Module statkit.feature_selection

Select features using statistical hypothesis testing.

Classes

class StatisticalTestFilter (statistical_test: Literal['kolmogorov-smirnov', 'mann-whitney-u', 'epps-singleton'] = 'kolmogorov-smirnov',
p_value: float = 0.05,
multiple_testing: Literal['benjamini-hochberg', 'bonferroni'] = 'benjamini-hochberg',
invert: bool = False,
**kwargs)

Expand source code

class StatisticalTestFilter(BaseEstimator, SelectorMixin):
    """Select columns with significant difference between labels.

    Test which features the distribution of the postive class is stastistically
    different from the negative class, using multiple testing correction. Keep only the
    features that passed the statistical test.
    """

    def _apply_test(
        self,
        X_pos: NDArray,
        X_neg: NDArray,
        multiple_testing: Literal[
            "benjamini-hochberg", "bonferroni"
        ] = "benjamini-hochberg",
    ) -> DataFrame:
        """Column-wise test between positive and negative group."""
        columns = tuple(range(X_pos.shape[1]))
        if hasattr(self, "feature_names_in_"):
            columns = self.feature_names_in_

        result = DataFrame(columns=["statistic", "pvalue"], index=columns)

        # Perform test for each feature.
        for i, col in enumerate(columns):
            try:
                statistic, p_value = self.test_(
                    X_pos[:, i], X_neg[:, i], **self.test_kwargs_
                )
            except (linalg.LinAlgError, ValueError):
                statistic, p_value = nan, nan
            result.loc[col] = [statistic, p_value]  # type: ignore

        # Apply multiple-testing correction.
        if multiple_testing == "benjamini-hochberg":
            reject, pvalue_corrected = fdrcorrection(result.pvalue, alpha=self.p_value)
        elif multiple_testing == "bonferroni":
            reject, pvalue_corrected, _, _ = multipletests(
                result.pvalue, alpha=self.p_value, method="bonferroni"
            )

        result["pvalue-corrected"] = pvalue_corrected
        result["reject"] = reject

        return result

    def __init__(
        self,
        statistical_test: Literal[
            "kolmogorov-smirnov", "mann-whitney-u", "epps-singleton"
        ] = "kolmogorov-smirnov",
        p_value: float = 0.05,
        multiple_testing: Literal[
            "benjamini-hochberg", "bonferroni"
        ] = "benjamini-hochberg",
        invert: bool = False,
        **kwargs,
    ):
        """
        Args:
            statistical_test: Test for difference in feature distributions
                between labels.
            p_value: The null hypothesis rejection probability (including
                `correction`).
            multiple_testing: What type of correction strategy to apply to account for
                multiple testing.
            invert: Invert selection, by keeping only the non-significant (instead of
                significant) columns.
        """
        super().__init__(**kwargs)
        self.statistical_test = statistical_test
        self.p_value = p_value
        self.multiple_testing = multiple_testing
        self.invert = invert

    def _get_support_mask(self):
        """Compute support mask of features."""
        if self.invert:
            return ~self.scores_["reject"]
        return self.scores_["reject"]

    def fit(self, X, y):
        """Perform column-wise statistical test."""
        check_X_y(X, y)
        _check_feature_names(self, X, reset=True)

        self.test_kwargs_ = {}
        statistical_functions = {
            "mann-whitney-u": mann_whitney_u,
            "kolmogorov-smirnov": kolmogorov_smirnov,
            "epps-singleton": epps_singleton,
        }

        if self.statistical_test not in statistical_functions.keys():
            raise KeyError(f"Unknown statistical method {self.statistical_test}.")

        self.test_ = statistical_functions[self.statistical_test]

        # Only allow two classes right now.
        self.classes_ = unique_labels(y)
        assert len(self.classes_) == 2
        X_neg = X[y == self.classes_[0]]
        X_pos = X[y == self.classes_[1]]

        if isinstance(X, DataFrame):
            X_neg = X_neg.to_numpy()
            X_pos = X_pos.to_numpy()

        self.scores_ = self._apply_test(
            X_pos, X_neg, multiple_testing=self.multiple_testing
        )

        return self

Select columns with significant difference between labels.

Test which features the distribution of the postive class is stastistically different from the negative class, using multiple testing correction. Keep only the features that passed the statistical test.

Args

statistical_test

Test for difference in feature distributions between labels.

p_value

The null hypothesis rejection probability (including correction).

multiple_testing

What type of correction strategy to apply to account for multiple testing.

invert

Invert selection, by keeping only the non-significant (instead of significant) columns.

Ancestors

• sklearn.base.BaseEstimator
• sklearn.utils._repr_html.base.ReprHTMLMixin
• sklearn.utils._repr_html.base._HTMLDocumentationLinkMixin
• sklearn.utils._metadata_requests._MetadataRequester
• sklearn.feature_selection._base.SelectorMixin
• sklearn.base.TransformerMixin
• sklearn.utils._set_output._SetOutputMixin

Methods

def fit(self, X, y)

Expand source code

def fit(self, X, y):
    """Perform column-wise statistical test."""
    check_X_y(X, y)
    _check_feature_names(self, X, reset=True)

    self.test_kwargs_ = {}
    statistical_functions = {
        "mann-whitney-u": mann_whitney_u,
        "kolmogorov-smirnov": kolmogorov_smirnov,
        "epps-singleton": epps_singleton,
    }

    if self.statistical_test not in statistical_functions.keys():
        raise KeyError(f"Unknown statistical method {self.statistical_test}.")

    self.test_ = statistical_functions[self.statistical_test]

    # Only allow two classes right now.
    self.classes_ = unique_labels(y)
    assert len(self.classes_) == 2
    X_neg = X[y == self.classes_[0]]
    X_pos = X[y == self.classes_[1]]

    if isinstance(X, DataFrame):
        X_neg = X_neg.to_numpy()
        X_pos = X_pos.to_numpy()

    self.scores_ = self._apply_test(
        X_pos, X_neg, multiple_testing=self.multiple_testing
    )

    return self

Perform column-wise statistical test.