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f3983bc 577b539 f3983bc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 | import os
import logging
import pandas as pd
import numpy as np
import joblib
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LogisticRegression
from sklearn.svm import LinearSVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import (
accuracy_score, precision_score, recall_score,
f1_score, classification_report, confusion_matrix
)
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
log = logging.getLogger(__name__)
# Paths
BASE = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
DATA_PATH = os.path.join(BASE, "data", "balanced_corpus.csv")
MODELS_DIR = os.path.join(BASE, "models")
RESULTS_DIR = os.path.join(BASE, "results")
os.makedirs(MODELS_DIR, exist_ok=True)
os.makedirs(RESULTS_DIR, exist_ok=True)
# Load data
log.info("Loading balanced corpus...")
df = pd.read_csv(DATA_PATH)
df = df.dropna(subset=["preprocessed_text"])
log.info(f"Dataset: {df.shape[0]} rows | {df['toxic'].value_counts().to_dict()}")
X = df["preprocessed_text"]
y = df["toxic"]
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y
)
log.info(f"Train: {len(X_train)} | Test: {len(X_test)}")
# Vectorizers
vectorizers = {
"tfidf": TfidfVectorizer(max_features=50000, ngram_range=(1, 1)),
"bow": CountVectorizer(max_features=50000, ngram_range=(1, 1)),
}
# Models
models = {
"naive_bayes": MultinomialNB(),
"logistic_regression": LogisticRegression(max_iter=1000, random_state=42),
"svm": LinearSVC(max_iter=2000, random_state=42),
}
# train
results = []
for vec_name, vectorizer in vectorizers.items():
log.info(f"Fitting vectorizer: {vec_name}")
X_train_vec = vectorizer.fit_transform(X_train)
X_test_vec = vectorizer.transform(X_test)
# save vectorizer
vec_path = os.path.join(MODELS_DIR, f"vectorizer_{vec_name}.joblib")
joblib.dump(vectorizer, vec_path)
log.info(f"Saved vectorizer → {vec_path}")
for model_name, model in models.items():
log.info(f"Training: {model_name} + {vec_name}")
model_clone = type(model)(**model.get_params())
model_clone.fit(X_train_vec, y_train)
y_pred = model_clone.predict(X_test_vec)
acc = accuracy_score(y_test, y_pred)
prec = precision_score(y_test, y_pred, average="weighted")
rec = recall_score(y_test, y_pred, average="weighted")
f1 = f1_score(y_test, y_pred, average="weighted")
results.append({
"model": model_name,
"vectorizer": vec_name,
"accuracy": round(acc, 4),
"precision": round(prec, 4),
"recall": round(rec, 4),
"f1": round(f1, 4),
})
log.info(f" acc={acc:.4f} | prec={prec:.4f} | rec={rec:.4f} | f1={f1:.4f}")
print(f"\n{'='*60}")
print(f"{model_name.upper()} + {vec_name.upper()}")
print(f"{'='*60}")
print(classification_report(y_test, y_pred, target_names=["Non-Toxic", "Toxic"]))
# save model
model_path = os.path.join(MODELS_DIR, f"{model_name}_{vec_name}.joblib")
joblib.dump(model_clone, model_path)
log.info(f" Saved model → {model_path}")
# confusion matrix plot
cm = confusion_matrix(y_test, y_pred)
fig, ax = plt.subplots(figsize=(5, 4))
sns.heatmap(
cm, annot=True, fmt="d", cmap="Blues",
xticklabels=["Non-Toxic", "Toxic"],
yticklabels=["Non-Toxic", "Toxic"],
ax=ax
)
ax.set_title(f"Confusion Matrix\n{model_name} + {vec_name}")
ax.set_ylabel("Actual")
ax.set_xlabel("Predicted")
plt.tight_layout()
cm_path = os.path.join(RESULTS_DIR, f"cm_{model_name}_{vec_name}.png")
plt.savefig(cm_path, dpi=150)
plt.close()
# Save comparison table
results_df = pd.DataFrame(results).sort_values("f1", ascending=False)
results_path = os.path.join(RESULTS_DIR, "model_comparison.csv")
results_df.to_csv(results_path, index=False)
print(f"\n{'='*60}")
print("MODEL COMPARISON (sorted by F1)")
print(f"{'='*60}")
print(results_df.to_string(index=False))
# Model comparison bar chart
fig, ax = plt.subplots(figsize=(10, 5))
x = np.arange(len(results_df))
bars = ax.bar(x, results_df["f1"], color=["#4C72B0", "#DD8452", "#55A868",
"#C44E52", "#8172B2", "#937860"])
ax.set_xticks(x)
ax.set_xticklabels(
[f"{r['model'].replace('_', ' ').title()}\n({r['vectorizer'].upper()})"
for _, r in results_df.iterrows()],
fontsize=9
)
ax.set_ylabel("Weighted F1 Score")
ax.set_title("Model Comparison — Classical Baselines")
ax.set_ylim(0.85, 1.0)
for bar, val in zip(bars, results_df["f1"]):
ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.001,
f"{val:.4f}", ha="center", va="bottom", fontsize=9)
plt.tight_layout()
chart_path = os.path.join(RESULTS_DIR, "model_comparison_chart.png")
plt.savefig(chart_path, dpi=150)
plt.close()
log.info(f"Saved comparison chart → {chart_path}")
log.info("Done.") |