fwhm_scaling_RFC.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Importing"
      ],
      "metadata": {
        "id": "pnwymD4X85c0"
      }
    },
    {
      "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": "tALiwbv4879K"
      },
      "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": "NQIWOsnX8-1n",
        "outputId": "10df6976-2c8b-459c-e4b5-827be68f6c58"
      },
      "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": "DRFUzWAt9D2O"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data"
      ],
      "metadata": {
        "id": "bqsd59LxAOa_"
      }
    },
    {
      "cell_type": "code",
      "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",
        "  \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",
        "\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",
        "      ## 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",
        "      # 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",
        "      dif_r =  len(r_stances)-len(r_swings_time)\n",
        "      if (dif_r>0):\n",
        "        r_stances=r_stances[:-dif_r]\n",
        "      elif(dif_r < 0):\n",
        "        r_swings_time = r_swings_time[:dif_r]\n",
        "       \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",
        "    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",
        "  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 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 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')"
      ],
      "metadata": {
        "id": "o0mJeejW9HTE"
      },
      "execution_count": 233,
      "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": [
            "58c6e018608841b0839b5528caca29f8",
            "15293797434c4e06b346329fd854ed10",
            "c05e438c941c438a80a737d98d674037",
            "18f9be5af9864ab7bffe3bcc48dc84e7",
            "f66d8471da114638bf0d3922aff43e0b",
            "4651c372655e4bf6ac650ee7c70c1989",
            "4802f8a9044348df976a27b1494a63ac",
            "d3b2463394174ecca7cb26d9c00730c7",
            "b5b62769fbaa4455b272690cf9c062bf",
            "e43645327f5249c7924ae004e60943cf",
            "8724cf24e0444b3d88dc07b5c55d21f4"
          ]
        },
        "id": "egqpTbBzPm5a",
        "outputId": "4d4e83c4-51d9-433b-99a4-23dd238e629a"
      },
      "execution_count": 234,
      "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": "58c6e018608841b0839b5528caca29f8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 194,
      "metadata": {
        "id": "XzE-whMYt6OS"
      },
      "outputs": [],
      "source": [
        "# read prepare data\n",
        "data = Data( 0, \"\", \"\",parquet_path)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### SHOULD be applied for all dataframes after `explode`\n",
        "data.right_stances_df = data.right_stances_df.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "QPAtCXsZ9XMu"
      },
      "execution_count": 239,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.right_stances_df['max_peak'] = data.right_stances_df['r_stances'].apply(lambda x: max(x))"
      ],
      "metadata": {
        "id": "jAv9Aehm9Y1S"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## FWHM "
      ],
      "metadata": {
        "id": "Jw5nm2ppAVsr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "the scaling should be as folloowing\n",
        "find the points X_left and X_right you can scale the actual data. I.e. x1 = (x-X_left)/(X_right-X_left), y1= y/Y_max. After that you can select a fixed x range e.g. np.linspace(-0.5,1.5,100) and get interpolated scaled shape for each siganl"
      ],
      "metadata": {
        "id": "8LfYENAwApVU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this code, we first calculate the FWHM.\n",
        "We then scale the signal by dividing the x values by the FWHM and dividing the y values by the maximum y value. \n",
        "We also interpolate the scaled signal onto a fixed x range using np.interp. Finally, we plot the original and scaled signals, as well as the interpolated scaled signal. \n",
        "The result should show that the peak is now narrower and taller in the scaled signal, and that the interpolated scaled signal has the same shape as the original signal."
      ],
      "metadata": {
        "id": "_L029nyWAzix"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Function for calculating & scaling by FWHM"
      ],
      "metadata": {
        "id": "cEElHZWycBj3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def fwhm(y,l_inter_x, r_inter_x):\n",
        "  # Find the peak location\n",
        "  x= np.linspace (0,len(y)*0.01,len(y))\n",
        "  peak_idx = np.argmax(y)\n",
        "  peak_pos = x[peak_idx]\n",
        "\n",
        "  # Calculate the FWHM\n",
        "  half_max = y[peak_idx] / 2\n",
        "  left_idx = np.argmin(np.abs(y[:peak_idx] - half_max))\n",
        "  right_idx = np.argmin(np.abs(y[peak_idx:] - half_max)) + peak_idx\n",
        "  fwhm = x[right_idx] - x[left_idx]\n",
        "\n",
        "  # Scale the signal by the FWHM\n",
        "  scaled_x = (x - x[left_idx]) / fwhm\n",
        "  scaled_y = y / np.max(y)\n",
        "\n",
        "  # Interpolate the scaled signal onto a fixed x range\n",
        "  new_x = np.linspace(l_inter_x, r_inter_x, 100)\n",
        "  new_y = np.interp(new_x, scaled_x, scaled_y)\n",
        "\n",
        "  return [fwhm,max(y),scaled_x,scaled_y,new_x,new_y,peak_pos]"
      ],
      "metadata": {
        "id": "vuVHxQtuXmBf"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Function for plotting the results"
      ],
      "metadata": {
        "id": "XGsiFE5TcHNg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_signals(x, y,scaled_x, scaled_y,new_x, new_y,peak_pos):\n",
        "  # Plot the original and scaled signals\n",
        "  plt.plot(x, y, label='Original')\n",
        "  plt.plot(scaled_x, scaled_y, label='Scaled')\n",
        "  plt.plot(new_x, new_y, label='Interpolated')\n",
        "  plt.axvline(peak_pos, color='r', linestyle='--', label='Peak Position')\n",
        "  plt.legend()\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "3SKCwbUAa3Z3"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generating a sample signal with a Gaussian-shaped peak and applying FWHM"
      ],
      "metadata": {
        "id": "54t2Slt_cQS4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "##### EXAMPLE ######\n",
        "# Generate a sample signal with a peak\n",
        "x= np.linspace (0,1000*0.01,1000)\n",
        "y = np.exp(-(x - 5)**2 / 2) + np.random.normal(0, 0.1, size=1000)\n",
        "\n",
        "fwhm_value, max_peak,scaled_x, scaled_y, new_x ,new_y,peak_pos = fwhm ( y, -0.5,1.5)\n",
        "plot_signals(x, y,scaled_x, scaled_y,new_x, new_y,peak_pos)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "yZt4XTTuYDPI",
        "outputId": "de19efb2-6527-463c-d575-39c78a42cdf3"
      },
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exaple from data"
      ],
      "metadata": {
        "id": "xGwWAnJlceLY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### Example from Data ###\n",
        "y =  data.right_stances_df['r_stances'][4]  \n",
        "x = np.linspace (0,len(y)*0.01,len(y))\n",
        "fwhm_value, max_peak,scaled_x, scaled_y, new_x ,new_y,peak_pos = fwhm ( y, -0.4,1.4)\n",
        "\n",
        "## Plot with log scale\n",
        "plt.plot(x, y, label='Original')\n",
        "plt.plot(scaled_x, scaled_y, label='Scaled')\n",
        "plt.plot(new_x, new_y, label='Interpolated')\n",
        "plt.yscale('log')\n",
        "plt.axvline(peak_pos, color='r', linestyle='--', label='Peak Position')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "eveIcktnS8om",
        "outputId": "45411e8d-f0b5-4095-beb0-d2f1d3e131b7"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Interpolate the scaled signal onto a fixed x range\n",
        "plt.plot(scaled_x, scaled_y, label='Scaled')\n",
        "plt.plot(new_x, new_y, label='Interpolated')\n",
        "plt.axvline(peak_pos, color='r', linestyle='--', label='Peak Position')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "_qv0doivT4tV",
        "outputId": "e87d2686-fd2b-4d3b-a94d-ae8d584d9e09"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Applying the scaling on the stances from the dataset"
      ],
      "metadata": {
        "id": "KvZE9n9shOOe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tqdm.pandas()"
      ],
      "metadata": {
        "id": "r8th9p4InZIJ"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "s=data.right_stances_df['r_stances'].progress_map(lambda x: fwhm(x, -0.4,1.4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "50ff8013a57f4a4ba8419449bbe14682",
            "384aeeacc44f499a88ea93bcfa77bca3",
            "8adc54ec32c24ac28d28ad858c4128b2",
            "e6bf0e9b7e514a02841685a988685ba6",
            "b8aca60292a440bbaf29b665d987e764",
            "808e93fcc29d4a5d90d15b3f633f883a",
            "313b2d2717e14671abf951b4f3ab0c49",
            "a621fd83cd6849c1a8a6aa3c46f8cd03",
            "992d74ab0def417c8f2e7ed054d68f12",
            "eb597d11f77b479f8b998af88a93903e",
            "e75bb41f7c8647b6a54e988d6abc7613"
          ]
        },
        "id": "-SX8Wl5NhSpG",
        "outputId": "16754640-828f-4e69-ee3e-21fb0502085d"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/18387 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "50ff8013a57f4a4ba8419449bbe14682"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.right_stances_df[['fwhm_value','max_peak' ,'scaled_x', 'scaled_y', 'new_x' ,'new_y','peak_pos']] = pd.DataFrame(s.tolist(), index= data.right_stances_df.index)"
      ],
      "metadata": {
        "id": "-00PK-4Z6yGB"
      },
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.right_stances_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        },
        "id": "lTf8d-8k7Cny",
        "outputId": "b4c20f6d-a453-4e2e-97e6-7c01db40900e"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ID  level  y                                          r_stances  \\\n",
              "0  GaPt03    3.0  1  [20.79, 27.72, 32.45, 37.07, 63.69, 110.33, 13...   \n",
              "1  GaPt03    3.0  1  [52.8, 69.19, 57.64, 51.7, 60.94, 92.84, 140.1...   \n",
              "2  GaPt03    3.0  1  [23.1, 32.45, 39.38, 46.31, 116.82, 155.43, 17...   \n",
              "3  GaPt03    3.0  1  [23.1, 41.69, 55.55, 119.13, 154.55, 200.42, 2...   \n",
              "4  GaPt03    3.0  1  [25.41, 37.07, 55.55, 106.37, 152.02, 182.05, ...   \n",
              "\n",
              "                                      r_strides_time  r_swings_time  \\\n",
              "0  [1.59, 1.27, 1.41, 1.49, 1.56, 1.41, 1.5, 1.32...           0.39   \n",
              "1  [1.59, 1.27, 1.41, 1.49, 1.56, 1.41, 1.5, 1.32...           0.21   \n",
              "2  [1.59, 1.27, 1.41, 1.49, 1.56, 1.41, 1.5, 1.32...           0.41   \n",
              "3  [1.59, 1.27, 1.41, 1.49, 1.56, 1.41, 1.5, 1.32...           0.43   \n",
              "4  [1.59, 1.27, 1.41, 1.49, 1.56, 1.41, 1.5, 1.32...           0.41   \n",
              "\n",
              "                                      r_stances_time  \\\n",
              "0  [1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...   \n",
              "1  [1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...   \n",
              "2  [1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...   \n",
              "3  [1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...   \n",
              "4  [1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...   \n",
              "\n",
              "                                        r_peaks_heel  \\\n",
              "0  [107.8, 71.17, 111.1, 141.24, 101.2, 123.42, 1...   \n",
              "1  [107.8, 71.17, 111.1, 141.24, 101.2, 123.42, 1...   \n",
              "2  [107.8, 71.17, 111.1, 141.24, 101.2, 123.42, 1...   \n",
              "3  [107.8, 71.17, 111.1, 141.24, 101.2, 123.42, 1...   \n",
              "4  [107.8, 71.17, 111.1, 141.24, 101.2, 123.42, 1...   \n",
              "\n",
              "                                         r_peaks_toe  max_peak  fwhm_value  \\\n",
              "0  [80.63, 134.42, 80.63, 71.61, 98.78, 130.02, 8...    731.61    0.927731   \n",
              "1  [80.63, 134.42, 80.63, 71.61, 98.78, 130.02, 8...    774.73    0.747048   \n",
              "2  [80.63, 134.42, 80.63, 71.61, 98.78, 130.02, 8...    717.75    0.696970   \n",
              "3  [80.63, 134.42, 80.63, 71.61, 98.78, 130.02, 8...    742.50    0.757143   \n",
              "4  [80.63, 134.42, 80.63, 71.61, 98.78, 130.02, 8...    779.79    0.887719   \n",
              "\n",
              "                                            scaled_x  \\\n",
              "0  [-0.13043478260869568, -0.11956521739130437, -...   \n",
              "1  [-0.14864864864864863, -0.13513513513513511, -...   \n",
              "2  [-0.17391304347826086, -0.15942028985507248, -...   \n",
              "3  [-0.16000000000000003, -0.1466666666666667, -0...   \n",
              "4  [-0.11363636363636363, -0.10227272727272727, -...   \n",
              "\n",
              "                                            scaled_y  \\\n",
              "0  [0.02841677943166442, 0.03788903924221922, 0.0...   \n",
              "1  [0.06815277580576458, 0.08930853329547067, 0.0...   \n",
              "2  [0.03218390804597701, 0.04521072796934866, 0.0...   \n",
              "3  [0.031111111111111117, 0.056148148148148155, 0...   \n",
              "4  [0.032585696148963186, 0.0475384398363662, 0.0...   \n",
              "\n",
              "                                               new_x  \\\n",
              "0  [-0.4, -0.38181818181818183, -0.36363636363636...   \n",
              "1  [-0.4, -0.38181818181818183, -0.36363636363636...   \n",
              "2  [-0.4, -0.38181818181818183, -0.36363636363636...   \n",
              "3  [-0.4, -0.38181818181818183, -0.36363636363636...   \n",
              "4  [-0.4, -0.38181818181818183, -0.36363636363636...   \n",
              "\n",
              "                                               new_y  peak_pos  \n",
              "0  [0.02841677943166442, 0.02841677943166442, 0.0...  0.443697  \n",
              "1  [0.06815277580576458, 0.06815277580576458, 0.0...  0.545143  \n",
              "2  [0.03218390804597701, 0.03218390804597701, 0.0...  0.646465  \n",
              "3  [0.031111111111111117, 0.031111111111111117, 0...  0.656190  \n",
              "4  [0.032585696148963186, 0.032585696148963186, 0...  0.716228  "
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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>r_stances</th>\n",
              "      <th>r_strides_time</th>\n",
              "      <th>r_swings_time</th>\n",
              "      <th>r_stances_time</th>\n",
              "      <th>r_peaks_heel</th>\n",
              "      <th>r_peaks_toe</th>\n",
              "      <th>max_peak</th>\n",
              "      <th>fwhm_value</th>\n",
              "      <th>scaled_x</th>\n",
              "      <th>scaled_y</th>\n",
              "      <th>new_x</th>\n",
              "      <th>new_y</th>\n",
              "      <th>peak_pos</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>[20.79, 27.72, 32.45, 37.07, 63.69, 110.33, 13...</td>\n",
              "      <td>[1.59, 1.27, 1.41, 1.49, 1.56, 1.41, 1.5, 1.32...</td>\n",
              "      <td>0.39</td>\n",
              "      <td>[1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...</td>\n",
              "      <td>[107.8, 71.17, 111.1, 141.24, 101.2, 123.42, 1...</td>\n",
              "      <td>[80.63, 134.42, 80.63, 71.61, 98.78, 130.02, 8...</td>\n",
              "      <td>731.61</td>\n",
              "      <td>0.927731</td>\n",
              "      <td>[-0.13043478260869568, -0.11956521739130437, -...</td>\n",
              "      <td>[0.02841677943166442, 0.03788903924221922, 0.0...</td>\n",
              "      <td>[-0.4, -0.38181818181818183, -0.36363636363636...</td>\n",
              "      <td>[0.02841677943166442, 0.02841677943166442, 0.0...</td>\n",
              "      <td>0.443697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GaPt03</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>[52.8, 69.19, 57.64, 51.7, 60.94, 92.84, 140.1...</td>\n",
              "      <td>[1.59, 1.27, 1.41, 1.49, 1.56, 1.41, 1.5, 1.32...</td>\n",
              "      <td>0.21</td>\n",
              "      <td>[1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...</td>\n",
              "      <td>[107.8, 71.17, 111.1, 141.24, 101.2, 123.42, 1...</td>\n",
              "      <td>[80.63, 134.42, 80.63, 71.61, 98.78, 130.02, 8...</td>\n",
              "      <td>774.73</td>\n",
              "      <td>0.747048</td>\n",
              "      <td>[-0.14864864864864863, -0.13513513513513511, -...</td>\n",
              "      <td>[0.06815277580576458, 0.08930853329547067, 0.0...</td>\n",
              "      <td>[-0.4, -0.38181818181818183, -0.36363636363636...</td>\n",
              "      <td>[0.06815277580576458, 0.06815277580576458, 0.0...</td>\n",
              "      <td>0.545143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GaPt03</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>[23.1, 32.45, 39.38, 46.31, 116.82, 155.43, 17...</td>\n",
              "      <td>[1.59, 1.27, 1.41, 1.49, 1.56, 1.41, 1.5, 1.32...</td>\n",
              "      <td>0.41</td>\n",
              "      <td>[1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...</td>\n",
              "      <td>[107.8, 71.17, 111.1, 141.24, 101.2, 123.42, 1...</td>\n",
              "      <td>[80.63, 134.42, 80.63, 71.61, 98.78, 130.02, 8...</td>\n",
              "      <td>717.75</td>\n",
              "      <td>0.696970</td>\n",
              "      <td>[-0.17391304347826086, -0.15942028985507248, -...</td>\n",
              "      <td>[0.03218390804597701, 0.04521072796934866, 0.0...</td>\n",
              "      <td>[-0.4, -0.38181818181818183, -0.36363636363636...</td>\n",
              "      <td>[0.03218390804597701, 0.03218390804597701, 0.0...</td>\n",
              "      <td>0.646465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GaPt03</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>[23.1, 41.69, 55.55, 119.13, 154.55, 200.42, 2...</td>\n",
              "      <td>[1.59, 1.27, 1.41, 1.49, 1.56, 1.41, 1.5, 1.32...</td>\n",
              "      <td>0.43</td>\n",
              "      <td>[1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...</td>\n",
              "      <td>[107.8, 71.17, 111.1, 141.24, 101.2, 123.42, 1...</td>\n",
              "      <td>[80.63, 134.42, 80.63, 71.61, 98.78, 130.02, 8...</td>\n",
              "      <td>742.50</td>\n",
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              "      <td>[0.031111111111111117, 0.031111111111111117, 0...</td>\n",
              "      <td>0.656190</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GaPt03</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>[25.41, 37.07, 55.55, 106.37, 152.02, 182.05, ...</td>\n",
              "      <td>[1.59, 1.27, 1.41, 1.49, 1.56, 1.41, 1.5, 1.32...</td>\n",
              "      <td>0.41</td>\n",
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              "      <td>779.79</td>\n",
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              "      <td>[-0.4, -0.38181818181818183, -0.36363636363636...</td>\n",
              "      <td>[0.032585696148963186, 0.032585696148963186, 0...</td>\n",
              "      <td>0.716228</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### train-test data"
      ],
      "metadata": {
        "id": "HGVWSzWxAFEt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "e3Zr-FjvA6wE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = data.right_stances_df[[ 'new_y','r_swings_time','max_peak','fwhm_value']]\n",
        "y = data.right_stances_df[[ 'y']]"
      ],
      "metadata": {
        "id": "rm61Wcd1AK3G"
      },
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(df1, y, random_state=42)"
      ],
      "metadata": {
        "id": "b_buddF67G21"
      },
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Reshape the data to be fit in the model (RF)"
      ],
      "metadata": {
        "id": "k-YsVQCzAr8A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Train"
      ],
      "metadata": {
        "id": "UyhQwikGBnAs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_train_data = np.append( np.asarray(X_train['new_y'].tolist()) , np.asarray(X_train[[\"r_swings_time\" ]]),  axis=1)\n",
        "scaled_train_data = np.append( scaled_train_data , np.asarray(X_train[[\"max_peak\" ]]),  axis=1)\n",
        "scaled_train_data = np.append( scaled_train_data , np.asarray(X_train[[\"fwhm_value\" ]]),  axis=1)"
      ],
      "metadata": {
        "id": "zB9OVtMLAjd2"
      },
      "execution_count": 179,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Test\n"
      ],
      "metadata": {
        "id": "9AZIALHgBpQN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_test_data = np.append( np.asarray(X_test['new_y'].tolist()) , np.asarray(X_test[[\"r_swings_time\" ]]),  axis=1)\n",
        "scaled_test_data = np.append( scaled_test_data , np.asarray(X_test[[\"max_peak\" ]]),  axis=1)\n",
        "scaled_test_data = np.append( scaled_test_data , np.asarray(X_test[[\"fwhm_value\" ]]),  axis=1)"
      ],
      "metadata": {
        "id": "y4BKYQCcBB4W"
      },
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Reshape the data for Hybrid model"
      ],
      "metadata": {
        "id": "RYLLcevuF1de"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### train data"
      ],
      "metadata": {
        "id": "rpi5Fca5Ga4u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train['new_y'] = X_train['new_y'].apply(np.array)"
      ],
      "metadata": {
        "id": "ZvKh4mmqNfR1"
      },
      "execution_count": 185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = X_train[['new_y', 'r_swings_time','max_peak','fwhm_value']]"
      ],
      "metadata": {
        "id": "goDGDcl7M89x"
      },
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "itemized_train_data = x.to_numpy()"
      ],
      "metadata": {
        "id": "fWR4Y_V4Mk9G"
      },
      "execution_count": 187,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Test data"
      ],
      "metadata": {
        "id": "YkLk7213GeCO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_test['new_y'] = X_test['new_y'].apply(np.array)"
      ],
      "metadata": {
        "id": "mKMpUwPQSlYN"
      },
      "execution_count": 188,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test = X_test[['new_y', 'r_swings_time','max_peak','fwhm_value']]"
      ],
      "metadata": {
        "id": "FLlBZd9wStrN"
      },
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "itemized_test_data = x_test.to_numpy()"
      ],
      "metadata": {
        "id": "DFm8ZMqUSvYd"
      },
      "execution_count": 191,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest with 200 estimators"
      ],
      "metadata": {
        "id": "Y52AEp-Rr9fa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train a Random Forest classifier on the training set\n",
        "rf_classifier = RandomForestClassifier(n_estimators=200, random_state=42)\n",
        "rf_classifier.fit(scaled_train_data, y_train.values.ravel())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "kq4udiavdDoW",
        "outputId": "ee25f537-315b-4722-b52e-6d276da256f6"
      },
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=200, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=200, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=200, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the performance of the classifier on the testing set\n",
        "y_pred = rf_classifier.predict(scaled_test_data)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f'Accuracy: {accuracy}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mLJwmO1pdSmb",
        "outputId": "0cbf1a57-c016-42d3-b97b-e658e6b469dc"
      },
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9188601261692408\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Add data features"
      ],
      "metadata": {
        "id": "KugYK8JqKbp5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have r_strides_time, r_peaks_toe, r_peaks_heel as extra features"
      ],
      "metadata": {
        "id": "23JneVKRSt7S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.right_stances_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3cqKGfkgSkRT",
        "outputId": "6d3fd38f-c7d5-4fc6-a784-ea90d2593d3b"
      },
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ID  level  y                                          r_stances  \\\n",
              "0  GaPt03    3.0  1  [20.79, 27.72, 32.45, 37.07, 63.69, 110.33, 13...   \n",
              "0  GaPt03    3.0  1  [52.8, 69.19, 57.64, 51.7, 60.94, 92.84, 140.1...   \n",
              "0  GaPt03    3.0  1  [23.1, 32.45, 39.38, 46.31, 116.82, 155.43, 17...   \n",
              "0  GaPt03    3.0  1  [23.1, 41.69, 55.55, 119.13, 154.55, 200.42, 2...   \n",
              "0  GaPt03    3.0  1  [25.41, 37.07, 55.55, 106.37, 152.02, 182.05, ...   \n",
              "\n",
              "  r_strides_time r_swings_time  \\\n",
              "0           1.59          0.39   \n",
              "0           1.27          0.21   \n",
              "0           1.41          0.41   \n",
              "0           1.49          0.43   \n",
              "0           1.56          0.41   \n",
              "\n",
              "                                      r_stances_time r_peaks_heel r_peaks_toe  \n",
              "0  [1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...        107.8       80.63  \n",
              "0  [1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...        71.17      134.42  \n",
              "0  [1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...        111.1       80.63  \n",
              "0  [1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...       141.24       71.61  \n",
              "0  [1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...        101.2       98.78  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f063d7d3-6d15-4ab9-97a2-d32e514d17b6\">\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>r_stances</th>\n",
              "      <th>r_strides_time</th>\n",
              "      <th>r_swings_time</th>\n",
              "      <th>r_stances_time</th>\n",
              "      <th>r_peaks_heel</th>\n",
              "      <th>r_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>[20.79, 27.72, 32.45, 37.07, 63.69, 110.33, 13...</td>\n",
              "      <td>1.59</td>\n",
              "      <td>0.39</td>\n",
              "      <td>[1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...</td>\n",
              "      <td>107.8</td>\n",
              "      <td>80.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GaPt03</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>[52.8, 69.19, 57.64, 51.7, 60.94, 92.84, 140.1...</td>\n",
              "      <td>1.27</td>\n",
              "      <td>0.21</td>\n",
              "      <td>[1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...</td>\n",
              "      <td>71.17</td>\n",
              "      <td>134.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GaPt03</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>[23.1, 32.45, 39.38, 46.31, 116.82, 155.43, 17...</td>\n",
              "      <td>1.41</td>\n",
              "      <td>0.41</td>\n",
              "      <td>[1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...</td>\n",
              "      <td>111.1</td>\n",
              "      <td>80.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GaPt03</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>[23.1, 41.69, 55.55, 119.13, 154.55, 200.42, 2...</td>\n",
              "      <td>1.49</td>\n",
              "      <td>0.43</td>\n",
              "      <td>[1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...</td>\n",
              "      <td>141.24</td>\n",
              "      <td>71.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GaPt03</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>[25.41, 37.07, 55.55, 106.37, 152.02, 182.05, ...</td>\n",
              "      <td>1.56</td>\n",
              "      <td>0.41</td>\n",
              "      <td>[1.19, 1.05, 0.99, 1.05, 1.1400000000000001, 0...</td>\n",
              "      <td>101.2</td>\n",
              "      <td>98.78</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f063d7d3-6d15-4ab9-97a2-d32e514d17b6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f063d7d3-6d15-4ab9-97a2-d32e514d17b6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f063d7d3-6d15-4ab9-97a2-d32e514d17b6');\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",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.right_stances_df['r_stances'] = data.right_stances_df['r_stances'].apply(np.array)"
      ],
      "metadata": {
        "id": "R1YB9j-rUIvM"
      },
      "execution_count": 243,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### SCALING\n",
        "tqdm.pandas()\n",
        "s=data.right_stances_df['r_stances'].progress_map(lambda x: fwhm(x, -0.4,1.4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "74c1cd87d0434f078e724e77c57f9ba5",
            "b7c8fd0a56794d18b3914d1f53d5b4fb",
            "8b4348ea0baa4e6fa6bb4ebfde7ae2ae",
            "07978b1f04994936a6133c486f9a07ac",
            "2928392687bd4b2ca52944a5f845cae1",
            "6b0e37f730a048d0b4225f80733c6114",
            "41df9e2301424911b92fa42f89ab8988",
            "dda2a7d49328457bb2839ff20426eafc",
            "d3963af0fce24206a9cfbf015d4fe8e0",
            "a3f36682ec934855b6b91bb42887df82",
            "2bd325200efe44e1982f7b3ad5479650"
          ]
        },
        "id": "UGdgAYmPS4TC",
        "outputId": "e57d7613-e67d-4107-dc5d-b29748fabc9b"
      },
      "execution_count": 244,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/18290 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "74c1cd87d0434f078e724e77c57f9ba5"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.right_stances_df[['fwhm_value','max_peak' ,'scaled_x', 'scaled_y', 'new_x' ,'new_y','peak_pos']] = pd.DataFrame(s.tolist(), index= data.right_stances_df.index)"
      ],
      "metadata": {
        "id": "gnuo2IUBTs0r"
      },
      "execution_count": 245,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### Train - Test data\n",
        "df1 = data.right_stances_df[[ 'new_y','r_swings_time','max_peak','fwhm_value', 'r_strides_time', 'r_peaks_toe', 'r_peaks_heel' ]]\n",
        "y = data.right_stances_df[[ 'y']]\n",
        "X_train, X_test, y_train, y_test = train_test_split(df1, y, random_state=42)"
      ],
      "metadata": {
        "id": "fEfDSGqxUe0h"
      },
      "execution_count": 247,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### Reformat data\n",
        "scaled_train_data = np.append( np.asarray(X_train['new_y'].tolist()) , np.asarray(X_train[[\"r_swings_time\" ]]),  axis=1)\n",
        "scaled_train_data = np.append( scaled_train_data , np.asarray(X_train[[\"max_peak\" ]]),  axis=1)\n",
        "scaled_train_data = np.append( scaled_train_data , np.asarray(X_train[[\"fwhm_value\" ]]),  axis=1)\n",
        "scaled_train_data = np.append( scaled_train_data , np.asarray(X_train[[\"r_strides_time\" ]]),  axis=1)\n",
        "scaled_train_data = np.append( scaled_train_data , np.asarray(X_train[[\"r_peaks_toe\" ]]),  axis=1)\n",
        "scaled_train_data = np.append( scaled_train_data , np.asarray(X_train[[\"r_peaks_heel\" ]]),  axis=1)\n",
        "\n",
        "scaled_test_data = np.append( np.asarray(X_test['new_y'].tolist()) , np.asarray(X_test[[\"r_swings_time\" ]]),  axis=1)\n",
        "scaled_test_data = np.append( scaled_test_data , np.asarray(X_test[[\"max_peak\" ]]),  axis=1)\n",
        "scaled_test_data = np.append( scaled_test_data , np.asarray(X_test[[\"fwhm_value\" ]]),  axis=1)\n",
        "scaled_test_data = np.append( scaled_test_data , np.asarray(X_test[[\"r_strides_time\" ]]),  axis=1)\n",
        "scaled_test_data = np.append( scaled_test_data , np.asarray(X_test[[\"r_peaks_toe\" ]]),  axis=1)\n",
        "scaled_test_data = np.append( scaled_test_data , np.asarray(X_test[[\"r_peaks_heel\" ]]),  axis=1)"
      ],
      "metadata": {
        "id": "WmByvct6UZQq"
      },
      "execution_count": 248,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Random forest classifier 1"
      ],
      "metadata": {
        "id": "3ZVLinpBYLzT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train a Random Forest classifier on the training set\n",
        "rf_classifier = RandomForestClassifier(n_estimators=200,  n_jobs=2)\n",
        "rf_classifier.fit(scaled_train_data, y_train.values.ravel())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "DnZeJaF2VYsj",
        "outputId": "94a71801-2a24-446e-bf7b-1ee6f2641221"
      },
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=200, n_jobs=2)"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=200, n_jobs=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=200, n_jobs=2)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 270
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the performance of the classifier on the testing set\n",
        "y_pred = rf_classifier.predict(scaled_test_data)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f'Accuracy: {accuracy}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SGTRTrMpVcn5",
        "outputId": "58a93fc1-10af-4a6f-9c53-9b92ce3a59cd"
      },
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9448939427071944\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prec = precision_score(y_test, y_pred)\n",
        "rec = recall_score(y_test, y_pred)\n",
        "f1 = f1_score(y_test, y_pred)"
      ],
      "metadata": {
        "id": "hGAoOJXxYSbz"
      },
      "execution_count": 272,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Precision: {}\".format(prec))\n",
        "print(\"Recall: {}\".format(rec))\n",
        "print(\"F1: {}\".format(f1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Ihn1imEYT4U",
        "outputId": "f6c2f4dc-0264-442e-c269-ea0e2824cb1d"
      },
      "execution_count": 273,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 0.9356300997280145\n",
            "Recall: 0.9875598086124402\n",
            "F1: 0.9608938547486033\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save the model\n",
        "with open(\"/content/drive/MyDrive/ITMO-Master's/Thesis/3rd_semester/Models/rf_classifier_7_features\", 'wb') as f:\n",
        "    pickle.dump(rf_classifier, f)"
      ],
      "metadata": {
        "id": "kMlTOHzjbCID"
      },
      "execution_count": 274,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the model                                                                                                                                                                                                       \n",
        "\n",
        "with open(\"/content/drive/MyDrive/ITMO-Master's/Thesis/3rd_semester/Models/rf_classifier_7_features\", 'rb') as f:\n",
        "    rf = pickle.load(f)"
      ],
      "metadata": {
        "id": "u19fPakRbX1F"
      },
      "execution_count": 275,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Random Forest Classifier 2"
      ],
      "metadata": {
        "id": "vmZFFG_PYPUD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train a Random Forest classifier on the training set\n",
        "rf_classifier = RandomForestClassifier(n_estimators=600, random_state=42)\n",
        "rf_classifier.fit(scaled_train_data, y_train.values.ravel())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "Zroh29t0WFOL",
        "outputId": "e32ffeaa-5352-49d9-f43c-761c471cc28b"
      },
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=600, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=600, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=600, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the performance of the classifier on the testing set\n",
        "y_pred = rf_classifier.predict(scaled_test_data)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f'Accuracy: {accuracy}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P8QMD-aFWGNh",
        "outputId": "415ccb09-a98c-49a7-dc7f-814188571f16"
      },
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9444565930461404\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_classifier.score(scaled_test_data, np.ravel(y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HSB3hAwXZH0j",
        "outputId": "04eed797-c6d6-487e-d232-016e9202c199"
      },
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9444565930461404"
            ]
          },
          "metadata": {},
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prec = precision_score(y_test, y_pred)\n",
        "rec = recall_score(y_test, y_pred)\n",
        "f1 = f1_score(y_test, y_pred)"
      ],
      "metadata": {
        "id": "2SzyT0lFX6r9"
      },
      "execution_count": 253,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Precision: {}\".format(prec))\n",
        "print(\"Recall: {}\".format(rec))\n",
        "print(\"F1: {}\".format(f1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b2FxGWuvYFGj",
        "outputId": "4a906c4d-8b4a-4ba8-873b-b68c0378b075"
      },
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 0.9363829142684035\n",
            "Recall: 0.9859649122807017\n",
            "F1: 0.9605344934742076\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Random classifier multiple"
      ],
      "metadata": {
        "id": "tiULaT3AYp9q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_score_RFC=[]\n",
        "\n",
        "for n in [5, 10, 20, 40, 100, 200]:\n",
        "    clf = RandomForestClassifier(n_estimators = int(n), n_jobs=2)\n",
        "    clf.fit(scaled_train_data, np.ravel(y_train))\n",
        "    acc = clf.score(scaled_test_data, np.ravel(y_test))\n",
        "    test_score_RFC.append(acc)  "
      ],
      "metadata": {
        "id": "i8hdmKSRYvlM"
      },
      "execution_count": 262,
      "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 test set: {:.2f}'.format(tr_sc))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m9zIjz6pYF56",
        "outputId": "8d4252e1-850f-4ff3-9562-4259a78e0ddb"
      },
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estimator = 5\n",
            "Accuracy of Random Forest Classifier on test set: 0.90\n",
            "Estimator = 10\n",
            "Accuracy of Random Forest Classifier on test set: 0.92\n",
            "Estimator = 20\n",
            "Accuracy of Random Forest Classifier on test set: 0.93\n",
            "Estimator = 40\n",
            "Accuracy of Random Forest Classifier on test set: 0.94\n",
            "Estimator = 100\n",
            "Accuracy of Random Forest Classifier on test set: 0.94\n",
            "Estimator = 200\n",
            "Accuracy of Random Forest Classifier on test set: 0.94\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_clf = clf.predict(scaled_test_data)\n",
        "prec = precision_score(y_test, prediction_clf)\n",
        "rec = recall_score(y_test, prediction_clf)\n",
        "f1 = f1_score(y_test, 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": "8Yc4Q-zYaBb8",
        "outputId": "cbeab586-5404-44f3-8325-88ffcb7ed929"
      },
      "execution_count": 265,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 0.935591170244935\n",
            "Recall: 0.9869218500797449\n",
            "F1: 0.9605712511642347\n"
          ]
        }
      ]
    }
  ]
}