Adapt to pandas 3.0

README.md

@@ -37,9 +37,9 @@ pip install -U khiops
 Other installation methods are documented at the [Khiops website][khiops-install].
 
 ### Requirements
-- [Python][python] (>=3.8)
-- [Pandas][pandas] (>=0.25.3)
-- [Scikit-Learn][sklearn] (>=0.22.2)
+- [Python][python] (>=3.10)
+- [Pandas][pandas] (>=2.3.3)
+- [Scikit-Learn][sklearn] (>=1.7.2)
 
 [pandas]: https://pandas.pydata.org
 [sklearn]: https://scikit-learn.org/stable

doc/requirements.txt

@@ -4,6 +4,6 @@ ipykernel>=6.9.1
 nbconvert==6.4.4
 nbformat==5.3.0
 numpydoc>=1.5.0
-pandas>=0.25.3,<=2.3.3
+pandas>=2.3.3,<4.0.0
 scikit-learn>=1.7.2,<1.9.0
 sphinx-copybutton>=0.5.0

khiops/sklearn/dataset.py

@@ -13,7 +13,6 @@
 
 import numpy as np
 import pandas as pd
-import sklearn
 from scipy import sparse as sp
 from sklearn.utils import check_array
 from sklearn.utils.validation import column_or_1d
@@ -33,6 +32,13 @@
 #   pylint --disable=all --enable=invalid-names dataset.py
 # pylint: disable=invalid-name
 
+# Set a special pandas option to force the new string data type (`StringDtype`)
+# even for version 2.0 which is still required for python 3.10.
+# This new string data type no longer maps to a NumPy data type.
+# Hence, code assuming NumPy type compatibility will break unless
+# this string data type is handled separately.
+pd.options.future.infer_string = True
+
 
 def check_dataset_spec(ds_spec):
     """Checks that a dataset spec is valid
@@ -393,16 +399,11 @@ def write_internal_data_table(dataframe, file_path_or_stream):
 
 
 def _column_or_1d_with_dtype(y, dtype=None):
-    # 'dtype' has been introduced on `column_or_1d' since Scikit-learn 1.2;
-    if sklearn.__version__ < "1.2":
-        if pd.api.types.is_string_dtype(dtype) and y.isin(["True", "False"]).all():
-            warnings.warn(
-                "'y' stores strings restricted to 'True'/'False' values: "
-                "The predict method may return a bool vector."
-            )
-        return column_or_1d(y, warn=True)
-    else:
-        return column_or_1d(y, warn=True, dtype=dtype)
+    """Checks the data is of the provided `dtype`.
+    If a problem is detected a warning is printed or an error raised,
+    otherwise the pandas object is transformed into a numpy.array
+    """
+    return column_or_1d(y, warn=True, dtype=dtype)
 
 
 class Dataset:
@@ -607,16 +608,54 @@ def _init_target_column(self, y):
         # pandas.Series, pandas.DataFrame or numpy.ndarray
         else:
             if hasattr(y, "dtype"):
+                if not isinstance(y, np.ndarray):
+                    # Since pandas 3.0, numbers and boolean values in an array
+                    # but with a carriage-return are wrongly inferred first
+                    # respectively as `object` dtype instead of `int64` and
+                    # `object` dtype instead of `bool`.
+                    # Forcing pandas to `infer_objects` fixes the error
+                    if pd.api.types.is_object_dtype(y):
+                        y = y.infer_objects()
+
+                # Since pandas 3.0 (and even in 2.0 if the option is activated)
+                # a new `StringDtype` is used to handle strings.
+                # It does not match any longer the one recognized by numpy.
+                # An issue was created on scikit-learn
+                # https://github.com/scikit-learn/scikit-learn/issues/33383
+                # Until it is fixed, 'y' is not checked by
+                # `_column_or_1d_with_dtype` when pandas dtype is `StringDtype`.
+
                 if isinstance(y.dtype, pd.CategoricalDtype):
                     y_checked = _column_or_1d_with_dtype(
-                        y, dtype=y.dtype.categories.dtype
+                        y,
+                        dtype=(
+                            y.dtype.categories.dtype
+                            if not pd.api.types.is_string_dtype(
+                                y.dtype.categories.dtype
+                            )
+                            else None
+                        ),
                     )
                 else:
-                    y_checked = _column_or_1d_with_dtype(y, dtype=y.dtype)
+                    y_checked = _column_or_1d_with_dtype(
+                        y,
+                        dtype=(
+                            y.dtype
+                            if not pd.api.types.is_string_dtype(y.dtype)
+                            else None
+                        ),
+                    )
             elif hasattr(y, "dtypes"):
                 if isinstance(y.dtypes.iloc[0], pd.CategoricalDtype):
                     y_checked = _column_or_1d_with_dtype(
-                        y, dtype=y.dtypes.iloc[0].categories.dtype
+                        y,
+                        dtype=(
+                            y.dtypes.iloc[0].categories.dtype
+                            if not pd.api.types.is_string_dtype(
+                                y.dtypes.iloc[0].categories.dtype
+                            )
+                            else None
+                        ),
                     )
                 else:
                     y_checked = _column_or_1d_with_dtype(y)
@@ -965,21 +1004,16 @@ def __init__(self, name, dataframe, key=None):
 
         # Initialize feature columns and verify their types
         self.column_ids = self.data_source.columns.values
-        if not np.issubdtype(self.column_ids.dtype, np.integer):
-            if np.issubdtype(self.column_ids.dtype, object):
-                for i, column_id in enumerate(self.column_ids):
-                    if not isinstance(column_id, str):
-                        raise TypeError(
-                            f"Dataframe column ids must be either all integers or "
-                            f"all strings. Column id at index {i} ('{column_id}') is"
-                            f" of type '{type(column_id).__name__}'"
-                        )
-            else:
-                raise TypeError(
-                    f"Dataframe column ids must be either all integers or "
-                    f"all strings. The column index has dtype "
-                    f"'{self.column_ids.dtype}'"
-                )
+        # Ensure the feature columns are either all string
+        # or all numeric but not a mix of both.
+        if not pd.api.types.is_numeric_dtype(
+            self.column_ids
+        ) and not pd.api.types.is_string_dtype(self.column_ids):
+            raise TypeError(
+                "Dataframe column ids must be either all integers or "
+                "all strings. Columns have the following mixed types: "
+                f"{sorted(set([type(cid).__name__ for cid in self.column_ids]))}."
+            )
 
         # Initialize Khiops types
         self.khiops_types = {}
@@ -988,7 +1022,11 @@ def __init__(self, name, dataframe, key=None):
             column_numpy_type = column.dtype
             column_max_size = None
             if isinstance(column_numpy_type, pd.StringDtype):
-                column_max_size = column.str.len().max()
+                # If a value is missing in column,
+                # column.str.len() would be typed as float64 instead of int64
+                # Until this is changed, the type is forced to int64
+                # cf https://github.com/pandas-dev/pandas/issues/51948
+                column_max_size = column.str.len().astype(pd.Int64Dtype()).max()
             self.khiops_types[column_id] = get_khiops_type(
                 column_numpy_type, column_max_size
             )

khiops/sklearn/estimators.py

@@ -2021,7 +2021,7 @@ def predict_proba(self, X):
             y_probas, (pd.DataFrame, np.ndarray)
         ), "y_probas is not a Pandas DataFrame nor Numpy array"
         y_probas = y_probas.reindex(
-            self._sorted_prob_variable_names(), axis=1, copy=False
+            self._sorted_prob_variable_names(), axis=1
         ).to_numpy(copy=False)
 
         assert isinstance(y_probas, (str, np.ndarray)), "Expected str or np.ndarray"
@@ -2265,7 +2265,7 @@ def predict(self, X):
 
         # Transform to np.ndarray
         if isinstance(y_pred, pd.DataFrame):
-            y_pred = y_pred.astype("float64", copy=False).to_numpy(copy=False).ravel()
+            y_pred = y_pred.astype("float64").to_numpy(copy=False).ravel()
 
         assert isinstance(y_pred, (str, np.ndarray)), "Expected str or np.array"
         return y_pred

packaging/conda/meta.yaml

@@ -24,7 +24,7 @@ requirements:
   run:
     - python
     - khiops-core =11.0.0
-    - pandas >=0.25.3,<=2.3.3
+    - pandas >=2.3.3,<4.0.0
     - scikit-learn>=1.7.2,<1.9.0
   run_constrained:
     # do not necessary use the latest version

pyproject.toml

@@ -105,7 +105,7 @@ classifiers = [
 requires-python = ">=3.8"
 dependencies = [
     # do not use the latest versions, to avoid undesired breaking changes
-    "pandas>=0.25.3,<=2.3.3",
+    "pandas>=2.3.3,<4.0.0",
     "scikit-learn>=1.7.2,<1.9.0",
 ]
 

tests/test_dataset_class.py

@@ -503,6 +503,13 @@ def test_out_file_from_dataframe_monotable(self):
         ref_table = spec["main_table"][0]
         ref_table["class"] = y
 
+        # Since pandas 3.0 the default precision for parsing a datetime
+        # is now microseconds (us) instead of nanoseconds (ns)
+        # unless enough precision is given.
+        # Unfortunately only the changelog states this, not the docstring.
+        # To avoid any comparison error in tests
+        # we need set the required precision (ns) to the datetime
+        ref_table["Date"] = ref_table["Date"].astype("datetime64[ns]")
         # Check that the dataframes are equal
         assert_frame_equal(
             ref_table.sort_values(by="User_ID").reset_index(drop=True),

tests/test_dataset_errors.py

@@ -595,6 +595,6 @@ def test_pandas_table_column_ids_must_all_be_int_or_str(self):
         output_error_msg = str(context.exception)
         expected_msg = (
             "Dataframe column ids must be either all integers or all "
-            "strings. Column id at index 0 ('1') is of type 'int'"
+            "strings. Columns have the following mixed types: ['int', 'str']."
         )
         self.assertEqual(output_error_msg, expected_msg)