Utilities to run app

utils.py

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+from __future__ import annotations
+
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+from sklearn.base import ClassifierMixin
+from sklearn.pipeline import make_pipeline
+from sklearn.metrics import roc_curve, auc
+from sklearn.datasets import make_classification
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import FunctionTransformer, OneHotEncoder
+from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, RandomTreesEmbedding
+
+
+def create_and_split_dataset(n_samples: int) -> list[tuple[np.ndarray, np.array]]:
+    # Create Data
+    X, y = make_classification(n_samples=n_samples, random_state=10)
+
+    # Split Data
+    X_full_train, X_test, y_full_train, y_test = train_test_split(
+        X, y, test_size=0.5, random_state=10
+    )
+
+    # Split Data for Ensemble and Linear Model
+    X_train_ensemble, X_train_linear, y_train_ensemble, y_train_linear = train_test_split(
+        X_full_train, y_full_train, test_size=0.5, random_state=10
+    )
+
+    return (X_train_ensemble, y_train_ensemble), (X_train_linear, y_train_linear), (X_test, y_test)
+
+def rf_apply(X, model):
+    return model.apply(X)
+
+def gbdt_apply(X, model):
+    return model.apply(X)[:, :, 0]
+
+def plot_roc(X: np.ndarray, y:np.array, models: tuple[str, ClassifierMixin]):
+    fig = go.Figure()
+
+    fig.add_shape(
+        type='line', line=dict(dash='dash'),
+        x0=0, x1=1, y0=0, y1=1
+    )
+
+    for model_name, model in models: 
+        y_score = model.predict_proba(X)[:, 1]
+        fpr, tpr, _ = roc_curve(y, y_score)
+        auc_val = auc(fpr, tpr) 
+        name = f"{model_name} (AUC={auc_val:.4f})"
+        fig.add_trace(go.Scatter(x=fpr, y=tpr, name=name, mode='lines'))
+        
+    fig.update_layout(
+        title="Model ROC Curve Comparison", 
+        xaxis_title='False Positive Rate',
+        yaxis_title='True Positive Rate',
+        width=1000, height=600
+    )
+
+    return fig