[MRG] Make sure all API methods accept sample_domain as None

examples/datasets/plot_dataset_from_moons_distribution.py

@@ -28,7 +28,9 @@
     random_state=RANDOM_SEED
 )
 
-X_source, y_source, X_target, y_target = source_target_split(X, y, sample_domain)
+X_source, X_target, y_source, y_target = source_target_split(
+    X, y, sample_domain=sample_domain
+)
 
 fig, (ax1, ax2) = plt.subplots(1, 2, sharex="row", sharey="row", figsize=(8, 4))
 fig.suptitle('One source and one target', fontsize=14)
@@ -75,8 +77,8 @@
     random_state=RANDOM_SEED
 )
 
-X_source, y_source, domain_source, X_target, y_target, domain_target = (
-    source_target_split(X, y, sample_domain, return_domain=True)
+X_source, X_target, y_source, y_target, domain_source, domain_target = (
+    source_target_split(X, y, sample_domain, sample_domain=sample_domain)
 )
 fig, (ax1, ax2) = plt.subplots(1, 2, sharex="row", sharey="row", figsize=(8, 4))
 fig.suptitle('Multi-source and Multi-target', fontsize=14)

examples/datasets/plot_shifted_dataset.py

@@ -41,7 +41,9 @@ def plot_shifted_dataset(shift, random_state=42):
         random_state=random_state,
     )
 
-    X_source, y_source, X_target, y_target = source_target_split(X, y, sample_domain)
+    X_source, X_target, y_source, y_target = source_target_split(
+        X, y, sample_domain=sample_domain
+    )
 
     fig, (ax1, ax2) = plt.subplots(1, 2, sharex="row", sharey="row", figsize=(8, 4))
     fig.suptitle(shift.replace("_", " ").title(), fontsize=14)

examples/datasets/plot_variable_frequency_dataset.py

@@ -33,7 +33,9 @@
     random_state=RANDOM_SEED
 )
 
-X_source, y_source, X_target, y_target = source_target_split(X, y, sample_domain)
+X_source, X_target, y_source, y_target = source_target_split(
+    X, y, sample_domain=sample_domain
+)
 
 # %% Visualize the signal
 

examples/methods/plot_optimal_transport_da.py

@@ -44,7 +44,9 @@
 )
 
 
-X_source, y_source, X_target, y_target = source_target_split(X, y, sample_domain)
+X_source, X_target, y_source, y_target = source_target_split(
+    X, y, sample_domain=sample_domain
+)
 
 
 n_tot_source = X_source.shape[0]

examples/plot_method_comparison.py

@@ -117,7 +117,7 @@
     # preprocess dataset, split into training and test part
     X, y, sample_domain = ds
 
-    Xs, ys, Xt, yt = source_target_split(X, y, sample_domain=sample_domain)
+    Xs, Xt, ys, yt = source_target_split(X, y, sample_domain=sample_domain)
 
     x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
     y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5

examples/validation/plot_cross_val_score_for_da.py

@@ -39,7 +39,9 @@
 )
 
 X, y, sample_domain = dataset.pack_train(as_sources=['s'], as_targets=['t'])
-X_source, y_source, X_target, y_target = source_target_split(X, y, sample_domain)
+X_source, X_target, y_source, y_target = source_target_split(
+    X, y, sample_domain=sample_domain
+)
 cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=0)
 
 # %%

skada/__init__.py

@@ -39,7 +39,7 @@
     TransferComponentAnalysis,
 )
 from ._pipeline import make_da_pipeline
-from ._utils import source_target_split
+from .utils import source_target_split
 
 
 # make sure that the usage of the library is not possible

skada/_mapping.py

@@ -10,9 +10,12 @@
 from ot import da
 
 from .base import BaseAdapter, clone
-from ._utils import (
+from .utils import (
     check_X_domain,
     check_X_y_domain,
+    source_target_split
+)
+from ._utils import (
     _estimate_covariance,
     _merge_source_target,
 )
@@ -45,13 +48,9 @@ def fit(self, X, y=None, sample_domain=None):
         self : object
             Returns self.
         """
-        X, y, X_target, y_target = check_X_y_domain(
-            X,
-            y,
-            sample_domain,
-            allow_multi_source=True,
-            allow_multi_target=True,
-            return_joint=False,
+        X, y, sample_domain = check_X_y_domain(X, y, sample_domain)
+        X, X_target, y, y_target = source_target_split(
+            X, y, sample_domain=sample_domain
         )
         transport = self._create_transport_estimator()
         self.ot_transport_ = clone(transport)
@@ -80,13 +79,13 @@ def adapt(self, X, y=None, sample_domain=None):
             The weights of the samples.
         """
         # xxx(okachaiev): implement auto-infer for sample_domain
-        X_source, X_target = check_X_domain(
+        X, sample_domain = check_X_domain(
             X,
             sample_domain,
             allow_multi_source=True,
-            allow_multi_target=True,
-            return_joint=False,
+            allow_multi_target=True
         )
+        X_source, X_target = source_target_split(X, sample_domain=sample_domain)
         # in case of prediction we would get only target samples here,
         # thus there's no need to perform any transformations
         if X_source.shape[0] > 0:
@@ -604,13 +603,14 @@ def fit(self, X, y=None, sample_domain=None):
         self : object
             Returns self.
         """
-        X_source, X_target = check_X_domain(
+        X, sample_domain = check_X_domain(
             X,
             sample_domain,
             allow_multi_source=True,
-            allow_multi_target=True,
-            return_joint=False,
+            allow_multi_target=True
         )
+        X_source, X_target = source_target_split(X, sample_domain=sample_domain)
+
         cov_source_ = _estimate_covariance(X_source, shrinkage=self.reg)
         cov_target_ = _estimate_covariance(X_target, shrinkage=self.reg)
         self.cov_source_inv_sqrt_ = _invsqrtm(cov_source_)
@@ -638,13 +638,14 @@ def adapt(self, X, y=None, sample_domain=None):
         weights : None
             No weights are returned here.
         """
-        X_source, X_target = check_X_domain(
+        X, sample_domain = check_X_domain(
             X,
             sample_domain,
             allow_multi_source=True,
-            allow_multi_target=True,
-            return_joint=False,
+            allow_multi_target=True
         )
+        X_source, X_target = source_target_split(X, sample_domain=sample_domain)
+
         X_source_adapt = np.dot(X_source, self.cov_source_inv_sqrt_)
         X_source_adapt = np.dot(X_source_adapt, self.cov_target_sqrt_)
         X_adapt = _merge_source_target(X_source_adapt, X_target, sample_domain)

skada/_reweight.py

@@ -16,7 +16,8 @@
 from sklearn.utils.validation import check_is_fitted
 
 from .base import AdaptationOutput, BaseAdapter, clone
-from ._utils import _estimate_covariance, check_X_domain
+from .utils import check_X_domain, source_target_split, extract_source_indices
+from ._utils import _estimate_covariance
 from ._pipeline import make_da_pipeline
 
 
@@ -62,11 +63,12 @@ def fit(self, X, y=None, sample_domain=None):
             Returns self.
         """
         # xxx(okachaiev): that's the reason we need a way to cache this call
-        X_source, X_target = check_X_domain(
+        X, sample_domain = check_X_domain(
             X,
-            sample_domain,
-            return_joint=False,
+            sample_domain
         )
+        X_source, X_target = source_target_split(X, sample_domain=sample_domain)
+
         self.weight_estimator_source_ = clone(self.weight_estimator)
         self.weight_estimator_target_ = clone(self.weight_estimator)
         self.weight_estimator_source_.fit(X_source)
@@ -94,11 +96,12 @@ def adapt(self, X, y=None, sample_domain=None):
         weights : array-like, shape (n_samples,)
             The weights of the samples.
         """
-        source_idx = check_X_domain(
+        X, sample_domain = check_X_domain(
             X,
-            sample_domain,
-            return_indices=True,
+            sample_domain
         )
+        source_idx = extract_source_indices(sample_domain)
+
         # xxx(okachaiev): move this to API
         if source_idx.sum() > 0:
             source_idx, = np.where(source_idx)
@@ -197,11 +200,12 @@ def fit(self, X, y=None, sample_domain=None):
         self : object
             Returns self.
         """
-        X_source, X_target = check_X_domain(
+        X, sample_domain = check_X_domain(
             X,
-            sample_domain,
-            return_joint=False
+            sample_domain
         )
+        X_source, X_target = source_target_split(X, sample_domain=sample_domain)
+
         self.mean_source_ = X_source.mean(axis=0)
         self.cov_source_ = _estimate_covariance(X_source, shrinkage=self.reg)
         self.mean_target_ = X_target.mean(axis=0)
@@ -230,11 +234,12 @@ def adapt(self, X, y=None, sample_domain=None):
             The weights of the samples.
         """
         check_is_fitted(self)
-        source_idx = check_X_domain(
+        X, sample_domain = check_X_domain(
             X,
-            sample_domain,
-            return_indices=True,
+            sample_domain
         )
+        source_idx = extract_source_indices(sample_domain)
+
         # xxx(okachaiev): move this to API
         if source_idx.sum() > 0:
             source_idx, = np.where(source_idx)
@@ -337,11 +342,12 @@ def fit(self, X, y=None, sample_domain=None):
         self : object
             Returns self.
         """
-        source_idx = check_X_domain(
+        X, sample_domain = check_X_domain(
             X,
-            sample_domain,
-            return_indices=True
+            sample_domain
         )
+        source_idx = extract_source_indices(sample_domain)
+
         source_idx, = np.where(source_idx)
         self.domain_classifier_ = clone(self.domain_classifier)
         y_domain = np.ones(X.shape[0], dtype=np.int32)
@@ -371,11 +377,12 @@ def adapt(self, X, y=None, sample_domain=None, **kwargs):
             The weights of the samples.
         """
         check_is_fitted(self)
-        source_idx = check_X_domain(
+        X, sample_domain = check_X_domain(
             X,
-            sample_domain,
-            return_indices=True,
+            sample_domain
         )
+        source_idx = extract_source_indices(sample_domain)
+
         # xxx(okachaiev): move this to API
         if source_idx.sum() > 0:
             source_idx, = np.where(source_idx)
@@ -505,13 +512,14 @@ def fit(self, X, y=None, sample_domain=None, **kwargs):
         self : object
             Returns self.
         """
-        X_source, X_target = check_X_domain(
+        X, sample_domain = check_X_domain(
             X,
             sample_domain,
             allow_multi_source=True,
-            allow_multi_target=True,
-            return_joint=False,
+            allow_multi_target=True
         )
+        X_source, X_target = source_target_split(X, sample_domain=sample_domain)
+
         if isinstance(self.gamma, list):
             self.best_gamma_ = self._likelihood_cross_validation(
                 self.gamma, X_source, X_target
@@ -599,11 +607,12 @@ def adapt(self, X, y=None, sample_domain=None, **kwargs):
         weights : array-like, shape (n_samples,)
             The weights of the samples.
         """
-        source_idx = check_X_domain(
+        X, sample_domain = check_X_domain(
             X,
-            sample_domain,
-            return_indices=True,
+            sample_domain
         )
+        source_idx = extract_source_indices(sample_domain)
+
         if source_idx.sum() > 0:
             source_idx, = np.where(source_idx)
             A = pairwise_kernels(

skada/_subspace.py

@@ -12,7 +12,8 @@
 from sklearn.svm import SVC
 
 from .base import BaseAdapter
-from ._utils import check_X_domain, _merge_source_target
+from .utils import check_X_domain, source_target_split
+from ._utils import _merge_source_target
 from ._pipeline import make_da_pipeline
 
 
@@ -79,13 +80,14 @@ def adapt(self, X, y=None, sample_domain=None, **kwargs):
         weights : None
             No weights are returned here.
         """
-        X_source, X_target = check_X_domain(
+        X, sample_domain = check_X_domain(
             X,
             sample_domain,
             allow_multi_source=True,
             allow_multi_target=True,
-            return_joint=False,
         )
+        X_source, X_target = source_target_split(X, sample_domain=sample_domain)
+
         if X_source.shape[0]:
             X_source = np.dot(self.pca_source_.transform(X_source), self.M_)
         if X_target.shape[0]:
@@ -111,13 +113,14 @@ def fit(self, X, y=None, sample_domain=None, **kwargs):
         self : object
             Returns self.
         """
-        X_source, X_target = check_X_domain(
+        X, sample_domain = check_X_domain(
             X,
             sample_domain,
             allow_multi_source=True,
             allow_multi_target=True,
-            return_joint=False,
         )
+        X_source, X_target = source_target_split(X, sample_domain=sample_domain)
+
         if self.n_components is None:
             n_components = min(min(X_source.shape), min(X_target.shape))
         else:
@@ -245,13 +248,13 @@ def fit(self, X, y=None, sample_domain=None, **kwargs):
         self : object
             Returns self.
         """
-        self.X_source_, self.X_target_ = check_X_domain(
+        X, sample_domain = check_X_domain(
             X,
             sample_domain,
             allow_multi_source=True,
             allow_multi_target=True,
-            return_joint=False,
         )
+        self.X_source_, self.X_target_ = source_target_split(X, sample_domain=sample_domain)
 
         Kss = pairwise_kernels(self.X_source_, metric=self.kernel)
         Ktt = pairwise_kernels(self.X_target_, metric=self.kernel)
@@ -306,13 +309,14 @@ def adapt(self, X, y=None, sample_domain=None, **kwargs):
         weights : None
             No weights are returned here.
         """
-        X_source, X_target = check_X_domain(
+        X, sample_domain = check_X_domain(
             X,
             sample_domain,
             allow_multi_source=True,
             allow_multi_target=True,
-            return_joint=False,
         )
+        X_source, X_target = source_target_split(X, sample_domain=sample_domain)
+
         if np.array_equal(X_source, self.X_source_) and np.array_equal(
             X_target, self.X_target_
         ):