Update TimeGAN (#18)

* Update test_timegan.py

* Update timeGAN.py

* make keras-like

* fix linting

tests/test_timegan.py

@@ -6,6 +6,46 @@
 from tensorflow import keras
 
 
+def test_timegan():
+    latent_dim = 24
+    feature_dim = 6
+    seq_len = 24
+    batch_size = 2
+
+    dataset = _gen_dataset(batch_size, seq_len, feature_dim)
+    timegan = tsgm.models.timeGAN.TimeGAN(
+        seq_len=seq_len, module="gru", hidden_dim=latent_dim, n_features=feature_dim, n_layers=3, batch_size=batch_size
+    )
+    timegan.compile()
+    timegan.fit(dataset, epochs=1)
+
+    _check_internals(timegan)
+
+    # Check generation
+    generated_samples = timegan.generate(1)
+    assert generated_samples.shape == (1, seq_len, feature_dim)
+
+
+def test_timegan_on_dataset():
+    latent_dim = 24
+    feature_dim = 6
+    seq_len = 24
+    batch_size = 16
+
+    dataset = _gen_tf_dataset(batch_size, seq_len, feature_dim)  # tf.data.Dataset
+    timegan = tsgm.models.timeGAN.TimeGAN(
+        seq_len=seq_len, module="gru", hidden_dim=latent_dim, n_features=feature_dim, n_layers=3, batch_size=batch_size
+    )
+    timegan.compile()
+    timegan.fit(dataset, epochs=1)
+
+    _check_internals(timegan)
+
+    # Check generation
+    generated_samples = timegan.generate(1)
+    assert generated_samples.shape == (1, seq_len, feature_dim)
+
+
 def _gen_dataset(no, seq_len, dim):
     """Sine data generation.
     Args:
@@ -42,18 +82,15 @@ def _gen_dataset(no, seq_len, dim):
     return data
 
 
-def test_timegan():
-    latent_dim = 24
-    feature_dim = 6
-    seq_len = 24
-    batch_size = 2
+def _gen_tf_dataset(no, seq_len, dim):
+    dataset = _gen_dataset(no, seq_len, dim)
+    dataset = tf.convert_to_tensor(dataset, dtype=tf.float32)
+    dataset = tf.data.Dataset.from_tensors(dataset).unbatch().batch(no)
+
+    return dataset
 
-    dataset = _gen_dataset(batch_size, seq_len, feature_dim)
-    timegan = tsgm.models.timeGAN.TimeGAN(
-        seq_len=seq_len, module="gru", hidden_dim=latent_dim, n_features=feature_dim, n_layers=3, batch_size=batch_size
-    )
-    timegan.compile()
-    timegan.fit(dataset, epochs=1)
+
+def _check_internals(timegan):
 
     # Check internal nets
     assert timegan.generator is not None
@@ -74,8 +111,3 @@ def test_timegan():
     assert timegan.embedder_opt is not None
     assert timegan.autoencoder_opt is not None
     assert timegan.adversarialsup_opt is not None
-
-    # Check generation
-    generated_samples = timegan.generate(1)
-    assert generated_samples.shape == (1, seq_len, feature_dim)
-

tsgm/models/timeGAN.py

@@ -47,7 +47,7 @@ def labels(self) -> list:
         return list(self.keys())
 
 
-class TimeGAN:
+class TimeGAN(keras.Model):
     """
     Time-series Generative Adversarial Networks (TimeGAN)
 
@@ -68,6 +68,7 @@ def __init__(
         batch_size: int = 256,
         gamma: float = 1.0,
     ):
+        super().__init__()
         self.seq_len = seq_len
         self.hidden_dim = hidden_dim
         self.dim = n_features
@@ -453,10 +454,12 @@ def _get_data_batch(self, data, n_windows: int) -> typing.Iterator:
 
     def fit(
         self,
-        data: TensorLike,
+        data: typing.Union[TensorLike, tf.data.Dataset],
         epochs: int,
         checkpoints_interval: typing.Optional[int] = None,
         generate_synthetic: tuple = (),
+        *args,
+        **kwargs,
     ):
         """
         :param data: TensorLike, the training data
@@ -479,14 +482,20 @@ def fit(
             self._mse is None or self._bce is None
         ), "One of the loss functions is not defined. Please call .compile() to set them"
 
+        # take tf.data.Dataset | TensorLike
+        if isinstance(data, tf.data.Dataset):
+            batches = iter(data.repeat())
+        else:
+            batches = self._get_data_batch(data, n_windows=len(data))
+
         # Define the model
         self._define_timegan()
 
         # 1. Embedding network training
         print("Start Embedding Network Training")
 
         for epoch in tqdm(range(epochs), desc="Autoencoder - training"):
-            X_ = next(self._get_data_batch(data, n_windows=len(data)))
+            X_ = next(batches)
             step_e_loss_0 = self._train_autoencoder(X_, self.autoencoder_opt)
 
             # Checkpoint
@@ -501,7 +510,7 @@ def fit(
 
         # Adversarial Supervised network training
         for epoch in tqdm(range(epochs), desc="Adversarial Supervised - training"):
-            X_ = next(self._get_data_batch(data, n_windows=len(data)))
+            X_ = next(batches)
             step_g_loss_s = self._train_supervisor(X_, self.adversarialsup_opt)
 
             # Checkpoint
@@ -523,7 +532,7 @@ def fit(
 
             # Generator training (twice more than discriminator training)
             for kk in range(2):
-                X_ = next(self._get_data_batch(data, n_windows=len(data)))
+                X_ = next(batches)
                 Z_ = next(self.get_noise_batch())
                 # --------------------------
                 # Train the generator
@@ -541,7 +550,7 @@ def fit(
                 # --------------------------
                 _, step_e_loss_t0 = self._train_embedder(X_, self.embedder_opt)
 
-            X_ = next(self._get_data_batch(data, n_windows=len(data)))
+            X_ = next(batches)
             Z_ = next(self.get_noise_batch())
             step_d_loss = self._check_discriminator_loss(X_, Z_)
             if step_d_loss > 0.15: