apply revisions + add typeguard

Author: Francisco Santos

examples/timeseries/tscwgan_example.py

@@ -24,7 +24,7 @@
 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, 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,

requirements.txt

@@ -7,3 +7,4 @@ tensorflow==2.4.*
 easydict==1.9
 pmlb==1.0.*
 tqdm<5.0
+typeguard==2.13.*

src/ydata_synthetic/preprocessing/timeseries/stock.py

@@ -2,21 +2,21 @@
     Get the stock data from Yahoo finance data
     Data from the period 01 January 2017 - 24 January 2021
 """
-from typing import Union, List
+from typing import Optional, List
 
 import pandas as pd
+from typeguard import typechecked
 
 from ydata_synthetic.preprocessing.timeseries.utils import real_data_loading
 
-def transformations(path, seq_len: int, cols: Union[str, List] = None):
+@typechecked
+def transformations(path, seq_len: int, cols: Optional[List] = None):
     """Apply min max scaling and roll windows of a temporal dataset.
 
     Args:
         path(str): path to a csv temporal dataframe
         seq_len(int): length of the rolled sequences
         cols (Union[str, List]): Column or list of columns to be used"""
-    if isinstance(cols, str):
-        cols = [cols]
     if isinstance(cols, list):
         stock_df = pd.read_csv(path)[cols]
     else:

src/ydata_synthetic/synthesizers/regular/cramergan/model.py

@@ -171,7 +171,7 @@ def save(self, path):
         super().save(path)
 
 
-class Generator(Model):
+class Generator(tf.keras.Model):
     def __init__(self, batch_size):
         """Simple generator with dense feedforward layers."""
         self.batch_size = batch_size
@@ -184,7 +184,7 @@ def build_model(self, input_shape, dim, data_dim):
         x = Dense(data_dim)(x)
         return Model(inputs=input_, outputs=x)
 
-class Critic(Model):
+class Critic(tf.keras.Model):
     def __init__(self, batch_size):
         """Simple critic with dense feedforward and dropout layers."""
         self.batch_size = batch_size