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import streamlit as st
# Define Hyperparameter Options
def get_hyperparams_ui(model_name):
"""Generate UI components for model-specific hyperparameters."""
hyperparams = {}
if model_name in ["Random Forest Regressor", "Random Forest"]:
hyperparams["n_estimators"] = st.number_input("Number of Trees (n_estimators)", min_value=10, max_value=500, value=100)
hyperparams["max_depth"] = st.number_input("Max Depth", min_value=1, max_value=50, value=10)
hyperparams["min_samples_split"] = st.number_input("Min Samples Split", min_value=2, max_value=10, value=2)
elif model_name in ["XGBoost Regressor", "XGBoost"]:
hyperparams["n_estimators"] = st.number_input("Number of Boosting Rounds (n_estimators)", min_value=10, max_value=500, value=100)
hyperparams["learning_rate"] = st.slider("Learning Rate", 0.01, 1.0, 0.1)
hyperparams["max_depth"] = st.number_input("Max Depth", min_value=1, max_value=50, value=6)
elif model_name == "Linear Regression":
st.info("No hyperparameters required for Linear Regression.")
# New Regression Models:
elif model_name == "Polynomial Regression":
hyperparams["degree"] = st.number_input("Degree of Polynomial Features", min_value=2, max_value=10, value=2)
# You may add additional hyperparameters for the underlying LinearRegression if needed
elif model_name == "Ridge Regression":
hyperparams["alpha"] = st.slider("Regularization Strength (alpha)", 0.01, 10.0, 1.0)
hyperparams["solver"] = st.selectbox("Solver", ["auto", "svd", "cholesky", "lsqr", "sparse_cg", "sag", "saga", "lbfgs"])
elif model_name == "Lasso Regression":
hyperparams["alpha"] = st.slider("Regularization Strength (alpha)", 0.01, 10.0, 1.0)
hyperparams["max_iter"] = st.number_input("Max Iterations", min_value=100, max_value=1000, value=1000)
elif model_name == "Logistic Regression":
hyperparams["C"] = st.slider("Regularization Strength (C)", 0.01, 10.0, 1.0)
hyperparams["max_iter"] = st.number_input("Max Iterations", min_value=100, max_value=1000, value=200)
elif model_name == "Support Vector Regressor":
hyperparams["C"] = st.slider("Regularization parameter (C)", 0.1, 100.0, 1.0)
hyperparams["epsilon"] = st.slider("Epsilon", 0.0, 1.0, 0.1)
hyperparams["kernel"] = st.selectbox("Kernel", ["linear", "rbf", "poly", "sigmoid"])
elif model_name == "Decision Tree Regressor":
hyperparams["max_depth"] = st.number_input("Max Depth", min_value=1, max_value=50, value=10)
hyperparams["min_samples_split"] = st.number_input("Min Samples Split", min_value=2, max_value=10, value=2)
elif model_name == "K-Nearest Neighbors Regressor":
hyperparams["n_neighbors"] = st.number_input("Number of Neighbors", min_value=1, max_value=100, value=5)
hyperparams["weights"] = st.selectbox("Weight Function", ["uniform", "distance"])
elif model_name == "ElasticNet":
hyperparams["alpha"] = st.slider("Alpha", 0.01, 10.0, 1.0)
hyperparams["l1_ratio"] = st.slider("L1 Ratio", 0.0, 1.0, 0.5)
elif model_name == "Gradient Boosting Regressor":
hyperparams["n_estimators"] = st.number_input("Number of Estimators", min_value=10, max_value=500, value=100)
hyperparams["learning_rate"] = st.slider("Learning Rate", 0.01, 1.0, 0.1)
hyperparams["max_depth"] = st.number_input("Max Depth", min_value=1, max_value=20, value=3)
elif model_name == "AdaBoost Regressor":
hyperparams["n_estimators"] = st.number_input("Number of Estimators", min_value=10, max_value=500, value=50)
hyperparams["learning_rate"] = st.slider("Learning Rate", 0.01, 1.0, 0.1)
elif model_name == "Bayesian Ridge":
hyperparams["alpha_1"] = st.slider("Alpha 1", 1e-6, 1e-1, 1e-4, format="%.6f")
hyperparams["alpha_2"] = st.slider("Alpha 2", 1e-6, 1e-1, 1e-4, format="%.6f")
hyperparams["lambda_1"] = st.slider("Lambda 1", 1e-6, 1e-1, 1e-4, format="%.6f")
hyperparams["lambda_2"] = st.slider("Lambda 2", 1e-6, 1e-1, 1e-4, format="%.6f")
# --- Additional Classification Models ---
elif model_name == "Support Vector Classifier":
hyperparams["C"] = st.slider("Regularization parameter (C)", 0.1, 100.0, 1.0)
hyperparams["kernel"] = st.selectbox("Kernel", ["linear", "rbf", "poly", "sigmoid"])
elif model_name == "Decision Tree Classifier":
hyperparams["max_depth"] = st.number_input("Max Depth", min_value=1, max_value=50, value=10)
hyperparams["min_samples_split"] = st.number_input("Min Samples Split", min_value=2, max_value=10, value=2)
elif model_name == "K-Nearest Neighbors Classifier":
hyperparams["n_neighbors"] = st.number_input("Number of Neighbors", min_value=1, max_value=100, value=5)
hyperparams["weights"] = st.selectbox("Weight Function", ["uniform", "distance"])
elif model_name == "Gradient Boosting Classifier":
hyperparams["n_estimators"] = st.number_input("Number of Estimators", min_value=10, max_value=500, value=100)
hyperparams["learning_rate"] = st.slider("Learning Rate", 0.01, 1.0, 0.1)
hyperparams["max_depth"] = st.number_input("Max Depth", min_value=1, max_value=20, value=3)
elif model_name == "AdaBoost Classifier":
hyperparams["n_estimators"] = st.number_input("Number of Estimators", min_value=10, max_value=500, value=50)
hyperparams["learning_rate"] = st.slider("Learning Rate", 0.01, 1.0, 0.1)
elif model_name == "Gaussian Naive Bayes":
hyperparams["var_smoothing"] = st.slider("Var Smoothing", 1e-12, 1e-8, 1e-9, format="%.12f")
elif model_name == "Quadratic Discriminant Analysis":
hyperparams["reg_param"] = st.slider("Regularization Parameter", 0.0, 1.0, 0.0)
elif model_name == "Linear Discriminant Analysis":
hyperparams["solver"] = st.selectbox("Solver", ["svd", "lsqr", "eigen"])
return hyperparams
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