Add seed

multilabel_oversampling/multilabel_oversampling.py

@@ -30,13 +30,14 @@ def create_fake_data(size=1, seed=1):
 
 class MultilabelOversampler:
 
- def __init__(self, number_of_adds=1000, number_of_tries=100, tqdm_disable=False, details=False, plot=True):
+ def __init__(self, number_of_adds=1000, number_of_tries=100, seed=1, tqdm_disable=False, details=False, plot=True):
  """
 
  
  Args:
  number_of_add: Maximum number of new rows add to df. Total number of iterations.
  number_of_tries: Maximum number of draws from df within total number of iterations.
+ seed: Seed for row sampling in `fit` function
  tqdm_disable: Enable progress bar for each iteration.
  details: Enable detailed feedback for each try
  plot: Plot all tries (iteration vs. std) after process is finished.
@@ -50,9 +51,11 @@ def __init__(self, number_of_adds=1000, number_of_tries=100, tqdm_disable=False,
  else:
  self.number_of_tries = 1e6
 
+ self.seed = seed
  self.tqdm_disable = tqdm_disable
  self.details = details
  self.plot = plot
+
 
 
  def fit(self, df, target_list=["y1", "y2", "y3", "y4"]):
@@ -76,7 +79,7 @@ def fit(self, df, target_list=["y1", "y2", "y3", "y4"]):
  # Take random row and add to df_new
  not_working = []
  for try_ in tqdm(range(self.number_of_tries), desc=f"Iter {iter_}", disable=True):
- random_row = df.sample(n = 1)
+ random_row = df.sample(n = 1, random_state=self.seed)
  df_interim = pd.concat((df_new, random_row))
  new_std = df_interim[self.target_list].sum().std()
  # If std improves add row, otherwise add to not_working list