Update ag_news.py

examples/ag_news.py

@@ -4,6 +4,8 @@
 
 from npc_gzip.compressors.base import BaseCompressor
 from npc_gzip.compressors.gzip_compressor import GZipCompressor
+from npc_gzip.compressors.bz2_compressor import Bz2Compressor
+from npc_gzip.compressors.lzma_compressor import LzmaCompressor
 from npc_gzip.knn_classifier import KnnClassifier
 
 
@@ -43,7 +45,7 @@ def get_data() -> tuple:
     return (train, test)
 
 
-def fit_model(
+def fit_model_gzip(
     train_text: np.ndarray, train_labels: np.ndarray, distance_metric: str = "ncd"
 ) -> KnnClassifier:
     """
@@ -68,15 +70,45 @@ def fit_model(
 
     return model
 
+def fit_model_bz2(
+    train_text: np.ndarray, train_labels: np.ndarray, distance_metric: str = "ncd"
+) -> KnnClassifier:
+
+    compressor: BaseCompressor = Bz2Compressor()
+    model: KnnClassifier = KnnClassifier(
+        compressor=compressor,
+        training_inputs=train_text,
+        training_labels=train_labels,
+        distance_metric=distance_metric,
+    )
+
+    return model
+
+def fit_model_lzma(
+    train_text: np.ndarray, train_labels: np.ndarray, distance_metric: str = "ncd"
+) -> KnnClassifier:
+
+    compressor: BaseCompressor = LzmaCompressor()
+    model: KnnClassifier = KnnClassifier(
+        compressor=compressor,
+        training_inputs=train_text,
+        training_labels=train_labels,
+        distance_metric=distance_metric,
+    )
+
+    return model
+
 
 def main() -> None:
     print("Fetching data...")
     ((train_text, train_labels), (test_text, test_labels)) = get_data()
 
-    print("Fitting model...")
-    model = fit_model(train_text, train_labels)
     random_indicies = np.random.choice(test_text.shape[0], 1000, replace=False)
 
+    print("Fitting model...")
+    print("gzip")
+    model = fit_model_gzip(train_text, train_labels)
+
     sample_test_text = test_text[random_indicies]
     sample_test_labels = test_labels[random_indicies]
 
@@ -94,6 +126,39 @@ def main() -> None:
 
     print(classification_report(sample_test_labels, labels.reshape(-1)))
 
+
+"""
+
+    print("Fitting model...")
+    print("bz2")
+    model = fit_model_bz2(train_text, train_labels)
+
+    print("Generating predictions...")
+
+    (distances, labels, similar_samples) = model.predict(
+        sample_test_text, top_k, sampling_percentage=0.01
+    )
+
+    print(classification_report(sample_test_labels, labels.reshape(-1)))\
+
+
+
+
+    print("Fitting model...")
+    print("lzma")
+    model = fit_model_lzma(train_text, train_labels)
+
+    print("Generating predictions...")
+
+    (distances, labels, similar_samples) = model.predict(
+        sample_test_text, top_k, sampling_percentage=0.01
+    )
+
+    print(classification_report(sample_test_labels, labels.reshape(-1)))
+
+
+"""
+
 
 if __name__ == "__main__":
     main()