Hello ,

I am facing some issues with Preprocessing. When I a running the section with preprocessing this is what I get:

AttributeError: module 'sklearn.preprocessing' has no attribute 'new_dataset'

Here is the code of yours. Am I missing any steps?

Edit Author: Ray

IMPORTING IMPORTANT LIBRARIES

import pandas as pd import matplotlib.pyplot as plt import numpy as np import math from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error from keras.models import Sequential from keras.layers import Dense, Activation from keras.layers import LSTM from sklearn import preprocessing # how to import preprocessing import sklearn.preprocessing import numpy as np

FOR REPRODUCIBILITY

np.random.seed(7)

IMPORTING DATASET

dataset = pd.read_csv('C:/Users/ray/Documents/Python Scripts/LSTM-Stock-prediction-master/apple_share_price.csv', usecols=[1,2,3,4]) dataset = dataset.reindex(index = dataset.index[::-1])

CREATING OWN INDEX FOR FLEXIBILITY

obs = np.arange(1, len(dataset) + 1, 1)

TAKING DIFFERENT INDICATORS FOR PREDICTION

OHLC_avg = dataset.mean(axis = 1) HLC_avg = dataset[['High', 'Low', 'Close']].mean(axis = 1) close_val = dataset[['Close']]

PLOTTING ALL INDICATORS IN ONE PLOT

plt.plot(obs, OHLC_avg, 'r', label = 'OHLC avg') plt.plot(obs, HLC_avg, 'b', label = 'HLC avg') plt.plot(obs, close_val, 'g', label = 'Closing price') plt.legend(loc = 'upper right') plt.show()

PREPARATION OF TIME SERIES DATASET

OHLC_avg = np.reshape(OHLC_avg.values, (len(OHLC_avg),1)) # 1664 scaler = MinMaxScaler(feature_range=(0, 1)) OHLC_avg = scaler.fit_transform(OHLC_avg)

TRAIN-TEST SPLIT

train_OHLC = int(len(OHLC_avg) * 0.75) test_OHLC = len(OHLC_avg) - train_OHLC train_OHLC, test_OHLC = OHLC_avg[0:train_OHLC,:], OHLC_avg[train_OHLC:len(OHLC_avg),:]

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ step_size = 1

FUNCTION TO CREATE 1D DATA INTO TIME SERIES DATASET

def new_dataset(dataset, step_size): trainX, trainY = [], [] for i in range(len(dataset)-step_size-1): a = dataset[i:(i+step_size), 0] trainX.append(a) trainY.append(dataset[i + step_size, 0]) return np.array(trainX), np.array(trainY)

TIME-SERIES DATASET (FOR TIME T, VALUES FOR TIME T+1)

trainX, trainY = sklearn.preprocessing.new_dataset(train_OHLC, 1) testX, testY = sklearn.preprocessing.new_dataset(test_OHLC, 1) +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

RESHAPING TRAIN AND TEST DATA

trainX = np.reshape(train_OHLC, (train_OHLC.shape[0], 1, train_OHLC.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) step_size = 1

LSTM MODEL

model = Sequential() model.add(LSTM(32, input_shape=(1, step_size), return_sequences = True)) model.add(LSTM(16)) model.add(Dense(1)) model.add(Activation('linear'))

MODEL COMPILING AND TRAINING

model.compile(loss='mean_squared_error', optimizer='adagrad') # Try SGD, adam, adagrad and compare!!! model.fit(trainX, trainY, epochs=5, batch_size=1, verbose=2)

PREDICTION

trainPredict = model.predict(trainX) testPredict = model.predict(testX)

DE-NORMALIZING FOR PLOTTING

trainPredict = scaler.inverse_transform(trainPredict) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict) testY = scaler.inverse_transform([testY])

TRAINING RMSE

trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0])) print('Train RMSE: %.2f' % (trainScore))

TEST RMSE

testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0])) print('Test RMSE: %.2f' % (testScore))

CREATING SIMILAR DATASET TO PLOT TRAINING PREDICTIONS

trainPredictPlot = np.empty_like(OHLC_avg) trainPredictPlot[:, :] = np.nan trainPredictPlot[step_size:len(trainPredict)+step_size, :] = trainPredict

CREATING SIMILAR DATASSET TO PLOT TEST PREDICTIONS

testPredictPlot = np.empty_like(OHLC_avg) testPredictPlot[:, :] = np.nan testPredictPlot[len(trainPredict)+(step_size*2)+1:len(OHLC_avg)-1, :] = testPredict

DE-NORMALIZING MAIN DATASET

OHLC_avg = scaler.inverse_transform(OHLC_avg)

PLOT OF MAIN OHLC VALUES, TRAIN PREDICTIONS AND TEST PREDICTIONS

plt.plot(OHLC_avg, 'g', label = 'original dataset') plt.plot(trainPredictPlot, 'r', label = 'training set') plt.plot(testPredictPlot, 'b', label = 'predicted stock price/test set') plt.legend(loc = 'upper right') plt.xlabel('Time in Days') plt.ylabel('OHLC Value of Apple Stocks') plt.show()

PREDICT FUTURE VALUES

last_val = testPredict[-1] last_val_scaled = last_val/last_val next_val = model.predict(np.reshape(last_val_scaled, (1,1,1))) print "Last Day Value:", np.asscalar(last_val) print "Next Day Value:", np.asscalar(last_val*next_val)

print np.append(last_val, next_val)

NourozR / Stock-Price-Prediction-LSTM

Issues with preprocessing #6