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import mlflow
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import GridSearchCV
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from main import X_train, X_test, y_train, y_test
from sklearn.neighbors import KNeighborsClassifier
from urllib.parse import urlparse
def train_and_evaluate_with_mlflow(model, param_grid, X_train, X_test, y_train, y_test, model_name, **kwargs):
"""
Train a machine learning model using GridSearchCV and evaluate its performance,
with all results and the model itself logged to MLflow.
Parameters:
- model: The machine learning model to train.
- param_grid: Dictionary with parameters names as keys and lists of parameter settings to try as values.
- X_train: Training data features.
- X_test: Testing data features.
- y_train: Training data labels.
- y_test: Testing data labels.
- model_name: The name of the model (for MLflow logging).
- **kwargs: Additional keyword arguments to pass to the GridSearchCV.
Returns:
- The best estimator from GridSearchCV.
"""
with mlflow.start_run():
mlflow.set_experiment("Student Status Prediction")
# Perform grid search to find the best parameters
grid_search = GridSearchCV(estimator=model, param_grid=param_grid, **kwargs)
grid_search.fit(X_train, y_train)
# Extract information from the grid search for logging
cv_results_df = pd.DataFrame(grid_search.cv_results_)
# Get the top 5 best parameter combinations by rank_test_score
top5_results = cv_results_df.sort_values('rank_test_score').head(5)
# Log the best parameters
best_params = grid_search.best_params_
mlflow.log_params(best_params)
# Evaluate the model
best_model = grid_search.best_estimator_
y_pred = best_model.predict(X_test)
# Log the performance metrics
mlflow.log_metric("accuracy", accuracy_score(y_test, y_pred))
mlflow.log_metric("precision", precision_score(y_test, y_pred, average='weighted'))
mlflow.log_metric("recall", recall_score(y_test, y_pred, average='weighted'))
mlflow.log_metric("f1", f1_score(y_test, y_pred, average='weighted'))
# Log the top 5 best results as an artifact
top5_results.to_csv("top5_results.csv", index=False)
mlflow.log_artifact("top5_results.csv")
# Log the best model in MLflow
mlflow.sklearn.log_model(best_model, model_name)
# For remote server only (Dagshub)
remote_server_uri = "https://dagshub.com/Danjari/Dropout.mlflow"
mlflow.set_tracking_uri(remote_server_uri)
tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
# Model registry does not work with file store
if tracking_url_type_store != "file":
# Register the model
# There are other ways to use the Model Registry, which depends on the use case,
# please refer to the doc for more information:
# https://mlflow.org/docs/latest/model-registry.html#api-workflow
mlflow.sklearn.log_model(best_model, "model", registered_model_name=model_name)
else:
mlflow.sklearn.log_model(best_model, "model")
return best_model
# Decision Tree hyperparameters
dt_param_grid = {
'max_depth': [3, 4,5,6, 10],
'min_samples_leaf': [1, 2, 4]
}
# KNN hyperparameters
k_list = list(range(1, 101))
knn_param_grid = {
'n_neighbors': k_list
}
# Set the MLflow experiment name
# mlflow.set_experiment("Model Comparison Experiment")
# Run Decision Tree experiment
train_and_evaluate_with_mlflow(
DecisionTreeClassifier(random_state=42),
dt_param_grid,
X_train, X_test, y_train, y_test,
model_name="DecisionTree",
cv=5
)
# Run KNN experiment
train_and_evaluate_with_mlflow(
KNeighborsClassifier(),
knn_param_grid,
X_train, X_test, y_train, y_test,
model_name="KNN",
cv=5
)
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