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lstm_predict.py
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from math import sqrt
from numpy import concatenate
from matplotlib import pyplot
import pandas as pd
from datetime import datetime
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense,Dropout
from keras.layers import LSTM
from keras.optimizers import RMSprop
import plotly.offline as py
import plotly.graph_objs as go
import numpy as np
import seaborn as sns
#py.init_notebook_mode(connected=True)
#%matplotlib inline
import quandl
data = quandl.get('BCHARTS/KRAKENUSD', returns='pandas')
data['Weighted Price'].replace(0, np.nan, inplace=True)
data['Weighted Price'].fillna(method='ffill', inplace=True)
btc_trace = go.Scatter(x=data.index, y=data['Weighted Price'], name= 'Price')
#py.iplot([btc_trace])
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset) - look_back):
a = dataset[i:(i + look_back), 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
print(len(dataY))
return np.array(dataX), np.array(dataY)
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
n_vars = 1 if type(data) is list else data.shape[1]
df = pd.DataFrame(data)
cols, names = list(), list()
# input sequence (t-n, ... t-1)
for i in range(n_in, 0, -1):
cols.append(df.shift(i))
names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
# forecast sequence (t, t+1, ... t+n)
for i in range(0, n_out):
cols.append(df.shift(-i))
if i == 0:
names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
else:
names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
# put it all together
agg = pd.concat(cols, axis=1)
agg.columns = names
# drop rows with NaN values
if dropnan:
agg.dropna(inplace=True)
return agg
values = data[['Weighted Price'] + ['Volume (BTC)'] + ['Volume (Currency)']].values
values = values.astype('float32')
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(values)
reframed = series_to_supervised(scaled, 1, 1)
#reframed = series_to_supervised(values, 1, 1)
reframed.drop(reframed.columns[[4,5]], axis=1, inplace=True)
#taking weighted volume and volumeC and next days weighted to predict
values = reframed.values
n_train_hours = int(len(values) * 0.75)
train = values[:n_train_hours, :]
test = values[n_train_hours:, :]
# split into input and outputs
train_X, train_y = train[:, :-1], train[:, -1]
test_X, test_y = test[:, :-1], test[:, -1]
train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))
look_back = 1
# reshape input to be 3D [samples, timesteps, features]
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
predictDates = data.tail(len(testX)).index
model = Sequential()
model.add(LSTM(150,dropout=0 ,return_state=False,input_shape=(train_X.shape[1], train_X.shape[2])))
#model.add(Dense(64, activation='linear'))
model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')
multi_history = model.fit(train_X, train_y, epochs=300, batch_size=100, validation_data=(test_X, test_y), verbose=0, shuffle=False)
pyplot.plot(multi_history.history['val_loss'], label='multi_test')
pyplot.legend()
pyplot.show()
yhat = model.predict(test_X)
pyplot.plot(yhat, label='predict')
pyplot.plot(test_y, label='true')
pyplot.legend()
pyplot.show()
test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))
# invert scaling for forecast
inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1)
inv_yhat = scaler.inverse_transform(inv_yhat)
inv_yhat = inv_yhat[:,0]
# invert scaling for actual
test_y = test_y.reshape((len(test_y), 1))
inv_y = concatenate((test_y, test_X[:, 1:]), axis=1)
inv_y = scaler.inverse_transform(inv_y)
inv_y = inv_y[:,0]
rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
print('Test RMSE: %.3f' % rmse)
actual_chart = go.Scatter(x=predictDates, y=inv_y, name= 'Actual Price')
multi_predict_chart = go.Scatter(x=predictDates, y=inv_yhat, name= 'Multi Predict Price')
from plotly.offline import init_notebook_mode, iplot
from plotly.graph_objs import *
init_notebook_mode(connected=False)
py.offline.plot([ multi_predict_chart, actual_chart])