File remove-nose.patch of Package python-apricot-select
Index: apricot-0.6.1/setup.py
===================================================================
--- apricot-0.6.1.orig/setup.py
+++ apricot-0.6.1/setup.py
@@ -14,6 +14,5 @@ setup(
"scipy >= 1.0.0",
"numba >= 0.43.0",
"tqdm >= 4.24.0",
- "nose"
],
)
Index: apricot-0.6.1/tests/test_optimizers/test_knapsack_facility_location.py
===================================================================
--- apricot-0.6.1.orig/tests/test_optimizers/test_knapsack_facility_location.py
+++ apricot-0.6.1/tests/test_optimizers/test_knapsack_facility_location.py
@@ -13,7 +13,6 @@ from sklearn.datasets import load_digits
from sklearn.metrics import pairwise_distances
-from nose.tools import assert_less_equal
from numpy.testing import assert_almost_equal
from numpy.testing import assert_array_equal
from numpy.testing import assert_array_almost_equal
@@ -141,21 +140,21 @@ def test_digits_naive():
model.fit(X_digits_cupy, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_ranking)
assert_array_almost_equal(model.gains, digits_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 25)
+ assert sum(X_digits_costs[model.ranking]) <= 25
def test_digits_lazy():
model = FacilityLocationSelection(25, 'cosine', optimizer='lazy')
model.fit(X_digits_cupy, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_ranking)
assert_array_almost_equal(model.gains, digits_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 25)
+ assert sum(X_digits_costs[model.ranking]) <= 25
def test_digits_two_stage():
model = FacilityLocationSelection(25, 'cosine', optimizer='two-stage')
model.fit(X_digits_cupy, sample_cost=X_digits_costs)
assert_array_equal(model.ranking[:50], digits_ranking[:50])
assert_array_almost_equal(model.gains[:50], digits_gains[:50], 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 25)
+ assert sum(X_digits_costs[model.ranking]) <= 25
def test_digits_greedi_nn():
model = FacilityLocationSelection(25, 'cosine', optimizer='greedi',
@@ -164,7 +163,7 @@ def test_digits_greedi_nn():
model.fit(X_digits_cupy, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_greedi_ranking)
assert_array_almost_equal(model.gains, digits_greedi_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 25)
+ assert sum(X_digits_costs[model.ranking]) <= 25
def test_digits_greedi_ll():
model = FacilityLocationSelection(25, 'cosine', optimizer='greedi',
@@ -173,7 +172,7 @@ def test_digits_greedi_ll():
model.fit(X_digits_cupy, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_greedi_ranking)
assert_array_almost_equal(model.gains, digits_greedi_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 25)
+ assert sum(X_digits_costs[model.ranking]) <= 25
def test_digits_greedi_ln():
model = FacilityLocationSelection(25, 'cosine', optimizer='greedi',
@@ -182,7 +181,7 @@ def test_digits_greedi_ln():
model.fit(X_digits_cupy, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_greedi_ranking)
assert_array_almost_equal(model.gains, digits_greedi_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 25)
+ assert sum(X_digits_costs[model.ranking]) <= 25
def test_digits_greedi_nl():
model = FacilityLocationSelection(25, 'cosine', optimizer='greedi',
@@ -191,14 +190,14 @@ def test_digits_greedi_nl():
model.fit(X_digits_cupy, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_greedi_ranking)
assert_array_almost_equal(model.gains, digits_greedi_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 25)
+ assert sum(X_digits_costs[model.ranking]) <= 25
def test_digits_approximate():
model = FacilityLocationSelection(25, 'cosine', optimizer='approximate-lazy')
model.fit(X_digits_cupy, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_approx_ranking)
assert_array_almost_equal(model.gains, digits_approx_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 25)
+ assert sum(X_digits_costs[model.ranking]) <= 25
def test_digits_stochastic():
model = FacilityLocationSelection(25, 'cosine', optimizer='stochastic',
@@ -206,7 +205,7 @@ def test_digits_stochastic():
model.fit(X_digits_cupy, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_stochastic_ranking)
assert_array_almost_equal(model.gains, digits_stochastic_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 25)
+ assert sum(X_digits_costs[model.ranking]) <= 25
def test_digits_sample():
model = FacilityLocationSelection(25, 'cosine', optimizer='sample',
@@ -214,7 +213,7 @@ def test_digits_sample():
model.fit(X_digits_cupy, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_sample_ranking)
assert_array_almost_equal(model.gains, digits_sample_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 25)
+ assert sum(X_digits_costs[model.ranking]) <= 25
def test_digits_modular():
model = FacilityLocationSelection(25, 'cosine', optimizer='modular',
@@ -222,4 +221,4 @@ def test_digits_modular():
model.fit(X_digits_cupy, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_modular_ranking)
assert_array_almost_equal(model.gains, digits_modular_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 25)
+ assert sum(X_digits_costs[model.ranking]) <= 25
Index: apricot-0.6.1/tests/test_optimizers/test_knapsack_feature_based.py
===================================================================
--- apricot-0.6.1.orig/tests/test_optimizers/test_knapsack_feature_based.py
+++ apricot-0.6.1/tests/test_optimizers/test_knapsack_feature_based.py
@@ -13,7 +13,6 @@ from sklearn.datasets import load_digits
from sklearn.metrics import pairwise_distances
-from nose.tools import assert_less_equal
from numpy.testing import assert_almost_equal
from numpy.testing import assert_array_equal
from numpy.testing import assert_array_almost_equal
@@ -317,21 +316,21 @@ def test_digits_naive():
model.fit(X_digits, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_ranking)
assert_array_almost_equal(model.gains, digits_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 100)
+ assert sum(X_digits_costs[model.ranking]) <= 100
def test_digits_lazy():
model = FeatureBasedSelection(100, 'sqrt', optimizer='lazy')
model.fit(X_digits, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_ranking)
assert_array_almost_equal(model.gains, digits_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 100)
+ assert sum(X_digits_costs[model.ranking]) <= 100
def test_digits_two_stage():
model = FeatureBasedSelection(100, 'sqrt', optimizer='two-stage')
model.fit(X_digits, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_ranking)
assert_array_almost_equal(model.gains, digits_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 100)
+ assert sum(X_digits_costs[model.ranking]) <= 100
def test_digits_greedi_nn():
model = FeatureBasedSelection(100, 'sqrt', optimizer='greedi',
@@ -340,7 +339,7 @@ def test_digits_greedi_nn():
model.fit(X_digits, sample_cost=X_digits_costs)
assert_array_equal(model.ranking[:50], digits_greedi_ranking[:50])
assert_array_almost_equal(model.gains[:50], digits_greedi_gains[:50], 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 100)
+ assert sum(X_digits_costs[model.ranking]) <= 100
def test_digits_greedi_ll():
model = FeatureBasedSelection(100, 'sqrt', optimizer='greedi',
@@ -349,7 +348,7 @@ def test_digits_greedi_ll():
model.fit(X_digits, sample_cost=X_digits_costs)
assert_array_equal(model.ranking[:30], digits_greedi_ranking[:30])
assert_array_almost_equal(model.gains[:30], digits_greedi_gains[:30], 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 100)
+ assert sum(X_digits_costs[model.ranking]) <= 100
def test_digits_greedi_ln():
model = FeatureBasedSelection(100, 'sqrt', optimizer='greedi',
@@ -358,7 +357,7 @@ def test_digits_greedi_ln():
model.fit(X_digits, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_greedi_ranking)
assert_array_almost_equal(model.gains, digits_greedi_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 100)
+ assert sum(X_digits_costs[model.ranking]) <= 100
def test_digits_greedi_nl():
model = FeatureBasedSelection(100, 'sqrt', optimizer='greedi',
@@ -367,14 +366,14 @@ def test_digits_greedi_nl():
model.fit(X_digits, sample_cost=X_digits_costs)
assert_array_equal(model.ranking[:30], digits_greedi_ranking[:30])
assert_array_almost_equal(model.gains[:30], digits_greedi_gains[:30], 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 100)
+ assert sum(X_digits_costs[model.ranking]) <= 100
def test_digits_approximate():
model = FeatureBasedSelection(100, 'sqrt', optimizer='approximate-lazy')
model.fit(X_digits, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_approx_ranking)
assert_array_almost_equal(model.gains, digits_approx_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 100)
+ assert sum(X_digits_costs[model.ranking]) <= 100
def test_digits_stochastic():
model = FeatureBasedSelection(100, 'sqrt', optimizer='stochastic',
@@ -382,7 +381,7 @@ def test_digits_stochastic():
model.fit(X_digits, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_stochastic_ranking)
assert_array_almost_equal(model.gains, digits_stochastic_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 100)
+ assert sum(X_digits_costs[model.ranking]) <= 100
def test_digits_sample():
model = FeatureBasedSelection(100, 'sqrt', optimizer='sample',
@@ -390,7 +389,7 @@ def test_digits_sample():
model.fit(X_digits, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_sample_ranking)
assert_array_almost_equal(model.gains, digits_sample_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 100)
+ assert sum(X_digits_costs[model.ranking]) <= 100
def test_digits_modular():
model = FeatureBasedSelection(100, 'sqrt', optimizer='modular',
@@ -398,4 +397,4 @@ def test_digits_modular():
model.fit(X_digits, sample_cost=X_digits_costs)
assert_array_equal(model.ranking, digits_modular_ranking)
assert_array_almost_equal(model.gains, digits_modular_gains, 4)
- assert_less_equal(sum(X_digits_costs[model.ranking]), 100)
+ assert sum(X_digits_costs[model.ranking]) <= 100
