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42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
import warnings
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from mfs import MFS
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from mfs.Metrics import Metrics
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from stree import Stree
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import numpy as np
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from scipy.io import arff
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mfsc = MFS(discrete=False)
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filename = "conn-bench-sonar-mines-rocks.arff"
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data, meta = arff.loadarff(filename)
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train = np.array([data[i] for i in meta])
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X = train.T
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X = X[:, :-1].astype("float64")
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y = data["clase"]
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m, n = X.shape
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print("* Differential entropy in X")
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for i in range(n):
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print(i, Metrics.differential_entropy(X[:, i], k=10))
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print("* Information Gain")
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print("- Continuous features")
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print(Metrics.information_gain_cont(X, y))
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for i in range(n):
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print(i, Metrics.information_gain_cont(X[:, i], y))
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# Classification
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warnings.filterwarnings("ignore")
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print("CFS")
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cfs_f = mfsc.cfs(X, y).get_results()
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print(cfs_f)
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print("FCBF")
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fcfb_f = mfsc.fcbf(X, y, 5e-2).get_results()
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print(fcfb_f, len(fcfb_f))
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print("X.shape=", X.shape)
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clf = Stree(random_state=0)
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print("Accuracy whole dataset", clf.fit(X, y).score(X, y))
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clf = Stree(random_state=0)
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print("Accuracy cfs", clf.fit(X[:, cfs_f], y).score(X[:, cfs_f], y))
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clf = Stree(random_state=0)
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subf = fcfb_f
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print("Accuracy fcfb", clf.fit(X[:, subf], y).score(X[:, subf], y))
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