add example, remove added attribute of basemodel

remove changes on gitignore

removed unused n_feats argument

Author: Francisco Santos

.gitignore

@@ -374,5 +374,3 @@ DerivedData/
 # User created
 VERSION
 version.py
-local_test_*.py
-local_test_*.ipynb

examples/timeseries/tscwgan_example.py

@@ -0,0 +1,60 @@
+from pandas import DataFrame
+from numpy import squeeze
+
+from ydata_synthetic.postprocessing.timeseries.inverse_preprocesser import inverse_transform
+from ydata_synthetic.preprocessing.timeseries import processed_stock
+from ydata_synthetic.synthesizers.timeseries import TSCWGAN
+from ydata_synthetic.synthesizers import ModelParameters, TrainParameters
+
+model = TSCWGAN
+
+#Define the GAN and training parameters
+noise_dim = 32
+dim = 128
+seq_len = 48
+cond_dim = 24
+batch_size = 128
+
+log_step = 100
+epochs = 300+1
+learning_rate = 5e-4
+beta_1 = 0.5
+beta_2 = 0.9
+models_dir = './cache'
+critic_iter = 5
+
+# Get transformed data stock - Univariate
+data, processed_data, scaler = processed_stock(path='./data/stock_data.csv', seq_len=seq_len, cols = 'Open')
+data_sample = processed_data[0]
+
+model_parameters = ModelParameters(batch_size=batch_size,
+                           lr=learning_rate,
+                           betas=(beta_1, beta_2),
+                           noise_dim=noise_dim,
+                           n_cols=seq_len,
+                           layers_dim=dim,
+                           condition = cond_dim)
+
+train_args = TrainParameters(epochs=epochs,
+                             sample_interval=log_step,
+                             critic_iter=critic_iter)
+
+#Training the TSCWGAN model
+synthesizer = model(model_parameters, gradient_penalty_weight=10)
+synthesizer.train(processed_data, train_args)
+
+#Saving the synthesizer to later generate new events
+synthesizer.save(path='./tscwgan_stock.pkl')
+
+#Loading the synthesizer
+synth = model.load(path='./tscwgan_stock.pkl')
+
+#Sampling the data
+#Note that the data returned is not inverse processed.
+step = int(len(processed_data)/(5-1))
+cond_array = DataFrame(data=[squeeze(processed_data[i][:cond_dim], axis=1) for i in range(0, len(processed_data), step)])
+
+data_sample = synth.sample(cond_array, 200)
+
+# Inverting the scaling of the synthetic samples
+data_sample = inverse_transform(data_sample, scaler)