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fix docstring identation
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@nikml
nikml committed Nov 13, 2023
1 parent d8a282b commit be9f99c
Showing 1 changed file with 50 additions and 50 deletions.
100 changes: 50 additions & 50 deletions nannyml/drift/ranker.py
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Expand Up @@ -199,56 +199,56 @@ class CorrelationRanker:
"""Ranks the features according to their correlation with changes in realized or estimated performance.
Examples
--------
>>> import nannyml as nml
>>> from IPython.display import display
>>> reference_df, analysis_df, analysis_target_df = nml.load_synthetic_car_loan_dataset()
>>> analysis_full_df = analysis_df.merge(analysis_target_df, left_index=True, right_index=True)
>>> column_names = [
... 'car_value', 'salary_range', 'debt_to_income_ratio', 'loan_length', 'repaid_loan_on_prev_car',
... 'size_of_downpayment', 'driver_tenure', 'y_pred_proba', 'y_pred', 'repaid'
>>> ]
>>> univ_calc = nml.UnivariateDriftCalculator(
... column_names=column_names,
... treat_as_categorical=['y_pred', 'repaid'],
... timestamp_column_name='timestamp',
... continuous_methods=['kolmogorov_smirnov', 'jensen_shannon'],
... categorical_methods=['chi2', 'jensen_shannon'],
... chunk_size=5000
>>> )
>>> univ_calc.fit(reference_df)
>>> univariate_results = univ_calc.calculate(analysis_full_df)
>>> realized_calc = nml.PerformanceCalculator(
... y_pred_proba='y_pred_proba',
... y_pred='y_pred',
... y_true='repaid',
... timestamp_column_name='timestamp',
... problem_type='classification_binary',
... metrics=['roc_auc', 'recall',],
... chunk_size=5000)
>>> realized_calc.fit(reference_df)
>>> realized_perf_results = realized_calc.calculate(analysis_full_df)
>>> ranker2 = nml.CorrelationRanker()
>>> # ranker fits on one metric and reference period data only
>>> ranker2.fit(
... realized_perf_results.filter(period='reference', metrics=['recall']))
>>> # ranker ranks on one drift method and one performance metric
>>> correlation_ranked_features2 = ranker2.rank(
... univariate_results.filter(period='analysis', methods=['jensen_shannon']),
... realized_perf_results.filter(period='analysis', metrics=['recall']),
... only_drifting = False)
>>> display(correlation_ranked_features2)
column_name pearsonr_correlation pearsonr_pvalue has_drifted rank
0 repaid_loan_on_prev_car 0.96897 3.90719e-06 True 1
1 y_pred_proba 0.966157 5.50918e-06 True 2
2 loan_length 0.965298 6.08385e-06 True 3
3 car_value 0.963623 7.33185e-06 True 4
4 salary_range 0.963456 7.46561e-06 True 5
5 size_of_downpayment 0.308948 0.385072 False 6
6 debt_to_income_ratio 0.307373 0.387627 False 7
7 y_pred -0.357571 0.310383 False 8
8 repaid -0.395842 0.257495 False 9
9 driver_tenure -0.575807 0.0815202 False 10
--------
>>> import nannyml as nml
>>> from IPython.display import display
>>> reference_df, analysis_df, analysis_target_df = nml.load_synthetic_car_loan_dataset()
>>> analysis_full_df = analysis_df.merge(analysis_target_df, left_index=True, right_index=True)
>>> column_names = [
... 'car_value', 'salary_range', 'debt_to_income_ratio', 'loan_length', 'repaid_loan_on_prev_car',
... 'size_of_downpayment', 'driver_tenure', 'y_pred_proba', 'y_pred', 'repaid'
>>> ]
>>> univ_calc = nml.UnivariateDriftCalculator(
... column_names=column_names,
... treat_as_categorical=['y_pred', 'repaid'],
... timestamp_column_name='timestamp',
... continuous_methods=['kolmogorov_smirnov', 'jensen_shannon'],
... categorical_methods=['chi2', 'jensen_shannon'],
... chunk_size=5000
>>> )
>>> univ_calc.fit(reference_df)
>>> univariate_results = univ_calc.calculate(analysis_full_df)
>>> realized_calc = nml.PerformanceCalculator(
... y_pred_proba='y_pred_proba',
... y_pred='y_pred',
... y_true='repaid',
... timestamp_column_name='timestamp',
... problem_type='classification_binary',
... metrics=['roc_auc', 'recall',],
... chunk_size=5000)
>>> realized_calc.fit(reference_df)
>>> realized_perf_results = realized_calc.calculate(analysis_full_df)
>>> ranker2 = nml.CorrelationRanker()
>>> # ranker fits on one metric and reference period data only
>>> ranker2.fit(
... realized_perf_results.filter(period='reference', metrics=['recall']))
>>> # ranker ranks on one drift method and one performance metric
>>> correlation_ranked_features2 = ranker2.rank(
... univariate_results.filter(period='analysis', methods=['jensen_shannon']),
... realized_perf_results.filter(period='analysis', metrics=['recall']),
... only_drifting = False)
>>> display(correlation_ranked_features2)
column_name pearsonr_correlation pearsonr_pvalue has_drifted rank
0 repaid_loan_on_prev_car 0.96897 3.90719e-06 True 1
1 y_pred_proba 0.966157 5.50918e-06 True 2
2 loan_length 0.965298 6.08385e-06 True 3
3 car_value 0.963623 7.33185e-06 True 4
4 salary_range 0.963456 7.46561e-06 True 5
5 size_of_downpayment 0.308948 0.385072 False 6
6 debt_to_income_ratio 0.307373 0.387627 False 7
7 y_pred -0.357571 0.310383 False 8
8 repaid -0.395842 0.257495 False 9
9 driver_tenure -0.575807 0.0815202 False 10
"""

def __init__(self) -> None:
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