Merge remote-tracking branch 'origin/main'

cca_zoo/_base.py

@@ -56,7 +56,7 @@ def __init__(
 
     def _validate_data(self, views: Iterable[np.ndarray]):
         if self.copy_data:
-            views= [
+            views = [
                 check_array(
                     view,
                     copy=True,
@@ -68,7 +68,7 @@ def _validate_data(self, views: Iterable[np.ndarray]):
                 for view in views
             ]
         else:
-            views= [
+            views = [
                 check_array(
                     view,
                     copy=False,
@@ -88,7 +88,6 @@ def _validate_data(self, views: Iterable[np.ndarray]):
         self.n_samples_ = views[0].shape[0]
         return views
 
-
     def _check_params(self):
         """
         Checks the parameters of the model.
@@ -129,16 +128,15 @@ def transform(
 
         """
         check_is_fitted(self)
-        views =[
-                check_array(
-                    view,
-                    copy=True,
-                    accept_sparse=False,
-                    accept_large_sparse=False,
-
-                )
-                for view in views
-            ]
+        views = [
+            check_array(
+                view,
+                copy=True,
+                accept_sparse=False,
+                accept_large_sparse=False,
+            )
+            for view in views
+        ]
         transformed_views = []
         for i, view in enumerate(views):
             transformed_view = view @ self.weights_[i]

cca_zoo/linear/_gradient/_ey.py

@@ -106,6 +106,10 @@ def __init__(self, views, batch_size=None):
 
     def __getitem__(self, index):
         views = [view[index] for view in self.views]
-        independent_index = index if self.batch_size is None else self.random_state.randint(0, len(self))
+        independent_index = (
+            index
+            if self.batch_size is None
+            else self.random_state.randint(0, len(self))
+        )
         independent_views = [view[independent_index] for view in self.views]
         return {"views": views, "independent_views": independent_views}

cca_zoo/linear/_grcca.py

@@ -62,7 +62,9 @@ def _weights(self, eigvals, eigvecs, views, feature_groups=None, **kwargs):
         # Loop through c and add group means to splits if c > 0
         self.splits = [
             n_features + n_groups if c > 0 else n_features
-            for n_features, n_groups, c in zip(self.n_features_in_, self.n_groups_, self.c)
+            for n_features, n_groups, c in zip(
+                self.n_features_in_, self.n_groups_, self.c
+            )
         ]
 
         # Add zero at the beginning and compute cumulative sum of splits

cca_zoo/linear/_iterative/_base.py

@@ -51,7 +51,7 @@ def fit(self, views: Iterable[np.ndarray], y=None, **kwargs):
 
     def _fit(self, views: Iterable[np.ndarray]):
         views = self._validate_data(views)
-        self.random_state= check_random_state(self.random_state)
+        self.random_state = check_random_state(self.random_state)
         self._initialize(views)
         self._check_params()
         # Solve using alternating optimisation across the representations until convergence

cca_zoo/linear/_iterative/_scca_span.py

@@ -99,7 +99,7 @@ def _update_weights(self, views: np.ndarray, i: int) -> None:
 
     def _initialize_variables(self, views):
         self.max_obj = [0, 0]
-        cov = cross_cov(views[0], views[1],rowvar=False)
+        cov = cross_cov(views[0], views[1], rowvar=False)
         # Perform SVD on im and obtain individual matrices
         P, D, Q = np.linalg.svd(cov, full_matrices=True)
         self.P = P[:, : self.rank]

cca_zoo/linear/_partialcca.py

@@ -42,7 +42,6 @@ def fit(self, views: Iterable[np.ndarray], y=None, partials=None, **kwargs):
             views, y=y, partials=partials, **kwargs
         )  # call the parent class fit method
 
-
     def _process_data(self, views, partials=None, **kwargs):
         if partials is None:
             return super()._process_data(views, **kwargs)
@@ -63,7 +62,9 @@ def transform(self, views: Iterable[np.ndarray], partials=None, **kwargs):
         if partials is None:
             return super().transform(views, **kwargs)
         else:
-            check_is_fitted(self)  # check if the model has been fitted before transforming
+            check_is_fitted(
+                self
+            )  # check if the model has been fitted before transforming
             transformed_views = []
             for i, (view) in enumerate(views):
                 transformed_view = (

cca_zoo/linear/_tcca.py

@@ -38,6 +38,7 @@ class TCCA(MCCA):
     >>> model = TCCA()
     >>> model.fit((X1,X2,X3)).score((X1,X2,X3))
     """
+
     def fit(self, views: Iterable[np.ndarray], y=None, **kwargs):
         # Validate the input data
         views = self._validate_data(views)
@@ -60,12 +61,16 @@ def fit(self, views: Iterable[np.ndarray], y=None, **kwargs):
                 M = np.expand_dims(M, -1) @ el
         M = np.mean(M, 0)
         tl.set_backend("numpy")
-        M_parafac = parafac(M, self.latent_dimensions, verbose=False, random_state=self.random_state, init="random")
+        M_parafac = parafac(
+            M,
+            self.latent_dimensions,
+            verbose=False,
+            random_state=self.random_state,
+            init="random",
+        )
         self.weights_ = [
             cov_invsqrt @ fac
-            for i, (cov_invsqrt, fac) in enumerate(
-                zip(covs_invsqrt, M_parafac.factors)
-            )
+            for i, (cov_invsqrt, fac) in enumerate(zip(covs_invsqrt, M_parafac.factors))
         ]
         return self
 

test/test_common.py

@@ -54,7 +54,9 @@ def fit(self, views, y=None):
     def transform(self, views, y=None):
         return views[0]
 
-random_state=0
+
+random_state = 0
+
 
 @pytest.mark.parametrize(
     "estimator",