Pandas>1.5 update (#257)

* fix nlargest issue

* correct df appending syntax

Author: ppdebreuck

modnet/preprocessing.py

@@ -1,34 +1,31 @@
 # coding: utf-8
 # Distributed under the terms of the MIT License.
 
-""" This module defines the :class:`MODData` class, featurizer functions
+"""This module defines the :class:`MODData` class, featurizer functions
 and functions to compute normalized mutual information (NMI) and relevance redundancy
 (RR) between descriptors.
 
 """
 
 from __future__ import annotations
 
-from pathlib import Path
-from typing import Dict, List, Union, Optional, Callable, Hashable, Iterable, Tuple
 from functools import partial
+from multiprocessing import Pool
+from pathlib import Path
+from typing import Callable, Dict, Hashable, Iterable, List, Optional, Tuple, Union
 
-from pymatgen.core import Structure, Composition
-
-from sklearn.feature_selection import mutual_info_regression, mutual_info_classif
-from sklearn.utils import resample
-from sklearn.preprocessing import MinMaxScaler
-
-import pandas as pd
 import numpy as np
+import pandas as pd
 import tqdm
-from multiprocessing import Pool
+from pymatgen.core import Composition, Structure
+from sklearn.feature_selection import mutual_info_classif, mutual_info_regression
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.utils import resample
 
-from modnet.featurizers import MODFeaturizer, clean_df
 from modnet import __version__
+from modnet.featurizers import MODFeaturizer, clean_df
 from modnet.utils import LOG
 
-
 DATABASE = pd.DataFrame([])
 
 
@@ -50,7 +47,6 @@ def compute_mi(
     random_state=None,
     n_neighbors=3,
 ):
-
     mi = mutual_info_regression(
         x.reshape(-1, 1),
         y,
@@ -364,6 +360,10 @@ def get_features_relevance_redundancy(
         list: List of dictionaries containing the results of the relevance-redundancy selection algorithm.
 
     """
+
+    # nmi should be of numeric type (pandas>1.5 nlargest compatibility)
+    target_nmi = target_nmi.apply(pd.to_numeric, errors="coerce")
+
     # Initial checks
     if set(cross_nmi.index) != set(cross_nmi.columns):
         raise ValueError(
@@ -607,9 +607,9 @@ def __init__(
         """
 
         from modnet.featurizers.presets import (
-            FEATURIZER_PRESETS,
-            DEFAULT_FEATURIZER,
             DEFAULT_COMPOSITION_ONLY_FEATURIZER,
+            DEFAULT_FEATURIZER,
+            FEATURIZER_PRESETS,
         )
 
         self.__modnet_version__ = __version__
@@ -946,9 +946,15 @@ def rebalance(self):
                             self.df_structure.iloc[idxs],
                             n_samples=int(max_support - support[i]),
                         )
-                        self.df_featurized = self.df_featurized.append(sampled_x)
-                        self.df_targets = self.df_targets.append(sampled_y)
-                        self.df_structure = self.df_structure.append(sampled_struct)
+                        self.df_featurized = pd.concat(
+                            [self.df_featurized, sampled_x], ignore_index=True
+                        )
+                        self.df_targets = pd.concat(
+                            [self.df_targets, sampled_y], ignore_index=True
+                        )
+                        self.df_structure = pd.concat(
+                            [self.df_structure, sampled_struct], ignore_index=True
+                        )
 
     @property
     def structures(self) -> List[Union[Structure, CompositionContainer]]: