Baseline_models.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "!! Data Class is edited\n"
      ],
      "metadata": {
        "id": "STL5SddAmLGz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The goal here is to apply RFC on statical data after filtering the data"
      ],
      "metadata": {
        "id": "aWgsWfG7eqnd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importing"
      ],
      "metadata": {
        "id": "W1EC3EBUfA7M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn import decomposition\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import decomposition\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score,precision_score, recall_score, f1_score, auc, roc_curve\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "import os\n",
        "\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "import shutil\n",
        "\n",
        "from itertools import groupby\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from statistics import mean\n",
        "\n",
        "import scipy.stats as stats\n",
        "from scipy.stats import skew\n",
        "from scipy.stats import kurtosis\n",
        "from scipy.fftpack import fft\n",
        "from scipy import signal\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "import pickle\n",
        "\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import LSTM, Dense, Input, Flatten, Conv1D, MaxPooling1D, Concatenate, Dropout\n",
        "from keras.optimizers import Adam\n",
        "from keras.callbacks import EarlyStopping\n",
        "from keras.utils.vis_utils import plot_model\n"
      ],
      "metadata": {
        "id": "AO-oBmyoepkP"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OKPdgoE-fGy9",
        "outputId": "69a10a86-939d-41d2-d595-daa7f499355d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# path of original data folder\n",
        "data_path = \"/content/drive/MyDrive/ITMO-Master's/Thesis/3rd_semester/Data\"\n",
        "# path of csv files, each file is a 2mins walk for a subject\n",
        "csvD_path = \"/content/drive/MyDrive/ITMO-Master's/Thesis/3rd_semester/Data/csv_files\"\n",
        "# In this file, the Null values are replaved with 0 in the HY scale - Null values were given to healthy control\n",
        "demographics = \"/content/drive/MyDrive/ITMO-Master's/Thesis/3rd_semester/Data/Processed_data/demographics_HYprocessed.csv\" \n",
        "dem_df = pd.read_csv(demographics)\n",
        "# parequet folder path\n",
        "parquet_path = \"/content/drive/MyDrive/ITMO-Master's/Thesis/3rd_semester/Data/parquet_files/\""
      ],
      "metadata": {
        "id": "7qroDiDOfINQ"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data"
      ],
      "metadata": {
        "id": "WDptCAzYepBm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "-pE1wLrOelU0"
      },
      "outputs": [],
      "source": [
        "class Data:\n",
        "  def __init__(self, prepare_or_get, data_folder, demographics_file, parquet_path):\n",
        "    '''\n",
        "    :param prepare_or_get:  1: prepare data , 0: load preloaded datas (npy)\n",
        "    :param data_folder: path for a folder containing csv files, file for each 2 mins walk (for indivdiual subject)\n",
        "    :param demographics_file: path for csv demographics file\n",
        "    :save_path: path where to save/read parquet files after preparing them\n",
        "    :\n",
        "    '''\n",
        "\n",
        "    self.data_path = data_folder\n",
        "    self.parquet_path= parquet_path\n",
        "    self.dem_path= demographics_file\n",
        "    self.scale=\"HoehnYahr\"            # fixed for now\n",
        "    self.seconds_to_discard = 20      # fixed for now\n",
        "    self.rows_to_discard = 100 * self.seconds_to_discard\n",
        "    \n",
        "    # data in arrays\n",
        "    self.signals_data = []\n",
        "    self.cycles_data=[]\n",
        "    self.statical_data=[]\n",
        "    self.raw_statics=[]\n",
        "  \n",
        "\n",
        "    if (prepare_or_get==1):\n",
        "      self.prepare_data()\n",
        "    elif (prepare_or_get==0):\n",
        "      self.get_data()\n",
        "  \n",
        "  def get_data(self):\n",
        "    self.signals_data_df= pd.read_parquet(self.parquet_path+'signals.gzip')\n",
        "    self.cycles_data_df=pd.read_parquet(self.parquet_path+'cyclels.gzip')\n",
        "    self.statical_data_df=pd.read_parquet(self.parquet_path+'statical.gzip')\n",
        "    self.left_stances_df=pd.read_parquet(self.parquet_path+'left_stances.gzip')\n",
        "    self.right_stances_df=pd.read_parquet(self.parquet_path+'right_stances.gzip')\n",
        "    self.raw_statics_df=pd.read_parquet(self.parquet_path+'raw_statics.gzip')\n",
        "\n",
        "  def prepare_data(self):\n",
        "    # loop over files, each file is a 2 min walk for a single individual\n",
        "    # files are expected to be in csv format\n",
        "    self.dem_df = pd.read_csv(self.dem_path)\n",
        "    for name in tqdm((os.listdir(self.data_path))):\n",
        "       \n",
        "      # id of subject\n",
        "      id = name.split('_')[0]\n",
        "\n",
        "      # disease level according to 'self.scale'\n",
        "      level = self.get_pd_level( id)\n",
        "\n",
        "      # parkinon's patient OR healthy control ?\n",
        "      if 'Co' in name:  \n",
        "        parkinson = 0 # Healthy control\n",
        "      elif 'Pt' in name:\n",
        "        parkinson = 1 # Parkinson's Patient\n",
        "\n",
        "      # read and filter one file\n",
        "      one_walk=self.read_filter(name)\n",
        "\n",
        "      # sum of values from all the left sensors\n",
        "      one_walk['Total_Force_Left'] = one_walk[list(one_walk.columns[0:8])].sum(axis=1)\n",
        "     \n",
        "      # sum of values from all the right sensors\n",
        "      one_walk['Total_Force_Right'] = one_walk[list(one_walk.columns[8:16])].sum(axis=1)\n",
        "      \n",
        "      # convert to lists\n",
        "      one_walk_lists=[]*18\n",
        "      for column in one_walk.columns.values.tolist():\n",
        "        one_walk_lists.append(one_walk[column].tolist()) \n",
        "      \n",
        "      # Raw statics\n",
        "      raw_statics_1 = self.get_statics_raw_siganls(one_walk)\n",
        "\n",
        "      ## Left foot related ##\n",
        "      l_stances, l_strides_time , l_swings_time, l_stances_time, l_indices = self.segment_signal(one_walk_lists[16])\n",
        "      # Find maximum VGRF at heel strike for each gait cycle \n",
        "      l_peaks_heel = self.get_max_per_gait_cycle(one_walk_lists[0],l_indices) # sensor 0 = left heal sensor\n",
        "      # Find maximum VGRF at toe off for each gait cycle\n",
        "      l_peaks_toe = self.get_max_per_gait_cycle(one_walk_lists[7],l_indices) # sensor 7 = left toe sensor\n",
        "\n",
        "      ## right foot related ##\n",
        "      r_stances, r_strides_time , r_swings_time, r_stances_time, r_indices = self.segment_signal(one_walk_lists[17])\n",
        "      # Find maximum VGRF at heel strike for each gait cycle \n",
        "      r_peaks_heel = self.get_max_per_gait_cycle(one_walk_lists[8],l_indices) # sensor 8 = right heal sensor\n",
        "      # Find maximum VGRF at toe off for each gait cycle\n",
        "      r_peaks_toe = self.get_max_per_gait_cycle(one_walk_lists[15],l_indices) # sensor 15 = right toe sensor\n",
        "\n",
        "\n",
        "      ## Saving data ##\n",
        "\n",
        "      # Raw Signals\n",
        "      self.signals_data.append([id,level,parkinson] +one_walk_lists)\n",
        "\n",
        "      # Raw Statics\n",
        "      self.raw_statics.append([id,level,parkinson] +raw_statics_1)\n",
        "\n",
        "      # Statical\n",
        "      self.statical_data.append( [ id, level, parkinson \n",
        "                                , mean (l_swings_time) , np.std(l_swings_time), (mean (l_swings_time) / np.std(l_swings_time) *100)\n",
        "                                , mean (l_strides_time) , np.std(l_strides_time) , (mean (l_strides_time) / np.std(l_strides_time)*100)\n",
        "                                , kurtosis(l_strides_time), skew(l_strides_time)\n",
        "                                , mean (l_stances_time) , np.std(l_stances_time) , ( mean (l_stances_time) / np.std(l_stances_time) *100) \n",
        "\n",
        "                                , mean (r_swings_time) , np.std(r_swings_time), (mean (r_swings_time) / np.std(r_swings_time) *100)\n",
        "                                , mean (r_strides_time) , np.std(r_strides_time) , (mean (r_strides_time) / np.std(r_strides_time)*100)\n",
        "                                , kurtosis(r_strides_time), skew(r_strides_time)\n",
        "                                , mean (r_stances_time) , np.std(r_stances_time) , ( mean (r_stances_time) / np.std(r_stances_time) *100)\n",
        "                                \n",
        "                                , mean (l_peaks_heel) , np.std(l_peaks_heel) ,mean (l_peaks_toe) , np.std(l_peaks_toe)\n",
        "                                , mean (r_peaks_heel) , np.std(r_peaks_heel) ,mean (r_peaks_toe) , np.std(r_peaks_toe)\n",
        "                                  ] )\n",
        "      \n",
        "      \n",
        "      # Segmented Signals\n",
        "      \n",
        "      # NEED TO BE MOVED TO SEPERATE FUNCTION\n",
        "      # only for segmented data, for a trial we need each stance phase with the following \n",
        "      # swing phases's time, so here we are adjusting the phases to be equal\n",
        "\n",
        "      dif_l =  len(l_stances)-len(l_swings_time)\n",
        "      if (dif_l > 0):\n",
        "        l_stances=l_stances[:-dif_l]\n",
        "      elif(dif_l < 0):\n",
        "        l_swings_time= l_swings_time[:dif_l]\n",
        "      \n",
        "      #print([len(r_peaks_toe), len(r_peaks_heel),len(r_strides_time),len(r_swings_time),len(r_stances)])\n",
        "      \n",
        "      min_r = min([len(r_peaks_toe), len(r_peaks_heel),len(r_strides_time),len(r_swings_time),len(r_stances)])\n",
        "      if (min_r -len(r_stances) !=0): r_stances=r_stances[:(min_r -len(r_stances) )]\n",
        "      if (min_r -len(r_swings_time) !=0): r_swings_time = r_swings_time[:(min_r -len(r_swings_time) )]\n",
        "      if (min_r -len(r_peaks_toe) !=0): r_peaks_toe = r_peaks_toe[:(min_r -len(r_peaks_toe) )]\n",
        "      if (min_r -len(r_peaks_heel) !=0): r_peaks_heel = r_peaks_heel[:(min_r -len(r_peaks_heel) )]\n",
        "      if (min_r -len(r_strides_time) !=0): r_strides_time = r_strides_time[:(min_r -len(r_strides_time) )]\n",
        "      \n",
        "      #print([len(r_peaks_toe), len(r_peaks_heel),len(r_strides_time),len(r_swings_time),len(r_stances)])\n",
        "     \n",
        "\n",
        "      self.cycles_data.append( [ id, level, parkinson ] \n",
        "                        + [l_stances] + [l_strides_time] + [l_swings_time] + [l_stances_time]\n",
        "                        + [r_stances] + [r_strides_time] + [r_swings_time] + [r_stances_time]\n",
        "                        + [l_peaks_heel] + [l_peaks_toe]\n",
        "                        + [r_peaks_heel] + [r_peaks_toe]\n",
        "                        )\n",
        "\n",
        "    # save to df\n",
        "    self.raw_statics_data_to_df()\n",
        "    self.statical_data_to_df()\n",
        "    self.cycles_data_to_df()\n",
        "    self.signal_data_to_df()\n",
        "    ## Get individual stances data frames\n",
        "    self.ind_stances_to_df()\n",
        "    ## writing files ##\n",
        "    self.save_files() \n",
        "\n",
        "\n",
        "  def get_pd_level(self, subject_id):\n",
        "    level = self.dem_df[self.scale][dem_df['ID'] == subject_id ].values[0]\n",
        "    return level\n",
        "\n",
        "  def read_filter(self,name):\n",
        "      # Reading each file, skipping 3 columns (time,total right forces & total left forces)\n",
        "      # For gait cycle segmentation, to eliminate the effect of gait initiation and termenation,\n",
        "      # the first and last N=20 seconds of VGRF data was discarded.\n",
        "\n",
        "      one_walk = pd.read_csv(self.data_path + '/' + name,\n",
        "                             skiprows=list(range(1,self.rows_to_discard+1)) ,\n",
        "                             skipfooter=self.rows_to_discard,\n",
        "                             usecols = np.arange(1,17),\n",
        "                             engine = 'python')  \n",
        "      \n",
        "      # Usually, Vertical Ground React Force VGRF values less than 20N are generated from noise\n",
        "      one_walk.where(one_walk > 20, 0, inplace=True)\n",
        "\n",
        "      return one_walk\n",
        "  \n",
        "  def get_statics_raw_siganls(self, individual_df):\n",
        "    static_co_df = []\n",
        "    df = individual_df\n",
        "    for col in df.columns:\n",
        "        for x in [\"Min\", \"Max\", \"Std\", \"Med\", \"Avg\", \"Skewness\", \"Kurtosis\"]:\n",
        "            colname = col +'_' +  x\n",
        "            if x == 'Min':\n",
        "                #static_co_df.loc[0, colname] = df[col].min(axis=0)\n",
        "                static_co_df.append(df[col].min(axis=0))\n",
        "                \n",
        "            if x == 'Max':\n",
        "                #static_co_df.loc[0, colname] = df[col].max(axis=0)\n",
        "                static_co_df.append(df[col].max(axis=0))\n",
        "                \n",
        "            if x == 'Std':\n",
        "                #static_co_df.loc[0, colname] = df[col].std(axis=0)\n",
        "                static_co_df.append(df[col].std(axis=0))\n",
        "\n",
        "            if x == 'Med':\n",
        "                #static_co_df.loc[0, colname] = df[col].median(axis=0)\n",
        "                static_co_df.append(df[col].median(axis=0))\n",
        "            if x == 'Avg':\n",
        "                #static_co_df.loc[0, colname] = df[col].mean(axis=0)\n",
        "                static_co_df.append(df[col].mean(axis=0))\n",
        "            if x == 'Skewness':\n",
        "                #static_co_df.loc[0, colname] = df[col].skew(axis=0)\n",
        "                static_co_df.append(df[col].skew(axis=0))\n",
        "                \n",
        "            if x == 'Kurtosis':\n",
        "                #static_co_df.loc[0, colname] = df[col].kurtosis(axis=0)\n",
        "                static_co_df.append(df[col].kurtosis(axis=0))\n",
        "    return static_co_df    \n",
        "\n",
        "  def segment_signal(self, signal):\n",
        "      # swing - stance phases repeatly\n",
        "      phases = [list(g) for k, g in groupby((signal), lambda x:x>0)]\n",
        "      \n",
        "      # Deleting the first and last phase, since the first and last 20 seconds were deleted,\n",
        "      # there's no garanty that the first and last phase are complete\n",
        "      # Hence, they are being deleted here just for safety\n",
        "\n",
        "      phases = phases[1:-1]\n",
        "\n",
        "      # filtering phases that is shorter than 20 becuase must propably it's noise\n",
        "      phases = [l for l in phases if len(l)>20]\n",
        "\n",
        "      # indices of the gait cycles\n",
        "      indices=[0]\n",
        "      for i in range(1, len(phases),2):\n",
        "        indices.append(indices[-1]+len(phases[i]) + len(phases[i-1]))\n",
        "\n",
        "      # stances phases\n",
        "      stances = [l for l in phases if any(l)]\n",
        "      \n",
        "      # strides times \n",
        "      strides_time =  [((len(phases[i]) + len(phases[i-1]) -1)*0.01 ) for i in range(1,len(phases),2)]\n",
        "\n",
        "      # swings time\n",
        "      swings_time = [(len(l)-1)*0.01 for l in phases if not all (l)]\n",
        "\n",
        "      # stance time \n",
        "      stances_time = [(len(l)-1)*0.01 for l in stances]\n",
        "\n",
        "      return stances, strides_time , swings_time, stances_time, indices\n",
        "\n",
        "  def get_max_per_gait_cycle(self, signal,indices):\n",
        "    gait_cycles = self.get_gait_cycles(signal, indices)\n",
        "    peaks = [max(cycle) for cycle in gait_cycles]\n",
        "    return peaks\n",
        "\n",
        "  def get_gait_cycles(self, signal, indices):\n",
        "    # get gait cycles\n",
        "    gait_cycles= [signal[indices[i]:indices[i+1]] for i in range(len(indices)-1)]\n",
        "    return gait_cycles\n",
        "\n",
        "  def signal_data_to_df(self):\n",
        "    self.signals_data_df = pd.DataFrame(self.signals_data, columns =[\"ID\", \"level\", \"y\", \"L1\", \"L2\",\"L3\", \"L4\",\"L5\",\"L6\",\"L7\",\"L8\",\n",
        "                                       \"R1\", \"R2\",\"R3\", \"R4\",\"R5\",\"R6\",\"R7\",\"R8\",\"Total_Force_Right\",\"Total_Force_Left\" ])\n",
        "     \n",
        "    \n",
        "  def cycles_data_to_df(self):\n",
        "    self.cycles_data_df = pd.DataFrame(self.cycles_data, columns =[\"ID\", \"level\", \"y\", \n",
        "                                                                   \"l_stances\", \"l_strides_time\", \"l_swings_time\" , \"l_stances_time\"\n",
        "                                                                  , \"r_stances\" , \"r_strides_time\" , \"r_swings_time\" , \"r_stances_time\"\n",
        "                                                                  , \"l_peaks_heel\" , \"l_peaks_toe\"\n",
        "                                                                  , \"r_peaks_heel\" , \"r_peaks_toe\" ])\n",
        "    \n",
        "\n",
        "  def statical_data_to_df(self):\n",
        "    self.statical_data_df = pd.DataFrame(self.statical_data, columns =[\"ID\", \"level\", \"y\"\n",
        "                                                                      , \"mean_left_swings_time\" , \"std_left_swings_time\", \"cv_left_swings_time\"\n",
        "                                                                      , \"mean_left_stride_time\" , \"std_left_stride_time\" ,\"cv_left_stride_time\"\n",
        "                                                                      , \"kurtosis_left_strides_time\", \"skew_left_strides_time\"\n",
        "                                                                      , \"mean_left_stance_time\" , \"std_left_stance_time\", \"cv_left_stance_time\"\n",
        "                                                                      , \"mean_right_swings_time\" , \"std_right_swings_time\", \"cv_right_swings_time\"\n",
        "                                                                      , \"mean_right_stride_time\" , \"std_right_stride_time\", \"cv_right_stride_time\"\n",
        "                                                                      , \"kurtosis_right_strides_time\", \"skew_right_strides_time\"\n",
        "                                                                      , \"mean_right_stance_time\" , \"std_right_stance_time\", \"cv_right_stance_time\"\n",
        "                                                                      , \"mean_left_peaks_heel\" , \"std_left_peaks_heel\"\n",
        "                                                                      , \"mean_left_peaks_toe\" , \"std_left_peaks_toe\"\n",
        "                                                                      , \"mean_right_peaks_heel\" , \"std_right_peaks_heel\"\n",
        "                                                                      , \"mean_right_peaks_toe\" , \"std_right_peaks_toe\"\n",
        "                                                                       ])\n",
        "  def raw_statics_data_to_df(self):\n",
        "    colnames=[\"ID\", \"level\", \"y\"]\n",
        "    for col in [\"Time\"\t,\"L1\",\t\"L2\",\t\"L3\"\t,\"L4\"\t\"L5\",\t\"L6\",\t\"L7\"\t,\"L8\",\t\"R1\",\t\"R2\"\t,\"R3\",\t\"R4\",\t\"R5\",\t\"R6\",\t\"R7\",\t\"R8\",\t\"Total_Force_Left\",\t\"Total_Force_Right\"]:\n",
        "        for x in [\"Min\", \"Max\", \"Std\", \"Med\", \"Avg\", \"Skewness\", \"Kurtosis\"]:\n",
        "            colnames.append( col +'_' +  x)\n",
        "    self.raw_statics_df = pd.DataFrame(self.raw_statics, columns =colnames)\n",
        "\n",
        "  def ind_stances_to_df(self):\n",
        "    #droping NNOT WORKING HERE\n",
        "    self.left_stances_df = self.cycles_data_df.explode([\"l_stances\",\"l_swings_time\"])\n",
        "    self.left_stances_df = self.left_stances_df.drop(columns=[\"r_stances\" ,\t\"r_strides_time\" ,\t\"r_swings_time\" ,\t\"r_stances_time\", \"r_peaks_heel\" ,\t\"r_peaks_toe\"], axis=1)\n",
        "\n",
        "    self.right_stances_df = self.cycles_data_df.explode([\"r_stances\",\"r_swings_time\",\"r_peaks_toe\",\"r_peaks_heel\",\"r_strides_time\"]) \n",
        "    self.right_stances_df = self.right_stances_df.drop(columns=[\"l_stances\" ,\t\"l_strides_time\" ,\t\"l_swings_time\" ,\t\"l_stances_time\", \"l_peaks_heel\" ,\t\"l_peaks_toe\"], axis=1)\n",
        "\n",
        "  def save_files(self):\n",
        "    self.signals_data_df.to_parquet(self.parquet_path+'signals.gzip')\n",
        "    self.cycles_data_df.to_parquet(self.parquet_path+'cyclels.gzip')\n",
        "    self.statical_data_df.to_parquet(self.parquet_path+'statical.gzip')\n",
        "    self.left_stances_df.to_parquet(self.parquet_path+'left_stances.gzip')\n",
        "    self.right_stances_df.to_parquet(self.parquet_path+'right_stances.gzip')\n",
        "    self.raw_statics_df.to_parquet(self.parquet_path+'raw_statics.gzip')\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# read prepare data\n",
        "data = Data( 0, \"\", \"\",parquet_path)"
      ],
      "metadata": {
        "id": "imFWeZFDfT1m"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare the data\n",
        "data = Data( 1, csvD_path, demographics,parquet_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "3d5c8d41beab4c05a2f8b0cb3e5e5877",
            "bfdb9f471bf24fa6a8d0c57d9d0cbbf3",
            "784a45a2d04b45e9b5fbfd7bcd8bed44",
            "2806355baa374cdcba01e9edf3ecf74c",
            "417e09519a3a42aca0bafa2cb78ea286",
            "ebfae1250fa34161b39a0608dd352a94",
            "b5f225cd4b4845cbbd33f0b20ba2fb68",
            "5dae84ff5d83401bba073abfdfbc45f0",
            "e2a0ff9ed1da4f3e8cf698fb16dbb241",
            "0b67c310342145a59af64f226a89e31a",
            "6cdfa188a126432489815146e9aa094f"
          ]
        },
        "id": "fd5B2fhJmB4f",
        "outputId": "1b0f1277-6666-461e-b728-2056bc4482e3"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/304 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d5c8d41beab4c05a2f8b0cb3e5e5877"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## RFC on statical on features"
      ],
      "metadata": {
        "id": "S6DXU9bigka2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.statical_data_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "MGR-JR5efULd",
        "outputId": "3246caa7-3a11-44ba-a809-6f7ed4875e51"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ID  level  y  mean_left_swings_time  std_left_swings_time  \\\n",
              "0  GaPt03    3.0  1               0.448571              0.083739   \n",
              "1  SiPt02    2.5  1               0.331268              0.029973   \n",
              "2  SiPt04    2.0  1               0.455507              0.035976   \n",
              "3  GaPt04    2.5  1               0.394000              0.032052   \n",
              "4  SiPt05    2.5  1               0.400175              0.031091   \n",
              "\n",
              "   cv_left_swings_time  mean_left_stride_time  std_left_stride_time  \\\n",
              "0           535.677087               1.572000              0.218367   \n",
              "1          1105.212394               1.119155              0.085543   \n",
              "2          1266.124714               1.157101              0.070054   \n",
              "3          1229.250892               1.325333              0.340888   \n",
              "4          1287.119803               1.350169              1.388420   \n",
              "\n",
              "   cv_left_stride_time  kurtosis_left_strides_time  ...  \\\n",
              "0           719.890102                   16.574155  ...   \n",
              "1          1308.295978                   29.451513  ...   \n",
              "2          1651.731664                   15.113690  ...   \n",
              "3           388.788062                   49.578405  ...   \n",
              "4            97.245032                   25.169938  ...   \n",
              "\n",
              "   std_right_stance_time  cv_right_stance_time  mean_left_peaks_heel  \\\n",
              "0               0.121305            903.950417             70.221800   \n",
              "1               0.036636           2090.995326            185.811690   \n",
              "2               0.078629            900.765604            138.855072   \n",
              "3               0.195412            477.594621            253.696667   \n",
              "4               0.871567             97.966733            234.232881   \n",
              "\n",
              "   std_left_peaks_heel  mean_left_peaks_toe  std_left_peaks_toe  \\\n",
              "0            17.194543           152.352200           43.493919   \n",
              "1            42.181100           197.031690           25.260014   \n",
              "2            32.976581           226.016522           51.476278   \n",
              "3            23.614942           121.377667           13.603482   \n",
              "4            57.262434           161.804407           32.012626   \n",
              "\n",
              "   mean_right_peaks_heel  std_right_peaks_heel  mean_right_peaks_toe  \\\n",
              "0             106.444800             17.777830             95.807800   \n",
              "1             179.369718             36.238324            282.351408   \n",
              "2             190.405217             32.220226            227.854638   \n",
              "3             218.412333             31.152661            178.979167   \n",
              "4              83.113390             44.229563            271.906949   \n",
              "\n",
              "   std_right_peaks_toe  \n",
              "0            30.390518  \n",
              "1            27.798614  \n",
              "2            49.962947  \n",
              "3            12.964552  \n",
              "4            36.726919  \n",
              "\n",
              "[5 rows x 33 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e2307e9b-bd89-43e0-b117-641dba1ff551\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>level</th>\n",
              "      <th>y</th>\n",
              "      <th>mean_left_swings_time</th>\n",
              "      <th>std_left_swings_time</th>\n",
              "      <th>cv_left_swings_time</th>\n",
              "      <th>mean_left_stride_time</th>\n",
              "      <th>std_left_stride_time</th>\n",
              "      <th>cv_left_stride_time</th>\n",
              "      <th>kurtosis_left_strides_time</th>\n",
              "      <th>...</th>\n",
              "      <th>std_right_stance_time</th>\n",
              "      <th>cv_right_stance_time</th>\n",
              "      <th>mean_left_peaks_heel</th>\n",
              "      <th>std_left_peaks_heel</th>\n",
              "      <th>mean_left_peaks_toe</th>\n",
              "      <th>std_left_peaks_toe</th>\n",
              "      <th>mean_right_peaks_heel</th>\n",
              "      <th>std_right_peaks_heel</th>\n",
              "      <th>mean_right_peaks_toe</th>\n",
              "      <th>std_right_peaks_toe</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GaPt03</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.448571</td>\n",
              "      <td>0.083739</td>\n",
              "      <td>535.677087</td>\n",
              "      <td>1.572000</td>\n",
              "      <td>0.218367</td>\n",
              "      <td>719.890102</td>\n",
              "      <td>16.574155</td>\n",
              "      <td>...</td>\n",
              "      <td>0.121305</td>\n",
              "      <td>903.950417</td>\n",
              "      <td>70.221800</td>\n",
              "      <td>17.194543</td>\n",
              "      <td>152.352200</td>\n",
              "      <td>43.493919</td>\n",
              "      <td>106.444800</td>\n",
              "      <td>17.777830</td>\n",
              "      <td>95.807800</td>\n",
              "      <td>30.390518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SiPt02</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0.331268</td>\n",
              "      <td>0.029973</td>\n",
              "      <td>1105.212394</td>\n",
              "      <td>1.119155</td>\n",
              "      <td>0.085543</td>\n",
              "      <td>1308.295978</td>\n",
              "      <td>29.451513</td>\n",
              "      <td>...</td>\n",
              "      <td>0.036636</td>\n",
              "      <td>2090.995326</td>\n",
              "      <td>185.811690</td>\n",
              "      <td>42.181100</td>\n",
              "      <td>197.031690</td>\n",
              "      <td>25.260014</td>\n",
              "      <td>179.369718</td>\n",
              "      <td>36.238324</td>\n",
              "      <td>282.351408</td>\n",
              "      <td>27.798614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SiPt04</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.455507</td>\n",
              "      <td>0.035976</td>\n",
              "      <td>1266.124714</td>\n",
              "      <td>1.157101</td>\n",
              "      <td>0.070054</td>\n",
              "      <td>1651.731664</td>\n",
              "      <td>15.113690</td>\n",
              "      <td>...</td>\n",
              "      <td>0.078629</td>\n",
              "      <td>900.765604</td>\n",
              "      <td>138.855072</td>\n",
              "      <td>32.976581</td>\n",
              "      <td>226.016522</td>\n",
              "      <td>51.476278</td>\n",
              "      <td>190.405217</td>\n",
              "      <td>32.220226</td>\n",
              "      <td>227.854638</td>\n",
              "      <td>49.962947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GaPt04</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0.394000</td>\n",
              "      <td>0.032052</td>\n",
              "      <td>1229.250892</td>\n",
              "      <td>1.325333</td>\n",
              "      <td>0.340888</td>\n",
              "      <td>388.788062</td>\n",
              "      <td>49.578405</td>\n",
              "      <td>...</td>\n",
              "      <td>0.195412</td>\n",
              "      <td>477.594621</td>\n",
              "      <td>253.696667</td>\n",
              "      <td>23.614942</td>\n",
              "      <td>121.377667</td>\n",
              "      <td>13.603482</td>\n",
              "      <td>218.412333</td>\n",
              "      <td>31.152661</td>\n",
              "      <td>178.979167</td>\n",
              "      <td>12.964552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SiPt05</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0.400175</td>\n",
              "      <td>0.031091</td>\n",
              "      <td>1287.119803</td>\n",
              "      <td>1.350169</td>\n",
              "      <td>1.388420</td>\n",
              "      <td>97.245032</td>\n",
              "      <td>25.169938</td>\n",
              "      <td>...</td>\n",
              "      <td>0.871567</td>\n",
              "      <td>97.966733</td>\n",
              "      <td>234.232881</td>\n",
              "      <td>57.262434</td>\n",
              "      <td>161.804407</td>\n",
              "      <td>32.012626</td>\n",
              "      <td>83.113390</td>\n",
              "      <td>44.229563</td>\n",
              "      <td>271.906949</td>\n",
              "      <td>36.726919</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 33 columns</p>\n",
              "</div>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=data.statical_data_df['y']"
      ],
      "metadata": {
        "id": "5_wOdHiLf4FV"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X =data.statical_data_df[[\"mean_left_swings_time\" , \"std_left_swings_time\", \"cv_left_swings_time\"\n",
        "                                                                      , \"mean_left_stride_time\" , \"std_left_stride_time\" ,\"cv_left_stride_time\"\n",
        "                                                                      , \"kurtosis_left_strides_time\", \"skew_left_strides_time\"\n",
        "                                                                      , \"mean_left_stance_time\" , \"std_left_stance_time\", \"cv_left_stance_time\"\n",
        "                                                                      , \"mean_right_swings_time\" , \"std_right_swings_time\", \"cv_right_swings_time\"\n",
        "                                                                      , \"mean_right_stride_time\" , \"std_right_stride_time\", \"cv_right_stride_time\"\n",
        "                                                                      , \"kurtosis_right_strides_time\", \"skew_right_strides_time\"\n",
        "                                                                      , \"mean_right_stance_time\" , \"std_right_stance_time\", \"cv_right_stance_time\"\n",
        "                                                                      , \"mean_left_peaks_heel\" , \"std_left_peaks_heel\"\n",
        "                                                                      , \"mean_left_peaks_toe\" , \"std_left_peaks_toe\"\n",
        "                                                                      , \"mean_right_peaks_heel\" , \"std_right_peaks_heel\"\n",
        "                                                                      , \"mean_right_peaks_toe\" , \"std_right_peaks_toe\"]]"
      ],
      "metadata": {
        "id": "Uqeq_h_Tf47M"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_score_RFC=[]\n",
        "\n",
        "X_train, X_test, y_train1, y_test1 = train_test_split(X, np.ravel(y),random_state=64)\n",
        "y_train = pd.DataFrame(y_train1)\n",
        "y_test = pd.DataFrame(y_test1)\n",
        "\n",
        "for n in [5, 10, 20, 40, 100, 200]:\n",
        "    clf = RandomForestClassifier(n_estimators = int(n), n_jobs=2)\n",
        "    clf.fit(X_train, np.ravel(y_train))\n",
        "    acc = clf.score(X_test, np.ravel(y_test))\n",
        "    test_score_RFC.append(acc)\n",
        "     \n"
      ],
      "metadata": {
        "id": "8FWn0XDHfbYb"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for neighbor, tr_sc in zip(([5, 10, 20, 40, 100, 200]),test_score_RFC): \n",
        "    print(f\"Estimator = {neighbor}\")\n",
        "    print('Accuracy of Random Forest Classifier on training set: {:.2f}'.format(tr_sc))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2Dan8Qtgarx",
        "outputId": "f120d4ff-1c12-4cda-e654-c14d934466ce"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estimator = 5\n",
            "Accuracy of Random Forest Classifier on training set: 0.83\n",
            "Estimator = 10\n",
            "Accuracy of Random Forest Classifier on training set: 0.79\n",
            "Estimator = 20\n",
            "Accuracy of Random Forest Classifier on training set: 0.83\n",
            "Estimator = 40\n",
            "Accuracy of Random Forest Classifier on training set: 0.80\n",
            "Estimator = 100\n",
            "Accuracy of Random Forest Classifier on training set: 0.82\n",
            "Estimator = 200\n",
            "Accuracy of Random Forest Classifier on training set: 0.82\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_clf = clf.predict(X_test)\n",
        "prec = precision_score(y_test1, prediction_clf)\n",
        "rec = recall_score(y_test1, prediction_clf)\n",
        "f1 = f1_score(y_test1, prediction_clf)\n",
        "print(\"Precision: {}\".format(prec))\n",
        "print(\"Recall: {}\".format(rec))\n",
        "print(\"F1: {}\".format(f1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8_SS5wH-gdZE",
        "outputId": "c27c26a3-fd00-4875-a474-ddf5304fbb0d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 0.8620689655172413\n",
            "Recall: 0.8928571428571429\n",
            "F1: 0.8771929824561403\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## RF Classifier on statics of the raw sensors data"
      ],
      "metadata": {
        "id": "HDJEy_mSgoVU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.raw_statics_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "2Dq39qIvghCq",
        "outputId": "a7ffa6f7-692d-4cf8-8900-0a922eefe73b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ID  level  y  Time_Min  Time_Max    Time_Std  Time_Med    Time_Avg  \\\n",
              "0  GaCo01    0.0  0       0.0    480.92  142.772146     20.90  118.283573   \n",
              "1  GaPt03    3.0  1       0.0    106.92   28.711902      0.00   24.715777   \n",
              "2  SiPt02    2.5  1       0.0    246.95   34.643793      0.00   17.166232   \n",
              "3  SiPt04    2.0  1       0.0    188.32   33.281562      0.00   17.040191   \n",
              "4  GaPt04    2.5  1       0.0    304.70  100.625905     26.84   89.612593   \n",
              "\n",
              "   Time_Skewness  Time_Kurtosis  ...  Total_Force_Left_Avg  \\\n",
              "0       0.715433      -1.048771  ...            517.728542   \n",
              "1       0.746198      -0.681584  ...            334.245344   \n",
              "2       2.926951      10.058267  ...            462.035754   \n",
              "3       2.296395       5.177897  ...            353.097941   \n",
              "4       0.501348      -1.412808  ...            460.916353   \n",
              "\n",
              "   Total_Force_Left_Skewness  Total_Force_Left_Kurtosis  \\\n",
              "0                  -0.062798                  -1.823584   \n",
              "1                  -0.011145                  -1.690073   \n",
              "2                  -0.101435                  -1.832651   \n",
              "3                   0.033986                  -1.708469   \n",
              "4                  -0.041533                  -1.723508   \n",
              "\n",
              "   Total_Force_Right_Min  Total_Force_Right_Max  Total_Force_Right_Std  \\\n",
              "0                    0.0                1257.30             490.075715   \n",
              "1                    0.0                 784.85             299.651983   \n",
              "2                    0.0                 979.88             410.276345   \n",
              "3                    0.0                 901.78             354.901744   \n",
              "4                    0.0                1008.70             395.354583   \n",
              "\n",
              "   Total_Force_Right_Med  Total_Force_Right_Avg  Total_Force_Right_Skewness  \\\n",
              "0                 717.31             552.158582                   -0.097883   \n",
              "1                 377.63             356.892798                   -0.000753   \n",
              "2                 539.77             474.159044                   -0.066059   \n",
              "3                 576.84             397.830886                   -0.094729   \n",
              "4                 550.33             481.004502                   -0.086407   \n",
              "\n",
              "   Total_Force_Right_Kurtosis  \n",
              "0                   -1.850094  \n",
              "1                   -1.693112  \n",
              "2                   -1.815159  \n",
              "3                   -1.832501  \n",
              "4                   -1.699340  \n",
              "\n",
              "[5 rows x 129 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>level</th>\n",
              "      <th>y</th>\n",
              "      <th>Time_Min</th>\n",
              "      <th>Time_Max</th>\n",
              "      <th>Time_Std</th>\n",
              "      <th>Time_Med</th>\n",
              "      <th>Time_Avg</th>\n",
              "      <th>Time_Skewness</th>\n",
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              "      <th>...</th>\n",
              "      <th>Total_Force_Left_Avg</th>\n",
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              "      <th>Total_Force_Right_Min</th>\n",
              "      <th>Total_Force_Right_Max</th>\n",
              "      <th>Total_Force_Right_Std</th>\n",
              "      <th>Total_Force_Right_Med</th>\n",
              "      <th>Total_Force_Right_Avg</th>\n",
              "      <th>Total_Force_Right_Skewness</th>\n",
              "      <th>Total_Force_Right_Kurtosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GaCo01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>480.92</td>\n",
              "      <td>142.772146</td>\n",
              "      <td>20.90</td>\n",
              "      <td>118.283573</td>\n",
              "      <td>0.715433</td>\n",
              "      <td>-1.048771</td>\n",
              "      <td>...</td>\n",
              "      <td>517.728542</td>\n",
              "      <td>-0.062798</td>\n",
              "      <td>-1.823584</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1257.30</td>\n",
              "      <td>490.075715</td>\n",
              "      <td>717.31</td>\n",
              "      <td>552.158582</td>\n",
              "      <td>-0.097883</td>\n",
              "      <td>-1.850094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GaPt03</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>106.92</td>\n",
              "      <td>28.711902</td>\n",
              "      <td>0.00</td>\n",
              "      <td>24.715777</td>\n",
              "      <td>0.746198</td>\n",
              "      <td>-0.681584</td>\n",
              "      <td>...</td>\n",
              "      <td>334.245344</td>\n",
              "      <td>-0.011145</td>\n",
              "      <td>-1.690073</td>\n",
              "      <td>0.0</td>\n",
              "      <td>784.85</td>\n",
              "      <td>299.651983</td>\n",
              "      <td>377.63</td>\n",
              "      <td>356.892798</td>\n",
              "      <td>-0.000753</td>\n",
              "      <td>-1.693112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SiPt02</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>246.95</td>\n",
              "      <td>34.643793</td>\n",
              "      <td>0.00</td>\n",
              "      <td>17.166232</td>\n",
              "      <td>2.926951</td>\n",
              "      <td>10.058267</td>\n",
              "      <td>...</td>\n",
              "      <td>462.035754</td>\n",
              "      <td>-0.101435</td>\n",
              "      <td>-1.832651</td>\n",
              "      <td>0.0</td>\n",
              "      <td>979.88</td>\n",
              "      <td>410.276345</td>\n",
              "      <td>539.77</td>\n",
              "      <td>474.159044</td>\n",
              "      <td>-0.066059</td>\n",
              "      <td>-1.815159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SiPt04</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>188.32</td>\n",
              "      <td>33.281562</td>\n",
              "      <td>0.00</td>\n",
              "      <td>17.040191</td>\n",
              "      <td>2.296395</td>\n",
              "      <td>5.177897</td>\n",
              "      <td>...</td>\n",
              "      <td>353.097941</td>\n",
              "      <td>0.033986</td>\n",
              "      <td>-1.708469</td>\n",
              "      <td>0.0</td>\n",
              "      <td>901.78</td>\n",
              "      <td>354.901744</td>\n",
              "      <td>576.84</td>\n",
              "      <td>397.830886</td>\n",
              "      <td>-0.094729</td>\n",
              "      <td>-1.832501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GaPt04</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>304.70</td>\n",
              "      <td>100.625905</td>\n",
              "      <td>26.84</td>\n",
              "      <td>89.612593</td>\n",
              "      <td>0.501348</td>\n",
              "      <td>-1.412808</td>\n",
              "      <td>...</td>\n",
              "      <td>460.916353</td>\n",
              "      <td>-0.041533</td>\n",
              "      <td>-1.723508</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1008.70</td>\n",
              "      <td>395.354583</td>\n",
              "      <td>550.33</td>\n",
              "      <td>481.004502</td>\n",
              "      <td>-0.086407</td>\n",
              "      <td>-1.699340</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 129 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a3b0410-b1d4-421a-a7fa-346d7e49bf5a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "        buttonEl.style.display =\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5a3b0410-b1d4-421a-a7fa-346d7e49bf5a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.raw_statics_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1-ceV3lEmsed",
        "outputId": "089489f8-fe9f-4c18-fa2a-f1a973122214"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(304, 129)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=data.raw_statics_df['y']"
      ],
      "metadata": {
        "id": "h1_qhhrHpvQx"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "colnames=[]\n",
        "for col in [\"Time\"\t,\"L1\",\t\"L2\",\t\"L3\"\t,\"L4\"\t\"L5\",\t\"L6\",\t\"L7\"\t,\"L8\",\t\"R1\",\t\"R2\"\t,\"R3\",\t\"R4\",\t\"R5\",\t\"R6\",\t\"R7\",\t\"R8\",\t\"Total_Force_Left\",\t\"Total_Force_Right\"]:\n",
        "    for x in [\"Min\", \"Max\", \"Std\", \"Med\", \"Avg\", \"Skewness\", \"Kurtosis\"]:\n",
        "        colnames.append( col +'_' +  x)"
      ],
      "metadata": {
        "id": "2nFswq9Wp76K"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X =data.raw_statics_df[colnames]"
      ],
      "metadata": {
        "id": "qPH5vcdBpz-f"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_score_RFC=[]\n",
        "\n",
        "X_train, X_test, y_train1, y_test1 = train_test_split(X, np.ravel(y),random_state=64)\n",
        "y_train = pd.DataFrame(y_train1)\n",
        "y_test = pd.DataFrame(y_test1)\n",
        "\n",
        "for n in [5, 10, 20, 40, 100, 200]:\n",
        "    clf = RandomForestClassifier(n_estimators = int(n), n_jobs=2, random_state = 42)\n",
        "    clf.fit(X_train, np.ravel(y_train))\n",
        "    acc = clf.score(X_test, np.ravel(y_test))\n",
        "    test_score_RFC.append(acc)"
      ],
      "metadata": {
        "id": "Y8TDXbZgmoJT"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for neighbor, tr_sc in zip(([5, 10, 20, 40, 100, 200]),test_score_RFC): \n",
        "    print(f\"Estimator = {neighbor}\")\n",
        "    print('Accuracy of Random Forest Classifier on training set: {:.2f}'.format(tr_sc))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cED_rwhWnkgG",
        "outputId": "0ff5b9df-95bd-41b5-e7e1-402adc55551b"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estimator = 5\n",
            "Accuracy of Random Forest Classifier on training set: 0.86\n",
            "Estimator = 10\n",
            "Accuracy of Random Forest Classifier on training set: 0.86\n",
            "Estimator = 20\n",
            "Accuracy of Random Forest Classifier on training set: 0.83\n",
            "Estimator = 40\n",
            "Accuracy of Random Forest Classifier on training set: 0.88\n",
            "Estimator = 100\n",
            "Accuracy of Random Forest Classifier on training set: 0.92\n",
            "Estimator = 200\n",
            "Accuracy of Random Forest Classifier on training set: 0.91\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = RandomForestClassifier(n_estimators = 100, n_jobs=2,  random_state = 42)\n",
        "clf.fit(X_train, np.ravel(y_train))    \n",
        "\n",
        "prediction_clf = clf.predict(X_test)\n",
        "\n",
        "prec = precision_score(y_test1, prediction_clf)\n",
        "rec = recall_score(y_test1, prediction_clf)\n",
        "f1 = f1_score(y_test1, prediction_clf)\n",
        "print(\"Precision: {}\".format(prec))\n",
        "print(\"Recall: {}\".format(rec))\n",
        "print(\"F1: {}\".format(f1))   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AQPH4C2JnmN4",
        "outputId": "8b9d2d31-a08e-43dd-d13c-0e8688f9f9cb"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 0.9166666666666666\n",
            "Recall: 0.9821428571428571\n",
            "F1: 0.9482758620689654\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, prediction_clf)\n",
        "print(f'Accuracy: {accuracy}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vtXK1Dyjqhit",
        "outputId": "d19c9e09-d3d8-41c3-f2f1-ecc59b6a40ab"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9210526315789473\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = clf.score(X_test, np.ravel(y_test))\n",
        "print(acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O-rFB-X4q5oX",
        "outputId": "a0f651fe-4294-42c5-e365-4b2f314aabd5"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9210526315789473\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save the model\n",
        "with open(\"/content/drive/MyDrive/ITMO-Master's/Thesis/3rd_semester/Models/rf_classifier_raw_filterd_statics\", 'wb') as f:\n",
        "    pickle.dump(clf, f)"
      ],
      "metadata": {
        "id": "hKChxhVRr04v"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the model                                                                                                                                                                                                       \n",
        "\n",
        "with open(\"/content/drive/MyDrive/ITMO-Master's/Thesis/3rd_semester/Models/rf_classifier_raw_filterd_statics\", 'rb') as f:\n",
        "    rf = pickle.load(f)"
      ],
      "metadata": {
        "id": "Pw814GN3r16y"
      },
      "execution_count": 80,
      "outputs": []
    }
  ]
}