lstm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shared/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "import pandas\n",
    "import numpy as np\n",
    "\n",
    "import keras\n",
    "from keras.preprocessing import image\n",
    "from keras import applications\n",
    "from keras.models import Sequential\n",
    "from keras.applications import vgg16\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.applications.vgg16 import preprocess_input\n",
    "from keras.models import Model\n",
    "from keras.layers import Conv2D, Conv3D,Input, ZeroPadding3D, Reshape,LSTM\n",
    "from keras.layers.convolutional import Convolution2D, Convolution3D, MaxPooling2D, ZeroPadding2D,ZeroPadding3D \n",
    "from keras.layers.core import Reshape\n",
    "import os\n",
    "from keras.utils import np_utils\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.callbacks import CSVLogger\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Flatten, Dense, Dropout\n",
    "from keras.optimizers import SGD\n",
    "from keras.layers import Merge \n",
    "import numpy as np\n",
    "import keras\n",
    "import keras.backend as K\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.callbacks import LambdaCallback\n",
    "from keras import regularizers\n",
    "from keras import backend as K\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X = np.load(\"train_X_normalized_lstm_regression.pkl.npy\")\n",
    "# Y = np.load(\"train_Y_normalized_lstm_regression.pkl.npy\")\n",
    "X = np.load(\"train_X_normalized_lstm_classification.pkl.npy\")\n",
    "Y = np.load(\"train_Y_normalized_lstm_classification.pkl.npy\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(721699, 60, 5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.reshape(X.shape[0], X.shape[1], -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(721699, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(X):\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(LSTM(512,\n",
    "        input_shape=(X.shape[1],X.shape[2]),\n",
    "        return_sequences=True))\n",
    "    model.add(Dropout(0.5))\n",
    "\n",
    "    model.add(LSTM(\n",
    "        512,\n",
    "        return_sequences=False))\n",
    "    model.add(Dropout(0.5))\n",
    "\n",
    "    #model.add(Dense(1, activation = 'linear'))\n",
    "\n",
    "    #model.compile(loss=\"mse\", optimizer=\"rmsprop\")\n",
    "    \n",
    "    model.add(Dense(3, activation = 'softmax')) ############ change dense layer node number to number of baskets.\n",
    "    #Adm = keras.optimizers.Adam(lr = 0.001)\n",
    "    #model.compile(loss=\"categorical_crossentropy\", optimizer='Adam', metrics=['categorical_accuracy'])\n",
    "    model.compile(loss=\"categorical_crossentropy\", optimizer='Adam', metrics=['accuracy'])\n",
    "    \n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 60, 512)           1060864   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 60, 512)           0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 512)               2099200   \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 3)                 1539      \n",
      "=================================================================\n",
      "Total params: 3,161,603\n",
      "Trainable params: 3,161,603\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = build_model(X)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# activ_func = 'tanh'\n",
    "# dropout = 0.5\n",
    "# model = Sequential()\n",
    "# model.add(LSTM(512, return_sequences = False, input_shape=(X.shape[1],X.shape[2]), activation=activ_func))\n",
    "# model.add(Dropout(dropout))\n",
    "# # model.add(LSTM(512, return_sequences = True, activation=activ_func))\n",
    "# # model.add(Dropout(dropout))\n",
    "# # model.add(LSTM(512, activation=activ_func))\n",
    "# # model.add(Dropout(dropout))\n",
    "\n",
    "# model.add(Dense(1, activation = 'linear'))\n",
    "# #model.add(Dense(3, activation = 'softmax'))\n",
    "# Adm = keras.optimizers.Adam(lr = 0.001)\n",
    "\n",
    "# #model.compile(loss='categorical_crossentropy', optimizer=Adm, metrics=['accuracy'])\n",
    "# model.compile(loss='mean_squared_error', optimizer=Adm, metrics=['mean_squared_error'])\n",
    "# model.summary()\n",
    "\n",
    "\n",
    "# K.tensorflow_backend._get_available_gpus()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 649529 samples, validate on 72170 samples\n",
      "Epoch 1/4\n",
      "649529/649529 [==============================] - 863s 1ms/step - loss: 1.0432 - acc: 0.4221 - val_loss: 1.0298 - val_acc: 0.4265\n",
      "Epoch 2/4\n",
      "649529/649529 [==============================] - 840s 1ms/step - loss: 1.0339 - acc: 0.4247 - val_loss: 1.0210 - val_acc: 0.4270\n",
      "Epoch 3/4\n",
      "649529/649529 [==============================] - 841s 1ms/step - loss: 1.0297 - acc: 0.4259 - val_loss: 1.0221 - val_acc: 0.4234\n",
      "Epoch 4/4\n",
      "649529/649529 [==============================] - 842s 1ms/step - loss: 1.0353 - acc: 0.4254 - val_loss: 1.0299 - val_acc: 0.4266\n"
     ]
    }
   ],
   "source": [
    "res = model.fit(X, Y ,batch_size=512, epochs=4,verbose=1, shuffle=True,validation_split = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"lstm.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X = np.load(\"test_X_normalized_lstm_regression.pkl.npy\")\n",
    "test_Y = np.load(\"test_Y_normalized_lstm_regression.pkl.npy\")\n",
    "de_norm = np.load(\"de_norm.pkl.npy\")\n",
    "unnormalized_Y = np.load(\"unnormalized_Y.pkl.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_Y = model.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77830, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(test_X.shape[2]):\n",
    "#     ss = StandardScaler()\n",
    "#     test_X[:,:,i] = ss.inverse_transform(test_X[:,:,i])\n",
    "de_norm = de_norm[:,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def de_normalization(data, de_norm):\n",
    "    res = de_norm*(data+1)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8075.  ],\n",
       "       [8069.69],\n",
       "       [8067.21],\n",
       "       ...,\n",
       "       [7475.  ],\n",
       "       [7475.  ],\n",
       "       [7474.99]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "de_norm.reshape(de_norm.shape[0], -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02141422],\n",
       "       [-0.02237201],\n",
       "       [-0.0215968 ],\n",
       "       ...,\n",
       "       [-0.00993677],\n",
       "       [-0.00995627],\n",
       "       [-0.00995315]], dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "unnormalized_pred_Y = []\n",
    "for i in range(de_norm.shape[0]):\n",
    "    unnormalized_pred_Y.append(de_norm[i]*(pred_Y[i]+1))\n",
    "#unnormalized_pred_Y = de_norm*(pred_Y+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "unnormalized_pred_Y = np.array(unnormalized_pred_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7902.08  ],\n",
       "       [7889.155 ],\n",
       "       [7892.984 ],\n",
       "       ...,\n",
       "       [7400.7227],\n",
       "       [7400.5767],\n",
       "       [7400.591 ]], dtype=float32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unnormalized_pred_Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf3e378d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "t = np.arange(0,unnormalized_pred_Y.shape[0])\n",
    "plt.plot(t, unnormalized_pred_Y, unnormalized_Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unnormalized_pred_Y\n",
    "# unnormalized_Y\n",
    "np.savetxt(\"unnormalized_pred_Y.csv\", unnormalized_pred_Y, delimiter = \",\")\n",
    "np.savetxt(\"unnormalized_Y.csv\", unnormalized_Y, delimiter = \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "mse = mean_squared_error(unnormalized_pred_Y, unnormalized_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "450.95214354624073"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ],\n",
       "       [0.00045998, 0.00045998, 0.00045998, 0.00045998, 0.00045998],\n",
       "       [0.00117295, 0.00117295, 0.00117295, 0.00117295, 0.00117295],\n",
       "       [0.00206992, 0.00206992, 0.00206992, 0.00206992, 0.00206992],\n",
       "       [0.00206992, 0.00206992, 0.00206992, 0.00206992, 0.00206992],\n",
       "       [0.00206992, 0.00206992, 0.00206992, 0.00206992, 0.00206992],\n",
       "       [0.00206992, 0.00206992, 0.00206992, 0.00206992, 0.00206992],\n",
       "       [0.00232291, 0.00232291, 0.00232291, 0.00232291, 0.00232291],\n",
       "       [0.0025299 , 0.0025299 , 0.0025299 , 0.0025299 , 0.0025299 ],\n",
       "       [0.00271389, 0.00271389, 0.00271389, 0.00271389, 0.00271389],\n",
       "       [0.00271389, 0.00271389, 0.00271389, 0.00271389, 0.00271389],\n",
       "       [0.00213891, 0.00213891, 0.00213891, 0.00213891, 0.00213891],\n",
       "       [0.00179393, 0.00179393, 0.00179393, 0.00179393, 0.00179393],\n",
       "       [0.00177093, 0.00177093, 0.00177093, 0.00177093, 0.00177093],\n",
       "       [0.00177093, 0.00177093, 0.00177093, 0.00177093, 0.00177093],\n",
       "       [0.00108096, 0.00108096, 0.00108096, 0.00108096, 0.00108096],\n",
       "       [0.00036799, 0.00036799, 0.00036799, 0.00036799, 0.00036799],\n",
       "       [0.00016099, 0.00016099, 0.00016099, 0.00016099, 0.00016099],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ],\n",
       "       [0.00018399, 0.00018399, 0.00018399, 0.00018399, 0.00018399],\n",
       "       [0.00016099, 0.00016099, 0.00016099, 0.00016099, 0.00016099],\n",
       "       [0.00096596, 0.00096596, 0.00096596, 0.00096596, 0.00096596],\n",
       "       [0.00117295, 0.00117295, 0.00117295, 0.00117295, 0.00117295],\n",
       "       [0.00126495, 0.00126495, 0.00126495, 0.00126495, 0.00126495],\n",
       "       [0.00126495, 0.00126495, 0.00126495, 0.00126495, 0.00126495],\n",
       "       [0.00126495, 0.00126495, 0.00126495, 0.00126495, 0.00126495],\n",
       "       [0.00144894, 0.00144894, 0.00144894, 0.00144894, 0.00144894],\n",
       "       [0.00273689, 0.00273689, 0.00273689, 0.00273689, 0.00273689],\n",
       "       [0.00273689, 0.00273689, 0.00273689, 0.00273689, 0.00273689],\n",
       "       [0.00273689, 0.00273689, 0.00273689, 0.00273689, 0.00273689],\n",
       "       [0.00358786, 0.00358786, 0.00358786, 0.00358786, 0.00358786],\n",
       "       [0.00455382, 0.00455382, 0.00455382, 0.00455382, 0.00455382],\n",
       "       [0.00524379, 0.00524379, 0.00524379, 0.00524379, 0.00524379],\n",
       "       [0.0051058 , 0.0051058 , 0.0051058 , 0.0051058 , 0.0051058 ],\n",
       "       [0.0051058 , 0.0051058 , 0.0051058 , 0.0051058 , 0.0051058 ],\n",
       "       [0.00282889, 0.00282889, 0.00282889, 0.00282889, 0.00282889],\n",
       "       [0.00280589, 0.00280589, 0.00280589, 0.00280589, 0.00280589],\n",
       "       [0.00303588, 0.00303588, 0.00303588, 0.00303588, 0.00303588],\n",
       "       [0.00303588, 0.00303588, 0.00303588, 0.00303588, 0.00303588],\n",
       "       [0.00303588, 0.00303588, 0.00303588, 0.00303588, 0.00303588],\n",
       "       [0.00296688, 0.00296688, 0.00296688, 0.00296688, 0.00296688],\n",
       "       [0.00294388, 0.00294388, 0.00294388, 0.00294388, 0.00294388],\n",
       "       [0.00275989, 0.00275989, 0.00275989, 0.00275989, 0.00275989],\n",
       "       [0.00287489, 0.00287489, 0.00287489, 0.00287489, 0.00287489],\n",
       "       [0.00285189, 0.00285189, 0.00285189, 0.00285189, 0.00285189],\n",
       "       [0.0025989 , 0.0025989 , 0.0025989 , 0.0025989 , 0.0025989 ],\n",
       "       [0.00273689, 0.00273689, 0.00273689, 0.00273689, 0.00273689],\n",
       "       [0.00273689, 0.00273689, 0.00273689, 0.00273689, 0.00273689],\n",
       "       [0.00278289, 0.00278289, 0.00278289, 0.00278289, 0.00278289],\n",
       "       [0.00308188, 0.00308188, 0.00308188, 0.00308188, 0.00308188],\n",
       "       [0.00305888, 0.00305888, 0.00305888, 0.00305888, 0.00305888],\n",
       "       [0.00308188, 0.00308188, 0.00308188, 0.00308188, 0.00308188],\n",
       "       [0.0025759 , 0.0025759 , 0.0025759 , 0.0025759 , 0.0025759 ],\n",
       "       [0.00264489, 0.00264489, 0.00264489, 0.00264489, 0.00264489],\n",
       "       [0.00271389, 0.00271389, 0.00271389, 0.00271389, 0.00271389],\n",
       "       [0.00285189, 0.00285189, 0.00285189, 0.00285189, 0.00285189],\n",
       "       [0.00165593, 0.00165593, 0.00165593, 0.00165593, 0.00165593]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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