transformação por PCA
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pyFTS/common/transformations/pca.py
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36
pyFTS/common/transformations/pca.py
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from sklearn.decomposition import PCA
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from pyFTS.common.Transformations import Transformation
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import pandas as pd
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class PCATransformation(Transformation):
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def __init__(self):
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self.pca = PCA(n_components=2)
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self.is_multivariate = True
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def apply(self, data, param=None, **kwargs):
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endogen_variable = kwargs.get('endogen_variable', None)
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names = kwargs.get('names', ('x', 'y'))
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if endogen_variable not in data.columns:
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endogen_variable = None
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cols = data.columns[:-1] if endogen_variable is None else [col for col in data.columns if
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col != endogen_variable]
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self.pca.fit(data[cols].values)
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transformed = self.pca.transform(data[cols])
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new = pd.DataFrame(transformed, columns=list(names))
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new[endogen_variable] = data[endogen_variable].values
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return new
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if __name__ =="__main__":
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pd.set_option('max_columns', 50)
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file = '/home/matheus_cascalho/Documentos/matheus_cascalho/MINDS/TimeSeries_Lab/SOM/gas_concentration/ethylene_CO.csv'
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df = pd.read_csv(file)
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df = df[df['Time (seconds)'].apply(lambda x: x % 1 == 0)]
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ignore = list(df.columns)[:3]
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endogen_variable = 'TGS2602'
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pca = PCATransformation()
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cols = [col for col in df.columns if col not in ignore]
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# cols.append(endogen_variable)
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print(pca.apply(df[cols], endogen_variable=endogen_variable))
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