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data_preprocessing.py
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data_preprocessing.py
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import numpy as np
from math import ceil
def train_test_split(time_series, train_end_timestamp):
"""
Splits a given time series in training data and test data.
Args:
time_series (pandas.core.series.Series): input time series.
train_end_timestamp (string): final timestamp of training data.
Returns:
numpy.ndarray: training time series
numpy.ndarray: test time series
"""
train = time_series[:train_end_timestamp].values
test = time_series[train_end_timestamp:].values
return train, test
def train_val_test_split(time_series, train_start_timestamp, train_end_timestamp, val_end_timestamp):
"""
Splits a given time series in training data, validation data and test data.
Args:
time_series (pandas.core.series.Series): input time series.
train_start_timestamp (string): initial timestamp of training data
train_end_timestamp (string): final timestamp of training data
val_end_timestamp (string): final timestamp of validation data
Returns:
numpy.ndarray: training time series
numpy.ndarray: validation time series
numpy.ndarray: test time series
"""
train = time_series[train_start_timestamp:train_end_timestamp].values
val = time_series[train_end_timestamp:val_end_timestamp].values
test = time_series[val_end_timestamp:].values
return train, val, test
def normalize_data(data, min_value=0.0, max_value=1.0):
"""
Rescales the input subtracting min_power and dividing by max_value - min_value.
Args:
data (numpy.ndarray): data to be normalized.
min_value (float)
max_value (float)
Returns:
numpy.ndarray: normalized data.
"""
data -= min_value
data /= max_value - min_value
return data
def standardize_data(data, mu=0.0, sigma=1.0):
"""
Rescales the input subtracting mu and dividing by sigma.
Args:
data (numpy.ndarray): data to be standardized.
mu (float)
sigma (float)
Returns:
numpy.ndarray: standardized data
"""
data -= mu
data /= sigma
return data
def zero_pad(data, window_size):
"""
Pads with window_size / 2 zeros the given input.
Args:
data (numpy.ndarray): data to be padded.
window_size (int): parameter that controls the size of padding.
Returns:
numpy.ndarray: padded data.
"""
pad_width = ceil(window_size / 2)
padded = np.pad(data, (pad_width, pad_width), 'constant', constant_values=(0,0))
return padded