"""Utilities for input validation""" # Authors: Olivier Grisel # Gael Varoquaux # Andreas Mueller # Lars Buitinck # Alexandre Gramfort # Nicolas Tresegnie # Sylvain Marie # License: BSD 3 clause from functools import wraps import warnings import numbers import numpy as np import scipy.sparse as sp from inspect import signature, isclass, Parameter # mypy error: Module 'numpy.core.numeric' has no attribute 'ComplexWarning' from numpy.core.numeric import ComplexWarning # type: ignore import joblib from contextlib import suppress from .fixes import _object_dtype_isnan, parse_version from .. import get_config as _get_config from ..exceptions import PositiveSpectrumWarning from ..exceptions import NotFittedError from ..exceptions import DataConversionWarning FLOAT_DTYPES = (np.float64, np.float32, np.float16) def _deprecate_positional_args(func=None, *, version="1.0 (renaming of 0.25)"): """Decorator for methods that issues warnings for positional arguments. Using the keyword-only argument syntax in pep 3102, arguments after the * will issue a warning when passed as a positional argument. Parameters ---------- func : callable, default=None Function to check arguments on. version : callable, default="1.0 (renaming of 0.25)" The version when positional arguments will result in error. """ def _inner_deprecate_positional_args(f): sig = signature(f) kwonly_args = [] all_args = [] for name, param in sig.parameters.items(): if param.kind == Parameter.POSITIONAL_OR_KEYWORD: all_args.append(name) elif param.kind == Parameter.KEYWORD_ONLY: kwonly_args.append(name) @wraps(f) def inner_f(*args, **kwargs): extra_args = len(args) - len(all_args) if extra_args <= 0: return f(*args, **kwargs) # extra_args > 0 args_msg = ['{}={}'.format(name, arg) for name, arg in zip(kwonly_args[:extra_args], args[-extra_args:])] args_msg = ", ".join(args_msg) warnings.warn(f"Pass {args_msg} as keyword args. From version " f"{version} passing these as positional arguments " "will result in an error", FutureWarning) kwargs.update(zip(sig.parameters, args)) return f(**kwargs) return inner_f if func is not None: return _inner_deprecate_positional_args(func) return _inner_deprecate_positional_args def _assert_all_finite(X, allow_nan=False, msg_dtype=None): """Like assert_all_finite, but only for ndarray.""" # validation is also imported in extmath from .extmath import _safe_accumulator_op if _get_config()['assume_finite']: return X = np.asanyarray(X) # First try an O(n) time, O(1) space solution for the common case that # everything is finite; fall back to O(n) space np.isfinite to prevent # false positives from overflow in sum method. The sum is also calculated # safely to reduce dtype induced overflows. is_float = X.dtype.kind in 'fc' if is_float and (np.isfinite(_safe_accumulator_op(np.sum, X))): pass elif is_float: msg_err = "Input contains {} or a value too large for {!r}." if (allow_nan and np.isinf(X).any() or not allow_nan and not np.isfinite(X).all()): type_err = 'infinity' if allow_nan else 'NaN, infinity' raise ValueError( msg_err.format (type_err, msg_dtype if msg_dtype is not None else X.dtype) ) # for object dtype data, we only check for NaNs (GH-13254) elif X.dtype == np.dtype('object') and not allow_nan: if _object_dtype_isnan(X).any(): raise ValueError("Input contains NaN") @_deprecate_positional_args def assert_all_finite(X, *, allow_nan=False): """Throw a ValueError if X contains NaN or infinity. Parameters ---------- X : {ndarray, sparse matrix} allow_nan : bool, default=False """ _assert_all_finite(X.data if sp.issparse(X) else X, allow_nan) @_deprecate_positional_args def as_float_array(X, *, copy=True, force_all_finite=True): """Converts an array-like to an array of floats. The new dtype will be np.float32 or np.float64, depending on the original type. The function can create a copy or modify the argument depending on the argument copy. Parameters ---------- X : {array-like, sparse matrix} copy : bool, default=True If True, a copy of X will be created. If False, a copy may still be returned if X's dtype is not a floating point type. force_all_finite : bool or 'allow-nan', default=True Whether to raise an error on np.inf, np.nan, pd.NA in X. The possibilities are: - True: Force all values of X to be finite. - False: accepts np.inf, np.nan, pd.NA in X. - 'allow-nan': accepts only np.nan and pd.NA values in X. Values cannot be infinite. .. versionadded:: 0.20 ``force_all_finite`` accepts the string ``'allow-nan'``. .. versionchanged:: 0.23 Accepts `pd.NA` and converts it into `np.nan` Returns ------- XT : {ndarray, sparse matrix} An array of type float. """ if isinstance(X, np.matrix) or (not isinstance(X, np.ndarray) and not sp.issparse(X)): return check_array(X, accept_sparse=['csr', 'csc', 'coo'], dtype=np.float64, copy=copy, force_all_finite=force_all_finite, ensure_2d=False) elif sp.issparse(X) and X.dtype in [np.float32, np.float64]: return X.copy() if copy else X elif X.dtype in [np.float32, np.float64]: # is numpy array return X.copy('F' if X.flags['F_CONTIGUOUS'] else 'C') if copy else X else: if X.dtype.kind in 'uib' and X.dtype.itemsize <= 4: return_dtype = np.float32 else: return_dtype = np.float64 return X.astype(return_dtype) def _is_arraylike(x): """Returns whether the input is array-like.""" return (hasattr(x, '__len__') or hasattr(x, 'shape') or hasattr(x, '__array__')) def _num_features(X): """Return the number of features in an array-like X. This helper function tries hard to avoid to materialize an array version of X unless necessary. For instance, if X is a list of lists, this function will return the length of the first element, assuming that subsequent elements are all lists of the same length without checking. Parameters ---------- X : array-like array-like to get the number of features. Returns ------- features : int Number of features """ type_ = type(X) if type_.__module__ == "builtins": type_name = type_.__qualname__ else: type_name = f"{type_.__module__}.{type_.__qualname__}" message = ( "Unable to find the number of features from X of type " f"{type_name}" ) if not hasattr(X, '__len__') and not hasattr(X, 'shape'): if not hasattr(X, '__array__'): raise TypeError(message) # Only convert X to a numpy array if there is no cheaper, heuristic # option. X = np.asarray(X) if hasattr(X, 'shape'): if not hasattr(X.shape, '__len__') or len(X.shape) <= 1: message += f" with shape {X.shape}" raise TypeError(message) return X.shape[1] first_sample = X[0] # Do not consider an array-like of strings or dicts to be a 2D array if isinstance(first_sample, (str, bytes, dict)): message += (f" where the samples are of type " f"{type(first_sample).__qualname__}") raise TypeError(message) try: # If X is a list of lists, for instance, we assume that all nested # lists have the same length without checking or converting to # a numpy array to keep this function call as cheap as possible. return len(first_sample) except Exception as err: raise TypeError(message) from err def _num_samples(x): """Return number of samples in array-like x.""" message = 'Expected sequence or array-like, got %s' % type(x) if hasattr(x, 'fit') and callable(x.fit): # Don't get num_samples from an ensembles length! raise TypeError(message) if not hasattr(x, '__len__') and not hasattr(x, 'shape'): if hasattr(x, '__array__'): x = np.asarray(x) else: raise TypeError(message) if hasattr(x, 'shape') and x.shape is not None: if len(x.shape) == 0: raise TypeError("Singleton array %r cannot be considered" " a valid collection." % x) # Check that shape is returning an integer or default to len # Dask dataframes may not return numeric shape[0] value if isinstance(x.shape[0], numbers.Integral): return x.shape[0] try: return len(x) except TypeError as type_error: raise TypeError(message) from type_error def check_memory(memory): """Check that ``memory`` is joblib.Memory-like. joblib.Memory-like means that ``memory`` can be converted into a joblib.Memory instance (typically a str denoting the ``location``) or has the same interface (has a ``cache`` method). Parameters ---------- memory : None, str or object with the joblib.Memory interface Returns ------- memory : object with the joblib.Memory interface Raises ------ ValueError If ``memory`` is not joblib.Memory-like. """ if memory is None or isinstance(memory, str): if parse_version(joblib.__version__) < parse_version('0.12'): memory = joblib.Memory(cachedir=memory, verbose=0) else: memory = joblib.Memory(location=memory, verbose=0) elif not hasattr(memory, 'cache'): raise ValueError("'memory' should be None, a string or have the same" " interface as joblib.Memory." " Got memory='{}' instead.".format(memory)) return memory def check_consistent_length(*arrays): """Check that all arrays have consistent first dimensions. Checks whether all objects in arrays have the same shape or length. Parameters ---------- *arrays : list or tuple of input objects. Objects that will be checked for consistent length. """ lengths = [_num_samples(X) for X in arrays if X is not None] uniques = np.unique(lengths) if len(uniques) > 1: raise ValueError("Found input variables with inconsistent numbers of" " samples: %r" % [int(l) for l in lengths]) def _make_indexable(iterable): """Ensure iterable supports indexing or convert to an indexable variant. Convert sparse matrices to csr and other non-indexable iterable to arrays. Let `None` and indexable objects (e.g. pandas dataframes) pass unchanged. Parameters ---------- iterable : {list, dataframe, ndarray, sparse matrix} or None Object to be converted to an indexable iterable. """ if sp.issparse(iterable): return iterable.tocsr() elif hasattr(iterable, "__getitem__") or hasattr(iterable, "iloc"): return iterable elif iterable is None: return iterable return np.array(iterable) def indexable(*iterables): """Make arrays indexable for cross-validation. Checks consistent length, passes through None, and ensures that everything can be indexed by converting sparse matrices to csr and converting non-interable objects to arrays. Parameters ---------- *iterables : {lists, dataframes, ndarrays, sparse matrices} List of objects to ensure sliceability. """ result = [_make_indexable(X) for X in iterables] check_consistent_length(*result) return result def _ensure_sparse_format(spmatrix, accept_sparse, dtype, copy, force_all_finite, accept_large_sparse): """Convert a sparse matrix to a given format. Checks the sparse format of spmatrix and converts if necessary. Parameters ---------- spmatrix : sparse matrix Input to validate and convert. accept_sparse : str, bool or list/tuple of str String[s] representing allowed sparse matrix formats ('csc', 'csr', 'coo', 'dok', 'bsr', 'lil', 'dia'). If the input is sparse but not in the allowed format, it will be converted to the first listed format. True allows the input to be any format. False means that a sparse matrix input will raise an error. dtype : str, type or None Data type of result. If None, the dtype of the input is preserved. copy : bool Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion. force_all_finite : bool or 'allow-nan' Whether to raise an error on np.inf, np.nan, pd.NA in X. The possibilities are: - True: Force all values of X to be finite. - False: accepts np.inf, np.nan, pd.NA in X. - 'allow-nan': accepts only np.nan and pd.NA values in X. Values cannot be infinite. .. versionadded:: 0.20 ``force_all_finite`` accepts the string ``'allow-nan'``. .. versionchanged:: 0.23 Accepts `pd.NA` and converts it into `np.nan` Returns ------- spmatrix_converted : sparse matrix. Matrix that is ensured to have an allowed type. """ if dtype is None: dtype = spmatrix.dtype changed_format = False if isinstance(accept_sparse, str): accept_sparse = [accept_sparse] # Indices dtype validation _check_large_sparse(spmatrix, accept_large_sparse) if accept_sparse is False: raise TypeError('A sparse matrix was passed, but dense ' 'data is required. Use X.toarray() to ' 'convert to a dense numpy array.') elif isinstance(accept_sparse, (list, tuple)): if len(accept_sparse) == 0: raise ValueError("When providing 'accept_sparse' " "as a tuple or list, it must contain at " "least one string value.") # ensure correct sparse format if spmatrix.format not in accept_sparse: # create new with correct sparse spmatrix = spmatrix.asformat(accept_sparse[0]) changed_format = True elif accept_sparse is not True: # any other type raise ValueError("Parameter 'accept_sparse' should be a string, " "boolean or list of strings. You provided " "'accept_sparse={}'.".format(accept_sparse)) if dtype != spmatrix.dtype: # convert dtype spmatrix = spmatrix.astype(dtype) elif copy and not changed_format: # force copy spmatrix = spmatrix.copy() if force_all_finite: if not hasattr(spmatrix, "data"): warnings.warn("Can't check %s sparse matrix for nan or inf." % spmatrix.format, stacklevel=2) else: _assert_all_finite(spmatrix.data, allow_nan=force_all_finite == 'allow-nan') return spmatrix def _ensure_no_complex_data(array): if hasattr(array, 'dtype') and array.dtype is not None \ and hasattr(array.dtype, 'kind') and array.dtype.kind == "c": raise ValueError("Complex data not supported\n" "{}\n".format(array)) @_deprecate_positional_args def check_array(array, accept_sparse=False, *, accept_large_sparse=True, dtype="numeric", order=None, copy=False, force_all_finite=True, ensure_2d=True, allow_nd=False, ensure_min_samples=1, ensure_min_features=1, estimator=None): """Input validation on an array, list, sparse matrix or similar. By default, the input is checked to be a non-empty 2D array containing only finite values. If the dtype of the array is object, attempt converting to float, raising on failure. Parameters ---------- array : object Input object to check / convert. accept_sparse : str, bool or list/tuple of str, default=False String[s] representing allowed sparse matrix formats, such as 'csc', 'csr', etc. If the input is sparse but not in the allowed format, it will be converted to the first listed format. True allows the input to be any format. False means that a sparse matrix input will raise an error. accept_large_sparse : bool, default=True If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by accept_sparse, accept_large_sparse=False will cause it to be accepted only if its indices are stored with a 32-bit dtype. .. versionadded:: 0.20 dtype : 'numeric', type, list of type or None, default='numeric' Data type of result. If None, the dtype of the input is preserved. If "numeric", dtype is preserved unless array.dtype is object. If dtype is a list of types, conversion on the first type is only performed if the dtype of the input is not in the list. order : {'F', 'C'} or None, default=None Whether an array will be forced to be fortran or c-style. When order is None (default), then if copy=False, nothing is ensured about the memory layout of the output array; otherwise (copy=True) the memory layout of the returned array is kept as close as possible to the original array. copy : bool, default=False Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion. force_all_finite : bool or 'allow-nan', default=True Whether to raise an error on np.inf, np.nan, pd.NA in array. The possibilities are: - True: Force all values of array to be finite. - False: accepts np.inf, np.nan, pd.NA in array. - 'allow-nan': accepts only np.nan and pd.NA values in array. Values cannot be infinite. .. versionadded:: 0.20 ``force_all_finite`` accepts the string ``'allow-nan'``. .. versionchanged:: 0.23 Accepts `pd.NA` and converts it into `np.nan` ensure_2d : bool, default=True Whether to raise a value error if array is not 2D. allow_nd : bool, default=False Whether to allow array.ndim > 2. ensure_min_samples : int, default=1 Make sure that the array has a minimum number of samples in its first axis (rows for a 2D array). Setting to 0 disables this check. ensure_min_features : int, default=1 Make sure that the 2D array has some minimum number of features (columns). The default value of 1 rejects empty datasets. This check is only enforced when the input data has effectively 2 dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0 disables this check. estimator : str or estimator instance, default=None If passed, include the name of the estimator in warning messages. Returns ------- array_converted : object The converted and validated array. """ # store reference to original array to check if copy is needed when # function returns array_orig = array # store whether originally we wanted numeric dtype dtype_numeric = isinstance(dtype, str) and dtype == "numeric" dtype_orig = getattr(array, "dtype", None) if not hasattr(dtype_orig, 'kind'): # not a data type (e.g. a column named dtype in a pandas DataFrame) dtype_orig = None # check if the object contains several dtypes (typically a pandas # DataFrame), and store them. If not, store None. dtypes_orig = None has_pd_integer_array = False if hasattr(array, "dtypes") and hasattr(array.dtypes, '__array__'): # throw warning if columns are sparse. If all columns are sparse, then # array.sparse exists and sparsity will be perserved (later). with suppress(ImportError): from pandas.api.types import is_sparse if (not hasattr(array, 'sparse') and array.dtypes.apply(is_sparse).any()): warnings.warn( "pandas.DataFrame with sparse columns found." "It will be converted to a dense numpy array." ) dtypes_orig = list(array.dtypes) # pandas boolean dtype __array__ interface coerces bools to objects for i, dtype_iter in enumerate(dtypes_orig): if dtype_iter.kind == 'b': dtypes_orig[i] = np.dtype(object) elif dtype_iter.name.startswith(("Int", "UInt")): # name looks like an Integer Extension Array, now check for # the dtype with suppress(ImportError): from pandas import (Int8Dtype, Int16Dtype, Int32Dtype, Int64Dtype, UInt8Dtype, UInt16Dtype, UInt32Dtype, UInt64Dtype) if isinstance(dtype_iter, (Int8Dtype, Int16Dtype, Int32Dtype, Int64Dtype, UInt8Dtype, UInt16Dtype, UInt32Dtype, UInt64Dtype)): has_pd_integer_array = True if all(isinstance(dtype, np.dtype) for dtype in dtypes_orig): dtype_orig = np.result_type(*dtypes_orig) if dtype_numeric: if dtype_orig is not None and dtype_orig.kind == "O": # if input is object, convert to float. dtype = np.float64 else: dtype = None if isinstance(dtype, (list, tuple)): if dtype_orig is not None and dtype_orig in dtype: # no dtype conversion required dtype = None else: # dtype conversion required. Let's select the first element of the # list of accepted types. dtype = dtype[0] if has_pd_integer_array: # If there are any pandas integer extension arrays, array = array.astype(dtype) if force_all_finite not in (True, False, 'allow-nan'): raise ValueError('force_all_finite should be a bool or "allow-nan"' '. Got {!r} instead'.format(force_all_finite)) if estimator is not None: if isinstance(estimator, str): estimator_name = estimator else: estimator_name = estimator.__class__.__name__ else: estimator_name = "Estimator" context = " by %s" % estimator_name if estimator is not None else "" # When all dataframe columns are sparse, convert to a sparse array if hasattr(array, 'sparse') and array.ndim > 1: # DataFrame.sparse only supports `to_coo` array = array.sparse.to_coo() if array.dtype == np.dtype('object'): unique_dtypes = set( [dt.subtype.name for dt in array_orig.dtypes] ) if len(unique_dtypes) > 1: raise ValueError( "Pandas DataFrame with mixed sparse extension arrays " "generated a sparse matrix with object dtype which " "can not be converted to a scipy sparse matrix." "Sparse extension arrays should all have the same " "numeric type.") if sp.issparse(array): _ensure_no_complex_data(array) array = _ensure_sparse_format(array, accept_sparse=accept_sparse, dtype=dtype, copy=copy, force_all_finite=force_all_finite, accept_large_sparse=accept_large_sparse) else: # If np.array(..) gives ComplexWarning, then we convert the warning # to an error. This is needed because specifying a non complex # dtype to the function converts complex to real dtype, # thereby passing the test made in the lines following the scope # of warnings context manager. with warnings.catch_warnings(): try: warnings.simplefilter('error', ComplexWarning) if dtype is not None and np.dtype(dtype).kind in 'iu': # Conversion float -> int should not contain NaN or # inf (numpy#14412). We cannot use casting='safe' because # then conversion float -> int would be disallowed. array = np.asarray(array, order=order) if array.dtype.kind == 'f': _assert_all_finite(array, allow_nan=False, msg_dtype=dtype) array = array.astype(dtype, casting="unsafe", copy=False) else: array = np.asarray(array, order=order, dtype=dtype) except ComplexWarning as complex_warning: raise ValueError("Complex data not supported\n" "{}\n".format(array)) from complex_warning # It is possible that the np.array(..) gave no warning. This happens # when no dtype conversion happened, for example dtype = None. The # result is that np.array(..) produces an array of complex dtype # and we need to catch and raise exception for such cases. _ensure_no_complex_data(array) if ensure_2d: # If input is scalar raise error if array.ndim == 0: raise ValueError( "Expected 2D array, got scalar array instead:\narray={}.\n" "Reshape your data either using array.reshape(-1, 1) if " "your data has a single feature or array.reshape(1, -1) " "if it contains a single sample.".format(array)) # If input is 1D raise error if array.ndim == 1: raise ValueError( "Expected 2D array, got 1D array instead:\narray={}.\n" "Reshape your data either using array.reshape(-1, 1) if " "your data has a single feature or array.reshape(1, -1) " "if it contains a single sample.".format(array)) # make sure we actually converted to numeric: if dtype_numeric and array.dtype.kind in "OUSV": warnings.warn( "Arrays of bytes/strings is being converted to decimal " "numbers if dtype='numeric'. This behavior is deprecated in " "0.24 and will be removed in 1.1 (renaming of 0.26). Please " "convert your data to numeric values explicitly instead.", FutureWarning, stacklevel=2 ) try: array = array.astype(np.float64) except ValueError as e: raise ValueError( "Unable to convert array of bytes/strings " "into decimal numbers with dtype='numeric'") from e if not allow_nd and array.ndim >= 3: raise ValueError("Found array with dim %d. %s expected <= 2." % (array.ndim, estimator_name)) if force_all_finite: _assert_all_finite(array, allow_nan=force_all_finite == 'allow-nan') if ensure_min_samples > 0: n_samples = _num_samples(array) if n_samples < ensure_min_samples: raise ValueError("Found array with %d sample(s) (shape=%s) while a" " minimum of %d is required%s." % (n_samples, array.shape, ensure_min_samples, context)) if ensure_min_features > 0 and array.ndim == 2: n_features = array.shape[1] if n_features < ensure_min_features: raise ValueError("Found array with %d feature(s) (shape=%s) while" " a minimum of %d is required%s." % (n_features, array.shape, ensure_min_features, context)) if copy and np.may_share_memory(array, array_orig): array = np.array(array, dtype=dtype, order=order) return array def _check_large_sparse(X, accept_large_sparse=False): """Raise a ValueError if X has 64bit indices and accept_large_sparse=False """ if not accept_large_sparse: supported_indices = ["int32"] if X.getformat() == "coo": index_keys = ['col', 'row'] elif X.getformat() in ["csr", "csc", "bsr"]: index_keys = ['indices', 'indptr'] else: return for key in index_keys: indices_datatype = getattr(X, key).dtype if (indices_datatype not in supported_indices): raise ValueError("Only sparse matrices with 32-bit integer" " indices are accepted. Got %s indices." % indices_datatype) @_deprecate_positional_args def check_X_y(X, y, accept_sparse=False, *, accept_large_sparse=True, dtype="numeric", order=None, copy=False, force_all_finite=True, ensure_2d=True, allow_nd=False, multi_output=False, ensure_min_samples=1, ensure_min_features=1, y_numeric=False, estimator=None): """Input validation for standard estimators. Checks X and y for consistent length, enforces X to be 2D and y 1D. By default, X is checked to be non-empty and containing only finite values. Standard input checks are also applied to y, such as checking that y does not have np.nan or np.inf targets. For multi-label y, set multi_output=True to allow 2D and sparse y. If the dtype of X is object, attempt converting to float, raising on failure. Parameters ---------- X : {ndarray, list, sparse matrix} Input data. y : {ndarray, list, sparse matrix} Labels. accept_sparse : str, bool or list of str, default=False String[s] representing allowed sparse matrix formats, such as 'csc', 'csr', etc. If the input is sparse but not in the allowed format, it will be converted to the first listed format. True allows the input to be any format. False means that a sparse matrix input will raise an error. accept_large_sparse : bool, default=True If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by accept_sparse, accept_large_sparse will cause it to be accepted only if its indices are stored with a 32-bit dtype. .. versionadded:: 0.20 dtype : 'numeric', type, list of type or None, default='numeric' Data type of result. If None, the dtype of the input is preserved. If "numeric", dtype is preserved unless array.dtype is object. If dtype is a list of types, conversion on the first type is only performed if the dtype of the input is not in the list. order : {'F', 'C'}, default=None Whether an array will be forced to be fortran or c-style. copy : bool, default=False Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion. force_all_finite : bool or 'allow-nan', default=True Whether to raise an error on np.inf, np.nan, pd.NA in X. This parameter does not influence whether y can have np.inf, np.nan, pd.NA values. The possibilities are: - True: Force all values of X to be finite. - False: accepts np.inf, np.nan, pd.NA in X. - 'allow-nan': accepts only np.nan or pd.NA values in X. Values cannot be infinite. .. versionadded:: 0.20 ``force_all_finite`` accepts the string ``'allow-nan'``. .. versionchanged:: 0.23 Accepts `pd.NA` and converts it into `np.nan` ensure_2d : bool, default=True Whether to raise a value error if X is not 2D. allow_nd : bool, default=False Whether to allow X.ndim > 2. multi_output : bool, default=False Whether to allow 2D y (array or sparse matrix). If false, y will be validated as a vector. y cannot have np.nan or np.inf values if multi_output=True. ensure_min_samples : int, default=1 Make sure that X has a minimum number of samples in its first axis (rows for a 2D array). ensure_min_features : int, default=1 Make sure that the 2D array has some minimum number of features (columns). The default value of 1 rejects empty datasets. This check is only enforced when X has effectively 2 dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0 disables this check. y_numeric : bool, default=False Whether to ensure that y has a numeric type. If dtype of y is object, it is converted to float64. Should only be used for regression algorithms. estimator : str or estimator instance, default=None If passed, include the name of the estimator in warning messages. Returns ------- X_converted : object The converted and validated X. y_converted : object The converted and validated y. """ if y is None: raise ValueError("y cannot be None") X = check_array(X, accept_sparse=accept_sparse, accept_large_sparse=accept_large_sparse, dtype=dtype, order=order, copy=copy, force_all_finite=force_all_finite, ensure_2d=ensure_2d, allow_nd=allow_nd, ensure_min_samples=ensure_min_samples, ensure_min_features=ensure_min_features, estimator=estimator) if multi_output: y = check_array(y, accept_sparse='csr', force_all_finite=True, ensure_2d=False, dtype=None) else: y = column_or_1d(y, warn=True) _assert_all_finite(y) if y_numeric and y.dtype.kind == 'O': y = y.astype(np.float64) check_consistent_length(X, y) return X, y @_deprecate_positional_args def column_or_1d(y, *, warn=False): """ Ravel column or 1d numpy array, else raises an error. Parameters ---------- y : array-like warn : bool, default=False To control display of warnings. Returns ------- y : ndarray """ y = np.asarray(y) shape = np.shape(y) if len(shape) == 1: return np.ravel(y) if len(shape) == 2 and shape[1] == 1: if warn: warnings.warn("A column-vector y was passed when a 1d array was" " expected. Please change the shape of y to " "(n_samples, ), for example using ravel().", DataConversionWarning, stacklevel=2) return np.ravel(y) raise ValueError( "y should be a 1d array, " "got an array of shape {} instead.".format(shape)) def check_random_state(seed): """Turn seed into a np.random.RandomState instance Parameters ---------- seed : None, int or instance of RandomState If seed is None, return the RandomState singleton used by np.random. If seed is an int, return a new RandomState instance seeded with seed. If seed is already a RandomState instance, return it. Otherwise raise ValueError. """ if seed is None or seed is np.random: return np.random.mtrand._rand if isinstance(seed, numbers.Integral): return np.random.RandomState(seed) if isinstance(seed, np.random.RandomState): return seed raise ValueError('%r cannot be used to seed a numpy.random.RandomState' ' instance' % seed) def has_fit_parameter(estimator, parameter): """Checks whether the estimator's fit method supports the given parameter. Parameters ---------- estimator : object An estimator to inspect. parameter : str The searched parameter. Returns ------- is_parameter: bool Whether the parameter was found to be a named parameter of the estimator's fit method. Examples -------- >>> from sklearn.svm import SVC >>> has_fit_parameter(SVC(), "sample_weight") True """ return parameter in signature(estimator.fit).parameters @_deprecate_positional_args def check_symmetric(array, *, tol=1E-10, raise_warning=True, raise_exception=False): """Make sure that array is 2D, square and symmetric. If the array is not symmetric, then a symmetrized version is returned. Optionally, a warning or exception is raised if the matrix is not symmetric. Parameters ---------- array : {ndarray, sparse matrix} Input object to check / convert. Must be two-dimensional and square, otherwise a ValueError will be raised. tol : float, default=1e-10 Absolute tolerance for equivalence of arrays. Default = 1E-10. raise_warning : bool, default=True If True then raise a warning if conversion is required. raise_exception : bool, default=False If True then raise an exception if array is not symmetric. Returns ------- array_sym : {ndarray, sparse matrix} Symmetrized version of the input array, i.e. the average of array and array.transpose(). If sparse, then duplicate entries are first summed and zeros are eliminated. """ if (array.ndim != 2) or (array.shape[0] != array.shape[1]): raise ValueError("array must be 2-dimensional and square. " "shape = {0}".format(array.shape)) if sp.issparse(array): diff = array - array.T # only csr, csc, and coo have `data` attribute if diff.format not in ['csr', 'csc', 'coo']: diff = diff.tocsr() symmetric = np.all(abs(diff.data) < tol) else: symmetric = np.allclose(array, array.T, atol=tol) if not symmetric: if raise_exception: raise ValueError("Array must be symmetric") if raise_warning: warnings.warn("Array is not symmetric, and will be converted " "to symmetric by average with its transpose.", stacklevel=2) if sp.issparse(array): conversion = 'to' + array.format array = getattr(0.5 * (array + array.T), conversion)() else: array = 0.5 * (array + array.T) return array @_deprecate_positional_args def check_is_fitted(estimator, attributes=None, *, msg=None, all_or_any=all): """Perform is_fitted validation for estimator. Checks if the estimator is fitted by verifying the presence of fitted attributes (ending with a trailing underscore) and otherwise raises a NotFittedError with the given message. This utility is meant to be used internally by estimators themselves, typically in their own predict / transform methods. Parameters ---------- estimator : estimator instance estimator instance for which the check is performed. attributes : str, list or tuple of str, default=None Attribute name(s) given as string or a list/tuple of strings Eg.: ``["coef_", "estimator_", ...], "coef_"`` If `None`, `estimator` is considered fitted if there exist an attribute that ends with a underscore and does not start with double underscore. msg : str, default=None The default error message is, "This %(name)s instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator." For custom messages if "%(name)s" is present in the message string, it is substituted for the estimator name. Eg. : "Estimator, %(name)s, must be fitted before sparsifying". all_or_any : callable, {all, any}, default=all Specify whether all or any of the given attributes must exist. Returns ------- None Raises ------ NotFittedError If the attributes are not found. """ if isclass(estimator): raise TypeError("{} is a class, not an instance.".format(estimator)) if msg is None: msg = ("This %(name)s instance is not fitted yet. Call 'fit' with " "appropriate arguments before using this estimator.") if not hasattr(estimator, 'fit'): raise TypeError("%s is not an estimator instance." % (estimator)) if attributes is not None: if not isinstance(attributes, (list, tuple)): attributes = [attributes] attrs = all_or_any([hasattr(estimator, attr) for attr in attributes]) else: attrs = [v for v in vars(estimator) if v.endswith("_") and not v.startswith("__")] if not attrs: raise NotFittedError(msg % {'name': type(estimator).__name__}) def check_non_negative(X, whom): """ Check if there is any negative value in an array. Parameters ---------- X : {array-like, sparse matrix} Input data. whom : str Who passed X to this function. """ # avoid X.min() on sparse matrix since it also sorts the indices if sp.issparse(X): if X.format in ['lil', 'dok']: X = X.tocsr() if X.data.size == 0: X_min = 0 else: X_min = X.data.min() else: X_min = X.min() if X_min < 0: raise ValueError("Negative values in data passed to %s" % whom) def check_scalar(x, name, target_type, *, min_val=None, max_val=None): """Validate scalar parameters type and value. Parameters ---------- x : object The scalar parameter to validate. name : str The name of the parameter to be printed in error messages. target_type : type or tuple Acceptable data types for the parameter. min_val : float or int, default=None The minimum valid value the parameter can take. If None (default) it is implied that the parameter does not have a lower bound. max_val : float or int, default=None The maximum valid value the parameter can take. If None (default) it is implied that the parameter does not have an upper bound. Raises ------- TypeError If the parameter's type does not match the desired type. ValueError If the parameter's value violates the given bounds. """ if not isinstance(x, target_type): raise TypeError('`{}` must be an instance of {}, not {}.' .format(name, target_type, type(x))) if min_val is not None and x < min_val: raise ValueError('`{}`= {}, must be >= {}.'.format(name, x, min_val)) if max_val is not None and x > max_val: raise ValueError('`{}`= {}, must be <= {}.'.format(name, x, max_val)) def _check_psd_eigenvalues(lambdas, enable_warnings=False): """Check the eigenvalues of a positive semidefinite (PSD) matrix. Checks the provided array of PSD matrix eigenvalues for numerical or conditioning issues and returns a fixed validated version. This method should typically be used if the PSD matrix is user-provided (e.g. a Gram matrix) or computed using a user-provided dissimilarity metric (e.g. kernel function), or if the decomposition process uses approximation methods (randomized SVD, etc.). It checks for three things: - that there are no significant imaginary parts in eigenvalues (more than 1e-5 times the maximum real part). If this check fails, it raises a ``ValueError``. Otherwise all non-significant imaginary parts that may remain are set to zero. This operation is traced with a ``PositiveSpectrumWarning`` when ``enable_warnings=True``. - that eigenvalues are not all negative. If this check fails, it raises a ``ValueError`` - that there are no significant negative eigenvalues with absolute value more than 1e-10 (1e-6) and more than 1e-5 (5e-3) times the largest positive eigenvalue in double (simple) precision. If this check fails, it raises a ``ValueError``. Otherwise all negative eigenvalues that may remain are set to zero. This operation is traced with a ``PositiveSpectrumWarning`` when ``enable_warnings=True``. Finally, all the positive eigenvalues that are too small (with a value smaller than the maximum eigenvalue multiplied by 1e-12 (2e-7)) are set to zero. This operation is traced with a ``PositiveSpectrumWarning`` when ``enable_warnings=True``. Parameters ---------- lambdas : array-like of shape (n_eigenvalues,) Array of eigenvalues to check / fix. enable_warnings : bool, default=False When this is set to ``True``, a ``PositiveSpectrumWarning`` will be raised when there are imaginary parts, negative eigenvalues, or extremely small non-zero eigenvalues. Otherwise no warning will be raised. In both cases, imaginary parts, negative eigenvalues, and extremely small non-zero eigenvalues will be set to zero. Returns ------- lambdas_fixed : ndarray of shape (n_eigenvalues,) A fixed validated copy of the array of eigenvalues. Examples -------- >>> _check_psd_eigenvalues([1, 2]) # nominal case array([1, 2]) >>> _check_psd_eigenvalues([5, 5j]) # significant imag part Traceback (most recent call last): ... ValueError: There are significant imaginary parts in eigenvalues (1 of the maximum real part). Either the matrix is not PSD, or there was an issue while computing the eigendecomposition of the matrix. >>> _check_psd_eigenvalues([5, 5e-5j]) # insignificant imag part array([5., 0.]) >>> _check_psd_eigenvalues([-5, -1]) # all negative Traceback (most recent call last): ... ValueError: All eigenvalues are negative (maximum is -1). Either the matrix is not PSD, or there was an issue while computing the eigendecomposition of the matrix. >>> _check_psd_eigenvalues([5, -1]) # significant negative Traceback (most recent call last): ... ValueError: There are significant negative eigenvalues (0.2 of the maximum positive). Either the matrix is not PSD, or there was an issue while computing the eigendecomposition of the matrix. >>> _check_psd_eigenvalues([5, -5e-5]) # insignificant negative array([5., 0.]) >>> _check_psd_eigenvalues([5, 4e-12]) # bad conditioning (too small) array([5., 0.]) """ lambdas = np.array(lambdas) is_double_precision = lambdas.dtype == np.float64 # note: the minimum value available is # - single-precision: np.finfo('float32').eps = 1.2e-07 # - double-precision: np.finfo('float64').eps = 2.2e-16 # the various thresholds used for validation # we may wish to change the value according to precision. significant_imag_ratio = 1e-5 significant_neg_ratio = 1e-5 if is_double_precision else 5e-3 significant_neg_value = 1e-10 if is_double_precision else 1e-6 small_pos_ratio = 1e-12 if is_double_precision else 2e-7 # Check that there are no significant imaginary parts if not np.isreal(lambdas).all(): max_imag_abs = np.abs(np.imag(lambdas)).max() max_real_abs = np.abs(np.real(lambdas)).max() if max_imag_abs > significant_imag_ratio * max_real_abs: raise ValueError( "There are significant imaginary parts in eigenvalues (%g " "of the maximum real part). Either the matrix is not PSD, or " "there was an issue while computing the eigendecomposition " "of the matrix." % (max_imag_abs / max_real_abs)) # warn about imaginary parts being removed if enable_warnings: warnings.warn("There are imaginary parts in eigenvalues (%g " "of the maximum real part). Either the matrix is not" " PSD, or there was an issue while computing the " "eigendecomposition of the matrix. Only the real " "parts will be kept." % (max_imag_abs / max_real_abs), PositiveSpectrumWarning) # Remove all imaginary parts (even if zero) lambdas = np.real(lambdas) # Check that there are no significant negative eigenvalues max_eig = lambdas.max() if max_eig < 0: raise ValueError("All eigenvalues are negative (maximum is %g). " "Either the matrix is not PSD, or there was an " "issue while computing the eigendecomposition of " "the matrix." % max_eig) else: min_eig = lambdas.min() if (min_eig < -significant_neg_ratio * max_eig and min_eig < -significant_neg_value): raise ValueError("There are significant negative eigenvalues (%g" " of the maximum positive). Either the matrix is " "not PSD, or there was an issue while computing " "the eigendecomposition of the matrix." % (-min_eig / max_eig)) elif min_eig < 0: # Remove all negative values and warn about it if enable_warnings: warnings.warn("There are negative eigenvalues (%g of the " "maximum positive). Either the matrix is not " "PSD, or there was an issue while computing the" " eigendecomposition of the matrix. Negative " "eigenvalues will be replaced with 0." % (-min_eig / max_eig), PositiveSpectrumWarning) lambdas[lambdas < 0] = 0 # Check for conditioning (small positive non-zeros) too_small_lambdas = (0 < lambdas) & (lambdas < small_pos_ratio * max_eig) if too_small_lambdas.any(): if enable_warnings: warnings.warn("Badly conditioned PSD matrix spectrum: the largest " "eigenvalue is more than %g times the smallest. " "Small eigenvalues will be replaced with 0." "" % (1 / small_pos_ratio), PositiveSpectrumWarning) lambdas[too_small_lambdas] = 0 return lambdas def _check_sample_weight(sample_weight, X, dtype=None, copy=False): """Validate sample weights. Note that passing sample_weight=None will output an array of ones. Therefore, in some cases, you may want to protect the call with: if sample_weight is not None: sample_weight = _check_sample_weight(...) Parameters ---------- sample_weight : {ndarray, Number or None}, shape (n_samples,) Input sample weights. X : {ndarray, list, sparse matrix} Input data. dtype: dtype, default=None dtype of the validated `sample_weight`. If None, and the input `sample_weight` is an array, the dtype of the input is preserved; otherwise an array with the default numpy dtype is be allocated. If `dtype` is not one of `float32`, `float64`, `None`, the output will be of dtype `float64`. copy : bool, default=False If True, a copy of sample_weight will be created. Returns ------- sample_weight : ndarray of shape (n_samples,) Validated sample weight. It is guaranteed to be "C" contiguous. """ n_samples = _num_samples(X) if dtype is not None and dtype not in [np.float32, np.float64]: dtype = np.float64 if sample_weight is None: sample_weight = np.ones(n_samples, dtype=dtype) elif isinstance(sample_weight, numbers.Number): sample_weight = np.full(n_samples, sample_weight, dtype=dtype) else: if dtype is None: dtype = [np.float64, np.float32] sample_weight = check_array( sample_weight, accept_sparse=False, ensure_2d=False, dtype=dtype, order="C", copy=copy ) if sample_weight.ndim != 1: raise ValueError("Sample weights must be 1D array or scalar") if sample_weight.shape != (n_samples,): raise ValueError("sample_weight.shape == {}, expected {}!" .format(sample_weight.shape, (n_samples,))) return sample_weight def _allclose_dense_sparse(x, y, rtol=1e-7, atol=1e-9): """Check allclose for sparse and dense data. Both x and y need to be either sparse or dense, they can't be mixed. Parameters ---------- x : {array-like, sparse matrix} First array to compare. y : {array-like, sparse matrix} Second array to compare. rtol : float, default=1e-7 Relative tolerance; see numpy.allclose. atol : float, default=1e-9 absolute tolerance; see numpy.allclose. Note that the default here is more tolerant than the default for numpy.testing.assert_allclose, where atol=0. """ if sp.issparse(x) and sp.issparse(y): x = x.tocsr() y = y.tocsr() x.sum_duplicates() y.sum_duplicates() return (np.array_equal(x.indices, y.indices) and np.array_equal(x.indptr, y.indptr) and np.allclose(x.data, y.data, rtol=rtol, atol=atol)) elif not sp.issparse(x) and not sp.issparse(y): return np.allclose(x, y, rtol=rtol, atol=atol) raise ValueError("Can only compare two sparse matrices, not a sparse " "matrix and an array") def _check_fit_params(X, fit_params, indices=None): """Check and validate the parameters passed during `fit`. Parameters ---------- X : array-like of shape (n_samples, n_features) Data array. fit_params : dict Dictionary containing the parameters passed at fit. indices : array-like of shape (n_samples,), default=None Indices to be selected if the parameter has the same size as `X`. Returns ------- fit_params_validated : dict Validated parameters. We ensure that the values support indexing. """ from . import _safe_indexing fit_params_validated = {} for param_key, param_value in fit_params.items(): if (not _is_arraylike(param_value) or _num_samples(param_value) != _num_samples(X)): # Non-indexable pass-through (for now for backward-compatibility). # https://github.com/scikit-learn/scikit-learn/issues/15805 fit_params_validated[param_key] = param_value else: # Any other fit_params should support indexing # (e.g. for cross-validation). fit_params_validated[param_key] = _make_indexable(param_value) fit_params_validated[param_key] = _safe_indexing( fit_params_validated[param_key], indices ) return fit_params_validated