galaxy.metrics
==============

.. py:module:: galaxy.metrics

.. autoapi-nested-parse::

   Script for model performance evaluation and metrics visualization.



Functions
---------

.. autoapisummary::

   galaxy.metrics.probabilities_hist
   galaxy.metrics.plot_roc_curve
   galaxy.metrics.plot_pr_curve
   galaxy.metrics.plot_confusion_matrices
   galaxy.metrics.plot_red_shift
   galaxy.metrics.plot_loss_by_model
   galaxy.metrics.plot_accuracies_by_model
   galaxy.metrics.modelPerformance
   galaxy.metrics.combine_metrics


Module Contents
---------------

.. py:function:: probabilities_hist(predictions_clusters: numpy.ndarray, predictions_non_clusters: numpy.ndarray, pdf: matplotlib.backends.backend_pdf.PdfPages) -> None

   Plots histogram of prediction probabilities.

   Args:
       predictions_clusters (np.ndarray): Probabilities for the cluster class.
       predictions_non_clusters (np.ndarray): Probabilities for the non-cluster class.
       pdf (PdfPages): PDF file to save the plots.


.. py:function:: plot_roc_curve(pdf: matplotlib.backends.backend_pdf.PdfPages, predictions: pandas.DataFrame) -> None

   Plots the ROC curve.

   Args:
       pdf (PdfPages): PDF file to save the plots.
       predictions (pd.DataFrame): DataFrame containing true labels and predicted probabilities.


.. py:function:: plot_pr_curve(pdf: matplotlib.backends.backend_pdf.PdfPages, predictions: pandas.DataFrame) -> float

   Plots the precision-recall curve.

   Args:
       pdf (PdfPages): PDF file to save the plots.
       predictions (pd.DataFrame): DataFrame containing true labels and predicted probabilities.

   Returns:
       float: Area under the precision-recall curve (PR AUC).


.. py:function:: plot_confusion_matrices(pdf: matplotlib.backends.backend_pdf.PdfPages, predictions: pandas.DataFrame, classes: List[str]) -> Tuple[int, int, int, int]

   Plots confusion matrices.

   Args:
       pdf (PdfPages): PDF file to save the plots.
       predictions (pd.DataFrame): DataFrame containing true and predicted labels.
       classes (List[str]): Class labels.

   Returns:
       Tuple[int, int, int, int]: Counts for TN, FP, FN, TP from the confusion matrix.


.. py:function:: plot_red_shift(pdf, predictions: pandas.DataFrame)

.. py:function:: plot_loss_by_model(train_table_data: List[Tuple[int, float]], val_table_data: List[Tuple[int, float]], pdf: matplotlib.backends.backend_pdf.PdfPages) -> None

   Plots loss curves for training and validation.

   Args:
       train_table_data (List[Tuple[int, float]]): Training loss data.
       val_table_data (List[Tuple[int, float]]): Validation loss data.
       pdf (PdfPages): PDF file to save the plots.


.. py:function:: plot_accuracies_by_model(train_table_data, val_table_data, pdf)

.. py:function:: modelPerformance(model_name: str, optimizer_name: str, predictions: pandas.DataFrame, classes: List[str], train_table_data: Optional[List[Tuple[int, float, float]]] = None, val_table_data: Optional[List[Tuple[int, float, float]]] = None, f_beta: float = 2.0) -> None

   Plots distributions of probabilities of classes, ROC and Precision-Recall curves, change of loss and accuracy throughout training,
   confusion matrix and its weighted version and saves them in .png files,
   counts accuracy, precision, recall, false positive rate and f1-score and saves them in .txt file

   Args:
       model_name (str): Name of the model.
       optimizer_name (str): Name of the optimizer.
       predictions (pd.DataFrame): DataFrame with true labels, predicted labels, and probabilities.
       classes (List[str]): Class labels.
       train_table_data (Optional[List[Tuple[int, float, float]]], optional): Training data for plotting. Defaults to None.
       val_table_data (Optional[List[Tuple[int, float, float]]], optional): Validation data for plotting. Defaults to None.
       f_beta (float, optional): Beta value for F-beta score calculation. Defaults to 2.0.


.. py:function:: combine_metrics(selected_models: List[Tuple[str, Any]], optimizer_name: str) -> pandas.DataFrame

   Combines metrics for all selected models into a single CSV file.

   Args:
       selected_models (List[Tuple[str, Any]]): List of selected models.
       optimizer_name (str): Name of the optimizer.

   Returns:
       pd.DataFrame: Combined metrics DataFrame.