visualizing_gpr.py

import numpy as np
import sys
import matplotlib.pyplot as plt
import os
import pickle
sys.path.append(r"C:\Users\hoodl\Documents\GitHub\DataAnalysis")
from GPR_Optimizer import GPR_Optimizer
import time
import h5py
#%%

# Function to plot data from an .h5 file
def plot_from_h5(file_path, x_dataset_name, y_dataset_name):
    # Open the .h5 file
    with h5py.File(file_path, 'r') as file:
        # The datasets are in the 'datasets' group, so we prefix the names with 'datasets/'
        x_data = file[f'datasets/{x_dataset_name}'][:]
        y_data = file[f'datasets/{y_dataset_name}'][:]
        
    # Plotting the graph with points only, no lines
    plt.figure(figsize=(10, 5))
    plt.scatter(x_data, y_data, marker='o')  # Changed from plot to scatter for points
    plt.xlabel(x_dataset_name)
    plt.ylabel(y_dataset_name)
    plt.title(f'{y_dataset_name} as a Function of {x_dataset_name}')
    plt.grid(True)
    plt.show()
    
def print_most_recently_modified_file(directory):
    # Ensure the provided directory exists
    if not os.path.exists(directory):
        print(f"The directory {directory} does not exist.")
        return

    # Initialize variables to track the most recent file
    most_recent_file = None
    most_recent_mod_time = 0
    
    # Iterate over all files in the directory
    for filename in os.listdir(directory):
        file_path = os.path.join(directory, filename)

        # Check if it's a file and not a directory
        if os.path.isfile(file_path):
            mod_time = os.path.getmtime(file_path)

            # Update the most recent file and its modification time
            if mod_time > most_recent_mod_time:
                most_recent_mod_time = mod_time
                most_recent_file = filename


    print(f"Importing: {most_recent_file}")

    return str(directory)+str(most_recent_file)
#%%
loop = False
if loop == True:
    while 1:
        path = print_most_recently_modified_file(r'C:\\Users\\hoodl\\Desktop\\save_opt_folder\\')
        with open(path, 'rb') as file: opt = pickle.load(file)
        
        res = opt.get_result()
        print("Best Point:", np.round(res.x,3), res.fun)
        
        plt.figure()
        opt.plot_objective()
        plt.title("Best Point:"+str(np.round(res.x,3))+str(res.fun), y=3.5)
        plt.show()
        
        time.sleep(10)
else:
    path = print_most_recently_modified_file(r'C:\\Users\\hoodl\\Desktop\\save_opt_folder\\')
    with open(path, 'rb') as file: opt = pickle.load(file)
    
    res = opt.get_result()
    # print("Best Point:", np.round(res.x,3), res.fun)
    
    for i in np.argsort(res['func_vals'])[:5]:
        print(repr(np.round(res['x_iters'][i],3)), res['func_vals'][i])
    print('\n')
        
    
    try:
        plt.figure()
        opt.plot_convergence()
        plt.show()
        
        # plt.figure()
        # opt.plot_objective()  # Partial dependence (can take > 1 min to run for large num of variable)
        # plt.title("Best Point:"+str(np.round(res.x,3))+str(res.fun), y=3.5)
        # plt.show()
        
        plt.figure()
        opt.plot_hyperparameters()
        plt.show()
        
        plt.figure()
        opt.plot_cross_section() # X-section through best point (let me know if you want predicted best point rather than best point)
        plt.show()
        
        print("Predicted best:", np.round(opt.expected_minimum()[0],3))
        
        
        
            
    except Exception as e:
        print("Only",len(res['x_iters']),"points so far!",e)
        
#%%


# plot_from_h5(r'C:\Users\hoodl\Documents\GitHub\Artiq\Experiments\results\2024-01-19\20\000061050-gpr_manager.h5', 
#              'variable', 'loading_rate_roi0')

#%%
if 0:
    print("self.v subs:",
    repr(np.round(
    [1.966+0.066-0.386+-0.106, -2.041+0.99 -3.585 +3.647, 1.115-0.995+5.558+1.627, -5.313-0.371-0.333+0.139, -1.2]
    ,3)))

if 1:
    x = repr(np.round(
    [ 1.54-0.33-1.48,  -0.989-2.325-0.32,  7.305+1.646+0.128, -5.878+2.10-1.15  ]+[0.488-0.213 -0.173+0.671, -1.044+0.505+1.217+1.065, 8.714+0.551-2.43-1.391, -9.288+3.58+0.25-3.49]
    ,3))
    
    opt_x = [ 2.081, -0.374, -0.746, -0.094, -0.031,  0.08 ,  0.898, -0.23 ]