playground.ipynb

{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6-final"
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  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.6 64-bit",
   "metadata": {
    "interpreter": {
     "hash": "df955ce39d0f31d56d4bb2fe0a613e5326ba60723fd33d8303a3aede8f65715c"
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 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "# Imports"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM,Dense ,Dropout, Bidirectional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of                       Formatted Date        Summary Precip Type  \\\n",
       "0      2006-04-01 00:00:00.000 +0200  Partly Cloudy        rain   \n",
       "1      2006-04-01 01:00:00.000 +0200  Partly Cloudy        rain   \n",
       "2      2006-04-01 02:00:00.000 +0200  Mostly Cloudy        rain   \n",
       "3      2006-04-01 03:00:00.000 +0200  Partly Cloudy        rain   \n",
       "4      2006-04-01 04:00:00.000 +0200  Mostly Cloudy        rain   \n",
       "...                              ...            ...         ...   \n",
       "96448  2016-09-09 19:00:00.000 +0200  Partly Cloudy        rain   \n",
       "96449  2016-09-09 20:00:00.000 +0200  Partly Cloudy        rain   \n",
       "96450  2016-09-09 21:00:00.000 +0200  Partly Cloudy        rain   \n",
       "96451  2016-09-09 22:00:00.000 +0200  Partly Cloudy        rain   \n",
       "96452  2016-09-09 23:00:00.000 +0200  Partly Cloudy        rain   \n",
       "\n",
       "       Temperature (C)  Apparent Temperature (C)  Humidity  Wind Speed (km/h)  \\\n",
       "0             9.472222                  7.388889      0.89            14.1197   \n",
       "1             9.355556                  7.227778      0.86            14.2646   \n",
       "2             9.377778                  9.377778      0.89             3.9284   \n",
       "3             8.288889                  5.944444      0.83            14.1036   \n",
       "4             8.755556                  6.977778      0.83            11.0446   \n",
       "...                ...                       ...       ...                ...   \n",
       "96448        26.016667                 26.016667      0.43            10.9963   \n",
       "96449        24.583333                 24.583333      0.48            10.0947   \n",
       "96450        22.038889                 22.038889      0.56             8.9838   \n",
       "96451        21.522222                 21.522222      0.60            10.5294   \n",
       "96452        20.438889                 20.438889      0.61             5.8765   \n",
       "\n",
       "       Wind Bearing (degrees)  Visibility (km)  Loud Cover  \\\n",
       "0                       251.0          15.8263         0.0   \n",
       "1                       259.0          15.8263         0.0   \n",
       "2                       204.0          14.9569         0.0   \n",
       "3                       269.0          15.8263         0.0   \n",
       "4                       259.0          15.8263         0.0   \n",
       "...                       ...              ...         ...   \n",
       "96448                    31.0          16.1000         0.0   \n",
       "96449                    20.0          15.5526         0.0   \n",
       "96450                    30.0          16.1000         0.0   \n",
       "96451                    20.0          16.1000         0.0   \n",
       "96452                    39.0          15.5204         0.0   \n",
       "\n",
       "       Pressure (millibars)                           Daily Summary  \n",
       "0                   1015.13       Partly cloudy throughout the day.  \n",
       "1                   1015.63       Partly cloudy throughout the day.  \n",
       "2                   1015.94       Partly cloudy throughout the day.  \n",
       "3                   1016.41       Partly cloudy throughout the day.  \n",
       "4                   1016.51       Partly cloudy throughout the day.  \n",
       "...                     ...                                     ...  \n",
       "96448               1014.36  Partly cloudy starting in the morning.  \n",
       "96449               1015.16  Partly cloudy starting in the morning.  \n",
       "96450               1015.66  Partly cloudy starting in the morning.  \n",
       "96451               1015.95  Partly cloudy starting in the morning.  \n",
       "96452               1016.16  Partly cloudy starting in the morning.  \n",
       "\n",
       "[96453 rows x 12 columns]>"
      ]
     },
     "metadata": {},
     "execution_count": 36
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/weatherHistory.csv\")\n",
    "data.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Index(['Formatted Date', 'Summary', 'Precip Type', 'Temperature (C)',\n",
       "       'Apparent Temperature (C)', 'Humidity', 'Wind Speed (km/h)',\n",
       "       'Wind Bearing (degrees)', 'Visibility (km)', 'Loud Cover',\n",
       "       'Pressure (millibars)', 'Daily Summary'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "execution_count": 37
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "source": [
    "# __CLEANING__"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Formatted Date                0\n",
       "Summary                       0\n",
       "Precip Type                 517\n",
       "Temperature (C)               0\n",
       "Apparent Temperature (C)      0\n",
       "Humidity                      0\n",
       "Wind Speed (km/h)             0\n",
       "Wind Bearing (degrees)        0\n",
       "Visibility (km)               0\n",
       "Loud Cover                    0\n",
       "Pressure (millibars)          0\n",
       "Daily Summary                 0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 38
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Formatted Date              0\n",
       "Summary                     0\n",
       "Precip Type                 0\n",
       "Temperature (C)             0\n",
       "Apparent Temperature (C)    0\n",
       "Humidity                    0\n",
       "Wind Speed (km/h)           0\n",
       "Wind Bearing (degrees)      0\n",
       "Visibility (km)             0\n",
       "Loud Cover                  0\n",
       "Pressure (millibars)        0\n",
       "Daily Summary               0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "data['Precip Type'] = data['Precip Type'].replace(np.NaN, 'N/A')\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "# heat map of correlation of features\n",
    "correlation_matrix = data.corr()\n",
    "fig = plt.figure(figsize=(9,6))\n",
    "sns.heatmap(correlation_matrix,vmax=0.8,square = True, annot= True)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "# __SAMPLING__"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Original Data Stats: \n",
      "\n",
      "       Temperature (C)  Apparent Temperature (C)      Humidity  \\\n",
      "count     96453.000000              96453.000000  96453.000000   \n",
      "mean         11.932678                 10.855029      0.734899   \n",
      "std           9.551546                 10.696847      0.195473   \n",
      "min         -21.822222                -27.716667      0.000000   \n",
      "25%           4.688889                  2.311111      0.600000   \n",
      "50%          12.000000                 12.000000      0.780000   \n",
      "75%          18.838889                 18.838889      0.890000   \n",
      "max          39.905556                 39.344444      1.000000   \n",
      "\n",
      "       Wind Speed (km/h)  Wind Bearing (degrees)  Visibility (km)  Loud Cover  \\\n",
      "count       96453.000000            96453.000000     96453.000000     96453.0   \n",
      "mean           10.810640              187.509232        10.347325         0.0   \n",
      "std             6.913571              107.383428         4.192123         0.0   \n",
      "min             0.000000                0.000000         0.000000         0.0   \n",
      "25%             5.828200              116.000000         8.339800         0.0   \n",
      "50%             9.965900              180.000000        10.046400         0.0   \n",
      "75%            14.135800              290.000000        14.812000         0.0   \n",
      "max            63.852600              359.000000        16.100000         0.0   \n",
      "\n",
      "       Pressure (millibars)  \n",
      "count          96453.000000  \n",
      "mean            1003.235956  \n",
      "std              116.969906  \n",
      "min                0.000000  \n",
      "25%             1011.900000  \n",
      "50%             1016.450000  \n",
      "75%             1021.090000  \n",
      "max             1046.380000  \n",
      "\n",
      "--------\n",
      "\n",
      "New Sample Data Stats: \n",
      "\n",
      "       Temperature (C)  Apparent Temperature (C)     Humidity  \\\n",
      "count      9645.000000               9645.000000  9645.000000   \n",
      "mean         11.938708                 10.851584     0.734762   \n",
      "std           9.639018                 10.789988     0.197633   \n",
      "min         -20.555556                -25.922222     0.000000   \n",
      "25%           4.455556                  2.222222     0.600000   \n",
      "50%          12.022222                 12.022222     0.780000   \n",
      "75%          18.861111                 18.861111     0.900000   \n",
      "max          39.588889                 37.933333     1.000000   \n",
      "\n",
      "       Wind Speed (km/h)  Wind Bearing (degrees)  Visibility (km)  Loud Cover  \\\n",
      "count        9645.000000             9645.000000      9645.000000      9645.0   \n",
      "mean           10.733876              186.152929        10.353604         0.0   \n",
      "std             6.867283              107.134520         4.239981         0.0   \n",
      "min             0.000000                0.000000         0.000000         0.0   \n",
      "25%             5.635000              113.000000         8.372000         0.0   \n",
      "50%             9.901500              178.000000        10.030300         0.0   \n",
      "75%            14.087500              290.000000        14.908600         0.0   \n",
      "max            50.135400              359.000000        16.100000         0.0   \n",
      "\n",
      "       Pressure (millibars)  \n",
      "count           9645.000000  \n",
      "mean            1002.443519  \n",
      "std              120.141251  \n",
      "min                0.000000  \n",
      "25%             1011.840000  \n",
      "50%             1016.390000  \n",
      "75%             1021.110000  \n",
      "max             1045.930000  \n"
     ]
    }
   ],
   "source": [
    "\n",
    "# *****************************\n",
    "# HIGHLY IMPORTANT\n",
    "# *****************************\n",
    "\n",
    "# Sample data\n",
    "print(\"Original Data Stats: \\n\")\n",
    "print(data.describe())\n",
    "\n",
    "print('\\n--------\\n')\n",
    "\n",
    "print(\"New Sample Data Stats: \\n\")\n",
    "# all_data['year'].fillna(0).astype(int)\n",
    "# all_data['mass'].fillna(0).astype(int)\n",
    "\n",
    "\n",
    "all_data = data.sample(frac=0.1)  # 10% sample set\n",
    "print(all_data.describe())"
   ]
  },
  {
   "source": [
    "## Get data to train for model \n",
    "## Were predicting temperature so using that"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[ 9.47222222],\n",
       "       [ 9.35555556],\n",
       "       [ 9.37777778],\n",
       "       ...,\n",
       "       [22.03888889],\n",
       "       [21.52222222],\n",
       "       [20.43888889]])"
      ]
     },
     "metadata": {},
     "execution_count": 42
    }
   ],
   "source": [
    "temp_data = data.iloc[:,3:4].values #temperature column only\n",
    "temp_data"
   ]
  },
  {
   "source": [
    "## Min Max Scaling"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_max = MinMaxScaler(feature_range=(0,1))\n",
    "temp_data = min_max.fit_transform(temp_data)"
   ]
  },
  {
   "source": [
    "## Train and Test Data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = []\n",
    "y_train = []\n",
    "n_future = 7 # next 4 days temperature forecast\n",
    "n_past = 30 # Past 30 days \n",
    "for i in range(0,len(temp_data)-n_past-n_future+1):\n",
    "    x_train.append(temp_data[i : i + n_past , 0])     \n",
    "    y_train.append(temp_data[i + n_past : i + n_past + n_future , 0 ])\n",
    "x_train , y_train = np.array(x_train), np.array(y_train)\n",
    "x_train = np.reshape(x_train, (x_train.shape[0] , x_train.shape[1], 1) )"
   ]
  },
  {
   "source": [
    "## Build Layers"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/50\n",
      "754/754 [==============================] - 184s 244ms/step - loss: 0.0143 - acc: 0.1805\n",
      "Epoch 2/50\n",
      "754/754 [==============================] - 172s 228ms/step - loss: 0.0050 - acc: 0.3165\n",
      "Epoch 3/50\n",
      "754/754 [==============================] - 174s 230ms/step - loss: 0.0036 - acc: 0.3920\n",
      "Epoch 4/50\n",
      "754/754 [==============================] - 197s 261ms/step - loss: 0.0030 - acc: 0.4464\n",
      "Epoch 5/50\n",
      "754/754 [==============================] - 202s 268ms/step - loss: 0.0027 - acc: 0.4817\n",
      "Epoch 6/50\n",
      "754/754 [==============================] - 209s 277ms/step - loss: 0.0025 - acc: 0.5035\n",
      "Epoch 7/50\n",
      "754/754 [==============================] - 203s 269ms/step - loss: 0.0024 - acc: 0.5166\n",
      "Epoch 8/50\n",
      "754/754 [==============================] - 196s 260ms/step - loss: 0.0024 - acc: 0.5293\n",
      "Epoch 9/50\n",
      "754/754 [==============================] - 197s 262ms/step - loss: 0.0023 - acc: 0.5345\n",
      "Epoch 10/50\n",
      "754/754 [==============================] - 201s 266ms/step - loss: 0.0023 - acc: 0.5380\n",
      "Epoch 11/50\n",
      "754/754 [==============================] - 201s 266ms/step - loss: 0.0023 - acc: 0.5364\n",
      "Epoch 12/50\n",
      "754/754 [==============================] - 222s 294ms/step - loss: 0.0022 - acc: 0.5385\n",
      "Epoch 13/50\n",
      "754/754 [==============================] - 182s 241ms/step - loss: 0.0022 - acc: 0.5394\n",
      "Epoch 14/50\n",
      "754/754 [==============================] - 160s 212ms/step - loss: 0.0022 - acc: 0.5415\n",
      "Epoch 15/50\n",
      "754/754 [==============================] - 159s 211ms/step - loss: 0.0021 - acc: 0.5445\n",
      "Epoch 16/50\n",
      "754/754 [==============================] - 161s 213ms/step - loss: 0.0021 - acc: 0.5425\n",
      "Epoch 17/50\n",
      "754/754 [==============================] - 162s 215ms/step - loss: 0.0021 - acc: 0.5449\n",
      "Epoch 18/50\n",
      "754/754 [==============================] - 160s 212ms/step - loss: 0.0021 - acc: 0.5435\n",
      "Epoch 19/50\n",
      "754/754 [==============================] - 181s 240ms/step - loss: 0.0020 - acc: 0.5431\n",
      "Epoch 20/50\n",
      "754/754 [==============================] - 187s 248ms/step - loss: 0.0020 - acc: 0.5446\n",
      "Epoch 21/50\n",
      "754/754 [==============================] - 127s 168ms/step - loss: 0.0020 - acc: 0.5452\n",
      "Epoch 22/50\n",
      "754/754 [==============================] - 143s 189ms/step - loss: 0.0020 - acc: 0.5482\n",
      "Epoch 23/50\n",
      "754/754 [==============================] - 167s 221ms/step - loss: 0.0020 - acc: 0.5501\n",
      "Epoch 24/50\n",
      "754/754 [==============================] - 176s 233ms/step - loss: 0.0019 - acc: 0.5491\n",
      "Epoch 25/50\n",
      "754/754 [==============================] - 155s 206ms/step - loss: 0.0019 - acc: 0.5501\n",
      "Epoch 26/50\n",
      "754/754 [==============================] - 156s 207ms/step - loss: 0.0019 - acc: 0.5513\n",
      "Epoch 27/50\n",
      "754/754 [==============================] - 146s 194ms/step - loss: 0.0019 - acc: 0.5518\n",
      "Epoch 28/50\n",
      "754/754 [==============================] - 123s 164ms/step - loss: 0.0019 - acc: 0.5542\n",
      "Epoch 29/50\n",
      "754/754 [==============================] - 123s 163ms/step - loss: 0.0019 - acc: 0.5557\n",
      "Epoch 30/50\n",
      "754/754 [==============================] - 138s 183ms/step - loss: 0.0019 - acc: 0.5547\n",
      "Epoch 31/50\n",
      "754/754 [==============================] - 127s 168ms/step - loss: 0.0019 - acc: 0.5554\n",
      "Epoch 32/50\n",
      "754/754 [==============================] - 134s 177ms/step - loss: 0.0018 - acc: 0.5581\n",
      "Epoch 33/50\n",
      "754/754 [==============================] - 130s 172ms/step - loss: 0.0018 - acc: 0.5558\n",
      "Epoch 34/50\n",
      "754/754 [==============================] - 133s 176ms/step - loss: 0.0018 - acc: 0.5557\n",
      "Epoch 35/50\n",
      "754/754 [==============================] - 135s 180ms/step - loss: 0.0018 - acc: 0.5570\n",
      "Epoch 36/50\n",
      "754/754 [==============================] - 121s 161ms/step - loss: 0.0018 - acc: 0.5563\n",
      "Epoch 37/50\n",
      "754/754 [==============================] - 128s 170ms/step - loss: 0.0018 - acc: 0.5578\n",
      "Epoch 38/50\n",
      "754/754 [==============================] - 165s 219ms/step - loss: 0.0018 - acc: 0.5602\n",
      "Epoch 39/50\n",
      "754/754 [==============================] - 172s 228ms/step - loss: 0.0018 - acc: 0.5584\n",
      "Epoch 40/50\n",
      "754/754 [==============================] - 112s 148ms/step - loss: 0.0018 - acc: 0.5600\n",
      "Epoch 41/50\n",
      "754/754 [==============================] - 216s 287ms/step - loss: 0.0017 - acc: 0.5592\n",
      "Epoch 42/50\n",
      "754/754 [==============================] - 90s 120ms/step - loss: 0.0017 - acc: 0.5592\n",
      "Epoch 43/50\n",
      "754/754 [==============================] - 94s 124ms/step - loss: 0.0017 - acc: 0.5602\n",
      "Epoch 44/50\n",
      "754/754 [==============================] - 98s 130ms/step - loss: 0.0017 - acc: 0.5580\n",
      "Epoch 45/50\n",
      "754/754 [==============================] - 100s 132ms/step - loss: 0.0017 - acc: 0.5601\n",
      "Epoch 46/50\n",
      "754/754 [==============================] - 106s 141ms/step - loss: 0.0017 - acc: 0.5606\n",
      "Epoch 47/50\n",
      "754/754 [==============================] - 104s 137ms/step - loss: 0.0017 - acc: 0.5594\n",
      "Epoch 48/50\n",
      "754/754 [==============================] - 2137s 3s/step - loss: 0.0017 - acc: 0.5607\n",
      "Epoch 49/50\n",
      "754/754 [==============================] - 96s 128ms/step - loss: 0.0017 - acc: 0.5612\n",
      "Epoch 50/50\n",
      "754/754 [==============================] - 126s 168ms/step - loss: 0.0017 - acc: 0.5612\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1454671d0>"
      ]
     },
     "metadata": {},
     "execution_count": 53
    }
   ],
   "source": [
    "regressor = Sequential()\n",
    "regressor.add(Bidirectional(LSTM(units=30, return_sequences=True, input_shape = (x_train.shape[1],1) ) ))\n",
    "regressor.add(Dropout(0.2))\n",
    "regressor.add(LSTM(units= 30 , return_sequences=True))\n",
    "regressor.add(Dropout(0.2))\n",
    "regressor.add(LSTM(units= 30 , return_sequences=True))\n",
    "regressor.add(Dropout(0.2))\n",
    "regressor.add(LSTM(units= 30))\n",
    "regressor.add(Dropout(0.2))\n",
    "regressor.add(Dense(units = n_future,activation='linear'))\n",
    "regressor.compile(optimizer='adam', loss='mean_squared_error',metrics=['acc'])\n",
    "regressor.fit(x_train, y_train, epochs=50,batch_size=128 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}