qsprpred.plotting package

Submodules

qsprpred.plotting.base_plot module

This module contains the base class for all model plots.

class qsprpred.plotting.base_plot.ModelPlot(models: list[qsprpred.models.model.QSPRModel])

Bases: ABC

Base class for all model plots.

Variables:

• models (list[QSPRModel]) – list of models to plot

• modelOuts (dict[QSPRModel, str]) – dictionary of model output paths

• modelNames (dict[QSPRModel, str]) – dictionary of model names

• cvPaths (dict[QSPRModel, str]) – dictionary of models mapped to their cross-validation set results paths

• indPaths (dict[QSPRModel, str]) – dictionary of models mapped to their independent test set results paths

Initialize the base class for all model plots.

Parameters:

models (list[QSPRModel]) – list of models to plot

checkModel(model: QSPRModel) → tuple[str, str]

Check if the model has been evaluated and saved. If not, raise an exception.

Parameters:

model (QSPRModel) – model to check

Returns:

path to the cross-validation set results file indPath (str): path to the independent test set results file

Return type:

cvPath (str)

Raises:

ValueError – if the model type is not supported

abstract getSupportedTasks() → list[str]

Get the types of models this plotter supports.

Returns:

list of supported TargetTasks

Return type:

list of TargetTasks

abstract make(save: bool = True, show: bool = False) → Any

Make the plot.

Opens a window to show the plot or returns a plot representation that can be directly shown in a notebook or saved to a file.

Parameters:

• save (bool) – whether to save the plot to a file

• show (bool) – whether to show the plot in a window

Returns:

plot representation

Return type:

plot (Any)

qsprpred.plotting.classification module

Plotting functions for classification models.

class qsprpred.plotting.classification.CalibrationPlot(models: list[qsprpred.models.model.QSPRModel])

Bases: ClassifierPlot

Plot of calibration curve for a given model.

Initialize the base class for all model plots.

Parameters:

models (list[QSPRModel]) – list of models to plot

calculateMultiClassMetrics(df, average_type, n_classes)

Calculate metrics for a given dataframe.

calculateSingleClassMetrics(df)

Calculate metrics for a given dataframe.

checkModel(model: QSPRModel) → tuple[str, str]

Check if the model has been evaluated and saved. If not, raise an exception.

Parameters:

model (QSPRModel) – model to check

Returns:

path to the cross-validation set results file indPath (str): path to the independent test set results file

Return type:

cvPath (str)

Raises:

ValueError – if the model type is not supported

getSummary()

Get summary statistics for classification results.

getSupportedTasks() → List[ModelTasks]

Return a list of tasks supported by this plotter.

make(save: bool = True, show: bool = False, property_name: str | None = None, validation: str = 'cv', fig_size: tuple = (6, 6)) → list[matplotlib.axes._axes.Axes]

Make the plot for a given validation type.

Parameters:

• property_name (str) – name of the property to plot (should correspond to the prefix of the column names in the data files). If None, the first property in the model’s targetProperties list will be used.

• validation (str) – The type of validation data to use. Can be either ‘cv’ for cross-validation or ‘ind’ for independent test set.

• fig_size (tuple) – The size of the figure to create.

• save (bool) – Whether to save the plot to a file.

• show (bool) – Whether to display the plot.

Returns:

A list of matplotlib axes objects containing the plots.

Return type:

axes (list[plt.Axes])

makeCV(model: QSPRModel, property_name: str, n_bins: int = 10) → Axes

Make the plot for a given model using cross-validation data.

Parameters:

• model (QSPRModel) – the model to plot the data from.

• property_name (str) – name of the property to plot (should correspond to the prefix of the column names in the data files).

• n_bins (int) – The number of bins to use for the calibration curve.

Returns:

the axes object containing the plot.

Return type:

ax (matplotlib.axes.Axes)

makeInd(model: QSPRModel, property_name: str, n_bins: int = 10) → Axes

Make the plot for a given model using independent test data.

Parameters:

• model (QSPRModel) – the model to plot the data from.

• property_name (str) – name of the property to plot (should correspond to the prefix of the column names in the data files).

• n_bins (int) – The number of bins to use for the calibration curve.

Returns:

the axes object containing the plot.

Return type:

ax (matplotlib.axes.Axes)

prepareAssessment(assessment_df: DataFrame) → DataFrame

Prepare assessment dataframe for plotting

Parameters:

assessment_df (pd.DataFrame) – the assessment dataframe containing the experimental and predicted values for each property. The dataframe should have the following columns: QSPRID, Fold (opt.), <property_name>_<suffixes>_<Label/Prediction/ProbabilityClass_X>

Returns:

The dataframe containing the assessment results, columns: QSPRID, Fold, Property, Label, Prediction, Class, Set

Return type:

pd.DataFrame

prepareClassificationResults() → DataFrame

Prepare classification results dataframe for plotting.

Returns:

the dataframe containing the classficiation results, columns: Model, QSPRID, Fold, Property, Label, Prediction, Set

Return type:

pd.DataFrame

class qsprpred.plotting.classification.ClassifierPlot(models: list[qsprpred.models.model.QSPRModel])

Bases: ModelPlot, ABC

Base class for plots of classification models.

Initialize the base class for all model plots.

Parameters:

models (list[QSPRModel]) – list of models to plot

calculateMultiClassMetrics(df, average_type, n_classes)

Calculate metrics for a given dataframe.

calculateSingleClassMetrics(df)

Calculate metrics for a given dataframe.

checkModel(model: QSPRModel) → tuple[str, str]

Check if the model has been evaluated and saved. If not, raise an exception.

Parameters:

model (QSPRModel) – model to check

Returns:

path to the cross-validation set results file indPath (str): path to the independent test set results file

Return type:

cvPath (str)

Raises:

ValueError – if the model type is not supported

getSummary()

Get summary statistics for classification results.

getSupportedTasks() → List[ModelTasks]

Return a list of tasks supported by this plotter.

abstract make(save: bool = True, show: bool = False) → Any

Make the plot.

Opens a window to show the plot or returns a plot representation that can be directly shown in a notebook or saved to a file.

Parameters:

• save (bool) – whether to save the plot to a file

• show (bool) – whether to show the plot in a window

Returns:

plot representation

Return type:

plot (Any)

prepareAssessment(assessment_df: DataFrame) → DataFrame

Prepare assessment dataframe for plotting

Parameters:

assessment_df (pd.DataFrame) – the assessment dataframe containing the experimental and predicted values for each property. The dataframe should have the following columns: QSPRID, Fold (opt.), <property_name>_<suffixes>_<Label/Prediction/ProbabilityClass_X>

Returns:

The dataframe containing the assessment results, columns: QSPRID, Fold, Property, Label, Prediction, Class, Set

Return type:

pd.DataFrame

prepareClassificationResults() → DataFrame

Prepare classification results dataframe for plotting.

Returns:

the dataframe containing the classficiation results, columns: Model, QSPRID, Fold, Property, Label, Prediction, Set

Return type:

pd.DataFrame

class qsprpred.plotting.classification.ConfusionMatrixPlot(models: list[qsprpred.models.model.QSPRModel])

Bases: ClassifierPlot

Plot of confusion matrix for a given model as a heatmap.

Initialize the base class for all model plots.

Parameters:

models (list[QSPRModel]) – list of models to plot

calculateMultiClassMetrics(df, average_type, n_classes)

Calculate metrics for a given dataframe.

calculateSingleClassMetrics(df)

Calculate metrics for a given dataframe.

checkModel(model: QSPRModel) → tuple[str, str]

Check if the model has been evaluated and saved. If not, raise an exception.

Parameters:

model (QSPRModel) – model to check

Returns:

path to the cross-validation set results file indPath (str): path to the independent test set results file

Return type:

cvPath (str)

Raises:

ValueError – if the model type is not supported

getConfusionMatrixDict(df: DataFrame) → dict

Create dictionary of confusion matrices for each model, property and fold

Parameters:

df (pd.DataFrame) – the dataframe containing the classficiation results, columns: Model, QSPRID, Fold, Property, Label, Prediction, Set

Returns:

dictionary of confusion matrices for each model, property and fold

Return type:

dict

getSummary()

Get summary statistics for classification results.

getSupportedTasks() → List[ModelTasks]

Return a list of tasks supported by this plotter.

make(save: bool = True, show: bool = False, out_path: str | None = None) → tuple[dict, matplotlib.axes._axes.Axes]

Make confusion matrix heatmap for each model, property and fold

Parameters:

• save (bool) – whether to save the plot

• show (bool) – whether to show the plot

• out_path (str | None) – path to save the plot to, e.g. “results/plot.png”, the plots will be saved to this path with the plot identifier appended before the extension, If None, the plots will be saved to each model’s output directory.

Returns:

dictionary of confusion matrices for each model, property and fold list[plt.axes.Axes]:

a list of matplotlib axes objects containing the plots.

Return type:

dict

prepareAssessment(assessment_df: DataFrame) → DataFrame

Prepare assessment dataframe for plotting

Parameters:

assessment_df (pd.DataFrame) – the assessment dataframe containing the experimental and predicted values for each property. The dataframe should have the following columns: QSPRID, Fold (opt.), <property_name>_<suffixes>_<Label/Prediction/ProbabilityClass_X>

Returns:

The dataframe containing the assessment results, columns: QSPRID, Fold, Property, Label, Prediction, Class, Set

Return type:

pd.DataFrame

prepareClassificationResults() → DataFrame

Prepare classification results dataframe for plotting.

Returns:

the dataframe containing the classficiation results, columns: Model, QSPRID, Fold, Property, Label, Prediction, Set

Return type:

pd.DataFrame

class qsprpred.plotting.classification.MetricsPlot(models: List[QSPRModel], metrics: List[Literal['f1', 'matthews_corrcoef', 'precision', 'recall', 'accuracy', 'calibration_error', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo']] = ['f1', 'matthews_corrcoef', 'precision', 'recall', 'accuracy', 'calibration_error', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo'])

Bases: ClassifierPlot

Plot of metrics for a given model.

Variables:

• models (list) – A list of QSPRModel objects to plot the data from.

• metrics (list) – A list of metrics to plot, choose from: f1, matthews_corrcoef, precision, recall, accuracy, roc_auc, roc_auc_ovr, roc_auc_ovo and calibration_error

Initialise the metrics plot.

Parameters:

• models (list) – A list of QSPRModel objects to plot the data from.

• metrics (list) – A list of metrics to plot.

calculateMultiClassMetrics(df, average_type, n_classes)

Calculate metrics for a given dataframe.

calculateSingleClassMetrics(df)

Calculate metrics for a given dataframe.

checkModel(model: QSPRModel) → tuple[str, str]

Check if the model has been evaluated and saved. If not, raise an exception.

Parameters:

model (QSPRModel) – model to check

Returns:

path to the cross-validation set results file indPath (str): path to the independent test set results file

Return type:

cvPath (str)

Raises:

ValueError – if the model type is not supported

getSummary()

Get summary statistics for classification results.

getSupportedTasks() → List[ModelTasks]

Return a list of tasks supported by this plotter.

make(save: bool = True, show: bool = False, out_path: str | None = None) → tuple[List[seaborn.axisgrid.FacetGrid], pandas.core.frame.DataFrame]

Make the plot for a given validation type.

Parameters:

• property_name (str) – name of the property to plot (should correspond to the prefix of the column names in the data files).

• save (bool) – Whether to save the plot to a file.

• show (bool) – Whether to display the plot.

• out_path (str | None) – Path to save the plots to, e.g. “results/plot.png”, the plot will be saved to this path with the metric name appended before the extension, e.g. “results/plot_roc_auc.png”. If None, the plots will be saved to each model’s output directory.

Returns:

the seaborn FacetGrid objects used to make the plot pd.DataFrame:

A dataframe containing the summary data generated.

Return type:

figures (list[sns.FacetGrid])

prepareAssessment(assessment_df: DataFrame) → DataFrame

Prepare assessment dataframe for plotting

Parameters:

assessment_df (pd.DataFrame) – the assessment dataframe containing the experimental and predicted values for each property. The dataframe should have the following columns: QSPRID, Fold (opt.), <property_name>_<suffixes>_<Label/Prediction/ProbabilityClass_X>

Returns:

The dataframe containing the assessment results, columns: QSPRID, Fold, Property, Label, Prediction, Class, Set

Return type:

pd.DataFrame

prepareClassificationResults() → DataFrame

Prepare classification results dataframe for plotting.

Returns:

the dataframe containing the classficiation results, columns: Model, QSPRID, Fold, Property, Label, Prediction, Set

Return type:

pd.DataFrame

class qsprpred.plotting.classification.PRCPlot(models: list[qsprpred.models.model.QSPRModel])

Bases: ClassifierPlot

Plot of Precision-Recall curve for a given model.

Initialize the base class for all model plots.

Parameters:

models (list[QSPRModel]) – list of models to plot

calculateMultiClassMetrics(df, average_type, n_classes)

Calculate metrics for a given dataframe.

calculateSingleClassMetrics(df)

Calculate metrics for a given dataframe.

checkModel(model: QSPRModel) → tuple[str, str]

Check if the model has been evaluated and saved. If not, raise an exception.

Parameters:

model (QSPRModel) – model to check

Returns:

path to the cross-validation set results file indPath (str): path to the independent test set results file

Return type:

cvPath (str)

Raises:

ValueError – if the model type is not supported

getSummary()

Get summary statistics for classification results.

getSupportedTasks() → List[ModelTasks]

Return a list of tasks supported by this plotter.

make(save: bool = True, show: bool = False, property_name: str | None = None, validation: str = 'cv', fig_size: tuple = (6, 6))

Make the plot for a given validation type.

Parameters:

• property_name (str) – name of the property to plot (should correspond to the prefix of the column names in the data files). If None, the first property in the model’s targetProperties list will be used.

• validation (str) – The type of validation data to use. Can be either ‘cv’ for cross-validation or ‘ind’ for independent test set.

• fig_size (tuple) – The size of the figure to create.

• save (bool) – Whether to save the plot to a file.

• show (bool) – Whether to display the plot.

Returns:

A list of matplotlib axes objects containing the plots.

Return type:

axes (list)

makeCV(model: QSPRModel, property_name: str) → Axes

Make the plot for a given model using cross-validation data.

Parameters:

• model (QSPRModel) – the model to plot the data from.

• property_name (str) – name of the property to plot (should correspond to the prefix of the column names in the data files).

Returns:

the axes object containing the plot.

Return type:

ax (matplotlib.axes.Axes)

makeInd(model: QSPRModel, property_name: str) → Axes

Make the plot for a given model using independent test data.

Parameters:

• model (QSPRModel) – the model to plot the data from.

• property_name (str) – name of the property to plot (should correspond to the prefix of the column names in the data files).

Returns:

the axes object containing the plot.

Return type:

ax (matplotlib.axes.Axes)

prepareAssessment(assessment_df: DataFrame) → DataFrame

Prepare assessment dataframe for plotting

Parameters:

assessment_df (pd.DataFrame) – the assessment dataframe containing the experimental and predicted values for each property. The dataframe should have the following columns: QSPRID, Fold (opt.), <property_name>_<suffixes>_<Label/Prediction/ProbabilityClass_X>

Returns:

The dataframe containing the assessment results, columns: QSPRID, Fold, Property, Label, Prediction, Class, Set

Return type:

pd.DataFrame

prepareClassificationResults() → DataFrame

Prepare classification results dataframe for plotting.

Returns:

the dataframe containing the classficiation results, columns: Model, QSPRID, Fold, Property, Label, Prediction, Set

Return type:

pd.DataFrame

class qsprpred.plotting.classification.ROCPlot(models: list[qsprpred.models.model.QSPRModel])

Bases: ClassifierPlot

Plot of ROC-curve (receiver operating characteristic curve) for a given classification model.

Initialize the base class for all model plots.

Parameters:

models (list[QSPRModel]) – list of models to plot

calculateMultiClassMetrics(df, average_type, n_classes)

Calculate metrics for a given dataframe.

calculateSingleClassMetrics(df)

Calculate metrics for a given dataframe.

checkModel(model: QSPRModel) → tuple[str, str]

Check if the model has been evaluated and saved. If not, raise an exception.

Parameters:

model (QSPRModel) – model to check

Returns:

path to the cross-validation set results file indPath (str): path to the independent test set results file

Return type:

cvPath (str)

Raises:

ValueError – if the model type is not supported

getSummary()

Get summary statistics for classification results.

getSupportedTasks() → List[ModelTasks]

Return a list of tasks supported by this plotter.

make(save: bool = True, show: bool = False, property_name: str | None = None, validation: str = 'cv', fig_size: tuple = (6, 6)) → list[matplotlib.axes._axes.Axes]

Make the ROC plot for given validation sets.

Parameters:

• property_name (str) – name of the predicted property to plot (should correspond to the prefix of the column names in cvPaths or indPaths files). If None, the first property in the model’s targetProperties list will be used.

• validation (str) – The type of validation set to read data for. Can be either ‘cv’ for cross-validation or ‘ind’ for independent test set.

• fig_size (tuple) – The size of the figure to create.

• save (bool) – Whether to save the plot to a file.

• show (bool) – Whether to display the plot.

Returns:

A list of matplotlib axes objects containing the plots.

Return type:

axes (list[plt.Axes])

makeCV(model: QSPRModel, property_name: str) → Axes

Make the plot for a given model using cross-validation data.

Many thanks to the scikit-learn documentation since the code below borrows heavily from the example at:

https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc_crossval.html

Parameters:

• model (QSPRModel) – the model to plot the data from.

• property_name (str) – name of the property to plot (should correspond to the prefix of the column names in the data files).

Returns:

the axes object containing the plot.

Return type:

ax (matplotlib.axes.Axes)

makeInd(model: QSPRModel, property_name: str) → Axes

Make the ROC plot for a given model using independent test data.

Parameters:

• model (QSPRModel) – the model to plot the data from.

• property_name (str) – name of the property to plot (should correspond to the prefix of the column names in the data files).

Returns:

the axes object containing the plot.

Return type:

ax (matplotlib.axes.Axes)

prepareAssessment(assessment_df: DataFrame) → DataFrame

Prepare assessment dataframe for plotting

Parameters:

assessment_df (pd.DataFrame) – the assessment dataframe containing the experimental and predicted values for each property. The dataframe should have the following columns: QSPRID, Fold (opt.), <property_name>_<suffixes>_<Label/Prediction/ProbabilityClass_X>

Returns:

The dataframe containing the assessment results, columns: QSPRID, Fold, Property, Label, Prediction, Class, Set

Return type:

pd.DataFrame

prepareClassificationResults() → DataFrame

Prepare classification results dataframe for plotting.

Returns:

the dataframe containing the classficiation results, columns: Model, QSPRID, Fold, Property, Label, Prediction, Set

Return type:

pd.DataFrame

qsprpred.plotting.grid_visualizers module

qsprpred.plotting.grid_visualizers.interactive_grid(mols, *args, molsPerRow=5, **kwargs)

install mols2grid with pip to use

qsprpred.plotting.grid_visualizers.smiles_to_grid(smiles, *args, mols_per_row=5, impl: ~typing.Callable = <function MolsToGridImage>, **kwargs)

qsprpred.plotting.grid_visualizers.table_to_grid(table: ~qsprpred.data.tables.base.MoleculeDataTable, mols_per_row: int = 5, impl: ~typing.Callable = <function MolsToGridImage>, *args, **kwargs)

qsprpred.plotting.regression module

Module for plotting regression models.

class qsprpred.plotting.regression.CorrelationPlot(models: list[qsprpred.models.model.QSPRModel])

Bases: RegressionPlot

Class to plot the results of regression models. Plot predicted pX_train vs real pX_train.

Initialize the base class for all model plots.

Parameters:

models (list[QSPRModel]) – list of models to plot

checkModel(model: QSPRModel) → tuple[str, str]

Check if the model has been evaluated and saved. If not, raise an exception.

Parameters:

model (QSPRModel) – model to check

Returns:

path to the cross-validation set results file indPath (str): path to the independent test set results file

Return type:

cvPath (str)

Raises:

ValueError – if the model type is not supported

getSummary()

calculate the R2 and RMSE for each model per set (cross-validation or independent test)

getSupportedTasks() → list[qsprpred.tasks.ModelTasks]

Return a list of supported model tasks.

make(save: bool = True, show: bool = False, out_path: str | None = None) → tuple[seaborn.axisgrid.FacetGrid, pandas.core.frame.DataFrame]

Plot the results of regression models. Plot predicted pX_train vs real pX_train.

Parameters:

• save (bool) – whether to save the plot

• show (bool) – whether to show the plot

• out_path (str | None) – path to save the plot to, e.g. “results/plot.png”, if None, the plot will be saved to each model’s output directory.

Returns:

the seaborn FacetGrid object used to make the plot pd.DataFrame:

the summary data used to make the plot

Return type:

g (sns.FacetGrid)

prepareAssessment(assessment_df: DataFrame) → DataFrame

Prepare assessment dataframe for plotting

Parameters:

assessment_df (pd.DataFrame) – the assessment dataframe containing the experimental and predicted values for each property. The dataframe should have the following columns: QSPRID, Fold (opt.), <property_name>_<suffixes>_<Label/Prediction>

Returns:

The dataframe containing the assessment results, columns: QSPRID, Fold, Property, Label, Prediction, Set

Return type:

pd.DataFrame

prepareRegressionResults() → DataFrame

Prepare regression results dataframe for plotting.

Returns:

the dataframe containing the regression results, columns: Model, QSPRID, Fold, Property, Label, Prediction, Set

Return type:

pd.DataFrame

class qsprpred.plotting.regression.RegressionPlot(models: list[qsprpred.models.model.QSPRModel])

Bases: ModelPlot, ABC

Base class for all regression plots.

Initialize the base class for all model plots.

Parameters:

models (list[QSPRModel]) – list of models to plot

checkModel(model: QSPRModel) → tuple[str, str]

Check if the model has been evaluated and saved. If not, raise an exception.

Parameters:

model (QSPRModel) – model to check

Returns:

path to the cross-validation set results file indPath (str): path to the independent test set results file

Return type:

cvPath (str)

Raises:

ValueError – if the model type is not supported

getSummary()

calculate the R2 and RMSE for each model per set (cross-validation or independent test)

getSupportedTasks() → list[qsprpred.tasks.ModelTasks]

Return a list of supported model tasks.

abstract make(save: bool = True, show: bool = False) → Any

Make the plot.

Opens a window to show the plot or returns a plot representation that can be directly shown in a notebook or saved to a file.

Parameters:

• save (bool) – whether to save the plot to a file

• show (bool) – whether to show the plot in a window

Returns:

plot representation

Return type:

plot (Any)

prepareAssessment(assessment_df: DataFrame) → DataFrame

Prepare assessment dataframe for plotting

Parameters:

assessment_df (pd.DataFrame) – the assessment dataframe containing the experimental and predicted values for each property. The dataframe should have the following columns: QSPRID, Fold (opt.), <property_name>_<suffixes>_<Label/Prediction>

Returns:

The dataframe containing the assessment results, columns: QSPRID, Fold, Property, Label, Prediction, Set

Return type:

pd.DataFrame

prepareRegressionResults() → DataFrame

Prepare regression results dataframe for plotting.

Returns:

the dataframe containing the regression results, columns: Model, QSPRID, Fold, Property, Label, Prediction, Set

Return type:

pd.DataFrame

class qsprpred.plotting.regression.WilliamsPlot(models: list[qsprpred.models.model.QSPRModel], datasets: list[qsprpred.data.tables.qspr.QSPRDataset])

Bases: RegressionPlot

Williams plot; plot of standardized residuals versus leverages

Initialize the base class for all model plots.

Parameters:

models (list[QSPRModel]) – list of models to plot

checkModel(model: QSPRModel) → tuple[str, str]

Check if the model has been evaluated and saved. If not, raise an exception.

Parameters:

model (QSPRModel) – model to check

Returns:

path to the cross-validation set results file indPath (str): path to the independent test set results file

Return type:

cvPath (str)

Raises:

ValueError – if the model type is not supported

getSummary()

calculate the R2 and RMSE for each model per set (cross-validation or independent test)

getSupportedTasks() → list[qsprpred.tasks.ModelTasks]

Return a list of supported model tasks.

make(save: bool = True, show: bool = False, out_path: str | None = None) → tuple[seaborn.axisgrid.FacetGrid, pandas.core.frame.DataFrame, List[float]]

make Williams plot

Parameters:

• save (bool) – whether to save the plot

• show (bool) – whether to show the plot

• out_path (str | None) – path to save the plot to, e.g. “results/plot.png”, if None, the plot will be saved to each model’s output directory.

Returns:

the seaborn FacetGrid object used to make the plot pd.DataFrame:

the leverages and standardized residuals for each compound

dict[str, float]:

the h* values for the datasets

Return type:

g (sns.FacetGrid)

prepareAssessment(assessment_df: DataFrame) → DataFrame

Prepare assessment dataframe for plotting

Parameters:

assessment_df (pd.DataFrame) – the assessment dataframe containing the experimental and predicted values for each property. The dataframe should have the following columns: QSPRID, Fold (opt.), <property_name>_<suffixes>_<Label/Prediction>

Returns:

The dataframe containing the assessment results, columns: QSPRID, Fold, Property, Label, Prediction, Set

Return type:

pd.DataFrame

prepareRegressionResults() → DataFrame

Prepare regression results dataframe for plotting.

Returns:

the dataframe containing the regression results, columns: Model, QSPRID, Fold, Property, Label, Prediction, Set

Return type:

pd.DataFrame

qsprpred.plotting.tests module

Tests for plotting module.

class qsprpred.plotting.tests.ConfusionMatrixPlotTest(methodName='runTest')

Bases: PlottingTest

Test confusion matrix plotting class.

Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.

classmethod addClassCleanup(function, /, *args, **kwargs)

Same as addCleanup, except the cleanup items are called even if setUpClass fails (unlike tearDownClass).

addCleanup(function, /, *args, **kwargs)

Add a function, with arguments, to be called when the test is completed. Functions added are called on a LIFO basis and are called after tearDown on test failure or success.

Cleanup items are called even if setUp fails (unlike tearDown).

addTypeEqualityFunc(typeobj, function)

Add a type specific assertEqual style function to compare a type.

This method is for use by TestCase subclasses that need to register their own type equality functions to provide nicer error messages.

Parameters:

• typeobj – The data type to call this function on when both values are of the same type in assertEqual().

• function – The callable taking two arguments and an optional msg= argument that raises self.failureException with a useful error message when the two arguments are not equal.

assertAlmostEqual(first, second, places=None, msg=None, delta=None)

Fail if the two objects are unequal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is more than the given delta.

Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).

If the two objects compare equal then they will automatically compare almost equal.

assertCountEqual(first, second, msg=None)

Asserts that two iterables have the same elements, the same number of times, without regard to order.

self.assertEqual(Counter(list(first)),

Counter(list(second)))

Example:

• [0, 1, 1] and [1, 0, 1] compare equal.

• [0, 0, 1] and [0, 1] compare unequal.

assertDictEqual(d1, d2, msg=None)

assertEqual(first, second, msg=None)

Fail if the two objects are unequal as determined by the ‘==’ operator.

assertFalse(expr, msg=None)

Check that the expression is false.

assertGreater(a, b, msg=None)

Just like self.assertTrue(a > b), but with a nicer default message.

assertGreaterEqual(a, b, msg=None)

Just like self.assertTrue(a >= b), but with a nicer default message.

assertIn(member, container, msg=None)

Just like self.assertTrue(a in b), but with a nicer default message.

assertIs(expr1, expr2, msg=None)

Just like self.assertTrue(a is b), but with a nicer default message.

assertIsInstance(obj, cls, msg=None)

Same as self.assertTrue(isinstance(obj, cls)), with a nicer default message.

assertIsNone(obj, msg=None)

Same as self.assertTrue(obj is None), with a nicer default message.

assertIsNot(expr1, expr2, msg=None)

Just like self.assertTrue(a is not b), but with a nicer default message.

assertIsNotNone(obj, msg=None)

Included for symmetry with assertIsNone.

assertLess(a, b, msg=None)

Just like self.assertTrue(a < b), but with a nicer default message.

assertLessEqual(a, b, msg=None)

Just like self.assertTrue(a <= b), but with a nicer default message.

assertListEqual(list1, list2, msg=None)

A list-specific equality assertion.

Parameters:

• list1 – The first list to compare.

• list2 – The second list to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertLogs(logger=None, level=None)

Fail unless a log message of level level or higher is emitted on logger_name or its children. If omitted, level defaults to INFO and logger defaults to the root logger.

This method must be used as a context manager, and will yield a recording object with two attributes: output and records. At the end of the context manager, the output attribute will be a list of the matching formatted log messages and the records attribute will be a list of the corresponding LogRecord objects.

Example:

with self.assertLogs('foo', level='INFO') as cm:
    logging.getLogger('foo').info('first message')
    logging.getLogger('foo.bar').error('second message')
self.assertEqual(cm.output, ['INFO:foo:first message',
                             'ERROR:foo.bar:second message'])

assertMultiLineEqual(first, second, msg=None)

Assert that two multi-line strings are equal.

assertNoLogs(logger=None, level=None)

Fail unless no log messages of level level or higher are emitted on logger_name or its children.

This method must be used as a context manager.

assertNotAlmostEqual(first, second, places=None, msg=None, delta=None)

Fail if the two objects are equal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is less than the given delta.

Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).

Objects that are equal automatically fail.

assertNotEqual(first, second, msg=None)

Fail if the two objects are equal as determined by the ‘!=’ operator.

assertNotIn(member, container, msg=None)

Just like self.assertTrue(a not in b), but with a nicer default message.

assertNotIsInstance(obj, cls, msg=None)

Included for symmetry with assertIsInstance.

assertNotRegex(text, unexpected_regex, msg=None)

Fail the test if the text matches the regular expression.

assertRaises(expected_exception, *args, **kwargs)

Fail unless an exception of class expected_exception is raised by the callable when invoked with specified positional and keyword arguments. If a different type of exception is raised, it will not be caught, and the test case will be deemed to have suffered an error, exactly as for an unexpected exception.

If called with the callable and arguments omitted, will return a context object used like this:

with self.assertRaises(SomeException):
    do_something()

An optional keyword argument ‘msg’ can be provided when assertRaises is used as a context object.

The context manager keeps a reference to the exception as the ‘exception’ attribute. This allows you to inspect the exception after the assertion:

with self.assertRaises(SomeException) as cm:
    do_something()
the_exception = cm.exception
self.assertEqual(the_exception.error_code, 3)

assertRaisesRegex(expected_exception, expected_regex, *args, **kwargs)

Asserts that the message in a raised exception matches a regex.

Parameters:

• expected_exception – Exception class expected to be raised.

• expected_regex – Regex (re.Pattern object or string) expected to be found in error message.

• args – Function to be called and extra positional args.

• kwargs – Extra kwargs.

• msg – Optional message used in case of failure. Can only be used when assertRaisesRegex is used as a context manager.

assertRegex(text, expected_regex, msg=None)

Fail the test unless the text matches the regular expression.

assertSequenceEqual(seq1, seq2, msg=None, seq_type=None)

An equality assertion for ordered sequences (like lists and tuples).

For the purposes of this function, a valid ordered sequence type is one which can be indexed, has a length, and has an equality operator.

Parameters:

• seq1 – The first sequence to compare.

• seq2 – The second sequence to compare.

• seq_type – The expected datatype of the sequences, or None if no datatype should be enforced.

• msg – Optional message to use on failure instead of a list of differences.

assertSetEqual(set1, set2, msg=None)

A set-specific equality assertion.

Parameters:

• set1 – The first set to compare.

• set2 – The second set to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertSetEqual uses ducktyping to support different types of sets, and is optimized for sets specifically (parameters must support a difference method).

assertTrue(expr, msg=None)

Check that the expression is true.

assertTupleEqual(tuple1, tuple2, msg=None)

A tuple-specific equality assertion.

Parameters:

• tuple1 – The first tuple to compare.

• tuple2 – The second tuple to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertWarns(expected_warning, *args, **kwargs)

Fail unless a warning of class warnClass is triggered by the callable when invoked with specified positional and keyword arguments. If a different type of warning is triggered, it will not be handled: depending on the other warning filtering rules in effect, it might be silenced, printed out, or raised as an exception.

If called with the callable and arguments omitted, will return a context object used like this:

with self.assertWarns(SomeWarning):
    do_something()

An optional keyword argument ‘msg’ can be provided when assertWarns is used as a context object.

The context manager keeps a reference to the first matching warning as the ‘warning’ attribute; similarly, the ‘filename’ and ‘lineno’ attributes give you information about the line of Python code from which the warning was triggered. This allows you to inspect the warning after the assertion:

with self.assertWarns(SomeWarning) as cm:
    do_something()
the_warning = cm.warning
self.assertEqual(the_warning.some_attribute, 147)

assertWarnsRegex(expected_warning, expected_regex, *args, **kwargs)

Asserts that the message in a triggered warning matches a regexp. Basic functioning is similar to assertWarns() with the addition that only warnings whose messages also match the regular expression are considered successful matches.

Parameters:

• expected_warning – Warning class expected to be triggered.

• expected_regex – Regex (re.Pattern object or string) expected to be found in error message.

• args – Function to be called and extra positional args.

• kwargs – Extra kwargs.

• msg – Optional message used in case of failure. Can only be used when assertWarnsRegex is used as a context manager.

clearGenerated()

Remove the directories that are used for testing.

countTestCases()

createLargeMultitaskDataSet(name='QSPRDataset_multi_test', target_props=[{'name': 'HBD', 'task': <TargetTasks.MULTICLASS: 'MULTICLASS'>, 'th': [-1, 1, 2, 100]}, {'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42)

Create a large dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• preparation_settings (dict) – dictionary containing preparation settings

• random_state (int) – random state to use for splitting and shuffling

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createLargeTestDataSet(name='QSPRDataset_test_large', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42, n_jobs=1, chunk_size=None)

Create a large dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• preparation_settings (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createSmallTestDataSet(name='QSPRDataset_test_small', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42)

Create a small dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• preparation_settings (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createTestDataSetFromFrame(df, name='QSPRDataset_test', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], random_state=None, prep=None, n_jobs=1, chunk_size=None)

Create a dataset for testing purposes from the given data frame.

Parameters:

• df (pd.DataFrame) – data frame containing the dataset

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• prep (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

debug()

Run the test without collecting errors in a TestResult

defaultTestResult()

classmethod doClassCleanups()

Execute all class cleanup functions. Normally called for you after tearDownClass.

doCleanups()

Execute all cleanup functions. Normally called for you after tearDown.

classmethod enterClassContext(cm)

Same as enterContext, but class-wide.

enterContext(cm)

Enters the supplied context manager.

If successful, also adds its __exit__ method as a cleanup function and returns the result of the __enter__ method.

fail(msg=None)

Fail immediately, with the given message.

failureException

alias of AssertionError

classmethod getAllDescriptors()

Return a list of (ideally) all available descriptor sets. For now they need to be added manually to the list below.

TODO: would be nice to create the list automatically by implementing a descriptor set registry that would hold all installed descriptor sets.

Returns:

list of DescriptorCalculator objects

Return type:

list

getBigDF()

Get a large data frame for testing purposes.

Returns:

a pandas.DataFrame containing the dataset

Return type:

pd.DataFrame

classmethod getDataPrepGrid()

Return a list of many possible combinations of descriptor calculators, splits, feature standardizers, feature filters and data filters. Again, this is not exhaustive, but should cover a lot of cases.

Returns:

a generator that yields tuples of all possible combinations as stated above, each tuple is defined as: (descriptor_calculator, split, feature_standardizer, feature_filters, data_filters)

Return type:

grid

classmethod getDefaultCalculatorCombo()

Makes a list of default descriptor calculators that can be used in tests. It creates a calculator with only morgan fingerprints and rdkit descriptors, but also one with them both to test behaviour with multiple descriptor sets. Override this method if you want to test with other descriptor sets and calculator combinations.

Returns:

list of created DescriptorCalculator objects

Return type:

list

static getDefaultPrep()

Return a dictionary with default preparation settings.

getModel(name: str, alg: ~typing.Type = <class 'sklearn.ensemble._forest.RandomForestClassifier'>) → SklearnModel

Get a model for testing.

Parameters:

• dataset (QSPRDataset) – Dataset to use for model.

• name (str) – Name of model.

• alg (Type, optional) – Algorithm to use for model. Defaults to RandomForestClassifier.

Returns:

The new model.

Return type:

SklearnModel

classmethod getPrepCombos()

Return a list of all possible preparation combinations as generated by getDataPrepGrid as well as their names. The generated list can be used to parameterize tests with the given named combinations.

Returns:

list of `list`s of all possible combinations of preparation

Return type:

list

getSmallDF()

Get a small data frame for testing purposes.

Returns:

a pandas.DataFrame containing the dataset

Return type:

pd.DataFrame

id()

longMessage = True

maxDiff = 640

run(result=None)

setUp()

Hook method for setting up the test fixture before exercising it.

classmethod setUpClass()

Hook method for setting up class fixture before running tests in the class.

setUpPaths()

Set up the test environment.

shortDescription()

Returns a one-line description of the test, or None if no description has been provided.

The default implementation of this method returns the first line of the specified test method’s docstring.

skipTest(reason)

Skip this test.

subTest(msg=<object object>, **params)

Return a context manager that will return the enclosed block of code in a subtest identified by the optional message and keyword parameters. A failure in the subtest marks the test case as failed but resumes execution at the end of the enclosed block, allowing further test code to be executed.

tearDown()

Remove all files and directories that are used for testing.

classmethod tearDownClass()

Hook method for deconstructing the class fixture after running all tests in the class.

testPlotSingle = None

testPlotSingle_0_binary(**kw)

Test plotting confusion matrix for single task [with _=’binary’, task=’binary’, th=[6.5]].

testPlotSingle_1_multi_class(**kw)

Test plotting confusion matrix for single task [with _=’multi_class’, task=’multi_class’, th=[0, 2, 10, 1100]].

validate_split(dataset)

Check if the split has the data it should have after splitting.

class qsprpred.plotting.tests.CorrPlotTest(methodName='runTest')

Bases: PlottingTest

Test correlation plotting class.

Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.

classmethod addClassCleanup(function, /, *args, **kwargs)

Same as addCleanup, except the cleanup items are called even if setUpClass fails (unlike tearDownClass).

addCleanup(function, /, *args, **kwargs)

Add a function, with arguments, to be called when the test is completed. Functions added are called on a LIFO basis and are called after tearDown on test failure or success.

Cleanup items are called even if setUp fails (unlike tearDown).

addTypeEqualityFunc(typeobj, function)

Add a type specific assertEqual style function to compare a type.

This method is for use by TestCase subclasses that need to register their own type equality functions to provide nicer error messages.

Parameters:

• typeobj – The data type to call this function on when both values are of the same type in assertEqual().

• function – The callable taking two arguments and an optional msg= argument that raises self.failureException with a useful error message when the two arguments are not equal.

assertAlmostEqual(first, second, places=None, msg=None, delta=None)

Fail if the two objects are unequal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is more than the given delta.

Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).

If the two objects compare equal then they will automatically compare almost equal.

assertCountEqual(first, second, msg=None)

Asserts that two iterables have the same elements, the same number of times, without regard to order.

self.assertEqual(Counter(list(first)),

Counter(list(second)))

Example:

• [0, 1, 1] and [1, 0, 1] compare equal.

• [0, 0, 1] and [0, 1] compare unequal.

assertDictEqual(d1, d2, msg=None)

assertEqual(first, second, msg=None)

Fail if the two objects are unequal as determined by the ‘==’ operator.

assertFalse(expr, msg=None)

Check that the expression is false.

assertGreater(a, b, msg=None)

Just like self.assertTrue(a > b), but with a nicer default message.

assertGreaterEqual(a, b, msg=None)

Just like self.assertTrue(a >= b), but with a nicer default message.

assertIn(member, container, msg=None)

Just like self.assertTrue(a in b), but with a nicer default message.

assertIs(expr1, expr2, msg=None)

Just like self.assertTrue(a is b), but with a nicer default message.

assertIsInstance(obj, cls, msg=None)

Same as self.assertTrue(isinstance(obj, cls)), with a nicer default message.

assertIsNone(obj, msg=None)

Same as self.assertTrue(obj is None), with a nicer default message.

assertIsNot(expr1, expr2, msg=None)

Just like self.assertTrue(a is not b), but with a nicer default message.

assertIsNotNone(obj, msg=None)

Included for symmetry with assertIsNone.

assertLess(a, b, msg=None)

Just like self.assertTrue(a < b), but with a nicer default message.

assertLessEqual(a, b, msg=None)

Just like self.assertTrue(a <= b), but with a nicer default message.

assertListEqual(list1, list2, msg=None)

A list-specific equality assertion.

Parameters:

• list1 – The first list to compare.

• list2 – The second list to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertLogs(logger=None, level=None)

Fail unless a log message of level level or higher is emitted on logger_name or its children. If omitted, level defaults to INFO and logger defaults to the root logger.

This method must be used as a context manager, and will yield a recording object with two attributes: output and records. At the end of the context manager, the output attribute will be a list of the matching formatted log messages and the records attribute will be a list of the corresponding LogRecord objects.

Example:

with self.assertLogs('foo', level='INFO') as cm:
    logging.getLogger('foo').info('first message')
    logging.getLogger('foo.bar').error('second message')
self.assertEqual(cm.output, ['INFO:foo:first message',
                             'ERROR:foo.bar:second message'])

assertMultiLineEqual(first, second, msg=None)

Assert that two multi-line strings are equal.

assertNoLogs(logger=None, level=None)

Fail unless no log messages of level level or higher are emitted on logger_name or its children.

This method must be used as a context manager.

assertNotAlmostEqual(first, second, places=None, msg=None, delta=None)

Fail if the two objects are equal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is less than the given delta.

Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).

Objects that are equal automatically fail.

assertNotEqual(first, second, msg=None)

Fail if the two objects are equal as determined by the ‘!=’ operator.

assertNotIn(member, container, msg=None)

Just like self.assertTrue(a not in b), but with a nicer default message.

assertNotIsInstance(obj, cls, msg=None)

Included for symmetry with assertIsInstance.

assertNotRegex(text, unexpected_regex, msg=None)

Fail the test if the text matches the regular expression.

assertRaises(expected_exception, *args, **kwargs)

Fail unless an exception of class expected_exception is raised by the callable when invoked with specified positional and keyword arguments. If a different type of exception is raised, it will not be caught, and the test case will be deemed to have suffered an error, exactly as for an unexpected exception.

If called with the callable and arguments omitted, will return a context object used like this:

with self.assertRaises(SomeException):
    do_something()

An optional keyword argument ‘msg’ can be provided when assertRaises is used as a context object.

The context manager keeps a reference to the exception as the ‘exception’ attribute. This allows you to inspect the exception after the assertion:

with self.assertRaises(SomeException) as cm:
    do_something()
the_exception = cm.exception
self.assertEqual(the_exception.error_code, 3)

assertRaisesRegex(expected_exception, expected_regex, *args, **kwargs)

Asserts that the message in a raised exception matches a regex.

Parameters:

• expected_exception – Exception class expected to be raised.

• expected_regex – Regex (re.Pattern object or string) expected to be found in error message.

• args – Function to be called and extra positional args.

• kwargs – Extra kwargs.

• msg – Optional message used in case of failure. Can only be used when assertRaisesRegex is used as a context manager.

assertRegex(text, expected_regex, msg=None)

Fail the test unless the text matches the regular expression.

assertSequenceEqual(seq1, seq2, msg=None, seq_type=None)

An equality assertion for ordered sequences (like lists and tuples).

For the purposes of this function, a valid ordered sequence type is one which can be indexed, has a length, and has an equality operator.

Parameters:

• seq1 – The first sequence to compare.

• seq2 – The second sequence to compare.

• seq_type – The expected datatype of the sequences, or None if no datatype should be enforced.

• msg – Optional message to use on failure instead of a list of differences.

assertSetEqual(set1, set2, msg=None)

A set-specific equality assertion.

Parameters:

• set1 – The first set to compare.

• set2 – The second set to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertSetEqual uses ducktyping to support different types of sets, and is optimized for sets specifically (parameters must support a difference method).

assertTrue(expr, msg=None)

Check that the expression is true.

assertTupleEqual(tuple1, tuple2, msg=None)

A tuple-specific equality assertion.

Parameters:

• tuple1 – The first tuple to compare.

• tuple2 – The second tuple to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertWarns(expected_warning, *args, **kwargs)

Fail unless a warning of class warnClass is triggered by the callable when invoked with specified positional and keyword arguments. If a different type of warning is triggered, it will not be handled: depending on the other warning filtering rules in effect, it might be silenced, printed out, or raised as an exception.

If called with the callable and arguments omitted, will return a context object used like this:

with self.assertWarns(SomeWarning):
    do_something()

An optional keyword argument ‘msg’ can be provided when assertWarns is used as a context object.

The context manager keeps a reference to the first matching warning as the ‘warning’ attribute; similarly, the ‘filename’ and ‘lineno’ attributes give you information about the line of Python code from which the warning was triggered. This allows you to inspect the warning after the assertion:

with self.assertWarns(SomeWarning) as cm:
    do_something()
the_warning = cm.warning
self.assertEqual(the_warning.some_attribute, 147)

assertWarnsRegex(expected_warning, expected_regex, *args, **kwargs)

Asserts that the message in a triggered warning matches a regexp. Basic functioning is similar to assertWarns() with the addition that only warnings whose messages also match the regular expression are considered successful matches.

Parameters:

• expected_warning – Warning class expected to be triggered.

• expected_regex – Regex (re.Pattern object or string) expected to be found in error message.

• args – Function to be called and extra positional args.

• kwargs – Extra kwargs.

• msg – Optional message used in case of failure. Can only be used when assertWarnsRegex is used as a context manager.

clearGenerated()

Remove the directories that are used for testing.

countTestCases()

createLargeMultitaskDataSet(name='QSPRDataset_multi_test', target_props=[{'name': 'HBD', 'task': <TargetTasks.MULTICLASS: 'MULTICLASS'>, 'th': [-1, 1, 2, 100]}, {'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42)

Create a large dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• preparation_settings (dict) – dictionary containing preparation settings

• random_state (int) – random state to use for splitting and shuffling

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createLargeTestDataSet(name='QSPRDataset_test_large', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42, n_jobs=1, chunk_size=None)

Create a large dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• preparation_settings (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createSmallTestDataSet(name='QSPRDataset_test_small', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42)

Create a small dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• preparation_settings (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createTestDataSetFromFrame(df, name='QSPRDataset_test', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], random_state=None, prep=None, n_jobs=1, chunk_size=None)

Create a dataset for testing purposes from the given data frame.

Parameters:

• df (pd.DataFrame) – data frame containing the dataset

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• prep (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

debug()

Run the test without collecting errors in a TestResult

defaultTestResult()

classmethod doClassCleanups()

Execute all class cleanup functions. Normally called for you after tearDownClass.

doCleanups()

Execute all cleanup functions. Normally called for you after tearDown.

classmethod enterClassContext(cm)

Same as enterContext, but class-wide.

enterContext(cm)

Enters the supplied context manager.

If successful, also adds its __exit__ method as a cleanup function and returns the result of the __enter__ method.

fail(msg=None)

Fail immediately, with the given message.

failureException

alias of AssertionError

classmethod getAllDescriptors()

Return a list of (ideally) all available descriptor sets. For now they need to be added manually to the list below.

TODO: would be nice to create the list automatically by implementing a descriptor set registry that would hold all installed descriptor sets.

Returns:

list of DescriptorCalculator objects

Return type:

list

getBigDF()

Get a large data frame for testing purposes.

Returns:

a pandas.DataFrame containing the dataset

Return type:

pd.DataFrame

classmethod getDataPrepGrid()

Return a list of many possible combinations of descriptor calculators, splits, feature standardizers, feature filters and data filters. Again, this is not exhaustive, but should cover a lot of cases.

Returns:

a generator that yields tuples of all possible combinations as stated above, each tuple is defined as: (descriptor_calculator, split, feature_standardizer, feature_filters, data_filters)

Return type:

grid

classmethod getDefaultCalculatorCombo()

Makes a list of default descriptor calculators that can be used in tests. It creates a calculator with only morgan fingerprints and rdkit descriptors, but also one with them both to test behaviour with multiple descriptor sets. Override this method if you want to test with other descriptor sets and calculator combinations.

Returns:

list of created DescriptorCalculator objects

Return type:

list

static getDefaultPrep()

Return a dictionary with default preparation settings.

getModel(name: str, alg: ~typing.Type = <class 'sklearn.ensemble._forest.RandomForestClassifier'>) → SklearnModel

Get a model for testing.

Parameters:

• dataset (QSPRDataset) – Dataset to use for model.

• name (str) – Name of model.

• alg (Type, optional) – Algorithm to use for model. Defaults to RandomForestClassifier.

Returns:

The new model.

Return type:

SklearnModel

classmethod getPrepCombos()

Return a list of all possible preparation combinations as generated by getDataPrepGrid as well as their names. The generated list can be used to parameterize tests with the given named combinations.

Returns:

list of `list`s of all possible combinations of preparation

Return type:

list

getSmallDF()

Get a small data frame for testing purposes.

Returns:

a pandas.DataFrame containing the dataset

Return type:

pd.DataFrame

id()

longMessage = True

maxDiff = 640

run(result=None)

setUp()

Hook method for setting up the test fixture before exercising it.

classmethod setUpClass()

Hook method for setting up class fixture before running tests in the class.

setUpPaths()

Set up the test environment.

shortDescription()

Returns a one-line description of the test, or None if no description has been provided.

The default implementation of this method returns the first line of the specified test method’s docstring.

skipTest(reason)

Skip this test.

subTest(msg=<object object>, **params)

Return a context manager that will return the enclosed block of code in a subtest identified by the optional message and keyword parameters. A failure in the subtest marks the test case as failed but resumes execution at the end of the enclosed block, allowing further test code to be executed.

tearDown()

Remove all files and directories that are used for testing.

classmethod tearDownClass()

Hook method for deconstructing the class fixture after running all tests in the class.

testPlotSingle()

Test plotting correlation for single task.

validate_split(dataset)

Check if the split has the data it should have after splitting.

class qsprpred.plotting.tests.MetricsPlotTest(methodName='runTest')

Bases: PlottingTest

Test metrics plotting class.

Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.

classmethod addClassCleanup(function, /, *args, **kwargs)

Same as addCleanup, except the cleanup items are called even if setUpClass fails (unlike tearDownClass).

addCleanup(function, /, *args, **kwargs)

Add a function, with arguments, to be called when the test is completed. Functions added are called on a LIFO basis and are called after tearDown on test failure or success.

Cleanup items are called even if setUp fails (unlike tearDown).

addTypeEqualityFunc(typeobj, function)

Add a type specific assertEqual style function to compare a type.

This method is for use by TestCase subclasses that need to register their own type equality functions to provide nicer error messages.

Parameters:

• typeobj – The data type to call this function on when both values are of the same type in assertEqual().

• function – The callable taking two arguments and an optional msg= argument that raises self.failureException with a useful error message when the two arguments are not equal.

assertAlmostEqual(first, second, places=None, msg=None, delta=None)

Fail if the two objects are unequal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is more than the given delta.

Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).

If the two objects compare equal then they will automatically compare almost equal.

assertCountEqual(first, second, msg=None)

Asserts that two iterables have the same elements, the same number of times, without regard to order.

self.assertEqual(Counter(list(first)),

Counter(list(second)))

Example:

• [0, 1, 1] and [1, 0, 1] compare equal.

• [0, 0, 1] and [0, 1] compare unequal.

assertDictEqual(d1, d2, msg=None)

assertEqual(first, second, msg=None)

Fail if the two objects are unequal as determined by the ‘==’ operator.

assertFalse(expr, msg=None)

Check that the expression is false.

assertGreater(a, b, msg=None)

Just like self.assertTrue(a > b), but with a nicer default message.

assertGreaterEqual(a, b, msg=None)

Just like self.assertTrue(a >= b), but with a nicer default message.

assertIn(member, container, msg=None)

Just like self.assertTrue(a in b), but with a nicer default message.

assertIs(expr1, expr2, msg=None)

Just like self.assertTrue(a is b), but with a nicer default message.

assertIsInstance(obj, cls, msg=None)

Same as self.assertTrue(isinstance(obj, cls)), with a nicer default message.

assertIsNone(obj, msg=None)

Same as self.assertTrue(obj is None), with a nicer default message.

assertIsNot(expr1, expr2, msg=None)

Just like self.assertTrue(a is not b), but with a nicer default message.

assertIsNotNone(obj, msg=None)

Included for symmetry with assertIsNone.

assertLess(a, b, msg=None)

Just like self.assertTrue(a < b), but with a nicer default message.

assertLessEqual(a, b, msg=None)

Just like self.assertTrue(a <= b), but with a nicer default message.

assertListEqual(list1, list2, msg=None)

A list-specific equality assertion.

Parameters:

• list1 – The first list to compare.

• list2 – The second list to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertLogs(logger=None, level=None)

Fail unless a log message of level level or higher is emitted on logger_name or its children. If omitted, level defaults to INFO and logger defaults to the root logger.

This method must be used as a context manager, and will yield a recording object with two attributes: output and records. At the end of the context manager, the output attribute will be a list of the matching formatted log messages and the records attribute will be a list of the corresponding LogRecord objects.

Example:

with self.assertLogs('foo', level='INFO') as cm:
    logging.getLogger('foo').info('first message')
    logging.getLogger('foo.bar').error('second message')
self.assertEqual(cm.output, ['INFO:foo:first message',
                             'ERROR:foo.bar:second message'])

assertMultiLineEqual(first, second, msg=None)

Assert that two multi-line strings are equal.

assertNoLogs(logger=None, level=None)

Fail unless no log messages of level level or higher are emitted on logger_name or its children.

This method must be used as a context manager.

assertNotAlmostEqual(first, second, places=None, msg=None, delta=None)

Fail if the two objects are equal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is less than the given delta.

Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).

Objects that are equal automatically fail.

assertNotEqual(first, second, msg=None)

Fail if the two objects are equal as determined by the ‘!=’ operator.

assertNotIn(member, container, msg=None)

Just like self.assertTrue(a not in b), but with a nicer default message.

assertNotIsInstance(obj, cls, msg=None)

Included for symmetry with assertIsInstance.

assertNotRegex(text, unexpected_regex, msg=None)

Fail the test if the text matches the regular expression.

assertRaises(expected_exception, *args, **kwargs)

Fail unless an exception of class expected_exception is raised by the callable when invoked with specified positional and keyword arguments. If a different type of exception is raised, it will not be caught, and the test case will be deemed to have suffered an error, exactly as for an unexpected exception.

If called with the callable and arguments omitted, will return a context object used like this:

with self.assertRaises(SomeException):
    do_something()

An optional keyword argument ‘msg’ can be provided when assertRaises is used as a context object.

The context manager keeps a reference to the exception as the ‘exception’ attribute. This allows you to inspect the exception after the assertion:

with self.assertRaises(SomeException) as cm:
    do_something()
the_exception = cm.exception
self.assertEqual(the_exception.error_code, 3)

assertRaisesRegex(expected_exception, expected_regex, *args, **kwargs)

Asserts that the message in a raised exception matches a regex.

Parameters:

• expected_exception – Exception class expected to be raised.

• expected_regex – Regex (re.Pattern object or string) expected to be found in error message.

• args – Function to be called and extra positional args.

• kwargs – Extra kwargs.

• msg – Optional message used in case of failure. Can only be used when assertRaisesRegex is used as a context manager.

assertRegex(text, expected_regex, msg=None)

Fail the test unless the text matches the regular expression.

assertSequenceEqual(seq1, seq2, msg=None, seq_type=None)

An equality assertion for ordered sequences (like lists and tuples).

For the purposes of this function, a valid ordered sequence type is one which can be indexed, has a length, and has an equality operator.

Parameters:

• seq1 – The first sequence to compare.

• seq2 – The second sequence to compare.

• seq_type – The expected datatype of the sequences, or None if no datatype should be enforced.

• msg – Optional message to use on failure instead of a list of differences.

assertSetEqual(set1, set2, msg=None)

A set-specific equality assertion.

Parameters:

• set1 – The first set to compare.

• set2 – The second set to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertSetEqual uses ducktyping to support different types of sets, and is optimized for sets specifically (parameters must support a difference method).

assertTrue(expr, msg=None)

Check that the expression is true.

assertTupleEqual(tuple1, tuple2, msg=None)

A tuple-specific equality assertion.

Parameters:

• tuple1 – The first tuple to compare.

• tuple2 – The second tuple to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertWarns(expected_warning, *args, **kwargs)

Fail unless a warning of class warnClass is triggered by the callable when invoked with specified positional and keyword arguments. If a different type of warning is triggered, it will not be handled: depending on the other warning filtering rules in effect, it might be silenced, printed out, or raised as an exception.

If called with the callable and arguments omitted, will return a context object used like this:

with self.assertWarns(SomeWarning):
    do_something()

An optional keyword argument ‘msg’ can be provided when assertWarns is used as a context object.

The context manager keeps a reference to the first matching warning as the ‘warning’ attribute; similarly, the ‘filename’ and ‘lineno’ attributes give you information about the line of Python code from which the warning was triggered. This allows you to inspect the warning after the assertion:

with self.assertWarns(SomeWarning) as cm:
    do_something()
the_warning = cm.warning
self.assertEqual(the_warning.some_attribute, 147)

assertWarnsRegex(expected_warning, expected_regex, *args, **kwargs)

Asserts that the message in a triggered warning matches a regexp. Basic functioning is similar to assertWarns() with the addition that only warnings whose messages also match the regular expression are considered successful matches.

Parameters:

• expected_warning – Warning class expected to be triggered.

• expected_regex – Regex (re.Pattern object or string) expected to be found in error message.

• args – Function to be called and extra positional args.

• kwargs – Extra kwargs.

• msg – Optional message used in case of failure. Can only be used when assertWarnsRegex is used as a context manager.

clearGenerated()

Remove the directories that are used for testing.

countTestCases()

createLargeMultitaskDataSet(name='QSPRDataset_multi_test', target_props=[{'name': 'HBD', 'task': <TargetTasks.MULTICLASS: 'MULTICLASS'>, 'th': [-1, 1, 2, 100]}, {'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42)

Create a large dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• preparation_settings (dict) – dictionary containing preparation settings

• random_state (int) – random state to use for splitting and shuffling

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createLargeTestDataSet(name='QSPRDataset_test_large', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42, n_jobs=1, chunk_size=None)

Create a large dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• preparation_settings (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createSmallTestDataSet(name='QSPRDataset_test_small', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42)

Create a small dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• preparation_settings (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createTestDataSetFromFrame(df, name='QSPRDataset_test', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], random_state=None, prep=None, n_jobs=1, chunk_size=None)

Create a dataset for testing purposes from the given data frame.

Parameters:

• df (pd.DataFrame) – data frame containing the dataset

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• prep (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

debug()

Run the test without collecting errors in a TestResult

defaultTestResult()

classmethod doClassCleanups()

Execute all class cleanup functions. Normally called for you after tearDownClass.

doCleanups()

Execute all cleanup functions. Normally called for you after tearDown.

classmethod enterClassContext(cm)

Same as enterContext, but class-wide.

enterContext(cm)

Enters the supplied context manager.

If successful, also adds its __exit__ method as a cleanup function and returns the result of the __enter__ method.

fail(msg=None)

Fail immediately, with the given message.

failureException

alias of AssertionError

classmethod getAllDescriptors()

Return a list of (ideally) all available descriptor sets. For now they need to be added manually to the list below.

TODO: would be nice to create the list automatically by implementing a descriptor set registry that would hold all installed descriptor sets.

Returns:

list of DescriptorCalculator objects

Return type:

list

getBigDF()

Get a large data frame for testing purposes.

Returns:

a pandas.DataFrame containing the dataset

Return type:

pd.DataFrame

classmethod getDataPrepGrid()

Return a list of many possible combinations of descriptor calculators, splits, feature standardizers, feature filters and data filters. Again, this is not exhaustive, but should cover a lot of cases.

Returns:

a generator that yields tuples of all possible combinations as stated above, each tuple is defined as: (descriptor_calculator, split, feature_standardizer, feature_filters, data_filters)

Return type:

grid

classmethod getDefaultCalculatorCombo()

Makes a list of default descriptor calculators that can be used in tests. It creates a calculator with only morgan fingerprints and rdkit descriptors, but also one with them both to test behaviour with multiple descriptor sets. Override this method if you want to test with other descriptor sets and calculator combinations.

Returns:

list of created DescriptorCalculator objects

Return type:

list

static getDefaultPrep()

Return a dictionary with default preparation settings.

getModel(name: str, alg: ~typing.Type = <class 'sklearn.ensemble._forest.RandomForestClassifier'>) → SklearnModel

Get a model for testing.

Parameters:

• dataset (QSPRDataset) – Dataset to use for model.

• name (str) – Name of model.

• alg (Type, optional) – Algorithm to use for model. Defaults to RandomForestClassifier.

Returns:

The new model.

Return type:

SklearnModel

classmethod getPrepCombos()

Return a list of all possible preparation combinations as generated by getDataPrepGrid as well as their names. The generated list can be used to parameterize tests with the given named combinations.

Returns:

list of `list`s of all possible combinations of preparation

Return type:

list

getSmallDF()

Get a small data frame for testing purposes.

Returns:

a pandas.DataFrame containing the dataset

Return type:

pd.DataFrame

id()

longMessage = True

maxDiff = 640

run(result=None)

setUp()

Hook method for setting up the test fixture before exercising it.

classmethod setUpClass()

Hook method for setting up class fixture before running tests in the class.

setUpPaths()

Set up the test environment.

shortDescription()

Returns a one-line description of the test, or None if no description has been provided.

The default implementation of this method returns the first line of the specified test method’s docstring.

skipTest(reason)

Skip this test.

subTest(msg=<object object>, **params)

Return a context manager that will return the enclosed block of code in a subtest identified by the optional message and keyword parameters. A failure in the subtest marks the test case as failed but resumes execution at the end of the enclosed block, allowing further test code to be executed.

tearDown()

Remove all files and directories that are used for testing.

classmethod tearDownClass()

Hook method for deconstructing the class fixture after running all tests in the class.

testPlotSingle = None

testPlotSingle_0_binary(**kw)

Test plotting metrics for single task single class and multi-class [with _=’binary’, task=’binary’, th=[6.5]].

testPlotSingle_1_multi_class(**kw)

Test plotting metrics for single task single class and multi-class [with _=’multi_class’, task=’multi_class’, th=[0, 2, 10, 1100]].

validate_split(dataset)

Check if the split has the data it should have after splitting.

class qsprpred.plotting.tests.PlottingTest(methodName='runTest')

Bases: ModelDataSetsPathMixIn, QSPRTestCase

Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.

classmethod addClassCleanup(function, /, *args, **kwargs)

Same as addCleanup, except the cleanup items are called even if setUpClass fails (unlike tearDownClass).

addCleanup(function, /, *args, **kwargs)

Add a function, with arguments, to be called when the test is completed. Functions added are called on a LIFO basis and are called after tearDown on test failure or success.

Cleanup items are called even if setUp fails (unlike tearDown).

addTypeEqualityFunc(typeobj, function)

Add a type specific assertEqual style function to compare a type.

This method is for use by TestCase subclasses that need to register their own type equality functions to provide nicer error messages.

Parameters:

• typeobj – The data type to call this function on when both values are of the same type in assertEqual().

• function – The callable taking two arguments and an optional msg= argument that raises self.failureException with a useful error message when the two arguments are not equal.

assertAlmostEqual(first, second, places=None, msg=None, delta=None)

Fail if the two objects are unequal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is more than the given delta.

Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).

If the two objects compare equal then they will automatically compare almost equal.

assertCountEqual(first, second, msg=None)

Asserts that two iterables have the same elements, the same number of times, without regard to order.

self.assertEqual(Counter(list(first)),

Counter(list(second)))

Example:

• [0, 1, 1] and [1, 0, 1] compare equal.

• [0, 0, 1] and [0, 1] compare unequal.

assertDictEqual(d1, d2, msg=None)

assertEqual(first, second, msg=None)

Fail if the two objects are unequal as determined by the ‘==’ operator.

assertFalse(expr, msg=None)

Check that the expression is false.

assertGreater(a, b, msg=None)

Just like self.assertTrue(a > b), but with a nicer default message.

assertGreaterEqual(a, b, msg=None)

Just like self.assertTrue(a >= b), but with a nicer default message.

assertIn(member, container, msg=None)

Just like self.assertTrue(a in b), but with a nicer default message.

assertIs(expr1, expr2, msg=None)

Just like self.assertTrue(a is b), but with a nicer default message.

assertIsInstance(obj, cls, msg=None)

Same as self.assertTrue(isinstance(obj, cls)), with a nicer default message.

assertIsNone(obj, msg=None)

Same as self.assertTrue(obj is None), with a nicer default message.

assertIsNot(expr1, expr2, msg=None)

Just like self.assertTrue(a is not b), but with a nicer default message.

assertIsNotNone(obj, msg=None)

Included for symmetry with assertIsNone.

assertLess(a, b, msg=None)

Just like self.assertTrue(a < b), but with a nicer default message.

assertLessEqual(a, b, msg=None)

Just like self.assertTrue(a <= b), but with a nicer default message.

assertListEqual(list1, list2, msg=None)

A list-specific equality assertion.

Parameters:

• list1 – The first list to compare.

• list2 – The second list to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertLogs(logger=None, level=None)

Fail unless a log message of level level or higher is emitted on logger_name or its children. If omitted, level defaults to INFO and logger defaults to the root logger.

This method must be used as a context manager, and will yield a recording object with two attributes: output and records. At the end of the context manager, the output attribute will be a list of the matching formatted log messages and the records attribute will be a list of the corresponding LogRecord objects.

Example:

with self.assertLogs('foo', level='INFO') as cm:
    logging.getLogger('foo').info('first message')
    logging.getLogger('foo.bar').error('second message')
self.assertEqual(cm.output, ['INFO:foo:first message',
                             'ERROR:foo.bar:second message'])

assertMultiLineEqual(first, second, msg=None)

Assert that two multi-line strings are equal.

assertNoLogs(logger=None, level=None)

Fail unless no log messages of level level or higher are emitted on logger_name or its children.

This method must be used as a context manager.

assertNotAlmostEqual(first, second, places=None, msg=None, delta=None)

Fail if the two objects are equal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is less than the given delta.

Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).

Objects that are equal automatically fail.

assertNotEqual(first, second, msg=None)

Fail if the two objects are equal as determined by the ‘!=’ operator.

assertNotIn(member, container, msg=None)

Just like self.assertTrue(a not in b), but with a nicer default message.

assertNotIsInstance(obj, cls, msg=None)

Included for symmetry with assertIsInstance.

assertNotRegex(text, unexpected_regex, msg=None)

Fail the test if the text matches the regular expression.

assertRaises(expected_exception, *args, **kwargs)

Fail unless an exception of class expected_exception is raised by the callable when invoked with specified positional and keyword arguments. If a different type of exception is raised, it will not be caught, and the test case will be deemed to have suffered an error, exactly as for an unexpected exception.

If called with the callable and arguments omitted, will return a context object used like this:

with self.assertRaises(SomeException):
    do_something()

An optional keyword argument ‘msg’ can be provided when assertRaises is used as a context object.

The context manager keeps a reference to the exception as the ‘exception’ attribute. This allows you to inspect the exception after the assertion:

with self.assertRaises(SomeException) as cm:
    do_something()
the_exception = cm.exception
self.assertEqual(the_exception.error_code, 3)

assertRaisesRegex(expected_exception, expected_regex, *args, **kwargs)

Asserts that the message in a raised exception matches a regex.

Parameters:

• expected_exception – Exception class expected to be raised.

• expected_regex – Regex (re.Pattern object or string) expected to be found in error message.

• args – Function to be called and extra positional args.

• kwargs – Extra kwargs.

• msg – Optional message used in case of failure. Can only be used when assertRaisesRegex is used as a context manager.

assertRegex(text, expected_regex, msg=None)

Fail the test unless the text matches the regular expression.

assertSequenceEqual(seq1, seq2, msg=None, seq_type=None)

An equality assertion for ordered sequences (like lists and tuples).

For the purposes of this function, a valid ordered sequence type is one which can be indexed, has a length, and has an equality operator.

Parameters:

• seq1 – The first sequence to compare.

• seq2 – The second sequence to compare.

• seq_type – The expected datatype of the sequences, or None if no datatype should be enforced.

• msg – Optional message to use on failure instead of a list of differences.

assertSetEqual(set1, set2, msg=None)

A set-specific equality assertion.

Parameters:

• set1 – The first set to compare.

• set2 – The second set to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertSetEqual uses ducktyping to support different types of sets, and is optimized for sets specifically (parameters must support a difference method).

assertTrue(expr, msg=None)

Check that the expression is true.

assertTupleEqual(tuple1, tuple2, msg=None)

A tuple-specific equality assertion.

Parameters:

• tuple1 – The first tuple to compare.

• tuple2 – The second tuple to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertWarns(expected_warning, *args, **kwargs)

Fail unless a warning of class warnClass is triggered by the callable when invoked with specified positional and keyword arguments. If a different type of warning is triggered, it will not be handled: depending on the other warning filtering rules in effect, it might be silenced, printed out, or raised as an exception.

If called with the callable and arguments omitted, will return a context object used like this:

with self.assertWarns(SomeWarning):
    do_something()

An optional keyword argument ‘msg’ can be provided when assertWarns is used as a context object.

The context manager keeps a reference to the first matching warning as the ‘warning’ attribute; similarly, the ‘filename’ and ‘lineno’ attributes give you information about the line of Python code from which the warning was triggered. This allows you to inspect the warning after the assertion:

with self.assertWarns(SomeWarning) as cm:
    do_something()
the_warning = cm.warning
self.assertEqual(the_warning.some_attribute, 147)

assertWarnsRegex(expected_warning, expected_regex, *args, **kwargs)

Asserts that the message in a triggered warning matches a regexp. Basic functioning is similar to assertWarns() with the addition that only warnings whose messages also match the regular expression are considered successful matches.

Parameters:

• expected_warning – Warning class expected to be triggered.

• expected_regex – Regex (re.Pattern object or string) expected to be found in error message.

• args – Function to be called and extra positional args.

• kwargs – Extra kwargs.

• msg – Optional message used in case of failure. Can only be used when assertWarnsRegex is used as a context manager.

clearGenerated()

Remove the directories that are used for testing.

countTestCases()

createLargeMultitaskDataSet(name='QSPRDataset_multi_test', target_props=[{'name': 'HBD', 'task': <TargetTasks.MULTICLASS: 'MULTICLASS'>, 'th': [-1, 1, 2, 100]}, {'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42)

Create a large dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• preparation_settings (dict) – dictionary containing preparation settings

• random_state (int) – random state to use for splitting and shuffling

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createLargeTestDataSet(name='QSPRDataset_test_large', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42, n_jobs=1, chunk_size=None)

Create a large dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• preparation_settings (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createSmallTestDataSet(name='QSPRDataset_test_small', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42)

Create a small dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• preparation_settings (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createTestDataSetFromFrame(df, name='QSPRDataset_test', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], random_state=None, prep=None, n_jobs=1, chunk_size=None)

Create a dataset for testing purposes from the given data frame.

Parameters:

• df (pd.DataFrame) – data frame containing the dataset

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• prep (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

debug()

Run the test without collecting errors in a TestResult

defaultTestResult()

classmethod doClassCleanups()

Execute all class cleanup functions. Normally called for you after tearDownClass.

doCleanups()

Execute all cleanup functions. Normally called for you after tearDown.

classmethod enterClassContext(cm)

Same as enterContext, but class-wide.

enterContext(cm)

Enters the supplied context manager.

If successful, also adds its __exit__ method as a cleanup function and returns the result of the __enter__ method.

fail(msg=None)

Fail immediately, with the given message.

failureException

alias of AssertionError

classmethod getAllDescriptors()

Return a list of (ideally) all available descriptor sets. For now they need to be added manually to the list below.

TODO: would be nice to create the list automatically by implementing a descriptor set registry that would hold all installed descriptor sets.

Returns:

list of DescriptorCalculator objects

Return type:

list

getBigDF()

Get a large data frame for testing purposes.

Returns:

a pandas.DataFrame containing the dataset

Return type:

pd.DataFrame

classmethod getDataPrepGrid()

Return a list of many possible combinations of descriptor calculators, splits, feature standardizers, feature filters and data filters. Again, this is not exhaustive, but should cover a lot of cases.

Returns:

a generator that yields tuples of all possible combinations as stated above, each tuple is defined as: (descriptor_calculator, split, feature_standardizer, feature_filters, data_filters)

Return type:

grid

classmethod getDefaultCalculatorCombo()

Makes a list of default descriptor calculators that can be used in tests. It creates a calculator with only morgan fingerprints and rdkit descriptors, but also one with them both to test behaviour with multiple descriptor sets. Override this method if you want to test with other descriptor sets and calculator combinations.

Returns:

list of created DescriptorCalculator objects

Return type:

list

static getDefaultPrep()

Return a dictionary with default preparation settings.

getModel(name: str, alg: ~typing.Type = <class 'sklearn.ensemble._forest.RandomForestClassifier'>) → SklearnModel

Get a model for testing.

Parameters:

• dataset (QSPRDataset) – Dataset to use for model.

• name (str) – Name of model.

• alg (Type, optional) – Algorithm to use for model. Defaults to RandomForestClassifier.

Returns:

The new model.

Return type:

SklearnModel

classmethod getPrepCombos()

Return a list of all possible preparation combinations as generated by getDataPrepGrid as well as their names. The generated list can be used to parameterize tests with the given named combinations.

Returns:

list of `list`s of all possible combinations of preparation

Return type:

list

getSmallDF()

Get a small data frame for testing purposes.

Returns:

a pandas.DataFrame containing the dataset

Return type:

pd.DataFrame

id()

longMessage = True

maxDiff = 640

run(result=None)

setUp()

Hook method for setting up the test fixture before exercising it.

classmethod setUpClass()

Hook method for setting up class fixture before running tests in the class.

setUpPaths()

Set up the test environment.

shortDescription()

Returns a one-line description of the test, or None if no description has been provided.

The default implementation of this method returns the first line of the specified test method’s docstring.

skipTest(reason)

Skip this test.

subTest(msg=<object object>, **params)

Return a context manager that will return the enclosed block of code in a subtest identified by the optional message and keyword parameters. A failure in the subtest marks the test case as failed but resumes execution at the end of the enclosed block, allowing further test code to be executed.

tearDown()

Remove all files and directories that are used for testing.

classmethod tearDownClass()

Hook method for deconstructing the class fixture after running all tests in the class.

validate_split(dataset)

Check if the split has the data it should have after splitting.

class qsprpred.plotting.tests.ROCPlotTest(methodName='runTest')

Bases: PlottingTest

Test ROC curve plotting class.

Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.

classmethod addClassCleanup(function, /, *args, **kwargs)

Same as addCleanup, except the cleanup items are called even if setUpClass fails (unlike tearDownClass).

addCleanup(function, /, *args, **kwargs)

Add a function, with arguments, to be called when the test is completed. Functions added are called on a LIFO basis and are called after tearDown on test failure or success.

Cleanup items are called even if setUp fails (unlike tearDown).

addTypeEqualityFunc(typeobj, function)

Add a type specific assertEqual style function to compare a type.

This method is for use by TestCase subclasses that need to register their own type equality functions to provide nicer error messages.

Parameters:

• typeobj – The data type to call this function on when both values are of the same type in assertEqual().

• function – The callable taking two arguments and an optional msg= argument that raises self.failureException with a useful error message when the two arguments are not equal.

assertAlmostEqual(first, second, places=None, msg=None, delta=None)

Fail if the two objects are unequal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is more than the given delta.

Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).

If the two objects compare equal then they will automatically compare almost equal.

assertCountEqual(first, second, msg=None)

Asserts that two iterables have the same elements, the same number of times, without regard to order.

self.assertEqual(Counter(list(first)),

Counter(list(second)))

Example:

• [0, 1, 1] and [1, 0, 1] compare equal.

• [0, 0, 1] and [0, 1] compare unequal.

assertDictEqual(d1, d2, msg=None)

assertEqual(first, second, msg=None)

Fail if the two objects are unequal as determined by the ‘==’ operator.

assertFalse(expr, msg=None)

Check that the expression is false.

assertGreater(a, b, msg=None)

Just like self.assertTrue(a > b), but with a nicer default message.

assertGreaterEqual(a, b, msg=None)

Just like self.assertTrue(a >= b), but with a nicer default message.

assertIn(member, container, msg=None)

Just like self.assertTrue(a in b), but with a nicer default message.

assertIs(expr1, expr2, msg=None)

Just like self.assertTrue(a is b), but with a nicer default message.

assertIsInstance(obj, cls, msg=None)

Same as self.assertTrue(isinstance(obj, cls)), with a nicer default message.

assertIsNone(obj, msg=None)

Same as self.assertTrue(obj is None), with a nicer default message.

assertIsNot(expr1, expr2, msg=None)

Just like self.assertTrue(a is not b), but with a nicer default message.

assertIsNotNone(obj, msg=None)

Included for symmetry with assertIsNone.

assertLess(a, b, msg=None)

Just like self.assertTrue(a < b), but with a nicer default message.

assertLessEqual(a, b, msg=None)

Just like self.assertTrue(a <= b), but with a nicer default message.

assertListEqual(list1, list2, msg=None)

A list-specific equality assertion.

Parameters:

• list1 – The first list to compare.

• list2 – The second list to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertLogs(logger=None, level=None)

Fail unless a log message of level level or higher is emitted on logger_name or its children. If omitted, level defaults to INFO and logger defaults to the root logger.

This method must be used as a context manager, and will yield a recording object with two attributes: output and records. At the end of the context manager, the output attribute will be a list of the matching formatted log messages and the records attribute will be a list of the corresponding LogRecord objects.

Example:

with self.assertLogs('foo', level='INFO') as cm:
    logging.getLogger('foo').info('first message')
    logging.getLogger('foo.bar').error('second message')
self.assertEqual(cm.output, ['INFO:foo:first message',
                             'ERROR:foo.bar:second message'])

assertMultiLineEqual(first, second, msg=None)

Assert that two multi-line strings are equal.

assertNoLogs(logger=None, level=None)

Fail unless no log messages of level level or higher are emitted on logger_name or its children.

This method must be used as a context manager.

assertNotAlmostEqual(first, second, places=None, msg=None, delta=None)

Fail if the two objects are equal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is less than the given delta.

Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).

Objects that are equal automatically fail.

assertNotEqual(first, second, msg=None)

Fail if the two objects are equal as determined by the ‘!=’ operator.

assertNotIn(member, container, msg=None)

Just like self.assertTrue(a not in b), but with a nicer default message.

assertNotIsInstance(obj, cls, msg=None)

Included for symmetry with assertIsInstance.

assertNotRegex(text, unexpected_regex, msg=None)

Fail the test if the text matches the regular expression.

assertRaises(expected_exception, *args, **kwargs)

Fail unless an exception of class expected_exception is raised by the callable when invoked with specified positional and keyword arguments. If a different type of exception is raised, it will not be caught, and the test case will be deemed to have suffered an error, exactly as for an unexpected exception.

If called with the callable and arguments omitted, will return a context object used like this:

with self.assertRaises(SomeException):
    do_something()

An optional keyword argument ‘msg’ can be provided when assertRaises is used as a context object.

The context manager keeps a reference to the exception as the ‘exception’ attribute. This allows you to inspect the exception after the assertion:

with self.assertRaises(SomeException) as cm:
    do_something()
the_exception = cm.exception
self.assertEqual(the_exception.error_code, 3)

assertRaisesRegex(expected_exception, expected_regex, *args, **kwargs)

Asserts that the message in a raised exception matches a regex.

Parameters:

• expected_exception – Exception class expected to be raised.

• expected_regex – Regex (re.Pattern object or string) expected to be found in error message.

• args – Function to be called and extra positional args.

• kwargs – Extra kwargs.

• msg – Optional message used in case of failure. Can only be used when assertRaisesRegex is used as a context manager.

assertRegex(text, expected_regex, msg=None)

Fail the test unless the text matches the regular expression.

assertSequenceEqual(seq1, seq2, msg=None, seq_type=None)

An equality assertion for ordered sequences (like lists and tuples).

For the purposes of this function, a valid ordered sequence type is one which can be indexed, has a length, and has an equality operator.

Parameters:

• seq1 – The first sequence to compare.

• seq2 – The second sequence to compare.

• seq_type – The expected datatype of the sequences, or None if no datatype should be enforced.

• msg – Optional message to use on failure instead of a list of differences.

assertSetEqual(set1, set2, msg=None)

A set-specific equality assertion.

Parameters:

• set1 – The first set to compare.

• set2 – The second set to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertSetEqual uses ducktyping to support different types of sets, and is optimized for sets specifically (parameters must support a difference method).

assertTrue(expr, msg=None)

Check that the expression is true.

assertTupleEqual(tuple1, tuple2, msg=None)

A tuple-specific equality assertion.

Parameters:

• tuple1 – The first tuple to compare.

• tuple2 – The second tuple to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertWarns(expected_warning, *args, **kwargs)

Fail unless a warning of class warnClass is triggered by the callable when invoked with specified positional and keyword arguments. If a different type of warning is triggered, it will not be handled: depending on the other warning filtering rules in effect, it might be silenced, printed out, or raised as an exception.

If called with the callable and arguments omitted, will return a context object used like this:

with self.assertWarns(SomeWarning):
    do_something()

An optional keyword argument ‘msg’ can be provided when assertWarns is used as a context object.

The context manager keeps a reference to the first matching warning as the ‘warning’ attribute; similarly, the ‘filename’ and ‘lineno’ attributes give you information about the line of Python code from which the warning was triggered. This allows you to inspect the warning after the assertion:

with self.assertWarns(SomeWarning) as cm:
    do_something()
the_warning = cm.warning
self.assertEqual(the_warning.some_attribute, 147)

assertWarnsRegex(expected_warning, expected_regex, *args, **kwargs)

Asserts that the message in a triggered warning matches a regexp. Basic functioning is similar to assertWarns() with the addition that only warnings whose messages also match the regular expression are considered successful matches.

Parameters:

• expected_warning – Warning class expected to be triggered.

• expected_regex – Regex (re.Pattern object or string) expected to be found in error message.

• args – Function to be called and extra positional args.

• kwargs – Extra kwargs.

• msg – Optional message used in case of failure. Can only be used when assertWarnsRegex is used as a context manager.

clearGenerated()

Remove the directories that are used for testing.

countTestCases()

createLargeMultitaskDataSet(name='QSPRDataset_multi_test', target_props=[{'name': 'HBD', 'task': <TargetTasks.MULTICLASS: 'MULTICLASS'>, 'th': [-1, 1, 2, 100]}, {'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42)

Create a large dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• preparation_settings (dict) – dictionary containing preparation settings

• random_state (int) – random state to use for splitting and shuffling

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createLargeTestDataSet(name='QSPRDataset_test_large', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42, n_jobs=1, chunk_size=None)

Create a large dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• preparation_settings (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createSmallTestDataSet(name='QSPRDataset_test_small', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42)

Create a small dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• preparation_settings (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createTestDataSetFromFrame(df, name='QSPRDataset_test', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], random_state=None, prep=None, n_jobs=1, chunk_size=None)

Create a dataset for testing purposes from the given data frame.

Parameters:

• df (pd.DataFrame) – data frame containing the dataset

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• prep (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

debug()

Run the test without collecting errors in a TestResult

defaultTestResult()

classmethod doClassCleanups()

Execute all class cleanup functions. Normally called for you after tearDownClass.

doCleanups()

Execute all cleanup functions. Normally called for you after tearDown.

classmethod enterClassContext(cm)

Same as enterContext, but class-wide.

enterContext(cm)

Enters the supplied context manager.

If successful, also adds its __exit__ method as a cleanup function and returns the result of the __enter__ method.

fail(msg=None)

Fail immediately, with the given message.

failureException

alias of AssertionError

classmethod getAllDescriptors()

Return a list of (ideally) all available descriptor sets. For now they need to be added manually to the list below.

TODO: would be nice to create the list automatically by implementing a descriptor set registry that would hold all installed descriptor sets.

Returns:

list of DescriptorCalculator objects

Return type:

list

getBigDF()

Get a large data frame for testing purposes.

Returns:

a pandas.DataFrame containing the dataset

Return type:

pd.DataFrame

classmethod getDataPrepGrid()

Return a list of many possible combinations of descriptor calculators, splits, feature standardizers, feature filters and data filters. Again, this is not exhaustive, but should cover a lot of cases.

Returns:

a generator that yields tuples of all possible combinations as stated above, each tuple is defined as: (descriptor_calculator, split, feature_standardizer, feature_filters, data_filters)

Return type:

grid

classmethod getDefaultCalculatorCombo()

Makes a list of default descriptor calculators that can be used in tests. It creates a calculator with only morgan fingerprints and rdkit descriptors, but also one with them both to test behaviour with multiple descriptor sets. Override this method if you want to test with other descriptor sets and calculator combinations.

Returns:

list of created DescriptorCalculator objects

Return type:

list

static getDefaultPrep()

Return a dictionary with default preparation settings.

getModel(name: str, alg: ~typing.Type = <class 'sklearn.ensemble._forest.RandomForestClassifier'>) → SklearnModel

Get a model for testing.

Parameters:

• dataset (QSPRDataset) – Dataset to use for model.

• name (str) – Name of model.

• alg (Type, optional) – Algorithm to use for model. Defaults to RandomForestClassifier.

Returns:

The new model.

Return type:

SklearnModel

classmethod getPrepCombos()

Return a list of all possible preparation combinations as generated by getDataPrepGrid as well as their names. The generated list can be used to parameterize tests with the given named combinations.

Returns:

list of `list`s of all possible combinations of preparation

Return type:

list

getSmallDF()

Get a small data frame for testing purposes.

Returns:

a pandas.DataFrame containing the dataset

Return type:

pd.DataFrame

id()

longMessage = True

maxDiff = 640

run(result=None)

setUp()

Hook method for setting up the test fixture before exercising it.

classmethod setUpClass()

Hook method for setting up class fixture before running tests in the class.

setUpPaths()

Set up the test environment.

shortDescription()

Returns a one-line description of the test, or None if no description has been provided.

The default implementation of this method returns the first line of the specified test method’s docstring.

skipTest(reason)

Skip this test.

subTest(msg=<object object>, **params)

Return a context manager that will return the enclosed block of code in a subtest identified by the optional message and keyword parameters. A failure in the subtest marks the test case as failed but resumes execution at the end of the enclosed block, allowing further test code to be executed.

tearDown()

Remove all files and directories that are used for testing.

classmethod tearDownClass()

Hook method for deconstructing the class fixture after running all tests in the class.

testPlotSingle()

Test plotting ROC curve for single task.

validate_split(dataset)

Check if the split has the data it should have after splitting.

class qsprpred.plotting.tests.WilliamsPlotTest(methodName='runTest')

Bases: PlottingTest

Test plotting Williams plot for single task.

Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.

classmethod addClassCleanup(function, /, *args, **kwargs)

Same as addCleanup, except the cleanup items are called even if setUpClass fails (unlike tearDownClass).

addCleanup(function, /, *args, **kwargs)

Add a function, with arguments, to be called when the test is completed. Functions added are called on a LIFO basis and are called after tearDown on test failure or success.

Cleanup items are called even if setUp fails (unlike tearDown).

addTypeEqualityFunc(typeobj, function)

Add a type specific assertEqual style function to compare a type.

This method is for use by TestCase subclasses that need to register their own type equality functions to provide nicer error messages.

Parameters:

• typeobj – The data type to call this function on when both values are of the same type in assertEqual().

• function – The callable taking two arguments and an optional msg= argument that raises self.failureException with a useful error message when the two arguments are not equal.

assertAlmostEqual(first, second, places=None, msg=None, delta=None)

Fail if the two objects are unequal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is more than the given delta.

Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).

If the two objects compare equal then they will automatically compare almost equal.

assertCountEqual(first, second, msg=None)

Asserts that two iterables have the same elements, the same number of times, without regard to order.

self.assertEqual(Counter(list(first)),

Counter(list(second)))

Example:

• [0, 1, 1] and [1, 0, 1] compare equal.

• [0, 0, 1] and [0, 1] compare unequal.

assertDictEqual(d1, d2, msg=None)

assertEqual(first, second, msg=None)

Fail if the two objects are unequal as determined by the ‘==’ operator.

assertFalse(expr, msg=None)

Check that the expression is false.

assertGreater(a, b, msg=None)

Just like self.assertTrue(a > b), but with a nicer default message.

assertGreaterEqual(a, b, msg=None)

Just like self.assertTrue(a >= b), but with a nicer default message.

assertIn(member, container, msg=None)

Just like self.assertTrue(a in b), but with a nicer default message.

assertIs(expr1, expr2, msg=None)

Just like self.assertTrue(a is b), but with a nicer default message.

assertIsInstance(obj, cls, msg=None)

Same as self.assertTrue(isinstance(obj, cls)), with a nicer default message.

assertIsNone(obj, msg=None)

Same as self.assertTrue(obj is None), with a nicer default message.

assertIsNot(expr1, expr2, msg=None)

Just like self.assertTrue(a is not b), but with a nicer default message.

assertIsNotNone(obj, msg=None)

Included for symmetry with assertIsNone.

assertLess(a, b, msg=None)

Just like self.assertTrue(a < b), but with a nicer default message.

assertLessEqual(a, b, msg=None)

Just like self.assertTrue(a <= b), but with a nicer default message.

assertListEqual(list1, list2, msg=None)

A list-specific equality assertion.

Parameters:

• list1 – The first list to compare.

• list2 – The second list to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertLogs(logger=None, level=None)

Fail unless a log message of level level or higher is emitted on logger_name or its children. If omitted, level defaults to INFO and logger defaults to the root logger.

This method must be used as a context manager, and will yield a recording object with two attributes: output and records. At the end of the context manager, the output attribute will be a list of the matching formatted log messages and the records attribute will be a list of the corresponding LogRecord objects.

Example:

with self.assertLogs('foo', level='INFO') as cm:
    logging.getLogger('foo').info('first message')
    logging.getLogger('foo.bar').error('second message')
self.assertEqual(cm.output, ['INFO:foo:first message',
                             'ERROR:foo.bar:second message'])

assertMultiLineEqual(first, second, msg=None)

Assert that two multi-line strings are equal.

assertNoLogs(logger=None, level=None)

Fail unless no log messages of level level or higher are emitted on logger_name or its children.

This method must be used as a context manager.

assertNotAlmostEqual(first, second, places=None, msg=None, delta=None)

Fail if the two objects are equal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is less than the given delta.

Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).

Objects that are equal automatically fail.

assertNotEqual(first, second, msg=None)

Fail if the two objects are equal as determined by the ‘!=’ operator.

assertNotIn(member, container, msg=None)

Just like self.assertTrue(a not in b), but with a nicer default message.

assertNotIsInstance(obj, cls, msg=None)

Included for symmetry with assertIsInstance.

assertNotRegex(text, unexpected_regex, msg=None)

Fail the test if the text matches the regular expression.

assertRaises(expected_exception, *args, **kwargs)

Fail unless an exception of class expected_exception is raised by the callable when invoked with specified positional and keyword arguments. If a different type of exception is raised, it will not be caught, and the test case will be deemed to have suffered an error, exactly as for an unexpected exception.

If called with the callable and arguments omitted, will return a context object used like this:

with self.assertRaises(SomeException):
    do_something()

An optional keyword argument ‘msg’ can be provided when assertRaises is used as a context object.

The context manager keeps a reference to the exception as the ‘exception’ attribute. This allows you to inspect the exception after the assertion:

with self.assertRaises(SomeException) as cm:
    do_something()
the_exception = cm.exception
self.assertEqual(the_exception.error_code, 3)

assertRaisesRegex(expected_exception, expected_regex, *args, **kwargs)

Asserts that the message in a raised exception matches a regex.

Parameters:

• expected_exception – Exception class expected to be raised.

• expected_regex – Regex (re.Pattern object or string) expected to be found in error message.

• args – Function to be called and extra positional args.

• kwargs – Extra kwargs.

• msg – Optional message used in case of failure. Can only be used when assertRaisesRegex is used as a context manager.

assertRegex(text, expected_regex, msg=None)

Fail the test unless the text matches the regular expression.

assertSequenceEqual(seq1, seq2, msg=None, seq_type=None)

An equality assertion for ordered sequences (like lists and tuples).

For the purposes of this function, a valid ordered sequence type is one which can be indexed, has a length, and has an equality operator.

Parameters:

• seq1 – The first sequence to compare.

• seq2 – The second sequence to compare.

• seq_type – The expected datatype of the sequences, or None if no datatype should be enforced.

• msg – Optional message to use on failure instead of a list of differences.

assertSetEqual(set1, set2, msg=None)

A set-specific equality assertion.

Parameters:

• set1 – The first set to compare.

• set2 – The second set to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertSetEqual uses ducktyping to support different types of sets, and is optimized for sets specifically (parameters must support a difference method).

assertTrue(expr, msg=None)

Check that the expression is true.

assertTupleEqual(tuple1, tuple2, msg=None)

A tuple-specific equality assertion.

Parameters:

• tuple1 – The first tuple to compare.

• tuple2 – The second tuple to compare.

• msg – Optional message to use on failure instead of a list of differences.

assertWarns(expected_warning, *args, **kwargs)

Fail unless a warning of class warnClass is triggered by the callable when invoked with specified positional and keyword arguments. If a different type of warning is triggered, it will not be handled: depending on the other warning filtering rules in effect, it might be silenced, printed out, or raised as an exception.

If called with the callable and arguments omitted, will return a context object used like this:

with self.assertWarns(SomeWarning):
    do_something()

An optional keyword argument ‘msg’ can be provided when assertWarns is used as a context object.

The context manager keeps a reference to the first matching warning as the ‘warning’ attribute; similarly, the ‘filename’ and ‘lineno’ attributes give you information about the line of Python code from which the warning was triggered. This allows you to inspect the warning after the assertion:

with self.assertWarns(SomeWarning) as cm:
    do_something()
the_warning = cm.warning
self.assertEqual(the_warning.some_attribute, 147)

assertWarnsRegex(expected_warning, expected_regex, *args, **kwargs)

Asserts that the message in a triggered warning matches a regexp. Basic functioning is similar to assertWarns() with the addition that only warnings whose messages also match the regular expression are considered successful matches.

Parameters:

• expected_warning – Warning class expected to be triggered.

• expected_regex – Regex (re.Pattern object or string) expected to be found in error message.

• args – Function to be called and extra positional args.

• kwargs – Extra kwargs.

• msg – Optional message used in case of failure. Can only be used when assertWarnsRegex is used as a context manager.

clearGenerated()

Remove the directories that are used for testing.

countTestCases()

createLargeMultitaskDataSet(name='QSPRDataset_multi_test', target_props=[{'name': 'HBD', 'task': <TargetTasks.MULTICLASS: 'MULTICLASS'>, 'th': [-1, 1, 2, 100]}, {'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42)

Create a large dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• preparation_settings (dict) – dictionary containing preparation settings

• random_state (int) – random state to use for splitting and shuffling

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createLargeTestDataSet(name='QSPRDataset_test_large', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42, n_jobs=1, chunk_size=None)

Create a large dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• preparation_settings (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createSmallTestDataSet(name='QSPRDataset_test_small', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], preparation_settings=None, random_state=42)

Create a small dataset for testing purposes.

Parameters:

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• preparation_settings (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

createTestDataSetFromFrame(df, name='QSPRDataset_test', target_props=[{'name': 'CL', 'task': <TargetTasks.REGRESSION: 'REGRESSION'>}], random_state=None, prep=None, n_jobs=1, chunk_size=None)

Create a dataset for testing purposes from the given data frame.

Parameters:

• df (pd.DataFrame) – data frame containing the dataset

• name (str) – name of the dataset

• target_props (List of dicts or TargetProperty) – list of target properties

• random_state (int) – random state to use for splitting and shuffling

• prep (dict) – dictionary containing preparation settings

Returns:

a QSPRDataset object

Return type:

QSPRDataset

debug()

Run the test without collecting errors in a TestResult

defaultTestResult()

classmethod doClassCleanups()

Execute all class cleanup functions. Normally called for you after tearDownClass.

doCleanups()

Execute all cleanup functions. Normally called for you after tearDown.

classmethod enterClassContext(cm)

Same as enterContext, but class-wide.

enterContext(cm)

Enters the supplied context manager.

If successful, also adds its __exit__ method as a cleanup function and returns the result of the __enter__ method.

fail(msg=None)

Fail immediately, with the given message.

failureException

alias of AssertionError

classmethod getAllDescriptors()

Return a list of (ideally) all available descriptor sets. For now they need to be added manually to the list below.

TODO: would be nice to create the list automatically by implementing a descriptor set registry that would hold all installed descriptor sets.

Returns:

list of DescriptorCalculator objects

Return type:

list

getBigDF()

Get a large data frame for testing purposes.

Returns:

a pandas.DataFrame containing the dataset

Return type:

pd.DataFrame

classmethod getDataPrepGrid()

Return a list of many possible combinations of descriptor calculators, splits, feature standardizers, feature filters and data filters. Again, this is not exhaustive, but should cover a lot of cases.

Returns:

a generator that yields tuples of all possible combinations as stated above, each tuple is defined as: (descriptor_calculator, split, feature_standardizer, feature_filters, data_filters)

Return type:

grid

classmethod getDefaultCalculatorCombo()

Makes a list of default descriptor calculators that can be used in tests. It creates a calculator with only morgan fingerprints and rdkit descriptors, but also one with them both to test behaviour with multiple descriptor sets. Override this method if you want to test with other descriptor sets and calculator combinations.

Returns:

list of created DescriptorCalculator objects

Return type:

list

static getDefaultPrep()

Return a dictionary with default preparation settings.

getModel(name: str, alg: ~typing.Type = <class 'sklearn.ensemble._forest.RandomForestClassifier'>) → SklearnModel

Get a model for testing.

Parameters:

• dataset (QSPRDataset) – Dataset to use for model.

• name (str) – Name of model.

• alg (Type, optional) – Algorithm to use for model. Defaults to RandomForestClassifier.

Returns:

The new model.

Return type:

SklearnModel

classmethod getPrepCombos()

Return a list of all possible preparation combinations as generated by getDataPrepGrid as well as their names. The generated list can be used to parameterize tests with the given named combinations.

Returns:

list of `list`s of all possible combinations of preparation

Return type:

list

getSmallDF()

Get a small data frame for testing purposes.

Returns:

a pandas.DataFrame containing the dataset

Return type:

pd.DataFrame

id()

longMessage = True

maxDiff = 640

run(result=None)

setUp()

Hook method for setting up the test fixture before exercising it.

classmethod setUpClass()

Hook method for setting up class fixture before running tests in the class.

setUpPaths()

Set up the test environment.

shortDescription()

Returns a one-line description of the test, or None if no description has been provided.

The default implementation of this method returns the first line of the specified test method’s docstring.

skipTest(reason)

Skip this test.

subTest(msg=<object object>, **params)

Return a context manager that will return the enclosed block of code in a subtest identified by the optional message and keyword parameters. A failure in the subtest marks the test case as failed but resumes execution at the end of the enclosed block, allowing further test code to be executed.

tearDown()

Remove all files and directories that are used for testing.

classmethod tearDownClass()

Hook method for deconstructing the class fixture after running all tests in the class.

testPlotSingle()

Test plotting Williams plot for single task.

validate_split(dataset)

Check if the split has the data it should have after splitting.

Module contents