Merge pull request #261 from ntumlgroup/update_api

Update APIs for reading data

libmultilabel/linear/preprocessor.py

@@ -65,7 +65,7 @@ def load_data(self, training_data: Union[str, pd.DataFrame] = None,
             self.include_test_labels = include_test_labels
 
         if self.data_format == 'txt' or 'dataframe':
-            data = self._load_libmultilabel(training_data, test_data, eval)
+            data = self._load_text(training_data, test_data, eval)
         elif self.data_format == 'svm':
             data = self._load_svm(training_data, test_data, eval)
 
@@ -86,18 +86,12 @@ def load_data(self, training_data: Union[str, pd.DataFrame] = None,
 
         return data
 
-    def _load_libmultilabel(self, training_data, test_data, eval) -> 'dict[str, dict]':
+    def _load_text(self, training_data, test_data, eval) -> 'dict[str, dict]':
         datasets = defaultdict(dict)
         if test_data is not None:
-            if self.data_format == 'txt':
-                test_data = pd.read_csv(test_data, sep='\t', header=None,
-                                        error_bad_lines=False, warn_bad_lines=True, quoting=csv.QUOTE_NONE).fillna('')
             test = read_libmultilabel_format(test_data)
 
         if not eval:
-            if self.data_format == 'txt':
-                training_data = pd.read_csv(training_data, sep='\t', header=None,
-                                            error_bad_lines=False, warn_bad_lines=True, quoting=csv.QUOTE_NONE).fillna('')
             train = read_libmultilabel_format(training_data)
             self._generate_tfidf(train['text'])
 
@@ -145,7 +139,18 @@ def _generate_label_mapping(self, labels, classes=None):
         self.binarizer.fit(labels)
 
 
-def read_libmultilabel_format(data: pd.DataFrame) -> 'dict[str,list[str]]':
+def read_libmultilabel_format(data: Union[str, pd.DataFrame]) -> 'dict[str,list[str]]':
+    """Read multi-label text data from file or pandas dataframe.
+
+    Args:
+        data (Union[str, pd.DataFrame]): A file path to data in `LibMultiLabel format <https://www.csie.ntu.edu.tw/~cjlin/libmultilabel/cli/ov_data_format.html#libmultilabel-format>`_
+            or a pandas dataframe contains index (optional), label, and text.
+    Returns:
+        dict[str,list[str]]: A dictionary with a list of index (optional), label, and text.
+    """
+    if isinstance(data, str):
+        data = pd.read_csv(data, sep='\t', header=None,
+                           on_bad_lines='warn', quoting=csv.QUOTE_NONE).fillna('')
     data = data.astype(str)
     if data.shape[1] == 2:
         data.columns = ['label', 'text']
@@ -157,6 +162,7 @@ def read_libmultilabel_format(data: pd.DataFrame) -> 'dict[str,list[str]]':
     data['label'] = data['label'].map(lambda s: s.split())
     return data.to_dict('list')
 
+
 def read_libsvm_format(file_path: str) -> 'tuple[list[list[int]], sparse.csr_matrix]':
     """Read multi-label LIBSVM-format data.
 

libmultilabel/nn/data_utils.py

@@ -135,6 +135,10 @@ def _load_raw_data(data, is_test=False, tokenize_text=True, remove_no_label_data
     Returns:
         pandas.DataFrame: Data composed of index, label, and tokenized text.
     """
+    if isinstance(data, str):
+        logging.info(f'Load data from {data}.')
+        data = pd.read_csv(data, sep='\t', header=None,
+                           on_bad_lines='warn', quoting=csv.QUOTE_NONE).fillna('')
     data = data.astype(str)
     if data.shape[1] == 2:
         data.columns = ['label', 'text']
@@ -197,31 +201,19 @@ def load_datasets(
 
     datasets = {}
     if training_data is not None:
-        if isinstance(training_data, str):
-            logging.info(f'Load data from {training_data}.')
-            training_data = pd.read_csv(training_data, sep='\t', header=None,
-                                        error_bad_lines=False, warn_bad_lines=True, quoting=csv.QUOTE_NONE).fillna('')
-        datasets['train'] = _load_raw_data(training_data, tokenize_text=tokenize_text,
-                                           remove_no_label_data=remove_no_label_data)
+        datasets['train'] = _load_raw_data(
+            training_data, tokenize_text=tokenize_text, remove_no_label_data=remove_no_label_data)
 
     if val_data is not None:
-        if isinstance(val_data, str):
-            logging.info(f'Load data from {val_data}.')
-            val_data = pd.read_csv(val_data, sep='\t', header=None,
-                                   error_bad_lines=False, warn_bad_lines=True, quoting=csv.QUOTE_NONE).fillna('')
-        datasets['val'] = _load_raw_data(val_data, tokenize_text=tokenize_text,
-                                           remove_no_label_data=remove_no_label_data)   
+        datasets['val'] = _load_raw_data(
+            val_data, tokenize_text=tokenize_text, remove_no_label_data=remove_no_label_data)
     elif val_size > 0:
         datasets['train'], datasets['val'] = train_test_split(
             datasets['train'], test_size=val_size, random_state=42)
 
     if test_data is not None:
-        if isinstance(test_data, str):
-            logging.info(f'Load data from {test_data}.')
-            test_data = pd.read_csv(test_data, sep='\t', header=None,
-                                    error_bad_lines=False, warn_bad_lines=True, quoting=csv.QUOTE_NONE).fillna('')
-        datasets['test'] = _load_raw_data(test_data, is_test=True, tokenize_text=tokenize_text,
-                                          remove_no_label_data=remove_no_label_data)
+        datasets['test'] = _load_raw_data(
+            test_data, is_test=True, tokenize_text=tokenize_text, remove_no_label_data=remove_no_label_data)
 
     if merge_train_val:
         datasets['train'] = datasets['train'] + datasets['val']

requirements.txt

@@ -1,8 +1,8 @@
 nltk
-pandas
+pandas>1.3.0
 PyYAML
 scikit-learn
-torch>=1.12.0
+torch>=1.13.1
 torchmetrics==0.10.3
 torchtext>=0.13.0
 pytorch-lightning==1.7.7

setup.cfg

@@ -26,7 +26,7 @@ classifiers =
 packages = find:
 install_requires =
     nltk
-    pandas
+    pandas>1.3.0
     PyYAML
     scikit-learn
     torch>=1.13.1