qsprpred.models.assessment package

Subpackages

Submodules

qsprpred.models.assessment.classification module

qsprpred.models.assessment.classification.create_metrics_summary(model)[source]

qsprpred.models.assessment.methods module

This module holds assessment methods for QSPRModels

class qsprpred.models.assessment.methods.CrossValAssessor(scoring: str | Callable[[Iterable, Iterable], float], split: DataSplit | None = None, monitor: AssessorMonitor | None = None, use_proba: bool = True, mode: EarlyStoppingMode | None = None, round: int = 5, split_multitask_scores: bool = False)[source]

Bases: ModelAssessor

Perform cross validation on a model.

Variables:

  • useProba (bool) – use predictProba instead of predict for classification

  • monitor (AssessorMonitor) – monitor to use for assessment, if None, a BaseMonitor is used

  • mode (EarlyStoppingMode) – mode to use for early stopping

  • split (DataSplit) – split to use for cross validation (default: KFold, n_splits=5)

  • round (int) – number of decimal places to round predictions to (default: 5)

  • splitMultitaskScores (bool) – whether to split the scores per task for multitask models

Initialize the evaluation method class.

Parameters:

  • scoring – str | Callable[[Iterable, Iterable], float],

  • monitor (AssessorMonitor) – monitor to track the evaluation

  • use_proba (bool) – use probabilities for classification models

  • mode (EarlyStoppingMode) – early stopping mode for fitting

  • split_multitask_scores (bool) – whether to split the scores per task for multitask models

predictionsToDataFrame(model: QSPRModel, y: array, predictions: ndarray | list[numpy.ndarray], index: Series, extra_columns: dict[str, numpy.ndarray] | None = None) → DataFrame

Create a dataframe with true values and predictions.

Parameters:

  • model (QSPRModel) – model to evaluate

  • y (np.array) – target values

  • predictions (np.ndarray | list[np.ndarray]) – predictions

  • index (pd.Series) – index of the data set

  • extra_columns (dict[str, np.ndarray]) – extra columns to add to the output

class qsprpred.models.assessment.methods.ModelAssessor(scoring: str | Callable[[Iterable, Iterable], float], monitor: AssessorMonitor | None = None, use_proba: bool = True, mode: EarlyStoppingMode | None = None, split_multitask_scores: bool = False)[source]

Bases: ABC

Base class for assessment methods.

Variables:

  • scoreFunc (Metric) – scoring function to use, should match the output of the evaluation method (e.g. if the evaluation methods returns class probabilities, the scoring function support class probabilities)

  • monitor (AssessorMonitor) – monitor to use for assessment, if None, a BaseMonitor is used

  • useProba (bool) – use probabilities for classification models

  • mode (EarlyStoppingMode) – early stopping mode for fitting

  • splitMultitaskScores (bool) – whether to split the scores per task for multitask models

Initialize the evaluation method class.

Parameters:

  • scoring – str | Callable[[Iterable, Iterable], float],

  • monitor (AssessorMonitor) – monitor to track the evaluation

  • use_proba (bool) – use probabilities for classification models

  • mode (EarlyStoppingMode) – early stopping mode for fitting

  • split_multitask_scores (bool) – whether to split the scores per task for multitask models

predictionsToDataFrame(model: QSPRModel, y: array, predictions: ndarray | list[numpy.ndarray], index: Series, extra_columns: dict[str, numpy.ndarray] | None = None) → DataFrame[source]

Create a dataframe with true values and predictions.

Parameters:

  • model (QSPRModel) – model to evaluate

  • y (np.array) – target values

  • predictions (np.ndarray | list[np.ndarray]) – predictions

  • index (pd.Series) – index of the data set

  • extra_columns (dict[str, np.ndarray]) – extra columns to add to the output

class qsprpred.models.assessment.methods.TestSetAssessor(scoring: str | Callable[[Iterable, Iterable], float], monitor: AssessorMonitor | None = None, use_proba: bool = True, mode: EarlyStoppingMode | None = None, round: int = 5, split_multitask_scores: bool = False)[source]

Bases: ModelAssessor

Assess a model on a test set.

Attributes:+

useProba (bool): use predictProba instead of predict for classification monitor (AssessorMonitor): monitor to use for assessment, if None, a BaseMonitor

is used

mode (EarlyStoppingMode): mode to use for early stopping round (int): number of decimal places to round predictions to (default: 3) splitMultitaskScores (bool): whether to split the scores per task for multitask models

Initialize the evaluation method class.

Parameters:

  • scoring – str | Callable[[Iterable, Iterable], float],

  • monitor (AssessorMonitor) – monitor to track the evaluation

  • use_proba (bool) – use probabilities for classification models

  • mode (EarlyStoppingMode) – early stopping mode for fitting

  • split_multitask_scores (bool) – whether to split the scores per task for multitask models

predictionsToDataFrame(model: QSPRModel, y: array, predictions: ndarray | list[numpy.ndarray], index: Series, extra_columns: dict[str, numpy.ndarray] | None = None) → DataFrame

Create a dataframe with true values and predictions.

Parameters:

  • model (QSPRModel) – model to evaluate

  • y (np.array) – target values

  • predictions (np.ndarray | list[np.ndarray]) – predictions

  • index (pd.Series) – index of the data set

  • extra_columns (dict[str, np.ndarray]) – extra columns to add to the output

qsprpred.models.assessment.regression module

qsprpred.models.assessment.regression.create_correlation_summary(model)[source]

Module contents