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[MRG+2] Add float32 support for Linear Discriminant Analysis #13273

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Feb 27, 2019
Merged

[MRG+2] Add float32 support for Linear Discriminant Analysis #13273

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da6518a
[skip ci] Empty commit to trigger PR
thibsej Feb 26, 2019
a748478
Add dtype testing
thibsej Feb 26, 2019
4586496
Fix: dtype testing
thibsej Feb 26, 2019
f6c6bcc
Fix test_estimators[OneVsRestClassifier-check_estimators_dtypes]
thibsej Feb 26, 2019
3223d9c
TST refactor using parametrize + Add failing test for int32
thibsej Feb 26, 2019
49c7c38
Fix for int32
thibsej Feb 26, 2019
d8cd7cb
Fix code according to review + Fix PEP8 violation
thibsej Feb 26, 2019
36248e8
Fix dtype for int32 and complex
thibsej Feb 26, 2019
d657605
Fix pep8 violation
thibsej Feb 26, 2019
cf02ebb
Update whatsnew + test COSMIT
thibsej Feb 26, 2019
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10 changes: 6 additions & 4 deletions sklearn/discriminant_analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@
from .linear_model.base import LinearClassifierMixin
from .covariance import ledoit_wolf, empirical_covariance, shrunk_covariance
from .utils.multiclass import unique_labels
from .utils import check_array, check_X_y
from .utils import check_array, check_X_y, as_float_array
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Suggested change
from .utils import check_array, check_X_y, as_float_array
from .utils import check_array, check_X_y
from .utils import as_float_array

from .utils.validation import check_is_fitted
from .utils.multiclass import check_classification_targets
from .preprocessing import StandardScaler
Expand Down Expand Up @@ -427,7 +427,8 @@ def fit(self, X, y):
Target values.
"""
# FIXME: Future warning to be removed in 0.23
X, y = check_X_y(X, y, ensure_min_samples=2, estimator=self)
X = as_float_array(X)
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This line is necessary to get coefficients of type float32 output when X is of type int32. Removing this line gives coefficients of type float64.

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This is strange becasue the next check_X_y will convert to float anyway. It seems unecessary, isn't it?

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Oh I see why. So X will be converted to float64 and you expect it to be converted to float 32 because X is of type int32. I would say that there is no real reason for that. int32 can default to float64. I am fine with that.

@massich what is the mechanism in the other estimators that you modified?

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There was no mechanism set. I've the vague idea that we talked about it long ago, but I can't recall that we agreed on anything. To me it makes sense that if someone willing creates the data in 32, wants to keep it in 32 bits. But if such individual is there she/he will scream at us, or would had done it already. So I've no objection on defaulting to float64.

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An int32 is a "very big" int. Hence, it make sense to convert it to a float64. People who want to save memory with ints use int16. Indeed, an int has a larger span than a float for the same memory cost.

X, y = check_X_y(X, y, ensure_min_samples=2, estimator=self, dtype=[np.float64, np.float32])
self.classes_ = unique_labels(y)
n_samples, _ = X.shape
n_classes = len(self.classes_)
Expand Down Expand Up @@ -485,9 +486,10 @@ def fit(self, X, y):
raise ValueError("unknown solver {} (valid solvers are 'svd', "
"'lsqr', and 'eigen').".format(self.solver))
if self.classes_.size == 2: # treat binary case as a special case
self.coef_ = np.array(self.coef_[1, :] - self.coef_[0, :], ndmin=2)
self.coef_ = np.array(self.coef_[1, :] - self.coef_[0, :], ndmin=2,
dtype=X.dtype)
self.intercept_ = np.array(self.intercept_[1] - self.intercept_[0],
ndmin=1)
ndmin=1, dtype=X.dtype)
return self

def transform(self, X):
Expand Down
24 changes: 24 additions & 0 deletions sklearn/tests/test_discriminant_analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@
from sklearn.utils.testing import (assert_array_equal, assert_no_warnings,
assert_warns_message)
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_allclose
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises
Expand Down Expand Up @@ -296,6 +297,29 @@ def test_lda_dimension_warning(n_classes, n_features):
assert_warns_message(FutureWarning, future_msg, lda.fit, X, y)


@pytest.mark.parametrize("data_type, expected_type", [
(np.float32, np.float32), (np.float64, np.float64), (np.int32, np.float32),
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Convention for int32 is casting to float64.

(np.int64, np.float64)])
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I find this one more readable:

@pytest.mark.parametrize("data_type, expected_type", [
  (np.float32, np.float32),
  (np.float64, np.float64),
  (np.int32, np.float64),
  (np.int64, np.float64)
])

def test_lda_dtype_match(data_type, expected_type):
for (solver, shrinkage) in solver_shrinkage:
clf = LinearDiscriminantAnalysis(solver=solver, shrinkage=shrinkage)
clf.fit(X.astype(data_type), y.astype(data_type))
assert clf.coef_.dtype == expected_type


def test_lda_numeric_consistency_float32_float64():
for (solver, shrinkage) in solver_shrinkage:
clf_32 = LinearDiscriminantAnalysis(solver=solver, shrinkage=shrinkage)
clf_32.fit(X.astype(np.float32), y.astype(np.float32))
clf_64 = LinearDiscriminantAnalysis(solver=solver, shrinkage=shrinkage)
clf_64.fit(X.astype(np.float64), y.astype(np.float64))

# Check value consistency between types
rtol = 1e-6
assert_allclose(clf_32.coef_, clf_64.coef_.astype(np.float32),
rtol=rtol)
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do not use astype assert_allclose should be able to handle it.

        assert_allclose(clf_32.coef_, clf_64.coef_, rtol=rtol)



def test_qda():
# QDA classification.
# This checks that QDA implements fit and predict and returns
Expand Down