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Documentation link says it supports categorical columns. But the following code
import pandas as pd from evidently import ColumnMapping from evidently.dashboard import Dashboard from evidently.dashboard.tabs import ( RegressionPerformanceTab ) from sklearn import datasets iris = datasets.load_iris() iris_frame = pd.DataFrame(iris.data, columns=iris.feature_names) iris_frame['target'] = iris.target iris_frame['prediction'] = iris.target + 0.1 column_mapping = ColumnMapping(target='target', prediction='prediction') # first column as string iris_frame['sepal length (cm)'] = iris_frame['sepal length (cm)'].apply(lambda x: f"value_str_{x}") iris_data_drift_report = Dashboard(tabs=[RegressionPerformanceTab()]) iris_data_drift_report.calculate(iris_frame[:100], iris_frame[100:], column_mapping=column_mapping)
raises error
File "C:\...\lib\site-packages\evidently\analyzers\regression_performance_analyzer.py", line 234, in _error_cat_feature_bias majority=float(ref_overal_value), ValueError: could not convert string to float: 'value_str_value_str_5.0'
The text was updated successfully, but these errors were encountered:
Hi @iuiu34 , thank you for reporting this! Categorical columns are meant to be supported in all Dashboards. We add this fix to the closest release.
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Hi @iuiu34 ! Thanks for pointing this out! The bug is fixed. 👩🔧 The update will be published in the next release. #291
Liraim
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Documentation link says it supports categorical columns.
But the following code
raises error
The text was updated successfully, but these errors were encountered: