From e05dfd4a16d54707fee6fc0d998259d6c9ef3d3b Mon Sep 17 00:00:00 2001
From: Lorenzo Volpi <lorenzo.volpi@outlook.com>
Date: Sun, 26 Nov 2023 16:33:11 +0100
Subject: [PATCH] refatored random_state

---
 quacc/dataset.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/quacc/dataset.py b/quacc/dataset.py
index ce97aec..35a20db 100644
--- a/quacc/dataset.py
+++ b/quacc/dataset.py
@@ -2,7 +2,7 @@ import math
 import os
 import pickle
 import tarfile
-from typing import List
+from typing import List, Tuple
 
 import numpy as np
 import quapy as qp
@@ -11,6 +11,7 @@ from sklearn.conftest import fetch_rcv1
 from sklearn.utils import Bunch
 
 from quacc import utils
+from quacc.environment import env
 
 TRAIN_VAL_PROP = 0.5
 
@@ -190,7 +191,7 @@ class Dataset:
 
         return all_train, test
 
-    def __train_test(self):
+    def __train_test(self) -> Tuple[LabelledCollection, LabelledCollection]:
         all_train, test = {
             "spambase": self.__spambase,
             "imdb": self.__imdb,
@@ -205,7 +206,7 @@ class Dataset:
         all_train, test = self.__train_test()
 
         train, val = all_train.split_stratified(
-            train_prop=TRAIN_VAL_PROP, random_state=0
+            train_prop=TRAIN_VAL_PROP, random_state=env._R_SEED
         )
 
         return DatasetSample(train, val, test)
@@ -216,7 +217,9 @@ class Dataset:
         # resample all_train set to have (0.5, 0.5) prevalence
         at_positives = np.sum(all_train.y)
         all_train = all_train.sampling(
-            min(at_positives, len(all_train) - at_positives) * 2, 0.5, random_state=0
+            min(at_positives, len(all_train) - at_positives) * 2,
+            0.5,
+            random_state=env._R_SEED,
         )
 
         # sample prevalences
@@ -228,9 +231,9 @@ class Dataset:
         at_size = min(math.floor(len(all_train) * 0.5 / p) for p in prevs)
         datasets = []
         for p in 1.0 - prevs:
-            all_train_sampled = all_train.sampling(at_size, p, random_state=0)
+            all_train_sampled = all_train.sampling(at_size, p, random_state=env._R_SEED)
             train, validation = all_train_sampled.split_stratified(
-                train_prop=TRAIN_VAL_PROP, random_state=0
+                train_prop=TRAIN_VAL_PROP, random_state=env._R_SEED
             )
             datasets.append(DatasetSample(train, validation, test))