#=============================================================================== # Copyright 2014 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #=============================================================================== import numpy as np import daal4py as d4p from sklearn import get_config as _get_config from sklearn.utils.fixes import _object_dtype_isnan import warnings from contextlib import suppress import scipy.sparse as sp from numpy.core.numeric import ComplexWarning from sklearn.utils.validation import (_num_samples, _ensure_no_complex_data, _ensure_sparse_format, column_or_1d, check_consistent_length, _assert_all_finite) from sklearn.utils.extmath import _safe_accumulator_op from .._utils import (is_DataFrame, get_dtype, get_number_of_types, sklearn_check_version, PatchingConditionsChain) def _daal_assert_all_finite(X, allow_nan=False, msg_dtype=None, estimator_name=None, input_name=""): if _get_config()['assume_finite']: return # Data with small size has too big relative overhead # TODO: tune threshold size if hasattr(X, 'size'): if X.size < 32768: if sklearn_check_version("1.1"): _assert_all_finite(X, allow_nan=allow_nan, msg_dtype=msg_dtype, estimator_name=estimator_name, input_name=input_name) else: _assert_all_finite(X, allow_nan=allow_nan, msg_dtype=msg_dtype) return is_df = is_DataFrame(X) num_of_types = get_number_of_types(X) # if X is heterogeneous pandas.DataFrame then # covert it to a list of arrays if is_df and num_of_types > 1: lst = [] for idx in X: arr = X[idx].to_numpy() lst.append(arr if arr.flags['C_CONTIGUOUS'] else np.ascontiguousarray(arr)) else: X = np.asanyarray(X) is_df = False dt = np.dtype(get_dtype(X)) is_float = dt.kind in 'fc' msg_err = "Input {} contains {} or a value too large for {!r}." type_err = 'infinity' if allow_nan else 'NaN, infinity' err = msg_err.format( input_name, type_err, msg_dtype if msg_dtype is not None else dt) _patching_status = PatchingConditionsChain( 'sklearn.utils.validation._assert_all_finite') _dal_ready = _patching_status.and_conditions([ (X.ndim in [1, 2], "X has not 1 or 2 dimensions."), (not np.any(np.equal(X.shape, 0)), "X shape contains 0."), (dt in [np.float32, np.float64], "X dtype is not float32 or float64.")]) _patching_status.write_log() if _dal_ready: if X.ndim == 1: X = X.reshape((-1, 1)) x_for_daal = lst if is_df and num_of_types > 1 else X if dt == np.float64: if not d4p.daal_assert_all_finite(x_for_daal, allow_nan, 0): raise ValueError(err) elif dt == np.float32: if not d4p.daal_assert_all_finite(x_for_daal, allow_nan, 1): raise ValueError(err) # 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. elif is_float and (np.isfinite(_safe_accumulator_op(np.sum, X))): pass elif is_float: if allow_nan and np.isinf(X).any() or \ not allow_nan and not np.isfinite(X).all(): raise ValueError(err) # for object dtype data, we only check for NaNs (GH-13254) elif dt == np.dtype('object') and not allow_nan: if _object_dtype_isnan(X).any(): raise ValueError(f"Input {input_name} contains NaN") def _pandas_check_array(array, array_orig, force_all_finite, ensure_min_samples, ensure_min_features, copy, context): if force_all_finite: _daal_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: 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 = array.copy() return array def _daal_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 : 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. 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, 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 : 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. 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. """ 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 "" array_orig = array # a branch for heterogeneous pandas.DataFrame if is_DataFrame(array) and get_number_of_types(array) > 1: from pandas.api.types import is_sparse if hasattr(array, 'sparse') or \ not array.dtypes.apply(is_sparse).any(): return _pandas_check_array(array, array_orig, force_all_finite, ensure_min_samples, ensure_min_features, copy, context) # 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(np.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) # 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 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': _daal_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: 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) # doing nothing for DataFrame 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, stacklevel=2) # 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: _daal_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 _daal_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 : 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. 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, 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 : 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. 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 = _daal_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 = _daal_check_array(y, accept_sparse='csr', force_all_finite=True, ensure_2d=False, dtype=None) else: y = column_or_1d(y, warn=True) _daal_assert_all_finite(y) if y_numeric and hasattr(y, 'dtype') and y.dtype.kind == 'O': y = y.astype(np.float64) check_consistent_length(X, y) return X, y def _daal_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 = f"Unable to find the number of features from X of type {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 {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