{
 "cells": [
  {
   "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/recent_test/recent_test_scaled.csv')\n",
    "validation = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/scaled/validation_scaled.csv')\n"
   ]
  },
  {
   "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": [
    "train_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/vol/exp/train_EVMA_scaled.csv')\n",
    "test_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/vol/exp/test_EVMA_scaled.csv')\n",
    "val_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/vol/exp/val_EVMA_scaled.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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']\n",
    "del train_EVMA['Unnamed: 0']\n",
    "del test_EVMA['Unnamed: 0']\n",
    "del val_EVMA['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_EVMA = train_EVMA.rename(columns={'5':'5vol','10':'10vol','20':'20vol','60':'60vol','120':'120vol'})\n",
    "test_EVMA = test_EVMA.rename(columns={'5':'5vol','10':'10vol','20':'20vol','60':'60vol','120':'120vol'})\n",
    "val_EVMA = val_EVMA.rename(columns={'5':'5vol','10':'10vol','20':'20vol','60':'60vol','120':'120vol'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([train, train_EMA, train_EVMA], axis = 1)\n",
    "test = pd.concat([test, test_EMA, test_EVMA],axis = 1)\n",
    "validation = pd.concat([validation,val_EMA, val_EVMA], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
      "5vol      0\n",
      "10vol     0\n",
      "20vol     0\n",
      "60vol     0\n",
      "120vol    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",
      "5vol      0\n",
      "10vol     0\n",
      "20vol     0\n",
      "60vol     0\n",
      "120vol    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",
      "5vol      0\n",
      "10vol     0\n",
      "20vol     0\n",
      "60vol     0\n",
      "120vol    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": 13,
   "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature , label 분리\n",
    "feature_cols = ['open','high','low','vol','5','10','20','60','120','5vol','10vol','20vol','60vol','120vol']\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 20, 64)            20224     \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 50)                23000     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 43,275\n",
      "Trainable params: 43,275\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# keras 활용한 LSTM 모델 생성\n",
    "model = Sequential()\n",
    "model.add(LSTM(64, return_sequences=True, input_shape=(20,14)))\n",
    "model.add(LSTM(50, return_sequences=False))\n",
    "model.add(Dense(1, activation='linear'))\n",
    "model.compile(loss='mse',optimizer='adam')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 3444 samples, validate on 226 samples\n",
      "Epoch 1/100\n",
      "3444/3444 [==============================] - 52s 15ms/step - loss: 0.0028 - val_loss: 0.0146\n",
      "Epoch 2/100\n",
      "3444/3444 [==============================] - 53s 15ms/step - loss: 6.0028e-04 - val_loss: 0.0097\n",
      "Epoch 3/100\n",
      "3444/3444 [==============================] - 52s 15ms/step - loss: 4.5852e-04 - val_loss: 0.0075\n",
      "Epoch 4/100\n",
      "3444/3444 [==============================] - 49s 14ms/step - loss: 3.6769e-04 - val_loss: 0.0055\n",
      "Epoch 5/100\n",
      "3444/3444 [==============================] - 48s 14ms/step - loss: 3.3887e-04 - val_loss: 0.0046\n",
      "Epoch 6/100\n",
      "3444/3444 [==============================] - 52s 15ms/step - loss: 2.9594e-04 - val_loss: 0.0044\n",
      "Epoch 7/100\n",
      "3444/3444 [==============================] - 50s 14ms/step - loss: 2.8786e-04 - val_loss: 0.0039\n",
      "Epoch 8/100\n",
      "3444/3444 [==============================] - 51s 15ms/step - loss: 2.6590e-04 - val_loss: 0.0035\n",
      "Epoch 9/100\n",
      "3444/3444 [==============================] - 49s 14ms/step - loss: 2.4717e-04 - val_loss: 0.0036\n"
     ]
    }
   ],
   "source": [
    "early_stopping = EarlyStopping() \n",
    "hist = model.fit(X_train, y_train, validation_data=(X_val, y_val),batch_size=5, epochs=100,callbacks=[early_stopping])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191/191 [==============================] - 0s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test,y_test,batch_size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.003031257843673083\n"
     ]
    }
   ],
   "source": [
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "actual = pd.DataFrame(y_test,columns=[\"actual\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.691248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.736622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.747421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     actual\n",
       "0  0.691248\n",
       "1  0.736622\n",
       "2  0.747421"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = pd.DataFrame(predict,columns=[\"prediction\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.concat([actual, prediction],axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "result['investing'] = '-'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/190 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "  1%|          | 1/190 [00:00<00:21,  8.77it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n",
      "100%|██████████| 190/190 [00:12<00:00, 15.05it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "for i in tqdm(range(1,len(result))):\n",
    "    \n",
    "    real = result['actual'][i]-result['actual'][i-1] #실제 변화\n",
    "    pred = result['prediction'][i]-result['actual'][i-1] #예측한 변화 \n",
    "    if real * pred <= 0:\n",
    "        result['investing'][i] = 'fail'\n",
    "    else:\n",
    "        result['investing'][i] = 'success'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual</th>\n",
       "      <th>prediction</th>\n",
       "      <th>investing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.691248</td>\n",
       "      <td>0.703289</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.736622</td>\n",
       "      <td>0.653340</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.747421</td>\n",
       "      <td>0.723103</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.816973</td>\n",
       "      <td>0.749402</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.797308</td>\n",
       "      <td>0.783280</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.781431</td>\n",
       "      <td>0.783663</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.805932</td>\n",
       "      <td>0.753640</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.824146</td>\n",
       "      <td>0.800787</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.819230</td>\n",
       "      <td>0.796187</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.835993</td>\n",
       "      <td>0.817366</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.833978</td>\n",
       "      <td>0.806768</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.793520</td>\n",
       "      <td>0.819744</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.798275</td>\n",
       "      <td>0.784758</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.784736</td>\n",
       "      <td>0.784814</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.750645</td>\n",
       "      <td>0.785657</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.658769</td>\n",
       "      <td>0.733705</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.684720</td>\n",
       "      <td>0.652582</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.650064</td>\n",
       "      <td>0.660263</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.627095</td>\n",
       "      <td>0.635876</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.553997</td>\n",
       "      <td>0.631950</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.572937</td>\n",
       "      <td>0.546443</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.582527</td>\n",
       "      <td>0.557074</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.635477</td>\n",
       "      <td>0.608967</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.661670</td>\n",
       "      <td>0.585065</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.611057</td>\n",
       "      <td>0.656084</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.514426</td>\n",
       "      <td>0.597800</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.526999</td>\n",
       "      <td>0.496118</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.465264</td>\n",
       "      <td>0.503983</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.389587</td>\n",
       "      <td>0.480900</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.333414</td>\n",
       "      <td>0.379324</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>0.882818</td>\n",
       "      <td>0.935797</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>0.889426</td>\n",
       "      <td>0.877169</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>0.831721</td>\n",
       "      <td>0.867201</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>0.840506</td>\n",
       "      <td>0.831463</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>0.868391</td>\n",
       "      <td>0.838094</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>0.887814</td>\n",
       "      <td>0.856841</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>0.920616</td>\n",
       "      <td>0.881978</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>0.928191</td>\n",
       "      <td>0.887928</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>0.951564</td>\n",
       "      <td>0.918288</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>0.952531</td>\n",
       "      <td>0.907593</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>0.966070</td>\n",
       "      <td>0.951820</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>0.969536</td>\n",
       "      <td>0.942905</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>0.945277</td>\n",
       "      <td>0.955160</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>0.923920</td>\n",
       "      <td>0.938551</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>0.907076</td>\n",
       "      <td>0.911032</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>0.913201</td>\n",
       "      <td>0.904509</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>0.930287</td>\n",
       "      <td>0.910905</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>0.938749</td>\n",
       "      <td>0.897511</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>0.922711</td>\n",
       "      <td>0.931549</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>0.930932</td>\n",
       "      <td>0.898203</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>0.920777</td>\n",
       "      <td>0.918876</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>0.906834</td>\n",
       "      <td>0.921127</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>0.917876</td>\n",
       "      <td>0.891305</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>0.891844</td>\n",
       "      <td>0.897526</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>0.824629</td>\n",
       "      <td>0.868539</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0.859687</td>\n",
       "      <td>0.842922</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>0.909010</td>\n",
       "      <td>0.834881</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>0.923195</td>\n",
       "      <td>0.896110</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>0.985090</td>\n",
       "      <td>0.915937</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0.989845</td>\n",
       "      <td>0.941439</td>\n",
       "      <td>fail</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>191 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       actual  prediction investing\n",
       "0    0.691248    0.703289         -\n",
       "1    0.736622    0.653340      fail\n",
       "2    0.747421    0.723103      fail\n",
       "3    0.816973    0.749402   success\n",
       "4    0.797308    0.783280   success\n",
       "5    0.781431    0.783663   success\n",
       "6    0.805932    0.753640      fail\n",
       "7    0.824146    0.800787      fail\n",
       "8    0.819230    0.796187   success\n",
       "9    0.835993    0.817366      fail\n",
       "10   0.833978    0.806768   success\n",
       "11   0.793520    0.819744   success\n",
       "12   0.798275    0.784758      fail\n",
       "13   0.784736    0.784814   success\n",
       "14   0.750645    0.785657      fail\n",
       "15   0.658769    0.733705   success\n",
       "16   0.684720    0.652582      fail\n",
       "17   0.650064    0.660263   success\n",
       "18   0.627095    0.635876   success\n",
       "19   0.553997    0.631950      fail\n",
       "20   0.572937    0.546443      fail\n",
       "21   0.582527    0.557074      fail\n",
       "22   0.635477    0.608967   success\n",
       "23   0.661670    0.585065      fail\n",
       "24   0.611057    0.656084   success\n",
       "25   0.514426    0.597800   success\n",
       "26   0.526999    0.496118      fail\n",
       "27   0.465264    0.503983   success\n",
       "28   0.389587    0.480900      fail\n",
       "29   0.333414    0.379324   success\n",
       "..        ...         ...       ...\n",
       "161  0.882818    0.935797   success\n",
       "162  0.889426    0.877169      fail\n",
       "163  0.831721    0.867201   success\n",
       "164  0.840506    0.831463      fail\n",
       "165  0.868391    0.838094      fail\n",
       "166  0.887814    0.856841      fail\n",
       "167  0.920616    0.881978      fail\n",
       "168  0.928191    0.887928      fail\n",
       "169  0.951564    0.918288      fail\n",
       "170  0.952531    0.907593      fail\n",
       "171  0.966070    0.951820      fail\n",
       "172  0.969536    0.942905      fail\n",
       "173  0.945277    0.955160   success\n",
       "174  0.923920    0.938551   success\n",
       "175  0.907076    0.911032   success\n",
       "176  0.913201    0.904509      fail\n",
       "177  0.930287    0.910905      fail\n",
       "178  0.938749    0.897511      fail\n",
       "179  0.922711    0.931549   success\n",
       "180  0.930932    0.898203      fail\n",
       "181  0.920777    0.918876   success\n",
       "182  0.906834    0.921127      fail\n",
       "183  0.917876    0.891305      fail\n",
       "184  0.891844    0.897526   success\n",
       "185  0.824629    0.868539   success\n",
       "186  0.859687    0.842922   success\n",
       "187  0.909010    0.834881      fail\n",
       "188  0.923195    0.896110      fail\n",
       "189  0.985090    0.915937      fail\n",
       "190  0.989845    0.941439      fail\n",
       "\n",
       "[191 rows x 3 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fail       111\n",
       "success     79\n",
       "-            1\n",
       "Name: investing, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['investing'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5842105263157895"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "111/190"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.arange(3)\n",
    "case = ['case1', 'case2', 'case3']\n",
    "values = [0.00224, 0.00292, 0.00303]\n",
    "\n",
    "plt.bar(x, values,width=0.3)\n",
    "plt.xticks(x, case)\n",
    "plt.title(\"mse\")\n",
    "plt.show()"
   ]
  },
  {
   "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
}