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generate_suppl_plots.py
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#####################################################################################
######### Code to generate the plots in the supplementary material section ##########
#####################################################################################
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score
import seaborn as sns
import matplotlib.pylab as plt
#FIGURE S1: NN
list_hyparams = []
list_models=[[268,268,268,268],
[200,200,200,200],
[100,100,100,100],
[50,50,50,50],
[268,200,100,50],
[268,128,64,32],
[128,64,32,16],
[512,256,128,64],
[250,250,250,250],
[250,250,250],
[250,250],
[250],
[500,500,500,500],
[500,500,500],
[500,500],
[500],
[125,125,125,125],
[125,125,125],
[125,125],
[125]
]
list_hyparams.append(list_models)
list_cm=[]
acc_nn=[]
for i in range(20):
acc_temp=[]
cm_temp=[]
for j in range(50):
results=pd.read_csv('./results/task_tr_task_test_nn_models/categorical_'+str(i)+'_'+ str(j)+'_pred.csv')
acc_temp.append(accuracy_score(results['true'].values,results['pred'].values))
acc_nn.append(acc_temp)
acc_nn = np.asarray(acc_nn).reshape(20,5,10)
plt.style.use('default')
fig, ax = plt.subplots()
for i in range(acc_nn.shape[0]):
temp=acc_nn[i,:,:].squeeze()
i = i + 1
y_to_plot=np.linspace(i-0.25, i+0.25, num=5)
x_to_plot = 100*np.mean(temp, axis=1)
err_to_plot = 100*np.std(temp, axis=1)
ax.errorbar(y=y_to_plot, x=x_to_plot, xerr=err_to_plot,
c=sns.xkcd_rgb["dark salmon"],
markersize=2,
elinewidth=1,
ecolor='g',
fmt='o',
capthick=2)
ax.plot([np.mean(x_to_plot), np.mean(x_to_plot)], [i-0.25,i+0.25],
lw =3, color='k')
ax.set_yticks(range(1,21))
ax.set_yticklabels([str(elem) for elem in list_models],
size='small',fontweight='bold')
ax.set_title('NEURAL NETWORKS',size = 20,fontweight='bold')
ax.set_ylabel('')
ax.set_xlabel('Accuracy (%)',size =15,fontweight='bold')
plt.savefig('./results/plots/supplementary/S1_fig.tiff',
dpi=300,bbox_inches='tight')
#FIGURE S2: RF multi
folder_path='./results/rf_multi_grid/'
params = np.load(folder_path + 'param_grid.npy')
list_hyparams.append(params)
acc_rf_multi = []
for i in range(50):
res=pd.read_csv(folder_path + 'res_fold_' + str(i)+'.csv')
acc_rf_multi.append([accuracy_score(res.iloc[:,0], res.iloc[:,j]) for j in range(1,res.shape[1])])
acc_rf_multi = np.asarray(acc_rf_multi)
acc_rf_multi = acc_rf_multi.reshape(5,10,acc_rf_multi.shape[1])
acc_rf_multi = np.moveaxis(acc_rf_multi, -1, 0)
fig, axs = plt.subplots(ncols=2, sharey=True)
axs=axs.flatten()
for i in range(acc_rf_multi.shape[0]):
temp=acc_rf_multi[i,:,:].squeeze()
i = i + 1
y_to_plot=np.linspace(i-0.25, i+0.25, num=5)
x_to_plot = 100*np.mean(temp, axis=1)
err_to_plot = 100*np.std(temp, axis=1)
#print(x_to_plot)
#ax.scatter(x=x_to_plot, y=100*y_to_plot, s=4, c=sns.xkcd_rgb["dark salmon"])
axs[0].errorbar(y=y_to_plot, x=x_to_plot, xerr=err_to_plot,
c=sns.xkcd_rgb["dark salmon"],
markersize=2,
elinewidth=1,
ecolor='g',
fmt='o',
capthick=2)
axs[0].plot([np.mean(x_to_plot), np.mean(x_to_plot)], [i-0.25,i+0.25],
lw =3, color='k')
axs[0].set_yticks(range(1,acc_rf_multi.shape[0]+1))
axs[0].set_yticklabels([str(param).replace("estimators", "trees") for param in params],
size='small',fontweight='bold')
axs[0].set_title('RF MULTI',size = 20,fontweight='bold')
axs[0].set_ylabel('')
axs[0].set_xlabel('Accuracy (%)',size =15,fontweight='bold')
#plt.savefig('./results/plots/supplementary/accuracies_tfmri_rf_multi.png',
# dpi=600,bbox_inches='tight')
#FIGURE S3: RF ovr
folder_path='./results/rf_ovr_grid/'
params = np.load(folder_path + 'param_grid.npy')
list_hyparams.append(params)
acc_rf_ovr = []
for i in range(50):
res=pd.read_csv(folder_path + 'res_fold_' + str(i)+'.csv')
acc_rf_ovr.append([accuracy_score(res.iloc[:,0], res.iloc[:,j]) for j in range(1,res.shape[1])])
acc_rf_ovr = np.asarray(acc_rf_ovr)
acc_rf_ovr = acc_rf_ovr.reshape(5,10,acc_rf_ovr.shape[1])
acc_rf_ovr = np.moveaxis(acc_rf_ovr, -1, 0)
for i in range(acc_rf_ovr.shape[0]):
temp=acc_rf_ovr[i,:,:].squeeze()
i = i + 1
y_to_plot=np.linspace(i-0.25, i+0.25, num=5)
x_to_plot = 100*np.mean(temp, axis=1)
err_to_plot = 100*np.std(temp, axis=1)
axs[1].errorbar(y=y_to_plot, x=x_to_plot, xerr=err_to_plot,
c=sns.xkcd_rgb["dark salmon"],
markersize=2,
elinewidth=1,
ecolor='g',
fmt='o',
capthick=2)
axs[1].plot([np.mean(x_to_plot), np.mean(x_to_plot)], [i-0.25,i+0.25],
lw =3, color='k')
axs[1].set_title('RF OVR ',size = 20,fontweight='bold')
axs[1].set_ylabel('')
axs[1].set_xlabel('Accuracy (%)',size =15,fontweight='bold')
plt.savefig('./results/plots/supplementary/S2_fig.tiff',
dpi=300,bbox_inches='tight')
#FIGURE S4: SVM Linear
folder_path='./results/svm_ovr_linear/'
params = np.load(folder_path + 'param_grid.npy')
list_hyparams.append(params)
acc_svm_linear = []
for i in range(50):
res=pd.read_csv(folder_path + 'res_fold_' + str(i)+'.csv')
acc_svm_linear.append([accuracy_score(res.iloc[:,0], res.iloc[:,j]) for j in range(1,res.shape[1])])
acc_svm_linear = np.asarray(acc_svm_linear)
acc_svm_linear = acc_svm_linear.reshape(5, 10, acc_svm_linear.shape[1])
acc_svm_linear = np.moveaxis(acc_svm_linear, -1, 0)
fig, ax = plt.subplots()
for i in range(acc_svm_linear.shape[0]):
temp=acc_svm_linear[i,:,:].squeeze()
i = i + 1
y_to_plot=np.linspace(i-0.25, i+0.25, num=5)
x_to_plot = 100*np.mean(temp, axis=1)
err_to_plot = 100*np.std(temp, axis=1)
ax.errorbar(y=y_to_plot, x=x_to_plot, xerr=err_to_plot,
c=sns.xkcd_rgb["dark salmon"],
markersize=2,
elinewidth=1,
ecolor='g',
fmt='o',
capthick=2)
ax.plot([np.mean(x_to_plot), np.mean(x_to_plot)], [i-0.25,i+0.25],
lw =3, color='k')
ax.set_yticks(range(1,acc_svm_linear.shape[0]+1))
ax.set_yticklabels([str(param).replace('alpha','C') for param in params],
size='small',fontweight='bold')
ax.set_title('SVM LINEAR KERNEAL' ,size = 20,fontweight='bold')
ax.set_ylabel('')
ax.set_xlabel('Accuracy (%)',size =15,fontweight='bold')
plt.savefig('./results/plots/supplementary/S3_fig.tiff',
dpi=300,bbox_inches='tight')
#FIGURE S4: SVM KERNEL
folder_path='./results/svm_ovr_kernel/'
params = np.load(folder_path + 'param_grid.npy')
list_hyparams.append(params)
acc_svm_kernel = []
for i in range(50):
res=pd.read_csv(folder_path + 'res_fold_' + str(i)+'.csv')
acc_svm_kernel.append([accuracy_score(res.iloc[:,0], res.iloc[:,j]) for j in range(1,res.shape[1])])
acc_svm_kernel = np.asarray(acc_svm_kernel)
acc_svm_kernel = acc_svm_kernel.reshape(5,10,acc_svm_kernel.shape[1])
acc_svm_kernel = np.moveaxis(acc_svm_kernel, -1, 0)
fig, ax = plt.subplots()
for i in range(acc_svm_kernel.shape[0]):
temp=acc_svm_kernel[i,:,:].squeeze()
i = i + 1
y_to_plot=np.linspace(i-0.25, i+0.25, num=5)
x_to_plot = 100*np.mean(temp, axis=1)
err_to_plot = 100*np.std(temp, axis=1)
ax.errorbar(y=y_to_plot, x=x_to_plot, xerr=err_to_plot,
c=sns.xkcd_rgb["dark salmon"],
markersize=2,
elinewidth=1,
ecolor='g',
fmt='o',
capthick=2)
ax.plot([np.mean(x_to_plot), np.mean(x_to_plot)], [i-0.25,i+0.25],
lw =3, color='k')
ax.set_yticks(range(1,acc_svm_kernel.shape[0]+1))
ax.set_yticklabels([str(param).replace('alpha','C').replace('gamma', '$\gamma$') for param in params],
size='small',fontweight='bold')
ax.set_title('SVM RBF KERNEL' ,size = 20,fontweight='bold')
ax.set_ylabel('')
ax.set_xlabel('Accuracy (%)',size =15,fontweight='bold')
plt.savefig('./results/plots/supplementary/S4_fig.tiff',
dpi=300,bbox_inches='tight')