\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":231},"id":"Kf3YBfjqmXbR","executionInfo":{"status":"error","timestamp":1636110945921,"user_tz":-60,"elapsed":4,"user":{"displayName":"SILVIO DI MARTINO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"02482830676185149902"}},"outputId":"914d8f9f-83f9-4a99-930c-42aba0941ffc"},"source":["cf_matrix = confusion_matrix(Y_test, predictionsLC)\n","tn, fp, fn, tp = cf_matrix.ravel()\n","labels = ['TN=%d'%tn,\"FP=%d\"%fp,\"FN=%d\"%fn,\"TP=%d\"%tp]\n","print(labels)\n","labels = np.asarray(labels).reshape(2,2)\n","sns.heatmap(cf_matrix, annot=labels, fmt='', cmap='Red')\n","plt.ylabel('True label')\n","plt.xlabel('Predicted label')\n","axis_labels = ['0', '1']\n","plt.xticks([0.5,1.5], axis_labels)\n","plt.yticks([0.5,1.5], axis_labels)\n","plt.show()"],"execution_count":1,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-7b46855d93b4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcf_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictionsLC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcf_matrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'TN=%d'\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0mtn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"FP=%d\"\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"FN=%d\"\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"TP=%d\"\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0mtp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'confusion_matrix' is not defined"]}]}]}