from utils.DataSaver import DataSaver
from utils.draw import draw_participant_and_simulation
import numpy as np
import matplotlib.pyplot as plt
participant_analysis_data = DataSaver.load_from_file("analysis_result.pkl")
model_names = ["MF", "MB", "MF_no_reinit", "MF_attention"
] # could be "MF", "MB", "MF_no_reinit", "MF_attention"
model_number = len(model_names)
simulate_analysis_data = {}
for name in model_names:
simulate_analysis_data[name] = []
NUMBER_OF_REPEAT = 50
for i in range(NUMBER_OF_REPEAT):
for name in model_names:
analysis_result = DataSaver.load_from_file(
"simulate_analysis_result/" + name + "_simulate_analysis_result" +
str(i) + ".pkl")
simulate_analysis_data[name].append(analysis_result)
def draw(metric, method, simulate_data, participant_data, randomized):
draw_participant_and_simulation(
participant_data,
simulate_data,
metric,
title=metric + "_in_" + method + "_method_" +
import matplotlib.pyplot as plt
model_names = ["MF_attention"
] # could be "MF", "MB", "MF_no_reinit", "MF_attention"
randomized = True
model_number = len(model_names)
simulate_analysis_data = {}
for name in model_names:
simulate_analysis_data[name] = []
NUMBER_OF_REPEAT = 50
for i in range(NUMBER_OF_REPEAT):
for name in model_names:
analysis_result = DataSaver.load_from_file(
"simulate_analysis_result_all_" +
("randomized" if randomized else "block") + "/" + name +
"_simulate_analysis_result" + str(i) + ".pkl")
simulate_analysis_data[name].append(analysis_result)
def draw(metric, method, simulate_data_even, simulate_data_odd):
draw_even_odd_simulation(
simulate_data_even,
simulate_data_odd,
metric,
title=metric + "_in_" + method + "_method_" +
("randomized" if randomized else "block") + "_even_odd_comparison",
save_path="participant_simulation_comparison_even_odd/",
save=True,
show=True,
smooth=False)
from utils.DataSaver import DataSaver
import numpy as np
import matplotlib.pyplot as plt
participant_analysis_data = DataSaver.load_from_file("analysis_result.pkl")
step_data = participant_analysis_data.get_data("step")
mean = np.mean(step_data, axis=1)
sum = np.sum(step_data, axis=1)
plt.hist(mean)
plt.show()
plt.hist(sum)
plt.show()
for i in range(36):
if sum[i] > 1324:
print(i)
import json
import os
import numpy as np
import pandas
from data_analysis.analysis_optimal import optimal_probability
from utils.DataSaver import DataSaver
if __name__ == "__main__":
NUMBER_OF_PARTICIPANT = 36
TRIAL_LENGTH = 144
NEED_UPDATE_DATA_FRAME = False
saver = DataSaver([
"step", "time", "normalized_time", "optimal", "optimal_inner",
"optimal_outer", "optimal_last"
], NUMBER_OF_PARTICIPANT, TRIAL_LENGTH)
all_data = [[] for _ in range(NUMBER_OF_PARTICIPANT)]
optimal_data_frame = pandas.DataFrame(columns=[
"step", "reaction_time", "normalized_reaction_time", "optimal_p",
"optimal_inner", "optimal_outer", "optimal_last", "timestep", "block",
"condition", "participant"
])
for lists in os.listdir("data/"):
path = os.path.join("data/", lists)
print("Loading ", path)
split = lists.split("_")
participant_id = int(split[0])
rawFile = open(path, "r")
from utils.DataSaver import DataSaver
from utils.draw import draw_participant_and_simulation
import matplotlib.pyplot as plt
participant_analysis_data = DataSaver.load_from_file("analysis_result.pkl")
simulate_analysis_data = {
"MF": DataSaver.load_from_file("MF_simulate_analysis_result.pkl"),
"MB": DataSaver.load_from_file("MB_simulate_analysis_result.pkl")
}
def draw(metric, method, randomized=True):
if randomized:
draw_participant_and_simulation(
participant_analysis_data.get_data(metric)[::2, :],
simulate_analysis_data[method].get_data(metric)[::2, :],
metric,
title=metric + "_in_" + method + "_method_randomized_condition",
save_path="participant_simulation_comparison_all/",
save=False,
show=False)
else:
draw_participant_and_simulation(
participant_analysis_data.get_data(metric)[1::2, :],
simulate_analysis_data[method].get_data(metric)[1::2, :],
metric,
title=metric + "_in_" + method + "_method_block_condition",
save_path="participant_simulation_comparison_all/",
save=False,
show=False)
from utils.DataSaver import DataSaver
from data_analysis.analysis_optimal import optimal_probability
MODEL_NAME = "MF_attention"
f = open(MODEL_NAME + "_fit_result.csv", "r")
fit_result = csv.DictReader(f)
NUMBER_OF_PARTICIPANT = 36
TRIAL_LENGTH = 144
NUMBER_OF_REPEAT = 50
savers = []
for _ in range(NUMBER_OF_REPEAT):
savers.append(
DataSaver(
["optimal", "optimal_inner", "optimal_outer", "optimal_last"],
NUMBER_OF_PARTICIPANT, TRIAL_LENGTH))
for participant_param in fit_result:
participant_id = int(participant_param["participant"])
# Use the fitted model to simulate
BASE_PATH = "simulate_data/%s/%02d/" % (MODEL_NAME, participant_id)
# simulate_MF(randomized=participant_id % 2 == 0,
# alpha=float(participant_param["alpha"]),
# tau=float(participant_param["tau"]),
# repeat=NUMBER_OF_REPEAT,
# gamma=float(participant_param["gamma"]),
# forget=float(participant_param["forget_MF"]),
# path=BASE_PATH,
# seed=participant_id)
# simulate_MB(randomized=participant_id % 2 == 0,
def save_results_to_csv(results):
saver = DataSaver(results_dir=RESULTS_DIR, results_ext=RESULTS_EXT)
saver.save(result=results, filename=RESULTS_FILENAME)
from utils.DataSaver import DataSaver
def transform_and_plot(data, data_name):
randomized_data = data[::2, :]
draw_metrics(randomized_data,
data_name,
"%s under %s condition in participants" % (data_name, "randomized"),
# extra_data_names=["MF_randomized", "optimal_randomized", "random_randomized", "MB_randomized"],
smooth=True,
save_npy=False,
draw_individual=True,
annotate_block_mean=False)
block_data = data[1::2, :]
draw_metrics(block_data,
data_name,
"%s under %s condition in participants" % (data_name, "block"),
# extra_data_names=["MF_block", "optimal_block", "random_block", "MB_block"],
smooth=True,
save_npy=False,
draw_individual=True,
annotate_block_mean=False)
if __name__ == "__main__":
saver = DataSaver.load_from_file("analysis_result.pkl")
for metric in saver.data_names:
transform_and_plot(saver.get_data(metric), metric)