From 167685bbd3c3f02e818612c05eb15be7d917eb4a Mon Sep 17 00:00:00 2001 From: Khuyen Tran Date: Fri, 30 May 2025 16:30:26 -0500 Subject: [PATCH] Revert "Adding the GPR Notebook for Hyperparameter tuning" --- GPR_Optimization.py | 181 -------------------------------- public/GPR_Optimization.html | 82 --------------- public/index.html | 16 ++- public/llm/diffbot_llm.html | 82 +++++++++++++++ public/llm/lchain_deepseek.html | 82 +++++++++++++++ public/llm/lchain_ollama.html | 82 +++++++++++++++ public/temp.html | 82 --------------- public/temp/temp.html | 82 --------------- 8 files changed, 258 insertions(+), 431 deletions(-) delete mode 100644 GPR_Optimization.py delete mode 100644 public/GPR_Optimization.html create mode 100644 public/llm/diffbot_llm.html create mode 100644 public/llm/lchain_deepseek.html create mode 100644 public/llm/lchain_ollama.html delete mode 100644 public/temp.html delete mode 100644 public/temp/temp.html diff --git a/GPR_Optimization.py b/GPR_Optimization.py deleted file mode 100644 index 7d6f814e..00000000 --- a/GPR_Optimization.py +++ /dev/null @@ -1,181 +0,0 @@ -# /// script -# requires-python = ">=3.12" -# dependencies = [ -# "marimo", -# "matplotlib==3.10.3", -# "numpy==2.2.6", -# "scikit-learn==1.6.1", -# "scipy==1.15.3", -# ] -# /// - -import marimo - -__generated_with = "0.13.7" -app = marimo.App(width="medium") - - -@app.cell -def _(): - import numpy as np - import matplotlib.pyplot as plt - from sklearn.gaussian_process import GaussianProcessRegressor - from sklearn.gaussian_process.kernels import Matern, WhiteKernel, ConstantKernel as C - - def black_box_function(x): - return - (np.sin(3*x) + 0.5 * x) - return ( - C, - GaussianProcessRegressor, - Matern, - WhiteKernel, - black_box_function, - np, - plt, - ) - - -@app.cell -def _(black_box_function, np, plt): - X = np.linspace(0, 5.5, 1000).reshape(-1, 1) - y = black_box_function(X) - plt.plot(X, y) - plt.title("Black-box function") - plt.xlabel("x") - plt.ylabel("f(x)") - plt.show() - return X, y - - -@app.cell -def _(black_box_function, np): - X_grid = np.linspace(0, 2, 100).reshape(-1, 1) - y_grid = black_box_function(X_grid) - x_best = X_grid[np.argmax(y_grid)] - return - - -@app.cell -def _(black_box_function, np): - # Initial sample points (simulate prior evaluations) - X_sample = np.array([[1.0], [3.0], [5.5]]) - y_sample = black_box_function(X_sample) - return X_sample, y_sample - - -@app.cell -def _(C, GaussianProcessRegressor, Matern, WhiteKernel, X_sample, y_sample): - # Define the kernel - kernel = C(1.0) * Matern(length_scale=1.0, nu=2.5) + WhiteKernel(noise_level=1e-5, noise_level_bounds=(1e-10, 1e1)) - - # Create and fit the Gaussian Process model - gpr = GaussianProcessRegressor(kernel=kernel, alpha=0.0) - gpr.fit(X_sample, y_sample) - return (gpr,) - - -@app.cell -def _(X, X_sample, gpr, plt, y, y_sample): - # Predict across the domain - mu, std = gpr.predict(X, return_std=True) - - # Plot the result - plt.figure(figsize=(10, 5)) - plt.plot(X, y, 'k--', label="True function") - plt.plot(X, mu, 'b-', label="GPR mean") - plt.fill_between(X.ravel(), mu - std, mu + std, alpha=0.3, label="Uncertainty") - plt.scatter(X_sample, y_sample, c='red', label="Samples") - plt.legend() - plt.title("Gaussian Process Fit") - plt.xlabel("x") - plt.ylabel("f(x)") - plt.show() - return - - -@app.cell -def _(np): - from scipy.stats import norm - - def expected_improvement(X, X_sample, y_sample, model, xi=0.01): - mu, std = model.predict(X, return_std=True) - mu_sample_opt = np.min(y_sample) - - with np.errstate(divide='warn'): - imp = mu_sample_opt - mu - xi # because we are minimizing - Z = imp / std - ei = imp * norm.cdf(Z) + std * norm.pdf(Z) - ei[std == 0.0] = 0.0 - - return ei - - return (expected_improvement,) - - -@app.cell -def _(X, X_sample, expected_improvement, gpr, np, plt, y_sample): - ei = expected_improvement(X, X_sample, y_sample, gpr) - - plt.figure(figsize=(10, 4)) - plt.plot(X, ei, label="Expected Improvement") - plt.axvline(X[np.argmax(ei)], color='r', linestyle='--', label="Next sample point") - plt.title("Acquisition Function (Expected Improvement)") - plt.xlabel("x") - plt.ylabel("EI(x)") - plt.legend() - plt.show() - - return - - -@app.cell -def _(X, black_box_function, expected_improvement, gpr, np): - def bayesian_optimization(n_iter=10): - # Initial data - X_sample = np.array([[1.0], [2.5], [4.0]]) - y_sample = black_box_function(X_sample) - - for i in range(n_iter): - gpr.fit(X_sample, y_sample) - ei = expected_improvement(X, X_sample, y_sample, gpr) - x_next = X[np.argmax(ei)].reshape(-1, 1) - - # Evaluate the function at the new point - y_next = black_box_function(x_next) - - # Add the new sample to our dataset - X_sample = np.vstack((X_sample, x_next)) - y_sample = np.append(y_sample, y_next) - return X_sample, y_sample - - return (bayesian_optimization,) - - -@app.cell -def _(bayesian_optimization): - X_opt, y_opt = bayesian_optimization(n_iter=10) - - return X_opt, y_opt - - -@app.cell -def _(X, X_opt, black_box_function, plt, y_opt): - # Plot final sampled points - plt.plot(X, black_box_function(X), 'k--', label="True function") - plt.scatter(X_opt, y_opt, c='red', label="Sampled Points") - plt.title("Bayesian Optimization with Gaussian Process") - plt.xlabel("x") - plt.ylabel("f(x)") - plt.legend() - plt.show() - - return - - -@app.cell -def _(): - return - - -if __name__ == "__main__": - app.run() diff --git a/public/GPR_Optimization.html b/public/GPR_Optimization.html deleted file mode 100644 index 6bb3dc95..00000000 --- a/public/GPR_Optimization.html +++ /dev/null @@ -1,82 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - GPR Optimization - - - - - - -
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