1 files changed, 0 insertions, 50 deletions
diff --git a/tests/dga/ml_tests/scikit-learn_tests/train_script.py b/tests/dga/ml_tests/scikit-learn_tests/train_script.py
deleted file mode 100644
index 040c3966a..000000000
--- a/tests/dga/ml_tests/scikit-learn_tests/train_script.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from sklearn.model_selection import train_test_split
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.preprocessing import LabelEncoder
-from sklearn.neural_network import MLPClassifier
-from sklearn.metrics import classification_report, accuracy_score
-from sklearn.decomposition import TruncatedSVD
-import pandas as pd
-import time
-import joblib
-
-df = pd.read_csv("../dga_domains_full.csv", header=None, names=["label", "family", "domain"])
-df = df[["label", "domain"]]
-
-# Label Encoding and Domain Vectorization Representation
-label_encoder = LabelEncoder()
-df["label_encoded"] = label_encoder.fit_transform(df["label"])
-vectorizer = CountVectorizer(analyzer='char_wb', ngram_range=(2, 4))  # Use 2 to 4 character n-grams
-X = vectorizer.fit_transform(df["domain"])
-joblib.dump(label_encoder, "label_encoder.joblib")
-
-# Dimensionality Reduction
-svd = TruncatedSVD(n_components=100)  # Set the number of components as needed
-X_reduced = svd.fit_transform(X)
-
-# Suddividere il dataset in training e test
-X_train, X_test, y_train, y_test = train_test_split(X_reduced, df["label_encoded"], test_size=0.1, shuffle=True, random_state=27)
-joblib.dump(X_test, "X_test.joblib")
-joblib.dump(y_test, "y_test.joblib")
-
-# Inizializzazione e addestramento
-mlp = MLPClassifier(hidden_layer_sizes=(100,), max_iter=300, random_state=27)
-
-start = time.time()
-mlp.fit(X_train, y_train)
-print(f"Tempo di addestramento: {time.time()-start:.2f} secondi")
-
-# Fare previsioni sul set di test
-start = time.time()
-y_pred = mlp.predict(X_test)
-print(f"Tempo di previsione: {time.time()-start:.2f} secondi")
-
-# Valutare le prestazioni del modello
-accuracy = accuracy_score(y_test, y_pred)
-report = classification_report(y_test, y_pred, target_names=label_encoder.classes_)
-
-# Stampa i risultati
-print(f"Accuratezza: {accuracy:.4f}")
-print("\nClassification Report:")
-print(report)
-joblib.dump(mlp, 'mlp_model.joblib')
-\ No newline at end of file