Based on: Utilities for developer
Assert
validation.assert_all_finite: Throw an error if array contains NaNs or Infs.
_testing.assert_allclose: quasi equality of arrays, using a tol parameter
Formater / converter
validation.as_float_array: convert input to array of float
validation.check_array:
check that input is a 2D array. Sparse matrix and other dimensions can be optionally allowed
call assert_all_finite
validation.check_X_y:
check that X and y have consistent lengths
call check_array on X, column_or_1d on y (use multi_output=True for multilabel y)
validation.indexable: check that input arrays have consistent length and can be sliced or indexed (useful for cross-validation)
do not use np.asanyarray or np.atleast_2d since it lets np.matrix through (different API from np.array)
Random state
validation.check_random_state: randomness must be handle with np.random.RandomState only
Estimators
validation.check_is_fitted: check that estimator has been fitted before calling predict or transform method
validation.has_fit_parameter: check that the fit method has a given parameter
all_estimators: return a list of all scikit-learn estimators
multiclass.type_of_target: return the type of the target y among continuous, continuous-multioutput, binary, multiclass, multiclass-multioutput, multilabel-indicator or unknown
multiclass.unique_labels: Extract an ordered array of unique labels, "multiclass-multioutput" input type not allowed.
Warnings & Exceptions
utils.deprecated: decorator to mark a function or class as deprecated
exceptions.ConvergenceWarning: Custom warning to catch convergence problems (used in Lasso)