Prosup_regression_selected_features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from Data_preparation_Library import *\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm.notebook import tqdm\n",
    "from time import time\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.models import load_model\n",
    "from random import choice\n",
    "from scipy import signal\n",
    "from pickle import load,dump\n",
    "import math\n",
    "from scipy.stats import pearsonr\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import backend as K\n",
    "from scipy import signal\n",
    "from tsfresh import extract_features\n",
    "from tsfresh import select_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Velocity'}>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "file = 'Subjects/Ingy/computed_Bulb.csv'\n",
    "emg_labels = ['EMG1','EMG2','EMG3','EMG4','EMG5','EMG6','EMG7','EMG8']\n",
    "angle_lables = ['Pronation_Angle'] # 'Pronation_Angle','Flexion_Angle',  'Radial_Angle','Elbow_Joint_Angle'\n",
    "df = pd.read_csv(file,index_col=0)[4500:85000]#[62500:69000]##[72500:82500]\n",
    "df[angle_lables].plot(figsize=(16,5),title=file,grid=True)\n",
    "\n",
    "# arr = np.diff([0]+list(df['Pronation_Angle']))\n",
    "arr = np.diff(list(df['Pronation_Angle'])+[list(df['Pronation_Angle'].values)[-1]])\n",
    "arr = arr/(1/2000)\n",
    "b,a = signal.butter(1, 1,fs=2000)\n",
    "arr = signal.lfilter(b, a,arr)\n",
    "pd.DataFrame(arr).plot(figsize=(16,5),title='Velocity',grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def selected_feature_df(df):\n",
    "    t = np.linspace(0,int(df.shape[0]/2000),df.shape[0])\n",
    "    df['time'] = t\n",
    "    EMG1 = df[['EMG1','time']]\n",
    "    EMG6 = df[['EMG6','time']]\n",
    "    EMG1_parameters = {'abs_energy': None,\n",
    "                       'fft_coefficient': [{'coeff': 0, 'attr': 'abs'}],\n",
    "                      'root_mean_square': None}\n",
    "    EMG6_parameters = {'count_above': [{'t': 0}],\n",
    "                       'count_below': [{'t': 0}],\n",
    "                       'benford_correlation': None,\n",
    "                       'fft_coefficient': [{'coeff': 0, 'attr': 'angle'}],\n",
    "                       'range_count': [{'min': 0, 'max': 1000000000000}]}\n",
    "    EMG1_features = extract_features(EMG1, default_fc_parameters=EMG1_parameters,column_id='time')\n",
    "    EMG6_features = extract_features(EMG6, default_fc_parameters=EMG6_parameters,column_id='time')\n",
    "    emg_labels = ['EMG1', 'EMG2', 'EMG3', 'EMG4', 'EMG5', 'EMG6','EMG7', 'EMG8']\n",
    "    angle_lables = ['Pronation_Angle','Flexion_Angle',  'Radial_Angle','Elbow_Joint_Angle']\n",
    "    feature_df = pd.DataFrame(np.hstack((EMG1_features,EMG6_features)),columns=emg_labels)\n",
    "    for labels in angle_lables:\n",
    "        feature_df[labels] = df[labels].values\n",
    "    return feature_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def selected_feature_df(df):\n",
    "    emg_labels = ['EMG1', 'EMG2', 'EMG3', 'EMG4', 'EMG5', 'EMG6','EMG7', 'EMG8']\n",
    "    angle_lables = ['Pronation_Angle','Flexion_Angle',  'Radial_Angle','Elbow_Joint_Angle']\n",
    "    df = df[emg_labels+angle_lables]\n",
    "    t = np.linspace(0,int(df.shape[0]/2000),df.shape[0])\n",
    "    df['time'] = t\n",
    "    \n",
    "    EMG_parameters = {'fft_coefficient': [{'coeff': 0, 'attr': 'abs'}]}\n",
    "    \n",
    "    EMG_features = extract_features(df[emg_labels+['time']], default_fc_parameters=EMG_parameters,column_id='time')\n",
    "    EMG_features.columns = emg_labels\n",
    "    for labels in angle_lables:\n",
    "        EMG_features[labels] = df[labels].values\n",
    "    return EMG_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prep Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "window = 250"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Subjects/Ingy/computed_Bulb.csv',\n",
       " 'Subjects/Ingy/computed_Bulb01.csv',\n",
       " 'Subjects/Ingy/computed_Bulb02.csv']"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = []\n",
    "# subjects = ['Fazil','Nikhil','Varun','Abhishek','Jai', 'Deepti', 'Nirmayee'] #'Fazil/day_04_14_21' ,'Ingy'\n",
    "subjects = ['Ingy'] #'Fazil/day_04_14_21' ,'Varun'\n",
    "for subj in subjects:\n",
    "    path = 'Subjects/'+subj\n",
    "    trial_names = ['Bulb'] # 'Bulb','Hammer' ,'Cup','Screw'\n",
    "#     trial_names = ['Bulb','Screw','Hammer' ,'Cup',] #\n",
    "    for exp in trial_names:\n",
    "        for i in range(3):\n",
    "            if(i==0):\n",
    "                files.append(path+'/computed_'+exp+'.csv')\n",
    "            else:\n",
    "                files.append(path+'/computed_'+exp+'0'+str(i)+'.csv')\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_df = []\n",
    "for file in files:\n",
    "    files_df.append(pd.read_csv(file,index_col=0)[4500:85000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_df = filter_df(files_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove Test Reps Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_rep_files = ['Subjects/Ingy/computed_Bulb02.csv', \n",
    "                  'Subjects/Ingy/computed_Screw02.csv']\n",
    "tr_range = [29000,37000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_df = []\n",
    "for file in files:\n",
    "    if file in test_rep_files:\n",
    "        df = pd.read_csv(file,index_col=0)[4500:85000]\n",
    "        df = df.drop(df.index[tr_range[0]:tr_range[1]])\n",
    "    else:\n",
    "        df = pd.read_csv(file,index_col=0)[4500:85000]\n",
    "    files_df.append(df)\n",
    "files_df = filter_df(files_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Only Removed Reps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\The Stuffs\\Awear\\Final_Project\\wrist_data_collection_AWEAR\\Data_preparation_Library.py:467: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  files_df[i][emg_labels] = np.array(emg_df)\n",
      "C:\\Users\\moham\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\pandas\\core\\indexing.py:1736: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  isetter(loc, value[:, i].tolist())\n"
     ]
    }
   ],
   "source": [
    "files_df = []\n",
    "files = test_rep_files\n",
    "for file in files:\n",
    "    df = pd.read_csv(file,index_col=0)\n",
    "    files_df.append(df[tr_range[0]:tr_range[1]])\n",
    "files_df = filter_df(files_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EMG Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2149c343e5c0453db3ef9c5aea83a86c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-74-83226be1db8c>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['time'] = t\n",
      "Feature Extraction: 100%|██████████████████████████████████████████████████████████████| 30/30 [00:50<00:00,  1.70s/it]\n",
      "<ipython-input-74-83226be1db8c>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['time'] = t\n",
      "Feature Extraction: 100%|██████████████████████████████████████████████████████████████| 30/30 [00:53<00:00,  1.78s/it]\n",
      "<ipython-input-74-83226be1db8c>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['time'] = t\n",
      "Feature Extraction: 100%|██████████████████████████████████████████████████████████████| 30/30 [00:56<00:00,  1.88s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "files_df_new = []\n",
    "for df in tqdm(files_df):\n",
    "    files_df_new.append(selected_feature_df(df))\n",
    "files_df = files_df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "del files_df_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b798133bf8de491ea5bae0e6c544163f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(48150, 250, 8)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = multiple_prep_data_velocity(files_df,window,interval=5,Normalize=False,rms=False,angle_label ='Pronation_Angle')\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "del files_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Window Normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_mean = np.zeros(X[0].shape)\n",
    "for x in X:\n",
    "    X_mean = X_mean + x\n",
    "X_mean = X_mean/X.shape[0]\n",
    "X_std = np.sqrt(variance(X))\n",
    "np.save('mean_std_prep',np.array([X_mean,X_std]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_mean = np.load('mean_std_prep.npy')[0]\n",
    "X_std = np.load('mean_std_prep.npy')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf4a7a9d6697419399b23ab0a108f682",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=38850.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "X_new = []\n",
    "for x in tqdm(X):\n",
    "    X_new.append((x-X_mean)/X_std)\n",
    "X = np.array(X_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Min Max Normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('max_min_prep',np.array([X.max(),X.min()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_max = np.load('max_min_prep.npy')[0]\n",
    "X_min = np.load('max_min_prep.npy')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f8b76e4a73a4e5a8c2d5708525e980d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=38850.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "X_new = []\n",
    "for x in tqdm(X):\n",
    "    X_new.append((x-X_min)/(X_max - X_min))\n",
    "X = np.array(X_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Train Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(269640, 250, 8)\n",
      "(67410, 250, 8)\n",
      "(269640, 1)\n",
      "(67410, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2)\n",
    "for data in [X_train, X_test, y_train, y_test]:\n",
    "    print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('prepared_data/X_train',X_train)\n",
    "np.save('prepared_data/X_test',X_test)\n",
    "np.save('prepared_data/y_train',y_train)\n",
    "np.save('prepared_data/y_test',y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('prepared_data/X',X)\n",
    "np.save('prepared_data/y',y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.load('prepared_data/X_train.npy')\n",
    "X_test = np.load('prepared_data/X_test.npy')\n",
    "y_train = np.load('prepared_data/y_train.npy')\n",
    "y_test = np.load('prepared_data/y_test.npy')\n",
    "X = np.load('prepared_data/X.npy')\n",
    "y = np.load('prepared_data/y.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(269640, 250, 8)\n",
      "(67410, 250, 8)\n",
      "(48150, 250, 8)\n",
      "(269640, 1)\n",
      "(67410, 1)\n",
      "(48150, 1)\n"
     ]
    }
   ],
   "source": [
    "for data in [X_train, X_test, X, y_train, y_test, y]:\n",
    "    print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Network Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import Flatten\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.layers import Conv1D\n",
    "from tensorflow.keras.layers import MaxPooling1D\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from tensorflow.keras.layers import LSTM\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow import keras\n",
    "n_outputs = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_filepath = 'best_model_reg.hdf5'\n",
    "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath=checkpoint_filepath,\n",
    "    save_weights_only=False,\n",
    "    monitor='Pearson_r',\n",
    "    mode='max',\n",
    "    save_best_only=True)\n",
    "\n",
    "def pearson_r_lossmax(y_true, y_pred):\n",
    "    x = y_true\n",
    "    y = y_pred\n",
    "    mx = K.mean(x, axis=0)\n",
    "    my = K.mean(y, axis=0)\n",
    "    xm, ym = x - mx, y - my\n",
    "    r_num = K.sum(xm * ym)\n",
    "    x_square_sum = K.sum(xm * xm)\n",
    "    y_square_sum = K.sum(ym * ym)\n",
    "    r_den = K.sqrt(x_square_sum * y_square_sum)\n",
    "    r = r_num / r_den\n",
    "    return -K.mean(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_20\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_273 (Conv1D)          (None, 248, 256)          6400      \n",
      "_________________________________________________________________\n",
      "conv1d_274 (Conv1D)          (None, 246, 256)          196864    \n",
      "_________________________________________________________________\n",
      "conv1d_275 (Conv1D)          (None, 244, 256)          196864    \n",
      "_________________________________________________________________\n",
      "conv1d_276 (Conv1D)          (None, 242, 256)          196864    \n",
      "_________________________________________________________________\n",
      "conv1d_277 (Conv1D)          (None, 240, 64)           49216     \n",
      "_________________________________________________________________\n",
      "conv1d_278 (Conv1D)          (None, 238, 64)           12352     \n",
      "_________________________________________________________________\n",
      "conv1d_279 (Conv1D)          (None, 236, 64)           12352     \n",
      "_________________________________________________________________\n",
      "conv1d_280 (Conv1D)          (None, 234, 64)           12352     \n",
      "_________________________________________________________________\n",
      "dropout_17 (Dropout)         (None, 234, 64)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_281 (Conv1D)          (None, 232, 32)           6176      \n",
      "_________________________________________________________________\n",
      "conv1d_282 (Conv1D)          (None, 230, 32)           3104      \n",
      "_________________________________________________________________\n",
      "conv1d_283 (Conv1D)          (None, 228, 32)           3104      \n",
      "_________________________________________________________________\n",
      "conv1d_284 (Conv1D)          (None, 226, 32)           3104      \n",
      "_________________________________________________________________\n",
      "conv1d_285 (Conv1D)          (None, 224, 16)           1552      \n",
      "_________________________________________________________________\n",
      "conv1d_286 (Conv1D)          (None, 222, 16)           784       \n",
      "_________________________________________________________________\n",
      "conv1d_287 (Conv1D)          (None, 220, 16)           784       \n",
      "_________________________________________________________________\n",
      "conv1d_288 (Conv1D)          (None, 218, 16)           784       \n",
      "_________________________________________________________________\n",
      "batch_normalization_19 (Batc (None, 218, 16)           64        \n",
      "_________________________________________________________________\n",
      "flatten_19 (Flatten)         (None, 3488)              0         \n",
      "_________________________________________________________________\n",
      "dense_57 (Dense)             (None, 16)                55824     \n",
      "_________________________________________________________________\n",
      "dense_58 (Dense)             (None, 8)                 136       \n",
      "_________________________________________________________________\n",
      "dense_59 (Dense)             (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 758,689\n",
      "Trainable params: 758,657\n",
      "Non-trainable params: 32\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "n_outputs = 1\n",
    "filter_size = 3\n",
    "in_shape = (X_train.shape[1],X_train.shape[2])\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv1D(filters=256, kernel_size=filter_size, activation='relu', input_shape=in_shape))\n",
    "model.add(Conv1D(filters=256, kernel_size=filter_size, activation='relu'))\n",
    "model.add(Conv1D(filters=256, kernel_size=filter_size, activation='relu'))\n",
    "model.add(Conv1D(filters=256, kernel_size=filter_size, activation='relu'))\n",
    "\n",
    "model.add(Conv1D(filters=64, kernel_size=filter_size, activation='relu'))\n",
    "model.add(Conv1D(filters=64, kernel_size=filter_size, activation='relu'))\n",
    "model.add(Conv1D(filters=64, kernel_size=filter_size, activation='relu'))\n",
    "model.add(Conv1D(filters=64, kernel_size=filter_size, activation='relu'))\n",
    "\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Conv1D(filters=32, kernel_size=filter_size, activation='relu'))\n",
    "model.add(Conv1D(filters=32, kernel_size=filter_size, activation='relu'))\n",
    "model.add(Conv1D(filters=32, kernel_size=filter_size, activation='relu'))\n",
    "model.add(Conv1D(filters=32, kernel_size=filter_size, activation='relu'))\n",
    "\n",
    "# model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Conv1D(filters=16, kernel_size=filter_size, activation='relu'))\n",
    "model.add(Conv1D(filters=16, kernel_size=filter_size, activation='relu'))\n",
    "model.add(Conv1D(filters=16, kernel_size=filter_size, activation='relu'))\n",
    "model.add(Conv1D(filters=16, kernel_size=filter_size, activation='relu'))\n",
    "\n",
    "# model.add(Dropout(0.5))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(16,activation='relu'))\n",
    "model.add(Dense(8,activation='relu'))\n",
    "# model.add(Dense(8,activation='relu'))\n",
    "\n",
    "model.add(Dense(n_outputs,activation='linear'))\n",
    "\n",
    "opt = keras.optimizers.Adam(learning_rate=0.0001)\n",
    "model.compile(loss='mean_squared_error', optimizer=opt,metrics=[pearson_r])\n",
    "# model.compile(loss=pearson_r_lossmax, optimizer='adam',metrics=[pearson_r])\n",
    "\n",
    "# model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "# model.compile(loss='mean_squared_error', optimizer='adam',metrics=[pearson_r])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "  6/527 [..............................] - ETA: 1:41 - loss: 288.7948 - pearson_r: 0.0075 - ETA: 1:42 - loss: 271.0541 - pearson_r: 0.0"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-08-04 00:01:09,915 [Geometry3D WARNING] Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0681s vs `on_train_batch_end` time: 0.1343s). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "527/527 [==============================] - 133s 249ms/step - loss: 260.0422 - pearson_r: -9.4487e-04 - val_loss: 264.3693 - val_pearson_r: 0.0308\n",
      "Pearson_r_Test=  0.03162\n",
      "Epoch 2/50\n",
      "527/527 [==============================] - 152s 289ms/step - loss: 260.0358 - pearson_r: -5.8587e-04 - val_loss: 264.3668 - val_pearson_r: 0.0588\n",
      "Pearson_r_Test=  0.07642\n",
      "Epoch 3/50\n",
      "527/527 [==============================] - 153s 290ms/step - loss: 260.0346 - pearson_r: -9.0343e-05 - val_loss: 264.3664 - val_pearson_r: -0.0042\n",
      "Pearson_r_Test=  -0.0215\n",
      "Epoch 4/50\n",
      " 17/527 [..............................] - ETA: 2:20 - loss: 257.6219 - pearson_r: -0.0112"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-98-f09228668cce>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcnn_history\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m50\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbatch_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m512\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalidation_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtest_loss\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmodel_checkpoint_callback\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1186\u001b[0m               \u001b[0mlogs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtmp_logs\u001b[0m  \u001b[1;31m# No error, now safe to assign to logs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1187\u001b[0m               \u001b[0mend_step\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstep\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep_increment\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1188\u001b[1;33m               \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mend_step\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1189\u001b[0m               \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1190\u001b[0m                 \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\keras\\callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[1;34m(self, batch, logs)\u001b[0m\n\u001b[0;32m    455\u001b[0m     \"\"\"\n\u001b[0;32m    456\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_should_call_train_batch_hooks\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 457\u001b[1;33m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_batch_hook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'end'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlogs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    458\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    459\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mon_test_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\keras\\callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook\u001b[1;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[0;32m    315\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_batch_begin_hook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    316\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'end'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 317\u001b[1;33m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_batch_end_hook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    318\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    319\u001b[0m       \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Unrecognized hook: {}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhook\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\keras\\callbacks.py\u001b[0m in \u001b[0;36m_call_batch_end_hook\u001b[1;34m(self, mode, batch, logs)\u001b[0m\n\u001b[0;32m    335\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_time\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    336\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 337\u001b[1;33m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_batch_hook_helper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhook_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    338\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    339\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_batches_for_timing_check\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\keras\\callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook_helper\u001b[1;34m(self, hook_name, batch, logs)\u001b[0m\n\u001b[0;32m    373\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    374\u001b[0m       \u001b[0mhook\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcallback\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhook_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 375\u001b[1;33m       \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    376\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    377\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_timing\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\keras\\callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[1;34m(self, batch, logs)\u001b[0m\n\u001b[0;32m   1027\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1028\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mon_train_batch_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1029\u001b[1;33m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_batch_update_progbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1030\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1031\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mon_test_batch_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\keras\\callbacks.py\u001b[0m in \u001b[0;36m_batch_update_progbar\u001b[1;34m(self, batch, logs)\u001b[0m\n\u001b[0;32m   1099\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1100\u001b[0m       \u001b[1;31m# Only block async when verbose = 1.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1101\u001b[1;33m       \u001b[0mlogs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msync_to_numpy_or_python_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1102\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseen\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfinalize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\keras\\utils\\tf_utils.py\u001b[0m in \u001b[0;36msync_to_numpy_or_python_type\u001b[1;34m(tensors)\u001b[0m\n\u001b[0;32m    517\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mt\u001b[0m  \u001b[1;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    518\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 519\u001b[1;33m   \u001b[1;32mreturn\u001b[0m \u001b[0mnest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtensors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    520\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    521\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\util\\nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[1;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[0;32m    865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    866\u001b[0m   return pack_sequence_as(\n\u001b[1;32m--> 867\u001b[1;33m       \u001b[0mstructure\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    868\u001b[0m       expand_composites=expand_composites)\n\u001b[0;32m    869\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\util\\nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    866\u001b[0m   return pack_sequence_as(\n\u001b[1;32m--> 867\u001b[1;33m       \u001b[0mstructure\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    868\u001b[0m       expand_composites=expand_composites)\n\u001b[0;32m    869\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\keras\\utils\\tf_utils.py\u001b[0m in \u001b[0;36m_to_single_numpy_or_python_type\u001b[1;34m(t)\u001b[0m\n\u001b[0;32m    513\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    514\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 515\u001b[1;33m       \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    516\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    517\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mt\u001b[0m  \u001b[1;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1092\u001b[0m     \"\"\"\n\u001b[0;32m   1093\u001b[0m     \u001b[1;31m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1094\u001b[1;33m     \u001b[0mmaybe_arr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1095\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_arr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1096\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1058\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_numpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1059\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1060\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_numpy_internal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1061\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1062\u001b[0m       \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "cnn_history=model.fit(X_train,y_train,epochs = 50,batch_size = 512,validation_data = (X_test,y_test),callbacks=[test_loss(X,y),model_checkpoint_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = load_model('best_model_reg.hdf5')\n",
    "best_model.save('models/prosup_velocity_model_prep_{}.hdf5'.format(250))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_history_df = pd.DataFrame(cnn_history.history) \n",
    "hist_csv_file = 'regression_history.csv'\n",
    "with open(hist_csv_file, mode='w') as f:\n",
    "    cnn_history_df.to_csv(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_history_df = pd.read_csv('regression_history.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnn_history_df[['loss', 'val_loss']].plot(figsize=(10,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnn_history_df['Pearson_r'].plot(figsize=(10,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "window = 250\n",
    "best_model = load_model('models/prosup_velocity_model_{}_p86.hdf5'.format(window))\n",
    "# best_model = load_model('best_model_reg.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'Subjects/Fazil/computed_Screw02.csv'\n",
    "df = pd.read_csv(file,index_col=0)[29000:37000]#[72500:82500]#[4500:85000]#\n",
    "df = filter_df([df])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = prep_data_velocity(df,window,interval=10,Normalize=True,rms=False,angle_label ='Pronation_Angle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1, y1 = prep_data_velocity(df,window,interval=10,Normalize=True,rms=False,angle_label ='Pronation_Angle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1550, 250, 8) (1550, 1)\n"
     ]
    }
   ],
   "source": [
    "X = np.vstack((X,X1))\n",
    "y = np.vstack((y,y1))\n",
    "print(X.shape,y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28d294e6636943e3bbc7a104c1c5a4c3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3670.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "X_mean = np.load('mean_std_master.npy')[0]\n",
    "X_std = np.load('mean_std_master.npy')[1]\n",
    "X_new = []\n",
    "for x in tqdm(X):\n",
    "    X_new.append((x-X_mean)/X_std)\n",
    "X = np.array(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25/25 [==============================] - 0s 7ms/step - loss: 0.0064\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_model = model\n",
    "pred = test_model.predict(X)\n",
    "test_model.evaluate(X,y)\n",
    "\n",
    "plt.figure(figsize=(16,5))\n",
    "pred = filter_array(pred,order=1,cf=50,fs=2000)\n",
    "# y = filter_array(y,order=1,cf=100,fs=2000)\n",
    "plt.plot(pred)\n",
    "plt.plot(y)\n",
    "plt.title('True Velocity Vs Predicted Velocity',size=20)\n",
    "plt.ylabel('Ang Velocity (deg/s)',size=15)\n",
    "plt.xlabel('Time(s)', size=15)\n",
    "plt.legend(['Predicted', 'True'], loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE Loss - 152.06050731563397\n",
      "RMSE - 12.331281657460995\n",
      "Pearson Correlation Coefficient - (-0.3867194906965817, 4.7429795576640977e-29)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import pearsonr\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print('MSE Loss -',mean_squared_error(y,pred))\n",
    "print('RMSE -',mean_squared_error(y,pred,squared=False))\n",
    "print('Pearson Correlation Coefficient -',pearsonr(y.flatten(),pred.flatten()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pearsonr 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def correlation_coefficient_loss(y_true, y_pred):\n",
    "    x = y_true\n",
    "    y = y_pred\n",
    "    mx = K.mean(x)\n",
    "    my = K.mean(y)\n",
    "    xm, ym = x-mx, y-my\n",
    "    r_num = K.sum(xm * ym)\n",
    "    r_den = K.sum(K.sum(K.square(xm)) * K.sum(K.square(ym)))\n",
    "    r = r_num / r_den\n",
    "    return 1 - r**2\n",
    "    \n",
    "def pearson_r(y_true, y_pred):\n",
    "    x = y_true\n",
    "    y = y_pred\n",
    "    mx = K.mean(x, axis=0)\n",
    "    my = K.mean(y, axis=0)\n",
    "    xm, ym = x - mx, y - my\n",
    "    r_num = K.sum(xm * ym)\n",
    "    x_square_sum = K.sum(xm * xm)\n",
    "    y_square_sum = K.sum(ym * ym)\n",
    "    r_den = K.sqrt(x_square_sum * y_square_sum)\n",
    "    r = r_num / r_den\n",
    "    return K.mean(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class test_loss(tf.keras.callbacks.Callback):\n",
    "\n",
    "    def __init__(self, X,y):\n",
    "        super(test_loss, self).__init__()\n",
    "        self.X_t = X\n",
    "        self.y_t = y\n",
    "        \n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        logs['Pearson_r'] = float('-inf')\n",
    "        pred = self.model.predict(self.X_t)\n",
    "        score = pearsonr(pred.flatten(),self.y_t.flatten())[0]\n",
    "        logs['Pearson_r'] = np.round(score, 5)\n",
    "        print('Pearson_r_Test= ',np.round(score, 5)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
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