def metric_details_by_lickwell(path, timeshift):
lick_id_details, lick_id_details_k = get_metric_details(
path + "output/", timeshift)
fractions_decoded_2, std_2, n_2 = return_fraction_decoded_and_std(
lick_id_details=lick_id_details,
lick_id_details_k=lick_id_details_k,
parameter=lick_id_details.licks_decoded,
filter=lick_id_details.next_well_licked_2)
fractions_decoded_3, std_3, n_3 = return_fraction_decoded_and_std(
lick_id_details=lick_id_details,
lick_id_details_k=lick_id_details_k,
parameter=lick_id_details.licks_decoded,
filter=lick_id_details.next_well_licked_3)
fractions_decoded_4, std_4, n_4 = return_fraction_decoded_and_std(
lick_id_details=lick_id_details,
lick_id_details_k=lick_id_details_k,
parameter=lick_id_details.licks_decoded,
filter=lick_id_details.next_well_licked_4)
fractions_decoded_5, std_5, n_5 = return_fraction_decoded_and_std(
lick_id_details=lick_id_details,
lick_id_details_k=lick_id_details_k,
parameter=lick_id_details.licks_decoded,
filter=lick_id_details.next_well_licked_5)
bar_values = [
fractions_decoded_2, fractions_decoded_3, fractions_decoded_4,
fractions_decoded_5
]
std_well = [std_2, std_3, std_4, std_5]
n_well = [n_2, n_3, n_4, n_5]
# so error bars show up correctly
std_lower, std_upper = get_corrected_std(bar_values, std_well)
return bar_values, std_lower, std_upper, n_well
from src.metrics import print_metric_details, get_metric_details
from random import seed
import numpy as np
if __name__ == '__main__':
# Removes a portion of the lick events and checks if performance changes
model_path = "C:/Users/NN/Desktop/Master/experiments/Lickwell_prediction/"
path_hc = "C:/Users/NN/Desktop/Master/experiments/Lickwell_prediction/MLP_PFC/output/"
path_pfc = model_path + "MLP_PFC_tleave/output/"
save_path = model_path
barcolor = "darkviolet"
add_trial_numbers = True
# load fraction and std data
lick_id_details, lick_id_details_k = get_metric_details(path_hc, 1)
# return_sample_count_by_lick_id(lick_id_details_k_1)
x_1, std_1, n_1 = get_accuracy_for_comparison_2(lick_id_details,
lick_id_details_k)
std_lower_1, std_upper_1 = get_corrected_std(x_1, std_1)
lick_id_details, lick_id_details_k = get_metric_details(path_hc, -1)
# return_sample_count_by_lick_id(lick_id_details_k_1)
x_2, std_2, n_2 = get_accuracy_for_comparison_2(lick_id_details,
lick_id_details_k)
std_lower_2, std_upper_2 = get_corrected_std(x_2, std_2)
shift = -1
lick_id_details, lick_id_details_k = get_metric_details(path_pfc, 1)
# return_sample_count_by_lick_id(lick_id_details_k_1)
x_3, std_3, n_3 = get_accuracy_for_comparison_2(lick_id_details,
# c = "goldenrod"
# ax.plot(ie,color=c,marker="X")
# y = []
# # ax.plot(total_fraction,color="r")
# plt.show()
y_list = []
n_list = []
for shift in [1,-1]: # TODO
for path in model_path_list:
y_n = []
n_n = []
for i in plotrange:
lick_id_details, lick_id_details_k = get_metric_details(path+"output/", shift,pathname_metadata="_"+str(i))
y,std,n = return_fraction_decoded_and_std(lick_id_details=lick_id_details,
lick_id_details_k=lick_id_details_k,
parameter=lick_id_details.fraction_decoded,
filter=lick_id_details.valid_licks)
y_n.append(y)
n_n.append(np.sum(n))
y_list.append(y_n)
n_list.append(n_n)#
y_list_1 = y_list[0]
n_list_1 = n_list[0]
y_list_2 = y_list[1]
n_list_2 = n_list[1]
y_list_3 = y_list[2]
n_list_3 = n_list[2]
y_list_4 = y_list[3]
if __name__ == '__main__':
# Removes a portion of the lick events and checks if performance changes
model_path = "C:/Users/NN/Desktop/Master/experiments/Lickwell_prediction/MLP_HC/"
save_path=model_path+"images/approximate_accuracy.png"
barcolor = "darkviolet"
add_trial_numbers = True
shift = -1
# load fraction and std data
path_hc = model_path + "output/"
lick_id_details, lick_id_details_k = get_metric_details(path_hc, shift)
x_1, std_1, n_1 = get_accuracy(lick_id_details, lick_id_details_k)
x_2, std_2, n_2 = get_accuracy(lick_id_details, lick_id_details_k, d=1)
x_3, std_3, n_3 = get_accuracy(lick_id_details, lick_id_details_k, d=2)
path_pfc = "C:/Users/NN/Desktop/Master/experiments/Lickwell_prediction/MLP_PFC/output/"
lick_id_details, lick_id_details_k = get_metric_details(path_pfc, shift)
x_4, std_4, n_4 = get_accuracy(lick_id_details, lick_id_details_k)
x_5, std_5, n_5 = get_accuracy(lick_id_details, lick_id_details_k,d=1)
x_6, std_6, n_6 = get_accuracy(lick_id_details, lick_id_details_k,d=2)
std_lower_1, std_upper_1 = get_corrected_std(x_1, std_1)
std_lower_2, std_upper_2 = get_corrected_std(x_2, std_2)
std_lower_3, std_upper_3 = get_corrected_std(x_3, std_3)
std_lower_4, std_upper_4 = get_corrected_std(x_4, std_4)
std_lower_5, std_upper_5 = get_corrected_std(x_5, std_5)
std_lower_6, std_upper_6 = get_corrected_std(x_6, std_6)
def plot_performance_comparison(path_1,
shift_1,
path_2,
shift_2,
title_1,
title_2,
save_path,
barcolor="darkviolet",
add_trial_numbers=False):
# load fraction and std data
lick_id_details_1, lick_id_details_k_1 = get_metric_details(
path_1, shift_1)
# return_sample_count_by_lick_id(lick_id_details_k_1)
x_1, std_1, n_1 = get_accuracy_for_comparison(lick_id_details_1,
lick_id_details_k_1)
lick_id_details_2, lick_id_details_k_2 = get_metric_details(
path_2, shift_2)
x_2, std_2, n_2 = get_accuracy_for_comparison(lick_id_details_2,
lick_id_details_k_2)
std_lower_1, std_upper_1 = get_corrected_std(x_1, std_1)
std_lower_2, std_upper_2 = get_corrected_std(x_2, std_2)
# plot bar charts
width = 0.75
fontsize = 12
font = {'family': 'normal', 'size': 12}
matplotlib.rc('font', **font)
matplotlib.rc('xtick', labelsize=fontsize - 3)
ind = np.arange(5) # the x locations for the groups
fig, (ax1, ax2) = plt.subplots(2)
ax1.set_ylim(0, 1.0)
ax2.set_ylim(0, 1.0)
ax_b1 = ax1.twinx()
ax_b2 = ax2.twinx()
error_kw = {'capsize': 5, 'capthick': 1, 'ecolor': 'black'}
ax1.bar(ind,
x_1,
color=barcolor,
yerr=[std_lower_1, std_upper_1],
error_kw=error_kw,
align='center')
ax1.set_xticks(ind)
ax1.set_xticklabels([
'all licks', 'target correct', 'target false', 'prior switch',
'after switch'
])
ax_b1.set_ylim(0, 1.0)
ax1.set_title(title_1)
ax1.set_ylabel("fraction decoded correctly", fontsize=fontsize)
if add_trial_numbers is True:
for i, j in zip(ind, x_1):
if j < 0.2:
offset = 0.1
else:
offset = -0.1
ax1.annotate(int(n_1[i]), xy=(i - 0.1, j + offset))
ax2.bar(ind,
x_2,
color=barcolor,
yerr=[std_lower_2, std_upper_2],
error_kw=error_kw,
align='center')
ax2.set_xticks(ind)
ax2.set_xticklabels([
'all licks', 'target correct', 'target false', 'prior switch',
'after switch'
])
ax_b2.set_ylim(0, 1.0)
ax2.set_title(title_2)
ax2.set_ylabel("fraction decoded correctly", fontsize=fontsize)
if add_trial_numbers is True:
for i, j in zip(ind, x_2):
if j < 0.2:
offset = 0.1
else:
offset = -0.1
ax2.annotate(int(n_2[i]), xy=(i - 0.1, j + offset))
plt.tight_layout(pad=0.1, w_pad=0.5, h_pad=0)
plt.savefig(save_path)
pass
from src.metrics import print_metric_details, get_metric_details
from random import seed
import numpy as np
if __name__ == '__main__':
# Removes a portion of the lick events and checks if performance changes
model_path = "C:/Users/NN/Desktop/Master/experiments/Lickwell_prediction/MLP_HC_prior_post/"
path = model_path + "output/"
save_path = model_path + "images/"
barcolor = "darkviolet"
# load fraction and std data
shift = 1
lick_id_details, lick_id_details_k = get_metric_details(
path, shift, pathname_metadata="_prior")
# return_sample_count_by_lick_id(lick_id_details_k_1)
x_1, std_1, n_1 = get_accuracy_for_comparison_2(lick_id_details,
lick_id_details_k)
std_lower_1, std_upper_1 = get_corrected_std(x_1, std_1)
lick_id_details, lick_id_details_k = get_metric_details(
path, shift, pathname_metadata="_post")
# return_sample_count_by_lick_id(lick_id_details_k_1)
x_2, std_2, n_2 = get_accuracy_for_comparison_2(lick_id_details,
lick_id_details_k)
std_lower_2, std_upper_2 = get_corrected_std(x_2, std_2)
shift = -1
lick_id_details, lick_id_details_k = get_metric_details(
path, shift, pathname_metadata="_prior")
# return_sample_count_by_lick_id(lick_id_details_k_1)