random_state updated

quacc/evaluation/baseline.py

@@ -17,6 +17,7 @@ import baselines.impweight as iw
 import baselines.mandoline as mandolib
 import baselines.rca as rcalib
 from baselines.utils import clone_fit
+from quacc.environment import env
 
 from .report import EvaluationReport
 
@@ -169,7 +170,7 @@ def doc(
     predict_method="predict_proba",
 ):
     c_model_predict = getattr(c_model, predict_method)
-    val1, val2 = validation.split_stratified(train_prop=0.5, random_state=0)
+    val1, val2 = validation.split_stratified(train_prop=0.5, random_state=env._R_SEED)
     val1_probs = c_model_predict(val1.X)
     val1_mc = np.max(val1_probs, axis=-1)
     val1_preds = np.argmax(val1_probs, axis=-1)
@@ -281,7 +282,7 @@ def rca_star(
     """elsahar19"""
     c_model_predict = getattr(c_model, predict_method)
     validation1, validation2 = validation.split_stratified(
-        train_prop=0.5, random_state=0
+        train_prop=0.5, random_state=env._R_SEED
     )
     val1_pred = c_model_predict(validation1.X)
     c_model1 = clone_fit(c_model, validation1.X, val1_pred)
@@ -318,7 +319,7 @@ def gde(
     predict_method="predict",
 ) -> EvaluationReport:
     c_model_predict = getattr(c_model, predict_method)
-    val1, val2 = validation.split_stratified(train_prop=0.5, random_state=0)
+    val1, val2 = validation.split_stratified(train_prop=0.5, random_state=env._R_SEED)
     c_model1 = clone_fit(c_model, val1.X, val1.y)
     c_model1_predict = getattr(c_model1, predict_method)
     c_model2 = clone_fit(c_model, val2.X, val2.y)

quacc/evaluation/method.py

@@ -8,6 +8,7 @@ from sklearn.linear_model import LogisticRegression
 from sklearn.svm import LinearSVC
 
 import quacc as qc
+from quacc.environment import env
 from quacc.evaluation.report import EvaluationReport
 from quacc.method.base import BQAE, MCAE, BaseAccuracyEstimator
 from quacc.method.model_selection import GridSearchAE
@@ -97,7 +98,7 @@ class EvaluationMethodGridSearch(EvaluationMethod):
     pg: str = "sld"
 
     def __call__(self, c_model, validation, protocol) -> EvaluationReport:
-        v_train, v_val = validation.split_stratified(0.6, random_state=0)
+        v_train, v_val = validation.split_stratified(0.6, random_state=env._R_SEED)
         __grid = _param_grid.get(self.pg, {})
         est = GridSearchAE(
             model=self.get_est(c_model),
@@ -122,7 +123,7 @@ def __sld_lr():
 
 
 def __kde_lr():
-    return KDEy(LogisticRegression())
+    return KDEy(LogisticRegression(), random_state=env._R_SEED)
 
 
 def __sld_lsvc():

quacc/method/model_selection.py

@@ -13,6 +13,7 @@ from sklearn.base import BaseEstimator
 import quacc as qc
 import quacc.error
 from quacc.data import ExtendedCollection, ExtendedData
+from quacc.environment import env
 from quacc.evaluation import evaluate
 from quacc.logger import SubLogger
 from quacc.method.base import (
@@ -251,7 +252,7 @@ class MCAEgsq(MultiClassAccuracyEstimator):
 
     def fit(self, train: LabelledCollection):
         self.e_train = self.extend(train)
-        t_train, t_val = self.e_train.split_stratified(0.6, random_state=0)
+        t_train, t_val = self.e_train.split_stratified(0.6, random_state=env._R_SEED)
         self.quantifier = GridSearchQ(
             deepcopy(self.quantifier),
             param_grid=self.param_grid,
@@ -304,7 +305,7 @@ class BQAEgsq(BinaryQuantifierAccuracyEstimator):
 
         self.quantifiers = []
         for e_train in self.e_trains:
-            t_train, t_val = e_train.split_stratified(0.6, random_state=0)
+            t_train, t_val = e_train.split_stratified(0.6, random_state=env._R_SEED)
             quantifier = GridSearchQ(
                 model=deepcopy(self.quantifier),
                 param_grid=self.param_grid,