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from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from time import perf_counter as timer # ✅ Use perf_counter for better precision
import joblib
import os
def get_model_instance(model_name, params):
if model_name == "Logistic Regression":
return LogisticRegression(**params)
elif model_name == "Decision Tree":
return DecisionTreeClassifier(**params)
elif model_name == "Random Forest":
return RandomForestClassifier(**params)
elif model_name == "SVM":
return SVC(**params)
elif model_name == "Naive Bayes":
return GaussianNB(**params)
else:
raise ValueError(f"Unsupported model: {model_name}")
def train_models(X_train, X_test, y_train, y_test, selected_models, model_params, preprocessing_steps, experiment_name="DefaultExperiment", dataset_name="Uploaded CSV"):
results = {}
for model_name in selected_models:
params = model_params.get(model_name, {})
model = get_model_instance(model_name, params)
# ✅ Measure training time with high precision
start_time = timer()
model.fit(X_train, y_train)
training_time = round(timer() - start_time, 4)
# ✅ Measure model size
temp_model_path = f"models/{model_name.replace(' ', '_')}_temp.joblib"
os.makedirs("models", exist_ok=True)
joblib.dump(model, temp_model_path)
model_size = os.path.getsize(temp_model_path)
os.remove(temp_model_path)
# ✅ Predictions
y_train_pred = model.predict(X_train)
y_test_pred = model.predict(X_test)
# ✅ Inference time on a single sample
single_sample = X_test.iloc[[0]] if hasattr(X_test, "iloc") else X_test[0].reshape(1, -1)
start_inf = timer()
_ = model.predict(single_sample)
inference_time = round(timer() - start_inf, 6)
# ✅ Evaluation metrics
metrics = {
"accuracy_train": accuracy_score(y_train, y_train_pred),
"accuracy_test": accuracy_score(y_test, y_test_pred),
"precision_train": precision_score(y_train, y_train_pred, average='weighted', zero_division=0),
"precision_test": precision_score(y_test, y_test_pred, average='weighted', zero_division=0),
"recall_train": recall_score(y_train, y_train_pred, average='weighted', zero_division=0),
"recall_test": recall_score(y_test, y_test_pred, average='weighted', zero_division=0),
"f1_score_train": f1_score(y_train, y_train_pred, average='weighted', zero_division=0),
"f1_score_test": f1_score(y_test, y_test_pred, average='weighted', zero_division=0),
"inference_time": inference_time
}
results[model_name] = {
"model": model,
"metrics": metrics,
"training_time": training_time,
"inference_time": inference_time,
"model_size": model_size
}
return results
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