slideflow.stats

This module contains internal utility functions for generating and evaluating model predictions and metrics.

df_from_pred(y_true: List[Any] | None, y_pred: List[Any], y_std: List[Any] | None, tile_to_slides: List | ndarray, locations: List | ndarray | None = None) → DataFrame

Converts arrays of model predictions to a pandas dataframe.

Parameters:

• y_true (list(np.ndarray)) – List of y_true numpy arrays, one array for each outcome. For linear outcomes, the length of the outer list should be one, and the second shape dimension of the numpy array should be the number of linear outcomes.

• y_pred (list(np.ndarray)) – List of y_pred numpy arrays, one array for each outcome. For linear outcomes, the length of the outer list should be one, and the second shape dimension of the numpy array should be the number of linear outcomes.

• y_std (list(np.ndarray)) – List of uncertainty numpy arrays, formatted in the same way as y_pred.

• tile_to_slides (np.ndarray) – Array of slide names for each tile. Length should match the numpy arrays in y_true, y_pred, and y_std.

Returns:

DataFrame of predictions.

Return type:

DataFrame

eval_dataset(model: tf.keras.Model | torch.nn.Module, dataset: tf.data.Dataset | torch.utils.data.DataLoader, model_type: str, num_tiles: int = 0, uq: bool = False, uq_n: int = 30, reduce_method: str | Callable = 'average', patients: Dict[str, str] | None = None, outcome_names: List[str] | None = None, loss: Callable | None = None, torch_args: SimpleNamespace | None = None) → Tuple[DataFrame, float, float]

Generates predictions and accuracy/loss from a given model and dataset.

Parameters:

• model (str) – Path to PyTorch model.

• dataset (tf.data.Dataset) – PyTorch dataloader.

• model_type (str, optional) – ‘categorical’, ‘linear’, or ‘cph’. If multiple linear outcomes are present, y_true is stacked into a single vector for each image. Defaults to ‘categorical’.

• num_tiles (int, optional) – Used for progress bar with Tensorflow. Defaults to 0.

• uq_n (int, optional) – Number of forward passes to perform when calculating MC Dropout uncertainty. Defaults to 30.

• reduce_method (str, optional) – Reduction method for calculating slide-level and patient-level predictions for categorical outcomes. Options include ‘average’, ‘mean’, ‘proportion’, ‘median’, ‘sum’, ‘min’, ‘max’, or a callable function. ‘average’ and ‘mean’ are synonymous, with both options kept for backwards compatibility. If ‘average’ or ‘mean’, will reduce with average of each logit across tiles. If ‘proportion’, will convert tile predictions into onehot encoding then reduce by averaging these onehot values. For all other values, will reduce with the specified function, applied via the pandas DataFrame.agg() function. Defaults to ‘average’.

• patients (dict, optional) – Dictionary mapping slide names to patient names. Required for generating patient-level metrics.

• outcome_names (list, optional) – List of str, names for outcomes. Defaults to None (outcomes will not be named).

• torch_args (namespace) – Used for PyTorch models. Namespace containing num_slide_features, slide_input, update_corrects, and update_loss functions.

Returns:

pd.DataFrame, accuracy, loss

group_reduce(df: DataFrame, method: str | Callable = 'average', patients: Dict[str, str] | None = None) → Dict[str, DataFrame]

Reduces tile-level predictions to group-level predictions.

Parameters:

• df (DataFrame) – Tile-level predictions.

• method (str, optional) – Reduction method for calculating slide-level and patient-level predictions for categorical outcomes. Options include ‘average’, ‘mean’, ‘proportion’, ‘median’, ‘sum’, ‘min’, ‘max’, or a callable function. ‘average’ and ‘mean’ are synonymous, with both options kept for backwards compatibility. If ‘average’ or ‘mean’, will reduce with average of each logit across tiles. If ‘proportion’, will convert tile predictions into onehot encoding then reduce by averaging these onehot values. For all other values, will reduce with the specified function, applied via the pandas DataFrame.agg() function. Defaults to ‘average’.

• patients (dict, optional) – Dictionary mapping slide names to patient names. Required for generating patient-level metrics.

metrics_from_dataset(model: tf.keras.Model | torch.nn.Module, model_type: str, patients: Dict[str, str], dataset: tf.data.Dataset | torch.utils.data.DataLoader, num_tiles: int = 0, outcome_names: List[str] | None = None, reduce_method: str | Callable = 'average', label: str = '', save_predictions: str | bool = False, data_dir: str = '', uq: bool = False, loss: Callable | None = None, torch_args: SimpleNamespace | None = None, **kwargs) → Tuple[Dict, float, float]

Evaluate performance of a given model on a given TFRecord dataset, generating a variety of statistical outcomes and graphs.

Parameters:

• model (tf.keras.Model or torch.nn.Module) – Keras/Torch model to eval.

• model_type (str) – ‘categorical’, ‘linear’, or ‘cph’.

• patients (dict) – Dictionary mapping slidenames to patients.

• dataset (tf.data.Dataset or torch.utils.data.DataLoader) – Dataset.

• num_tiles (int, optional) – Number of total tiles expected in dataset. Used for progress bar. Defaults to 0.

Keyword Arguments:

• outcome_names (list, optional) – List of str, names for outcomes. Defaults to None.

• reduce_method (str, optional) – Reduction method for calculating slide-level and patient-level predictions for categorical outcomes. Options include ‘average’, ‘mean’, ‘proportion’, ‘median’, ‘sum’, ‘min’, ‘max’, or a callable function. ‘average’ and ‘mean’ are synonymous, with both options kept for backwards compatibility. If ‘average’ or ‘mean’, will reduce with average of each logit across tiles. If ‘proportion’, will convert tile predictions into onehot encoding then reduce by averaging these onehot values. For all other values, will reduce with the specified function, applied via the pandas DataFrame.agg() function. Defaults to ‘average’.

• label (str, optional) – Label prefix/suffix for saving. Defaults to None.

• save_predictions (bool, optional) – Save tile, slide, and patient-level predictions to CSV. Defaults to True.

• data_dir (str) – Path to data directory for saving. Defaults to empty string (current directory).

• neptune_run (neptune.Run, optional) – Neptune run in which to log results. Defaults to None.

Returns:

metrics [dict], accuracy [float], loss [float]

name_columns(df: DataFrame, model_type: str, outcome_names: List[str] | None = None)

Renames columns in a DataFrame to correspond to the given outcome names.

Assumes the DataFrame supplied was generated by sf.stats.df_from_pred().

Parameters:

• df (DataFrame) – DataFrame from sf.stats.df_from_pred(), containing predictions and labels.

• model_type (str) – Type of model (‘categorical’, ‘linear’, or ‘cph’).

• outcome_names (list(str)), optional) – Outcome names to apply to the DataFrame. If this is from a CPH model, the standard names “time” and “event” will be used.

Raises:

• ValueError – If outcome_names are not supplied and it is not a CPH model.

• errors.StatsError – If the length of outcome_names is incompatible with the DataFrame.

Returns:

DataFrame with renamed columns.

Return type:

DataFrame

predict_dataset(model: tf.keras.Model | torch.nn.Module, dataset: tf.data.Dataset | torch.utils.data.DataLoader, model_type: str, num_tiles: int = 0, uq: bool = False, uq_n: int = 30, reduce_method: str | Callable = 'average', patients: Dict[str, str] | None = None, outcome_names: List[str] | None = None, torch_args: SimpleNamespace | None = None) → Dict[str, DataFrame]

Generates predictions from model and dataset.

Parameters:

• model (str) – Path to PyTorch model.

• dataset (tf.data.Dataset) – PyTorch dataloader.

• model_type (str, optional) – ‘categorical’, ‘linear’, or ‘cph’. If multiple linear outcomes are present, y_true is stacked into a single vector for each image. Defaults to ‘categorical’.

• num_tiles (int, optional) – Used for progress bar with Tensorflow. Defaults to 0.

• uq_n (int, optional) – Number of forward passes to perform when calculating MC Dropout uncertainty. Defaults to 30.

• reduce_method (str, optional) – Reduction method for calculating slide-level and patient-level predictions for categorical outcomes. Options include ‘average’, ‘mean’, ‘proportion’, ‘median’, ‘sum’, ‘min’, ‘max’, or a callable function. ‘average’ and ‘mean’ are synonymous, with both options kept for backwards compatibility. If ‘average’ or ‘mean’, will reduce with average of each logit across tiles. If ‘proportion’, will convert tile predictions into onehot encoding then reduce by averaging these onehot values. For all other values, will reduce with the specified function, applied via the pandas DataFrame.agg() function. Defaults to ‘average’.

• patients (dict, optional) – Dictionary mapping slide names to patient names. Required for generating patient-level metrics.

• outcome_names (list, optional) – List of str, names for outcomes. Defaults to None (outcomes will not be named).

• torch_args (namespace) – Used for PyTorch backend. Namespace containing num_slide_features and slide_input.

Returns:

Dictionary with keys ‘tile’, ‘slide’, and ‘patient’, and values containing DataFrames with tile-, slide-, and patient-level predictions.

Return type:

Dict[str, pd.DataFrame]

calculate_centroid(act: Dict[str, ndarray]) → Tuple[Dict[str, int], Dict[str, ndarray]]

Calcultes slide-level centroid indices for a provided activations dict.

Parameters:

activations (dict) – Dict mapping slide names to ndarray of activations across tiles, of shape (n_tiles, n_features)

Returns:

A tuple containing

dict: Dict mapping slides to index of tile nearest to centroid

dict: Dict mapping slides to activations of tile nearest to centroid

get_centroid_index(arr: ndarray) → int

Calculate index nearest to centroid from a given 2D input array.