convert floats to int correctly (#211)

convert floats to int correctly

.flake8

@@ -1,3 +1,3 @@
 [flake8]
 ignore = E401,W503,W504
-max-line-length = 80
+max-line-length = 100

docs/source/conf.py

@@ -89,7 +89,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = 'en'
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.

examples/plot_swimmer.py

@@ -73,7 +73,7 @@
 # ``min_pts`` should be half of the number of bootstraps.
 
 nboot = 20
-min_pts = nboot / 2
+min_pts = max(nboot // 2, 1)
 ranks = [16]
 
 shape = corrupted.shape

src/pyuoi/decomposition/NMF.py

@@ -119,7 +119,7 @@ def __initialize(self, **kwargs):
                 raise ValueError('dbscan must be an instance, not a class.')
             self.cluster = cluster
         else:
-            self.cluster = DBSCAN(min_samples=self.n_boots / 2)
+            self.cluster = DBSCAN(min_samples=max(self.n_boots // 2, 1))
 
         # initialize non-negative regression solver
         if nnreg is None:
@@ -423,7 +423,7 @@ def __init__(
 
         # create DBSCAN solver
         if db_min_samples is None:
-            db_min_samples = n_boots / 2
+            db_min_samples = max(n_boots // 2, 1)
         dbscan = DBSCAN(eps=db_eps,
                         min_samples=db_min_samples,
                         metric=db_metric,

tests/test_mpi/test_mpi_utils.py

@@ -128,4 +128,4 @@ def test_Gatherv_random_rows():
     data = Gatherv_rows(data, comm, root)
 
     if rank == root:
-        assert(data.shape[0] == np.sum(sizes))
+        assert data.shape[0] == np.sum(sizes)

tests/test_nmf.py

@@ -39,7 +39,7 @@ def test_UoI_NMF_Base_initialization():
     assert_array_equal(uoi.ranks, np.arange(2, ranks + 1))
     assert uoi.nmf.solver == 'mu'
     assert uoi.nmf.beta_loss == 'kullback-leibler'
-    assert uoi.cluster.min_samples == n_boots / 2
+    assert uoi.cluster.min_samples == max(n_boots // 2, 1)
 
 
 @pytest.mark.fast
@@ -51,7 +51,7 @@ def test_UoI_NMF_initialization():
     assert_array_equal(uoi.ranks, np.arange(2, ranks + 1))
     assert uoi.nmf.solver == 'mu'
     assert uoi.nmf.beta_loss == 'kullback-leibler'
-    assert uoi.cluster.min_samples == n_boots / 2
+    assert uoi.cluster.min_samples == max(n_boots // 2, 1)
     assert uoi.cons_meth == np.mean
 
 

tests/test_scores.py

@@ -101,7 +101,7 @@ def test_LinearRegressor_scoring_defaults():
     support = np.ones(X.shape[1]).astype(bool)
     # r2 - must use test data
     uoi = UoI_Lasso(estimation_score='r2')
-    assert(uoi._estimation_target == 1)
+    assert uoi._estimation_target == 1
 
     score = uoi._score_predictions('r2', fitter, X, y, support,
                                    (train_idxs, test_idxs))
@@ -111,22 +111,22 @@ def test_LinearRegressor_scoring_defaults():
                             fitter.predict(X_train[:, support]))
     # BIC - must use train data
     uoi = UoI_Lasso(estimation_score='BIC')
-    assert(uoi._estimation_target == 0)
+    assert uoi._estimation_target == 0
     score = -1 * uoi._score_predictions('BIC', fitter, X, y, support,
                                         (train_idxs, test_idxs))
     assert_equal(BIC(ll, *X_train.T.shape), score)
 
     # AIC - must use train data
     uoi = UoI_Lasso(estimation_score='AIC')
-    assert(uoi._estimation_target == 0)
+    assert uoi._estimation_target == 0
 
     score = -1 * uoi._score_predictions('AIC', fitter, X, y, support,
                                         (train_idxs, test_idxs))
     assert_equal(AIC(ll, X_train.shape[1]), score)
 
     # AICc - must use train data
     uoi = UoI_Lasso(estimation_score='AICc')
-    assert(uoi._estimation_target == 0)
+    assert uoi._estimation_target == 0
 
     score = -1 * uoi._score_predictions('AICc', fitter, X, y, support,
                                         (train_idxs, test_idxs))
@@ -158,15 +158,15 @@ def test_GeneralizedLinearRegressor_scoring_defaults():
 
     # acc - must use test data
     uoi = UoI_L1Logistic(estimation_score='acc')
-    assert(uoi._estimation_target == 1)
+    assert uoi._estimation_target == 1
     uoi.classes_ = np.unique(y)
     score = uoi._score_predictions('acc', fitter, X, y, support,
                                    (train_idxs, test_idxs))
     assert_equal(accuracy_score(y_test, fitter.predict(X_test)), score)
 
     # log - must use test data. Note the sign difference
     uoi = UoI_L1Logistic(estimation_score='log')
-    assert(uoi._estimation_target == 1)
+    assert uoi._estimation_target == 1
     uoi.classes_ = np.unique(y)
     score = uoi._score_predictions('log', fitter, X, y, support,
                                    (train_idxs, test_idxs))
@@ -180,23 +180,23 @@ def test_GeneralizedLinearRegressor_scoring_defaults():
     total_ll = ll * X_train.shape[0]
     # BIC - must use train data
     uoi = UoI_L1Logistic(estimation_score='BIC')
-    assert(uoi._estimation_target == 0)
+    assert uoi._estimation_target == 0
     uoi.classes_ = np.unique(y)
     score = -1 * uoi._score_predictions('BIC', fitter, X, y, support,
                                         (train_idxs, test_idxs))
     assert_equal(BIC(total_ll, *X_train.T.shape), score)
 
     # AIC
     uoi = UoI_L1Logistic(estimation_score='AIC')
-    assert(uoi._estimation_target == 0)
+    assert uoi._estimation_target == 0
     uoi.classes_ = np.unique(y)
     score = -1 * uoi._score_predictions('AIC', fitter, X, y, support,
                                         (train_idxs, test_idxs))
     assert_equal(AIC(total_ll, X_train.shape[1]), score)
 
     # AICc
     uoi = UoI_L1Logistic(estimation_score='AICc')
-    assert(uoi._estimation_target == 0)
+    assert uoi._estimation_target == 0
     uoi.classes_ = np.unique(y)
     score = -1 * uoi._score_predictions('AICc', fitter, X, y, support,
                                         (train_idxs, test_idxs))
@@ -210,7 +210,7 @@ def test_estimation_target():
     uoi = UoI_Lasso(estimation_score='r2', estimation_target='train')
 
     # train gets converted to the index 0
-    assert(uoi._estimation_target == 0)
+    assert uoi._estimation_target == 0
 
     # Assess BIC on test data
     uoi = UoI_Lasso(estimation_score='BIC', estimation_target='test')
@@ -219,25 +219,25 @@ def test_estimation_target():
     uoi = UoI_ElasticNet(estimation_score='r2', estimation_target='train')
 
     # train gets converted to the index 0
-    assert(uoi._estimation_target == 0)
+    assert uoi._estimation_target == 0
 
     # Assess BIC on test data
     uoi = UoI_ElasticNet(estimation_score='BIC', estimation_target='test')
 
-    assert(uoi._estimation_target == 1)
+    assert uoi._estimation_target == 1
 
     uoi = UoI_L1Logistic(estimation_score='acc', estimation_target='train')
 
-    assert(uoi._estimation_target == 0)
+    assert uoi._estimation_target == 0
 
     uoi = UoI_L1Logistic(estimation_score='BIC', estimation_target='test')
 
-    assert(uoi._estimation_target == 1)
+    assert uoi._estimation_target == 1
 
     uoi = UoI_Poisson(estimation_score='acc', estimation_target='train')
 
-    assert(uoi._estimation_target == 0)
+    assert uoi._estimation_target == 0
 
     uoi = UoI_Poisson(estimation_score='BIC', estimation_target='test')
 
-    assert(uoi._estimation_target == 1)
+    assert uoi._estimation_target == 1

tests/test_uoi_lasso.py

@@ -262,10 +262,10 @@ def test_choice_of_solver():
     '''Tests whether one can correctly switch between solvers in UoI Lasso'''
 
     uoi1 = UoI_Lasso(solver='cd')
-    assert(isinstance(uoi1._selection_lm, Lasso))
+    assert isinstance(uoi1._selection_lm, Lasso)
 
     uoi2 = UoI_Lasso(solver='pyc')
-    assert(isinstance(uoi2._selection_lm, PycLasso))
+    assert isinstance(uoi2._selection_lm, PycLasso)
 
 
 @pytest.mark.skipif(pycasso is not None, reason='pycasso is installed')
@@ -275,7 +275,7 @@ def test_pycasso_error():
 
     with pytest.raises(ImportError):
         uoi2 = UoI_Lasso(solver='pyc')
-        assert(isinstance(uoi2._selection_lm, PycLasso))
+        assert isinstance(uoi2._selection_lm, PycLasso)
 
 
 @pytest.mark.skipif(pycasso is None, reason='pycasso not installed')
@@ -285,11 +285,11 @@ def test_pyclasso():
     pyclasso = PycLasso()
     # Test that we can set params correctly
     pyclasso.set_params(fit_intercept=True)
-    assert(pyclasso.fit_intercept)
+    assert pyclasso.fit_intercept
     pyclasso.set_params(max_iter=500)
-    assert(pyclasso.max_iter == 500)
+    assert pyclasso.max_iter == 500
     pyclasso.set_params(alphas=np.arange(100))
-    assert(np.array_equal(pyclasso.alphas, np.arange(100)))
+    assert np.array_equal(pyclasso.alphas, np.arange(100))
 
     # Test that spurious parameters are rejected
     try:
@@ -313,10 +313,10 @@ def test_pyclasso():
     alphas = _alpha_grid(X, y)
     pyclasso.set_params(alphas=alphas)
     pyclasso.fit(X, y)
-    assert(np.array_equal(pyclasso.coef_.shape, (100, 3)))
+    assert np.array_equal(pyclasso.coef_.shape, (100, 3))
     y_pred = pyclasso.predict(X)
     scores = np.array([r2_score(y, y_pred[:, j]) for j in range(100)])
-    assert(np.allclose(1, max(scores)))
+    assert np.allclose(1, max(scores))
 
 
 @pytest.mark.skipif(pycasso is None, reason='pycasso not installed')