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# Author: Lars Buitinck
# License: BSD 3 clause
import array
from cpython cimport array
cimport cython
from libc.stdlib cimport abs
cimport numpy as np
import numpy as np
from ..externals.six import string_types
from sklearn.utils.murmurhash cimport murmurhash3_bytes_s32
np.import_array()
@cython.boundscheck(False)
@cython.cdivision(True)
def transform(raw_X, Py_ssize_t n_features, dtype):
"""Guts of FeatureHasher.transform.
Returns
-------
n_samples : integer
indices, indptr, values : lists
For constructing a scipy.sparse.csr_matrix.
"""
assert n_features > 0
cdef np.int32_t h
cdef double value
cdef array.array indices
cdef array.array indptr
indices = array.array("i")
indptr = array.array("i", [0])
# Since Python array does not understand Numpy dtypes, we grow the indices
# and values arrays ourselves. Use a Py_ssize_t capacity for safety.
cdef Py_ssize_t capacity = 8192 # arbitrary
cdef np.int32_t size = 0
cdef np.ndarray values = np.empty(capacity, dtype=dtype)
for x in raw_X:
for f, v in x:
if isinstance(v, string_types):
f = "%s%s%s" % (f, '=', v)
value = 1
else:
value = v
if value == 0:
continue
if isinstance(f, unicode):
f = f.encode("utf-8")
# Need explicit type check because Murmurhash does not propagate
# all exceptions. Add "except *" there?
elif not isinstance(f, bytes):
raise TypeError("feature names must be strings")
h = murmurhash3_bytes_s32(f, 0)
array.resize_smart(indices, len(indices) + 1)
indices[len(indices) - 1] = abs(h) % n_features
value *= (h >= 0) * 2 - 1
values[size] = value
size += 1
if size == capacity:
capacity *= 2
# can't use resize member because there might be multiple
# references to the arrays due to Cython's error checking
values = np.resize(values, capacity)
array.resize_smart(indptr, len(indptr) + 1)
indptr[len(indptr) - 1] = size
if len(indices):
indices_a = np.frombuffer(indices, dtype=np.int32)
else: # workaround for NumPy < 1.7.0
indices_a = np.empty(0, dtype=np.int32)
return (indices_a, np.frombuffer(indptr, dtype=np.int32), values[:size])
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