remove scikit-learn<1.6.0 limit (#1451)

---------

Co-authored-by: Mikhail Sveshnikov <[email protected]>

setup.py

@@ -53,7 +53,7 @@
     install_requires=[
         "plotly>=5.10.0,<6",
         "statsmodels>=0.12.2",
-        "scikit-learn>=1.0.1,<1.6.0",
+        "scikit-learn>=1.0.1",
         "pandas[parquet]>=1.3.5",
         "numpy>=1.22.0,<2.1",
         "nltk>=3.6.7",

src/evidently/metrics/classification_performance/classification_dummy_metric.py

@@ -132,7 +132,12 @@ def calculate(self, data: InputData) -> ClassificationDummyMetricResults:
             coeff_precision = min(1.0, (1 - threshold) / 0.5)
             neg_label_precision = precision_score(target, dummy_preds, pos_label=labels[1]) * coeff_precision
             neg_label_recall = recall_score(target, dummy_preds, pos_label=labels[1]) * coeff_recall
-            f1_label2_value = 2 * neg_label_precision * neg_label_recall / (neg_label_precision + neg_label_recall)
+            f1_label2_denominator = neg_label_precision + neg_label_recall
+            f1_label2_value = (
+                2 * neg_label_precision * neg_label_recall / f1_label2_denominator
+                if f1_label2_denominator != 0
+                else float("nan")
+            )
             metrics_matrix = {
                 str(labels[0]): ClassMetric(
                     precision=current_dummy.precision,
@@ -242,16 +247,19 @@ def correction_for_threshold(
             fpr = dummy_results.fpr * coeff_recall
             fnr = dummy_results.fnr * coeff_precision
 
+        f1_denominator = dummy_results.precision * coeff_precision + dummy_results.recall * coeff_recall
+
+        f1 = (
+            2 * dummy_results.precision * coeff_precision * dummy_results.recall * coeff_recall / f1_denominator
+            if f1_denominator != 0
+            else float("nan")
+        )
+
         return DatasetClassificationQuality(
             accuracy=dummy_results.accuracy,
             precision=dummy_results.precision * coeff_precision,
             recall=dummy_results.recall * coeff_recall,
-            f1=2
-            * dummy_results.precision
-            * coeff_precision
-            * dummy_results.recall
-            * coeff_recall
-            / (dummy_results.precision * coeff_precision + dummy_results.recall * coeff_recall),
+            f1=f1,
             roc_auc=0.5,
             log_loss=None,
             tpr=tpr,

src/evidently/metrics/regression_performance/regression_dummy_metric.py

@@ -5,12 +5,12 @@
 import pandas as pd
 from sklearn.metrics import mean_absolute_error
 from sklearn.metrics import mean_absolute_percentage_error
-from sklearn.metrics import mean_squared_error
 
 from evidently.base_metric import InputData
 from evidently.base_metric import Metric
 from evidently.base_metric import MetricResult
 from evidently.metrics.regression_performance.regression_quality import RegressionQualityMetric
+from evidently.metrics.utils import root_mean_squared_error_compat
 from evidently.model.widget import BaseWidgetInfo
 from evidently.options.base import AnyOptions
 from evidently.renderers.base_renderer import MetricRenderer
@@ -76,10 +76,9 @@ def calculate(self, data: InputData) -> RegressionDummyMetricResults:
         )
         # rmse
         dummy_preds = data.current_data[target_name].mean()
-        rmse_default = mean_squared_error(
+        rmse_default = root_mean_squared_error_compat(
             y_true=data.current_data[target_name],
             y_pred=[dummy_preds] * data.current_data.shape[0],
-            squared=False,
         )
         # mape default values
         # optimal constant for mape
@@ -118,10 +117,9 @@ def calculate(self, data: InputData) -> RegressionDummyMetricResults:
             )
             # rmse
             dummy_preds = data.reference_data[target_name].mean()
-            rmse_by_ref = mean_squared_error(
+            rmse_by_ref = root_mean_squared_error_compat(
                 y_true=data.current_data[target_name],
                 y_pred=[dummy_preds] * data.current_data.shape[0],
-                squared=False,
             )
             # mape default values
             # optimal constant for mape

src/evidently/metrics/regression_performance/regression_performance_metrics.py

@@ -5,7 +5,6 @@
 import numpy as np
 from sklearn.metrics import mean_absolute_error
 from sklearn.metrics import mean_absolute_percentage_error
-from sklearn.metrics import mean_squared_error
 from sklearn.metrics import r2_score
 
 from evidently.base_metric import InputData
@@ -19,6 +18,7 @@
 from evidently.metrics.regression_performance.objects import RegressionMetricsScatter
 from evidently.metrics.regression_performance.utils import apply_func_to_binned_data
 from evidently.metrics.utils import make_target_bins_for_reg_plots
+from evidently.metrics.utils import root_mean_squared_error_compat
 from evidently.model.widget import BaseWidgetInfo
 from evidently.renderers.base_renderer import MetricRenderer
 from evidently.renderers.base_renderer import default_renderer
@@ -123,10 +123,9 @@ def calculate(self, data: InputData) -> RegressionPerformanceMetricsResults:
             y_true=data.current_data[data.column_mapping.target],
             y_pred=data.current_data[data.column_mapping.prediction],
         )
-        rmse_score_value = mean_squared_error(
+        rmse_score_value = root_mean_squared_error_compat(
             y_true=data.current_data[data.column_mapping.target],
             y_pred=data.current_data[data.column_mapping.prediction],
-            squared=False,
         )
 
         # mae default values
@@ -138,16 +137,14 @@ def calculate(self, data: InputData) -> RegressionPerformanceMetricsResults:
         # rmse default values
         rmse_ref = None
         if data.reference_data is not None:
-            rmse_ref = mean_squared_error(
+            rmse_ref = root_mean_squared_error_compat(
                 y_true=data.reference_data[data.column_mapping.target],
                 y_pred=data.reference_data[data.column_mapping.prediction],
-                squared=False,
             )
         dummy_preds = data.current_data[data.column_mapping.target].mean()
-        rmse_default = mean_squared_error(
+        rmse_default = root_mean_squared_error_compat(
             y_true=data.current_data[data.column_mapping.target],
             y_pred=[dummy_preds] * data.current_data.shape[0],
-            squared=False,
         )
         # mape default values
         # optimal constant for mape
@@ -211,7 +208,7 @@ def calculate(self, data: InputData) -> RegressionPerformanceMetricsResults:
             ["r2_score", "rmse", "mean_abs_error", "mean_abs_perc_error"],
             [
                 r2_score,
-                lambda x, y: mean_squared_error(x, y, squared=False),
+                lambda x, y: root_mean_squared_error_compat(x, y),
                 mean_absolute_error,
                 mean_absolute_percentage_error,
             ],

src/evidently/metrics/regression_performance/regression_quality.py

@@ -6,7 +6,6 @@
 import numpy as np
 from sklearn.metrics import mean_absolute_error
 from sklearn.metrics import mean_absolute_percentage_error
-from sklearn.metrics import mean_squared_error
 from sklearn.metrics import r2_score
 
 from evidently.base_metric import InputData
@@ -21,6 +20,7 @@
 from evidently.metrics.regression_performance.regression_performance_metrics import RegressionMetrics
 from evidently.metrics.regression_performance.utils import apply_func_to_binned_data
 from evidently.metrics.utils import make_target_bins_for_reg_plots
+from evidently.metrics.utils import root_mean_squared_error_compat
 from evidently.model.widget import BaseWidgetInfo
 from evidently.renderers.base_renderer import MetricRenderer
 from evidently.renderers.base_renderer import default_renderer
@@ -116,10 +116,9 @@ def calculate(self, data: InputData) -> RegressionQualityMetricResults:
             y_true=data.current_data[target_name],
             y_pred=data.current_data[prediction_name],
         )
-        rmse_score_value = mean_squared_error(
+        rmse_score_value = root_mean_squared_error_compat(
             y_true=data.current_data[target_name],
             y_pred=data.current_data[prediction_name],
-            squared=False,
         )
 
         # mae default values
@@ -131,16 +130,14 @@ def calculate(self, data: InputData) -> RegressionQualityMetricResults:
         # rmse default values
         rmse_ref = None
         if data.reference_data is not None:
-            rmse_ref = mean_squared_error(
+            rmse_ref = root_mean_squared_error_compat(
                 y_true=data.reference_data[target_name],
                 y_pred=data.reference_data[prediction_name],
-                squared=False,
             )
         dummy_preds = data.current_data[target_name].mean()
-        rmse_default = mean_squared_error(
+        rmse_default = root_mean_squared_error_compat(
             y_true=data.current_data[target_name],
             y_pred=[dummy_preds] * data.current_data.shape[0],
-            squared=False,
         )
         # mape default values
         # optimal constant for mape
@@ -207,7 +204,7 @@ def calculate(self, data: InputData) -> RegressionQualityMetricResults:
             ["r2_score", "rmse", "mean_abs_error", "mean_abs_perc_error"],
             [
                 r2_score,
-                lambda x, y: mean_squared_error(x, y, squared=False),
+                lambda x, y: root_mean_squared_error_compat(x, y),
                 mean_absolute_error,
                 mean_absolute_percentage_error,
             ],

src/evidently/metrics/utils.py

@@ -1,4 +1,25 @@
 import pandas as pd
+import sklearn.metrics
+
+use_new_root_mean_squared_error = hasattr(sklearn.metrics, "root_mean_squared_error")
+
+
+def root_mean_squared_error_compat(y_true, y_pred):
+    """
+    Compute the Root Mean Squared Error (RMSE) in a way that is compatible
+    with both old and new versions of scikit-learn.
+
+    In scikit-learn >= 1.6.0, uses sklearn.metrics.root_mean_squared_error.
+    In earlier versions, uses mean_squared_error with squared=False.
+    """
+    if use_new_root_mean_squared_error:
+        from sklearn.metrics import root_mean_squared_error
+
+        return root_mean_squared_error(y_true, y_pred)
+
+    from sklearn.metrics import mean_squared_error
+
+    return mean_squared_error(y_true, y_pred, squared=False)
 
 
 def make_target_bins_for_reg_plots(