revert TS data processor integration

src/ydata_synthetic/synthesizers/timeseries/tscwgan/model.py

@@ -6,7 +6,6 @@
 from tqdm import trange
 from numpy import array, vstack, hstack
 from numpy.random import normal
-from typing import List
 
 from tensorflow import concat, float32, convert_to_tensor, reshape, GradientTape, reduce_mean, tile
 from tensorflow import data as tfdata
@@ -24,9 +23,9 @@ class TSCWGAN(BaseModel):
 
     def __init__(self, model_parameters, gradient_penalty_weight=10):
         """Create a base TSCWGAN."""
-        super().__init__(model_parameters)
         self.gradient_penalty_weight = gradient_penalty_weight
         self.cond_dim = model_parameters.condition
+        super().__init__(model_parameters)
 
     def define_gan(self):
         self.generator = Generator(self.batch_size). \
@@ -45,18 +44,14 @@ def define_gan(self):
         score = concat([cond, gen], axis=1)
         score = self.critic(score)
 
-    def train(self, data, train_arguments: TrainParameters, num_cols: List[str], cat_cols: List[str],
-              preprocess: bool = True):
-        super().train(data, num_cols, cat_cols, preprocess)
-
-        processed_data = self.processor.transform(data)
-        real_batches = self.get_batch_data(processed_data)
+    def train(self, data, train_arguments: TrainParameters):
+        real_batches = self.get_batch_data(data)
         noise_batches = self.get_batch_noise()
 
         for epoch in trange(train_arguments.epochs):
             for i in range(train_arguments.critic_iter):
                 real_batch = next(real_batches)
-                noise_batch = next(noise_batches)[:len(real_batch)]  # Truncate noise tensor to real data shape
+                noise_batch = next(noise_batches)[:len(real_batch)]  # Truncate the noise tensor in the shape of the real data tensor
 
                 c_loss = self.update_critic(real_batch, noise_batch)
 
@@ -149,10 +144,9 @@ def get_batch_data(self, data, n_windows= None):
 
     def sample(self, condition: array, n_samples: int = 100, seq_len: int = 24):
         """For a given condition, produce n_samples of length seq_len.
-        The samples are returned in the original data format (any preprocessing transformation is inverted).
 
         Args:
-            condition (numpy.array): Condition for the generated samples, must have the same length .
+            condition (numpy.array): Condition for the generated samples, must have the same length.
             n_samples (int): Minimum number of generated samples (returns always a multiple of batch_size).
             seq_len (int): Length of the generated samples.
 
@@ -175,7 +169,7 @@ def sample(self, condition: array, n_samples: int = 100, seq_len: int = 24):
                 data_.append(records)
             data_ = hstack(data_)[:, :seq_len]
             data.append(data_)
-        return self.processor.inverse_transform(array(vstack(data)))
+        return array(vstack(data))
 
 
 class Generator(Model):