414 lines
13 KiB
Python
414 lines
13 KiB
Python
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""" Utility functions for sparse matrix module
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"""
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import sys
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import operator
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import numpy as np
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from scipy._lib._util import prod
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import scipy.sparse as sp
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__all__ = ['upcast', 'getdtype', 'getdata', 'isscalarlike', 'isintlike',
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'isshape', 'issequence', 'isdense', 'ismatrix', 'get_sum_dtype']
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supported_dtypes = [np.bool_, np.byte, np.ubyte, np.short, np.ushort, np.intc,
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np.uintc, np.int_, np.uint, np.longlong, np.ulonglong,
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np.single, np.double,
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np.longdouble, np.csingle, np.cdouble, np.clongdouble]
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_upcast_memo = {}
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def upcast(*args):
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"""Returns the nearest supported sparse dtype for the
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combination of one or more types.
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upcast(t0, t1, ..., tn) -> T where T is a supported dtype
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Examples
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--------
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>>> upcast('int32')
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<type 'numpy.int32'>
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>>> upcast('bool')
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<type 'numpy.bool_'>
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>>> upcast('int32','float32')
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<type 'numpy.float64'>
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>>> upcast('bool',complex,float)
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<type 'numpy.complex128'>
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"""
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t = _upcast_memo.get(hash(args))
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if t is not None:
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return t
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upcast = np.result_type(*args)
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for t in supported_dtypes:
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if np.can_cast(upcast, t):
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_upcast_memo[hash(args)] = t
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return t
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raise TypeError('no supported conversion for types: %r' % (args,))
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def upcast_char(*args):
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"""Same as `upcast` but taking dtype.char as input (faster)."""
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t = _upcast_memo.get(args)
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if t is not None:
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return t
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t = upcast(*map(np.dtype, args))
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_upcast_memo[args] = t
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return t
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def upcast_scalar(dtype, scalar):
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"""Determine data type for binary operation between an array of
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type `dtype` and a scalar.
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"""
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return (np.array([0], dtype=dtype) * scalar).dtype
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def downcast_intp_index(arr):
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"""
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Down-cast index array to np.intp dtype if it is of a larger dtype.
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Raise an error if the array contains a value that is too large for
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intp.
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"""
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if arr.dtype.itemsize > np.dtype(np.intp).itemsize:
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if arr.size == 0:
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return arr.astype(np.intp)
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maxval = arr.max()
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minval = arr.min()
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if maxval > np.iinfo(np.intp).max or minval < np.iinfo(np.intp).min:
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raise ValueError("Cannot deal with arrays with indices larger "
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"than the machine maximum address size "
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"(e.g. 64-bit indices on 32-bit machine).")
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return arr.astype(np.intp)
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return arr
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def to_native(A):
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"""
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Ensure that the data type of the NumPy array `A` has native byte order.
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`A` must be a NumPy array. If the data type of `A` does not have native
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byte order, a copy of `A` with a native byte order is returned. Otherwise
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`A` is returned.
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"""
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dt = A.dtype
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if dt.isnative:
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# Don't call `asarray()` if A is already native, to avoid unnecessarily
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# creating a view of the input array.
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return A
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return np.asarray(A, dtype=dt.newbyteorder('native'))
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def getdtype(dtype, a=None, default=None):
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"""Function used to simplify argument processing. If 'dtype' is not
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specified (is None), returns a.dtype; otherwise returns a np.dtype
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object created from the specified dtype argument. If 'dtype' and 'a'
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are both None, construct a data type out of the 'default' parameter.
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Furthermore, 'dtype' must be in 'allowed' set.
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"""
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# TODO is this really what we want?
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if dtype is None:
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try:
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newdtype = a.dtype
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except AttributeError as e:
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if default is not None:
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newdtype = np.dtype(default)
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else:
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raise TypeError("could not interpret data type") from e
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else:
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newdtype = np.dtype(dtype)
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if newdtype == np.object_:
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raise ValueError(
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"object dtype is not supported by sparse matrices"
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)
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return newdtype
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def getdata(obj, dtype=None, copy=False):
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"""
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This is a wrapper of `np.array(obj, dtype=dtype, copy=copy)`
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that will generate a warning if the result is an object array.
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"""
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data = np.array(obj, dtype=dtype, copy=copy)
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# Defer to getdtype for checking that the dtype is OK.
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# This is called for the validation only; we don't need the return value.
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getdtype(data.dtype)
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return data
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def get_index_dtype(arrays=(), maxval=None, check_contents=False):
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"""
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Based on input (integer) arrays `a`, determine a suitable index data
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type that can hold the data in the arrays.
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Parameters
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----------
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arrays : tuple of array_like
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Input arrays whose types/contents to check
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maxval : float, optional
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Maximum value needed
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check_contents : bool, optional
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Whether to check the values in the arrays and not just their types.
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Default: False (check only the types)
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Returns
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-------
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dtype : dtype
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Suitable index data type (int32 or int64)
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"""
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int32min = np.int32(np.iinfo(np.int32).min)
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int32max = np.int32(np.iinfo(np.int32).max)
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# not using intc directly due to misinteractions with pythran
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dtype = np.int32 if np.intc().itemsize == 4 else np.int64
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if maxval is not None:
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maxval = np.int64(maxval)
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if maxval > int32max:
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dtype = np.int64
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if isinstance(arrays, np.ndarray):
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arrays = (arrays,)
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for arr in arrays:
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arr = np.asarray(arr)
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if not np.can_cast(arr.dtype, np.int32):
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if check_contents:
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if arr.size == 0:
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# a bigger type not needed
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continue
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elif np.issubdtype(arr.dtype, np.integer):
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maxval = arr.max()
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minval = arr.min()
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if minval >= int32min and maxval <= int32max:
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# a bigger type not needed
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continue
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dtype = np.int64
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break
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return dtype
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def get_sum_dtype(dtype):
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"""Mimic numpy's casting for np.sum"""
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if dtype.kind == 'u' and np.can_cast(dtype, np.uint):
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return np.uint
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if np.can_cast(dtype, np.int_):
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return np.int_
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return dtype
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def isscalarlike(x):
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"""Is x either a scalar, an array scalar, or a 0-dim array?"""
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return np.isscalar(x) or (isdense(x) and x.ndim == 0)
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def isintlike(x):
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"""Is x appropriate as an index into a sparse matrix? Returns True
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if it can be cast safely to a machine int.
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"""
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# Fast-path check to eliminate non-scalar values. operator.index would
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# catch this case too, but the exception catching is slow.
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if np.ndim(x) != 0:
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return False
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try:
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operator.index(x)
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except (TypeError, ValueError):
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try:
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loose_int = bool(int(x) == x)
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except (TypeError, ValueError):
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return False
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if loose_int:
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msg = "Inexact indices into sparse matrices are not allowed"
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raise ValueError(msg)
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return loose_int
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return True
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def isshape(x, nonneg=False):
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"""Is x a valid 2-tuple of dimensions?
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If nonneg, also checks that the dimensions are non-negative.
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"""
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try:
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# Assume it's a tuple of matrix dimensions (M, N)
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(M, N) = x
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except Exception:
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return False
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else:
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if isintlike(M) and isintlike(N):
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if np.ndim(M) == 0 and np.ndim(N) == 0:
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if not nonneg or (M >= 0 and N >= 0):
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return True
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return False
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def issequence(t):
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return ((isinstance(t, (list, tuple)) and
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(len(t) == 0 or np.isscalar(t[0]))) or
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(isinstance(t, np.ndarray) and (t.ndim == 1)))
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def ismatrix(t):
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return ((isinstance(t, (list, tuple)) and
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len(t) > 0 and issequence(t[0])) or
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(isinstance(t, np.ndarray) and t.ndim == 2))
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def isdense(x):
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return isinstance(x, np.ndarray)
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def validateaxis(axis):
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if axis is not None:
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axis_type = type(axis)
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# In NumPy, you can pass in tuples for 'axis', but they are
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# not very useful for sparse matrices given their limited
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# dimensions, so let's make it explicit that they are not
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# allowed to be passed in
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if axis_type == tuple:
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raise TypeError(("Tuples are not accepted for the 'axis' "
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"parameter. Please pass in one of the "
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"following: {-2, -1, 0, 1, None}."))
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# If not a tuple, check that the provided axis is actually
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# an integer and raise a TypeError similar to NumPy's
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if not np.issubdtype(np.dtype(axis_type), np.integer):
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raise TypeError("axis must be an integer, not {name}"
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.format(name=axis_type.__name__))
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if not (-2 <= axis <= 1):
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raise ValueError("axis out of range")
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def check_shape(args, current_shape=None):
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"""Imitate numpy.matrix handling of shape arguments"""
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if len(args) == 0:
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raise TypeError("function missing 1 required positional argument: "
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"'shape'")
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elif len(args) == 1:
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try:
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shape_iter = iter(args[0])
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except TypeError:
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new_shape = (operator.index(args[0]), )
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else:
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new_shape = tuple(operator.index(arg) for arg in shape_iter)
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else:
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new_shape = tuple(operator.index(arg) for arg in args)
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if current_shape is None:
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if len(new_shape) != 2:
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raise ValueError('shape must be a 2-tuple of positive integers')
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elif any(d < 0 for d in new_shape):
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raise ValueError("'shape' elements cannot be negative")
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else:
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# Check the current size only if needed
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current_size = prod(current_shape)
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# Check for negatives
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negative_indexes = [i for i, x in enumerate(new_shape) if x < 0]
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if len(negative_indexes) == 0:
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new_size = prod(new_shape)
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if new_size != current_size:
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raise ValueError('cannot reshape array of size {} into shape {}'
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.format(current_size, new_shape))
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elif len(negative_indexes) == 1:
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skip = negative_indexes[0]
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specified = prod(new_shape[0:skip] + new_shape[skip+1:])
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unspecified, remainder = divmod(current_size, specified)
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if remainder != 0:
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err_shape = tuple('newshape' if x < 0 else x for x in new_shape)
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raise ValueError('cannot reshape array of size {} into shape {}'
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''.format(current_size, err_shape))
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new_shape = new_shape[0:skip] + (unspecified,) + new_shape[skip+1:]
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else:
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raise ValueError('can only specify one unknown dimension')
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if len(new_shape) != 2:
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raise ValueError('matrix shape must be two-dimensional')
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return new_shape
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def check_reshape_kwargs(kwargs):
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"""Unpack keyword arguments for reshape function.
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This is useful because keyword arguments after star arguments are not
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allowed in Python 2, but star keyword arguments are. This function unpacks
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'order' and 'copy' from the star keyword arguments (with defaults) and
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throws an error for any remaining.
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"""
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order = kwargs.pop('order', 'C')
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copy = kwargs.pop('copy', False)
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if kwargs: # Some unused kwargs remain
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raise TypeError('reshape() got unexpected keywords arguments: {}'
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.format(', '.join(kwargs.keys())))
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return order, copy
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def is_pydata_spmatrix(m):
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"""
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Check whether object is pydata/sparse matrix, avoiding importing the module.
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"""
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base_cls = getattr(sys.modules.get('sparse'), 'SparseArray', None)
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return base_cls is not None and isinstance(m, base_cls)
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###############################################################################
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# Wrappers for NumPy types that are deprecated
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# Numpy versions of these functions raise deprecation warnings, the
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# ones below do not.
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def matrix(*args, **kwargs):
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return np.array(*args, **kwargs).view(np.matrix)
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def asmatrix(data, dtype=None):
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if isinstance(data, np.matrix) and (dtype is None or data.dtype == dtype):
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return data
|
||
|
|
return np.asarray(data, dtype=dtype).view(np.matrix)
|
||
|
|
|
||
|
|
###############################################################################
|
||
|
|
|
||
|
|
|
||
|
|
def _todata(s: 'sp.spmatrix') -> np.ndarray:
|
||
|
|
"""Access nonzero values, possibly after summing duplicates.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
s : sparse matrix
|
||
|
|
Input sparse matrix.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
data: ndarray
|
||
|
|
Nonzero values of the array, with shape (s.nnz,)
|
||
|
|
|
||
|
|
"""
|
||
|
|
if isinstance(s, sp._data._data_matrix):
|
||
|
|
return s._deduped_data()
|
||
|
|
|
||
|
|
if isinstance(s, sp.dok_matrix):
|
||
|
|
return np.fromiter(s.values(), dtype=s.dtype, count=s.nnz)
|
||
|
|
|
||
|
|
if isinstance(s, sp.lil_matrix):
|
||
|
|
data = np.empty(s.nnz, dtype=s.dtype)
|
||
|
|
sp._csparsetools.lil_flatten_to_array(s.data, data)
|
||
|
|
return data
|
||
|
|
|
||
|
|
return s.tocoo()._deduped_data()
|