test/Code/test_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Top Three models are:** \n",
    "### _**a) Logit**_\n",
    "### _**b) Balanced Random Forest**_\n",
    "### _**c) Decision Tree**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from iexfinance.stocks import Stock\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "from newsapi.newsapi_client import NewsApiClient\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "from datetime import date, datetime, timedelta\n",
    "import os \n",
    "import nltk\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "analyzer = SentimentIntensityAnalyzer()\n",
    "from iexfinance.stocks import get_historical_data\n",
    "import pickle\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer, PorterStemmer\n",
    "from string import punctuation\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from alpha_vantage.timeseries import TimeSeries\n",
    "import time\n",
    "%matplotlib inline\n",
    "from datetime import datetime as dt\n",
    "from dateutil.tz import gettz\n",
    "import hvplot.pandas\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from newsapi import NewsApiClient\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "from datetime import datetime, timedelta\n",
    "from iexfinance.stocks import get_historical_data\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "from sklearn import tree\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "# Needed for decision tree visualization\n",
    "import pydotplus\n",
    "from IPython.display import Image\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     /Users/ava/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('vader_lexicon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_df(ticker, lag=0):\n",
    "    \n",
    "    sp500_csv = Path(\"../../project_code/Data/sp500_constituents.csv\")\n",
    "    sp500_df = pd.read_csv(sp500_csv)\n",
    "    sp500_df['Ticker'] = sp500_df['Symbol']\n",
    "    sp500_df.drop(columns='Sector',inplace=True)\n",
    "    sp500_df.set_index('Ticker', inplace=True)\n",
    "    \n",
    "    stock_dict = sp500_df.T.to_dict('list')\n",
    "    \n",
    "    from datetime import date, datetime, timedelta\n",
    "    newsapi = NewsApiClient(api_key=os.environ[\"NEWS_API_KEY\"])\n",
    "    pickle.dump(newsapi,open('newsapi.pickle','wb'))\n",
    "    current_date = date.today()\n",
    "    past_date = date.today() - timedelta(days=30)\n",
    "    \n",
    "    def get_headlines(keyword):\n",
    "        all_headlines = []  #empty list for all headlines\n",
    "        all_dates = [] #empyt list for all dates\n",
    "        date = current_date #today's date\n",
    "        #print(f\"Fetching news about '{keyword}'\") #prints single string of function starting\n",
    "        #print(\"*\" * 30) #creating a line of stars for readability\n",
    "        while date > past_date: #establishes length of dates being pulled by for the \n",
    "            #lenght of the difference between today and past_date \n",
    "            #print(f\"retrieving news from: {date}\")#printing string for loop readability  \n",
    "            articles = newsapi.get_everything(   #pulling articles through API\n",
    "                q=keyword,\n",
    "                from_param=str(date),\n",
    "                to=str(date),\n",
    "                language=\"en\",\n",
    "                sort_by=\"relevancy\",\n",
    "                page=1,\n",
    "            )\n",
    "            headlines = []\n",
    "            for i in range(0, len(articles[\"articles\"])):\n",
    "\n",
    "                #pulling the content part of the dict\n",
    "                headlines.append(articles[\"articles\"][i][\"content\"])\n",
    "            all_headlines.append(headlines)\n",
    "            all_dates.append(date)\n",
    "            date = date - timedelta(days=1) #moving through the days            \n",
    "        return all_headlines, all_dates\n",
    "    \n",
    "    headlines, dates = get_headlines(stock_dict[ticker][1])\n",
    "    \n",
    "    df = pd.DataFrame(headlines)\n",
    "    df.index = pd.to_datetime(dates)\n",
    "    \n",
    "    concatinated_list = []\n",
    "    i = 0\n",
    "    while i < len(df):\n",
    "        date = df.index[i]\n",
    "        daily_data = df.iloc[i,:].dropna().to_list()\n",
    "        daily_data = \"\".join(daily_data)\n",
    "        concatinated_list.append({\"Date\":date,\n",
    "                                  \"articles\":daily_data\n",
    "                                  })\n",
    "        i = i + 1\n",
    "        \n",
    "    df = pd.DataFrame(concatinated_list)\n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    \n",
    "    def tokenizer(text):\n",
    "        sw = set(stopwords.words('english'))\n",
    "        regex = re.compile(\"[^a-zA-Z ]\")\n",
    "        re_clean = regex.sub('', text)\n",
    "        words = word_tokenize(re_clean)\n",
    "        lem = [lemmatizer.lemmatize(word) for word in words]\n",
    "        tokens = [word.lower() for word in lem if word.lower() not in sw]\n",
    "\n",
    "        return tokens\n",
    "\n",
    "    df[\"tokens\"] = [tokenizer(i) for i in df[\"articles\"]]\n",
    "    df[\"Clean Words\"] = [\" \".join(i)for i in df[\"tokens\"]]\n",
    "    \n",
    "    def get_sentiment(dataframe):\n",
    "    # list of sentiment objects observed\n",
    "        df_sentiments = []\n",
    "        i = 0 \n",
    "        # for rows in df: # the next time you put a for-loop outside of a try fn \n",
    "        # there will be a muckduck\n",
    "        # for loops are used for interating through one object ie. for every item in list \n",
    "        # while loops can be used for interating through multiple objects on the same \n",
    "        # index ie. 1st item in ...for 2nd item in... for 3rd item in.. \n",
    "\n",
    "\n",
    "        while i < len(dataframe[\"Clean Words\"]): # if we did len of df, then it would be \n",
    "            # the length of columns by doing len of df[col], then its length of rows\n",
    "\n",
    "            # to get values for the sentiment parameters\n",
    "            text = dataframe[\"Clean Words\"][i] \n",
    "            date = dataframe[\"Date\"][i]\n",
    "\n",
    "            # activate sentiment analysis\n",
    "            sentiment = analyzer.polarity_scores(text)  ## Attribute Error is applied for \n",
    "                                                        ## NoneTypes in \n",
    "            compound = sentiment[\"compound\"]\n",
    "            pos = sentiment[\"pos\"]\n",
    "            neu = sentiment[\"neu\"]\n",
    "            neg = sentiment[\"neg\"]\n",
    "\n",
    "            # append results of sentiment analysis per row of sentiment parameters df\n",
    "            df_sentiments.append({\n",
    "                    \"text\": text,\n",
    "                    \"date\": date,\n",
    "                    \"compound\": compound,\n",
    "                    \"positive\": pos,\n",
    "                    \"negative\": neg,\n",
    "                    \"neutral\": neu\n",
    "                })\n",
    "            i += 1\n",
    "\n",
    "        # Create DataFrame\n",
    "        final_df = pd.DataFrame(df_sentiments)\n",
    "\n",
    "        # Reorder DataFrame columns\n",
    "        cols = [\"date\", \"text\", \"compound\", \"positive\", \"negative\", \"neutral\"]\n",
    "        final_list = final_df[cols]\n",
    "\n",
    "        return final_list\n",
    "\n",
    "    df = get_sentiment(df)\n",
    "    df = df.set_index('date')\n",
    "    df = df.sort_values(by='date',ascending=True)\n",
    "\n",
    "    # setting start and end date for the past four weeks\n",
    "    # 29 days needed instead of 28 days so that we get 28 days of return when we calculate\n",
    "    end_date_stock = datetime.now()\n",
    "    start_date_stock = end_date_stock + timedelta(-31)\n",
    "\n",
    "    # getting data from the API and adding to DataFrame\n",
    "    returns_df = get_historical_data(ticker, start_date_stock, end_date_stock,  \n",
    "                                     output_format='pandas')\n",
    "    returns_df.drop(columns=['open','high','low','volume'],inplace=True)\n",
    "    returns_df = returns_df.pct_change() * 100\n",
    "    returns_df.dropna(inplace=True)\n",
    "    returns_df.isnull().sum()\n",
    "    returns_df.rename(columns={'close':'return'}, inplace=True)\n",
    "    \n",
    "    combined_df = pd.concat([df, returns_df], axis=1)\n",
    "\n",
    "    # Function takes in an NLP/stock returns dataframe and calculates the average polarity\n",
    "    # scores over non-trading days and replaces scores of the first trading day after\n",
    "    # non-trading days with average score of previous days.\n",
    "    def cleaned_df(dataframe):\n",
    "        compound = []\n",
    "        positive = []\n",
    "        negative = []\n",
    "        neutral = []\n",
    "\n",
    "        for index, row in dataframe.iterrows():\n",
    "\n",
    "            # If daily return is null value for a given day, append polarity scores to their\n",
    "            # respective lists.\n",
    "            if pd.isnull(row['return']):\n",
    "                compound.append(row['compound'])\n",
    "                positive.append(row['positive'])\n",
    "                negative.append(row['negative'])\n",
    "                neutral.append(row['neutral'])\n",
    "                dataframe.drop(index=index, inplace=True)\n",
    "\n",
    "            elif pd.notnull(row['return']):\n",
    "                \n",
    "                # The list of compound polarity scores will be empty if the stock was traded\n",
    "                # on the previous day; therefore, move along.\n",
    "                if len(compound) == 0:\n",
    "                    pass\n",
    "\n",
    "                # If the list is not empty, then at least one day prior was a non-trading \n",
    "                # day. Append the current day's scores to the list and calculate the mean \n",
    "                # for each score. Then replace the current day's polarity scores with the \n",
    "                # average scores of today and previous non-trading days.\n",
    "                else:\n",
    "                    compound.append(row['compound'])\n",
    "                    compound_mean = np.mean(compound)\n",
    "                    compound = []\n",
    "\n",
    "                    positive.append(row['positive'])\n",
    "                    positive_mean = np.mean(positive)\n",
    "                    positive = []\n",
    "\n",
    "                    negative.append(row['negative'])\n",
    "                    negative_mean = np.mean(negative)\n",
    "                    negative = []\n",
    "\n",
    "                    neutral.append(row['neutral'])\n",
    "                    neutral_mean = np.mean(neutral)\n",
    "                    neutral = []\n",
    "\n",
    "                    dataframe.at[index, 'compound'] = compound_mean\n",
    "                    dataframe.at[index, 'positive'] = positive_mean\n",
    "                    dataframe.at[index, 'negative'] = negative_mean\n",
    "                    dataframe.at[index, 'neutral'] = neutral_mean\n",
    "\n",
    "            else:\n",
    "                pass\n",
    "\n",
    "        return dataframe.sort_index(ascending=True)\n",
    "    \n",
    "    # Shift the return column up to adjust for a lag in stock reaction to sentiments.\n",
    "    final_df = cleaned_df(combined_df)\n",
    "    final_df['return'] = final_df['return'].shift(-lag)\n",
    "    final_df.dropna(inplace=True)\n",
    "    \n",
    "    return final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker = 'DIS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dis_df = create_df('DIS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 836,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .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>text</th>\n",
       "      <th>compound</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "      <th>neutral</th>\n",
       "      <th>return</th>\n",
       "      <th>return_sign</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-19</th>\n",
       "      <td>health worker screen patient coronavirus march...</td>\n",
       "      <td>0.985100</td>\n",
       "      <td>0.117000</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.839000</td>\n",
       "      <td>6.903153</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-20</th>\n",
       "      <td>disney said friday postponing launch eponymous...</td>\n",
       "      <td>0.898800</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.804000</td>\n",
       "      <td>-9.428000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-23</th>\n",
       "      <td>health worker screen patient coronavirus march...</td>\n",
       "      <td>0.970400</td>\n",
       "      <td>0.125667</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.803333</td>\n",
       "      <td>-0.255873</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-24</th>\n",
       "      <td>disney streaming service walt disney company h...</td>\n",
       "      <td>0.996800</td>\n",
       "      <td>0.192000</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.761000</td>\n",
       "      <td>14.412313</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-25</th>\n",
       "      <td>response coronavirus pandemic disney theme par...</td>\n",
       "      <td>0.995300</td>\n",
       "      <td>0.182000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.768000</td>\n",
       "      <td>2.660008</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-26</th>\n",
       "      <td>authors note article part series attempt ident...</td>\n",
       "      <td>0.998700</td>\n",
       "      <td>0.239000</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.741000</td>\n",
       "      <td>4.596446</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-27</th>\n",
       "      <td>disneyland walt disney world initially planned...</td>\n",
       "      <td>0.988600</td>\n",
       "      <td>0.114000</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.853000</td>\n",
       "      <td>-8.504176</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-30</th>\n",
       "      <td>enjoying killer queen blacki started playing k...</td>\n",
       "      <td>0.994267</td>\n",
       "      <td>0.157000</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.794333</td>\n",
       "      <td>3.526971</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>walt disneynysedis taking step conserve cash c...</td>\n",
       "      <td>0.982100</td>\n",
       "      <td>0.122000</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.821000</td>\n",
       "      <td>-3.206413</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>even though walt disney company laying constru...</td>\n",
       "      <td>0.985300</td>\n",
       "      <td>0.148000</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.779000</td>\n",
       "      <td>-1.739130</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         text  compound  \\\n",
       "date                                                                      \n",
       "2020-03-19  health worker screen patient coronavirus march...  0.985100   \n",
       "2020-03-20  disney said friday postponing launch eponymous...  0.898800   \n",
       "2020-03-23  health worker screen patient coronavirus march...  0.970400   \n",
       "2020-03-24  disney streaming service walt disney company h...  0.996800   \n",
       "2020-03-25  response coronavirus pandemic disney theme par...  0.995300   \n",
       "2020-03-26  authors note article part series attempt ident...  0.998700   \n",
       "2020-03-27  disneyland walt disney world initially planned...  0.988600   \n",
       "2020-03-30  enjoying killer queen blacki started playing k...  0.994267   \n",
       "2020-03-31  walt disneynysedis taking step conserve cash c...  0.982100   \n",
       "2020-04-01  even though walt disney company laying constru...  0.985300   \n",
       "\n",
       "            positive  negative   neutral     return  return_sign  \n",
       "date                                                              \n",
       "2020-03-19  0.117000     0.044  0.839000   6.903153          1.0  \n",
       "2020-03-20  0.106000     0.090  0.804000  -9.428000         -1.0  \n",
       "2020-03-23  0.125667     0.071  0.803333  -0.255873         -1.0  \n",
       "2020-03-24  0.192000     0.047  0.761000  14.412313          1.0  \n",
       "2020-03-25  0.182000     0.050  0.768000   2.660008          1.0  \n",
       "2020-03-26  0.239000     0.020  0.741000   4.596446          1.0  \n",
       "2020-03-27  0.114000     0.033  0.853000  -8.504176         -1.0  \n",
       "2020-03-30  0.157000     0.049  0.794333   3.526971          1.0  \n",
       "2020-03-31  0.122000     0.058  0.821000  -3.206413         -1.0  \n",
       "2020-04-01  0.148000     0.073  0.779000  -1.739130         -1.0  "
      ]
     },
     "execution_count": 836,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dis_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 837,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dis_df.to_pickle(f'../Data/{ticker}_sent_return.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 838,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just lag function\n",
    "def df_lag(df, lag=0):\n",
    "    df['return'] = df['return'].shift(-lag)\n",
    "    df.dropna(inplace=True)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 839,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_lag(dis_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 840,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['return_sign'] = np.sign(df['return'].values)\n",
    "df_class = df.copy() # save a clean copy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 841,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>compound</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "      <th>neutral</th>\n",
       "      <th>return</th>\n",
       "      <th>return_sign</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-19</th>\n",
       "      <td>health worker screen patient coronavirus march...</td>\n",
       "      <td>0.9851</td>\n",
       "      <td>0.117000</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.839000</td>\n",
       "      <td>6.903153</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-03-20</th>\n",
       "      <td>disney said friday postponing launch eponymous...</td>\n",
       "      <td>0.8988</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.804000</td>\n",
       "      <td>-9.428000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-23</th>\n",
       "      <td>health worker screen patient coronavirus march...</td>\n",
       "      <td>0.9704</td>\n",
       "      <td>0.125667</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.803333</td>\n",
       "      <td>-0.255873</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-24</th>\n",
       "      <td>disney streaming service walt disney company h...</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>0.192000</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.761000</td>\n",
       "      <td>14.412313</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-25</th>\n",
       "      <td>response coronavirus pandemic disney theme par...</td>\n",
       "      <td>0.9953</td>\n",
       "      <td>0.182000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.768000</td>\n",
       "      <td>2.660008</td>\n",
       "      <td>1.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                                         text  compound  \\\n",
       "date                                                                      \n",
       "2020-03-19  health worker screen patient coronavirus march...    0.9851   \n",
       "2020-03-20  disney said friday postponing launch eponymous...    0.8988   \n",
       "2020-03-23  health worker screen patient coronavirus march...    0.9704   \n",
       "2020-03-24  disney streaming service walt disney company h...    0.9968   \n",
       "2020-03-25  response coronavirus pandemic disney theme par...    0.9953   \n",
       "\n",
       "            positive  negative   neutral     return  return_sign  \n",
       "date                                                              \n",
       "2020-03-19  0.117000     0.044  0.839000   6.903153          1.0  \n",
       "2020-03-20  0.106000     0.090  0.804000  -9.428000         -1.0  \n",
       "2020-03-23  0.125667     0.071  0.803333  -0.255873         -1.0  \n",
       "2020-03-24  0.192000     0.047  0.761000  14.412313          1.0  \n",
       "2020-03-25  0.182000     0.050  0.768000   2.660008          1.0  "
      ]
     },
     "execution_count": 841,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 842,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>compound</th>\n",
       "      <th>positive</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-19</th>\n",
       "      <td>0.985100</td>\n",
       "      <td>0.117000</td>\n",
       "      <td>0.04400</td>\n",
       "      <td>0.839000</td>\n",
       "      <td>6.903153</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-03-20</th>\n",
       "      <td>0.898800</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.09000</td>\n",
       "      <td>0.804000</td>\n",
       "      <td>-9.428000</td>\n",
       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-03-23</th>\n",
       "      <td>0.970400</td>\n",
       "      <td>0.125667</td>\n",
       "      <td>0.07100</td>\n",
       "      <td>0.803333</td>\n",
       "      <td>-0.255873</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-24</th>\n",
       "      <td>0.996800</td>\n",
       "      <td>0.192000</td>\n",
       "      <td>0.04700</td>\n",
       "      <td>0.761000</td>\n",
       "      <td>14.412313</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-25</th>\n",
       "      <td>0.995300</td>\n",
       "      <td>0.182000</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.768000</td>\n",
       "      <td>2.660008</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-26</th>\n",
       "      <td>0.998700</td>\n",
       "      <td>0.239000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.741000</td>\n",
       "      <td>4.596446</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-27</th>\n",
       "      <td>0.988600</td>\n",
       "      <td>0.114000</td>\n",
       "      <td>0.03300</td>\n",
       "      <td>0.853000</td>\n",
       "      <td>-8.504176</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-30</th>\n",
       "      <td>0.994267</td>\n",
       "      <td>0.157000</td>\n",
       "      <td>0.04900</td>\n",
       "      <td>0.794333</td>\n",
       "      <td>3.526971</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>0.982100</td>\n",
       "      <td>0.122000</td>\n",
       "      <td>0.05800</td>\n",
       "      <td>0.821000</td>\n",
       "      <td>-3.206413</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>0.985300</td>\n",
       "      <td>0.148000</td>\n",
       "      <td>0.07300</td>\n",
       "      <td>0.779000</td>\n",
       "      <td>-1.739130</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>0.981600</td>\n",
       "      <td>0.136000</td>\n",
       "      <td>0.07300</td>\n",
       "      <td>0.791000</td>\n",
       "      <td>2.159713</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-03</th>\n",
       "      <td>0.977300</td>\n",
       "      <td>0.114000</td>\n",
       "      <td>0.05300</td>\n",
       "      <td>0.833000</td>\n",
       "      <td>-3.186553</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>0.991933</td>\n",
       "      <td>0.136333</td>\n",
       "      <td>0.04300</td>\n",
       "      <td>0.820333</td>\n",
       "      <td>6.071581</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-07</th>\n",
       "      <td>0.996500</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.04300</td>\n",
       "      <td>0.786000</td>\n",
       "      <td>1.667001</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-08</th>\n",
       "      <td>0.851500</td>\n",
       "      <td>0.104000</td>\n",
       "      <td>0.07800</td>\n",
       "      <td>0.819000</td>\n",
       "      <td>-0.167918</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-09</th>\n",
       "      <td>0.994400</td>\n",
       "      <td>0.147000</td>\n",
       "      <td>0.03300</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>3.393688</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-13</th>\n",
       "      <td>0.996475</td>\n",
       "      <td>0.189000</td>\n",
       "      <td>0.04425</td>\n",
       "      <td>0.766750</td>\n",
       "      <td>-0.956938</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-14</th>\n",
       "      <td>0.998000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.02400</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>2.444444</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-15</th>\n",
       "      <td>0.994700</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.04100</td>\n",
       "      <td>0.809000</td>\n",
       "      <td>-2.508724</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-16</th>\n",
       "      <td>0.997000</td>\n",
       "      <td>0.177000</td>\n",
       "      <td>0.02600</td>\n",
       "      <td>0.797000</td>\n",
       "      <td>-1.305988</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            compound  positive  negative   neutral     return  return_sign\n",
       "date                                                                      \n",
       "2020-03-19  0.985100  0.117000   0.04400  0.839000   6.903153          1.0\n",
       "2020-03-20  0.898800  0.106000   0.09000  0.804000  -9.428000         -1.0\n",
       "2020-03-23  0.970400  0.125667   0.07100  0.803333  -0.255873         -1.0\n",
       "2020-03-24  0.996800  0.192000   0.04700  0.761000  14.412313          1.0\n",
       "2020-03-25  0.995300  0.182000   0.05000  0.768000   2.660008          1.0\n",
       "2020-03-26  0.998700  0.239000   0.02000  0.741000   4.596446          1.0\n",
       "2020-03-27  0.988600  0.114000   0.03300  0.853000  -8.504176         -1.0\n",
       "2020-03-30  0.994267  0.157000   0.04900  0.794333   3.526971          1.0\n",
       "2020-03-31  0.982100  0.122000   0.05800  0.821000  -3.206413         -1.0\n",
       "2020-04-01  0.985300  0.148000   0.07300  0.779000  -1.739130         -1.0\n",
       "2020-04-02  0.981600  0.136000   0.07300  0.791000   2.159713          1.0\n",
       "2020-04-03  0.977300  0.114000   0.05300  0.833000  -3.186553         -1.0\n",
       "2020-04-06  0.991933  0.136333   0.04300  0.820333   6.071581          1.0\n",
       "2020-04-07  0.996500  0.171000   0.04300  0.786000   1.667001          1.0\n",
       "2020-04-08  0.851500  0.104000   0.07800  0.819000  -0.167918         -1.0\n",
       "2020-04-09  0.994400  0.147000   0.03300  0.820000   3.393688          1.0\n",
       "2020-04-13  0.996475  0.189000   0.04425  0.766750  -0.956938         -1.0\n",
       "2020-04-14  0.998000  0.200000   0.02400  0.775000   2.444444          1.0\n",
       "2020-04-15  0.994700  0.150000   0.04100  0.809000  -2.508724         -1.0\n",
       "2020-04-16  0.997000  0.177000   0.02600  0.797000  -1.305988         -1.0"
      ]
     },
     "execution_count": 842,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(columns=['text'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 843,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.to_pickle(f'../Data/{ticker}_clean.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle('../Data/DIS_clean.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>compound</th>\n",
       "      <th>positive</th>\n",
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       "      <th>date</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-18</th>\n",
       "      <td>0.9978</td>\n",
       "      <td>0.183000</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.802000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-19</th>\n",
       "      <td>0.9851</td>\n",
       "      <td>0.117000</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.839000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-20</th>\n",
       "      <td>0.8988</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.804000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-23</th>\n",
       "      <td>0.9704</td>\n",
       "      <td>0.125667</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.803333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-24</th>\n",
       "      <td>0.9968</td>\n",
       "      <td>0.192000</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.761000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            compound  positive  negative   neutral\n",
       "date                                              \n",
       "2020-03-18    0.9978  0.183000     0.015  0.802000\n",
       "2020-03-19    0.9851  0.117000     0.044  0.839000\n",
       "2020-03-20    0.8988  0.106000     0.090  0.804000\n",
       "2020-03-23    0.9704  0.125667     0.071  0.803333\n",
       "2020-03-24    0.9968  0.192000     0.047  0.761000"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(columns=['return', 'return_sign'])\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['return_sign']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>2020-03-18</th>\n",
       "      <td>-1.0</td>\n",
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       "      <th>2020-03-19</th>\n",
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       "      <th>2020-03-20</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-23</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-24</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            return_sign\n",
       "date                   \n",
       "2020-03-18         -1.0\n",
       "2020-03-19          1.0\n",
       "2020-03-20         -1.0\n",
       "2020-03-23         -1.0\n",
       "2020-03-24          1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = y.to_frame()\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker = 'DIS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3-layer Neural Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the features (X) and target (y) sets\n",
    "X = df.iloc[:, 0:4].values\n",
    "y = df[\"return_sign\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y= y.reshape(-1,1)\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 4)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler().fit(X)\n",
    "X = scaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating training and testing data sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, shuffle=False, random_state=42) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---- Could load a saved model alternatively ------\n",
    "\n",
    "from keras.models import load_model\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "# returns a compiled model\n",
    "# identical to the previous one\n",
    "nn_3 = tf.keras.models.load_model('../Models/../Models/disney_nn_loss_3_layers.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating validation data sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train_val, X_val, y_train_val, y_val = train_test_split(X_train, y_train, test_size = 0.3, shuffle=False, random_state=42) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the model - deep neural net\n",
    "#number_input_features = 4\n",
    "#hidden_nodes_layer1 = 12\n",
    "#hidden_nodes_layer2 = 8\n",
    "#hidden_nodes_layer3 = 3\n",
    "\n",
    "\n",
    "#nn = Sequential()\n",
    "# First hidden layer\n",
    "#nn.add(\n",
    "#    Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\")\n",
    "#)\n",
    "# Second hidden layer\n",
    "#nn.add(Dense(units=hidden_nodes_layer2, activation=\"sigmoid\"))\n",
    "\n",
    "# Third hidden layer\n",
    "#nn.add(Dense(units=hidden_nodes_layer3, activation=\"tanh\"))\n",
    "\n",
    "# Output layer\n",
    "#nn.add(Dense(units=1, activation=\"linear\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile model\n",
    "#nn.compile(loss=\"mean_squared_error\", optimizer=\"adam\", metrics=[\"mse\"])\n",
    "#nn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 9 samples, validate on 5 samples\n",
      "Epoch 1/200\n",
      "9/9 [==============================] - 0s 25ms/sample - loss: 1.1068 - mse: 1.1068 - val_loss: 1.2858 - val_mse: 1.2858\n",
      "Epoch 2/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.9934 - mse: 0.9934 - val_loss: 1.2151 - val_mse: 1.2151\n",
      "Epoch 3/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.8842 - mse: 0.8842 - val_loss: 1.1609 - val_mse: 1.1609\n",
      "Epoch 4/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.7943 - mse: 0.7943 - val_loss: 1.1233 - val_mse: 1.1233\n",
      "Epoch 5/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.7235 - mse: 0.7235 - val_loss: 1.0990 - val_mse: 1.0990\n",
      "Epoch 6/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.6680 - mse: 0.6680 - val_loss: 1.0852 - val_mse: 1.0852\n",
      "Epoch 7/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.6225 - mse: 0.6225 - val_loss: 1.0762 - val_mse: 1.0762\n",
      "Epoch 8/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.5832 - mse: 0.5832 - val_loss: 1.0692 - val_mse: 1.0692\n",
      "Epoch 9/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.5502 - mse: 0.5502 - val_loss: 1.0639 - val_mse: 1.0639\n",
      "Epoch 10/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.5204 - mse: 0.5204 - val_loss: 1.0597 - val_mse: 1.0597\n",
      "Epoch 11/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.4940 - mse: 0.4940 - val_loss: 1.0565 - val_mse: 1.0565\n",
      "Epoch 12/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.4704 - mse: 0.4704 - val_loss: 1.0543 - val_mse: 1.0543\n",
      "Epoch 13/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.4490 - mse: 0.4490 - val_loss: 1.0532 - val_mse: 1.0532\n",
      "Epoch 14/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.4295 - mse: 0.4295 - val_loss: 1.0531 - val_mse: 1.0531\n",
      "Epoch 15/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.4115 - mse: 0.4115 - val_loss: 1.0536 - val_mse: 1.0536\n",
      "Epoch 16/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.3949 - mse: 0.3949 - val_loss: 1.0551 - val_mse: 1.0551\n",
      "Epoch 17/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.3793 - mse: 0.3793 - val_loss: 1.0574 - val_mse: 1.0574\n",
      "Epoch 18/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.3647 - mse: 0.3647 - val_loss: 1.0605 - val_mse: 1.0605\n",
      "Epoch 19/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.3509 - mse: 0.3509 - val_loss: 1.0646 - val_mse: 1.0646\n",
      "Epoch 20/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.3379 - mse: 0.3379 - val_loss: 1.0696 - val_mse: 1.0696\n",
      "Epoch 21/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.3254 - mse: 0.3254 - val_loss: 1.0754 - val_mse: 1.0754\n",
      "Epoch 22/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.3139 - mse: 0.3139 - val_loss: 1.0791 - val_mse: 1.0791\n",
      "Epoch 23/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.3034 - mse: 0.3034 - val_loss: 1.0820 - val_mse: 1.0820\n",
      "Epoch 24/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.2933 - mse: 0.2933 - val_loss: 1.0852 - val_mse: 1.0852\n",
      "Epoch 25/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.2838 - mse: 0.2838 - val_loss: 1.0885 - val_mse: 1.0885\n",
      "Epoch 26/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.2751 - mse: 0.2751 - val_loss: 1.0957 - val_mse: 1.0957\n",
      "Epoch 27/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.2666 - mse: 0.2666 - val_loss: 1.1013 - val_mse: 1.1013\n",
      "Epoch 28/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.2591 - mse: 0.2591 - val_loss: 1.1062 - val_mse: 1.1062\n",
      "Epoch 29/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.2518 - mse: 0.2518 - val_loss: 1.1110 - val_mse: 1.1110\n",
      "Epoch 30/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.2446 - mse: 0.2446 - val_loss: 1.1161 - val_mse: 1.1161\n",
      "Epoch 31/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.2385 - mse: 0.2385 - val_loss: 1.1256 - val_mse: 1.1256\n",
      "Epoch 32/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.2322 - mse: 0.2322 - val_loss: 1.1330 - val_mse: 1.1330\n",
      "Epoch 33/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.2262 - mse: 0.2262 - val_loss: 1.1393 - val_mse: 1.1393\n",
      "Epoch 34/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.2205 - mse: 0.2205 - val_loss: 1.1456 - val_mse: 1.1456\n",
      "Epoch 35/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.2148 - mse: 0.2148 - val_loss: 1.1516 - val_mse: 1.1516\n",
      "Epoch 36/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.2097 - mse: 0.2097 - val_loss: 1.1577 - val_mse: 1.1577\n",
      "Epoch 37/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.2048 - mse: 0.2048 - val_loss: 1.1638 - val_mse: 1.1638\n",
      "Epoch 38/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.2002 - mse: 0.2002 - val_loss: 1.1756 - val_mse: 1.1756\n",
      "Epoch 39/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1953 - mse: 0.1953 - val_loss: 1.1847 - val_mse: 1.1847\n",
      "Epoch 40/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1912 - mse: 0.1912 - val_loss: 1.1922 - val_mse: 1.1922\n",
      "Epoch 41/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1873 - mse: 0.1873 - val_loss: 1.1991 - val_mse: 1.1991\n",
      "Epoch 42/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1834 - mse: 0.1834 - val_loss: 1.2059 - val_mse: 1.2059\n",
      "Epoch 43/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1797 - mse: 0.1797 - val_loss: 1.2125 - val_mse: 1.2125\n",
      "Epoch 44/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1758 - mse: 0.1758 - val_loss: 1.2191 - val_mse: 1.2191\n",
      "Epoch 45/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1728 - mse: 0.1728 - val_loss: 1.2256 - val_mse: 1.2256\n",
      "Epoch 46/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1698 - mse: 0.1698 - val_loss: 1.2322 - val_mse: 1.2322\n",
      "Epoch 47/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1668 - mse: 0.1668 - val_loss: 1.2386 - val_mse: 1.2386\n",
      "Epoch 48/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1640 - mse: 0.1640 - val_loss: 1.2449 - val_mse: 1.2449\n",
      "Epoch 49/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1614 - mse: 0.1614 - val_loss: 1.2511 - val_mse: 1.2511\n",
      "Epoch 50/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1585 - mse: 0.1585 - val_loss: 1.2570 - val_mse: 1.2570\n",
      "Epoch 51/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1566 - mse: 0.1566 - val_loss: 1.2711 - val_mse: 1.2711\n",
      "Epoch 52/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1545 - mse: 0.1545 - val_loss: 1.2808 - val_mse: 1.2808\n",
      "Epoch 53/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.1524 - mse: 0.1524 - val_loss: 1.2882 - val_mse: 1.2882\n",
      "Epoch 54/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.1504 - mse: 0.1504 - val_loss: 1.2945 - val_mse: 1.2945\n",
      "Epoch 55/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.1484 - mse: 0.1484 - val_loss: 1.3005 - val_mse: 1.3005\n",
      "Epoch 56/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1465 - mse: 0.1465 - val_loss: 1.3062 - val_mse: 1.3062\n",
      "Epoch 57/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1447 - mse: 0.1447 - val_loss: 1.3117 - val_mse: 1.3117\n",
      "Epoch 58/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1430 - mse: 0.1430 - val_loss: 1.3172 - val_mse: 1.3172\n",
      "Epoch 59/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1410 - mse: 0.1410 - val_loss: 1.3222 - val_mse: 1.3222\n",
      "Epoch 60/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1397 - mse: 0.1397 - val_loss: 1.3271 - val_mse: 1.3271\n",
      "Epoch 61/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1383 - mse: 0.1383 - val_loss: 1.3323 - val_mse: 1.3323\n",
      "Epoch 62/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1369 - mse: 0.1369 - val_loss: 1.3373 - val_mse: 1.3373\n",
      "Epoch 63/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1354 - mse: 0.1354 - val_loss: 1.3422 - val_mse: 1.3422\n",
      "Epoch 64/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1340 - mse: 0.1340 - val_loss: 1.3472 - val_mse: 1.3472\n",
      "Epoch 65/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1327 - mse: 0.1327 - val_loss: 1.3520 - val_mse: 1.3520\n",
      "Epoch 66/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1314 - mse: 0.1314 - val_loss: 1.3567 - val_mse: 1.3567\n",
      "Epoch 67/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1301 - mse: 0.1301 - val_loss: 1.3615 - val_mse: 1.3615\n",
      "Epoch 68/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1288 - mse: 0.1288 - val_loss: 1.3660 - val_mse: 1.3660\n",
      "Epoch 69/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1276 - mse: 0.1276 - val_loss: 1.3704 - val_mse: 1.3704\n",
      "Epoch 70/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1265 - mse: 0.1265 - val_loss: 1.3750 - val_mse: 1.3750\n",
      "Epoch 71/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1253 - mse: 0.1253 - val_loss: 1.3794 - val_mse: 1.3794\n",
      "Epoch 72/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1237 - mse: 0.1237 - val_loss: 1.3837 - val_mse: 1.3837\n",
      "Epoch 73/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1230 - mse: 0.1230 - val_loss: 1.3882 - val_mse: 1.3882\n",
      "Epoch 74/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1221 - mse: 0.1221 - val_loss: 1.3931 - val_mse: 1.3931\n",
      "Epoch 75/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1211 - mse: 0.1211 - val_loss: 1.3978 - val_mse: 1.3978\n",
      "Epoch 76/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1200 - mse: 0.1200 - val_loss: 1.4025 - val_mse: 1.4025\n",
      "Epoch 77/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1190 - mse: 0.1190 - val_loss: 1.4074 - val_mse: 1.4074\n",
      "Epoch 78/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1180 - mse: 0.1180 - val_loss: 1.4119 - val_mse: 1.4119\n",
      "Epoch 79/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1170 - mse: 0.1170 - val_loss: 1.4163 - val_mse: 1.4163\n",
      "Epoch 80/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1160 - mse: 0.1160 - val_loss: 1.4209 - val_mse: 1.4209\n",
      "Epoch 81/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1150 - mse: 0.1150 - val_loss: 1.4253 - val_mse: 1.4253\n",
      "Epoch 82/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1140 - mse: 0.1140 - val_loss: 1.4295 - val_mse: 1.4295\n",
      "Epoch 83/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1131 - mse: 0.1131 - val_loss: 1.4337 - val_mse: 1.4337\n",
      "Epoch 84/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1121 - mse: 0.1121 - val_loss: 1.4382 - val_mse: 1.4382\n",
      "Epoch 85/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1112 - mse: 0.1112 - val_loss: 1.4424 - val_mse: 1.4424\n",
      "Epoch 86/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1103 - mse: 0.1103 - val_loss: 1.4465 - val_mse: 1.4465\n",
      "Epoch 87/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1094 - mse: 0.1094 - val_loss: 1.4509 - val_mse: 1.4509\n",
      "Epoch 88/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1085 - mse: 0.1085 - val_loss: 1.4551 - val_mse: 1.4551\n",
      "Epoch 89/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1072 - mse: 0.1072 - val_loss: 1.4712 - val_mse: 1.4712\n",
      "Epoch 90/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1067 - mse: 0.1067 - val_loss: 1.4812 - val_mse: 1.4812\n",
      "Epoch 91/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1060 - mse: 0.1060 - val_loss: 1.4878 - val_mse: 1.4878\n",
      "Epoch 92/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1051 - mse: 0.1051 - val_loss: 1.4932 - val_mse: 1.4932\n",
      "Epoch 93/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1043 - mse: 0.1043 - val_loss: 1.4980 - val_mse: 1.4980\n",
      "Epoch 94/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1034 - mse: 0.1034 - val_loss: 1.5026 - val_mse: 1.5026\n",
      "Epoch 95/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1025 - mse: 0.1025 - val_loss: 1.5074 - val_mse: 1.5074\n",
      "Epoch 96/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1017 - mse: 0.1017 - val_loss: 1.5118 - val_mse: 1.5118\n",
      "Epoch 97/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1008 - mse: 0.1008 - val_loss: 1.5162 - val_mse: 1.5162\n",
      "Epoch 98/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.1000 - mse: 0.1000 - val_loss: 1.5207 - val_mse: 1.5207\n",
      "Epoch 99/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0992 - mse: 0.0992 - val_loss: 1.5249 - val_mse: 1.5249\n",
      "Epoch 100/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0983 - mse: 0.0983 - val_loss: 1.5291 - val_mse: 1.5291\n",
      "Epoch 101/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0975 - mse: 0.0975 - val_loss: 1.5332 - val_mse: 1.5332\n",
      "Epoch 102/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0967 - mse: 0.0967 - val_loss: 1.5377 - val_mse: 1.5377\n",
      "Epoch 103/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0959 - mse: 0.0959 - val_loss: 1.5419 - val_mse: 1.5419\n",
      "Epoch 104/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0951 - mse: 0.0951 - val_loss: 1.5461 - val_mse: 1.5461\n",
      "Epoch 105/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0939 - mse: 0.0939 - val_loss: 1.5507 - val_mse: 1.5507\n",
      "Epoch 106/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0934 - mse: 0.0934 - val_loss: 1.5552 - val_mse: 1.5552\n",
      "Epoch 107/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0928 - mse: 0.0928 - val_loss: 1.5599 - val_mse: 1.5599\n",
      "Epoch 108/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0920 - mse: 0.0920 - val_loss: 1.5650 - val_mse: 1.5650\n",
      "Epoch 109/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0912 - mse: 0.0912 - val_loss: 1.5698 - val_mse: 1.5698\n",
      "Epoch 110/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0904 - mse: 0.0904 - val_loss: 1.5743 - val_mse: 1.5743\n",
      "Epoch 111/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0897 - mse: 0.0897 - val_loss: 1.5788 - val_mse: 1.5788\n",
      "Epoch 112/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0888 - mse: 0.0888 - val_loss: 1.5835 - val_mse: 1.5835\n",
      "Epoch 113/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0881 - mse: 0.0881 - val_loss: 1.5879 - val_mse: 1.5879\n",
      "Epoch 114/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0873 - mse: 0.0873 - val_loss: 1.5921 - val_mse: 1.5921\n",
      "Epoch 115/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0865 - mse: 0.0865 - val_loss: 1.5963 - val_mse: 1.5963\n",
      "Epoch 116/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0857 - mse: 0.0857 - val_loss: 1.6009 - val_mse: 1.6009\n",
      "Epoch 117/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0850 - mse: 0.0850 - val_loss: 1.6052 - val_mse: 1.6052\n",
      "Epoch 118/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.0838 - mse: 0.0838 - val_loss: 1.6094 - val_mse: 1.6094\n",
      "Epoch 119/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.0834 - mse: 0.0834 - val_loss: 1.6144 - val_mse: 1.6144\n",
      "Epoch 120/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.0828 - mse: 0.0828 - val_loss: 1.6192 - val_mse: 1.6192\n",
      "Epoch 121/200\n",
      "9/9 [==============================] - 0s 2ms/sample - loss: 0.0821 - mse: 0.0821 - val_loss: 1.6240 - val_mse: 1.6240\n",
      "Epoch 122/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0814 - mse: 0.0814 - val_loss: 1.6288 - val_mse: 1.6288\n",
      "Epoch 123/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0806 - mse: 0.0806 - val_loss: 1.6338 - val_mse: 1.6338\n",
      "Epoch 124/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0798 - mse: 0.0798 - val_loss: 1.6384 - val_mse: 1.6384\n",
      "Epoch 125/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0791 - mse: 0.0791 - val_loss: 1.6427 - val_mse: 1.6427\n",
      "Epoch 126/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0783 - mse: 0.0783 - val_loss: 1.6471 - val_mse: 1.6471\n",
      "Epoch 127/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0776 - mse: 0.0776 - val_loss: 1.6518 - val_mse: 1.6518\n",
      "Epoch 128/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0768 - mse: 0.0768 - val_loss: 1.6561 - val_mse: 1.6561\n",
      "Epoch 129/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0761 - mse: 0.0761 - val_loss: 1.6604 - val_mse: 1.6604\n",
      "Epoch 130/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0750 - mse: 0.0750 - val_loss: 1.6651 - val_mse: 1.6651\n",
      "Epoch 131/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0746 - mse: 0.0746 - val_loss: 1.6698 - val_mse: 1.6698\n",
      "Epoch 132/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0740 - mse: 0.0740 - val_loss: 1.6747 - val_mse: 1.6747\n",
      "Epoch 133/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0734 - mse: 0.0734 - val_loss: 1.6957 - val_mse: 1.6957\n",
      "Epoch 134/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0727 - mse: 0.0727 - val_loss: 1.7080 - val_mse: 1.7080\n",
      "Epoch 135/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0720 - mse: 0.0720 - val_loss: 1.7155 - val_mse: 1.7155\n",
      "Epoch 136/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0712 - mse: 0.0712 - val_loss: 1.7211 - val_mse: 1.7211\n",
      "Epoch 137/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0705 - mse: 0.0705 - val_loss: 1.7258 - val_mse: 1.7258\n",
      "Epoch 138/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0698 - mse: 0.0698 - val_loss: 1.7304 - val_mse: 1.7304\n",
      "Epoch 139/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0691 - mse: 0.0691 - val_loss: 1.7355 - val_mse: 1.7355\n",
      "Epoch 140/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0684 - mse: 0.0684 - val_loss: 1.7405 - val_mse: 1.7405\n",
      "Epoch 141/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0677 - mse: 0.0677 - val_loss: 1.7455 - val_mse: 1.7455\n",
      "Epoch 142/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0667 - mse: 0.0667 - val_loss: 1.7505 - val_mse: 1.7505\n",
      "Epoch 143/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0663 - mse: 0.0663 - val_loss: 1.7561 - val_mse: 1.7561\n",
      "Epoch 144/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0657 - mse: 0.0657 - val_loss: 1.7618 - val_mse: 1.7618\n",
      "Epoch 145/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0651 - mse: 0.0651 - val_loss: 1.7673 - val_mse: 1.7673\n",
      "Epoch 146/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0644 - mse: 0.0644 - val_loss: 1.7724 - val_mse: 1.7724\n",
      "Epoch 147/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0637 - mse: 0.0637 - val_loss: 1.7779 - val_mse: 1.7779\n",
      "Epoch 148/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0631 - mse: 0.0631 - val_loss: 1.7831 - val_mse: 1.7831\n",
      "Epoch 149/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0624 - mse: 0.0624 - val_loss: 1.7882 - val_mse: 1.7882\n",
      "Epoch 150/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0617 - mse: 0.0617 - val_loss: 1.7931 - val_mse: 1.7931\n",
      "Epoch 151/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0610 - mse: 0.0610 - val_loss: 1.7983 - val_mse: 1.7983\n",
      "Epoch 152/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0604 - mse: 0.0604 - val_loss: 1.8033 - val_mse: 1.8033\n",
      "Epoch 153/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0597 - mse: 0.0597 - val_loss: 1.8081 - val_mse: 1.8081\n",
      "Epoch 154/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0587 - mse: 0.0587 - val_loss: 1.8129 - val_mse: 1.8129\n",
      "Epoch 155/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0584 - mse: 0.0584 - val_loss: 1.8187 - val_mse: 1.8187\n",
      "Epoch 156/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0579 - mse: 0.0579 - val_loss: 1.8246 - val_mse: 1.8246\n",
      "Epoch 157/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0573 - mse: 0.0573 - val_loss: 1.8302 - val_mse: 1.8302\n",
      "Epoch 158/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0566 - mse: 0.0566 - val_loss: 1.8357 - val_mse: 1.8357\n",
      "Epoch 159/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0560 - mse: 0.0560 - val_loss: 1.8415 - val_mse: 1.8415\n",
      "Epoch 160/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0553 - mse: 0.0553 - val_loss: 1.8468 - val_mse: 1.8468\n",
      "Epoch 161/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0546 - mse: 0.0546 - val_loss: 1.8517 - val_mse: 1.8517\n",
      "Epoch 162/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0540 - mse: 0.0540 - val_loss: 1.8568 - val_mse: 1.8568\n",
      "Epoch 163/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0534 - mse: 0.0534 - val_loss: 1.8622 - val_mse: 1.8622\n",
      "Epoch 164/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0527 - mse: 0.0527 - val_loss: 1.8671 - val_mse: 1.8671\n",
      "Epoch 165/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0521 - mse: 0.0521 - val_loss: 1.8722 - val_mse: 1.8722\n",
      "Epoch 166/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0512 - mse: 0.0512 - val_loss: 1.8773 - val_mse: 1.8773\n",
      "Epoch 167/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0508 - mse: 0.0508 - val_loss: 1.8830 - val_mse: 1.8830\n",
      "Epoch 168/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0504 - mse: 0.0504 - val_loss: 1.8893 - val_mse: 1.8893\n",
      "Epoch 169/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0498 - mse: 0.0498 - val_loss: 1.8951 - val_mse: 1.8951\n",
      "Epoch 170/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0492 - mse: 0.0492 - val_loss: 1.9009 - val_mse: 1.9009\n",
      "Epoch 171/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0485 - mse: 0.0485 - val_loss: 1.9064 - val_mse: 1.9064\n",
      "Epoch 172/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0479 - mse: 0.0479 - val_loss: 1.9117 - val_mse: 1.9117\n",
      "Epoch 173/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0473 - mse: 0.0473 - val_loss: 1.9168 - val_mse: 1.9168\n",
      "Epoch 174/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0467 - mse: 0.0467 - val_loss: 1.9219 - val_mse: 1.9219\n",
      "Epoch 175/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0462 - mse: 0.0462 - val_loss: 1.9462 - val_mse: 1.9462\n",
      "Epoch 176/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0456 - mse: 0.0456 - val_loss: 1.9599 - val_mse: 1.9599\n",
      "Epoch 177/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0450 - mse: 0.0450 - val_loss: 1.9678 - val_mse: 1.9678\n",
      "Epoch 178/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0441 - mse: 0.0441 - val_loss: 1.9738 - val_mse: 1.9738\n",
      "Epoch 179/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0438 - mse: 0.0438 - val_loss: 1.9798 - val_mse: 1.9798\n",
      "Epoch 180/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0433 - mse: 0.0433 - val_loss: 1.9857 - val_mse: 1.9857\n",
      "Epoch 181/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0428 - mse: 0.0428 - val_loss: 1.9915 - val_mse: 1.9915\n",
      "Epoch 182/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0422 - mse: 0.0422 - val_loss: 1.9973 - val_mse: 1.9973\n",
      "Epoch 183/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0416 - mse: 0.0416 - val_loss: 2.0030 - val_mse: 2.0030\n",
      "Epoch 184/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0410 - mse: 0.0410 - val_loss: 2.0090 - val_mse: 2.0090\n",
      "Epoch 185/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0404 - mse: 0.0404 - val_loss: 2.0144 - val_mse: 2.0144\n",
      "Epoch 186/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0399 - mse: 0.0399 - val_loss: 2.0195 - val_mse: 2.0195\n",
      "Epoch 187/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0393 - mse: 0.0393 - val_loss: 2.0246 - val_mse: 2.0246\n",
      "Epoch 188/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0388 - mse: 0.0388 - val_loss: 2.0300 - val_mse: 2.0300\n",
      "Epoch 189/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0379 - mse: 0.0379 - val_loss: 2.0356 - val_mse: 2.0356\n",
      "Epoch 190/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0376 - mse: 0.0376 - val_loss: 2.0413 - val_mse: 2.0413\n",
      "Epoch 191/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0372 - mse: 0.0372 - val_loss: 2.0471 - val_mse: 2.0471\n",
      "Epoch 192/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0367 - mse: 0.0367 - val_loss: 2.0527 - val_mse: 2.0527\n",
      "Epoch 193/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0362 - mse: 0.0362 - val_loss: 2.0584 - val_mse: 2.0584\n",
      "Epoch 194/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0356 - mse: 0.0356 - val_loss: 2.0643 - val_mse: 2.0643\n",
      "Epoch 195/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0351 - mse: 0.0351 - val_loss: 2.0697 - val_mse: 2.0697\n",
      "Epoch 196/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0345 - mse: 0.0345 - val_loss: 2.0748 - val_mse: 2.0748\n",
      "Epoch 197/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0340 - mse: 0.0340 - val_loss: 2.0797 - val_mse: 2.0797\n",
      "Epoch 198/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0335 - mse: 0.0335 - val_loss: 2.0845 - val_mse: 2.0845\n",
      "Epoch 199/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0330 - mse: 0.0330 - val_loss: 2.0894 - val_mse: 2.0894\n",
      "Epoch 200/200\n",
      "9/9 [==============================] - 0s 1ms/sample - loss: 0.0325 - mse: 0.0325 - val_loss: 2.0948 - val_mse: 2.0948\n"
     ]
    }
   ],
   "source": [
    "model_3 = nn_3.fit(X_train_val, y_train_val, batch_size=1, validation_data=(X_val, y_val), shuffle=False, epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DNDc309DQwGOPPQZAUVERU6dO5cEHHwSCkF2xYsWQ5r1r1y4qKirIzMzkueee4/3395y59sILL+TJJ59kyZIlfOQjHwHgIx/5CHfffTeNjY0AbNy4kW3btu3zutu3b6erq4tPfOITfPvb3+bVV18dUn29JVwLPS3NKM7NVJeLyBi2efNmLr/8cjo7O+nq6uLiiy/u2cg5WDfffDOXXnopc+bM4dRTT+Wggw4CYM6cOXzqU5/i2GOP5eCDD+bkk/dcyvW+++7jc5/7HN/5zndob2/nkksu4Zhjjhn0vC+77DI++tGPUlVVxbHHHrvXl1JWVhann346JSUlpKenA3DWWWexcuVKTjzxRCDY/fFXv/pVz/huGzdu5Morr6SrqwuA7373u4OuLZq4XVO0qqrKh3qBiw//4HmOmFjEHX8/Z5irEkkeK1eu5Igjjoh3GUmrq6uLOXPm8OCDDzJjxowRmUe0dWhmy9w96j6dCdflAlCSl6kuFxGJm7fffpvp06dzxhlnjFiYD0XCdbkAjMvPYlOdTgkqIvExc+ZM1q1bF+8y9pGgLfQstdBFYhCvLlXZf0NZdwkZ6KV5mexUoIv0Kycnhx07dijUE1D3+dBzcnIG9byE7HIpycuipb2LlvZOcjLTB36CSAqaPHky1dXV1NTUxLsUGYLuKxYNRkIGeuTRohOKc+NcjcjYlJmZOair3UjiS9guF4Da3TpaVESkW0IGeuQpdEVEJDBgoJvZ3Wa2zcze7GO8mdntZrbWzF43sxE/2qc0X+dzERHpLZYW+r3A2f2MPweYEd6uAX66/2X1b5zOuCgiso8BA93dFwE7+5nkAuC/PfAyUGJmE4arwGjU5SIisq/h6EOfBGyIeFwdDtuHmV1jZkvNbOn+7EqVlZFGfla6ulxERCIMR6BHO1Fx1CMZ3P0ud69y96ry8vL9mmlJXpauWiQiEmE4Ar0amBLxeDKwaRhet1+l+TqFrohIpOEI9EeBz4R7u5wA7HL3zcPwuv0qzctSl4uISIQBjxQ1s98ApwFlZlYN3AxkArj7ncDjwLnAWqAJuHKkio1UkpfFhp1NozErEZGEMGCgu/ulA4x34PphqyhG4/Iy1UIXEYmQkEeKQtBCr29pp7NLZ5ITEYEEDvTSvEzcYVezWukiIpDIgZ6vo0VFRCIlbKB3Hy2qfdFFRAIJG+g9p9DVhlERESChA11dLiIikRI20EvCFrpO0CUiEkjYQC/IziAz3dTlIiISSthANzNK8rLUQhcRCSVsoEOwYVTXFRURCSR0oJfkZWmjqIhIKKEDvTQvkzr1oYuIAAkf6FnsVAtdRARI8EDv3iganPBRRCS1JXSgl+Zl0t7p7G7rjHcpIiJxl9CBPi48QdfORnW7iIgkdKCXF2YDUNPYGudKRETiL6EDvawgDPQGBbqISEIHekXYQt+uFrqISGIH+rj8LMzUQhcRgQQP9Iz0NMblZakPXUSEBA90CDaMqoUuIpIkga4+dBGRJAj0sgK10EVEIAkCvbvLRYf/i0iqS/xAL8imtaOLxtaOeJciIhJXCR/oZYXB4f/qdhGRVJfwgV5ekAMo0EVEYgp0MzvbzFab2VozuyHK+IPM7Dkze83MXjezc4e/1Oh0PhcRkcCAgW5m6cAdwDnATOBSM5vZa7J/AR5w99nAJcBPhrvQvpQVBF0u29VCF5EUF0sLfR6w1t3XuXsbcD9wQa9pHCgK7xcDm4avxP6V5mWRnmZqoYtIyosl0CcBGyIeV4fDIi0EPm1m1cDjwBejvZCZXWNmS81saU1NzRDK3VdamlFWkMW2egW6iKS2WALdogzrvdP3pcC97j4ZOBf4pZnt89rufpe7V7l7VXl5+eCr7cOBxblsqW8ZttcTEUlEsQR6NTAl4vFk9u1SuQp4AMDd/wbkAGXDUWAsJpXksLGuebRmJyIyJsUS6EuAGWY21cyyCDZ6Ptprmg+AMwDM7AiCQB+ePpUYTCzOZVNds44WFZGUNmCgu3sH8AXgKWAlwd4sb5nZt8xsQTjZ14B/NLMVwG+AK3wU03ViSS4t7V3UNrWP1ixFRMacjFgmcvfHCTZ2Rg67KeL+28CHhre02E0syQVgU11zz4WjRURSTcIfKQowKQx09aOLSCpLikCfWBIc/r9JgS4iKSwpAn1cfhbZGWkKdBFJaUkR6GbGpJJcNtVpX3QRSV1JEegQbBhVH7qIpLIkCvQcdbmISEpLmkCfVJLHtoZWWjs6412KiEhcJE2gd+/psln96CKSopIm0A8enw/A+h2741yJiEh8JE2gTy0LAv297Qp0EUlNSRPoZQVZFGZnKNBFJGUlTaCbGdPK8xXoIpKykibQIeh2WVejQBeR1JRkgV7AxrpmWtq166KIpJ7kCvRy7ekiIqkrqQJ9WveeLup2EZEUlFSB3r3r4jptGBWRFJRUgZ6fncEBRdm8W9MY71JEREZdUgU6wIyKQtZsVaCLSOpJukA/YkIhq7c20NHZFe9SRERGVdIF+uEHFtHW0aUDjEQk5SRdoB8xoQiAtzfXx7kSEZHRlXSBPr2igMx0Y9WWhniXIiIyqpIu0LMy0jikvICVaqGLSIpJukAHmDmhSIEuIiknKQP9iAlFbK1vZefutniXIiIyapI20AHe3LgrzpWIiIyemALdzM42s9VmttbMbuhjmovN7G0ze8vMfj28ZQ7OrMnFALxeXRfPMkRERlXGQBOYWTpwB/B3QDWwxMwedfe3I6aZAXwd+JC715pZxUgVHIvi3EymleezfINa6CKSOmJpoc8D1rr7OndvA+4HLug1zT8Cd7h7LYC7bxveMgfv2MklLN9Qh7vHuxQRkVERS6BPAjZEPK4Oh0U6FDjUzP5qZi+b2dnRXsjMrjGzpWa2tKamZmgVx+iYKSVsb2xl866WEZ2PiMhYEUugW5RhvZu9GcAM4DTgUuDnZlayz5Pc73L3KnevKi8vH2ytg3LslGD2KzaoH11EUkMsgV4NTIl4PBnYFGWaR9y93d3fA1YTBHzcHD6hkKz0NJZrw6iIpIhYAn0JMMPMpppZFnAJ8Givaf4AnA5gZmUEXTDrhrPQwcrOSOeIiUW89r4CXURSw4CB7u4dwBeAp4CVwAPu/paZfcvMFoSTPQXsMLO3geeAf3b3HSNVdKyOnzqO5RvqdNFoEUkJMe2H7u6Pu/uh7n6Iu98aDrvJ3R8N77u7f9XdZ7r7LHe/fySLjtUJ08bR1tnFqx/UxrsUEZERl5RHinarqhxHmsHL63bGuxQRkRGX1IFelJPJrEnFvPxu3Ht/RERGXFIHOsAJ08azfEMdzW3qRxeR5JYSgd7W2cWy99WPLiLJLekDfd7UcWSmGy+sGdkjU0VE4i3pAz0/O4PjKsfxl3cU6CKS3JI+0AFOObScVVsa2Fqv87qISPJKiUA/9dDgvDFqpYtIMkuJQD/8wEIqCrMV6CKS1FIi0M2M0w4rZ9E7NbR1dMW7HBGREZESgQ5w1swDaWjp4OV1OshIRJJTygT6/Bll5GWl89RbW+JdiojIiEiZQM/JTOfUQ8v5n7e30tWly9KJSPJJmUAH+MiRB7KtoZXXdBUjEUlCKRXopx9eQVZ6Gn98vfcFl0REEl9KBXpxbianH17OYys206luFxFJMikV6AAXHDuJ7Y2t/E2n1BWRJJNygf7hwysoyM7gkeUb412KiMiwSrlAz8lM5+yjDuSJN7fQ1NYR73JERIZNygU6wMVVU2hs7eDxN7RPuogkj5QM9OMqS5lWls9vl3wQ71JERIZNSga6mfHJqiksWV/LuzWN8S5HRGRYpGSgA3xi7iQy041f/u39eJciIjIsUjbQKwpzOP/oiTy4dAO7mtvjXY6IyH5L2UAHuGr+VHa3daovXUSSQkoH+lGTijl+6jju/et6Ojp1nnQRSWwpHegAV588jU27WnjiTe3CKCKJLeUD/YzDK6gcn8fPX3wPd53fRUQSV0yBbmZnm9lqM1trZjf0M91FZuZmVjV8JY6stDTjs/OnsmJDHcver413OSIiQzZgoJtZOnAHcA4wE7jUzGZGma4Q+BLwynAXOdIumjuZ0rxMbn92bbxLEREZslha6POAte6+zt3bgPuBC6JM923g+0DLMNY3KvKyMrju1ENY9E4Ni9/bGe9yRESGJJZAnwRsiHhcHQ7rYWazgSnu/sf+XsjMrjGzpWa2tKamZtDFjqTPnFhJWUE2P3h6tfrSRSQhxRLoFmVYT+KZWRrwQ+BrA72Qu9/l7lXuXlVeXh57laMgNyudL5x+CIvf28lf1+pc6SKSeGIJ9GpgSsTjyUDkNdwKgaOA581sPXAC8GgibRjtdunxBzGxOEetdBFJSLEE+hJghplNNbMs4BLg0e6R7r7L3cvcvdLdK4GXgQXuvnREKh5B2RnpfPGMGSzfUMczK7fFuxwRkUEZMNDdvQP4AvAUsBJ4wN3fMrNvmdmCkS5wtF00dzJTy/L57uMraevQ0aMikjhi2g/d3R9390Pd/RB3vzUcdpO7Pxpl2tMSsXXeLTM9jZvOn8m67bu596X34l2OiEjMUv5I0WhOP7yCDx9ewY+fWcO2+oTbC1NEUpQCvQ/fPH8mbZ1dfO/J1fEuRUQkJgr0Pkwty+eq+dP4/avVvPqBTgkgImOfAr0fX/jwdA4oyuYbD71Bu06vKyJjnAK9HwXZGXz7gqNYtaWBO59/N97liIj0S4E+gLOOPJDzj57Avz+7ljVbG+JdjohInxToMbhlwZHkZ6fzv3//Op1dOoJURMYmBXoMxhdks3DBkbz2QR13v6h900VkbFKgx2jBMRM5a+YBfP+pVSzfUBfvckRE9qFAj5GZcdtFx1BRmMP1971KXVNbvEsSEdmLAn0QivMyueOyOWxraOGfHlyhMzKKyJiiQB+kY6eU8I1zj+CZldv42aJ18S5HRKSHAn0IrjipkvNmTeB7T67if97eGu9yREQABfqQmBk/+OQxzJpUzJfvf423Nu2Kd0kiIgr0ocrNSufnn6miODeTq3+xVGdlFJG4U6Dvh4qiHP7r8uOob27n8nuWsKupPd4liUgKU6Dvp5kTi/jpp+fy7rZGPnP3KzS0KNRFJD4U6MPglEPL+cllc3hrUz1X3LOE3a0d8S5JRFKQAn2YnDnzAP790tks31DHFfcsVveLiIw6BfowOmfWBH58ybEs31DHRXe+xMa65niXJCIpRIE+zM4/eiK/+Ow8ttS3cOEdf9UujSIyahToI+CkQ8r43XUnkZ5mfOpnL/PCmpp4lyQiKUCBPkIOO7CQhz5/EpNLc7nyniXc89f3dO4XERlRCvQRNKE4lweuO5FTDy3nlsfe5sp7l1DT0BrvskQkSSnQR1hRTiY/v7yKb11wJH97dwfn/HgRz63eFu+yRCQJKdBHgZnxmRMreeyL8ykryObKe5aw8NG3aGnvjHdpIpJEFOij6NADCvnD9R/iipMqufel9Xzsjr+yeosuPC0iw0OBPspyMtNZuOBI7rnyOLY3trLgP17k3r++R5cuPi0i+ymmQDezs81stZmtNbMbooz/qpm9bWavm9mfzezg4S81uZx+WAVPfPkUTjpkPAsfe5sLf/oSb1Rrn3URGboBA93M0oE7gHOAmcClZjaz12SvAVXufjTwO+D7w11oMiovzObuK47jh586ho21zSy440X+5Q9vsKNRe8KIyODF0kKfB6x193Xu3gbcD1wQOYG7P+fuTeHDl4HJw1tm8jIzLpw9mT9/7VQuP7GS3yzewKm3Pc9/PLuGpjad5EtEYhdLoE8CNkQ8rg6H9eUq4IloI8zsGjNbamZLa2p09GSk4txMFi44kqe+cjInHjKeHzz9Dqfd9jz3L/6Ajs6ueJcnIgkglkC3KMOibsEzs08DVcBt0ca7+13uXuXuVeXl5bFXmUKmVxTyn5+p4sHrTmRyaS43PPQGZ/7bX7jvlfe1m6OI9CuWQK8GpkQ8ngxs6j2RmZ0J3AgscHd1Au+n4yrH8fvPncTP/mEuxbmZ3Pjwm8z/3rPc8dxanZpXRKKygc4vYmYZwDvAGcBGYAnw9+7+VsQ0swk2hp7t7mtimXFVVZUvXbp0qHWnFHfnb+t28LO/rOMv79SQl5XOpfMO4soPVTK5NJ29QkgAAA1cSURBVC/e5YnIKDKzZe5eFXVcLCeMMrNzgR8B6cDd7n6rmX0LWOruj5rZM8AsYHP4lA/cfUF/r6lAH5q3N9Vz16J3eez1zXR2OfOmjuNjx07ivFkTKM7LjHd5IjLC9jvQR4ICff9U1zbx0Ksb+cPyjayr2U1munHaYRV87NhJnHFEBTmZ6fEuUURGgAI9ibk7b26s5w/LN/LYik1sa2glLyud+dPLOOOICk4/rIKKopx4lykiw0SBniI6u5y/vbuDJ9/azLMrt7FpVwsAsyYVM39GGfOmjqPq4FIKc9Q1I5KoFOgpyN1ZvbWBP6/cxnOrtrGiuo72TifN4MiJxRw/dRzzpo7juMpxlOZnxbtcEYmRAl1obuvktQ9qefm9nSx+bwevfVBHa0dwwNJhBxRyzJRiZk0u4ZjJxRx+YBFZGTpvm8hY1F+gZ4x2MRIfuVnpnDS9jJOmlwHQ2tHJ69W7WPzeTpas38kzK7fxwNJqALLS0zhiQiGzJhdz6AGFTK8oYHpFAeUF2ZhFO85MRMYCBXqKys5I57jKoMsFgi6a6tpmXq/exevVdayoruOR1zbR0LrnfDJFORlMryhgRsWekJ9eUcCkklzS0hT0IvGmLhfpk7uzpb6Ftdsa97q9W9PI9sa2nulyMtOYVlbAjAMKmF5ewCEVBRw0Lo8p4/IoztUGWJHhpC4XGRIzY0JxLhOKczl5xt7n3qnd3cbamr2Dfun6Wh5ZvvdZIYpzM8Nwz2XKuLzgfmnwd0JJDtkZ2l9eZLgo0GVISvOzOC5/T5dNt92tHby3fTfVtU18sLOJDTub+WBnE6s2N/DM29to63XmyPLCbCaW5DKpJIcJxbk99yeWBPfH52ep314kRgp0GVb52RkcNamYoyYV7zOuq8vZ2tDCBzuCsN+8q4VNdc1srGtm9ZYGnltVQ3OvM0pmZ6SF4Z7DxJ7Az90zrCRXR8WKhBToMmrS0vZ04Rw/bfw+492duqZ2NtY1s6n7tqul5/GiNTVsa2il92af0rxMDijKoaIohwOLsnvuH1AY3D+wOIfx+VlkpGtXTEluCnQZM8yM0vwsSvOzorbwAdo6uthavyfkN9Y2s7WhhS27WtnW0MLqLfXUNLTS+5rbaQZlBUHAB7fsnr/BF0EOFYXZlOZlaY8dSVgKdEkoWRlpTAn3oOlLZ5ezo7GVLfUtbK1vZWt9C9vC+1vqW6iubeLVD2rZubttn+dmpBllBdmUF2ZTUbj33+CW0/NYXT0y1ijQJemkpxkVYbdLf1o7OqlpCAK/O/hrGlqpaWhlW0Mrm3e18PrGXexo3LfFD1CYkxER+DmUF2RTUZS9529hcF+tfhktCnRJWdkZ6UwuzRvwIiEdnV3sbGpjW30rNY2t1HT/bQi6eWoaWnm9uo5t9a37bNSF4AumrCCrJ+C7fwGUF+59v7wwm8LsDO3VI0OmQBcZQEZ6GhWFOVQUDnwa4t2tHWxr2BP22+pb2R6G//bG4Ivg7c317GhsoyNKsz8rI43yvgK/IJvywizKC3IoL8wmN0tdPrI3BbrIMMrPzmBqdgZTy/L7na6ry6lrbu/p4ukO/ZqI8N+ws4lX369lZ1PbPnv2ABRkZ/S0/MsKsinJy6QoJ5Oi3EyKcjIoys1kfH424/KzKCsINjZnak+fpKZAF4mDtDRjXH4W4/KzOOzAwn6n7ejsYufutqDl39jK9r2Cv42ahhbe2dpAfUsH9c3tPWfRjKYoJ4PxBdmMD+c9viD8m5+9z/3SvCyddTPBKNBFxriM9LSYNvJ2a2nvpKGlg13NbexobGPn7ja2725jZ2MbO3e3smN3MPz9HU28+kEdO3dH3+gLwYbfsoLsni+fcXlBS39cfialecGw0nD4uIIsbQOIMwW6SJLJyUwnJzOd8sJsplcMPH1Xl7OruZ0du4Pw39HYGvX+hp1NrNhQR21TG+2d0b8BMtKsJ+BL8zODwO8O/l5fACV5mZTmZ5Gfla4vgWGiQBdJcWlpew7oioW709jaQe3udnY2tVEbBn5tU6+/u9t5Z2sjteGwvn4FZKWnUZyXSWleJiV5WZTkBq3/kvBXQPfw0vBLoCQvk5JcdQdFo0AXkUExMwpzMinMyeSg8f3v8tmts8upb97zBbBjdxu7mtqpbWqjtqmduqa2nvvv72hi+YY66pra9zmZW6SC7Iygld8T9JHhHwwvzs2kOC+TktzM4H5uZlKfAkKBLiIjLj3yV0D5wNND8Eugqa2T2qY26praqQu/AOrC4N8zPHi8YWcTtU3t1Le0R90rqFtBdgbFuUFLP/JvcW7WnmHdXwB5e74IChJg+4ACXUTGJDMjPzuD/OwMJpfG/rzOcJtAbVMbu5rbg1tT8LeuqZ265ra9hr2ztZG6pnZ2Nfe9bQCC8wEFu4RmUpQbfCkU5fR6HG18+DcnM23EvxAU6CKSVNIjdgkdDHenub1zT/CHgb+ruY365g7qW4LH9c3tPbuIrq1vpL6lnfrmjqhHCUfKSk+jKDeDopxMvvJ3h7LgmIn78zajUqCLiBD8IsjLyiAvK4MJxbmDfn5bR1cY7nsCf1dze0/gd4/b1dxOad7IXJpRgS4iMgyyMtIoC8/VEy/Ju7lXRCTFxBToZna2ma02s7VmdkOU8dlm9ttw/CtmVjnchYqISP8GDHQzSwfuAM4BZgKXmtnMXpNdBdS6+3Tgh8D3hrtQERHpXywt9HnAWndf5+5twP3ABb2muQD4RXj/d8AZNtZ32BQRSTKxBPokYEPE4+pwWNRp3L0D2AXscxVgM7vGzJaa2dKampqhVSwiIlHFEujRWtq9976PZRrc/S53r3L3qvLyGA8XExGRmMQS6NXAlIjHk4FNfU1jZhlAMbBzOAoUEZHYxBLoS4AZZjbVzLKAS4BHe03zKHB5eP8i4Fn3/s6mICIiw81iyV0zOxf4EZAO3O3ut5rZt4Cl7v6omeUAvwRmE7TML3H3dQO8Zg3w/hDrLgO2D/G5I22s1qa6Bkd1Dd5YrS3Z6jrY3aP2WccU6GONmS1196p41xHNWK1NdQ2O6hq8sVpbKtWlI0VFRJKEAl1EJEkkaqDfFe8C+jFWa1Ndg6O6Bm+s1pYydSVkH7qIiOwrUVvoIiLSiwJdRCRJJFygD3Qq31GsY4qZPWdmK83sLTP7cjh8oZltNLPl4e3cONS23szeCOe/NBw2zsz+x8zWhH8HcZXGYanpsIhlstzM6s3sK/FaXmZ2t5ltM7M3I4ZFXUYWuD38zL1uZnNGua7bzGxVOO+HzawkHF5pZs0Ry+7OUa6rz3VnZl8Pl9dqM/vISNXVT22/jahrvZktD4ePyjLrJx9G9jPm7glzIziw6V1gGpAFrABmxqmWCcCc8H4h8A7B6YUXAv8U5+W0HijrNez7wA3h/RuA78V5PW4BDo7X8gJOAeYAbw60jIBzgScIzll0AvDKKNd1FpAR3v9eRF2VkdPFYXlFXXfh/8EKIBuYGv7Ppo9mbb3G/z/gptFcZv3kw4h+xhKthR7LqXxHhbtvdvdXw/sNwEr2PQvlWBJ5iuNfAB+LYy1nAO+6+1CPFN5v7r6Ifc831NcyugD4bw+8DJSY2YTRqsvdn/bgLKYALxOcT2lU9bG8+nIBcL+7t7r7e8Bagv/dUa/NzAy4GPjNSM2/j5r6yocR/YwlWqDHcirfUWfBFZpmA6+Eg74Q/my6e7S7NkIOPG1my8zsmnDYAe6+GYIPG1ARh7q6XcLe/2DxXl7d+lpGY+lz91mClly3qWb2mpn9xcxOjkM90dbdWFpeJwNb3X1NxLBRXWa98mFEP2OJFugxnaZ3NJlZAfB74CvuXg/8FDgEOBbYTPBzb7R9yN3nEFxl6nozOyUONURlwQneFgAPhoPGwvIayJj43JnZjUAHcF84aDNwkLvPBr4K/NrMikaxpL7W3ZhYXqFL2bvxMKrLLEo+9DlplGGDXmaJFuixnMp31JhZJsHKus/dHwJw963u3unuXcB/MoI/Nfvi7pvCv9uAh8Matnb/hAv/bhvtukLnAK+6+9awxrgvrwh9LaO4f+7M7HLgfOAyDztdwy6NHeH9ZQR91YeOVk39rLu4Ly/oOZX3x4Hfdg8bzWUWLR8Y4c9YogV6LKfyHRVh39x/ASvd/d8ihkf2e10IvNn7uSNcV76ZFXbfJ9ig9iZ7n+L4cuCR0awrwl4tpngvr176WkaPAp8J90Q4AdjV/bN5NJjZ2cD/ARa4e1PE8HILrvmLmU0DZgD9nuV0mOvqa909ClxiwcXjp4Z1LR6tuiKcCaxy9+ruAaO1zPrKB0b6MzbSW3tHYOvxuQRbjN8FboxjHfMJfhK9DiwPb+cSnEb4jXD4o8CEUa5rGsEeBiuAt7qXEcElAf8MrAn/jovDMssDdgDFEcPisrwIvlQ2A+0EraOr+lpGBD+H7wg/c28AVaNc11qC/tXuz9md4bSfCNfxCuBV4KOjXFef6w64MVxeq4FzRntdhsPvBa7rNe2oLLN+8mFEP2M69F9EJEkkWpeLiIj0QYEuIpIkFOgiIklCgS4ikiQU6CIiSUKBLiKSJBToIiJJ4v8DsG8J3dLcMUMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model_3.history[\"loss\"])\n",
    "\n",
    "plt.title(\"loss_function_nn_disney_3_models - Training\")\n",
    "plt.legend([\"3 hidden layers\"])\n",
    "plt.savefig(f'../Images/loss_function_nn_{ticker}_3layers.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### _**An inflecting validation curve may imply that the model is OVERFITTING**_\n",
    "\" The plot of training loss continues to decrease with experience.\n",
    "The plot of validation loss decreases to a point and begins increasing again.\"\n",
    "\n",
    "_Source: https://machinelearningmastery.com/learning-curves-for-diagnosing-machine-learning-model-performance/_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": [
    "# Train vs test for deep net\n",
    "plt.plot(model_3.history[\"loss\"])\n",
    "plt.plot(model_3.history[\"val_loss\"])\n",
    "plt.title(f\"Data/loss_function_nn_{ticker} - Training Vs. Validation - 3 hidden layers\")\n",
    "plt.legend([\"train\", \"test\"])\n",
    "plt.savefig(f'../Images/loss_function_nn_{ticker}_train_val_3layer.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### _**Logit Model**_\n",
    "_**Model of Best Fit**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from collections import Counter\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from imblearn.metrics import classification_report_imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the features (X) and target (y) sets\n",
    "X = df.iloc[:, 0:4].values\n",
    "y = df[\"return_sign\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20,)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating training and testing data sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, shuffle=False, random_state=42) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create the StandardScaler instance\n",
    "scaler = StandardScaler()\n",
    "# Fit the Standard Scaler with the training data\n",
    "X_scaler = scaler.fit(X_train)\n",
    "\n",
    "# Scale the training data - only scale X_train and X_test data \n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=100000.0, class_weight=None, dual=False,\n",
       "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
       "                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ********* MODEL FITTING *************\n",
    "\n",
    "   # --------- Loigt -----------\n",
    "   # --------Start-------------\n",
    "   \n",
    "M = 'Logit'\n",
    "        \n",
    "from sklearn import linear_model \n",
    "\n",
    "lm = linear_model.LogisticRegression(C = 1e5)\n",
    "lm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=100000.0, class_weight=None, dual=False,\n",
       "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
       "                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(X_train, y_train)\n",
    "\n",
    "   # --------- Logit ------------\n",
    "   # ---------End -------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm_pred = lm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training vs. Testing - Logit\n",
      "Training Data Score: 0.7857\n",
      "Testing Data Score: 0.6667\n"
     ]
    }
   ],
   "source": [
    "# Score the accuracy\n",
    "print(\"Training vs. Testing - Logit\")\n",
    "print(f\"Training Data Score: {lm.score(X_train, y_train):,.04f}\")\n",
    "print(f\"Testing Data Score: {lm.score(X_test, y_test):,.04f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluating the Logit model in a nicer format\n",
    "\n",
    "# Calculating the confusion matrix\n",
    "cm_lm = confusion_matrix(y_test, lm_pred)\n",
    "cm_lm_df = pd.DataFrame(\n",
    "    cm_lm, index=[\"Actual -1\", \"Actual 1\"], columns=[\"Predicted -1\", \"Predicted 1\"]\n",
    ")\n",
    "\n",
    "# Calculating the accuracy score\n",
    "acc_lm_score = balanced_accuracy_score(y_test, lm_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix - Logit\n"
     ]
    },
    {
     "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>Predicted -1</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual -1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Predicted -1  Predicted 1\n",
       "Actual -1             1            2\n",
       "Actual 1              0            3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balanced Accuracy Score : 0.6667\n",
      "Classification Report - Logit\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       1.00      0.33      0.50         3\n",
      "         1.0       0.60      1.00      0.75         3\n",
      "\n",
      "    accuracy                           0.67         6\n",
      "   macro avg       0.80      0.67      0.62         6\n",
      "weighted avg       0.80      0.67      0.62         6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Displaying results\n",
    "print(\"Confusion Matrix - Logit\")\n",
    "display(cm_lm_df)\n",
    "print(f\"Balanced Accuracy Score : {acc_lm_score:,.04f}\")\n",
    "print(\"Classification Report - Logit\")\n",
    "print(classification_report(y_test, lm_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _SVM_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the features (X) and target (y) sets\n",
    "X = df.iloc[:, 0:4]\n",
    "y = df[\"return_sign\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating training and testing data sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, shuffle=False, random_state=42) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create the StandardScaler instance\n",
    "scaler = StandardScaler()\n",
    "# Fit the Standard Scaler with the training data\n",
    "X_scaler = scaler.fit(X_train)\n",
    "\n",
    "# Scale the training data - only scale X_train and X_test data \n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100000.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "    max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ********* MODEL FITTING *************\n",
    "\n",
    "# --------- SVM -----------\n",
    "# --------Start-------------\n",
    "   \n",
    "M = 'SVM'\n",
    "    \n",
    "from sklearn.svm import SVC\n",
    "    \n",
    "svm = SVC(C= 1e5, probability=True)\n",
    "svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_pred = svm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training vs. Testing - SVM\n",
      "Training Data Score: 0.7857\n",
      "Testing Data Score: 0.6667\n"
     ]
    }
   ],
   "source": [
    "# Score the accuracy\n",
    "print(\"Training vs. Testing - SVM\")\n",
    "print(f\"Training Data Score: {svm.score(X_train, y_train):,.04f}\")\n",
    "print(f\"Testing Data Score: {svm.score(X_test, y_test):,.04f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _Decision Tree_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the features (X) and target (y) sets\n",
    "X = df.iloc[:, 0:4]\n",
    "y = df[\"return_sign\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating training and testing data sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, shuffle=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create the StandardScaler instance\n",
    "scaler = StandardScaler()\n",
    "# Fit the Standard Scaler with the training data\n",
    "X_scaler = scaler.fit(X_train)\n",
    "\n",
    "# Scale the training data - only scale X_train and X_test data \n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the decision tree classifier instance\n",
    "model_tree = tree.DecisionTreeClassifier()\n",
    "\n",
    "# Fitting the model\n",
    "model_tree = model_tree.fit(X_train, y_train)\n",
    "\n",
    "# Making predictions using the testing data\n",
    "tree_pred = model_tree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training vs. Testing - Decision Tree\n",
      "Training Data Score: 1.0000\n",
      "Testing Data Score: 0.6667\n"
     ]
    }
   ],
   "source": [
    "# Score the accuracy\n",
    "print(\"Training vs. Testing - Decision Tree\")\n",
    "print(f\"Training Data Score: {model_tree.score(X_train, y_train):,.04f}\")\n",
    "print(f\"Testing Data Score: {model_tree.score(X_test, y_test):,.04f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluating the decision tree model\n",
    "\n",
    "# Calculating the confusion matrix\n",
    "cm = confusion_matrix(y_test, tree_pred)\n",
    "cm_df = pd.DataFrame(\n",
    "    cm, index=[\"Actual -1\", \"Actual 1\"], columns=[\"Predicted -1\", \"Predicted 1\"]\n",
    ")\n",
    "\n",
    "# Calculating the accuracy score\n",
    "acc_score = accuracy_score(y_test, tree_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix - Decision Tree\n"
     ]
    },
    {
     "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>Predicted -1</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual -1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Predicted -1  Predicted 1\n",
       "Actual -1             1            2\n",
       "Actual 1              0            3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score : 0.6667\n",
      "Classification Report - Decision Tree\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       1.00      0.33      0.50         3\n",
      "         1.0       0.60      1.00      0.75         3\n",
      "\n",
      "    accuracy                           0.67         6\n",
      "   macro avg       0.80      0.67      0.62         6\n",
      "weighted avg       0.80      0.67      0.62         6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Displaying results\n",
    "print(\"Confusion Matrix - Decision Tree\")\n",
    "display(cm_df)\n",
    "print(f\"Accuracy Score : {acc_score:,.04f}\")\n",
    "print(\"Classification Report - Decision Tree\")\n",
    "print(classification_report(y_test, tree_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tree Visualization\n",
    "\n",
    "# Graph tree\n",
    "from sklearn import tree\n",
    "\n",
    "# Needed for decision tree visualization\n",
    "import pydotplus\n",
    "from IPython.display import Image\n",
    "\n",
    "# Create DOT data\n",
    "dot_data = tree.export_graphviz(\n",
    "    model_tree, out_file=None, feature_names=X.columns, class_names=[\"-1\", \"1\"], filled=True\n",
    ")\n",
    "\n",
    "# Draw graph\n",
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "\n",
    "# Show graph\n",
    "#Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_window = 1\n",
    "ticker = 'DIS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the tree as PDF\n",
    "#graph.write_pdf(f\"../Images/{ticker}_{training_window}d_decision_tree_updated.pdf\")\n",
    "\n",
    "# Saving the tree as PNG\n",
    "#graph.write_png(f\"../Images/{ticker}_{training_window}d_decision_tree_updated.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## _Ensemble Learning_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from collections import Counter\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from imblearn.metrics import classification_report_imbalanced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_**Balanced Random Forest Classifier**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BalancedRandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                               criterion='gini', max_depth=None,\n",
       "                               max_features='auto', max_leaf_nodes=None,\n",
       "                               max_samples=None, min_impurity_decrease=0.0,\n",
       "                               min_samples_leaf=2, min_samples_split=2,\n",
       "                               min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                               n_jobs=None, oob_score=False, random_state=42,\n",
       "                               replacement=False, sampling_strategy='auto',\n",
       "                               verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with the RandomOversampler\n",
    "# fit Random Forest Classifier\n",
    "from imblearn.ensemble import BalancedRandomForestClassifier\n",
    "brf = BalancedRandomForestClassifier(n_estimators=100, random_state=42)\n",
    "brf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6666666666666666"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brf_pred = brf.predict(X_test)\n",
    "\n",
    "balanced_accuracy_score(y_test, brf_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training vs. Testing - Balanced Random Forest\n",
      "Training Data Score: 0.8571\n",
      "Testing Data Score: 0.6667\n"
     ]
    }
   ],
   "source": [
    "# Score the accuracy\n",
    "print(\"Training vs. Testing - Balanced Random Forest\")\n",
    "print(f\"Training Data Score: {brf.score(X_train, y_train):,.04f}\")\n",
    "print(f\"Testing Data Score: {brf.score(X_test, y_test):,.04f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix - Balanced Random Forest\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [0, 3]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "print(\"Confusion Matrix - Balanced Random Forest\")\n",
    "confusion_matrix(y_test, brf_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report - Balanced Random Forest on Imbalanced Data\n",
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "       -1.0       1.00      0.33      1.00      0.50      0.58      0.31         3\n",
      "        1.0       0.60      1.00      0.33      0.75      0.58      0.36         3\n",
      "\n",
      "avg / total       0.80      0.67      0.67      0.62      0.58      0.33         6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "\n",
    "print(\"Classification Report - Balanced Random Forest on Imbalanced Data\")\n",
    "print(classification_report_imbalanced(y_test, brf_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluating the Balanced Random Forest model in a nicer format\n",
    "\n",
    "# Calculating the confusion matrix\n",
    "cm_brf = confusion_matrix(y_test, brf_pred)\n",
    "cm_brf_df = pd.DataFrame(\n",
    "    cm_brf, index=[\"Actual -1\", \"Actual 1\"], columns=[\"Predicted -1\", \"Predicted 1\"]\n",
    ")\n",
    "\n",
    "# Calculating the accuracy score\n",
    "acc_brf_score = accuracy_score(y_test, brf_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix - Balanced Random Forest\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted -1</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual -1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Predicted -1  Predicted 1\n",
       "Actual -1             1            2\n",
       "Actual 1              0            3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score : 0.6667\n",
      "Classification Report - Balaced Random Forest\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       1.00      0.33      0.50         3\n",
      "         1.0       0.60      1.00      0.75         3\n",
      "\n",
      "    accuracy                           0.67         6\n",
      "   macro avg       0.80      0.67      0.62         6\n",
      "weighted avg       0.80      0.67      0.62         6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Displaying results\n",
    "print(\"Confusion Matrix - Balanced Random Forest\")\n",
    "display(cm_brf_df)\n",
    "print(f\"Accuracy Score : {acc_brf_score:,.04f}\")\n",
    "print(\"Classification Report - Balaced Random Forest\")\n",
    "print(classification_report(y_test, brf_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_**Easy Ensemble AdaBoost Classifier**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EasyEnsembleClassifier(base_estimator=None, n_estimators=100, n_jobs=None,\n",
       "                       random_state=42, replacement=False,\n",
       "                       sampling_strategy='auto', verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.ensemble import EasyEnsembleClassifier\n",
    "adb = EasyEnsembleClassifier(n_estimators=100, random_state=42)\n",
    "adb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3333333333333333"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "adb_pred = adb.predict(X_test)\n",
    "balanced_accuracy_score(y_test, adb_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix - Easy Emsemble AdaBoost\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [2, 1]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "print(\"Confusion Matrix - Easy Emsemble AdaBoost\")\n",
    "confusion_matrix(y_test, adb_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report - Easy Ensemble AdaBoost on Imbalanced Data\n",
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "       -1.0       0.33      0.33      0.33      0.33      0.33      0.11         3\n",
      "        1.0       0.33      0.33      0.33      0.33      0.33      0.11         3\n",
      "\n",
      "avg / total       0.33      0.33      0.33      0.33      0.33      0.11         6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "\n",
    "print(\"Classification Report - Easy Ensemble AdaBoost on Imbalanced Data\")\n",
    "print(classification_report_imbalanced(y_test, adb_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_**Gradient Boosting Classifier**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate:  0.05\n",
      "Accuracy score (training): 1.000\n",
      "Accuracy score (validation): 0.500\n",
      "\n",
      "Learning rate:  0.1\n",
      "Accuracy score (training): 1.000\n",
      "Accuracy score (validation): 0.333\n",
      "\n",
      "Learning rate:  0.25\n",
      "Accuracy score (training): 1.000\n",
      "Accuracy score (validation): 0.500\n",
      "\n",
      "Learning rate:  0.5\n",
      "Accuracy score (training): 1.000\n",
      "Accuracy score (validation): 0.500\n",
      "\n",
      "Learning rate:  0.75\n",
      "Accuracy score (training): 1.000\n",
      "Accuracy score (validation): 0.667\n",
      "\n",
      "Learning rate:  1\n",
      "Accuracy score (training): 1.000\n",
      "Accuracy score (validation): 0.500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# Create a classifier object\n",
    "learning_rates = [0.05, 0.1, 0.25, 0.5, 0.75, 1]\n",
    "for learning_rate in learning_rates:\n",
    "    classifier = GradientBoostingClassifier(n_estimators=20,\n",
    "                                            learning_rate=learning_rate,\n",
    "                                            max_features=4,\n",
    "                                            max_depth=3,\n",
    "                                            random_state=0)\n",
    "\n",
    "    # Fit the model\n",
    "    classifier.fit(X_train.values, y_train.ravel())\n",
    "    print(\"Learning rate: \", learning_rate)\n",
    "\n",
    "    # Score the model\n",
    "    print(\"Accuracy score (training): {0:.3f}\".format(\n",
    "        classifier.score(\n",
    "            X_train,\n",
    "            y_train.ravel())))\n",
    "    print(\"Accuracy score (validation): {0:.3f}\".format(\n",
    "        classifier.score(\n",
    "            X_test,\n",
    "            y_test.ravel())))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>Prediction</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Prediction  Actual\n",
       "0         1.0     1.0\n",
       "1        -1.0    -1.0\n",
       "2        -1.0     1.0\n",
       "3         1.0    -1.0\n",
       "4         1.0     1.0\n",
       "5         1.0    -1.0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Learning rates produces identical outcome\n",
    "#Use learning_rate of 0.75 for it is higher compared to the rest\n",
    "\n",
    "classifier = GradientBoostingClassifier(n_estimators=20,\n",
    "                                        learning_rate=0.05,\n",
    "                                        max_features=4,\n",
    "                                        max_depth=3,\n",
    "                                        random_state=42)\n",
    "\n",
    "# Fit the model\n",
    "classifier.fit(X_train, y_train.ravel())\n",
    "\n",
    "# Make Prediction\n",
    "grad_pred = classifier.predict(X_test)\n",
    "pd.DataFrame({\"Prediction\": grad_pred, \"Actual\": y_test.ravel()}).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score : 0.5\n"
     ]
    }
   ],
   "source": [
    "# Calculating the accuracy score\n",
    "acc_score = accuracy_score(y_test, grad_pred)\n",
    "print(f\"Accuracy Score : {acc_score}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix - Gradient Boosting\n"
     ]
    },
    {
     "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>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0            1            2\n",
       "Actual 1            1            2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate the confusion matrix\n",
    "cm = confusion_matrix(y_test, grad_pred)\n",
    "cm_df = pd.DataFrame(\n",
    "    cm, index=[\"Actual 0\", \"Actual 1\"],\n",
    "    columns=[\"Predicted 0\", \"Predicted 1\"]\n",
    ")\n",
    "\n",
    "# Displaying results\n",
    "print(\"Confusion Matrix - Gradient Boosting\")\n",
    "display(cm_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report - Gradient Boosting on Imbalanced Data\n",
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "       -1.0       0.50      0.33      0.67      0.40      0.47      0.21         3\n",
      "        1.0       0.50      0.67      0.33      0.57      0.47      0.23         3\n",
      "\n",
      "avg / total       0.50      0.50      0.50      0.49      0.47      0.22         6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Generate classification report\n",
    "print(\"Classification Report - Gradient Boosting on Imbalanced Data\")\n",
    "print(classification_report_imbalanced(y_test, grad_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Graph tree\n",
    "from sklearn import tree\n",
    "\n",
    "# Needed for decision tree visualization\n",
    "import pydotplus\n",
    "from IPython.display import Image\n",
    "\n",
    "dot_data = tree.export_graphviz(\n",
    "    classifier.estimators_[12, 0],\n",
    "    out_file=None, filled=True,\n",
    "    rounded=True,\n",
    "    special_characters=True,\n",
    "    proportion=True,\n",
    ")\n",
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "#Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the tree as PDF\n",
    "#graph.write_pdf(f\"../Images/{ticker}_GradientBoosting_tree_{training_window}_updated.pdf\")\n",
    "\n",
    "# Saving the tree as PNG\n",
    "#graph.write_png(f\"../Images/{ticker}_GradientBoosting_tree_{training_window}_updated.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the Precision Recall Curvey for both the SMOTEENN + Logistic Regression model\n",
    "# and the Balanced Random Forest model\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "probs_brf = brf.predict_proba(X_test)[:, 1]\n",
    "probs_adb = adb.predict_proba(X_test)[:, 1]\n",
    "probs_grad = classifier.predict_proba(X_test)[:, 1]\n",
    "\n",
    "precision_brf, recall_brf, _ = precision_recall_curve(y_test, probs_brf, pos_label=1)\n",
    "precision_adb, recall_adb, _ = precision_recall_curve(y_test, probs_adb, pos_label=1)\n",
    "precision_grad, recall_grad, _ = precision_recall_curve(y_test, probs_grad, pos_label=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(12,7))\n",
    "\n",
    "plt.plot(recall_brf, precision_brf, marker='.', color='darkviolet')\n",
    "plt.plot(recall_adb, precision_adb, marker='x', color='green')\n",
    "plt.plot(recall_grad, precision_grad, marker='^', color='deeppink')\n",
    "\n",
    "plt.suptitle('Precision-Recall Curve for Random Forest [.], Easy Emsemble AdaBoost [x] and Gradient Boosting [^]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the tree as PDF\n",
    "#graph.write_pdf(f\"../Images/pr_curve_brf_adb_grad_{ticker}_{training_window}d_updated.pdf\")\n",
    "\n",
    "# Saving the tree as PNG\n",
    "#graph.write_png(f\"../Images/pr_curve_brf_adb_grad_{ticker}_{training_window}d_updated.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mlenv",
   "language": "python",
   "name": "mlenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}