Implement pd.StringDtype handling

[Copilot]

pd.StringDtype was not supported in _process_pandas_column, causing a TypeError when processing DataFrames with string columns using pandas' nullable string dtype.

Changes

• Added StringDtype handling in _process_pandas_column (lines 1039-1056)

  • Checks isinstance(dt, pd.StringDtype) after CategoricalDtype
  • Handles missing values via .hasnans, .dropna(), and .notna()
  • Converts to numpy string arrays with .astype(np.str_, copy=False)
  • Supports both storage='python' and storage='pyarrow' backends

• Added test case test_unify_columns_pandas_missings_StringDtype

  • Validates StringDtype columns with missing values
  • Follows existing pattern for nullable types (Int64Dtype, BooleanDtype)

Example

import pandas as pd
from interpret.glassbox import ExplainableBoostingClassifier

# Now works with StringDtype columns
df = pd.DataFrame({
    'category': pd.Series(['A', 'B', None, 'C'], dtype=pd.StringDtype()),
    'city': pd.Series(['NYC', None, 'LA', 'SF'], dtype=pd.StringDtype()),
    'target': [0, 1, 0, 1]
})

ebm = ExplainableBoostingClassifier()
ebm.fit(df[['category', 'city']], df['target'])

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 72.77%. Comparing base (766b663) to head (cf10959).

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #647      +/-   ##
==========================================
+ Coverage   72.76%   72.77%   +0.01%     
==========================================
  Files          75       75              
  Lines       10447    10451       +4     
==========================================
+ Hits         7602     7606       +4     
  Misses       2845     2845              

Flag	Coverage Δ
bdist_linux_310_python	72.46% <100.00%> (+0.07%)	⬆️
bdist_linux_311_python	72.41% <100.00%> (-0.06%)	⬇️
bdist_linux_312_python	72.48% <100.00%> (+0.12%)	⬆️
bdist_linux_313_python	72.41% <100.00%> (+0.02%)	⬆️
bdist_mac_310_python	72.60% <100.00%> (+0.04%)	⬆️
bdist_mac_311_python	72.65% <100.00%> (+0.10%)	⬆️
bdist_mac_312_python	72.65% <100.00%> (+0.09%)	⬆️
bdist_mac_313_python	72.63% <100.00%> (+0.02%)	⬆️
bdist_win_310_python	72.67% <100.00%> (+0.01%)	⬆️
bdist_win_311_python	72.69% <100.00%> (+0.02%)	⬆️
bdist_win_312_python	72.67% <100.00%> (+0.09%)	⬆️
bdist_win_313_python	72.60% <100.00%> (+0.01%)	⬆️
sdist_linux_310_python	72.44% <100.00%> (+0.09%)	⬆️
sdist_linux_311_python	72.42% <100.00%> (+0.09%)	⬆️
sdist_linux_312_python	72.32% <100.00%> (+<0.01%)	⬆️
sdist_linux_313_python	72.42% <100.00%> (+0.01%)	⬆️
sdist_mac_310_python	72.53% <100.00%> (-0.03%)	⬇️
sdist_mac_311_python	72.55% <100.00%> (+0.17%)	⬆️
sdist_mac_312_python	72.55% <100.00%> (-0.01%)	⬇️
sdist_mac_313_python	72.55% <100.00%> (+0.01%)	⬆️
sdist_win_310_python	72.67% <100.00%> (+0.09%)	⬆️
sdist_win_311_python	72.60% <100.00%> (+0.06%)	⬆️
sdist_win_312_python	72.67% <100.00%> (+0.07%)	⬆️
sdist_win_313_python	72.67% <100.00%> (+0.09%)	⬆️

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