'awareness',
'confidence',
]
target_name = 'awareness'
experiment = 'e1'
# for some of the variables, we need to rescale them to a more preferable range like 0-1
name_for_scale = ['awareness']
# ['ah', 'av', 'bj', 'cm', 'db', 'ddb', 'fcm', 'kf', 'kk', 'ml', 'qa','sk', 'yv']
# get one of the participants' data
participant = 'db'
df_sub = df[df['participant'] == participant]
# for 1-back to 4-back
for n_back in np.arange(1,5):
X,y,groups = utils.get_features_targets_groups(
df_sub.dropna(),
n_back = n_back,
names = name_for_scale,
independent_variables = feature_names,
dependent_variable = [target_name,'correctness'])
X,y,groups = shuffle(X,y,groups)
y,correctness = y[:,0],y[:,1]
for model_name,model in utils.make_clfs().items():
cv = LeaveOneOut()
print('{}-back,{}'.format(n_back,model_name))
preds = cross_val_predict(model,X,y,groups=groups,cv=cv,method='predict',verbose=2,n_jobs=4)
df_pred_ = pd.DataFrame(np.vstack([preds,correctness]).T,columns = ['preds','correct'])
p_correct = float(np.sum(correctness == 1)+1) / (len(correctness)+1)
p_incorrect = float(np.sum(correctness == 0)+1) / (len(correctness)+1)
p_aware = float(np.sum(preds == 1)+1) / (len(preds)+1)
p_unaware = float(np.sum(preds == 0)+1) / (len(preds)+1)
p_correct_aware = float(np.sum(np.logical_and(correctness == 1, preds == 1))+1) / (len(df_pred_)+1)
p_correct_unaware = float(np.sum(np.logical_and(correctness == 1, preds == 0))+1) / (len(df_pred_)+1)
results_full = dict(
model = [],
train = [],
test = [],
score = [],
fold = [],
window = []
)
for n_back in range(5): # loop through the number of N-back trials
# get the features, targets, and subject groups for both experiments and the given n_back trial
X_att,y_att,groups_att = get_features_targets_groups(
att,# the loaded dataframe
n_back = n_back, # n_back trials
names = ['attention',# need to normalize to 0 and 1
'awareness',# need to normalize to 0 and 1
'confidence'],# need to normalize to 0 and 1
independent_variables = ['correct',
'awareness',
'confidence'],
dependent_variable = 'attention'
)
X_pos,y_pos,groups_pos = get_features_targets_groups(
pos,
n_back = n_back,
names = ['success',# need to normalize to 0 and 1
'awareness',# need to normalize to 0 and 1
'confidence'],# need to normalize to 0 and 1
independent_variables = ['correct',
'awareness',
'confidence'],
dependent_variable = 'success'
