Weighted EVREG feature selection algorithm #39
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LastShekel
requested changes
Jan 10, 2021
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| def weighted_evidential_regression(X, y, alpha=0.01, num_epochs=1000, p=2, k=None, radius=5.0): | ||
| """ | ||
| Calculates anova measure for each feature. |
| np.sum([__y_derivative(X, i, weights, p, radius, y) for i in range(X.shape[0])], axis=0) | ||
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| def weighted_evidential_regression(X, y, alpha=0.01, num_epochs=1000, p=2, k=None, radius=5.0): |
Collaborator
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Does it a filter algorithm?
Not embedded one?
Contributor
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Yes it is more of an embedded method, but it fits better the pipeline of univariate filter, as it needs cutting rule and provides feature metric that can be used as a rank.
� Conflicts: � ITMO_FS/filters/univariate/__init__.py
LastShekel
requested changes
Mar 22, 2021
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LastShekel
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LastShekel
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Delete pandas from requirements
| return sum(abs((first - second) * weights) ** p) ** (1.0 / p) | ||
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| def __rbf(distance, radius): |
Collaborator
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Its probably better to put these functions into utils or somewhere
| self.k = int(0.1 * X.shape[0]) | ||
| if self.k < 1: | ||
| self.k = X.shape[0] - 1 | ||
| print(self.k) |
Collaborator
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This should go to log or get deleted
| >>> y = np.array([1, 2, 3, 4, 5], dtype=np.integer) | ||
| >>> weighted_ev_reg = WeightedEvReg(cutting_rule=('K best', 2), num_epochs=100) | ||
| >>> weighted_ev_reg.fit(X, y) | ||
| >>> print(weighted_ev_reg.selected_features_) |
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do not print this
and check this test with pytest
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Pull Request Template
Description
Please include a summary of the change and which issue is fixed. Please also include relevant motivation and context. List any dependencies that are required for this change.
Fixes # (issue)
Type of change
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Just a basic run unit test - test_weighted_evreg in univariate_filter_test.
Checklist: