{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### case2. Simple Price Data (시가, 종가, 고가, 저가, 거래량) + Exponential Moving Average(종가)\n",
    "---\n",
    "단방향 LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Model, Sequential\n",
    "from keras.layers import Input, Dense, LSTM, Bidirectional\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras import backend as K\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/scaled/train_scaled.csv')\n",
    "test = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/scaled/test_scaled.csv')\n",
    "validation = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/scaled/validation_scaled.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/close/exp/train_EMA_scaled.csv')\n",
    "test_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/close/exp/test_EMA_scaled.csv')\n",
    "val_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/close/exp/val_EMA_scaled.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#불필요한 열 삭제\n",
    "\n",
    "del train['Unnamed: 0']\n",
    "del test['Unnamed: 0']\n",
    "del validation['Unnamed: 0']\n",
    "del train_EMA['Unnamed: 0']\n",
    "del test_EMA['Unnamed: 0']\n",
    "del val_EMA['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([train, train_EMA], axis = 1)\n",
    "test = pd.concat([test, test_EMA],axis = 1)\n",
    "validation = pd.concat([validation,val_EMA], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking if any null values are present in train\n",
      " close    0\n",
      "open     0\n",
      "high     0\n",
      "low      0\n",
      "vol      0\n",
      "5        0\n",
      "10       0\n",
      "20       0\n",
      "60       0\n",
      "120      0\n",
      "dtype: int64\n",
      "checking if any null values are present in test\n",
      " close    0\n",
      "open     0\n",
      "high     0\n",
      "low      0\n",
      "vol      0\n",
      "5        0\n",
      "10       0\n",
      "20       0\n",
      "60       0\n",
      "120      0\n",
      "dtype: int64\n",
      "checking if any null values are present in validation\n",
      " close    0\n",
      "open     0\n",
      "high     0\n",
      "low      0\n",
      "vol      0\n",
      "5        0\n",
      "10       0\n",
      "20       0\n",
      "60       0\n",
      "120      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# NULL value 없나 확인해보기 (지수이동평균의 경우는 결측치가 없다)\n",
    "print(\"checking if any null values are present in train\\n\", train.isna().sum())\n",
    "print(\"checking if any null values are present in test\\n\", test.isna().sum())\n",
    "print(\"checking if any null values are present in validation\\n\", validation.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_dataset(data, label, window_size = 20):\n",
    "    feature_list = []\n",
    "    label_list = []\n",
    "    for i in range(len(data)-window_size):\n",
    "        feature_list.append(np.array(data.iloc[i:i+window_size]))\n",
    "        label_list.append(np.array(label.iloc[i+window_size]))\n",
    "    return np.array(feature_list), np.array(label_list)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature , label 분리\n",
    "feature_cols = ['open','high','low','vol','5','10','20','60','120']\n",
    "label_cols = ['close']\n",
    "\n",
    "##----train_data----##\n",
    "train_feature = train[feature_cols]\n",
    "train_label = train[label_cols]\n",
    "train_feature, train_label = make_dataset(train_feature,train_label,20)\n",
    "\n",
    "##----test_data----##\n",
    "test_feature = test[feature_cols]\n",
    "test_label = test[label_cols]\n",
    "test_feature, test_label = make_dataset(test_feature,test_label,20)\n",
    "\n",
    "##----validation_data----##\n",
    "validation_feature = validation[feature_cols]\n",
    "validation_label = validation[label_cols]\n",
    "validation_feature, validation_label = make_dataset(validation_feature,validation_label,20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train, X_val, y_val = train_feature, train_label, validation_feature, validation_label\n",
    "X_test, y_test = test_feature, test_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3444, 20, 9)\n",
      "(3444, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_11 (LSTM)               (None, 20, 64)            18944     \n",
      "_________________________________________________________________\n",
      "lstm_12 (LSTM)               (None, 50)                23000     \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 41,995\n",
      "Trainable params: 41,995\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# keras 활용한 LSTM 모델 생성\n",
    "model = Sequential()\n",
    "##----Input Layer----##\n",
    "model.add(LSTM(64, return_sequences=True, input_shape=(20,9)))\n",
    "##----Hidden Layer----##\n",
    "model.add(LSTM(50, return_sequences=False))\n",
    "model.add(Dense(1, activation='linear'))\n",
    "##----Compile Model----##\n",
    "model.compile(loss='mse',optimizer='adam')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3444 samples, validate on 167 samples\n",
      "Epoch 1/100\n",
      "3444/3444 [==============================] - 12s 4ms/step - loss: 0.0276 - val_loss: 0.0287\n",
      "Epoch 2/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 0.0014 - val_loss: 0.0198\n",
      "Epoch 3/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 8.3635e-04 - val_loss: 0.0160\n",
      "Epoch 4/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 7.7347e-04 - val_loss: 0.0142\n",
      "Epoch 5/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 7.3175e-04 - val_loss: 0.0121\n",
      "Epoch 6/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 6.7373e-04 - val_loss: 0.0108\n",
      "Epoch 7/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 6.4270e-04 - val_loss: 0.0100\n",
      "Epoch 8/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 6.2100e-04 - val_loss: 0.0092\n",
      "Epoch 9/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 5.8556e-04 - val_loss: 0.0088\n",
      "Epoch 10/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 5.6867e-04 - val_loss: 0.0083\n",
      "Epoch 11/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 5.3832e-04 - val_loss: 0.0081\n",
      "Epoch 12/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 5.3748e-04 - val_loss: 0.0078\n",
      "Epoch 13/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 5.0169e-04 - val_loss: 0.0074\n",
      "Epoch 14/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 4.7160e-04 - val_loss: 0.0074\n",
      "Epoch 15/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 4.6457e-04 - val_loss: 0.0070\n",
      "Epoch 16/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 4.5361e-04 - val_loss: 0.0066\n",
      "Epoch 17/100\n",
      "3444/3444 [==============================] - 8s 2ms/step - loss: 5.1368e-04 - val_loss: 0.0074\n"
     ]
    }
   ],
   "source": [
    "early_stopping = EarlyStopping() \n",
    "hist = model.fit(X_train, y_train, validation_data=(X_val, y_val),batch_size=50, epochs=100,callbacks=[early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "226/226 [==============================] - 0s 662us/step\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test,y_test,batch_size=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.011599955008884447\n"
     ]
    }
   ],
   "source": [
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.plot(y_test, label='actual')\n",
    "plt.plot(predict, label=\"prediction\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x64b2a6e80>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 모델 학습 과정\n",
    "fig, loss_ax = plt.subplots()\n",
    "\n",
    "acc_ax = loss_ax.twinx()\n",
    "\n",
    "loss_ax.plot(hist.history['loss'], 'y', label='train loss')\n",
    "loss_ax.plot(hist.history['val_loss'], 'r', label='val loss')\n",
    "\n",
    "loss_ax.set_xlabel('epoch')\n",
    "loss_ax.set_ylabel('loss')\n",
    "\n",
    "\n",
    "loss_ax.legend(loc='upper left')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "batch_size -> 5, 10, 20, 30, 50 일때마다 score 저장해서 비교하기!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_list['50'] = score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'5': 0.004676613666424198,\n",
       " '10': 0.007281756650788331,\n",
       " '20': 0.00919954200168099,\n",
       " '30': 0.008342336613965114,\n",
       " '50': 0.011599955008884447}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_list = dict()"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}