Sampling procedures for testing models (testing
)¶

class
Orange.evaluation.testing.
Results
(data=None, nmethods=0, *, learners=None, train_data=None, nrows=None, nclasses=None, store_data=False, store_models=False, domain=None, actual=None, row_indices=None, predicted=None, probabilities=None, preprocessor=None, callback=None, n_jobs=1)[source]¶ Class for storing predictions in model testing.
 Attributes:
 data (Optional[Table]): Data used for testing. When data is stored,
 this is typically not a copy but a reference.
models (Optional[List[Model]]): A list of induced models.
 row_indices (np.ndarray): Indices of rows in data that were used in
 testing, stored as a numpy vector of length nrows. Values of actual[i], predicted[i] and probabilities[i] refer to the target value of instance data[row_indices[i]].
nrows (int): The number of test instances (including duplicates).
 actual (np.ndarray): Actual values of target variable;
 a numpy vector of length nrows and of the same type as data (or np.float32 if the type of data cannot be determined).
 predicted (np.ndarray): Predicted values of target variable;
 a numpy array of shape (numberofmethods, nrows) and of the same type as data (or np.float32 if the type of data cannot be determined).
 probabilities (Optional[np.ndarray]): Predicted probabilities
 (for discrete target variables); a numpy array of shape (numberofmethods, nrows, numberofclasses) of type np.float32.
 folds (List[Slice or List[int]]): A list of indices (or slice objects)
 corresponding to rows of each fold.

get_augmented_data
(model_names, include_attrs=True, include_predictions=True, include_probabilities=True)[source]¶ Return the data, augmented with predictions, probabilities (if the task is classification) and folds info. Predictions, probabilities and folds are inserted as meta attributes.
 Args:
 model_names (list): A list of strings containing learners’ names. include_attrs (bool): Flag that tells whether to include original attributes. include_predictions (bool): Flag that tells whether to include predictions. include_probabilities (bool): Flag that tells whether to include probabilities.
 Returns:
 Orange.data.Table: Data augmented with predictions, (probabilities) and (fold).

fit
(train_data, test_data=None)[source]¶ Fits self.learners using folds sampled from the provided data.
 Args:
train_data (Table): table to sample train folds test_data (Optional[Table]): tap to sample test folds
of None then train_data will be used

class
Orange.evaluation.testing.
CrossValidation
(data, learners, k=10, stratified=True, random_state=0, store_data=False, store_models=False, preprocessor=None, callback=None, warnings=None, n_jobs=1)[source]¶ Kfold cross validation.
If the constructor is given the data and a list of learning algorithms, it runs cross validation and returns an instance of Results containing the predicted values and probabilities.

k
¶ The number of folds.

random_state
¶


class
Orange.evaluation.testing.
CrossValidationFeature
(data, learners, feature, store_data=False, store_models=False, preprocessor=None, callback=None, n_jobs=1)[source]¶ Cross validation with folds according to values of a feature.

feature
¶ The feature defining the folds.


class
Orange.evaluation.testing.
LeaveOneOut
(data, learners, store_data=False, store_models=False, preprocessor=None, callback=None, n_jobs=1)[source]¶ Leaveoneout testing

class
Orange.evaluation.testing.
TestOnTestData
(train_data, test_data, learners, store_data=False, store_models=False, preprocessor=None, callback=None, n_jobs=1)[source]¶ Test on a separate test data set.

class
Orange.evaluation.testing.
TestOnTrainingData
(data, learners, store_data=False, store_models=False, preprocessor=None, callback=None, n_jobs=1)[source]¶ Trains and test on the same data

Orange.evaluation.testing.
sample
(table, n=0.7, stratified=False, replace=False, random_state=None)[source]¶ Samples data instances from a data table. Returns the sample and a data set from input data table that are not in the sample. Also uses several sampling functions from scikitlearn.
 table : data table
 A data table from which to sample.
 n : float, int (default = 0.7)
 If float, should be between 0.0 and 1.0 and represents the proportion of data instances in the resulting sample. If int, n is the number of data instances in the resulting sample.
 stratified : bool, optional (default = False)
 If true, sampling will try to consider class values and match distribution of class values in train and test subsets.
 replace : bool, optional (default = False)
 sample with replacement
 random_state : int or RandomState
 Pseudorandom number generator state used for random sampling.