Upload 7 files

ml_flow.py

@@ -0,0 +1,113 @@
+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
+)

requirements.txt

@@ -0,0 +1,12 @@
+streamlit
+pandas
+matplotlib
+seaborn
+numpy
+altair
+graphviz
+streamlit_option_menu
+scikit-learn
+Pillow
+shapash
+mlflow