Fix sklearn deprecation warning for parameter squared of function mean_squared_error

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,

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,

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
 
 from .CompositeModel import CompositeModel
@@ -164,8 +164,8 @@ def composite_metric(graph: CompositeModel, data: pd.DataFrame, percent=0.02):
  target = data_of_node_test.to_numpy()
  if node.content["type"] == "cont":
  predict = fitted_model.predict(features)
- mse = mean_squared_error(target, predict, squared=False) + 0.0000001
- a = norm.logpdf(target, loc=predict, scale=mse)
+ rmse = root_mean_squared_error(target, predict) + 0.0000001
+ a = norm.logpdf(target, loc=predict, scale=rmse)
  score += a.sum()
  else:
  predict_proba = fitted_model.predict_proba(features)

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

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())