DTM_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 sklearn.preprocessing import QuantileTransformer, StandardScaler\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 pickle import dump, load"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Velocity'}>"
      ]
     },
     "execution_count": 43,
     "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/Fazil/computed_Hammer02.csv'\n",
    "emg_labels = ['EMG1','EMG2','EMG3','EMG4','EMG5','EMG6','EMG7','EMG8']\n",
    "angle_lables = ['DTM_Angle'] # 'Pronation_Angle','Flexion_Angle',  'Radial_Angle','Elbow_Joint_Angle'\n",
    "df = pd.read_csv(file,index_col=0)[12000:50000]#[32000:40000]\n",
    "df[angle_lables].plot(figsize=(16,5),title=file,grid=True)\n",
    "\n",
    "arr = np.diff([df['DTM_Angle'].values[0]]+list(df['DTM_Angle']))\n",
    "arr = arr/(1/2000)\n",
    "b,a = signal.butter(3, 1,fs=2000)\n",
    "arr = signal.lfilter(b, a,arr)\n",
    "pd.DataFrame(arr).plot(figsize=(16,5),title='Velocity',grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prep Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "window = 250"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Subjects/Varun/computed_Hammer.csv',\n",
       " 'Subjects/Varun/computed_Hammer01.csv',\n",
       " 'Subjects/Varun/computed_Hammer02.csv',\n",
       " 'Subjects/Varun/computed_Cup.csv',\n",
       " 'Subjects/Varun/computed_Cup01.csv',\n",
       " 'Subjects/Varun/computed_Cup02.csv']"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = []\n",
    "# subjects = ['Fazil','Nikhil','Ingy','Abhishek','Jai', 'Deepti', 'Nirmayee'] #'Fazil/day_04_14_21' ,'Varun'\n",
    "subjects = ['Varun'] #'Fazil/day_04_14_21' ,'Varun'\n",
    "for subj in subjects:\n",
    "    path = 'Subjects/'+subj\n",
    "    trial_names = ['Hammer','Cup'] # '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": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_df = []\n",
    "for file in files:\n",
    "    files_df.append(pd.read_csv(file,index_col=0)[12000:50000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_df = filter_df(files_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove Test Reps Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_rep_files = ['Subjects/Fazil/computed_Hammer02.csv', \n",
    "                  'Subjects/Fazil/computed_Cup02.csv']\n",
    "tr_range = [32000,40000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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)[12000:50000]\n",
    "        df = df.drop(df.index[tr_range[0]:tr_range[1]])\n",
    "    else:\n",
    "        df = pd.read_csv(file,index_col=0)[12000:50000]\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": {},
   "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": [
    "## Rearrange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['EMG8', 'EMG1', 'EMG2', 'EMG3', 'EMG4', 'EMG5', 'EMG6', 'EMG7'],\n",
       " ['EMG2', 'EMG3', 'EMG4', 'EMG5', 'EMG6', 'EMG7', 'EMG8', 'EMG1']]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import deque\n",
    "rearr_labels = []\n",
    "emg_labels = ['EMG1','EMG2','EMG3','EMG4','EMG5','EMG6','EMG7','EMG8']\n",
    "labels = deque(emg_labels)\n",
    "labels.rotate(1)\n",
    "rearr_labels.append(list(labels))\n",
    "labels.rotate(-2)\n",
    "rearr_labels.append(list(labels))\n",
    "rearr_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea05068053c649498bbc19dfbf1c1fd9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=42.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "126"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files_df_rearrange = []\n",
    "for df in tqdm(files_df):\n",
    "    for lb in rearr_labels:\n",
    "        df[emg_labels] = df[lb]\n",
    "        files_df_rearrange.append(df) \n",
    "    \n",
    "files_df = files_df+files_df_rearrange\n",
    "len(files_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bfe87650a1c94ae5b472c46577d9731a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=6.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(22650, 250, 8)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = multiple_prep_data_velocity(files_df,window,interval=10,Normalize=True,rms=False,angle_label ='DTM_Angle')\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pd.Series(y.reshape((len(y)))).interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.values.reshape((len(y),1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vel_normalize(y,save=False):\n",
    "#     if save == True:\n",
    "#         qt = StandardScaler()\n",
    "#         qt.fit(y.reshape(1,-1))\n",
    "#         dump(qt, open('norm_vel_dtm.pkl', 'wb'))\n",
    "#     qt = load(open('norm_vel_dtm.pkl', 'rb'))\n",
    "#     yq = qt.transform(y.reshape(1,-1))\n",
    "#     return yq.reshape(((-1,1)))\n",
    "    y = y.reshape((len(y),))\n",
    "    y_mean = y.mean()\n",
    "    y_std = np.sqrt(variance(y))\n",
    "    y_new = (y - y_mean)/y_std\n",
    "    return y_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Window Normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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',np.array([X_mean,X_std]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_mean = np.load('mean_std_master.npy')[0]\n",
    "X_std = np.load('mean_std_master.npy')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "decdbe2b87654838a4bdcc9c9f680544",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=75870.0), 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": [
    "## Test Train Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(377640, 250, 8)\n",
      "(94410, 250, 8)\n",
      "(377640, 1)\n",
      "(94410, 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": 13,
   "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": 103,
   "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(377640, 250, 8)\n",
      "(94410, 250, 8)\n",
      "(377640, 1)\n",
      "(94410, 1)\n"
     ]
    }
   ],
   "source": [
    "for data in [X_train, X_test, y_train, y_test]:\n",
    "    print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Network Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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.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": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_42 (Conv1D)           (None, 248, 256)          6400      \n",
      "_________________________________________________________________\n",
      "conv1d_43 (Conv1D)           (None, 246, 64)           49216     \n",
      "_________________________________________________________________\n",
      "conv1d_44 (Conv1D)           (None, 244, 64)           12352     \n",
      "_________________________________________________________________\n",
      "conv1d_45 (Conv1D)           (None, 242, 32)           6176      \n",
      "_________________________________________________________________\n",
      "conv1d_46 (Conv1D)           (None, 240, 32)           3104      \n",
      "_________________________________________________________________\n",
      "conv1d_47 (Conv1D)           (None, 238, 32)           3104      \n",
      "_________________________________________________________________\n",
      "conv1d_48 (Conv1D)           (None, 236, 16)           1552      \n",
      "_________________________________________________________________\n",
      "conv1d_49 (Conv1D)           (None, 234, 16)           784       \n",
      "_________________________________________________________________\n",
      "conv1d_50 (Conv1D)           (None, 232, 16)           784       \n",
      "_________________________________________________________________\n",
      "conv1d_51 (Conv1D)           (None, 230, 16)           784       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 230, 16)           0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 230, 16)           64        \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 3680)              0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 16)                58896     \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 8)                 136       \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 143,361\n",
      "Trainable params: 143,329\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",
    "\n",
    "# model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model.compile(loss='mean_squared_error', optimizer='adam',metrics=[])\n",
    "\n",
    "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",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "  6/738 [..............................] - ETA: 49s - loss: 64.8734"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-07-28 13:28:04,530 [Geometry3D WARNING] Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0241s vs `on_train_batch_end` time: 0.0456s). Check your callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "738/738 [==============================] - 52s 70ms/step - loss: 42.1018 - val_loss: 34.8740\n",
      "Pearson_r_Test=  0.63541\n",
      "Epoch 2/25\n",
      "738/738 [==============================] - 57s 77ms/step - loss: 19.2358 - val_loss: 13.0781\n",
      "Pearson_r_Test=  0.85187\n",
      "Epoch 3/25\n",
      "738/738 [==============================] - 59s 80ms/step - loss: 11.0546 - val_loss: 13.0269\n",
      "Pearson_r_Test=  0.88294\n",
      "Epoch 4/25\n",
      "738/738 [==============================] - 59s 80ms/step - loss: 7.6593 - val_loss: 5.9258\n",
      "Pearson_r_Test=  0.90089\n",
      "Epoch 5/25\n",
      "738/738 [==============================] - 58s 79ms/step - loss: 5.8767 - val_loss: 4.7825\n",
      "Pearson_r_Test=  0.91023\n",
      "Epoch 6/25\n",
      "738/738 [==============================] - 59s 80ms/step - loss: 4.8784 - val_loss: 4.5108\n",
      "Pearson_r_Test=  0.90096\n",
      "Epoch 7/25\n",
      "738/738 [==============================] - 60s 81ms/step - loss: 4.1379 - val_loss: 6.3510\n",
      "Pearson_r_Test=  0.92105\n",
      "Epoch 8/25\n",
      "738/738 [==============================] - 60s 81ms/step - loss: 3.7164 - val_loss: 3.4990\n",
      "Pearson_r_Test=  0.91053\n",
      "Epoch 9/25\n",
      "738/738 [==============================] - 60s 81ms/step - loss: 3.2267 - val_loss: 2.7618\n",
      "Pearson_r_Test=  0.92364\n",
      "Epoch 10/25\n",
      "738/738 [==============================] - 60s 81ms/step - loss: 2.9350 - val_loss: 3.4501\n",
      "Pearson_r_Test=  0.91234\n",
      "Epoch 11/25\n",
      "738/738 [==============================] - 60s 81ms/step - loss: 2.9611 - val_loss: 2.8224\n",
      "Pearson_r_Test=  0.90469\n",
      "Epoch 12/25\n",
      "738/738 [==============================] - 60s 81ms/step - loss: 2.5123 - val_loss: 2.3048\n",
      "Pearson_r_Test=  0.91807\n",
      "Epoch 13/25\n",
      "738/738 [==============================] - 60s 81ms/step - loss: 2.4213 - val_loss: 2.2218\n",
      "Pearson_r_Test=  0.92649\n",
      "Epoch 14/25\n",
      "738/738 [==============================] - 60s 82ms/step - loss: 2.2788 - val_loss: 1.9656\n",
      "Pearson_r_Test=  0.92699\n",
      "Epoch 15/25\n",
      "738/738 [==============================] - 62s 84ms/step - loss: 2.1122 - val_loss: 2.8216\n",
      "Pearson_r_Test=  0.92574\n",
      "Epoch 16/25\n",
      "738/738 [==============================] - 60s 81ms/step - loss: 2.2468 - val_loss: 1.4843\n",
      "Pearson_r_Test=  0.92539\n",
      "Epoch 17/25\n",
      "738/738 [==============================] - 60s 82ms/step - loss: 1.8573 - val_loss: 1.8052\n",
      "Pearson_r_Test=  0.91648\n",
      "Epoch 18/25\n",
      "738/738 [==============================] - 61s 82ms/step - loss: 2.8344 - val_loss: 2.0970\n",
      "Pearson_r_Test=  0.9208\n",
      "Epoch 19/25\n",
      "738/738 [==============================] - 61s 83ms/step - loss: 1.6248 - val_loss: 1.4877\n",
      "Pearson_r_Test=  0.92384\n",
      "Epoch 20/25\n",
      "738/738 [==============================] - 61s 82ms/step - loss: 1.5891 - val_loss: 2.2795\n",
      "Pearson_r_Test=  0.9195\n",
      "Epoch 21/25\n",
      "738/738 [==============================] - 61s 83ms/step - loss: 1.6367 - val_loss: 2.9215\n",
      "Pearson_r_Test=  0.90832\n",
      "Epoch 22/25\n",
      "738/738 [==============================] - 60s 82ms/step - loss: 1.7735 - val_loss: 1.6612\n",
      "Pearson_r_Test=  0.92174\n",
      "Epoch 23/25\n",
      "738/738 [==============================] - 60s 82ms/step - loss: 1.5805 - val_loss: 1.4457\n",
      "Pearson_r_Test=  0.92469\n",
      "Epoch 24/25\n",
      "738/738 [==============================] - 60s 82ms/step - loss: 1.6078 - val_loss: 1.3054\n",
      "Pearson_r_Test=  0.93558\n",
      "Epoch 25/25\n",
      "738/738 [==============================] - 62s 85ms/step - loss: 1.4778 - val_loss: 1.6679\n",
      "Pearson_r_Test=  0.92854\n"
     ]
    }
   ],
   "source": [
    "cnn_history=model.fit(X_train,y_train,epochs = 25,batch_size = 512,validation_data = (X_test,y_test),callbacks=[test_loss(X,y),model_checkpoint_callback])#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = load_model('best_model_reg.hdf5')\n",
    "best_model.save('models/dtm_velocity_model_{}.hdf5'.format(250))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_history_df = pd.read_csv('regression_history.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 57,
     "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": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 58,
     "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "window = 250\n",
    "best_model = load_model('models/dtm_velocity_model_{}.hdf5'.format(window))\n",
    "# best_model = load_model('best_model_reg.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'Subjects/Fazil/computed_Hammer02.csv'\n",
    "df = pd.read_csv(file,index_col=0)[32000:40000]\n",
    "df = filter_df([df])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = prep_data_velocity(df,window,interval=10,Normalize=True,rms=False,angle_label ='DTM_Angle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'Subjects/Fazil/computed_Cup02.csv'\n",
    "df = pd.read_csv(file,index_col=0)[32000:40000]\n",
    "df = filter_df([df])[0]\n",
    "X2, y2 = prep_data_velocity(df,window,interval=10,Normalize=True,rms=False,angle_label ='DTM_Angle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1550, 250, 8) (1550, 1)\n"
     ]
    }
   ],
   "source": [
    "X = np.vstack((X,X2))\n",
    "y = np.vstack((y,y2))#.reshape((len(y)+len(y2),1))\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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25/25 [==============================] - 0s 4ms/step - loss: 5.3134\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 = best_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[200:])\n",
    "plt.plot(y[200:])\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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted_velocity</th>\n",
       "      <th>True_Velocity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.862834</td>\n",
       "      <td>-0.273847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.757517</td>\n",
       "      <td>-0.172759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.624163</td>\n",
       "      <td>-0.060968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.468122</td>\n",
       "      <td>0.061948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.323606</td>\n",
       "      <td>0.196465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>-12.298514</td>\n",
       "      <td>-10.210419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>-12.296154</td>\n",
       "      <td>-10.044868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>-12.277842</td>\n",
       "      <td>-9.875581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>-12.289894</td>\n",
       "      <td>-9.702754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>-12.324937</td>\n",
       "      <td>-9.526715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>575 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Predicted_velocity  True_Velocity\n",
       "0             -0.862834      -0.273847\n",
       "1             -0.757517      -0.172759\n",
       "2             -0.624163      -0.060968\n",
       "3             -0.468122       0.061948\n",
       "4             -0.323606       0.196465\n",
       "..                  ...            ...\n",
       "570          -12.298514     -10.210419\n",
       "571          -12.296154     -10.044868\n",
       "572          -12.277842      -9.875581\n",
       "573          -12.289894      -9.702754\n",
       "574          -12.324937      -9.526715\n",
       "\n",
       "[575 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(columns=['Predicted_velocity','True_Velocity'])\n",
    "df['Predicted_velocity'] = pred[200:]\n",
    "df['True_Velocity'] = y[200:]\n",
    "df.to_excel('DTM_velocity_regression.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE Loss - 5.0603022731028435\n",
      "RMSE - 2.2495115632294143\n",
      "Pearson Correlation Coefficient - (0.9321433936480011, 0.0)\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pearsonr 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
    "\n",
    "def correlation_coefficient(y_true, y_pred):\n",
    "    pearson_r, update_op = tf.contrib.metrics.streaming_pearson_correlation(y_pred, y_true)\n",
    "    # find all variables created for this metric\n",
    "    metric_vars = [i for i in tf.local_variables() if 'correlation_coefficient' in i.name.split('/')[1]]\n",
    "\n",
    "    # Add metric variables to GLOBAL_VARIABLES collection.\n",
    "    # They will be initialized for new session.\n",
    "    for v in metric_vars:\n",
    "        tf.add_to_collection(tf.GraphKeys.GLOBAL_VARIABLES, v)\n",
    "\n",
    "    # force to update metric values\n",
    "    with tf.control_dependencies([update_op]):\n",
    "        pearson_r = tf.identity(pearson_r)\n",
    "        return 1-pearson_r**2\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": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "system_sleep()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "system_shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypd = pd.Series(y.reshape((len(y)))).interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypd.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.0 64-bit",
   "language": "python",
   "name": "python38064bitcaed634bb72a48a5bf9eda0dc4f94389"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "metadata": {
   "interpreter": {
    "hash": "6ee35c36cb35713bb17262932a5363e33420bada462273a386b4b23ffd64fd21"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}