Prediction output

[Rajmehta123]

Hello @deshpandenu This is really a cool project. I am trying to use the model for prediction but unable to output it.

class Predict:
  def __init__(self, num_historical_days=20, days=10, pct_change=0, gan_model=f'{googlepath}/deployed_models/gan', cnn_modle=f'{googlepath}/deployed_models/cnn', xgb_model=f'{googlepath}/deployed_models/xgb'):
    self.data = []
    self.num_historical_days = num_historical_days
    self.gan_model = gan_model
    self.cnn_modle = cnn_modle
    self.xgb_model = xgb_model
    
    files = [f"{googlepath}stock_data/{f}" for f in os.listdir(f"{googlepath}stock_data")] 
    for file in files:
      
      print(file)
      df = pd.read_csv(file, index_col='timestamp', parse_dates=True)
      df = df[['open','high','low','close','volume']]
            # data for new column labels that will use the pct_change of the closing data.
            # pct_change measure change between current and prior element. Map these into a 1x2
            # array to show if the pct_change > (our desired threshold) or less than.
            
      df = ((df -
            df.rolling(num_historical_days).mean().shift(-num_historical_days))
            /(df.rolling(num_historical_days).max().shift(-num_historical_days)
            -df.rolling(num_historical_days).min().shift(-num_historical_days)))
      df = df.dropna()
      self.data.append((file.split('/')[-1], df.iloc[0], df[200:200+num_historical_days].values))
      #split the df into arrays of length num_historical_days and append
      # to data, i.e. array of df[curr - num_days : curr] -> a batch of values
      # appending if price went up or down in curr day of "i" we are lookin
      # at
      
      
  def gan_predict(self):
    tf.reset_default_graph()
    gan = GAN(num_features=5, num_historical_days=self.num_historical_days, generator_input_size=200, is_train=False)
    with tf.Session() as sess:
      sess.run(tf.global_variables_initializer())
      saver = tf.train.Saver()
      saver.restore(sess, self.gan_model)
      clf = joblib.load(self.xgb_model)
      for sym, date, data in self.data:
        features = sess.run(gan.features, feed_dict={gan.X:[data]})
        features = xgb.DMatrix(features)
        print('{} {} {}'.format(str(date).split(' ')[0], sym, clf.predict(features)[0][1] > 0.5))
        #predictions = np.array([x for x in gan_estimator.predict(p.gan_predict())])
        #print(predictions)

p = Predict()
p.gan_predict()

Questions:

1. How should I interpret the output? For eg: Is there a plot or time-series or forecasting that shows the prediction?
   After running the prediction, how to interpret the output?

2. The prediction is using gan to get features and use xgboost to predict. How to use the cnn model for stacking?