feat(adult): Add WGANGP example with adult census dataset.

examples/adult_wgangp.py

@@ -0,0 +1,27 @@
+from ydata_synthetic.preprocessing.adult import transformations
+from ydata_synthetic.synthesizers import WGAN_GP
+
+#Load and process the data
+data, processed_data, preprocessor = transformations()
+
+# WGAN_GP training
+#Defininf the training parameters of WGAN_GP
+
+noise_dim = 32
+dim = 128
+batch_size = 128
+
+log_step = 100
+epochs = 200+1
+learning_rate = 5e-4
+beta_1 = 0.5
+beta_2 = 0.9
+models_dir = './cache'
+
+gan_args = [batch_size, learning_rate, beta_1, beta_2, noise_dim, processed_data.shape[1], dim]
+train_args = ['', epochs, log_step]
+
+synthesizer = WGAN_GP(gan_args, n_critic=2)
+synthesizer.train(processed_data, train_args)
+
+synth_data = synthesizer.sample(1000)

src/ydata_synthetic/preprocessing/adult.py

@@ -6,11 +6,8 @@
 
 from pmlb import fetch_data
 
-def transformations(auto=True):
-    if auto:
-        data = fetch_data('adult')
-    else:
-        data = fetch_data('adult')
+def transformations():
+    data = fetch_data('adult')
 
     numerical_features = ['age', 'fnlwgt', 
                           'capital-gain', 'capital-loss',
@@ -29,8 +26,8 @@ def transformations(auto=True):
             ('num', numerical_transformer, numerical_features),
             ('cat', categorical_transformer, categorical_features)])
 
-    processed_data = preprocessor.fit_transform(data)
-    processed_data = pd.DataFrame.sparse.from_spmatrix(preprocessor.fit_transform(processed_data))
+    processed_data = pd.DataFrame.sparse.from_spmatrix(preprocessor.fit_transform(data))
+
     return data, processed_data, preprocessor