Neural Network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime \n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "#parse data \n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "#label encoding on categorical data \n",
    "\n",
    "#FAMA 49CRSP Common Stocks \n",
    "df = pd.read_csv('FAMA_49CRSP.csv', dtype={'public_date' : str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import sklearn.preprocessing \n",
    "from sklearn.preprocessing import KBinsDiscretizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#preprocessing here\n",
    "#sort by date \n",
    "df = df.sort_values(by = 'public_date', ascending = True)\n",
    "df = df.dropna()\n",
    "\n",
    "#encode integer categories into numbers \n",
    "label_encoder = LabelEncoder()\n",
    "integer_encoded = label_encoder.fit_transform(df.FFI49_desc)\n",
    "df.FFI49_desc = integer_encoded\n",
    "\n",
    "#df = df.dropna()\n",
    "\n",
    "ewlabels = df.indret_ew\n",
    "vwlabels = df.indret_vw\n",
    "\n",
    "df = df.drop(labels=['indret_ew', 'indret_vw'], axis=1)\n",
    "#3year on year change as a prediction feature, raw pct change \n",
    "yoythree = ewlabels.diff(periods = 3)\n",
    "#3 years rolling percent change, averaged ie. (y1-y2 + (y3-y2)change)/2 \n",
    "rollavgpct = ewlabels.rolling(3).mean()\n",
    "\n",
    "#drop first 3 years\n",
    "df = df.iloc[3:]\n",
    "ewlabels = ewlabels.iloc[3:]\n",
    "yoythree = yoythree.iloc[3:]\n",
    "#yoypctthree = yoypctthree.iloc[3:]\n",
    "rollavgpct = rollavgpct.iloc[3:]\n",
    "\n",
    "#add -1 and 1 so the bins will take on bins to be equal and set to max -1 and 1\n",
    "#extrema = pd.Series([-1,1])\n",
    "#ewnlabels = ewlabels.append(extrema)\n",
    "\n",
    "#make a new output (bucket by percentage?)\n",
    "# enc = KBinsDiscretizer(n_bins=8, encode='ordinal',strategy = 'uniform')\n",
    "# ewnlabels = np.asarray(ewnlabels)\n",
    "# ewnlabels = ewnlabels.reshape((-1,1))\n",
    "# labels_binned = enc.fit_transform(ewnlabels)\n",
    "\n",
    "# labels_binned = labels_binned[:-2]\n",
    "\n",
    "#1 Split-Timer series data, 0.64 Train, 0.16 dev, 0.2 Test\n",
    "#x_train, x_test, y_train, y_test = train_test_split(df, labels_binned, test_size = 0.2, shuffle = False)\n",
    "x_train, x_test, y_train, y_test = train_test_split(df, ewlabels, test_size = 0.2, shuffle = False)\n",
    "x_train, x_dev, y_train, y_dev = train_test_split(x_train, y_train, test_size = 0.2, shuffle = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13167, 70)\n",
      "(4115, 70)\n"
     ]
    }
   ],
   "source": [
    "def get_dates(x_train, x_dev, x_test):\n",
    "    train_dates = [datetime(year=int(x[0:4]), month=int(x[4:6]), day=int(x[6:8])) for x in x_train['public_date']] \n",
    "    dev_dates = [datetime(year=int(x[0:4]), month=int(x[4:6]), day=int(x[6:8])) for x in x_dev['public_date']]\n",
    "    test_dates = [datetime(year=int(x[0:4]), month=int(x[4:6]), day=int(x[6:8])) for x in x_test['public_date']]\n",
    "    \n",
    "    x_train = x_train.drop('public_date', axis=1)\n",
    "    x_dev = x_dev.drop('public_date', axis=1)\n",
    "    x_test = x_test.drop('public_date', axis=1)\n",
    "    \n",
    "    return train_dates, dev_dates, test_dates, x_train, x_dev, x_test\n",
    "\n",
    "train_dates, dev_dates, test_dates, x_train, x_dev, x_test = get_dates(x_train, x_dev, x_test)\n",
    "\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "\n",
    "fig1 = pyplot.figure(1, figsize = (6,6))\n",
    "pyplot.plot(train_dates, y_train, color = 'green', label = 'industry_ew_train')\n",
    "pyplot.plot(dev_dates, y_dev, color = 'yellow', label = 'industry_ew_dev')\n",
    "pyplot.plot(test_dates, y_test, color = 'red', label = 'industry_ew_test')\n",
    "pyplot.xlabel('Date')\n",
    "pyplot.ylabel('Industry Return (Equally Weighted)')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/klchou/.local/lib/python3.6/site-packages/ipykernel_launcher.py:14: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(32, input_dim=76, activation=\"relu\", kernel_initializer=\"uniform\")`\n",
      "  \n",
      "/home/klchou/.local/lib/python3.6/site-packages/ipykernel_launcher.py:15: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(16, activation=\"relu\", kernel_initializer=\"uniform\")`\n",
      "  from ipykernel import kernelapp as app\n",
      "/home/klchou/.local/lib/python3.6/site-packages/ipykernel_launcher.py:16: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(1, activation=\"sigmoid\", kernel_initializer=\"uniform\")`\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'x_tra' 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-30-2478b1ee21da>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m# For a binary classification problem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'binary_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'adam'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_tra\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_tra\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'x_tra' is not defined"
     ]
    }
   ],
   "source": [
    "#tutorial keras practice\n",
    "#https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/\n",
    "\n",
    "\n",
    "#####IGNORE THIS!!!!!!!!\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation,Softmax\n",
    "from keras.optimizers import SGD\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy\n",
    "\n",
    "model = Sequential()\n",
    "#parameters = number of neurons, initialization method, activation function\n",
    "model.add(Dense(32, input_dim=76, init = 'uniform', activation = 'relu'))\n",
    "model.add(Dense(16, init = 'uniform', activation = 'relu'))\n",
    "model.add(Dense(1, init = 'uniform', activation = 'sigmoid'))\n",
    "\n",
    "# For a binary classification problem\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model.fit(x_tra, y_tra, epochs=25, batch_size=32)\n",
    "\n",
    "\n",
    "print(\"----------------------------------------------------------\")\n",
    "scores = model.evaluate(x_tra,y_tra)\n",
    "print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n",
    "\n",
    "print(\"XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\")\n",
    "\n",
    "y_devpred = model.predict(x_dev)\n",
    "print(\"--------------------------------------------\")\n",
    "print(mean_squared_error(y_dev,y_devpred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "13167/13167 [==============================] - 1s 80us/step - loss: 0.0978 - mean_squared_error: 0.0978 - mean_absolute_error: 0.0904 - mean_absolute_percentage_error: 11898.8460 - cosine_proximity: -0.0873\n",
      "Epoch 2/50\n",
      "13167/13167 [==============================] - 1s 53us/step - loss: 0.0049 - mean_squared_error: 0.0049 - mean_absolute_error: 0.0523 - mean_absolute_percentage_error: 9403.1272 - cosine_proximity: -0.1198\n",
      "Epoch 3/50\n",
      "13167/13167 [==============================] - 1s 53us/step - loss: 0.0049 - mean_squared_error: 0.0049 - mean_absolute_error: 0.0523 - mean_absolute_percentage_error: 14084.0951 - cosine_proximity: -0.1306\n",
      "Epoch 4/50\n",
      "13167/13167 [==============================] - 1s 53us/step - loss: 0.0050 - mean_squared_error: 0.0050 - mean_absolute_error: 0.0529 - mean_absolute_percentage_error: 10834.7172 - cosine_proximity: -0.0948\n",
      "Epoch 5/50\n",
      "13167/13167 [==============================] - 1s 54us/step - loss: 0.0050 - mean_squared_error: 0.0050 - mean_absolute_error: 0.0532 - mean_absolute_percentage_error: 14446.1271 - cosine_proximity: -0.0945\n",
      "Epoch 6/50\n",
      "13167/13167 [==============================] - 1s 55us/step - loss: 0.0050 - mean_squared_error: 0.0050 - mean_absolute_error: 0.0528 - mean_absolute_percentage_error: 10738.8438 - cosine_proximity: -0.1146\n",
      "Epoch 7/50\n",
      "13167/13167 [==============================] - 1s 54us/step - loss: 0.0051 - mean_squared_error: 0.0051 - mean_absolute_error: 0.0538 - mean_absolute_percentage_error: 14942.3014 - cosine_proximity: -0.0852\n",
      "Epoch 8/50\n",
      "13167/13167 [==============================] - 1s 54us/step - loss: 0.0050 - mean_squared_error: 0.0050 - mean_absolute_error: 0.0528 - mean_absolute_percentage_error: 11129.7420 - cosine_proximity: -0.1118\n",
      "Epoch 9/50\n",
      "13167/13167 [==============================] - 1s 53us/step - loss: 0.0051 - mean_squared_error: 0.0051 - mean_absolute_error: 0.0539 - mean_absolute_percentage_error: 13104.1686 - cosine_proximity: -0.0931\n",
      "Epoch 10/50\n",
      "13167/13167 [==============================] - 1s 51us/step - loss: 0.0050 - mean_squared_error: 0.0050 - mean_absolute_error: 0.0529 - mean_absolute_percentage_error: 10869.8542 - cosine_proximity: -0.1071\n",
      "Epoch 11/50\n",
      "13167/13167 [==============================] - 1s 52us/step - loss: 0.0048 - mean_squared_error: 0.0048 - mean_absolute_error: 0.0513 - mean_absolute_percentage_error: 11216.9720 - cosine_proximity: -0.1666\n",
      "Epoch 12/50\n",
      "13167/13167 [==============================] - 1s 51us/step - loss: 0.0048 - mean_squared_error: 0.0048 - mean_absolute_error: 0.0513 - mean_absolute_percentage_error: 11209.6673 - cosine_proximity: -0.1653\n",
      "Epoch 13/50\n",
      "13167/13167 [==============================] - 1s 51us/step - loss: 0.0048 - mean_squared_error: 0.0048 - mean_absolute_error: 0.0512 - mean_absolute_percentage_error: 11523.7672 - cosine_proximity: -0.1683\n",
      "Epoch 14/50\n",
      "13167/13167 [==============================] - 1s 50us/step - loss: 0.0048 - mean_squared_error: 0.0048 - mean_absolute_error: 0.0512 - mean_absolute_percentage_error: 10803.8641 - cosine_proximity: -0.1675\n",
      "Epoch 15/50\n",
      "13167/13167 [==============================] - 1s 46us/step - loss: 0.0048 - mean_squared_error: 0.0048 - mean_absolute_error: 0.0511 - mean_absolute_percentage_error: 11353.2605 - cosine_proximity: -0.1704\n",
      "Epoch 16/50\n",
      "13167/13167 [==============================] - 1s 49us/step - loss: 0.0048 - mean_squared_error: 0.0048 - mean_absolute_error: 0.0511 - mean_absolute_percentage_error: 11367.2764 - cosine_proximity: -0.1689\n",
      "Epoch 17/50\n",
      "13167/13167 [==============================] - 1s 55us/step - loss: 0.0048 - mean_squared_error: 0.0048 - mean_absolute_error: 0.0510 - mean_absolute_percentage_error: 11237.7022 - cosine_proximity: -0.1694\n",
      "Epoch 18/50\n",
      "13167/13167 [==============================] - 1s 54us/step - loss: 0.0048 - mean_squared_error: 0.0048 - mean_absolute_error: 0.0512 - mean_absolute_percentage_error: 11316.1448 - cosine_proximity: -0.1689\n",
      "Epoch 19/50\n",
      "13167/13167 [==============================] - 1s 54us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0511 - mean_absolute_percentage_error: 11666.8128 - cosine_proximity: -0.1721\n",
      "Epoch 20/50\n",
      "13167/13167 [==============================] - 1s 51us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0510 - mean_absolute_percentage_error: 11166.1622 - cosine_proximity: -0.1713\n",
      "Epoch 21/50\n",
      "13167/13167 [==============================] - 1s 61us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0510 - mean_absolute_percentage_error: 10024.0921 - cosine_proximity: -0.1718\n",
      "Epoch 22/50\n",
      "13167/13167 [==============================] - 1s 64us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0510 - mean_absolute_percentage_error: 11821.5069 - cosine_proximity: -0.1724\n",
      "Epoch 23/50\n",
      "13167/13167 [==============================] - 1s 59us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0510 - mean_absolute_percentage_error: 11516.2538 - cosine_proximity: -0.1722\n",
      "Epoch 24/50\n",
      "13167/13167 [==============================] - 1s 53us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0510 - mean_absolute_percentage_error: 11580.2912 - cosine_proximity: -0.1727\n",
      "Epoch 25/50\n",
      "13167/13167 [==============================] - 1s 55us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0509 - mean_absolute_percentage_error: 11878.0242 - cosine_proximity: -0.1730\n",
      "Epoch 26/50\n",
      "13167/13167 [==============================] - 1s 62us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0510 - mean_absolute_percentage_error: 10919.9075 - cosine_proximity: -0.1727\n",
      "Epoch 27/50\n",
      "13167/13167 [==============================] - 1s 57us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0509 - mean_absolute_percentage_error: 10429.9875 - cosine_proximity: -0.1729\n",
      "Epoch 28/50\n",
      "13167/13167 [==============================] - 1s 56us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0509 - mean_absolute_percentage_error: 11543.5173 - cosine_proximity: -0.1729\n",
      "Epoch 29/50\n",
      "13167/13167 [==============================] - 1s 56us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0510 - mean_absolute_percentage_error: 12081.5344 - cosine_proximity: -0.1704\n",
      "Epoch 30/50\n",
      "13167/13167 [==============================] - 1s 54us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0510 - mean_absolute_percentage_error: 11238.2028 - cosine_proximity: -0.1712\n",
      "Epoch 31/50\n",
      "13167/13167 [==============================] - 1s 60us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0509 - mean_absolute_percentage_error: 11703.0555 - cosine_proximity: -0.1724\n",
      "Epoch 32/50\n",
      "13167/13167 [==============================] - 1s 59us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0510 - mean_absolute_percentage_error: 11189.4146 - cosine_proximity: -0.1716\n",
      "Epoch 33/50\n",
      "13167/13167 [==============================] - 1s 58us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0509 - mean_absolute_percentage_error: 11611.3966 - cosine_proximity: -0.1733\n",
      "Epoch 34/50\n",
      "13167/13167 [==============================] - 1s 53us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0509 - mean_absolute_percentage_error: 10826.7089 - cosine_proximity: -0.1730\n",
      "Epoch 35/50\n",
      "13167/13167 [==============================] - 1s 59us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0509 - mean_absolute_percentage_error: 11256.0200 - cosine_proximity: -0.1709\n",
      "Epoch 36/50\n",
      "13167/13167 [==============================] - 1s 57us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0509 - mean_absolute_percentage_error: 10728.3646 - cosine_proximity: -0.1745\n",
      "Epoch 37/50\n",
      "13167/13167 [==============================] - 1s 57us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0509 - mean_absolute_percentage_error: 11185.2698 - cosine_proximity: -0.1729\n",
      "Epoch 38/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13167/13167 [==============================] - 1s 53us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0509 - mean_absolute_percentage_error: 11272.0011 - cosine_proximity: -0.1738\n",
      "Epoch 39/50\n",
      "13167/13167 [==============================] - 1s 47us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0508 - mean_absolute_percentage_error: 11417.5079 - cosine_proximity: -0.1741\n",
      "Epoch 40/50\n",
      "13167/13167 [==============================] - 1s 48us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0509 - mean_absolute_percentage_error: 11735.0031 - cosine_proximity: -0.1739\n",
      "Epoch 41/50\n",
      "13167/13167 [==============================] - 1s 48us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0510 - mean_absolute_percentage_error: 11759.1908 - cosine_proximity: -0.1721\n",
      "Epoch 42/50\n",
      "13167/13167 [==============================] - 1s 45us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0510 - mean_absolute_percentage_error: 11215.0519 - cosine_proximity: -0.1713\n",
      "Epoch 43/50\n",
      "13167/13167 [==============================] - 1s 46us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0510 - mean_absolute_percentage_error: 10872.8437 - cosine_proximity: -0.1721\n",
      "Epoch 44/50\n",
      "13167/13167 [==============================] - 1s 48us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0509 - mean_absolute_percentage_error: 12083.1857 - cosine_proximity: -0.1726\n",
      "Epoch 45/50\n",
      "13167/13167 [==============================] - 1s 52us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0509 - mean_absolute_percentage_error: 10839.7540 - cosine_proximity: -0.1741\n",
      "Epoch 46/50\n",
      "13167/13167 [==============================] - 1s 56us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0509 - mean_absolute_percentage_error: 11690.3201 - cosine_proximity: -0.1741\n",
      "Epoch 47/50\n",
      "13167/13167 [==============================] - 1s 51us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0509 - mean_absolute_percentage_error: 11078.5919 - cosine_proximity: -0.1718\n",
      "Epoch 48/50\n",
      "13167/13167 [==============================] - 1s 61us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0508 - mean_absolute_percentage_error: 10589.9155 - cosine_proximity: -0.1738\n",
      "Epoch 49/50\n",
      "13167/13167 [==============================] - 1s 58us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0509 - mean_absolute_percentage_error: 11453.0846 - cosine_proximity: -0.1729\n",
      "Epoch 50/50\n",
      "13167/13167 [==============================] - 1s 54us/step - loss: 0.0047 - mean_squared_error: 0.0047 - mean_absolute_error: 0.0509 - mean_absolute_percentage_error: 12213.9835 - cosine_proximity: -0.1732\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------\n",
      "13167/13167 [==============================] - 0s 36us/step\n",
      "\n",
      "loss: 0.00%\n",
      "\n",
      "mean_squared_error: 0.00%\n",
      "\n",
      "mean_absolute_error: 0.05%\n",
      "\n",
      "mean_absolute_percentage_error: 8579.12%\n",
      "\n",
      "cosine_proximity: -0.18%\n",
      "----------------------------------------------------------\n",
      "3292/3292 [==============================] - 0s 21us/step\n",
      "\n",
      "loss: 0.01%\n",
      "\n",
      "mean_squared_error: 0.01%\n",
      "\n",
      "mean_absolute_error: 0.06%\n",
      "\n",
      "mean_absolute_percentage_error: 189.31%\n",
      "\n",
      "cosine_proximity: -0.12%\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
      "4115/4115 [==============================] - 0s 30us/step\n",
      "\n",
      "loss: 0.01%\n",
      "\n",
      "mean_squared_error: 0.01%\n",
      "\n",
      "mean_absolute_error: 0.05%\n",
      "\n",
      "mean_absolute_percentage_error: 2619.43%\n",
      "\n",
      "cosine_proximity: -0.16%\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Regression Model:\n",
    "#1 Layer: 76 -> 12.78% and 15.28%\n",
    "#2 Layer: 76,1 -> 0.40% and 0.48%\n",
    "#3 Layers: 76, 32, 1 -> 0% and 65%\n",
    "#4 layers: 76,48,32,1 + adam +  -> 60.52% and 56.70%\n",
    "#4 Layers: 76,32,16,1 -> 61.33% and 57.18%\n",
    "#4 Layers: 76,32,8,1 -> 0%\n",
    "#4 layers: 76,48,8,1 -> 0%\n",
    "#6 layers: 76,48,32,16,8,1 -> 20% and 0%\n",
    "\n",
    "import pandas\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "model = Sequential()\n",
    "#parameters = number of neurons, initialization method, activation function\n",
    "model.add(Dense(72, input_dim=x_train.shape[1], kernel_initializer='normal', activation='relu'))\n",
    "#model.add(Dense(48, kernel_initializer='normal',activation = 'relu'))\n",
    "model.add(Dense(32, kernel_initializer='normal',activation = 'relu'))\n",
    "#model.add(Dense(16, kernel_initializer='normal',activation = 'relu'))\n",
    "model.add(Dense(8, kernel_initializer='normal',activation = 'relu'))\n",
    "model.add(Dense(1, kernel_initializer='normal', activation = 'linear'))\n",
    "    \n",
    "# Compile model\n",
    "#opt = Adam(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08)\n",
    "#model.compile(loss='mean_squared_error', optimizer=opt, metrics=['accuracy'])\n",
    "#model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])\n",
    "model.compile(loss='mse', optimizer='adam', metrics=['mse', 'mae', 'mape', 'cosine'])\n",
    "history = model.fit(np.asarray(x_train), y_train, epochs=50)\n",
    "\n",
    "fig2 = pyplot.figure(2,figsize = (10,10))\n",
    "pyplot.plot(history.history['mean_squared_error'], color = 'blue')\n",
    "pyplot.plot(history.history['mean_absolute_error'], color = 'green')\n",
    "pyplot.plot(history.history['mean_absolute_percentage_error'], color = 'orange')\n",
    "pyplot.plot(history.history['cosine_proximity'], color = 'red')\n",
    "pyplot.ylim(-10,10)\n",
    "pyplot.show()\n",
    "\n",
    "#train set\n",
    "print(\"----------------------------------------------------------\")\n",
    "train_predictions = model.predict(x_train) \n",
    "scores = model.evaluate(np.asarray(x_train), y_train)\n",
    "for i in range(len(scores)):\n",
    "    print(\"\\n%s: %.2f%%\" % (model.metrics_names[i], scores[i]))\n",
    "\n",
    "#dev set\n",
    "print(\"----------------------------------------------------------\")\n",
    "dev_predictions = model.predict(x_dev) \n",
    "scores = model.evaluate(np.asarray(x_dev),y_dev)\n",
    "for i in range(len(scores)):\n",
    "    print(\"\\n%s: %.2f%%\" % (model.metrics_names[i], scores[i]))\n",
    "\n",
    "fig3 = pyplot.figure(3, figsize = (10,10))\n",
    "pyplot.plot(dev_dates, y_dev, color = 'green')\n",
    "pyplot.plot(dev_dates, dev_predictions, color = 'red')\n",
    "pyplot.show()\n",
    "\n",
    "#test set\n",
    "print(\"XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\")\n",
    "test_predictions = model.predict(x_test).reshape((-1))\n",
    "scores = model.evaluate(np.asarray(x_test),y_test)\n",
    "for i in range(len(scores)):\n",
    "    print(\"\\n%s: %.2f%%\" % (model.metrics_names[i], scores[i]))\n",
    "\n",
    "fig4 = pyplot.figure(4, figsize = (10,10))\n",
    "pyplot.plot(test_dates, y_test, color = 'green')\n",
    "pyplot.plot(test_dates, test_predictions, color = 'red')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/klchou/.local/lib/python3.6/site-packages/ipykernel_launcher.py:8: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(32, input_dim=72, activation=\"relu\", kernel_initializer=\"uniform\")`\n",
      "  \n",
      "/home/klchou/.local/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(16, activation=\"relu\", kernel_initializer=\"uniform\")`\n",
      "  if __name__ == '__main__':\n",
      "/home/klchou/.local/lib/python3.6/site-packages/ipykernel_launcher.py:10: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(8, activation=\"softmax\", kernel_initializer=\"uniform\")`\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Error when checking target: expected dense_33 to have shape (8,) but got array with shape (1,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-4e068c698de2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m               \u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'categorical_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m               metrics=['accuracy'])\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m    950\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    951\u001b[0m             \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 952\u001b[0;31m             batch_size=batch_size)\n\u001b[0m\u001b[1;32m    953\u001b[0m         \u001b[0;31m# Prepare validation data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    954\u001b[0m         \u001b[0mdo_validation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[0;34m(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)\u001b[0m\n\u001b[1;32m    787\u001b[0m                 \u001b[0mfeed_output_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m                 \u001b[0mcheck_batch_axis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;31m# Don't enforce the batch size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 789\u001b[0;31m                 exception_prefix='target')\n\u001b[0m\u001b[1;32m    790\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m             \u001b[0;31m# Generate sample-wise weight values given the `sample_weight` and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/keras/engine/training_utils.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[0;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[1;32m    136\u001b[0m                             \u001b[0;34m': expected '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' to have shape '\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m                             \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' but got array with shape '\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m                             str(data_shape))\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Error when checking target: expected dense_33 to have shape (8,) but got array with shape (1,)"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "from keras.optimizers import SGD\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(32, input_dim=x_train.shape[1], init = 'uniform', activation = 'relu'))\n",
    "model.add(Dense(16, init = 'uniform', activation = 'relu'))\n",
    "model.add(Dense(8, init = 'uniform', activation = 'softmax'))\n",
    "\n",
    "# For a multi-class classification problem\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.fit(x_train, y_train, epochs=25, batch_size=32)\n",
    "\n",
    "\n",
    "print(\"----------------------------------------------------------\")\n",
    "scores = model.evaluate(x_train,y_train)\n",
    "print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n",
    "\n",
    "print(\"XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\")\n",
    "y_devpred = model.predict(x_dev)\n",
    "print(\"--------------------------------------------\")\n",
    "print(mean_squared_error(y_dev,y_devpred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation,Softmax\n",
    "from keras.optimizers import SGD\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(32, input_shape = (x_train.shape)))\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "# For a multi-class classification problem\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# For a binary classification problem\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# For a mean squared error regression problem\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss='mse')\n",
    "\n",
    "model.fit(x_train, y_train, epochs=5, batch_size=32)\n",
    "y_devpred = model.predict(x_dev)\n",
    "print(\"--------------------------------------------\")\n",
    "print(mean_squared_error(y_dev,y_devpred))\n"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}