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

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import numpy as np
import pandas as pd
import signac
import os
from itertools import combinations
import signac

import bo_methods_lib
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)
# from sklearn.exceptions import InconsistentVersionWarning
# warnings.filterwarnings(action='ignore', category=InconsistentVersionWarning)

# Set Stuff
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_nonoise")

for mode in modes:
    for val in [11, 17, 2, 3, 15, 14, 12, 13, 10, 1]:
        criteria_dict = {
            "cs_name_val": val,
            "ep_enum_val": 1,
            "gp_package": "gpflow",
            "meth_name_val": {"$in": meth_name_val_list},
        }

        analyzer = General_Analysis(criteria_dict, project, mode, save_csv)
        plotters = Plotters(analyzer, save_figs)

        ###Get all data from experiments
        df_all_jobs, job_list, theta_true_data = analyzer.get_df_all_jobs(
            criteria_dict, save_csv
        )
        ### Get Best Data from ep experiment
        df_best, job_list_best = analyzer.get_best_data()

        # Loop over z_choices to make comparison line plots
        z_choices = ["sse", "min_sse", "acq"]
        titles = [
            "Min SSE Parameter Values",
            "Min SSE Parameter Values Overall",
            "Optimal Acq Func Parameter Values",
        ]

        # Make Parity Plots
        plotters.make_parity_plots()

        # Get best plots for all objectives with all 7 methods on each subplot
        plotters.plot_objs_all_methods(z_choices)

        # Get plot with each method on a different subplot for each obj
        for i in range(len(z_choices)):
            plotters.plot_one_obj_all_methods(z_choices[i])

        # Can optionally make plots for hyperparameters and theta values
        # for i in range(len(job_list_best)):
        #     #Plot hyperparameters
        #     plotters.plot_hypers(job_list_best[i])

        #     #Plot param values at min_sse, the best theta_values of min_sse overall, and param values at max ei
        #     for j in range(len(z_choices)):
        #         plotters.plot_thetas(job_list_best[i], z_choices[j], title = titles[j])