Integrated autoencoder fitting to coordinates class in data.py

deepof/data.py

@@ -24,6 +24,7 @@ from sklearn.impute import SimpleImputer
 from sklearn.manifold import TSNE
 from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder
 from tqdm import tqdm
+import deepof.models
 import deepof.pose_utils
 import deepof.utils
 import deepof.visuals
@@ -777,8 +778,67 @@ class coordinates:
             tag_dict, typ="rule-based", arena=self._arena, arena_dims=self._arena_dims
         )
 
-    def gmvae_embedding(self):
-        pass
+    @staticmethod
+    def deep_unsupervised_embedding(
+        preprocessed_object: np.array,
+        encoding_size: int,
+        batch_size: int = 256,
+        cp: bool = True,
+        hparams: dict = None,
+        kl_warmup: int = 0,
+        loss: str = "ELBO",
+        mmd_warmup: int = 0,
+        montecarlo_kl: int = 10,
+        n_components: int = 25,
+        outpath: str = ".",
+        phenotype_class: float = 0,
+        predictor: float = 0,
+        pretrained: str = False,
+        variational: bool = True,
+    ):
+
+        # Load all
+        X_train, y_train, X_val, y_val = preprocessed_object
+
+        if not variational:
+            encoder, decoder, ae = deepof.models.SEQ_2_SEQ_AE(
+                ({} if hparams is None else hparams)
+            ).build(X_train.shape)
+            return_list = (encoder, decoder, ae)
+
+        else:
+            (
+                encoder,
+                generator,
+                grouper,
+                ae,
+                kl_warmup_callback,
+                mmd_warmup_callback,
+            ) = deepof.models.SEQ_2_SEQ_GMVAE(
+                architecture_hparams=({} if hparams is None else hparams),
+                batch_size=batch_size,
+                compile_model=True,
+                encoding=encoding_size,
+                kl_warmup_epochs=kl_warmup,
+                loss=loss,
+                mmd_warmup_epochs=mmd_warmup,
+                montecarlo_kl=montecarlo_kl,
+                neuron_control=False,
+                number_of_components=n_components,
+                overlap_loss=False,
+                phenotype_prediction=phenotype_class,
+                predictor=predictor,
+            ).build(
+                X_train.shape
+            )
+            return_list = (encoder, generator, grouper, ae)
+
+        if pretrained:
+            ae.load_weights(pretrained)
+            return return_list
+        # returns a trained tensorflow model
+
+        return ae
 
 
 class table_dict(dict):
@@ -1033,7 +1093,7 @@ class table_dict(dict):
             if self._propagate_labels:
                 y_train = y_train[shuffle_train]
 
-        return X_train, y_train, X_test, y_test
+        return X_train, y_train, np.array(X_test), np.array(y_test)
 
     def random_projection(
         self, n_components: int = None, sample: int = 1000

deepof/models.py

@@ -521,7 +521,6 @@ class SEQ_2_SEQ_GMVAE:
     def build(self, input_shape: Tuple):
         """Builds the tf.keras model"""
 
-        print(input_shape)
         # Instanciate prior
         self.get_prior()