Function _compute_ks_test_p_value
has a Cognitive Complexity of 17 (exceeds 5 allowed). Consider refactoring. Open
def _compute_ks_test_p_value(
target_distribution: kll_doubles_sketch,
reference_distribution: kll_doubles_sketch,
quantiles: Optional[List[float]] = None,
) -> Optional[ColumnDriftValue]:
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Cognitive Complexity
Cognitive Complexity is a measure of how difficult a unit of code is to intuitively understand. Unlike Cyclomatic Complexity, which determines how difficult your code will be to test, Cognitive Complexity tells you how difficult your code will be to read and comprehend.
A method's cognitive complexity is based on a few simple rules:
- Code is not considered more complex when it uses shorthand that the language provides for collapsing multiple statements into one
- Code is considered more complex for each "break in the linear flow of the code"
- Code is considered more complex when "flow breaking structures are nested"
Further reading
Function _compute_chi_squared_test_p_value
has a Cognitive Complexity of 9 (exceeds 5 allowed). Consider refactoring. Open
def _compute_chi_squared_test_p_value(
target_distribution: FrequentStats, reference_distribution: FrequentStats
) -> Optional[ColumnDriftValue]:
"""
Calculate the Chi-Squared test p-value for two discrete distributions.
- Read upRead up
Cognitive Complexity
Cognitive Complexity is a measure of how difficult a unit of code is to intuitively understand. Unlike Cyclomatic Complexity, which determines how difficult your code will be to test, Cognitive Complexity tells you how difficult your code will be to read and comprehend.
A method's cognitive complexity is based on a few simple rules:
- Code is not considered more complex when it uses shorthand that the language provides for collapsing multiple statements into one
- Code is considered more complex for each "break in the linear flow of the code"
- Code is considered more complex when "flow breaking structures are nested"
Further reading
Function calculate_drift_values
has a Cognitive Complexity of 7 (exceeds 5 allowed). Consider refactoring. Open
def calculate_drift_values(
target_view: DatasetProfileView, reference_view: DatasetProfileView, statistic=False
) -> Dict[str, Optional[Union[ColumnDriftValue, ColumnDriftStatistic]]]:
"""Calculate drift values between both profiles. Applicable for numerical and categorical features.
- Read upRead up
Cognitive Complexity
Cognitive Complexity is a measure of how difficult a unit of code is to intuitively understand. Unlike Cyclomatic Complexity, which determines how difficult your code will be to test, Cognitive Complexity tells you how difficult your code will be to read and comprehend.
A method's cognitive complexity is based on a few simple rules:
- Code is not considered more complex when it uses shorthand that the language provides for collapsing multiple statements into one
- Code is considered more complex for each "break in the linear flow of the code"
- Code is considered more complex when "flow breaking structures are nested"
Further reading
Line too long (93 > 79 characters) Open
def _get_ks_p_value(target_view_column, reference_view_column) -> Optional[ColumnDriftValue]:
- Read upRead up
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Should be a ReferenceDistributionDiscreteMessage containing the frequent items,
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p_value : ColumnDriftValue or None. ColumnDriftValue has fields `p-value` with the test's result,
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start = min([target_kll_sketch.get_min_value(), ref_kll_sketch.get_min_value()])
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cdf_target = target_distribution.get_cdf([target_quantile_values[i]])[0]
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cdf_ref = reference_distribution.get_cdf([target_quantile_values[i]])[0]
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of 79 characters. For flowing long blocks of text (docstrings or
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A kll_floats_sketch (quantiles sketch) from the reference (expected) distribution's values
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def calculate_hellinger_distance(target_pmf: List[float], reference_pmf: List[float]) -> float:
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Uses the quantile values and the corresponding CDFs to calculate the approximate KS statistic.
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distance = euclidean(np.sqrt(target_pmf), np.sqrt(reference_pmf)) / np.sqrt(2)
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distance = calculate_hellinger_distance(target_pmf=target_pmf, reference_pmf=ref_pmf)
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"""Statistic for applied algorithm, along with the name of the applied algorithm."""
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Can be generated from a theoretical distribution, or another sample for the same feature.
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proportion_ref_dist_items = {k: v / ref_total_count for k, v in ref_dist_items.items()}
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from whylogs.core.view.dataset_profile_view import DatasetProfileView # type: ignore
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ref_frequency = int(proportion_ref_dist_items[item["value"]] * target_total_count)
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target_view: DatasetProfileView, reference_view: DatasetProfileView, statistic=False
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from whylogs.core.view.column_profile_view import ColumnProfileView # type: ignore
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A dictionary of the p-values, along with the type of test applied, for the given features.
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Uses the top frequent items summary, unique count estimate and total count estimate for each feature,
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def _get_chi2_p_value(target_view_column, reference_view_column) -> Optional[ColumnDriftValue]:
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"""p-value for applied statistical test, along with the name of the applied test."""
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target_frequent_stats: FrequentStats = get_frequent_stats(target_view_column, config=None)
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ref_frequent_stats: FrequentStats = get_frequent_stats(reference_view_column, config=None)
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of 79 characters. For flowing long blocks of text (docstrings or
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Line too long (87 > 79 characters) Open
Should be a ReferenceDistributionDiscreteMessage containing the frequent items,
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end = max([target_kll_sketch.get_max_value(), ref_kll_sketch.get_max_value()])
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Calculates drift only for features found in both profiles, and ignore those not found in either profile.
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drift_values: Dict[str, Optional[Union[ColumnDriftValue, ColumnDriftStatistic]]] = {}
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Bucket of quantiles used to get the CDF's for both target and reference profiles.
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m, n = sorted([target_distribution.get_n(), reference_distribution.get_n()], reverse=True)
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"""Calculates hellinger distance between two discrete probability distributions.
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The hellinger distance between the two discrete probability distributions.
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A kll_floats_sketch (quantiles sketch) from the target distribution's values
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Between 0 (identical distributions) and 1 (completely different distributions).
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target_view_column=target_view_column, reference_view_column=reference_view_column
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target_view_column=target_view_column, reference_view_column=reference_view_column
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The estimated p-value from the Chi-Squared test, applied on the target and reference distributions'
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target_freq_items, ref_freq_items = zero_padding_frequent_items(target_freq_items, ref_freq_items)
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"MIN_N_BUCKETS < 2 might lead to erroneous results for low-sized samples. Consider setting it to >=2."
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"""Calculate drift values between both profiles. Applicable for numerical and categorical features.
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target_view_column=target_view_column, reference_view_column=reference_view_column
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"""Compute the Kolmogorov-Smirnov test p-value of two continuous distributions.
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The estimated p-value from the parametrized KS test, applied on the target and reference distributions'
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of 79 characters. For flowing long blocks of text (docstrings or
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target_distribution=target_frequent_stats, reference_distribution=ref_frequent_stats
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of 79 characters. For flowing long blocks of text (docstrings or
comments), limiting the length to 72 characters is recommended.
Reports error E501.