Refactor welcome module

bluecast/blueprints/welcome.py

@@ -7,6 +7,7 @@
 from bluecast.blueprints.cast_cv import BlueCastCV
 from bluecast.blueprints.cast_cv_regression import BlueCastCVRegression
 from bluecast.blueprints.cast_regression import BlueCastRegression
+from bluecast.config.training_config import TrainingConfig
 
 
 class WelcomeToBlueCast:
@@ -15,19 +16,56 @@ def __init__(self):
         self.output = widgets.Output()
 
     def automl_configurator(self) -> None:
+        message = """
+        Welcome to BlueCast!\n
+        This configurator will help find the right configuration for each user in a non-programmatic way.
+        Make sure to store the instantiated WelcomeToBlueCast instance into a variable
+        to be able to retrieve the pre-configured automl instance after pressing submit.
+
+        # Sample usage:
+        welcome = WelcomeToBlueCast()
+        welcome.automl_configurator()
+        automl = welcome.automl_instance
+        automl.fit(df_train, target_col="target")
+        y_hat = automl.predict(df_val)\n
+        """
+        print(message)
+
         # Create widgets with tooltips
         task = self._create_task_widget()
         debug_mode = self._create_debug_mode_widget()
         n_models = self._create_n_models_widget()
         shap_values = self._create_shap_values_widget()
         n_folds = self._create_n_folds_widget()
         oof_storage_path = self._create_oof_storage_path_widget()
+
+        # New widgets for hyperparameter tuning options
+        hyperparameter_tuning_rounds = (
+            self._create_hyperparameter_tuning_rounds_widget()
+        )
+        hyperparameter_tuning_max_runtime_secs = (
+            self._create_hyperparameter_tuning_max_runtime_secs_widget()
+        )
+        plot_hyperparameter_tuning_overview = (
+            self._create_plot_hyperparameter_tuning_overview_widget()
+        )
+        show_detailed_tuning_logs = self._create_show_detailed_tuning_logs_widget()
+
         submit_button = self._create_submit_button()
 
         # Link the submit button to the on_submit_clicked function
         submit_button.on_click(
             lambda b: self.on_submit_clicked(
-                task, debug_mode, n_models, shap_values, n_folds, oof_storage_path
+                task,
+                debug_mode,
+                n_models,
+                shap_values,
+                n_folds,
+                oof_storage_path,
+                hyperparameter_tuning_rounds,
+                hyperparameter_tuning_max_runtime_secs,
+                plot_hyperparameter_tuning_overview,
+                show_detailed_tuning_logs,
             )
         )
 
@@ -39,15 +77,20 @@ def automl_configurator(self) -> None:
             shap_values,
             n_folds,
             oof_storage_path,
+            hyperparameter_tuning_rounds,
+            hyperparameter_tuning_max_runtime_secs,
+            plot_hyperparameter_tuning_overview,
+            show_detailed_tuning_logs,
             submit_button,
             self.output,
         )
 
+    # Widget creation functions
     def _create_task_widget(self):
         return widgets.Dropdown(
             options=["binary", "multiclass", "regression"],
             description="Task Type:",
-            tooltip="Select the type of problem you're solving: binary classification (i.e. True/False), multiclass classification (i.e. 'safe', 'risky', 'dangerous'), or regression (continous targets).",
+            tooltip="Select the type of problem you're solving: binary classification, multiclass classification, or regression.",
             style={"description_width": "initial"},
             layout=widgets.Layout(width="500px"),
         )
@@ -101,15 +144,66 @@ def _create_oof_storage_path_widget(self):
             layout=widgets.Layout(width="500px"),
         )
 
+    def _create_hyperparameter_tuning_rounds_widget(self):
+        return widgets.IntSlider(
+            value=200,
+            min=1,
+            max=500,
+            description="Tuning Rounds:",
+            tooltip="Specify the number of hyperparameter tuning rounds. The more rounds the longer the tuning process. The tuning wll stop earlier when 'Max Tuning Runtime (secs)' has been reached.",
+            style={"description_width": "initial"},
+            layout=widgets.Layout(width="500px"),
+        )
+
+    def _create_hyperparameter_tuning_max_runtime_secs_widget(self):
+        return widgets.IntSlider(
+            value=3600,
+            min=60,
+            max=14400,
+            description="Max Tuning Runtime (secs):",
+            tooltip="Specify the maximum runtime in seconds for hyperparameter tuning. (i.e. 3600 is one hour)",
+            style={"description_width": "initial"},
+            layout=widgets.Layout(width="500px"),
+        )
+
+    def _create_plot_hyperparameter_tuning_overview_widget(self):
+        return widgets.ToggleButtons(
+            options=[("Yes", True), ("No", False)],
+            description="Plot Tuning Overview:",
+            tooltip="Specify whether to plot the hyperparameter tuning overview. This will create charts showing which hyperparameters were most important and the evolution of losses during the tuning..",
+            style={"description_width": "initial"},
+            layout=widgets.Layout(width="500px"),
+        )
+
+    def _create_show_detailed_tuning_logs_widget(self):
+        return widgets.ToggleButtons(
+            options=[("Yes", True), ("No", False)],
+            description="Show Detailed Tuning Logs:",
+            tooltip="Specify whether to show detailed tuning logs during hyperparameter tuning. This will print every single tested hyperparameter set and the evaluation result.",
+            style={"description_width": "initial"},
+            layout=widgets.Layout(width="500px"),
+        )
+
     def _create_submit_button(self):
         return widgets.Button(
             description="Submit",
             button_style="success",
             layout=widgets.Layout(width="200px"),
         )
 
+    # Function handling form submission
     def on_submit_clicked(
-        self, task, debug_mode, n_models, shap_values, n_folds, oof_storage_path
+        self,
+        task,
+        debug_mode,
+        n_models,
+        shap_values,
+        n_folds,
+        oof_storage_path,
+        hyperparameter_tuning_rounds,
+        hyperparameter_tuning_max_runtime_secs,
+        plot_hyperparameter_tuning_overview,
+        show_detailed_tuning_logs,
     ):
         with self.output:
             clear_output(wait=True)
@@ -122,10 +216,24 @@ def on_submit_clicked(
             shap_values_value = shap_values.value
             n_folds_value = n_folds.value
             oof_storage_path_value = oof_storage_path.value
+            hyperparameter_tuning_rounds_value = hyperparameter_tuning_rounds.value
+            hyperparameter_tuning_max_runtime_secs_value = (
+                hyperparameter_tuning_max_runtime_secs.value
+            )
+            plot_tuning_overview_value = plot_hyperparameter_tuning_overview.value
+            show_detailed_logs_value = show_detailed_tuning_logs.value
 
             # Create the BlueCast instance
             self.automl_instance = self.instantiate_bluecast_instance(
-                task_type, n_models_value
+                task_type,
+                n_models_value,
+                n_folds_value,
+                oof_storage_path_value,
+                debug_mode_value,
+                hyperparameter_tuning_rounds_value,
+                hyperparameter_tuning_max_runtime_secs_value,
+                plot_tuning_overview_value,
+                show_detailed_logs_value,
             )
 
             # Update instance properties
@@ -134,24 +242,15 @@ def on_submit_clicked(
             self.update_debug_flag(debug_mode_value)
             self.update_hyperparam_folds(n_folds_value)
             self.update_oof_storage_path(oof_storage_path_value)
-            print("Finished configuration of BlueCast instance")
-
-    def instantiate_bluecast_instance(
-        self, task, n_models: int
-    ) -> Union[BlueCast, BlueCastCV, BlueCastRegression, BlueCastCVRegression]:
-        if task in ["binary", "multiclass"] and n_models == 1:
-            return BlueCast(class_problem=task)
-        elif task in ["binary", "multiclass"] and n_models > 1:
-            return BlueCastCV(class_problem=task)
-        elif task == "regression" and n_models == 1:
-            return BlueCastRegression(class_problem=task)
-        elif task == "regression" and n_models > 1:
-            return BlueCastCVRegression(class_problem=task)
-        else:
-            raise ValueError(
-                "No suitable configuration found. Please raise a GitHub issue."
+            self.update_hyperparameter_tuning_rounds(hyperparameter_tuning_rounds_value)
+            self.update_hyperparameter_tuning_max_runtime_secs(
+                hyperparameter_tuning_max_runtime_secs_value
             )
+            self.update_plot_hyperparameter_tuning_overview(plot_tuning_overview_value)
+            self.update_show_detailed_tuning_logs(show_detailed_logs_value)
+            print("Finished configuration of BlueCast instance")
 
+    # New update methods for hyperparameter tuning parameters
     def update_calc_shap_flag(self, calc_shap):
         self.automl_instance.conf_training.calculate_shap_values = calc_shap
 
@@ -165,3 +264,82 @@ def update_oof_storage_path(self, oof_storage_path):
         self.automl_instance.conf_training.out_of_fold_dataset_store_path = (
             oof_storage_path
         )
+
+    def update_hyperparameter_tuning_rounds(self, rounds):
+        self.automl_instance.conf_training.hyperparameter_tuning_rounds = rounds
+
+    def update_hyperparameter_tuning_max_runtime_secs(self, runtime_secs):
+        self.automl_instance.conf_training.hyperparameter_tuning_max_runtime_secs = (
+            runtime_secs
+        )
+
+    def update_plot_hyperparameter_tuning_overview(self, plot_tuning_overview):
+        self.automl_instance.conf_training.plot_hyperparameter_tuning_overview = (
+            plot_tuning_overview
+        )
+
+    def update_show_detailed_tuning_logs(self, show_logs):
+        self.automl_instance.conf_training.show_detailed_tuning_logs = show_logs
+
+    # Function to instantiate BlueCast based on the task type
+    def instantiate_bluecast_instance(
+        self,
+        task,
+        n_models: int,
+        n_folds: int,
+        oof_storage_path: str,
+        autotune_model: bool,
+        hyperparameter_tuning_rounds: int,
+        hyperparameter_tuning_max_runtime_secs: int,
+        plot_hyperparameter_tuning_overview: bool,
+        show_detailed_tuning_logs: bool,
+    ) -> Union[BlueCast, BlueCastCV, BlueCastRegression, BlueCastCVRegression]:
+        # Prepare the configuration for BlueCast
+        conf_training = TrainingConfig(
+            autotune_model=autotune_model,
+            hypertuning_cv_folds=n_folds,
+            out_of_fold_dataset_store_path=oof_storage_path,
+            hyperparameter_tuning_rounds=hyperparameter_tuning_rounds,
+            hyperparameter_tuning_max_runtime_secs=hyperparameter_tuning_max_runtime_secs,
+            plot_hyperparameter_tuning_overview=plot_hyperparameter_tuning_overview,
+            show_detailed_tuning_logs=show_detailed_tuning_logs,
+        )
+
+        if task in ["binary", "multiclass"]:
+            if n_models == 1:
+                automl_instance_bc = BlueCast(
+                    class_problem=task, conf_training=conf_training
+                )
+                print(
+                    f"Instantiated BlueCast instance with:\n automl_instance = BlueCast(class_problem={task})\n"
+                )
+                return automl_instance_bc
+            else:
+                automl_instance_bcc = BlueCastCV(
+                    class_problem=task, conf_training=conf_training
+                )
+                print(
+                    f"Instantiated BlueCast instance with:\n automl_instance = BlueCastCV(class_problem={task})\n"
+                )
+                return automl_instance_bcc
+        elif task == "regression":
+            if n_models == 1:
+                automl_instance_bcr = BlueCastRegression(
+                    class_problem=task, conf_training=conf_training
+                )
+                print(
+                    f"Instantiated BlueCast instance with:\n automl_instance = BlueCastRegression(class_problem={task})\n"
+                )
+                return automl_instance_bcr
+            else:
+                automl_instance_bcrc = BlueCastCVRegression(
+                    class_problem=task, conf_training=conf_training
+                )
+                print(
+                    f"Instantiated BlueCast instance with:\n automl_instance = BlueCastCVRegression(class_problem={task})\n"
+                )
+                return automl_instance_bcrc
+        else:
+            raise ValueError(
+                "No suitable configuration found. Please raise a GitHub issue."
+            )