wehub-resource-sync
66 lines
1.6 KiB
Python
66 lines
1.6 KiB
Python
# coding:utf-8
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import numpy as np
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from scipy import stats
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def f_entropy(p):
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# Convert values to probability
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p = np.bincount(p) / float(p.shape[0])
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ep = stats.entropy(p)
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if ep == -float("inf"):
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return 0.0
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return ep
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def information_gain(y, splits):
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splits_entropy = sum(
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[f_entropy(split) * (float(split.shape[0]) / y.shape[0]) for split in splits]
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)
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return f_entropy(y) - splits_entropy
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def mse_criterion(y, splits):
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y_mean = np.mean(y)
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return -sum(
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[
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np.sum((split - y_mean) ** 2) * (float(split.shape[0]) / y.shape[0])
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for split in splits
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]
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)
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def xgb_criterion(y, left, right, loss):
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left = loss.gain(left["actual"], left["y_pred"])
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right = loss.gain(right["actual"], right["y_pred"])
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initial = loss.gain(y["actual"], y["y_pred"])
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gain = left + right - initial
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return gain
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def get_split_mask(X, column, value):
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left_mask = X[:, column] < value
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right_mask = X[:, column] >= value
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return left_mask, right_mask
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def split(X, y, value):
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left_mask = X < value
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right_mask = X >= value
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return y[left_mask], y[right_mask]
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def split_dataset(X, target, column, value, return_X=True):
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left_mask, right_mask = get_split_mask(X, column, value)
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left, right = {}, {}
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for key in target.keys():
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left[key] = target[key][left_mask]
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right[key] = target[key][right_mask]
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if return_X:
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left_X, right_X = X[left_mask], X[right_mask]
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return left_X, right_X, left, right
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else:
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return left, right
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