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svm_all.py

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+import pandas as pd
+import numpy as np
+
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.svm import LinearSVC
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+
+
+# -----------------------
+# FILES
+# -----------------------
+train_file = "treniranje_sve_grupe.csv"
+test_files  = ["test grupa 1.csv", "test grupa 2.csv", "test grupa 3.csv", "test grupa 4.csv"]
+
+VALID_LABELS = ["positive", "negative", "neutral", "mixed", "sarcasm"]
+label_map = {"negative": 0, "neutral": 1, "positive": 2, "mixed":3, "sarcasm":4}
+
+
+# -----------------------
+# LOAD DATA
+# -----------------------
+def load_data(file):
+    df = pd.read_csv(file, sep=";")
+
+    df = df[["text", "label"]].dropna()
+
+    df["label"] = df["label"].astype(str).str.lower()
+    df = df[df["label"].isin(VALID_LABELS)]
+
+    X = df["text"].astype(str).values
+    y = np.array([label_map[l] for l in df["label"]])
+
+    return X, y
+
+
+# -----------------------
+# METRICS
+# -----------------------
+def evaluate(y_true, y_pred):
+    return {
+        "accuracy": accuracy_score(y_true, y_pred),
+        "precision": precision_score(y_true, y_pred, average="weighted", zero_division=0),
+        "recall": recall_score(y_true, y_pred, average="weighted", zero_division=0),
+        "f1": f1_score(y_true, y_pred, average="weighted", zero_division=0),
+    }
+
+
+# -----------------------
+# TRAIN
+# -----------------------
+X_train, y_train = load_data(train_file)
+
+vectorizer = TfidfVectorizer(stop_words="english", max_features=5000)
+X_train = vectorizer.fit_transform(X_train)
+
+model = LinearSVC()
+model.fit(X_train, y_train)
+
+print("\n===== SVM: Combined Training =====")
+
+# -----------------------
+# TEST
+# -----------------------
+for i, test_file in enumerate(test_files):
+    X_test, y_test = load_data(test_file)
+    X_test = vectorizer.transform(X_test)
+
+    y_pred = model.predict(X_test)
+
+    print(f"\nTest set {i+1}")
+    print(evaluate(y_test, y_pred))

svm_individual.py

@@ -0,0 +1,64 @@
+import pandas as pd
+import numpy as np
+
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.svm import LinearSVC
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+
+
+# -----------------------
+# CONFIG
+# -----------------------
+train_files = ["train grupa 1.csv", "train grupa 2.csv", "train grupa 3.csv", "train grupa 4.csv"]
+test_files  = ["test grupa 1.csv", "test grupa 2.csv", "test grupa 3.csv", "test grupa 4.csv"]
+
+VALID_LABELS = ["positive", "negative", "neutral","mixed","sarcasm"]
+label_map = {"negative": 0, "neutral": 1, "positive": 2, "mixed":3,"sarcasm":4}
+
+
+# -----------------------
+# HELPERS
+# -----------------------
+def load_data(file):
+    df = pd.read_csv(file, sep=";")
+    df = df[["text", "label"]]
+    df = df.dropna()
+    df["label"] = df["label"].astype(str).str.lower()
+    df = df[df["label"].isin(VALID_LABELS)]
+
+    X = df["text"].astype(str).values
+    y = np.array([label_map[l] for l in df["label"]])
+
+    return X, y
+
+
+def evaluate(y_true, y_pred):
+    return {
+        "accuracy": accuracy_score(y_true, y_pred),
+        "precision": precision_score(y_true, y_pred, average="weighted", zero_division=0),
+        "recall": recall_score(y_true, y_pred, average="weighted", zero_division=0),
+        "f1": f1_score(y_true, y_pred, average="weighted", zero_division=0),
+    }
+
+
+# -----------------------
+# RUN
+# -----------------------
+vectorizer = TfidfVectorizer(stop_words="english", max_features=5000)
+
+print("\n===== SVM: Individual Training =====")
+
+for i in range(4):
+    X_train, y_train = load_data(train_files[i])
+    X_test, y_test = load_data(test_files[i])
+
+    X_train = vectorizer.fit_transform(X_train)
+    X_test = vectorizer.transform(X_test)
+
+    model = LinearSVC()
+    model.fit(X_train, y_train)
+
+    y_pred = model.predict(X_test)
+
+    print(f"\nDataset {i+1}")
+    print(evaluate(y_test, y_pred))