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