mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
110 lines
3.4 KiB
Python
110 lines
3.4 KiB
Python
#%%
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from math import sqrt
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from numpy import concatenate
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import numpy as np
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from matplotlib import pyplot
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from pandas import read_csv
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from pandas import DataFrame
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from pandas import concat
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.preprocessing import LabelEncoder
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from sklearn.metrics import mean_squared_error
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from keras.models import Sequential
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from keras.layers import Dense
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from keras.layers import LSTM
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#%%
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# convert series to supervised learning
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def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
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n_vars = 1 if type(data) is list else data.shape[1]
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df = DataFrame(data)
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cols, names = list(), list()
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# input sequence (t-n, ... t-1)
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for i in range(n_in, 0, -1):
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cols.append(df.shift(i))
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names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
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# forecast sequence (t, t+1, ... t+n)
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for i in range(0, n_out):
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cols.append(df.shift(-i))
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if i == 0:
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names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
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else:
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names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
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# put it all together
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agg = concat(cols, axis=1)
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agg.columns = names
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# drop rows with NaN values
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if dropnan:
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agg.dropna(inplace=True)
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return agg
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#%% load dataset
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from load_data import load_files
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data = load_files('data/', True)
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data.reset_index(drop=True, inplace=True)
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data = data[[column for column in data.columns if not column.endswith('volume')]]
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data = data[["BTC_returns", "BTC_vol_10"]]
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values = data.values
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# ensure all data is float
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# values = values.astype('float32')
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#%%
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np.isposinf(values).sum()
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#%% normalize features
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scaler = MinMaxScaler(feature_range=(-1, 1))
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scaled = scaler.fit_transform(values)
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# specify the number of lag hours
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past = 10
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n_features = 8
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#%% frame as supervised learning
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reframed = series_to_supervised(scaled, past, 1)
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print(reframed.shape)
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#%% split into train and test sets
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values = reframed.values
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n_train_hours = 365 * 24
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train = values[:n_train_hours, :]
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test = values[n_train_hours:, :]
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# split into input and outputs
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n_obs = past * n_features
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train_X, train_y = train[:, :n_obs], train[:, -n_features]
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test_X, test_y = test[:, :n_obs], test[:, -n_features]
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print(train_X.shape, len(train_X), train_y.shape)
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# reshape input to be 3D [samples, timesteps, features]
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train_X = train_X.reshape((train_X.shape[0], past, n_features))
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test_X = test_X.reshape((test_X.shape[0], past, n_features))
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print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
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#%% design network
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model = Sequential()
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model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
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model.add(Dense(1))
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model.compile(loss='mae', optimizer='adam')
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#%% fit network
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history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)
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# plot history
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pyplot.plot(history.history['loss'], label='train')
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pyplot.plot(history.history['val_loss'], label='test')
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pyplot.legend()
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pyplot.show()
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# make a prediction
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yhat = model.predict(test_X)
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test_X = test_X.reshape((test_X.shape[0], n_hours*n_features))
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# invert scaling for forecast
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inv_yhat = concatenate((yhat, test_X[:, -7:]), axis=1)
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inv_yhat = scaler.inverse_transform(inv_yhat)
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inv_yhat = inv_yhat[:,0]
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# invert scaling for actual
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test_y = test_y.reshape((len(test_y), 1))
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inv_y = concatenate((test_y, test_X[:, -7:]), axis=1)
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inv_y = scaler.inverse_transform(inv_y)
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inv_y = inv_y[:,0]
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# calculate RMSE
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rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
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print('Test RMSE: %.3f' % rmse) |