8000 [MRG + 2] FIX dtypes to conform to the stricter type cast rules of numpy 1.10 by raghavrv · Pull Request #5398 · scikit-learn/scikit-learn · GitHub
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[MRG + 2] FIX dtypes to conform to the stricter type cast rules of numpy 1.10 #5398

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Merged
merged 5 commits into from
Oct 15, 2015
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[MRG + 2] FIX dtypes to conform to the stricter type cast rules of numpy 1.10 #5398

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4f4c359
FIX dtypes to conform to the stricter type cast rules of numpy 1.10
raghavrv Oct 14, 2015
affa122
MAINT Update the 3rd travis build to latest versions of numpy and scipy
lesteve Oct 14, 2015
21fab6e
FIX Move validation from helper to main function
raghavrv Oct 14, 2015
cac1d28
FIX AdaBoostRegressort test failure with numpy 1.10
lesteve Oct 14, 2015
47f19e9
MAINT add safe_{median|mean} for np 1.10.1
lesteve Oct 15, 2015
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4 changes: 2 additions & 2 deletions .travis.yml
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Expand Up @@ -17,8 +17,8 @@ env:
- DISTRIB="conda" PYTHON_VERSION="2.6" INSTALL_MKL="false"
NUMPY_VERSION="1.6.2" SCIPY_VERSION="0.11.0"
# This environment tests the newest supported anaconda env
- DISTRIB="conda" PYTHON_VERSION="3.4" INSTALL_MKL="true"
NUMPY_VERSION="1.8.1" SCIPY_VERSION="0.14.0"
- DISTRIB="conda" PYTHON_VERSION="3.5" INSTALL_MKL="true"
NUMPY_VERSION="1.10.1" SCIPY_VERSION="0.16.0"
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@lesteve @ogrisel Is this better than * since we know which version we are testing against?

install: source continuous_integration/install.sh
script: bash continuous_integration/test_script.sh
after_success:
Expand Down
16 changes: 8 additions & 8 deletions doc/modules/linear_model.rst
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Expand Up @@ -180,7 +180,7 @@ the parameter vector.
The implementation in the class :class:`Lasso` uses coordinate descent as
the algorithm to fit the coefficients. See :ref:`least_angle_regression`
for another implementation::

>>> from sklearn import linear_model
>>> clf = linear_model.Lasso(alpha = 0.1)
>>> clf.fit([[0, 0], [1, 1]], [0, 1])
Expand Down Expand Up @@ -1079,9 +1079,9 @@ of a given degree. It can be used as follows::
[4, 5]])
>>> poly = PolynomialFeatures(degree=2)
>>> poly.fit_transform(X)
array([[ 1, 0, 1, 0, 0, 1],
[ 1, 2, 3, 4, 6, 9],
[ 1, 4, 5, 16, 20, 25]])
array([[ 1., 0., 1., 0., 0., 1.],
[ 1., 2., 3., 4., 6., 9.],
[ 1., 4., 5., 16., 20., 25.]])

The features of ``X`` have been transformed from :math:`[x_1, x_2]` to
:math:`[1, x_1, x_2, x_1^2, x_1 x_2, x_2^2]`, and can now be used within
Expand Down Expand Up @@ -1125,10 +1125,10 @@ This way, we can solve the XOR problem with a linear classifier::
>>> y = X[:, 0] ^ X[:, 1]
>>> X = PolynomialFeatures(interaction_only=True).fit_transform(X)
>>> X
array([[1, 0, 0, 0],
[1, 0, 1, 0],
[1, 1, 0, 0],
[1, 1, 1, 1]])
array([[ 1., 0., 0., 0.],
[ 1., 0., 1., 0.],
[ 1., 1., 0., 0.],
[ 1., 1., 1., 1.]])
>>> clf = Perceptron(fit_intercept=False, n_iter=10, shuffle=False).fit(X, y)
>>> clf.score(X, y)
1.0
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12 changes: 6 additions & 6 deletions doc/modules/preprocessing.rst
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Expand Up @@ -484,9 +484,9 @@ Often it's useful to add complexity to the model by considering nonlinear featur
[4, 5]])
>>> poly = PolynomialFeatures(2)
>>> poly.fit_transform(X) # doctest: +ELLIPSIS
array([[ 1, 0, 1, 0, 0, 1],
[ 1, 2, 3, 4, 6, 9],
[ 1, 4, 5, 16, 20, 25]])
array([[ 1., 0., 1., 0., 0., 1.],
[ 1., 2., 3., 4., 6., 9.],
[ 1., 4., 5., 16., 20., 25.]])

The features of X have been transformed from :math:`(X_1, X_2)` to :math:`(1, X_1, X_2, X_1^2, X_1X_2, X_2^2)`.

Expand All @@ -499,9 +499,9 @@ In some cases, only interaction terms among features are required, and it can be
[6, 7, 8]])
>>> poly = PolynomialFeatures(degree=3, interaction_only=True)
>>> poly.fit_transform(X) # doctest: +ELLIPSIS
array([[ 1, 0, 1, 2, 0, 0, 2, 0],
[ 1, 3, 4, 5, 12, 15, 20, 60],
[ 1, 6, 7, 8, 42, 48, 56, 336]])
array([[ 1., 0., 1., 2., 0., 0., 2., 0.],
[ 1., 3., 4., 5., 12., 15., 20., 60.],
[ 1., 6., 7., 8., 42., 48., 56., 336.]])

The features of X have been transformed from :math:`(X_1, X_2, X_3)` to :math:`(1, X_1, X_2, X_3, X_1X_2, X_1X_3, X_2X_3, X_1X_2X_3)`.

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8 changes: 4 additions & 4 deletions sklearn/cross_decomposition/pls_.py
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Expand Up @@ -14,7 +14,7 @@
import numpy as np
from scipy import linalg
from ..utils import arpack
from ..utils.validation import check_is_fitted
from ..utils.validation import check_is_fitted, FLOAT_DTYPES

__all__ = ['PLSCanonical', 'PLSRegression', 'PLSSVD']

Expand Down Expand Up @@ -375,14 +375,14 @@ def transform(self, X, Y=None, copy=True):
x_scores if Y is not given, (x_scores, y_scores) otherwise.
"""
check_is_fitted(self, 'x_mean_')
X = check_array(X, copy=copy)
X = check_array(X, copy=copy, dtype=FLOAT_DTYPES)
# Normalize
X -= self.x_mean_
X /= self.x_std_
# Apply rotation
x_scores = np.dot(X, self.x_rotations_)
if Y is not None:
Y = check_array(Y, ensure_2d=False, copy=copy)
Y = check_array(Y, ensure_2d=False, copy=copy, dtype=FLOAT_DTYPES)
if Y.ndim == 1:
Y = Y.reshape(-1, 1)
Y -= self.y_mean_
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@GaelVaroquaux @ogrisel @lesteve I am also wondering if instead of explicitly typecasting Y to be float (assuming y_mean_ is float) should we -

  1. typecast y_mean_ to Y.dtype or
  2. typecast Y to y_mean_.dtype?

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This would (?) preserve int as it is without typecasting it when y_mean_ is also int!

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Okay!!

Expand Down Expand Up @@ -410,7 +410,7 @@ def predict(self, X, copy=True):
be an issue in high dimensional space.
"""
check_is_fitted(self, 'x_mean_')
X = check_array(X, copy=copy)
X = check_array(X, copy=copy, dtype=FLOAT_DTYPES)
# Normalize
X -= self.x_mean_
X /= self.x_std_
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Same question here w.r.t x_mean_ and x_std_ ?

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8 changes: 4 additions & 4 deletions sklearn/decomposition/fastica_.py
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Expand Up @@ -18,6 +18,7 @@
from ..utils import check_array, as_float_array, check_random_state
from ..utils.extmath import fast_dot
from ..utils.validation import check_is_fitted
from ..utils.validation import FLOAT_DTYPES

__all__ = ['fastica', 'FastICA']

Expand Down Expand Up @@ -261,7 +262,7 @@ def my_g(x):
fun_args = {} if fun_args is None else fun_args
# make interface compatible with other decompositions
# a copy is required only for non whitened data
X = check_array(X, copy=whiten).T
X = check_array(X, copy=whiten, dtype=FLOAT_DTYPES).T

alpha = fun_args.get('alpha', 1.0)
if not 1 <= alpha <= 2:
Expand Down Expand Up @@ -540,7 +541,7 @@ def transform(self, X, y=None, copy=True):
"""
check_is_fitted(self, 'mixing_')

X = check_array(X, copy=copy)
X = check_array(X, copy=copy, dtype=FLOAT_DTYPES)
if self.whiten:
X -= self.mean_

Expand All @@ -563,8 +564,7 @@ def inverse_transform(self, X, copy=True):
"""
check_is_fitted(self, 'mixing_')

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Why is that not check_array here?

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and why would we want to copy? fast_dot doesn't change inplace, right? That wouldn't make any sense.

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Was there an error here?

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Yes X += self.mean_ made numpy 0.10 raise an error since X is not explicitly float but mean_ was... (is this fix okay or should I rather do check_array(X, dtype=FLOAT_DTYPES)?)

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so even if self.mixing_ is float, fast_dot(X, self.mixing_.T) is not? That is somewhat surprising to me.

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fast_dot does casting according to my quick check on numpy 1.10

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Oh! So I must've done this pre-emptively I think... I'll check against master once to confirm!

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Have replaced with check_array...! And yes I did this pre-emptively since there was a failure w.r.t to fast ica here -

======================================================================
ERROR: sklearn.tests.test_common.test_non_meta_estimators('FastICA', <class 'sklearn.decomposition.fastica_.FastICA'>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest
    self.test(*self.arg)
  File "/scikit-learn/sklearn/utils/testing.py", line 317, in wrapper
    return fn(*args, **kwargs)
  File "/scikit-learn/sklearn/utils/estimator_checks.py", line 669, in check_estimators_dtypes
    estimator.fit(X_train, y)
  File "/scikit-learn/sklearn/decomposition/fastica_.py", line 522, in fit
    self._fit(X, compute_sources=False)
  File "/scikit-learn/sklearn/decomposition/fastica_.py", line 478, in _fit
    compute_sources=compute_sources, return_n_iter=True)
  File "/scikit-learn/sklearn/decomposition/fastica_.py", line 300, in fastica
    X -= X_mean[:, np.newaxis]
TypeError: Cannot cast ufunc subtract output from dtype('float64') to dtype('int64') with casting rule 'same_kind'

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ok

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Should we set copy=copy and self.whiten? There is no need to trigger a copy when self.whiten is False.

if copy:
X = X.copy()
X = check_array(X, copy=(copy and self.whiten), dtype=FLOAT_DTYPES)
X = fast_dot(X, self.mixing_.T)
if self.whiten:
X += self.mean_
Expand Down
5 changes: 3 additions & 2 deletions sklearn/ensemble/weight_boosting.py
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Expand Up @@ -29,7 +29,7 @@
from numpy.core.umath_tests import inner1d

from .base import BaseEnsemble
from ..base import ClassifierMixin, RegressorMixin
from ..base import ClassifierMixin, RegressorMixin, is_regressor
from ..externals import six
from ..externals.six.moves import zip
from ..externals.six.moves import xrange as range
Expand Down Expand Up @@ -107,7 +107,8 @@ def fit(self, X, y, sample_weight=None):
dtype = None
accept_sparse = ['csr', 'csc']

X, y = check_X_y(X, y, accept_sparse=accept_sparse, dtype=dtype)
X, y = check_X_y(X, y, accept_sparse=accept_sparse, dtype=dtype,
y_numeric=is_regressor(self))

if sample_weight is None:
# Initialize weights to 1 / n_samples
Expand Down
2 changes: 2 additions & 0 deletions sklearn/linear_model/least_angle.py
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Expand Up @@ -1074,6 +1074,8 @@ def fit(self, X, y):
"""
self.fit_path = True
X, y = check_X_y(X, y, y_numeric=True)
X = as_float_array(X, copy=self.copy_X)
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why? why not dtypes?

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is that for memory efficiency? Then at least y was already converted, right?

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You mean dtype=FLOAT_DTYPES in check_X_y correct?

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Ah, wait, I thought y_numeric made y floating, but that is only if it has dtype object. Never mind, sorry for the noise. I didn't have coffee yet.

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Actually we could squash the three lines into

X, y = check_X_y(X, y, y_numeric=True, dtype=FLOAT_DTYPES, copy=self.copy_X)

Can I do that without any side effect?

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well it wouldn't convert y and it would upcast int32 to float64

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Ah okay!!

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Well, if you think upcasting int32 to float64 is an issue, we should get it fixed everywhere.

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check_X_y never upcasts integers:

>>> check_X_y([[1], [2]], np.array([1, 2], dtype=np.int32), y_numeric=True, dtype=[np.float64, np.float32])[1].dtype
type('int32')

y_numeric=True just checks for non-object dtypes in y. We could benefit from a y_dtype=[np.int64, np.int32] option to replace y_numeric at some point but this is outside of the scope of this PR.

y = as_float_array(y, copy=self.copy_X)

# init cross-validation generator
cv = check_cv(self.cv, X, y, classifier=False)
Expand Down
30 changes: 14 additions & 16 deletions sklearn/preprocessing/data.py
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< 10000 td class="blob-code blob-code-context js-file-line"> self.K_fit_rows_.shape[0])[:, np.newaxis]
Original file line number Diff line number Diff line change
Expand Up @@ -323,7 +323,7 @@ def transform(self, X):
"""
check_is_fitted(self, 'scale_')

X = check_array(X, copy=self.copy, ensure_2d=False)
X = check_array(X, copy=self.copy, ensure_2d=False, dtype=FLOAT_DTYPES)
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The reason will be displayed to describe this comment to others. Learn m D96B ore.

That's good!

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😕 is that not going to cast a int32 dtype into a float64:

import numpy as np

from sklearn.utils.validation import check_array, FLOAT_DTYPES
check_array(np.arange(10, dtype='i4'), dtype=FLOAT_DTYPES).dtype

returns dtype('float64').

On the other hand as_float_array seems to do the right thing.

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Thanks loic! will fix it!!

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You get an array of twice the memory in an 'int32' cast to 'float64' case but this may not happen that much in practice I guess.

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I'm not sure that would be too difficult to do. But I also don't know if its high up in the priority list. We could fix that up if someone files an issue saying that their memory crashes after feeding in integer data to some model.

if X.ndim == 1:
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)

Expand All @@ -341,7 +341,7 @@ def inverse_transform(self, X):
"""
check_is_fitted(self, 'scale_')

X = check_array(X, copy=self.copy, ensure_2d=False)
X = check_array(X, copy=self.copy, ensure_2d=False, dtype=FLOAT_DTYPES)
if X.ndim == 1:
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)

Expand Down Expand Up @@ -1068,14 +1068,14 @@ class PolynomialFeatures(BaseEstimator, TransformerMixin):
[4, 5]])
>>> poly = PolynomialFeatures(2)
>>> poly.fit_transform(X)
array([[ 1, 0, 1, 0, 0, 1],
[ 1, 2, 3, 4, 6, 9],
[ 1, 4, 5, 16, 20, 25]])
array([[ 1., 0., 1., 0., 0., 1.],
[ 1., 2., 3., 4., 6., 9.],
[ 1., 4., 5., 16., 20., 25.]])
>>> poly = PolynomialFeatures(interaction_only=True)
>>> poly.fit_transform(X)
array([[ 1, 0, 1, 0],
[ 1, 2, 3, 6],
[ 1, 4, 5, 20]])
array([[ 1., 0., 1., 0.],
[ 1., 2., 3., 6.],
[ 1., 4., 5., 20.]])

Attributes
----------
Expand Down Expand Up @@ -1149,7 +1149,7 @@ def transform(self, X, y=None):
"""
check_is_fitted(self, ['n_input_features_', 'n_output_features_'])

X = check_array(X)
X = check_array(X, dtype=FLOAT_DTYPES)
n_samples, n_features = X.shape

if n_features != self.n_input_features_:
Expand Down Expand Up @@ -1313,7 +1313,7 @@ def binarize(X, threshold=0.0, copy=True):

Parameters
----------
XX : {array-like, sparse matrix}, shape [n_samples, n_features]
X : {array-like, sparse matrix}, shape [n_samples, n_features]
The data to binarize, element by element.
scipy.sparse matrices should be in CSR or CSC format to avoid an
un-necessary copy.
Expand Down Expand Up @@ -1438,7 +1438,7 @@ def fit(self, K, y=None):
-------
self : returns an instance of self.
"""
K = check_array(K)
K = check_array(K, dtype=FLOAT_DTYPES)
n_samples = K.shape[0]
self.K_fit_rows_ = np.sum(K, axis=0) / n_samples
self.K_fit_all_ = self.K_fit_rows_.sum() / n_samples
Expand All @@ -1461,9 +1461,7 @@ def transform(self, K, y=None, copy=True):
"""
check_is_fitted(self, 'K_fit_all_')

K = check_array(K)
if copy:
K = K.copy()
K = check_array(K, copy=copy, dtype=FLOAT_DTYPES)

K_pred_cols = (np.sum(K, axis=1) /
Expand Down Expand Up @@ -1503,7 +1501,7 @@ def add_dummy_feature(X, value=1.0):
array([[ 1., 0., 1.],
[ 1., 1., 0.]])
"""
X = check_array(X, accept_sparse=['csc', 'csr', 'coo'])
X = check_array(X, accept_sparse=['csc', 'csr', 'coo'], dtype=FLOAT_DTYPES)
n_samples, n_features = X.shape
shape = (n_samples, n_features + 1)
if sparse.issparse(X):
Expand Down Expand Up @@ -1558,7 +1556,7 @@ def _transform_selected(X, transform, selected="all", copy=True):
if selected == "all":
return transform(X)

X = check_array(X, accept_sparse='csc', copy=copy)
X = check_array(X, accept_sparse='csc', copy=copy, dtype=FLOAT_DTYPES)

if len(selected) == 0:
return X
Expand Down
8 changes: 8 additions & 0 deletions sklearn/preprocessing/tests/test_data.py
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@@ -1,3 +1,4 @@

# Authors:
#
# Giorgio Patrini
Expand Down Expand Up @@ -1247,6 +1248,13 @@ def test_binarizer():
X_bin = binarizer.transform(X)
if init is not list:
assert_true(X_bin is X)

binarizer = Binarizer(copy=False)
X_float = np.array([[1, 0, 5], [2, 3, -1]], dtype=np.float64)
X_bin = binarizer.transform(X_float)
if init is not list:
assert_true(X_bin is X_float)

X_bin = toarray(X_bin)
assert_equal(np.sum(X_bin == 0), 2)
assert_equal(np.sum(X_bin == 1), 4)
Expand Down
19 changes: 16 additions & 3 deletions sklearn/preprocessing/tests/test_imputation.py
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@@ -1,3 +1,4 @@

import numpy as np
from scipy import sparse

Expand All @@ -12,7 +13,7 @@
from sklearn import grid_search
from sklearn import tree
from sklearn.random_projection import sparse_random_matrix


def _check_statistics(X, X_true,
strategy, statistics, missing_values):
Expand Down Expand Up @@ -121,6 +122,18 @@ def test_imputation_mean_median_only_zero():
statistics_median, 0)


def safe_median(arr, *args, **kwargs):
# np.median([]) raises a TypeError for numpy >= 1.10.1
length = arr.size if hasattr(arr, 'size') else len(arr)
return np.nan if length == 0 else np.median(arr, *args, **kwargs)


def safe_mean(arr, *args, **kwargs):
# np.mean([]) raises a RuntimeWarning for numpy >= 1.10.1
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and before, I think. But the statement is not wrong, I guess ^^

length = arr.size if hasattr(arr, 'size') else len(arr)
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the line is missing here now.

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lol sorry :P done :)

return np.nan if length == 0 else np.mean(arr, *args, **kwargs)


def test_imputation_mean_median():
# Test imputation using the mean and median strategies, when
# missing_values != 0.
Expand All @@ -134,9 +147,9 @@ def test_imputation_mean_median():
values = np.arange(1, shape[0]+1)
values[4::2] = - values[4::2]

tests = [("mean", "NaN", lambda z, v, p: np.mean(np.hstack((z, v)))),
tests = [("mean", "NaN", lambda z, v, p: safe_mean(np.hstack((z, v)))),
("mean", 0, lambda z, v, p: np.mean(v)),
("median", "NaN", lambda z, v, p: np.median(np.hstack((z, v)))),
("median", "NaN", lambda z, v, p: safe_median(np.hstack((z, v)))),
("median", 0, lambda z, v, p: np.median(v))]

for strategy, test_missing_values, true_value_fun in tests:
Expand Down
0