GPBO_Emulators/make_1meth_hms.py at main · dowlinglab/GPBO_Emulators · GitHub

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import numpy as np
import signac
from itertools import combinations
from bo_methods_lib.bo_methods_lib.analyze_data import General_Analysis
from bo_methods_lib.bo_methods_lib.GPBO_Classes_plotters import Plotters

# Ignore warnings
import warnings

warnings.simplefilter("ignore", category=RuntimeWarning)
warnings.simplefilter("ignore", category=UserWarning)
warnings.simplefilter("ignore", category=DeprecationWarning)

meth_name_val_list = [1, 2, 3, 4, 5, 6, 7]
save_csv = True  # Set to False if you don't want to save/resave csvs
save_figs = True
modes = ["act", "gp", "acq"]
project = signac.get_project("GPBO_Fix")

for val in [15]:
    criteria_dict = {
        "cs_name_val": val,
        "ep_enum_val": 1,
        "gp_package": "gpflow",
        "meth_name_val": {"$in": meth_name_val_list},
    }

    for mode in modes:
        analyzer = General_Analysis(criteria_dict, project, mode, save_csv)
        plotters = Plotters(analyzer, save_figs)

        # Get Best Data from ep experiment
        ### Get Best Data from ep experiment
        df_best, job_list_best = analyzer.get_best_data()

        # Set z_choices and levels
        z_choices = ["sse_sim", "sse_mean", "sse_var", "acq"]
        levels = [100, 100, 100, 100]

        # Loop over best jobs
        for i in range(len(job_list_best)):
            # Get jobs, runs, and iters to examine
            job = job_list_best[i]
            run_num = int(df_best["Run Number"].iloc[i])
            bo_iter = int(df_best["BO Iter"].iloc[i])

            # Back out number of parameters
            string_val = df_best["Theta Min Obj"].iloc[0]
            try:
                numbers = [
                    float(num)
                    for num in string_val.replace("[", "").replace("]", "").split()
                ]
            except:
                numbers = [float(num) for num in string_val]

            # Create list of parameter pair combinations
            dim_theta = len(np.array(numbers).reshape(-1, 1))
            dim_list = np.linspace(0, dim_theta - 1, dim_theta)
            pairs = len((list(combinations(dim_list, 2))))

            # Loop over parameter pairs
            if pairs < 3:
                for pair in range(pairs):
                    plotters.plot_hms_gp_compare(
                        job, run_num, bo_iter, pair, z_choices, levels
                    )