Merge pull request #101 from jameschapman19/dev

A few bugs introduced from before. Testing looking done.

.github/workflows/python-package.yml

@@ -5,9 +5,9 @@ name: Python package
 
 on:
   push:
-    branches: [ main ]
+    branches: [ main, dev ]
   pull_request:
-    branches: [ main ]
+    branches: [ main, dev ]
 
 jobs:
   build:
@@ -41,11 +41,8 @@ jobs:
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: Run Test
-      run: pytest --cov-report=xml --cov=cca_zoo ./cca_zoo/test/
+      run: pytest --doctest-modules --cov-report=xml --cov=cca_zoo ./cca_zoo/test/
     - name: "Upload coverage to Codecov"
       uses: codecov/codecov-action@v1
       with:
         fail_ci_if_error: true
-    - name: Run doctests
-      run: |
-        pytest --doctest-modules --ignore=$MODULE_NAME/tests $MODULE_NAME

cca_zoo/data/simulated.py

@@ -10,16 +10,16 @@
 
 
 def generate_covariance_data(
-        n: int,
-        view_features: List[int],
-        latent_dims: int = 1,
-        view_sparsity: List[Union[int, float]] = None,
-        correlation: Union[List[float], float] = 1,
-        structure: Union[str, List[str]] = None,
-        sigma: List[float] = None,
-        decay: float = 0.5,
-        positive=None,
-        random_state: Union[int, np.random.RandomState] = None,
+    n: int,
+    view_features: List[int],
+    latent_dims: int = 1,
+    view_sparsity: List[Union[int, float]] = None,
+    correlation: Union[List[float], float] = 1,
+    structure: Union[str, List[str]] = None,
+    sigma: List[float] = None,
+    decay: float = 0.5,
+    positive=None,
+    random_state: Union[int, np.random.RandomState] = None,
 ):
     """
     Function to generate CCA dataset with defined population correlations
@@ -58,7 +58,7 @@ def generate_covariance_data(
             covs = []
             true_features = []
             for view_p, sparsity, view_structure, view_positive, view_sigma in zip(
-                    view_features, view_sparsity, structure, positive, sigma
+                view_features, view_sparsity, structure, positive, sigma
             ):
                 # Covariance Bit
                 if view_structure == "identity":
@@ -88,8 +88,8 @@ def generate_covariance_data(
                     ).T
                     random_state.shuffle(mask)
                     while (
-                            np.sum(np.unique(mask, axis=1, return_counts=True)[1] > 1) > 0
-                            or np.sum(np.sum(mask, axis=0) == 0) > 0
+                        np.sum(np.unique(mask, axis=1, return_counts=True)[1] > 1) > 0
+                        or np.sum(np.sum(mask, axis=0) == 0) > 0
                     ):
                         random_state.shuffle(mask)
                     weights = weights * mask
@@ -113,12 +113,12 @@ def generate_covariance_data(
                     # Cross Bit
                     cross += covs[i] @ A @ covs[j]
                 cov[
-                splits[i]: splits[i] + view_features[i],
-                splits[j]: splits[j] + view_features[j],
+                    splits[i] : splits[i] + view_features[i],
+                    splits[j] : splits[j] + view_features[j],
                 ] = cross
                 cov[
-                splits[j]: splits[j] + view_features[j],
-                splits[i]: splits[i] + view_features[i],
+                    splits[j] : splits[j] + view_features[j],
+                    splits[i] : splits[i] + view_features[i],
                 ] = cross.T
 
             X = np.zeros((n, sum(view_features)))
@@ -133,12 +133,12 @@ def generate_covariance_data(
 
 
 def generate_simple_data(
-        n: int,
-        view_features: List[int],
-        view_sparsity: List[int] = None,
-        eps: float = 0,
-        transform=True,
-        random_state=None,
+    n: int,
+    view_features: List[int],
+    view_sparsity: List[int] = None,
+    eps: float = 0,
+    transform=True,
+    random_state=None,
 ):
     """
     Simple latent variable model to generate data with one latent factor
@@ -200,9 +200,9 @@ def _gaussian(x, mu, sig, dn):
     :param dn:
     """
     return (
-            np.exp(-np.power(x - mu, 2.0) / (2 * np.power(sig, 2.0)))
-            * dn
-            / (np.sqrt(2 * np.pi) * sig)
+        np.exp(-np.power(x - mu, 2.0) / (2 * np.power(sig, 2.0)))
+        * dn
+        / (np.sqrt(2 * np.pi) * sig)
     )
 
 

cca_zoo/data/toy.py

@@ -16,7 +16,7 @@ class Split_MNIST_Dataset(Dataset):
     """
 
     def __init__(
-            self, mnist_type: str = "MNIST", train: bool = True, flatten: bool = True
+        self, mnist_type: str = "MNIST", train: bool = True, flatten: bool = True
     ):
         """
 
@@ -72,7 +72,7 @@ class Noisy_MNIST_Dataset(Dataset):
     """
 
     def __init__(
-            self, mnist_type: str = "MNIST", train: bool = True, flatten: bool = True
+        self, mnist_type: str = "MNIST", train: bool = True, flatten: bool = True
     ):
         """
 

cca_zoo/deepmodels/architectures.py

@@ -31,11 +31,11 @@ def forward(self, x):
 
 class Encoder(BaseEncoder):
     def __init__(
-            self,
-            latent_dims: int,
-            variational: bool = False,
-            feature_size: int = 784,
-            layer_sizes: Iterable = None,
+        self,
+        latent_dims: int,
+        variational: bool = False,
+        feature_size: int = 784,
+        layer_sizes: Iterable = None,
     ):
         super(Encoder, self).__init__(latent_dims, variational=variational)
         if layer_sizes is None:
@@ -78,11 +78,11 @@ def forward(self, x):
 
 class Decoder(BaseDecoder):
     def __init__(
-            self,
-            latent_dims: int,
-            feature_size: int = 784,
-            layer_sizes: list = None,
-            norm_output: bool = False,
+        self,
+        latent_dims: int,
+        feature_size: int = 784,
+        layer_sizes: list = None,
+        norm_output: bool = False,
     ):
         super(Decoder, self).__init__(latent_dims)
         if layer_sizes is None:
@@ -126,14 +126,14 @@ def forward(self, x):
 
 class CNNEncoder(BaseEncoder):
     def __init__(
-            self,
-            latent_dims: int,
-            variational: bool = False,
-            feature_size: Iterable = (28, 28),
-            channels: list = None,
-            kernel_sizes: list = None,
-            stride: list = None,
-            padding: list = None,
+        self,
+        latent_dims: int,
+        variational: bool = False,
+        feature_size: Iterable = (28, 28),
+        channels: list = None,
+        kernel_sizes: list = None,
+        stride: list = None,
+        padding: list = None,
     ):
         super(CNNEncoder, self).__init__(latent_dims, variational=variational)
         if channels is None:
@@ -199,14 +199,14 @@ def forward(self, x):
 
 class CNNDecoder(BaseDecoder):
     def __init__(
-            self,
-            latent_dims: int,
-            feature_size: Iterable = (28, 28),
-            channels: list = None,
-            kernel_sizes=None,
-            strides=None,
-            paddings=None,
-            norm_output: bool = False,
+        self,
+        latent_dims: int,
+        feature_size: Iterable = (28, 28),
+        channels: list = None,
+        kernel_sizes=None,
+        strides=None,
+        paddings=None,
+        norm_output: bool = False,
     ):
         super(CNNDecoder, self).__init__(latent_dims)
         if channels is None:
@@ -229,7 +229,7 @@ def __init__(
         # Loop backward through decoding layers in order to work out the dimensions at each layer - in particular the first
         # linear layer needs to know B*current_size*current_size*channels
         for l_id, (channel, kernel, stride, padding) in reversed(
-                list(enumerate(zip(channels, kernel_sizes, strides, paddings)))
+            list(enumerate(zip(channels, kernel_sizes, strides, paddings)))
         ):
             conv_layers.append(
                 torch.nn.Sequential(
@@ -302,10 +302,10 @@ def forward(self, x):
 # BrainNetCNN Network for fitting Gold-MSI on LSD dataset
 class BrainNetEncoder(BaseEncoder):
     def __init__(
-            self,
-            latent_dims: int,
-            variational: bool = False,
-            feature_size: Tuple[int] = (200, ...),
+        self,
+        latent_dims: int,
+        variational: bool = False,
+        feature_size: Tuple[int] = (200, ...),
     ):
         super(BrainNetEncoder, self).__init__(latent_dims, variational=variational)
         _check_feature_size(feature_size)

cca_zoo/deepmodels/dcca.py

@@ -17,12 +17,12 @@ class DCCA(_DCCA_base):
     """
 
     def __init__(
-            self,
-            latent_dims: int,
-            objective=objectives.MCCA,
-            encoders=None,
-            r: float = 0,
-            eps: float = 1e-3,
+        self,
+        latent_dims: int,
+        objective=objectives.MCCA,
+        encoders=None,
+        r: float = 0,
+        eps: float = 1e-3,
     ):
         """
         Constructor class for DCCA

cca_zoo/deepmodels/dcca_barlow_twins.py

@@ -17,10 +17,10 @@ class BarlowTwins(DCCA):
     """
 
     def __init__(
-            self,
-            latent_dims: int,
-            encoders: Iterable[BaseEncoder] = [Encoder, Encoder],
-            lam=1,
+        self,
+        latent_dims: int,
+        encoders: Iterable[BaseEncoder] = [Encoder, Encoder],
+        lam=1,
     ):
         """
         Constructor class for Barlow Twins

cca_zoo/deepmodels/dcca_noi.py

@@ -16,14 +16,14 @@ class DCCA_NOI(DCCA):
     """
 
     def __init__(
-            self,
-            latent_dims: int,
-            N: int,
-            encoders=None,
-            r: float = 0,
-            rho: float = 0.2,
-            eps: float = 1e-9,
-            shared_target: bool = False,
+        self,
+        latent_dims: int,
+        N: int,
+        encoders=None,
+        r: float = 0,
+        rho: float = 0.2,
+        eps: float = 1e-9,
+        shared_target: bool = False,
     ):
         """
         Constructor class for DCCA
@@ -58,7 +58,7 @@ def forward(self, *args):
         z = []
         # Users architecture + final linear layer
         for i, (encoder, linear_layer) in enumerate(
-                zip(self.encoders, self.linear_layers)
+            zip(self.encoders, self.linear_layers)
         ):
             z.append(linear_layer(encoder(args[i])))
         return z

cca_zoo/deepmodels/dcca_sdl.py

@@ -15,15 +15,15 @@ class DCCA_SDL(DCCA_NOI):
     """
 
     def __init__(
-            self,
-            latent_dims: int,
-            N: int,
-            encoders=None,
-            r: float = 0,
-            rho: float = 0.2,
-            eps: float = 1e-3,
-            shared_target: bool = False,
-            lam=0.5,
+        self,
+        latent_dims: int,
+        N: int,
+        encoders=None,
+        r: float = 0,
+        rho: float = 0.2,
+        eps: float = 1e-3,
+        shared_target: bool = False,
+        lam=0.5,
     ):
         """
         Constructor class for DCCA

cca_zoo/deepmodels/dccae.py

@@ -17,14 +17,14 @@ class DCCAE(_DCCA_base):
     """
 
     def __init__(
-            self,
-            latent_dims: int,
-            objective=objectives.MCCA,
-            encoders=None,
-            decoders=None,
-            r: float = 0,
-            eps: float = 1e-3,
-            lam=0.5,
+        self,
+        latent_dims: int,
+        objective=objectives.MCCA,
+        encoders=None,
+        decoders=None,
+        r: float = 0,
+        eps: float = 1e-3,
+        lam=0.5,
     ):
         """
         :param latent_dims: # latent dimensions

cca_zoo/deepmodels/dtcca.py

@@ -20,7 +20,7 @@ class DTCCA(DCCA):
     """
 
     def __init__(
-            self, latent_dims: int, encoders=None, r: float = 0, eps: float = 1e-3
+        self, latent_dims: int, encoders=None, r: float = 0, eps: float = 1e-3
     ):
         """
 

cca_zoo/deepmodels/dvcca.py

@@ -23,11 +23,11 @@ class DVCCA(_DCCA_base):
     """
 
     def __init__(
-            self,
-            latent_dims: int,
-            encoders=None,
-            decoders=None,
-            private_encoders: Iterable[BaseEncoder] = None,
+        self,
+        latent_dims: int,
+        encoders=None,
+        decoders=None,
+        private_encoders: Iterable[BaseEncoder] = None,
     ):
         """
         :param latent_dims: # latent dimensions

cca_zoo/deepmodels/objectives.py

@@ -199,10 +199,10 @@ def loss(self, H1, H2):
 
         SigmaHat12 = (1.0 / (n - 1)) * H1bar.T @ H2bar
         SigmaHat11 = (1 - self.r) * (
-                1.0 / (n - 1)
+            1.0 / (n - 1)
         ) * H1bar.T @ H1bar + self.r * torch.eye(o1, device=H1.device)
         SigmaHat22 = (1 - self.r) * (
-                1.0 / (n - 1)
+            1.0 / (n - 1)
         ) * H2bar.T @ H2bar + self.r * torch.eye(o2, device=H2.device)
 
         SigmaHat11RootInv = torch.linalg.inv(