paradance.evaluation.LogarithmPCACalculator

class paradance.evaluation.LogarithmPCACalculator(df: DataFrame, pca_calculator: SelfBalancingLogarithmPCACalculator, overall_score_lower_bound: float | None = None, overall_score_upper_bound: float | None = None, rerank_eval_str: str | None = None)[source]

A calculator for performing PCA (Principal Component Analysis) operations.

This calculator uses an instance of a SelfBalancingLogarithmPCACalculator to perform the underlying PCA calculations and updates.

pca_calculator

The PCA calculator instance used for performing PCA operations.

Type:: SelfBalancingLogarithmPCACalculator

df

A copy of the cleaned dataframe from the pca_calculator.

Type:: DataFrame

__init__(df: DataFrame, pca_calculator: SelfBalancingLogarithmPCACalculator, overall_score_lower_bound: float | None = None, overall_score_upper_bound: float | None = None, rerank_eval_str: str | None = None) → None[source]: Initializes the BaseCalculator.

Methods

`__init__`(df, pca_calculator[, ...])	Initializes the BaseCalculator.
`calculate_auc_triple_parameters`(grid_interval)
`calculate_corrcoef`(mask_column, ...)
`calculate_cumulative_deviation`(mask_column, ...)
`calculate_distinct_count_portfolio_concentration`(...)
`calculate_distinct_top_coverage`(mask_column, ...)
`calculate_inverse_pair`(target_column[, ...])
`calculate_log_mse`(target_column[, ...])
`calculate_mean`(mask_column, target_column, ...)
`calculate_neg_rank_ratio`([label_column])
`calculate_portfolio_concentration`(...)
`calculate_proportion`(mask_column, ...)
`calculate_standard_deviation`(mask_column, ...)
`calculate_tau`(target_column, groupby[, ...])
`calculate_top_coverage`(mask_column, ...)
`calculate_woauc`(groupby, target_column[, ...])
`calculate_wuauc`(mask_column, target_column, ...)
`clip_max`(left, right)	Clips the values in the right array or scalar to a maximum value specified by left.
`clip_min`(left, right)	Clips the values in the right array or scalar to a minimum value specified by left.
`get_overall_score`(weights_for_equation)	Calculates and assigns an overall score to each entry in the dataframe based on the provided weights for the PCA equation.
`initialize_local_dict`(weights_for_equation, ...)	Initializes a dictionary that can be used for additional calculations.
`rerank_with_side_information`()	Reranks the rows in the DataFrame based on side information.

get_overall_score(weights_for_equation: List[float]) → None[source]

Calculates and assigns an overall score to each entry in the dataframe based on the provided weights for the PCA equation.

This method updates the internal dataframe df with a new column overall_score that contains the calculated scores for each entry.

Parameters:: weights_for_equation (List[float]) – A list of weights for calculating the overall PCA score.