223 lines
7.6 KiB
Python
223 lines
7.6 KiB
Python
"""Utilities for input validation"""
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# Authors: Olivier Grisel and Gael Varoquaux and others (please update me)
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# License: BSD 3
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import warnings
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import numbers
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import numpy as np
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from scipy import sparse
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from .fixes import safe_copy
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def assert_all_finite(X):
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"""Throw a ValueError if X contains NaN or infinity.
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Input MUST be an np.ndarray instance or a scipy.sparse matrix."""
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# First try an O(n) time, O(1) space solution for the common case that
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# there everything is finite; fall back to O(n) space np.isfinite to
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# prevent false positives from overflow in sum method.
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if X.dtype.char in np.typecodes['AllFloat'] and not np.isfinite(X.sum()) \
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and not np.isfinite(X.data if sparse.issparse(X) else X).all():
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raise ValueError("array contains NaN or infinity")
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def safe_asarray(X, dtype=None, order=None):
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"""Convert X to an array or sparse matrix.
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Prevents copying X when possible; sparse matrices are passed through."""
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if not sparse.issparse(X):
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X = np.asarray(X, dtype, order)
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assert_all_finite(X)
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return X
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def as_float_array(X, copy=True):
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"""Converts an array-like to an array of floats
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The new dtype will be np.float32 or np.float64, depending on the original
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type. The function can create a copy or modify the argument depending
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on the argument copy.
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Parameters
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----------
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X : {array-like, sparse matrix}
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copy : bool, optional
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If True, a copy of X will be created. If False, a copy may still be
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returned if X's dtype is not a floating point type.
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Returns
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-------
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XT : {array, sparse matrix}
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An array of type np.float
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"""
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if isinstance(X, np.matrix) or (not isinstance(X, np.ndarray)
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and not sparse.issparse(X)):
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return safe_asarray(X, dtype=np.float64)
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elif sparse.issparse(X) and X.dtype in [np.float32, np.float64]:
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return X.copy() if copy else X
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elif X.dtype in [np.float32, np.float64]: # is numpy array
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return X.copy('F' if X.flags['F_CONTIGUOUS'] else 'C') if copy else X
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else:
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return X.astype(np.float32 if X.dtype == np.int32 else np.float64)
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def array2d(X, dtype=None, order=None, copy=False):
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"""Returns at least 2-d array with data from X"""
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if sparse.issparse(X):
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raise TypeError('A sparse matrix was passed, but dense data '
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'is required. Use X.todense() to convert to dense.')
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X_2d = np.asarray(np.atleast_2d(X), dtype=dtype, order=order)
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if X is X_2d and copy:
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X_2d = safe_copy(X_2d)
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return X_2d
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def atleast2d_or_csc(X, dtype=None, order=None, copy=False):
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"""Like numpy.atleast_2d, but converts sparse matrices to CSC format
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Also, converts np.matrix to np.ndarray.
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"""
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if sparse.issparse(X):
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# Note: order is ignored because CSR matrices hold data in 1-d arrays
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if dtype is None or X.dtype == dtype:
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X = X.tocsc()
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else:
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X = sparse.csc_matrix(X, dtype=dtype)
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else:
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X = array2d(X, dtype=dtype, order=order, copy=copy)
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assert_all_finite(X)
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return X
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def atleast2d_or_csr(X, dtype=None, order=None, copy=False):
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"""Like numpy.atleast_2d, but converts sparse matrices to CSR format
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Also, converts np.matrix to np.ndarray.
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"""
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if sparse.issparse(X):
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# Note: order is ignored because CSR matrices hold data in 1-d arrays
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if dtype is None or X.dtype == dtype:
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X = X.tocsr()
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else:
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X = sparse.csr_matrix(X, dtype=dtype)
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else:
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X = array2d(X, dtype=dtype, order=order, copy=copy)
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assert_all_finite(X)
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return X
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def _num_samples(x):
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"""Return number of samples in array-like x."""
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if not hasattr(x, '__len__') and not hasattr(x, 'shape'):
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raise TypeError("Expected sequence or array-like, got %r" % x)
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return x.shape[0] if hasattr(x, 'shape') else len(x)
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def check_arrays(*arrays, **options):
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"""Checked that all arrays have consistent first dimensions
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Parameters
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----------
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*arrays : sequence of arrays or scipy.sparse matrices with same shape[0]
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Python lists or tuples occurring in arrays are converted to 1D numpy
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arrays.
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sparse_format : 'csr', 'csc' or 'dense', None by default
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If not None, any scipy.sparse matrix is converted to
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Compressed Sparse Rows or Compressed Sparse Columns representations.
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If 'dense', an error is raised when a sparse array is
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passed.
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copy : boolean, False by default
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If copy is True, ensure that returned arrays are copies of the original
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(if not already converted to another format earlier in the process).
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check_ccontiguous : boolean, False by default
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Check that the arrays are C contiguous
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dtype : a numpy dtype instance, None by default
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Enforce a specific dtype.
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"""
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sparse_format = options.pop('sparse_format', None)
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if sparse_format not in (None, 'csr', 'csc', 'dense'):
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raise ValueError('Unexpected sparse format: %r' % sparse_format)
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copy = options.pop('copy', False)
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check_ccontiguous = options.pop('check_ccontiguous', False)
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dtype = options.pop('dtype', None)
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if options:
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raise TypeError("Unexpected keyword arguments: %r" % options.keys())
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if len(arrays) == 0:
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return None
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n_samples = _num_samples(arrays[0])
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checked_arrays = []
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for array in arrays:
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array_orig = array
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if array is None:
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# special case: ignore optional y=None kwarg pattern
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checked_arrays.append(array)
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continue
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size = _num_samples(array)
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if size != n_samples:
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raise ValueError("Found array with dim %d. Expected %d" % (
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size, n_samples))
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if sparse.issparse(array):
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if sparse_format == 'csr':
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array = array.tocsr()
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elif sparse_format == 'csc':
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array = array.tocsc()
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elif sparse_format == 'dense':
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raise TypeError('A sparse matrix was passed, but dense data '
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'is required. Use X.todense() to convert to dense.')
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if check_ccontiguous:
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array.data = np.ascontiguousarray(array.data, dtype=dtype)
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else:
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array.data = np.asarray(array.data, dtype=dtype)
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else:
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if check_ccontiguous:
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array = np.ascontiguousarray(array, dtype=dtype)
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else:
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array = np.asarray(array, dtype=dtype)
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if copy and array is array_orig:
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array = array.copy()
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checked_arrays.append(array)
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return checked_arrays
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def warn_if_not_float(X, estimator='This algorithm'):
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"""Warning utility function to check that data type is floating point"""
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if not isinstance(estimator, basestring):
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estimator = estimator.__class__.__name__
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if X.dtype.kind != 'f':
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warnings.warn("%s assumes floating point values as input, "
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"got %s" % (estimator, X.dtype))
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def check_random_state(seed):
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"""Turn seed into a np.random.RandomState instance
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If seed is None, return the RandomState singleton used by np.random.
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If seed is an int, return a new RandomState instance seeded with seed.
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If seed is already a RandomState instance, return it.
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Otherwise raise ValueError.
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"""
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if seed is None or seed is np.random:
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return np.random.mtrand._rand
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if isinstance(seed, numbers.Integral):
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return np.random.RandomState(seed)
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if isinstance(seed, np.random.RandomState):
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return seed
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raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
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' instance' % seed)
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