Fix sklearn deprecation warning for parameter squared of function mea…

…n_squared_error (#114) Replace mean_squared_error(..., squared=False) with root_mean_squared_error Co-authored-by: Jerzy Kamiński <[email protected]>
aimclub · Oct 11, 2024 · 938753c · 938753c
1 parent f521f95
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Showing 5 changed files with 12 additions and 11 deletions.
diff --git a/bamt/nodes/conditional_gaussian_node.py b/bamt/nodes/conditional_gaussian_node.py
@@ -7,7 +7,7 @@
 from pandas import DataFrame
 from sklearn import linear_model
 from sklearn.base import clone
-from sklearn.metrics import mean_squared_error as mse
+from sklearn.metrics import root_mean_squared_error as rmse
 
 from .base import BaseNode
 from .schema import CondGaussParams
@@ -51,8 +51,8 @@ def fit_parameters(self, data: DataFrame) -> Dict[str, Dict[str, CondGaussParams
                         new_data[self.cont_parents].values, new_data[self.name].values
                     )
                     predicted_value = model.predict(new_data[self.cont_parents].values)
-                    variance = mse(
-                        new_data[self.name].values, predicted_value, squared=False
+                    variance = rmse(
+                        new_data[self.name].values, predicted_value
                     )
                     hycprob[str(key_comb)] = {
                         "variance": variance,

diff --git a/bamt/nodes/gaussian_node.py b/bamt/nodes/gaussian_node.py
@@ -5,7 +5,7 @@
 import numpy as np
 from pandas import DataFrame
 from sklearn import linear_model
-from sklearn.metrics import mean_squared_error as mse
+from sklearn.metrics import root_mean_squared_error as rmse
 
 from .base import BaseNode
 from .schema import GaussianParams
@@ -30,7 +30,7 @@ def fit_parameters(self, data: DataFrame, **kwargs) -> GaussianParams:
         if parents:
             self.regressor.fit(data[parents].values, data[self.name].values, **kwargs)
             predicted_value = self.regressor.predict(data[parents].values)
-            variance = mse(data[self.name].values, predicted_value, squared=False)
+            variance = rmse(data[self.name].values, predicted_value)
             return {
                 "mean": np.nan,
                 "regressor_obj": self.regressor,

diff --git a/bamt/utils/composite_utils/CompositeGeneticOperators.py b/bamt/utils/composite_utils/CompositeGeneticOperators.py
@@ -5,7 +5,7 @@
 from golem.core.dag.graph_utils import ordered_subnodes_hierarchy
 from numpy import std, mean, log
 from scipy.stats import norm
-from sklearn.metrics import mean_squared_error
+from sklearn.metrics import root_mean_squared_error
 from sklearn.model_selection import train_test_split
 import numpy as np
 from .CompositeModel import CompositeModel
@@ -168,8 +168,9 @@ def composite_metric(graph: CompositeModel, data: pd.DataFrame):
 
             if node_type == "cont":
                 predictions = fitted_model.predict(features_test)
-                mse = mean_squared_error(target_test, predictions, squared=False) + 1e-7
-                score += norm.logpdf(target_test, loc=predictions, scale=mse).sum()
+                rmse = root_mean_squared_error(target_test, predictions, squared=False) + 1e-7
+                score += norm.logpdf(target_test, loc=predictions, scale=rmse).sum()
+
             else:
                 predict_proba = fitted_model.predict_proba(features_test)
                 probas = np.maximum(predict_proba[range(len(target_test)), [index_test_dict[x] for x in target_test]], 1e-7)

diff --git a/docs/source/examples/learn_sampling_predict.rst b/docs/source/examples/learn_sampling_predict.rst
@@ -13,7 +13,7 @@ Used imports:
     import matplotlib.pyplot as plt
 
     from sklearn import preprocessing
-    from sklearn.metrics import accuracy_score, mean_squared_error
+    from sklearn.metrics import accuracy_score
     from sklearn.ensemble import RandomForestClassifier
     from sklearn.neighbors import KNeighborsClassifier
     from sklearn.tree import DecisionTreeClassifier

diff --git a/tests/MainTest.py b/tests/MainTest.py
@@ -82,7 +82,7 @@
 # print(cont_bn.weights)
 # print(cont_bn2.weights)
 # print('RMSE on predicted values with continuous data: ' +
-#       f'{mse(cont_target, cont_predicted_values, squared=False)}')
+#       f'{rmse(cont_target, cont_predicted_values)}')
 # print(cont_bn.get_info())
 # print(cont_bn2.get_info())
 # print(synth_cont_data)
@@ -116,7 +116,7 @@
 # print(hybrid_bn2.weights)
 # print(hybrid_bn3.weights)
 # print('RMSE on predicted values with hybrid data: ' +
-#       f'{mse(hybrid_target, hybrid_predicted_values, squared=False)}')
+#       f'{rmse(hybrid_target, hybrid_predicted_values)}')
 # print(hybrid_bn.get_info())
 # print(hybrid_bn2.get_info())
 # print(hybrid_bn3.get_info())