graph_recognition_w_attn/plot_results.py

import os
import sys
import json
import pickle
import matplotlib.pyplot as plt
from tqdm import tqdm
import numpy as np
from config_ import ModelConfig
from train_and_eval import calculate_f1_score
from sklearn.metrics import f1_score

if len(sys.argv) < 2:
    data_dir = "datasets/kuramoto_dataset"
else:
    data_dir = "datasets/" + sys.argv[1]

datapoints = {}
THRESHOLD = 0.2

for folder in tqdm(os.listdir(data_dir)):
    num_agents = int(folder.split("_")[1]) # Extract num agents
    
    folder_path = os.path.join(data_dir, folder)

    for noise_level in os.listdir(os.path.join(folder_path, "results/NoiseType.NONE")):


        # Load model config from summary json
        with open(os.path.join(folder_path, "results/NoiseType.NONE", noise_level, "summary_results.json"), "r") as f:
            summary_results = json.load(f)

        
        for i, graph in enumerate(os.listdir(folder_path)):

            # train_summary_results
            summ_results = summary_results[i-1]

            if graph == "results": # ignore the result folder
                continue

            graph_path = os.path.join(folder_path, graph)

            # Load run data
            with open(os.path.join(folder_path, graph), "r") as f:
                run_data = json.load(f)

            true_graph = np.array(run_data["adjacency_matrix"])
            
            learned_graph = np.array(summ_results["raw_attention"])

            predicted_graph = (learned_graph > THRESHOLD).astype(int)

            true_flat = true_graph.flatten()
            pred_flat = predicted_graph.flatten()
            
            calc_f1_score = f1_score(true_flat, pred_flat)


            datapoints[num_agents] = datapoints.get(num_agents, [])
            datapoints[num_agents].append(calc_f1_score)


    for key in datapoints.keys():
        try:
            datapoints[key] = sum(datapoints[key])/len(datapoints[key])
        except:
            continue


    x = []
    y = []

    for item in datapoints.items():
        x.append(item[0])
        y.append(item[1])

    plt.plot(x, y)
    plt.show()