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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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sns.set()
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dataset = pd.read_pickle('../preprocessing/dataset.pkl')
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target = dataset.pop('target')
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# Feature evaluation
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from sklearn.ensemble import ExtraTreesClassifier
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from sklearn.feature_selection import SelectFromModel
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clf = ExtraTreesClassifier(n_estimators=1000)
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clf = clf.fit(dataset, target)
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model = SelectFromModel(clf, prefit=True, max_features = 6)
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print('Retaining features:')
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print(dataset.columns.values[model.get_support()])
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reducedDataset = pd.DataFrame(model.transform(dataset),
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columns = dataset.columns.values[model.get_support()])
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# Every combination of the 6 best features with length equal to 4 features
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import itertools
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featureCombinations = itertools.combinations(range(6), 4)
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for plotIndex, subset in enumerate(featureCombinations):
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featurePlot = sns.pairplot(data=(reducedDataset.iloc[:, list(subset)]).assign(target=target),
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hue='target', palette='Set1', vars=reducedDataset.columns.values[list(subset)]);
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featurePlot.fig.savefig("output/figure_" + str(plotIndex+1) + ".png")
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# sns.relplot(x="4HzMod", y="Flat", data=dataset[["4HzMod", "Flat"]], hue = target, style = target)
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# sns.jointplot(x="SLAtt", y="ZCR", data=dataset[["SLAtt", "ZCR"]]);
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# plt.show()
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