Changed formatting of files for black 23.1.0

Author: victorprincipe

skcosmo/_selection.py

@@ -99,7 +99,6 @@ def __init__(
         full=False,
         random_state=0,
     ):
-
         self.selection_type = selection_type
         self.n_to_select = n_to_select
         self.score_threshold = score_threshold
@@ -209,7 +208,6 @@ def fit(self, X, y=None, warm_start=False):
         n_iterations -= self.n_selected_
 
         for n in self.report_progress(range(n_iterations)):
-
             new_idx = self._get_best_new_selection(self.score, X, y)
             if new_idx is not None:
                 self._update_post_selection(X, y, new_idx)
@@ -479,7 +477,6 @@ def __init__(
         full=False,
         random_state=0,
     ):
-
         self.k = k
         self.tolerance = tolerance
         self.recompute_every = recompute_every
@@ -537,7 +534,6 @@ def _continue_greedy_search(self, X, y, n_to_select):
         """
 
         for c in self.selected_idx_:
-
             if self.recompute_every != 0 and (
                 np.linalg.norm(np.take(self.X_current_, [c], axis=self._axis))
                 > self.tolerance
@@ -612,7 +608,6 @@ def _update_post_selection(self, X, y, last_selected):
         self.pi_[last_selected] = 0.0
 
     def _orthogonalize(self, last_selected):
-
         if self._axis == 1:
             self.X_current_ = X_orthogonalizer(
                 x1=self.X_current_, c=last_selected, tol=self.tolerance
@@ -739,7 +734,6 @@ def _continue_greedy_search(self, X, y, n_to_select):
         """
 
         for c in self.selected_idx_:
-
             if self.recompute_every != 0 and (
                 np.linalg.norm(np.take(self.X_current_, [c], axis=self._axis))
                 > self.tolerance
@@ -834,7 +828,6 @@ def _compute_pi(self, X, y=None):
         return pi
 
     def _orthogonalize(self, last_selected):
-
         if self._axis == 1:
             self.X_current_ = X_orthogonalizer(
                 x1=self.X_current_, c=last_selected, tol=self.tolerance
@@ -992,7 +985,6 @@ def _init_greedy_search(self, X, y, n_to_select):
             raise ValueError("Invalid value of the initialize parameter")
 
     def _update_haussdorf(self, X, y, last_selected):
-
         self.haussdorf_at_select_[last_selected] = self.haussdorf_[last_selected]
 
         # distances of all points to the new point
@@ -1147,7 +1139,6 @@ def _init_greedy_search(self, X, y, n_to_select):
         self._update_post_selection(X, y, self.selected_idx_[0])
 
     def _update_haussdorf(self, X, y, last_selected):
-
         self.haussdorf_at_select_[last_selected] = self.haussdorf_[last_selected]
 
         # distances of all points to the new point

skcosmo/decomposition/_kernel_pcovr.py

@@ -211,7 +211,6 @@ def __init__(
         iterated_power="auto",
         random_state=None,
     ):
-
         self.mixing = mixing
         self.n_components = n_components
 
@@ -542,7 +541,6 @@ def score(self, X, Y):
         return -sum([Lkpca, Lkrr])
 
     def _decompose_truncated(self, mat):
-
         if not 1 <= self.n_components <= self.n_samples_:
             raise ValueError(
                 "n_components=%r must be between 1 and "
@@ -601,7 +599,6 @@ def _decompose_truncated(self, mat):
         return U, S, Vt
 
     def _decompose_full(self, mat):
-
         if self.n_components != "mle":
             if not (0 <= self.n_components <= self.n_samples_):
                 raise ValueError(

skcosmo/decomposition/_pcovr.py

@@ -216,7 +216,6 @@ def __init__(
         iterated_power="auto",
         random_state=None,
     ):
-
         self.mixing = mixing
         self.n_components = n_components
         self.space = space
@@ -481,7 +480,6 @@ def _fit_sample_space(self, X, Y, Yhat, W):
         self.ptx_ = T.T @ X
 
     def _decompose_truncated(self, mat):
-
         if not 1 <= self.n_components <= min(self.n_samples_, self.n_features_):
             raise ValueError(
                 "n_components=%r must be between 1 and "

skcosmo/preprocessing/_data.py

@@ -344,7 +344,6 @@ def fit(self, K=None, y=None, sample_weight=None):
             K_pred_cols = np.zeros((Kc.shape[0], 1))
 
         if self.with_trace:
-
             Kc -= self.K_fit_rows_
             Kc -= K_pred_cols
             Kc += self.K_fit_all_

skcosmo/sample_selection/_voronoi_fps.py

@@ -59,7 +59,6 @@ class VoronoiFPS(GreedySelector):
     def __init__(
         self, n_trial_calculation=4, full_fraction=None, initialize=0, **kwargs
     ):
-
         self.n_trial_calculation = n_trial_calculation
         self.full_fraction = full_fraction
         self.initialize = initialize

skcosmo/utils/_orthogonalizers.py

@@ -50,7 +50,6 @@ def X_orthogonalizer(x1, c=None, x2=None, tol=1e-12, copy=False):
         xnew = x1
 
     for i in range(cols.shape[-1]):
-
         col = cols[:, [i]]
 
         if np.linalg.norm(col) < tol:

skcosmo/utils/_pcovr_utils.py

@@ -157,7 +157,6 @@ def pcovr_covariance(
     C = np.zeros((X.shape[1], X.shape[1]), dtype=np.float64)
 
     if mixing < 1 or return_isqrt:
-
         if rank is None:
             rank = min(X.shape)
 

tests/test_kernel_pcovr.py

@@ -63,7 +63,6 @@ def test_lr_with_x_errors(self):
         prev_error = -1.0
 
         for i, mixing in enumerate(np.linspace(0, 1, 6)):
-
             kpcovr = KernelPCovR(mixing=mixing, n_components=2, tol=1e-12)
             kpcovr.fit(self.X, self.Y)
 
@@ -260,7 +259,6 @@ def test_none_regressor(self):
         self.assertTrue(kpcovr.regressor_ is not None)
 
     def test_incompatible_coef_shape(self):
-
         # self.Y is 2D with two targets
         # Don't need to test X shape, since this should
         # be caught by sklearn's _validate_data
@@ -437,7 +435,6 @@ def test_good_n_components(self):
         kpcovr.fit(self.X, self.Y)
 
         for svd_solver in ["auto", "full"]:
-
             # this one should pass
             kpcovr = self.model(n_components=2, svd_solver=svd_solver)
             kpcovr.fit(self.X, self.Y)

tests/test_orthogonalizers.py

@@ -19,7 +19,6 @@ def __init__(self, *args, **kwargs):
         self.random_state = np.random.RandomState(0)
 
     def setUp(self):
-
         self.n_samples = 100
         self.n_features = 100
 

tests/test_pcovr.py

@@ -97,7 +97,6 @@ def test_lr_with_x_errors(self):
         prev_error = -1.0
 
         for i, mixing in enumerate(np.linspace(0, 1, 11)):
-
             pcovr = self.model(mixing=mixing, n_components=2, tol=1e-12)
             pcovr.fit(self.X, self.Y)
 
@@ -206,7 +205,6 @@ def test_spaces_equivalent(self):
         are equivalent.
         """
         for alpha in np.linspace(0.01, 0.99, 11):
-
             with self.subTest(alpha=alpha, type="prediction"):
                 pcovr_ss = self.model(
                     n_components=2, mixing=alpha, tol=1e-12, space="sample"
@@ -285,7 +283,6 @@ def test_good_n_components(self):
         pcovr.fit(self.X, self.Y)
 
         for svd_solver in ["auto", "full"]:
-
             # this one should pass
             pcovr = self.model(n_components=2, svd_solver=svd_solver)
             pcovr.fit(self.X, self.Y)
@@ -489,7 +486,6 @@ def test_none_regressor(self):
         self.assertTrue(pcovr.regressor_ is not None)
 
     def test_incompatible_coef_shape(self):
-
         # self.Y is 2D with one target
         # Don't need to test X shape, since this should
         # be caught by sklearn's _validate_data