test_region_predict.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#invite people for the Kaggle party\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(region_predict.K2_bw_ts, columns=['bw','hour', 'lon','lat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation matrix\n",
    "corrmat = df.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(corrmat, vmax=.8, square=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "strategy use yesterday's hour:min, mre: 7.0%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x125a4c8d0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y1 = region_predict.K2_bw_ts[1440:2880][:,0]\n",
    "y2 = region_predict.K2_bw_ts[2880:1440*3][:,0]\n",
    "# strategy \n",
    "mre = np.mean(np.abs(y2 - y1)/ y1)\n",
    "print(\"strategy use yesterday's hour:min, mre: %.1f%%\" %(100 * mre))\n",
    "\n",
    "plt.plot(y1)\n",
    "plt.plot(y2)\n",
    "plt.legend(['y1','y2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [],
   "source": [
    "from region_predict import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'region_predict' from 'region_predict.py'>"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reload(region_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [],
   "source": [
    "    n = np.random.randint(N_data, size=1)\n",
    "    test_ts = np.array(range(n_test + lst_t[-1] + n_steps)) + n_start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, (18720, 4))"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(K2_bw_ts), K2_bw_ts[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./region_predict_model_20\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "global name 'X_test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-413-f1b3c83588cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_future\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_ts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/wangzhiguo/deeplearn/handson-ml/region_predict.py\u001b[0m in \u001b[0;36mmain\u001b[0;34m(n_future, training, test_ts, n)\u001b[0m\n\u001b[1;32m    175\u001b[0m             \u001b[0mY_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 177\u001b[0;31m                 \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    178\u001b[0m                 \u001b[0mY_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mY_pred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: global name 'X_test' is not defined"
     ]
    }
   ],
   "source": [
    "main(n_future, False, test_ts, n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1490,)"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_ts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 4 into shape (1,20,newaxis)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-414-434f12412d93>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime_series\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_ts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mn_steps\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_steps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 4 into shape (1,20,newaxis)"
     ]
    }
   ],
   "source": [
    "X_test = time_series(n, test_ts[i:i+n_steps]).reshape(1, n_steps, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10952, 10953, 10954, 10955, 10956, 10957, 10958, 10959, 10960,\n",
       "       10961, 10962, 10963, 10964, 10965, 10966, 10967, 10968, 10969,\n",
       "       10970, 10971])"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_ts[i:i+n_steps]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, array([23]), 20)"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i,n,n_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 4)"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_series(n, test_ts[i:i+n_steps]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([23]), (20,))"
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n, test_ts[i:i+n_steps].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 20, 4)"
      ]
     },
     "execution_count": 425,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_series(n, test_ts[i:i+n_steps].reshape(1, -1)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [],
   "source": [
    "def main(n_future, training=True, test_ts = None, n = None):\n",
    "    reset_graph()\n",
    "\n",
    "    X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "    y = tf.placeholder(tf.float32, [None, n_steps, n_outputs])\n",
    "\n",
    "    cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons, activation=tf.nn.relu)\n",
    "    rnn_outputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)\n",
    "\n",
    "    stacked_rnn_outputs = tf.reshape(rnn_outputs, [-1, n_neurons])\n",
    "    stacked_outputs = tf.layers.dense(stacked_rnn_outputs, n_outputs)\n",
    "    outputs = tf.reshape(stacked_outputs, [-1, n_steps, n_outputs])\n",
    "    # loss = tf.reduce_mean(tf.sqrt(tf.reduce_mean(tf.square(tf.subtract(y, outputs)))))\n",
    "    loss = tf.reduce_mean(tf.divide(tf.abs(outputs - y), y))\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "    training_op = optimizer.minimize(loss)\n",
    "\n",
    "    saver = tf.train.Saver()\n",
    "    fname = '%s_%s' %(fname_model, n_future)\n",
    "\n",
    "    with tf.Session() as sess:\n",
    "        if training:\n",
    "            if run_on:\n",
    "                saver.restore(sess, fname)\n",
    "            else:\n",
    "                init = tf.global_variables_initializer()\n",
    "                init.run()\n",
    "            for iteration in range(n_iterations):\n",
    "                X_batch, y_batch = next_batch(N_train, n_future)\n",
    "                sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "                if iteration % 100 == 0:\n",
    "                    mre = loss.eval(feed_dict={X: X_batch, y: y_batch})\n",
    "                    # mean relative error\n",
    "                    print(iteration, \"\\tmre: %.1f%%\" %(100. * mre))\n",
    "            saver.save(sess, fname)\n",
    "            return mre\n",
    "        else:\n",
    "            saver.restore(sess, fname)\n",
    "            Y_pred = []\n",
    "            for i in range(n_test):\n",
    "                X_test = time_series(n, test_ts[i:i+n_steps].reshape(1, -1))\n",
    "                y_pred = sess.run(outputs, feed_dict={X: X_test})\n",
    "                Y_pred.append(y_pred[0, -1, 0])\n",
    "            return Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./region_predict_model_20\n"
     ]
    }
   ],
   "source": [
    "res = main(n_future, False, test_ts, n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20,), (1, 4))"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_ts[:n_steps].shape, time_series(n, test_ts[:n_steps]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_predict(n, test_ts, Ys, lst_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([23]),\n",
       " array([10952, 10953, 10954, 10955, 10956, 10957, 10958, 10959, 10960,\n",
       "        10961, 10962, 10963, 10964, 10965, 10966, 10967, 10968, 10969,\n",
       "        10970, 10971]),\n",
       " array([9.138052 , 9.5048055, 9.5286095, 9.8576815, 9.0886631, 9.1676123,\n",
       "        8.9334036, 9.0478377, 9.1320669, 8.9982871, 9.1730606, 9.0952499,\n",
       "        8.9701062, 9.0757579, 9.0649218, 9.02737  , 9.0489399, 9.1335761,\n",
       "        8.9565575, 8.8421069]))"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n, test_ts[:n_steps], time_series(n, test_ts[:n_steps].reshape(1,-1))[0,:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_predict(n, test_ts, Ys, lst_t):\n",
    "    plt.title(\"Testing the model\", fontsize=14)\n",
    "    try:\n",
    "        plt.plot(\n",
    "            test_ts[:n_steps],\n",
    "            time_series(n, test_ts[:n_steps].reshape(1,-1))[0,:,0],\n",
    "            \"bo-\",\n",
    "            label=\"instance\")\n",
    "        plt.plot(\n",
    "            test_ts[n_steps:],\n",
    "            time_series(n, test_ts[n_steps:].reshape(1,-1))[0,:,0],\n",
    "            \"g--\",\n",
    "            label=\"target\")\n",
    "        for i, n_future in enumerate(lst_t):\n",
    "            plt.plot(\n",
    "                test_ts[n_steps + n_future:n_steps + n_future + n_test],\n",
    "                Ys[i],\n",
    "                label=\"predict_%s\" %n_future)\n",
    "    except:\n",
    "        print(n, n_steps)\n",
    "        raise\n",
    "    plt.legend(loc=\"best\")\n",
    "    plt.xlabel(\"Time\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  },
  "toc": {
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