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add datasets for tests
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20191
letter.arff
Executable file
20191
letter.arff
Executable file
File diff suppressed because it is too large
Load Diff
2306
mfeat-factors.arff
Executable file
2306
mfeat-factors.arff
Executable file
File diff suppressed because it is too large
Load Diff
65
sample.py
65
sample.py
@@ -9,61 +9,11 @@ from math import log2
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from scipy.io import arff
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import pandas as pd
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def entropy(y: np.array) -> float:
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"""Compute entropy of a labels set
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Parameters
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----------
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y : np.array
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set of labels
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Returns
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-------
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float
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entropy
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"""
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n_labels = len(y)
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if n_labels <= 1:
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return 0
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counts = np.bincount(y)
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proportions = counts / n_labels
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n_classes = np.count_nonzero(proportions)
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if n_classes <= 1:
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return 0
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entropy = 0.0
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# Compute standard entropy.
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for prop in proportions:
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if prop != 0.0:
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entropy -= prop * log2(prop, 2)
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return entropy
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def information_gain(
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labels: np.array, labels_up: np.array, labels_dn: np.array
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) -> float:
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imp_prev = entropy(labels)
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card_up = card_dn = imp_up = imp_dn = 0
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if labels_up is not None:
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card_up = labels_up.shape[0]
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imp_up = entropy(labels_up)
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if labels_dn is not None:
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card_dn = labels_dn.shape[0] if labels_dn is not None else 0
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imp_dn = entropy(labels_dn)
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samples = card_up + card_dn
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if samples == 0:
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return 0.0
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else:
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result = (
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imp_prev
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- (card_up / samples) * imp_up
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- (card_dn / samples) * imp_dn
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)
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return result
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class_name = "speaker"
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file_name = "kdd_JapaneseVowels.arff"
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# class_name = "speaker"
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# file_name = "kdd_JapaneseVowels.arff"
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class_name = "class"
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# file_name = "mfeat-factors.arff"
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file_name = "letter.arff"
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data = arff.loadarff(file_name)
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df = pd.DataFrame(data[0])
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df.dropna(axis=0, how="any", inplace=True)
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@@ -82,7 +32,8 @@ X = X.to_numpy()
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test = FImdlp()
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now = time.time()
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test.fit(X, y, features=[i for i in (range(3, 14))])
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# test.fit(X, y, features=[i for i in (range(3, 14))])
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test.fit(X, y)
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fit_time = time.time()
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print("Fitting: ", fit_time - now)
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now = time.time()
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@@ -92,7 +43,7 @@ print(test.get_cut_points())
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clf = RandomForestClassifier(random_state=0)
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print(clf.fit(Xt, y).score(Xt, y))
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print(Xt)
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# for proposal in [True, False]:
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# X = data.data
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# y = data.target
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