plots package

Submodules

plots.live_learning module

class plots.live_learning.PlotLearning(title, plot_save_as)

Bases: Callback

Callback to plot the learning curves of the model during training.

on_epoch_end(epoch, logs={})

Called at the end of an epoch.

Subclasses should override for any actions to run. This function should only be called during TRAIN mode.

Parameters:

• epoch – Integer, index of epoch.

• logs – Dict, metric results for this training epoch, and for the validation epoch if validation is performed. Validation result keys are prefixed with val_. For training epoch, the values of the Model’s metrics are returned. Example: {‘loss’: 0.2, ‘accuracy’: 0.7}.

on_train_begin(logs={})

Called at the beginning of training.

Subclasses should override for any actions to run.

Parameters:

logs – Dict. Currently no data is passed to this argument for this method but that may change in the future.

on_train_end(logs={})

Called at the end of training.

Subclasses should override for any actions to run.

Parameters:

logs – Dict. Currently the output of the last call to on_epoch_end() is passed to this argument for this method but that may change in the future.

plots.plots module

plots.plots.average_jaccard_similarity(rankings: List[List[str]]) → Tuple[float, float]

Calculate the average Jaccard similarity and standard deviation for a list of rankings

Parameters:

rankings (List[List[str]]) – List of rankings, where each ranking is a list of features

Returns:

Average Jaccard similarity and standard deviation across all pairs of rankings

Return type:

Tuple[float, float]

plots.plots.create_stats_df(unique_features, index_positions)

plots.plots.extract_values(file_path)

plots.plots.feature_ranking_plot(top_features, index_positions, bins_to_names, save_path, auto_open)

plots.plots.generate_plots(datasets_root, denoised_dataset_name, computations_root, dataset_name, auto_open=False)

plots.plots.get_index_positions(data)

plots.plots.jaccard_similarity(list1: List[str], list2: List[str]) → float

Calculate Jaccard Similarity between two lists.

The Jaccard similarity coefficient measures the size of the intersection divided by the size of the union of two sets. The resulting number is a scalar value representing the overall similarity (or stability) of the feature selection reflected by the different rankings. Note that the Jaccard similarity ranges from 0 to 1, where 1 signifies that the rankings are identical, and 0 signifies that the rankings do not share any features.

Parameters:

• list1 (List[str]) – First list of features

• list2 (List[str]) – Second list of features

Returns:

Jaccard similarity between the two lists

Return type:

float

plots.plots.load_csv_file(file_path)

plots.plots.load_pickle_file(file_path)

plots.plots.plot_ppm_with_selection(ds, pca_ds, top_features, bins_to_names, ppm_chart_title, save_path, auto_open, plot_median=False)

plots.plots.plot_ppm_with_selection_paper_modified(ds, pca_ds, top_features, bins_to_names, ppm_chart_title, save_path, auto_open, plot_median=False)

plots.plots.plot_pred_acc_and_sim_for_RFE(df, ordered_ranking, save_path, auto_open, metric_dict={'rfe_val_auc_shallow': 'AUC Accuracy'})

plots.plots.plot_pred_acc_multimetric_RFE(df, ordered_ranking, save_path, auto_open, metric_dict={'rfe_val_auc_shallow': 'AUC Accuracy'})

plots.plots.ranking_stats_to_txt(top_features, bins_to_names, save_path)

Process a DataFrame: add binned metabolites, binned metabolite range, sort by mean, and rename columns.

Parameters: top_features (pandas.DataFrame): Input DataFrame. bins_to_names (dict): Dictionary for mapping bin numbers to feature names.

Returns: pandas.DataFrame: The processed DataFrame.

plots.plots.sort_paths_by_epoch(paths)

plots.ppm_selected module

plots.ppm_selected.plot_ppm_with_selection(dataset_path, selected_feature_indicies, ppm_chart_title, SAVE_PATH, plot_median=False, bin=0.005, display_top_n_bin=20)

Module contents