library.phases.phases_implementation.modelling.results_analysis.result_analysis module

class library.phases.phases_implementation.modelling.results_analysis.result_analysis.InTuningResultAnalysis(phase_results_df: DataFrame)[source]

Bases: ResultAnalysis

extract_metrics()[source]
feature_importance()[source]
plot_results()[source]

scatterplot and histogram of the results

class library.phases.phases_implementation.modelling.results_analysis.result_analysis.PostTuningResultAnalysis(phase_results_df: DataFrame)[source]

Bases: ResultAnalysis

extract_metrics()[source]
feature_importance()[source]
plot_results()[source]

scatterplot and histogram of the results

class library.phases.phases_implementation.modelling.results_analysis.result_analysis.PreTuningResultAnalysis(phase_results_df: DataFrame)[source]

Bases: ResultAnalysis

extract_metrics()[source]
feature_importance()[source]
plot_results()[source]

scatterplot and histogram of the results

class library.phases.phases_implementation.modelling.results_analysis.result_analysis.ResultAnalysis(phase_results_df: DataFrame)[source]

Bases: ABC

abstractmethod extract_metrics()[source]
abstractmethod feature_importance()[source]
plot_multiple_model_metrics(feature_list)[source]

Plots bar charts of multiple performance metrics across different models.

Parameters:

  • feature_list (list of str)

  • plot. (List of metric names (features) to)

  • Behavior

  • --------

  • columns (- Creates a grid of bar plots with 2)

  • needed. (adjusting rows as)

  • phase_results_df. (- Each subplot shows the metric values for each model from)

  • values. (- Bars are annotated with their numeric)

  • layout. (- Unused subplots are removed for a cleaner)

  • readability. (- X-axis labels are rotated for better)

Return type:

None

abstractmethod plot_results()[source]

scatterplot and histogram of the results