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# Authors: Mathieu Blondel
# Olivier Grisel
# Peter Prettenhofer
# Lars Buitinck
# Giorgio Patrini
#
# License: BSD 3 clause
#!python
#cython: boundscheck=False, wraparound=False, cdivision=True
from libc.math cimport fabs, sqrt, pow
cimport numpy as np
import numpy as np
import scipy.sparse as sp
cimport cython
from cython cimport floating
from numpy.math cimport isnan
np.import_array()
ctypedef fused integral:
int
long long
ctypedef np.float64_t DOUBLE
def csr_row_norms(X):
"""L2 norm of each row in CSR matrix X."""
if X.dtype not in [np.float32, np.float64]:
X = X.astype(np.float64)
return _csr_row_norms(X.data, X.shape, X.indices, X.indptr)
def _csr_row_norms(np.ndarray[floating, ndim=1, mode="c"] X_data,
shape,
np.ndarray[integral, ndim=1, mode="c"] X_indices,
np.ndarray[integral, ndim=1, mode="c"] X_indptr):
cdef:
unsigned long long n_samples = shape[0]
unsigned long long n_features = shape[1]
np.ndarray[DOUBLE, ndim=1, mode="c"] norms
np.npy_intp i, j
double sum_
norms = np.zeros(n_samples, dtype=np.float64)
for i in range(n_samples):
sum_ = 0.0
for j in range(X_indptr[i], X_indptr[i + 1]):
sum_ += X_data[j] * X_data[j]
norms[i] = sum_
return norms
def csr_mean_variance_axis0(X):
"""Compute mean and variance along axis 0 on a CSR matrix
Parameters
----------
X : CSR sparse matrix, shape (n_samples, n_features)
Input data.
Returns
-------
means : float array with shape (n_features,)
Feature-wise means
variances : float array with shape (n_features,)
Feature-wise variances
"""
if X.dtype not in [np.float32, np.float64]:
X = X.astype(np.float64)
means, variances, _ = _csr_mean_variance_axis0(X.data, X.shape[0],
X.shape[1], X.indices)
return means, variances
def _csr_mean_variance_axis0(np.ndarray[floating, ndim=1, mode="c"] X_data,
unsigned long long n_samples,
unsigned long long n_features,
np.ndarray[integral, ndim=1] X_indices):
# Implement the function here since variables using fused types
# cannot be declared directly and can only be passed as function arguments
cdef:
np.npy_intp i
unsigned long long non_zero = X_indices.shape[0]
np.npy_intp col_ind
floating diff
# means[j] contains the mean of feature j
np.ndarray[floating, ndim=1] means
# variances[j] contains the variance of feature j
np.ndarray[floating, ndim=1] variances
if floating is float:
dtype = np.float32
else:
dtype = np.float64
means = np.zeros(n_features, dtype=dtype)
variances = np.zeros_like(means, dtype=dtype)
cdef:
# counts[j] contains the number of samples where feature j is non-zero
np.ndarray[np.int64_t, ndim=1] counts = np.zeros(n_features,
dtype=np.int64)
# counts_nan[j] contains the number of NaNs for feature j
np.ndarray[np.int64_t, ndim=1] counts_nan = np.zeros(n_features,
dtype=np.int64)
for i in xrange(non_zero):
col_ind = X_indices[i]
if not isnan(X_data[i]):
means[col_ind] += X_data[i]
else:
counts_nan[col_ind] += 1
for i in xrange(n_features):
means[i] /= (n_samples - counts_nan[i])
for i in xrange(non_zero):
col_ind = X_indices[i]
if not isnan(X_data[i]):
diff = X_data[i] - means[col_ind]
variances[col_ind] += diff * diff
counts[col_ind] += 1
for i in xrange(n_features):
variances[i] += (n_samples - counts_nan[i] - counts[i]) * means[i]**2
variances[i] /= (n_samples - counts_nan[i])
return means, variances, counts_nan
def csc_mean_variance_axis0(X):
"""Compute mean and variance along axis 0 on a CSC matrix
Parameters
----------
X : CSC sparse matrix, shape (n_samples, n_features)
Input data.
Returns
-------
means : float array with shape (n_features,)
Feature-wise means
variances : float array with shape (n_features,)
Feature-wise variances
"""
if X.dtype not in [np.float32, np.float64]:
X = X.astype(np.float64)
means, variances, _ = _csc_mean_variance_axis0(X.data, X.shape[0],
X.shape[1], X.indices,
X.indptr)
return means, variances
def _csc_mean_variance_axis0(np.ndarray[floating, ndim=1] X_data,
unsigned long long n_samples,
unsigned long long n_features,
np.ndarray[integral, ndim=1] X_indices,
np.ndarray[integral, ndim=1] X_indptr):
# Implement the function here since variables using fused types
# cannot be declared directly and can only be passed as function arguments
cdef:
np.npy_intp i, j
unsigned long long counts
unsigned long long startptr
unsigned long long endptr
floating diff
# means[j] contains the mean of feature j
np.ndarray[floating, ndim=1] means
# variances[j] contains the variance of feature j
np.ndarray[floating, ndim=1] variances
if floating is float:
dtype = np.float32
else:
dtype = np.float64
means = np.zeros(n_features, dtype=dtype)
variances = np.zeros_like(means, dtype=dtype)
cdef np.ndarray[np.int64_t, ndim=1] counts_nan = np.zeros(n_features,
dtype=np.int64)
for i in xrange(n_features):
startptr = X_indptr[i]
endptr = X_indptr[i + 1]
counts = endptr - startptr
for j in xrange(startptr, endptr):
if not isnan(X_data[j]):
means[i] += X_data[j]
else:
counts_nan[i] += 1
counts -= counts_nan[i]
means[i] /= (n_samples - counts_nan[i])
for j in xrange(startptr, endptr):
if not isnan(X_data[j]):
diff = X_data[j] - means[i]
variances[i] += diff * diff
variances[i] += (n_samples - counts_nan[i] - counts) * means[i]**2
variances[i] /= (n_samples - counts_nan[i])
return means, variances, counts_nan
def incr_mean_variance_axis0(X, last_mean, last_var, last_n):
"""Compute mean and variance along axis 0 on a CSR or CSC matrix.
last_mean, last_var are the statistics computed at the last step by this
function. Both must be initialized to 0.0. last_n is the
number of samples encountered until now and is initialized at 0.
Parameters
----------
X : CSR or CSC sparse matrix, shape (n_samples, n_features)
Input data.
last_mean : float array with shape (n_features,)
Array of feature-wise means to update with the new data X.
last_var : float array with shape (n_features,)
Array of feature-wise var to update with the new data X.
last_n : int array with shape (n_features,)
Number of samples seen so far, before X.
Returns
-------
updated_mean : float array with shape (n_features,)
Feature-wise means
updated_variance : float array with shape (n_features,)
Feature-wise variances
updated_n : int array with shape (n_features,)
Updated number of samples seen
Notes
-----
NaNs are ignored during the computation.
References
----------
T. Chan, G. Golub, R. LeVeque. Algorithms for computing the sample
variance: recommendations, The American Statistician, Vol. 37, No. 3,
pp. 242-247
Also, see the non-sparse implementation of this in
`utils.extmath._batch_mean_variance_update`.
"""
if X.dtype not in [np.float32, np.float64]:
X = X.astype(np.float64)
return _incr_mean_variance_axis0(X.data, X.shape[0], X.shape[1], X.indices,
X.indptr, X.format, last_mean, last_var,
last_n)
def _incr_mean_variance_axis0(np.ndarray[floating, ndim=1] X_data,
unsigned long long n_samples,
unsigned long long n_features,
np.ndarray[integral, ndim=1] X_indices,
np.ndarray[integral, ndim=1] X_indptr,
str X_format,
np.ndarray[floating, ndim=1] last_mean,
np.ndarray[floating, ndim=1] last_var,
np.ndarray[np.int64_t, ndim=1] last_n):
# Implement the function here since variables using fused types
# cannot be declared directly and can only be passed as function arguments
cdef:
np.npy_intp i
# last = stats until now
# new = the current increment
# updated = the aggregated stats
# when arrays, they are indexed by i per-feature
cdef:
np.ndarray[floating, ndim=1] new_mean
np.ndarray[floating, ndim=1] new_var
np.ndarray[floating, ndim=1] updated_mean
np.ndarray[floating, ndim=1] updated_var
if floating is float:
dtype = np.float32
else:
dtype = np.float64
new_mean = np.zeros(n_features, dtype=dtype)
new_var = np.zeros_like(new_mean, dtype=dtype)
updated_mean = np.zeros_like(new_mean, dtype=dtype)
updated_var = np.zeros_like(new_mean, dtype=dtype)
cdef:
np.ndarray[np.int64_t, ndim=1] new_n
np.ndarray[np.int64_t, ndim=1] updated_n
np.ndarray[floating, ndim=1] last_over_new_n
np.ndarray[np.int64_t, ndim=1] counts_nan
# Obtain new stats first
new_n = np.full(n_features, n_samples, dtype=np.int64)
updated_n = np.zeros_like(new_n, dtype=np.int64)
last_over_new_n = np.zeros_like(new_n, dtype=dtype)
if X_format == 'csr':
# X is a CSR matrix
new_mean, new_var, counts_nan = _csr_mean_variance_axis0(
X_data, n_samples, n_features, X_indices)
else:
# X is a CSC matrix
new_mean, new_var, counts_nan = _csc_mean_variance_axis0(
X_data, n_samples, n_features, X_indices, X_indptr)
for i in xrange(n_features):
new_n[i] -= counts_nan[i]
# First pass
cdef bint is_first_pass = True
for i in xrange(n_features):
if last_n[i] > 0:
is_first_pass = False
break
if is_first_pass:
return new_mean, new_var, new_n
# Next passes
for i in xrange(n_features):
updated_n[i] = last_n[i] + new_n[i]
last_over_new_n[i] = last_n[i] / new_n[i]
# Unnormalized stats
for i in xrange(n_features):
last_mean[i] *= last_n[i]
last_var[i] *= last_n[i]
new_mean[i] *= new_n[i]
new_var[i] *= new_n[i]
# Update stats
for i in xrange(n_features):
updated_var[i] = (last_var[i] + new_var[i] +
last_over_new_n[i] / updated_n[i] *
(last_mean[i] / last_over_new_n[i] - new_mean[i])**2)
updated_mean[i] = (last_mean[i] + new_mean[i]) / updated_n[i]
updated_var[i] /= updated_n[i]
return updated_mean, updated_var, updated_n
def inplace_csr_row_normalize_l1(X):
"""Inplace row normalize using the l1 norm"""
_inplace_csr_row_normalize_l1(X.data, X.shape, X.indices, X.indptr)
def _inplace_csr_row_normalize_l1(np.ndarray[floating, ndim=1] X_data,
shape,
np.ndarray[integral, ndim=1] X_indices,
np.ndarray[integral, ndim=1] X_indptr):
cdef unsigned long long n_samples = shape[0]
cdef unsigned long long n_features = shape[1]
# the column indices for row i are stored in:
# indices[indptr[i]:indices[i+1]]
# and their corresponding values are stored in:
# data[indptr[i]:indptr[i+1]]
cdef np.npy_intp i, j
cdef double sum_
for i in xrange(n_samples):
sum_ = 0.0
for j in xrange(X_indptr[i], X_indptr[i + 1]):
sum_ += fabs(X_data[j])
if sum_ == 0.0:
# do not normalize empty rows (can happen if CSR is not pruned
# correctly)
continue
for j in xrange(X_indptr[i], X_indptr[i + 1]):
X_data[j] /= sum_
def inplace_csr_row_normalize_l2(X):
"""Inplace row normalize using the l2 norm"""
_inplace_csr_row_normalize_l2(X.data, X.shape, X.indices, X.indptr)
def _inplace_csr_row_normalize_l2(np.ndarray[floating, ndim=1] X_data,
shape,
np.ndarray[integral, ndim=1] X_indices,
np.ndarray[integral, ndim=1] X_indptr):
cdef integral n_samples = shape[0]
cdef integral n_features = shape[1]
cdef np.npy_intp i, j
cdef double sum_
for i in xrange(n_samples):
sum_ = 0.0
for j in xrange(X_indptr[i], X_indptr[i + 1]):
sum_ += (X_data[j] * X_data[j])
if sum_ == 0.0:
# do not normalize empty rows (can happen if CSR is not pruned
# correctly)
continue
sum_ = sqrt(sum_)
for j in xrange(X_indptr[i], X_indptr[i + 1]):
X_data[j] /= sum_
def assign_rows_csr(X,
np.ndarray[np.npy_intp, ndim=1] X_rows,
np.ndarray[np.npy_intp, ndim=1] out_rows,
np.ndarray[floating, ndim=2, mode="c"] out):
"""Densify selected rows of a CSR matrix into a preallocated array.
Like out[out_rows] = X[X_rows].toarray() but without copying.
No-copy supported for both dtype=np.float32 and dtype=np.float64.
Parameters
----------
X : scipy.sparse.csr_matrix, shape=(n_samples, n_features)
X_rows : array, dtype=np.intp, shape=n_rows
out_rows : array, dtype=np.intp, shape=n_rows
out : array, shape=(arbitrary, n_features)
"""
cdef:
# npy_intp (np.intp in Python) is what np.where returns,
# but int is what scipy.sparse uses.
int i, ind, j
np.npy_intp rX
np.ndarray[floating, ndim=1] data = X.data
np.ndarray[int, ndim=1] indices = X.indices, indptr = X.indptr
if X_rows.shape[0] != out_rows.shape[0]:
raise ValueError("cannot assign %d rows to %d"
% (X_rows.shape[0], out_rows.shape[0]))
out[out_rows] = 0.
for i in range(X_rows.shape[0]):
rX = X_rows[i]
for ind in range(indptr[rX], indptr[rX + 1]):
j = indices[ind]
out[out_rows[i], j] = data[ind]
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