8000 [MRG+1] ENH: Swap rows in sparsefuncs by MechCoder · Pull Request #3104 · scikit-learn/scikit-learn · GitHub
[go: up one dir, main page]
Skip to content

[MRG+1] ENH: Swap rows in sparsefuncs #3104

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 4 commits into from
Apr 30, 2014
Merged

[MRG+1] ENH: Swap rows in sparsefuncs #3104

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
5119d68
ENH: Swap rows in sparsefuncs
MechCoder Apr 24, 2014
924db9b
Made the following changes
MechCoder Apr 24, 2014
31d438e
COSMIT: Replaced ptr1/2 with start/stop
MechCoder Apr 26, 2014
fc634f9
Made the following changes
MechCoder Apr 30, 2014
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
138 changes: 138 additions & 0 deletions sklearn/utils/sparsefuncs.py
View file
Open in desktop
9E88
Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@

# License: BSD 3 clause
import scipy.sparse as sp
import numpy as np

from .sparsefuncs_fast import (csr_mean_variance_axis0,
csc_mean_variance_axis0,
Expand Down Expand Up @@ -56,3 +57,140 @@ def inplace_column_scale(X, scale):
else:
raise TypeError(
"Unsupported type; expected a CSR or CSC sparse matrix.")


def inplace_swap_row_csc(X, m, n):
"""
Swaps two rows of a CSC matrix in-place.

Parameters
----------
X: scipy.sparse.csc_matrix, shape=(n_samples, n_features)
Matrix whose two rows are to be swapped.

m: int
Index of the row of X to be swapped.

n: int
Index of the row of X to be swapped.
"""
for t in [m, n]:
if isinstance(t, np.ndarray):
raise TypeError("m and n should be valid integers")

if m < 0:
m += X.shape[0]
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Just in case someone passes in a mutable (for instance a numpy int instead of a Python int), I would do 'm = m + X.shape[0]' here to avoid side effects.

Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Just out of curiousity (and for learning), could you please tell me how would the behaviour of the code that I've written, change for a numpy int ?

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I don't get that:

>>> x = y = np.int(0)
>>> y += 1
>>> x, y
(0, 1)

Gael, where you thinking of 0-d arrays?

>>> x = y = array(0)
>>> x += 1
>>> x, y
(array(1), array(1))

I'm not sure if guarding for this is worthwhile. It's very likely that a later refactoring round undoes this; callers should take care not to produce these values.

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Yes I was. 

Maybe I am paranoid but each time I see an in place modification I check that it does not apply to input arguments. Thus I think that l might see the potential problem also in a later code review. 

I think that it is good practice not to let such a code.  If m is an intégré there is no computational benefit to doing the +=.

-------- Original message --------
From: Lars Buitinck notifications@github.com
Date:29/04/2014 23:37 (GMT+01:00)
To: scikit-learn/scikit-learn scikit-learn@noreply.github.com
Cc: Gael Varoquaux gael.varoquaux@normalesup.org
Subject: Re: [scikit-learn] [MRG+1] ENH: Swap rows in sparsefuncs (#3104)
In sklearn/utils/sparsefuncs.py:

@@ -56,3 +57,109 @@ def inplace_column_scale(X, scale):
else:
raise TypeError(
"Unsupported type; expected a CSR or CSC sparse matrix.")
+
+
+def swap_row_csc(X, m, n):

  • """
  • Swaps two rows of a CSC matrix in-place.
  • Parameters
  • X : scipy.sparse.csc_matrix, shape=(n_samples, n_features)
  • m : int, index of first sample
  • n : int, index of second sample
  • """
  • if m < 0:
  •    m += X.shape[0]
    I don't get that:

x = y = np.int(0)
y += 1
x, y
(0, 1)
Gael, where you thinking of 0-d arrays?

x = y = array(0)
x += 1
x, y
(array(1), array(1))
I'm not sure if guarding for this is worthwhile. It's very likely that a later refactoring round undoes this; callers should take care not to produce these values.

—
Reply to this email directly or view it on GitHub.

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

It's a very Pythonic shorthand, though. I think it would be cleaner to typecheck and raise a TypeError for a 0-d array:

>>> isinstance(np.array(1), (np.integer, Integral))
False

Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

@GaelVaroquaux Is it just 0-D numpy arrays, or are there other cases also? Then it might be better to do m = m + X.shape[0]

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

It's a very Pythonic shorthand, though. I think it would be cleaner to
typecheck and raise a TypeError for a 0-d array:

isinstance(np.array(1), (np.integer, Integral))
False

If you whish.

if n < 0:
n += X.shape[0]
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Same remark.


m_mask = X.indices == m
X.indices[X.indices == n] = m
X.indices[m_mask] = n


def inplace_swap_row_csr(X, m, n):
"""
Swaps two rows of a CSR matrix in-place.

Parameters
----------
X: scipy.sparse.csr_matrix, shape=(n_samples, n_features)
Matrix whose two rows are to be swapped.

m: int
Index of the row of X to be swapped.

n: int
Index of the row of X to be swapped.
"""
for t in [m, n]:
if isinstance(t, np.ndarray):
raise TypeError("m and n should be valid integers")

if m < 0:
m += X.shape[0]
if n < 0:
n += X.shape[0]

# The following swapping makes life easier since m is assumed to be the
# smaller integer below.
if m > n:
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Can you tell us what you are doing here (a comment in the code) as I find this surprising.

Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

If m > n, then concatenate , won't work the way it is supposed to below. (Numpy slicing)

m, n = n, m

indptr = X.indptr
m_start = indptr[m]
m_stop = indptr[m + 1]
n_start = indptr[n]
n_stop = indptr[n + 1]
nz_m = m_stop - m_start
nz_n = n_stop - n_start


if nz_m != nz_n:
# Modify indptr first
X.indptr[m + 2:n] += nz_n - nz_m
X.indptr[m + 1] = m_start + nz_n
X.indptr[n] = n_stop - nz_m

X.indices = np.concatenate([X.indices[:m_start],
X.indices[n_start:n_stop],
X.indices[m_stop:n_start],
X.indices[m_start:m_stop],
X.indices[n_stop:]])
X.data = np.concatenate([X.data[:m_start],
X.data[n_start:n_stop],
X.data[m_stop:n_start],
X.data[m_start:m_stop],
X.data[n_stop:]])


def inplace_swap_row(X, m, n):
"""
Swaps two rows of a CSC/CSR matrix in-place.

Parameters
----------
X : CSR or CSC sparse matrix, shape=(n_samples, n_features)
Matrix whose two rows are to be swapped.

m: int
Index of the row of X to be swapped.

n: int
Index of the row of X to be swapped.
"""
if isinstance(X, sp.csc_matrix):
return inplace_swap_row_csc(X, m, n)
elif isinstance(X, sp.csr_matrix):
return inplace_swap_row_csr(X, m, n)
else:
raise TypeError(
"Unsupported type; expected a CSR or CSC sparse matrix.")
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Coming late to the party but it would be nice to report the actual type of sparse matrix with X.getformat() in the error message.



def inplace_swap_column(X, m, n):
"""
Swaps two columns of a CSC/CSR matrix in-place.

Parameters
----------
X : CSR or CSC sparse matrix, shape=(n_samples, n_features)
Matrix whose two columns are to be swapped.

m: int
Index of the column of X to be swapped.

n : int
Index of the column of X to be swapped.
"""
if m < 0:
m += X.shape[1]
if n < 0:
n += X.shape[1]
if isinstance(X, sp.csc_matrix):
return inplace_swap_row_csr(X, m, n)
elif isinstance(X, sp.csr_matrix):
return inplace_swap_row_csc(X, m, n)
else:
raise TypeError(
"Unsupported type; expected a CSR or CSC sparse matrix.")
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

same comment as above here.

57 changes: 56 additions & 1 deletion sklearn/utils/tests/test_sparsefuncs.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,10 +1,13 @@
import numpy as np
import scipy.sparse as sp

from scipy import linalg
from numpy.testing import assert_array_almost_equal, assert_array_equal

from sklearn.datasets import make_classification
from sklearn.utils.sparsefuncs import (mean_variance_axis0,
inplace_column_scale)
inplace_column_scale,
inplace_swap_row, inplace_swap_column)
from sklearn.utils.sparsefuncs_fast import assign_rows_csr
from sklearn.utils.testing import assert_raises

Expand Down Expand Up @@ -60,3 +63,55 @@ def test_inplace_column_scale():
assert_array_almost_equal(XA, Xc.toarray())
assert_array_almost_equal(XA, Xr.toarray())
assert_raises(TypeError, inplace_column_scale, X.tolil(), scale)


def test_inplace_swap_row():
X = np.array([[0, 3, 0],
[2, 4, 0],
[0, 0, 0],
[9, 8, 7],
[4, 0, 5]], dtype=np.float64)
X_csr = sp.csr_matrix(X)
X_csc = sp.csc_matrix(X)

swap = linalg.get_blas_funcs(('swap',), (X,))
swap = swap[0]
X[0], X[-1] = swap(X[0], X[-1])
inplace_swap_row(X_csr, 0, -1)
inplace_swap_row(X_csc, 0, -1)
assert_array_equal(X_csr.toarray(), X_csc.toarray())
assert_array_equal(X, X_csc.toarray())
assert_array_equal(X, X_csr.toarray())

X[2], X[3] = swap(X[2], X[3])
inplace_swap_row(X_csr, 2, 3)
inplace_swap_row(X_csc, 2, 3)
assert_array_equal(X_csr.toarray(), X_csc.toarray())
assert_array_equal(X, X_csc.toarray())
assert_array_equal(X, X_csr.toarray())

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

You should also add a test for the unsupported formats:

    assert_raises(TypeError, inplace_swap_row, X_csr.tolil())


def test_inplace_swap_column():
X = np.array([[0, 3, 0],
[2, 4, 0],
[0, 0, 0],
[9, 8, 7],
[4, 0, 5]], dtype=np.float64)
X_csr = sp.csr_matrix(X)
X_csc = sp.csc_matrix(X)

swap = linalg.get_blas_funcs(('swap',), (X,))
swap = swap[0]
X[:, 0], X[:, -1] = swap(X[:, 0], X[:, -1])
inplace_swap_column(X_csr, 0, -1)
inplace_swap_column(X_csc, 0, -1)
assert_array_equal(X_csr.toarray(), X_csc.toarray())
assert_array_equal(X, X_csc.toarray())
assert_array_equal(X, X_csr.toarray())

X[:, 0], X[:, 1] = swap(X[:, 0], X[:, 1])
inplace_swap_column(X_csr, 0, 1)
inplace_swap_column(X_csc, 0, 1)
assert_array_equal(X_csr.toarray(), X_csc.toarray())
assert_array_equal(X, X_csc.toarray())
assert_array_equal(X, X_csr.toarray())
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Please also check for unsupported input formats here.

0