"""Utilities for input validation""" # Authors: Olivier Grisel # Gael Varoquaux # Andreas Mueller # Lars Buitinck # Alexandre Gramfort # Nicolas Tresegnie # License: BSD 3 clause import warnings import numbers import numpy as np import scipy.sparse as sp from scipy import __version__ as scipy_version from distutils.version import LooseVersion from numpy.core.numeric import ComplexWarning from ..externals import six from .fixes import signature from .. import get_config as _get_config from ..exceptions import NonBLASDotWarning from ..exceptions import NotFittedError from ..exceptions import DataConversionWarning from ._joblib import Memory from ._joblib import __version__ as joblib_version FLOAT_DTYPES = (np.float64, np.float32, np.float16) # Silenced by default to reduce verbosity. Turn on at runtime for # performance profiling. warnings.simplefilter('ignore', NonBLASDotWarning) # checking whether large sparse are supported by scipy or not LARGE_SPARSE_SUPPORTED = LooseVersion(scipy_version) >= '0.14.0' def _assert_all_finite(X, allow_nan=False): """Like assert_all_finite, but only for ndarray.""" 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. is_float = X.dtype.kind in 'fc' if is_float and np.isfinite(X.sum()): 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, X.dtype)) def assert_all_finite(X, allow_nan=False): """Throw a ValueError if X contains NaN or infinity. Parameters ---------- X : array or sparse matrix allow_nan : bool """ _assert_all_finite(X.data if sp.issparse(X) else X, allow_nan) 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, optional 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 : boolean or 'allow-nan', (default=True) Whether to raise an error on np.inf and np.nan in X. The possibilities are: - True: Force all values of X to be finite. - False: accept both np.inf and np.nan in X. - 'allow-nan': accept only np.nan values in X. Values cannot be infinite. .. versionadded:: 0.20 ``force_all_finite`` accepts the string ``'allow-nan'``. Returns ------- XT : {array, sparse matrix} An array of type np.float """ if isinstance(X, np.matrix) or (not isinstance(X, np.ndarray) and not sp.issparse(X)): return check_array(X, ['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_samples(x): """Return number of samples in array-like x.""" if hasattr(x, 'fit') and callable(x.fit): # Don't get num_samples from an ensembles length! raise TypeError('Expected sequence or array-like, got ' 'estimator %s' % x) if not hasattr(x, '__len__') and not hasattr(x, 'shape'): if hasattr(x, '__array__'): x = np.asarray(x) else: raise TypeError("Expected sequence or array-like, got %s" % type(x)) if hasattr(x, 'shape'): 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] else: return len(x) else: return len(x) def _shape_repr(shape): """Return a platform independent representation of an array shape Under Python 2, the `long` type introduces an 'L' suffix when using the default %r format for tuples of integers (typically used to store the shape of an array). Under Windows 64 bit (and Python 2), the `long` type is used by default in numpy shapes even when the integer dimensions are well below 32 bit. The platform specific type causes string messages or doctests to change from one platform to another which is not desirable. Under Python 3, there is no more `long` type so the `L` suffix is never introduced in string representation. >>> _shape_repr((1, 2)) '(1, 2)' >>> one = 2 ** 64 / 2 ** 64 # force an upcast to `long` under Python 2 >>> _shape_repr((one, 2 * one)) '(1, 2)' >>> _shape_repr((1,)) '(1,)' >>> _shape_repr(()) '()' """ if len(shape) == 0: return "()" joined = ", ".join("%d" % e for e in shape) if len(shape) == 1: # special notation for singleton tuples joined += ',' return "(%s)" % joined 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, six.string_types): if LooseVersion(joblib_version) < '0.12': memory = Memory(cachedir=memory, verbose=0) else: memory = 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 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, arrays, sparse matrices List of objects to ensure sliceability. """ result = [] for X in iterables: if sp.issparse(X): result.append(X.tocsr()) elif hasattr(X, "__getitem__") or hasattr(X, "iloc"): result.append(X) elif X is None: result.append(X) else: result.append(np.array(X)) 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 : scipy sparse matrix Input to validate and convert. accept_sparse : string, boolean or list/tuple of strings 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 : string, type or None Data type of result. If None, the dtype of the input is preserved. copy : boolean Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion. force_all_finite : boolean or 'allow-nan', (default=True) Whether to raise an error on np.inf and np.nan in X. The possibilities are: - True: Force all values of X to be finite. - False: accept both np.inf and np.nan in X. - 'allow-nan': accept only np.nan values in X. Values cannot be infinite. .. versionadded:: 0.20 ``force_all_finite`` accepts the string ``'allow-nan'``. Returns ------- spmatrix_converted : scipy 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, six.string_types): 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) 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)) 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, warn_on_dtype=False, 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 : string, boolean or list/tuple of strings (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. .. deprecated:: 0.19 Passing 'None' to parameter ``accept_sparse`` in methods is deprecated in version 0.19 "and will be removed in 0.21. Use ``accept_sparse=False`` instead. 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 : string, type, list of types 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 : boolean (default=False) Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion. force_all_finite : boolean or 'allow-nan', (default=True) Whether to raise an error on np.inf and np.nan in array. The possibilities are: - True: Force all values of array to be finite. - False: accept both np.inf and np.nan in array. - 'allow-nan': accept only np.nan values in array. Values cannot be infinite. .. versionadded:: 0.20 ``force_all_finite`` accepts the string ``'allow-nan'``. ensure_2d : boolean (default=True) Whether to raise a value error if array is not 2D. allow_nd : boolean (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. warn_on_dtype : boolean (default=False) Raise DataConversionWarning if the dtype of the input data structure does not match the requested dtype, causing a memory copy. 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. """ # accept_sparse 'None' deprecation check if accept_sparse is None: warnings.warn( "Passing 'None' to parameter 'accept_sparse' in methods " "check_array and check_X_y is deprecated in version 0.19 " "and will be removed in 0.21. Use 'accept_sparse=False' " " instead.", DeprecationWarning) accept_sparse = False # 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, six.string_types) 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 if hasattr(array, "dtypes") and hasattr(array.dtypes, '__array__'): dtypes_orig = np.array(array.dtypes) 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 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, six.string_types): estimator_name = estimator else: estimator_name = estimator.__class__.__name__ else: estimator_name = "Estimator" context = " by %s" % estimator_name if estimator is not None else "" 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) array = np.asarray(array, dtype=dtype, order=order) except ComplexWarning: raise ValueError("Complex data not supported\n" "{}\n".format(array)) # 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)) # in the future np.flexible dtypes will be handled like object dtypes if dtype_numeric and np.issubdtype(array.dtype, np.flexible): warnings.warn( "Beginning in version 0.22, arrays of bytes/strings will be " "converted to decimal numbers if dtype='numeric'. " "It is recommended that you convert the array to " "a float dtype before using it in scikit-learn, " "for example by using " "your_array = your_array.astype(np.float64).", FutureWarning) # make sure we actually converted to numeric: if dtype_numeric and array.dtype.kind == "O": array = array.astype(np.float64) 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') shape_repr = _shape_repr(array.shape) 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, shape_repr, 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, shape_repr, ensure_min_features, context)) if warn_on_dtype and dtype_orig is not None and array.dtype != dtype_orig: msg = ("Data with input dtype %s was converted to %s%s." % (dtype_orig, array.dtype, context)) warnings.warn(msg, DataConversionWarning) if copy and np.may_share_memory(array, array_orig): array = np.array(array, dtype=dtype, order=order) if (warn_on_dtype and dtypes_orig is not None and {array.dtype} != set(dtypes_orig)): # if there was at the beginning some other types than the final one # (for instance in a DataFrame that can contain several dtypes) then # some data must have been converted msg = ("Data with input dtype %s were all converted to %s%s." % (', '.join(map(str, sorted(set(dtypes_orig)))), array.dtype, context)) warnings.warn(msg, DataConversionWarning, stacklevel=3) 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 and LARGE_SPARSE_SUPPORTED): 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): if not LARGE_SPARSE_SUPPORTED: raise ValueError("Scipy version %s does not support large" " indices, please upgrade your scipy" " to 0.14.0 or above" % scipy_version) raise ValueError("Only sparse matrices with 32-bit integer" " indices are accepted. Got %s indices." % indices_datatype) 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, warn_on_dtype=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 : nd-array, list or sparse matrix Input data. y : nd-array, list or sparse matrix Labels. accept_sparse : string, boolean or list of string (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. .. deprecated:: 0.19 Passing 'None' to parameter ``accept_sparse`` in methods is deprecated in version 0.19 "and will be removed in 0.21. Use ``accept_sparse=False`` instead. 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 : string, type, list of types 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. copy : boolean (default=False) Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion. force_all_finite : boolean or 'allow-nan', (default=True) Whether to raise an error on np.inf and np.nan in X. This parameter does not influence whether y can have np.inf or np.nan values. The possibilities are: - True: Force all values of X to be finite. - False: accept both np.inf and np.nan in X. - 'allow-nan': accept only np.nan values in X. Values cannot be infinite. .. versionadded:: 0.20 ``force_all_finite`` accepts the string ``'allow-nan'``. ensure_2d : boolean (default=True) Whether to raise a value error if X is not 2D. allow_nd : boolean (default=False) Whether to allow X.ndim > 2. multi_output : boolean (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 : boolean (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. warn_on_dtype : boolean (default=False) Raise DataConversionWarning if the dtype of the input data structure does not match the requested dtype, causing a memory copy. 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, warn_on_dtype=warn_on_dtype, estimator=estimator) if multi_output: y = check_array(y, '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 def column_or_1d(y, warn=False): """ Ravel column or 1d numpy array, else raises an error Parameters ---------- y : array-like warn : boolean, default False To control display of warnings. Returns ------- y : array """ 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("bad input shape {0}".format(shape)) def check_random_state(seed): """Turn seed into a np.random.RandomState instance Parameters ---------- seed : None | int | 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, np.integer)): 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 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 : nd-array or sparse matrix Input object to check / convert. Must be two-dimensional and square, otherwise a ValueError will be raised. tol : float Absolute tolerance for equivalence of arrays. Default = 1E-10. raise_warning : boolean (default=True) If True then raise a warning if conversion is required. raise_exception : boolean (default=False) If True then raise an exception if array is not symmetric. Returns ------- array_sym : ndarray or 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.") 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 def check_is_fitted(estimator, attributes, msg=None, all_or_any=all): """Perform is_fitted validation for estimator. Checks if the estimator is fitted by verifying the presence of "all_or_any" of the passed attributes and raises a NotFittedError with the given message. Parameters ---------- estimator : estimator instance. estimator instance for which the check is performed. attributes : attribute name(s) given as string or a list/tuple of strings Eg.: ``["coef_", "estimator_", ...], "coef_"`` msg : string The default error message is, "This %(name)s instance is not fitted yet. Call 'fit' with appropriate arguments before using this method." 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 msg is None: msg = ("This %(name)s instance is not fitted yet. Call 'fit' with " "appropriate arguments before using this method.") if not hasattr(estimator, 'fit'): raise TypeError("%s is not an estimator instance." % (estimator)) if not isinstance(attributes, (list, tuple)): attributes = [attributes] if not all_or_any([hasattr(estimator, attr) for attr in attributes]): 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 or sparse matrix Input data. whom : string Who passed X to this function. """ X = X.data if sp.issparse(X) else X if (X < 0).any(): raise ValueError("Negative values in data passed to %s" % whom)