feat: introduce train, validation and test splits

README.md

@@ -93,9 +93,11 @@ Portuguese
   
 ### Data Splits
 
-|  name   |train|
-|---------|----:|
-|hatebr|7000|
+The original authors of the dataset did not propose a standard data split. To address this, we use the Multi-label data stratification technique implemented at the scikit-multilearn library to propose a train-validation-test split. This method considers all classes for hate speech in the data and attempts to balance the representation of each class in the split.
+
+|  name   |train|validation|test|
+|---------|----:|----:|----:|
+|hatebr|4480|1120|1400|
 
 ## Considerations for Using the Data
 

hatebr.py

@@ -2,6 +2,7 @@
 
 import datasets
 import pandas as pd
+import json
 
 _CITATION = """
 @inproceedings{vargas2022hatebr,
@@ -37,6 +38,20 @@ _LABEL_INT_KEY = {
 
 _INT_LABEL_KEY = {v:k for k,v in _LABEL_INT_KEY.items()}
 
+def process_row(row, annotator_row):
+    categories = row["hate_speech"].split(",")
+    del row["hate_speech"]
+    for default_label in _LABEL_INT_KEY.keys():
+        row[default_label] = False
+    for int_label in categories:
+        row[_INT_LABEL_KEY[int(int_label)]] = True
+    
+    if isinstance(annotator_row, dict):
+        row["specialist_1_hate_speech"] = bool(int(annotator_row["Avaliador 1"]))
+        row["specialist_2_hate_speech"] = bool(int(annotator_row["Avaliador 2"]))
+        row["specialist_3_hate_speech"] = bool(int(annotator_row["Avaliador 3"]))
+    return row
+
 class Boun(datasets.GeneratorBasedBuilder):
 
     VERSION = datasets.Version("1.0.0")
@@ -72,28 +87,47 @@ class Boun(datasets.GeneratorBasedBuilder):
         downloaded_files = dl_manager.download(_URLS)
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, 
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": downloaded_files["train"],
+                    "annotators": downloaded_files["annotators"],
+                    "split": "train"
+                }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
                 gen_kwargs={
                     "filepath": downloaded_files["train"],
-                    "annotators": downloaded_files["annotators"]
+                    "annotators": downloaded_files["annotators"],
+                    "split": "validation"
                 }
-            )
+            ),    
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": downloaded_files["train"],
+                    "annotators": downloaded_files["annotators"],
+                    "split": "test"
+                }
+            )        
         ]
 
-    def _generate_examples(self, filepath, annotators):
-        def process_row(row, annotator_row):
-            categories = row["hate_speech"].split(",")
-            del row["hate_speech"]
-            for default_label in _LABEL_INT_KEY.keys():
-                row[default_label] = False
-            for int_label in categories:
-                row[_INT_LABEL_KEY[int(int_label)]] = True
-            
-            row["specialist_1_hate_speech"] = bool(int(annotator_row["Avaliador 1"]))
-            row["specialist_2_hate_speech"] = bool(int(annotator_row["Avaliador 2"]))
-            row["specialist_3_hate_speech"] = bool(int(annotator_row["Avaliador 3"]))
-            return row
+    def _generate_examples(self, filepath, annotators, split):
+
         records = pd.read_csv(filepath).to_dict("records")
         annotators = pd.read_csv(annotators).to_dict("records")
-        for idx, row in enumerate(records):
-            yield idx, process_row(row, annotators[idx])
+
+        with open("indexes.json", "r") as f:
+            indexes = json.load(f)
+
+        split_indexes = indexes[split]
+
+        selected_tuples = []
+        for idx, (row, annotator) in enumerate(zip(records, annotators)):
+            if idx in split_indexes:
+                selected_tuples.append((row, annotator))
+
+        processed_records = [ process_row(*args) for args in selected_tuples ]
+
+        for idx, row in enumerate(processed_records):
+            yield idx, row

indexes.json

@@ -0,0 +1 @@
+{"train": [0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 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sampling.py

@@ -0,0 +1,69 @@
+import pandas as pd
+import numpy as np
+from skmultilearn.model_selection import iterative_train_test_split
+from hatebr import process_row
+import json
+DATASET_URL = "https://raw.githubusercontent.com/franciellevargas/HateBR/2d18c5b9410c2dfdd6d5394caa54d608857dae7c/dataset/HateBR.csv"
+
+def generate_stratified_indexes(df, y_column="offensive_language"):
+    """Generates stratified train, validation, and test indexes for given DataFrame.
+
+    Args:
+        df: A pandas DataFrame.
+        y_column: A string indicating the name of the column representing the target variable (default: "offensive_language").
+
+    Returns:
+        A tuple of numpy arrays containing the train, validation, and test indexes for the input DataFrame:
+        X_train_indexes: An array of indexes representing the train data.
+        X_dev_indexes: An array of indexes representing the validation data.
+        X_test_indexes: An array of indexes representing the test data.
+        y_train_indexes: An array of indexes representing the train target values.
+        y_dev_indexes: An array of indexes representing the validation target values.
+        y_test_indexes: An array of indexes representing the test target values.
+    """
+    records = df.to_dict("records")
+    processed_records = [ process_row(row, None) for row in records ]
+    processed_df = pd.DataFrame(processed_records)
+
+    y = processed_df[[y_column]].to_numpy().astype(np.int32)
+
+    indices = np.arange(y.shape[0])
+    indices = indices.reshape(indices.shape[0], 1)
+
+    y = np.append(y, indices, axis=1)
+
+    processed_df.drop(columns=[y_column], inplace=True)
+    
+    X = processed_df.to_numpy().astype(np.int32)
+    X = np.append(X, indices, axis=1)
+
+    X_train_dev, y_train_dev, X_test, y_test = iterative_train_test_split(X, y, test_size = 0.2)
+    X_train, y_train, X_dev, y_dev = iterative_train_test_split(X_train_dev, y_train_dev, test_size = 0.2)
+
+    X_train_indexes = X_train[:, -1]
+    X_dev_indexes = X_dev[:, -1]
+    X_test_indexes = X_test[:, -1]
+
+    y_train_indexes = y_train[:, -1]
+    y_dev_indexes = y_dev[:, -1]
+    y_test_indexes = y_test[:, -1]
+
+    return X_train_indexes, X_dev_indexes, X_test_indexes, y_train_indexes, y_dev_indexes, y_test_indexes
+
+def main():
+    df = pd.read_csv(DATASET_URL)
+    df.drop(columns=["instagram_comments"], inplace=True)
+    X_train_indexes, X_dev_indexes, X_test_indexes, y_train_indexes, y_dev_indexes, y_test_indexes = generate_stratified_indexes(df)
+
+    final_indexes = {
+        "train": [int(x) for x in X_train_indexes],
+        "validation": [int(x) for x in X_dev_indexes],
+        "test": [int(x) for x in X_test_indexes]
+    }
+
+    with open("indexes.json", "w") as f:
+        json.dump(final_indexes, f)
+
+
+if __name__ == "__main__":
+    main()

testing.py

@@ -0,0 +1,51 @@
+import json
+import pytest
+from sampling import generate_stratified_indexes
+import numpy as np 
+import pandas as pd 
+import os 
+
+default_url = "https://raw.githubusercontent.com/franciellevargas/HateBR/2d18c5b9410c2dfdd6d5394caa54d608857dae7c/dataset/HateBR.csv"
+DATASET_URL = os.environ.get("DATASET_URL", default_url)
+
+
+@pytest.fixture(scope='module')
+def mock_data():
+    df = pd.read_csv(DATASET_URL)
+    df.drop("instagram_comments", axis=1, inplace=True)
+    df = df.head(100)
+    return df
+
+@pytest.fixture(scope='module')
+def indexes():
+    with open('indexes.json', 'r') as f:
+        generated_indexes = json.load(f)
+    return generated_indexes
+
+
+def test_generate_stratified_indexes_returns_arrays_of_equal_length(mock_data):
+    X_train_indexes, X_dev_indexes, X_test_indexes, y_train_indexes, y_dev_indexes, y_test_indexes = generate_stratified_indexes(mock_data)
+
+    assert len(X_train_indexes) == len(y_train_indexes)
+    assert len(X_dev_indexes) == len(y_dev_indexes)
+    assert len(X_test_indexes) == len(y_test_indexes)
+
+def test_generate_stratified_indexes_returns_equal_arrays(mock_data):
+    X_train_indexes, X_dev_indexes, X_test_indexes, y_train_indexes, y_dev_indexes, y_test_indexes = generate_stratified_indexes(mock_data)
+
+    assert np.array_equal(X_train_indexes, y_train_indexes)
+    assert np.array_equal(X_dev_indexes, y_dev_indexes)
+    assert np.array_equal(X_test_indexes, y_test_indexes)
+
+def test_no_repeated_indexes(indexes):
+    train_set = set(indexes['train'])
+    val_set = set(indexes['validation'])
+    test_set = set(indexes['test'])
+    # Check that there are no repeated indexes between sets
+    assert len(train_set.union(val_set).union(test_set)) == len(train_set) + len(val_set) + len(test_set)
+
+def test_all_indexes_present(indexes):
+    total_indexes = indexes['train'] + indexes['validation'] + indexes['test']
+    max_index = max(total_indexes)
+    # Check that all indexes in range of total combined length are present
+    assert set(total_indexes) == set(range(max_index+1))