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https://github.com/Doctorado-ML/FImdlp.git
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115 lines
2.8 KiB
Python
115 lines
2.8 KiB
Python
from sklearn.datasets import load_iris
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from fimdlp.mdlp import FImdlp
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from fimdlp.cppfimdlp import CFImdlp
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import numpy as np
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from math import log
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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 * log(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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data = load_iris()
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X = data.data
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y = data.target
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features = data.feature_names
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# test = FImdlp()
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# test.fit(X, y, features=features)
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# test.transform(X)
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# test.get_cut_points()
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test = CFImdlp(debug=True, proposed=False)
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# # k = test.cut_points(X[:, 0], y)
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# # print(k)
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# # k = test.cut_points_ant(X[:, 0], y)
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# # print(k)
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# # test.debug_points(X[:, 0], y)
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X = [5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9]
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indices = [4, 3, 6, 8, 2, 1, 5, 0, 9, 7]
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y = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2]
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# # To check
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# indices2 = np.argsort(X)
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# Xs = np.array(X)[indices2]
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# ys = np.array(y)[indices2]
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# test.fit(X[:, 0], y)
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test.fit(X, y)
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result = test.get_cut_points()
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for item in result:
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print(
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f"*Class={item['classNumber']} - ({item['start']:3d}, {item['end']:3d})"
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f" -> ({item['fromValue']:3.1f}, {item['toValue']:3.1f}]"
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)
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print(test.get_discretized_values())
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# print(Xs, ys)
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# print("**********************")
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# test = [(0, 3), (4, 4), (5, 5), (6, 8), (9, 9)]
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# print(ys)
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# for start, end in test:
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# print("Testing ", start, end, ys[:end], ys[end:])
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# print("Information gain: ", information_gain(ys, ys[:end], ys[end:]))
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# print(test.transform(X))
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# print(X)
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# print(indices)
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# print(np.array(X)[indices])
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# X = np.array(
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# [
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# [5.1, 3.5, 1.4, 0.2],
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# [5.2, 3.0, 1.4, 0.2],
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# [5.3, 3.2, 1.3, 0.2],
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# [5.3, 3.1, 1.5, 0.2],
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# ]
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# )
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# y = np.array([0, 0, 0, 1])
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# test.fit(X, y).transform(X)
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