questionso.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Physical GPUs, 1 Logical GPUs\n",
      "1899\n",
      "1589895492.178923\n",
      "AP시스템 \t 1  /  1899\n",
      "0.00056052476\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HL\\anaconda3\\envs\\me\\lib\\site-packages\\ipykernel_launcher.py:125: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n"
     ]
    }
   ],
   "source": [
    "# 0. 사용할 패키지 불러오기\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "# from keras.models import Sequential\n",
    "# from keras.layers import Dense, LSTM, Dropout, Conv2D, Reshape, TimeDistributed, Flatten, Conv1D,ConvLSTM2D, MaxPooling1D, BatchNormalization, Bidirectional, CuDNNLSTM\n",
    "# from keras.layers.core import Dense, Activation, Dropout\n",
    "# from keras import optimizers\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "# from keras import backend as K\n",
    "from keras.models import load_model\n",
    "import json\n",
    "import requests\n",
    "import os\n",
    "import pandas as pd\n",
    "import time\n",
    "# from keras.backend import tensorflow_backend as K\n",
    "import sys\n",
    "\n",
    "from tensorflow.keras import layers, optimizers, Sequential, metrics\n",
    "import datetime\n",
    "\n",
    "file_name =  os.path.basename(sys.argv[0])\n",
    "core = 1\n",
    "import tensorflow as tf\n",
    "\n",
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "  try:\n",
    "    # Currently, memory growth needs to be the same across GPUs\n",
    "    for gpu in gpus:\n",
    "      tf.config.experimental.set_memory_growth(gpu, True)\n",
    "    logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n",
    "    print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n",
    "  except RuntimeError as e:\n",
    "    # Memory growth must be set before GPUs have been initialized\n",
    "    print(e)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def create_dataset(signal_data, look_back=1):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(signal_data) - look_back):\n",
    "        dataX.append(signal_data[i:(i + look_back), :])\n",
    "        dataY.append(signal_data[i + look_back, -1])\n",
    "    return np.array(dataX), np.array(dataY)\n",
    "\n",
    "\n",
    "look_back = 20\n",
    "forecast = 20\n",
    "\n",
    "\n",
    "path = \"./csv/\"\n",
    "file_list = os.listdir(path)\n",
    "file_list_csv = [file for file in file_list if file.endswith(\".csv\")]\n",
    "\n",
    "\n",
    "alist = [file.replace(\".csv\", \"\") for file in file_list_csv ]\n",
    "size = len(alist)\n",
    "div = int(size)\n",
    "# alist = alist[div*(core-1):div*core]\n",
    "\n",
    "print(size)\n",
    "count=0\n",
    "\n",
    "print(start)\n",
    "\n",
    "\n",
    "stock = alist[4] \n",
    "\n",
    "\n",
    "\n",
    "print(stock,'\\t', cc, ' / ', len(alist))\n",
    "\n",
    "df = pd.read_csv('./csv/'+stock+'.csv') #this file is stock data including 'Close' value\n",
    "signal_data = df[[\"close\"]].values.astype('float32')\n",
    "total_data = df[[\"close\"]].values.astype('float32')\n",
    "\n",
    "\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "signal_data = scaler.fit_transform(signal_data)\n",
    "\n",
    "train_size = int(len(signal_data) * 0.80)\n",
    "test_size = len(signal_data) - train_size\n",
    "\n",
    "train = signal_data[0:train_size]\n",
    "test = signal_data[train_size: len(signal_data)]\n",
    "\n",
    "x_train, y_train = create_dataset(train, look_back)\n",
    "x_test, y_test = create_dataset(test, look_back)\n",
    "\n",
    "\n",
    "# K.clear_session()\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    layers.LSTM(10, input_shape=(None, x_train.shape[2]), dropout=0.2),\n",
    "    layers.Dense(1)\n",
    "])\n",
    "\n",
    "# callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, verbose=2)\n",
    "# model.compile(optimizer=optimizers.adam(lr=0.01), metrics='mse')\n",
    "\n",
    "model.compile(optimizer='adam', loss='mae')\n",
    "\n",
    "history = model.fit(x_train, y_train, epochs=50, batch_size=256, verbose=0)\n",
    "\n",
    "p = model.predict(x_test)\n",
    "mse = mean_squared_error(y_test, p)\n",
    "print(mse)\n",
    "print()\n",
    "\n",
    "inputs = total_data[len(total_data) - forecast - look_back:]\n",
    "inputs = scaler.transform(inputs)\n",
    "X_test = []\n",
    "for i in range(look_back, inputs.shape[0]):\n",
    "    X_test.append(inputs[i - look_back:i, :])\n",
    "X_test = np.array(X_test)\n",
    "predicted = model.predict(X_test)\n",
    "# predicted_stock_price = scaler.inverse_transform(predicted)\n",
    "result = max(predicted) - y_test[-1]\n",
    "result = np.asscalar(result)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.52040815],\n",
       "       [0.5374149 ],\n",
       "       [0.52721083],\n",
       "       [0.5068027 ],\n",
       "       [0.52040815],\n",
       "       [0.51700675],\n",
       "       [0.52040815],\n",
       "       [0.52380955],\n",
       "       [0.52040815],\n",
       "       [0.51700675],\n",
       "       [0.52040815],\n",
       "       [0.51700675],\n",
       "       [0.5068027 ],\n",
       "       [0.5102041 ],\n",
       "       [0.51700675],\n",
       "       [0.51700675],\n",
       "       [0.52040815],\n",
       "       [0.5136055 ],\n",
       "       [0.52040815],\n",
       "       [0.5102041 ],\n",
       "       [0.48299325],\n",
       "       [0.48639452],\n",
       "       [0.47619045],\n",
       "       [0.46938777],\n",
       "       [0.48299325],\n",
       "       [0.48299325],\n",
       "       [0.48639452],\n",
       "       [0.48979592],\n",
       "       [0.48979592],\n",
       "       [0.48979592],\n",
       "       [0.48639452],\n",
       "       [0.48979592],\n",
       "       [0.48639452],\n",
       "       [0.48979592],\n",
       "       [0.48639452],\n",
       "       [0.47959185],\n",
       "       [0.47278917],\n",
       "       [0.47278917],\n",
       "       [0.47959185],\n",
       "       [0.47619045]], dtype=float32)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.52040815],\n",
       "        [0.5374149 ],\n",
       "        [0.52721083],\n",
       "        [0.5068027 ],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.52380955],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.5068027 ],\n",
       "        [0.5102041 ],\n",
       "        [0.51700675],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ]],\n",
       "\n",
       "       [[0.5374149 ],\n",
       "        [0.52721083],\n",
       "        [0.5068027 ],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.52380955],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.5068027 ],\n",
       "        [0.5102041 ],\n",
       "        [0.51700675],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325]],\n",
       "\n",
       "       [[0.52721083],\n",
       "        [0.5068027 ],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.52380955],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.5068027 ],\n",
       "        [0.5102041 ],\n",
       "        [0.51700675],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452]],\n",
       "\n",
       "       [[0.5068027 ],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.52380955],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.5068027 ],\n",
       "        [0.5102041 ],\n",
       "        [0.51700675],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.47619045]],\n",
       "\n",
       "       [[0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.52380955],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.5068027 ],\n",
       "        [0.5102041 ],\n",
       "        [0.51700675],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.47619045],\n",
       "        [0.46938777]],\n",
       "\n",
       "       [[0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.52380955],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.5068027 ],\n",
       "        [0.5102041 ],\n",
       "        [0.51700675],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.47619045],\n",
       "        [0.46938777],\n",
       "        [0.48299325]],\n",
       "\n",
       "       [[0.52040815],\n",
       "        [0.52380955],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.5068027 ],\n",
       "        [0.5102041 ],\n",
       "        [0.51700675],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.47619045],\n",
       "        [0.46938777],\n",
       "        [0.48299325],\n",
       "        [0.48299325]],\n",
       "\n",
       "       [[0.52380955],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.5068027 ],\n",
       "        [0.5102041 ],\n",
       "        [0.51700675],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.47619045],\n",
       "        [0.46938777],\n",
       "        [0.48299325],\n",
       "        [0.48299325],\n",
       "        [0.48639452]],\n",
       "\n",
       "       [[0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.5068027 ],\n",
       "        [0.5102041 ],\n",
       "        [0.51700675],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.47619045],\n",
       "        [0.46938777],\n",
       "        [0.48299325],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.48979592]],\n",
       "\n",
       "       [[0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.5068027 ],\n",
       "        [0.5102041 ],\n",
       "        [0.51700675],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.47619045],\n",
       "        [0.46938777],\n",
       "        [0.48299325],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48979592]],\n",
       "\n",
       "       [[0.52040815],\n",
       "        [0.51700675],\n",
       "        [0.5068027 ],\n",
       "        [0.5102041 ],\n",
       "        [0.51700675],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.47619045],\n",
       "        [0.46938777],\n",
       "        [0.48299325],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48979592]],\n",
       "\n",
       "       [[0.51700675],\n",
       "        [0.5068027 ],\n",
       "        [0.5102041 ],\n",
       "        [0.51700675],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.47619045],\n",
       "        [0.46938777],\n",
       "        [0.48299325],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48639452]],\n",
       "\n",
       "       [[0.5068027 ],\n",
       "        [0.5102041 ],\n",
       "        [0.51700675],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.47619045],\n",
       "        [0.46938777],\n",
       "        [0.48299325],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.48979592]],\n",
       "\n",
       "       [[0.5102041 ],\n",
       "        [0.51700675],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.47619045],\n",
       "        [0.46938777],\n",
       "        [0.48299325],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48639452]],\n",
       "\n",
       "       [[0.51700675],\n",
       "        [0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.47619045],\n",
       "        [0.46938777],\n",
       "        [0.48299325],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.48979592]],\n",
       "\n",
       "       [[0.51700675],\n",
       "        [0.52040815],\n",
       "        [0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.47619045],\n",
       "        [0.46938777],\n",
       "        [0.48299325],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48639452]],\n",
       "\n",
       "       [[0.52040815],\n",
       "        [0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.47619045],\n",
       "        [0.46938777],\n",
       "        [0.48299325],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.47959185]],\n",
       "\n",
       "       [[0.5136055 ],\n",
       "        [0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.47619045],\n",
       "        [0.46938777],\n",
       "        [0.48299325],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.47959185],\n",
       "        [0.47278917]],\n",
       "\n",
       "       [[0.52040815],\n",
       "        [0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.47619045],\n",
       "        [0.46938777],\n",
       "        [0.48299325],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.47959185],\n",
       "        [0.47278917],\n",
       "        [0.47278917]],\n",
       "\n",
       "       [[0.5102041 ],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.47619045],\n",
       "        [0.46938777],\n",
       "        [0.48299325],\n",
       "        [0.48299325],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.48979592],\n",
       "        [0.48639452],\n",
       "        [0.47959185],\n",
       "        [0.47278917],\n",
       "        [0.47278917],\n",
       "        [0.47959185]]], dtype=float32)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I think this method is wrong\n",
    "# this method cannot predict next N-step\n",
    "# because this mtehod doesn't use model.predict function"
   ]
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}