89 lines
2.5 KiB
Python
89 lines
2.5 KiB
Python
import time
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import matplotlib.pyplot as plt
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import dill
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import numpy as np
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current_milli_time = lambda: int(round(time.time() * 1000))
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def uniquefilename(name):
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if '.' in name:
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tmp = name.split('.')
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return tmp[0] + str(current_milli_time()) + '.' + tmp[1]
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else:
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return name + str(current_milli_time())
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def showAndSaveImage(fig,file,flag,lgd=None):
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"""
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Show and image and save on file
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:param fig: Matplotlib Figure object
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:param file: filename to save the picture
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:param flag: if True the image will be saved
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:param lgd: legend
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"""
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if flag:
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plt.show()
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if lgd is not None:
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fig.savefig(uniquefilename(file), additional_artists=lgd,bbox_inches='tight') #bbox_extra_artists=(lgd,), )
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else:
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fig.savefig(uniquefilename(file))
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plt.close(fig)
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def enumerate2(xs, start=0, step=1):
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for x in xs:
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yield (start, x)
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start += step
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def sliding_window(data, windowsize, train=0.8, inc=0.1):
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"""
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Sliding window method of cross validation for time series
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:param data: the entire dataset
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:param windowsize: window size
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:param train: percentual of the window size will be used for training the models
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:param inc: percentual of data used for slide the window
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:return: window count, training set, test set
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"""
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l = len(data)
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ttrain = int(round(windowsize * train, 0))
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ic = int(round(windowsize * inc, 0))
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for count in np.arange(0,l-windowsize+ic,ic):
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if count + windowsize > l:
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_end = l
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else:
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_end = count + windowsize
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yield (count, data[count : count + ttrain], data[count + ttrain : _end] )
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def persist_obj(obj, file):
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"""
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Persist an object on filesystem. This function depends on Dill package
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:param obj: object on memory
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:param file: file name to store the object
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"""
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with open(file, 'wb') as _file:
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dill.dump(obj, _file)
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def load_obj(file):
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"""
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Load to memory an object stored filesystem. This function depends on Dill package
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:param file: file name where the object is stored
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:return: object
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"""
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with open(file, 'rb') as _file:
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obj = dill.load(_file)
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return obj
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def persist_env(file):
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"""
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Persist an entire environment on file. This function depends on Dill package
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:param file: file name to store the environment
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"""
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dill.dump_session(file)
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def load_env(file):
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dill.load_session(file) |