Validation set for Gradient Based Models woohoo

cca_zoo/deep/objectives.py

@@ -266,26 +266,26 @@ def loss(self, representations, independent_representations=None):
         }
 
 
-class CCA_SVDLoss(CCA_EYLoss):
-    def loss(self, representations, independent_representations=None):
-        C = torch.cov(torch.hstack(representations).T)
-        latent_dims = representations[0].shape[1]
-
-        Cxy = C[:latent_dims, latent_dims:]
-        Cxx = C[:latent_dims, :latent_dims]
-
-        if independent_representations is None:
-            Cyy = C[latent_dims:, latent_dims:]
-        else:
-            Cyy = torch.cov(independent_representations[1].T)
-
-        rewards = torch.trace(2 * Cxy)
-        penalties = torch.trace(Cxx @ Cyy)
-        return {
-            "objective": -rewards + penalties,  # return the negative objective value
-            "rewards": rewards,  # return the total rewards
-            "penalties": penalties,  # return the penalties matrix
-        }
+# class CCA_SVDLoss(CCA_EYLoss):
+#     def loss(self, representations, independent_representations=None):
+#         C = torch.cov(torch.hstack(representations).T)
+#         latent_dims = representations[0].shape[1]
+#
+#         Cxy = C[:latent_dims, latent_dims:]
+#         Cxx = C[:latent_dims, :latent_dims]
+#
+#         if independent_representations is None:
+#             Cyy = C[latent_dims:, latent_dims:]
+#         else:
+#             Cyy = torch.cov(independent_representations[1].T)
+#
+#         rewards = torch.trace(2 * Cxy)
+#         penalties = torch.trace(Cxx @ Cyy)
+#         return {
+#             "objective": -rewards + penalties,  # return the negative objective value
+#             "rewards": rewards,  # return the total rewards
+#             "penalties": penalties,  # return the penalties matrix
+#         }
 
 
 class PLS_EYLoss(CCA_EYLoss):
@@ -317,19 +317,19 @@ def get_AB(self, representations, weights=None):
         return A / len(representations), B / len(representations)
 
 
-class PLS_SVDLoss(PLS_EYLoss):
-    def loss(self, representations, weights=None):
-        C = cross_cov(representations[0], representations[1], rowvar=False)
-
-        Cxy = C
-        Cxx = weights[0].T @ weights[0] / representations[0].shape[0]
-        Cyy = weights[1].T @ weights[1] / representations[1].shape[0]
-
-        rewards = torch.trace(2 * Cxy)
-        penalties = torch.trace(Cxx @ Cyy)
-
-        return {
-            "objective": -rewards + penalties,
-            "rewards": rewards,
-            "penalties": penalties,
-        }
+# class PLS_SVDLoss(PLS_EYLoss):
+#     def loss(self, representations, weights=None):
+#         C = cross_cov(representations[0], representations[1], rowvar=False)
+#
+#         Cxy = C
+#         Cxx = weights[0].T @ weights[0] / representations[0].shape[0]
+#         Cyy = weights[1].T @ weights[1] / representations[1].shape[0]
+#
+#         rewards = torch.trace(2 * Cxy)
+#         penalties = torch.trace(Cxx @ Cyy)
+#
+#         return {
+#             "objective": -rewards + penalties,
+#             "rewards": rewards,
+#             "penalties": penalties,
+#         }

cca_zoo/linear/__init__.py

@@ -1,5 +1,5 @@
 from ._gcca import GCCA
-from ._gradient import CCA_EY, CCA_GHA, CCA_SVD, PLS_EY, PLSStochasticPower
+from ._gradient import CCA_EY, CCA_GHA, PLS_EY, PLSStochasticPower
 from ._grcca import GRCCA
 from ._iterative import (
     PLS_ALS,
@@ -39,6 +39,6 @@
     "CCA_EY",
     "PLS_EY",
     "CCA_GHA",
-    "CCA_SVD",
+    # "CCA_SVD",
     "PLSStochasticPower",
 ]

cca_zoo/linear/_gradient/__init__.py

@@ -2,13 +2,13 @@
 from ._gha import CCA_GHA
 
 from ._stochasticpls import PLSStochasticPower
-from ._svd import CCA_SVD
+# from ._svd import CCA_SVD
 
 __all__ = [
     "CCA_EY",
     "PLS_EY",
     "CCA_GHA",
-    "CCA_SVD",
+    # "CCA_SVD",
     # "PLS_SVD",
     "PLSStochasticPower",
 ]

cca_zoo/linear/_gradient/_base.py

@@ -34,7 +34,7 @@ def __init__(
         batch_size=None,
         dataloader_kwargs=None,
         epochs=1,
-        learning_rate=1,
+        learning_rate=5e-2,
         initialization: Union[str, callable] = "random",
         trainer_kwargs=None,
         optimizer_kwargs=None,

cca_zoo/probabilistic/__init__.py

@@ -1,6 +1,7 @@
 from ._cca import ProbabilisticCCA
 from ._plsregression import ProbabilisticPLSRegression
 from ._rcca import ProbabilisticRCCA
+
 # from ._pls import ProbabilisticPLS
 
 __all__ = [

cca_zoo/probabilistic/_cca.py

@@ -196,7 +196,7 @@ def _guide(self, views):
         n = X1.shape[0] if X1 is not None else X2.shape[0]
 
         with numpyro.plate("n", n):
-            z=numpyro.sample(
+            z = numpyro.sample(
                 "representations",
                 dist.MultivariateNormal(
                     jnp.zeros(self.latent_dimensions), jnp.eye(self.latent_dimensions)

cca_zoo/probabilistic/_rcca.py

@@ -144,17 +144,15 @@ def _model(self, views):
                 "X1",
                 dist.MultivariateNormal(
                     z @ W1.T + mu1,
-                    covariance_matrix=psi1
-                    + self.c[0] * jnp.eye(self.n_features_[0]),
+                    covariance_matrix=psi1 + self.c[0] * jnp.eye(self.n_features_[0]),
                 ),
                 obs=X1,
             )
             numpyro.sample(
                 "X2",
                 dist.MultivariateNormal(
                     z @ W2.T + mu2,
-                    covariance_matrix=psi2
-                    + self.c[1] * jnp.eye(self.n_features_[1]),
+                    covariance_matrix=psi2 + self.c[1] * jnp.eye(self.n_features_[1]),
                 ),
                 obs=X2,
             )

cca_zoo/visualisation/umap_scores.py

@@ -9,7 +9,7 @@ def _validate_plot_params(self):
         check_umap_support("UMAPScoreDisplay")
         check_seaborn_support("TSNEScoreDisplay")
 
-    def plot(self):
+    def plot(self, **kwargs):
         self._validate_plot_params()
         import umap
         import matplotlib.pyplot as plt

pyproject.toml

@@ -45,6 +45,7 @@ pytest-cov = "*"
 seaborn = "*"
 opentsne = "*"
 umap-learn = "*"
+rdata = "*"
 
 
 [build-system]

test/test_cross_corr.py

@@ -2,22 +2,25 @@
 
 from cca_zoo.utils.cross_correlation import cross_corrcoef, cross_cov
 
+N = 50
+features = [4, 6]
+
 
 def test_crosscorrcoef():
-    X = np.random.rand(100, 5)
-    Y = np.random.rand(100, 5) / 10
+    X = np.random.rand(N, features[0])
+    Y = np.random.rand(N, features[1]) / 10
 
-    m = np.corrcoef(X, Y, rowvar=False)[:5, 5:]
+    m = np.corrcoef(X, Y, rowvar=False)[:4, 4:]
     n = cross_corrcoef(X, Y, rowvar=False)
 
     assert np.allclose(m, n)
 
 
 def test_crosscov(bias=False):
-    X = np.random.rand(100, 5)
-    Y = np.random.rand(100, 5) / 10
+    X = np.random.rand(N, features[0])
+    Y = np.random.rand(N, features[1]) / 10
 
-    m = np.cov(X, Y, rowvar=False)[:5, 5:]
+    m = np.cov(X, Y, rowvar=False)[:4, 4:]
     n = cross_cov(X, Y, rowvar=False)
 
     assert np.allclose(m, n)

test/test_data.py

@@ -41,8 +41,8 @@ def test_cca_on_simulated_data_maintains_expected_correlation(
 # Additional test to verify the shape of generated data
 def test_simulated_data_shapes():
     data = JointData(
-        view_features=[10, 12], latent_dims=4, correlation=[0.8, 0.7, 0.6, 0.5]
+        view_features=[4, 6], latent_dims=4, correlation=[0.8, 0.7, 0.6, 0.5]
     )
-    x_train, y_train = data.sample(500)
-    assert x_train.shape == (500, 10)
-    assert y_train.shape == (500, 12)
+    x_train, y_train = data.sample(5)
+    assert x_train.shape == (5, 4)
+    assert y_train.shape == (5, 6)