LSTM Predict demand of bike share.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense,Dropout, Bidirectional\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from keras.utils import plot_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from keras.layers.merge import Concatenate\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "import datetime\n",
    "from datetime import date\n",
    "from pandas_datareader import data as pdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://towardsdatascience.com/demand-prediction-with-lstms-using-tensorflow-2-and-keras-in-python-1d1076fc89a0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      cnt   t1   t2    hum  wind_speed  weather_code  \\\n",
      "timestamp                                                              \n",
      "2015-01-04 00:00:00   182  3.0  2.0   93.0         6.0           3.0   \n",
      "2015-01-04 01:00:00   138  3.0  2.5   93.0         5.0           1.0   \n",
      "2015-01-04 02:00:00   134  2.5  2.5   96.5         0.0           1.0   \n",
      "2015-01-04 03:00:00    72  2.0  2.0  100.0         0.0           1.0   \n",
      "2015-01-04 04:00:00    47  2.0  0.0   93.0         6.5           1.0   \n",
      "...                   ...  ...  ...    ...         ...           ...   \n",
      "2017-01-03 19:00:00  1042  5.0  1.0   81.0        19.0           3.0   \n",
      "2017-01-03 20:00:00   541  5.0  1.0   81.0        21.0           4.0   \n",
      "2017-01-03 21:00:00   337  5.5  1.5   78.5        24.0           4.0   \n",
      "2017-01-03 22:00:00   224  5.5  1.5   76.0        23.0           4.0   \n",
      "2017-01-03 23:00:00   139  5.0  1.0   76.0        22.0           2.0   \n",
      "\n",
      "                     is_holiday  is_weekend  season  hour  day_of_month  \\\n",
      "timestamp                                                                 \n",
      "2015-01-04 00:00:00         0.0         1.0     3.0     0             4   \n",
      "2015-01-04 01:00:00         0.0         1.0     3.0     1             4   \n",
      "2015-01-04 02:00:00         0.0         1.0     3.0     2             4   \n",
      "2015-01-04 03:00:00         0.0         1.0     3.0     3             4   \n",
      "2015-01-04 04:00:00         0.0         1.0     3.0     4             4   \n",
      "...                         ...         ...     ...   ...           ...   \n",
      "2017-01-03 19:00:00         0.0         0.0     3.0    19             3   \n",
      "2017-01-03 20:00:00         0.0         0.0     3.0    20             3   \n",
      "2017-01-03 21:00:00         0.0         0.0     3.0    21             3   \n",
      "2017-01-03 22:00:00         0.0         0.0     3.0    22             3   \n",
      "2017-01-03 23:00:00         0.0         0.0     3.0    23             3   \n",
      "\n",
      "                     day_of_week  month  \n",
      "timestamp                                \n",
      "2015-01-04 00:00:00            6      1  \n",
      "2015-01-04 01:00:00            6      1  \n",
      "2015-01-04 02:00:00            6      1  \n",
      "2015-01-04 03:00:00            6      1  \n",
      "2015-01-04 04:00:00            6      1  \n",
      "...                          ...    ...  \n",
      "2017-01-03 19:00:00            1      1  \n",
      "2017-01-03 20:00:00            1      1  \n",
      "2017-01-03 21:00:00            1      1  \n",
      "2017-01-03 22:00:00            1      1  \n",
      "2017-01-03 23:00:00            1      1  \n",
      "\n",
      "[17414 rows x 13 columns]\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv('london_merged.csv', parse_dates=['timestamp'], index_col=\"timestamp\")\n",
    "df['hour'] = df.index.hour\n",
    "df['day_of_month'] = df.index.day\n",
    "df['day_of_week'] = df.index.dayofweek\n",
    "df['month'] = df.index.month\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "\n",
    "grouped.plot()\n",
    "plt.xticks(rotation=90)\n",
    "plt.show(\"bike share detail trend\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our goal is to predict the number of future bike shares given the historical data of London bike shares.\n"
     ]
    }
   ],
   "source": [
    "print(\"Our goal is to predict the number of future bike shares given the historical data of London bike shares.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(dataset, look_back=3):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset)-look_back-1):\n",
    "        a = dataset[i:(i+look_back)]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + look_back])\n",
    "    return np.array(dataX), np.array(dataY)\n",
    "\n",
    "def create_dataset2(X, y, time_steps=1):\n",
    "    Xs, ys = [], []\n",
    "    for i in range(len(X) - time_steps):\n",
    "        v = X.iloc[i:(i + time_steps)].values\n",
    "        Xs.append(v)\n",
    "        ys.append(y.iloc[i + time_steps])\n",
    "    return np.array(Xs), np.array(ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "#COLUMNS=['cnt']\n",
    "#dataset=df[COLUMNS]\n",
    "#dataset=np.array(dataset).reshape(-1,1)\n",
    "#scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "#dataset = scaler.fit_transform(np.array(dataset).reshape(-1,1))\n",
    "#train_size = int(len(dataset) * 0.70)\n",
    "#test_size = len(dataset) - train_size\n",
    "#train, test = dataset[0:train_size], dataset[train_size:len(dataset)]\n",
    "#print(len(train), len(test))\n",
    "\n",
    "look_back=10\n",
    "trainX=[]\n",
    "testX=[]\n",
    "y_train=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dnishimoto.BOISE\\AppData\\Roaming\\Python\\Python37\\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",
      "C:\\Users\\dnishimoto.BOISE\\AppData\\Roaming\\Python\\Python37\\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": [
    "train_size = int(len(df) * 0.70)\n",
    "test_size = len(df) - train_size\n",
    "train, test = df[0:train_size], df[train_size:len(df)]\n",
    "f_columns = ['t1', 't2', 'hum', 'wind_speed']\n",
    "f_transformer = RobustScaler()\n",
    "f_transformer = f_transformer.fit(train[f_columns].to_numpy())\n",
    "train.loc[:, f_columns] = f_transformer.transform(\n",
    "  train[f_columns].to_numpy()\n",
    ")\n",
    "\n",
    "test.loc[:, f_columns] = f_transformer.transform(\n",
    "  test[f_columns].to_numpy()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dnishimoto.BOISE\\AppData\\Local\\Continuum\\anaconda3\\envs\\deeplearning\\lib\\site-packages\\ipykernel_launcher.py:3: 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",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\dnishimoto.BOISE\\AppData\\Local\\Continuum\\anaconda3\\envs\\deeplearning\\lib\\site-packages\\ipykernel_launcher.py:4: 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",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "cnt_transformer = RobustScaler()\n",
    "cnt_transformer = cnt_transformer.fit(train[['cnt']])\n",
    "train['cnt'] = cnt_transformer.transform(train[['cnt']])\n",
    "test['cnt'] = cnt_transformer.transform(test[['cnt']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "#trainX, y_train = create_dataset(train, look_back)\n",
    "#testX, y_test = create_dataset(test, look_back)\n",
    "#X_train = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
    "#X_test = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))\n",
    "\n",
    "X_train, y_train = create_dataset2(train, train.cnt, look_back)\n",
    "X_test, y_test = create_dataset2(test, test.cnt, look_back)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12179, 10, 13) (12179,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_future = 1\n",
    "#features=2\n",
    "timeSteps=10\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "#model.add(Bidirectional(LSTM(units=50, return_sequences=True, \n",
    "#                             input_shape=(X_train.shape[1], 1))))\n",
    "\n",
    "model.add(Bidirectional(LSTM(units=128,return_sequences=True,input_shape=(X_train.shape[1], X_train.shape[2]))))\n",
    "\n",
    "\n",
    "model.add(LSTM(units= 128, return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units= 128))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(units = n_future))\n",
    "\n",
    "model.compile(optimizer=\"adam\", loss=\"mean_squared_error\", metrics=[\"acc\"])\n",
    "\n",
    "\n",
    "\n",
    "plot_model(model, to_file='model.png')\n",
    "img=plt.imread('model.png')\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10961 samples, validate on 1218 samples\n",
      "Epoch 1/10\n",
      "10961/10961 [==============================] - 23s 2ms/step - loss: 0.3205 - acc: 2.7370e-04 - val_loss: 0.3365 - val_acc: 8.2102e-04\n",
      "Epoch 2/10\n",
      "10961/10961 [==============================] - 24s 2ms/step - loss: 0.1585 - acc: 4.5616e-04 - val_loss: 0.2467 - val_acc: 8.2102e-04\n",
      "Epoch 3/10\n",
      "10961/10961 [==============================] - 25s 2ms/step - loss: 0.0866 - acc: 4.5616e-04 - val_loss: 0.1436 - val_acc: 8.2102e-04\n",
      "Epoch 4/10\n",
      "10961/10961 [==============================] - 30s 3ms/step - loss: 0.0611 - acc: 4.5616e-04 - val_loss: 0.0971 - val_acc: 8.2102e-04\n",
      "Epoch 5/10\n",
      "10961/10961 [==============================] - 30s 3ms/step - loss: 0.0481 - acc: 4.5616e-04 - val_loss: 0.0620 - val_acc: 8.2102e-04\n",
      "Epoch 6/10\n",
      "10961/10961 [==============================] - 25s 2ms/step - loss: 0.0395 - acc: 4.5616e-04 - val_loss: 0.0731 - val_acc: 8.2102e-04oss: 0.03 - ETA - ETA: 14s - loss: 0.0343 - acc: 7.87 - ETA: 14s - loss: 0.0342 - acc:  - ETA: 14s - loss: 0.0339 - acc: 7.38 - E - E - E - ETA: 7s  - ETA: 5s - loss: 0.0429 - acc: 4.7529e-0 - ETA: 5s - - ETA: 3s - loss:  - ETA: 1s - loss: \n",
      "Epoch 7/10\n",
      "10961/10961 [==============================] - 24s 2ms/step - loss: 0.0327 - acc: 4.5616e-04 - val_loss: 0.0760 - val_acc: 8.2102e-04 0.0331 \n",
      "Epoch 8/10\n",
      "10961/10961 [==============================] - 26s 2ms/step - loss: 0.0315 - acc: 4.5616e-04 - val_loss: 0.0605 - val_acc: 8.2102e-04- E - ETA: 10s - loss: 0.0332 - acc: 7.1023 -  - ETA: 9s - loss: 0.03 - ETA: 7s - los - E - ETA: 0s - loss: 0.0315 - acc: 3\n",
      "Epoch 9/10\n",
      "10961/10961 [==============================] - 27s 3ms/step - loss: 0.0276 - acc: 4.5616e-04 - val_loss: 0.0420 - val_acc: 8.2102e-04: 8s - loss: 0.0300 - acc: 5.29 - ETA: 8s - loss: 0.0304 - acc: 5.1 - ETA: 7s - loss: 0.0300 - a - ETA: 6s - loss: 0.0293 - acc - ETA: 5s - loss: - ETA: 3s - loss: 0.0290 - acc: - ETA: 2s - loss: 0.0284 - acc: 4.0193e - ETA: 2s - loss: 0.0282 - acc: 3.9809 - ETA: 1s - loss: 0.0281 - acc: 3. - ETA: 1s - loss: 0.0277 - acc: 3.8344 - ETA: 0s - loss: 0.0275 - ac\n",
      "Epoch 10/10\n",
      "10961/10961 [==============================] - 30s 3ms/step - loss: 0.0255 - acc: 4.5616e-04 - val_loss: 0.0520 - val_acc: 8.2102e-04A: 13s - loss: 0.0191 - acc - E - ETA: 10s - loss: 0.0265 - ETA: 10s - l\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(X_train, y_train,epochs=10,batch_size=32,\n",
    "    validation_split=0.1,\n",
    "    shuffle=False  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#print(X_test)\n",
    "prediction = model.predict(X_test)\n",
    "\n",
    "# shift train predictions for plotting\n",
    "trainPredictPlot = np.empty_like(df)\n",
    "trainPredictPlot[:, :] = np.nan\n",
    "trainPredictPlot[look_back:len(prediction)+look_back, :] = prediction\n",
    "# shift test predictions for plotting\n",
    "#plt.plot(scaler.inverse_transform(dataset))\n",
    "plt.plot(trainPredictPlot, color='red')\n",
    "plt.title(\"Predicted \")\n",
    "#plt.plot(testPredictPlot)\n",
    "#plt.legend(['Actual','Train','Test'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.48654709 -0.49925262 -0.50896861 ... -0.34603886 -0.43049327\n",
      " -0.49402093]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(y_test)\n",
    "#plt.plot(X_test.reshape(-1,1))\n",
    "#plt.title('Actual test dataset')\n",
    "#plt.show()\n",
    "plt.plot(y_test)\n",
    "plt.title('Actual test dataset')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}