cols = [
'model',
'pval',
'score_mean',
'score_std',
'train',
'window',
]
df_post = {name: [] for name in cols}
for (train, model_name, window), df_sub in df_plot.groupby(
['experiment_train', 'model', 'window']):
df_sub
ps = utils.resample_ttest(df_sub['score'].values,
0.5,
n_ps=100,
n_permutation=int(1e4),
one_tail=True)
df_post['model'].append(model_name)
df_post['window'].append(window)
df_post['train'].append(train)
df_post['score_mean'].append(df_sub['score'].values.mean())
df_post['score_std'].append(df_sub['score'].values.std())
df_post['pval'].append(ps.mean())
df_post = pd.DataFrame(df_post)
temp = []
for (train, model_name), df_sub in df_post.groupby(['train', 'model']):
df_sub = df_sub.sort_values(['pval'])
ps = df_sub['pval'].values
converter = utils.MCPConverter(pvals=ps)
d = converter.adjust_many()
feature_names,
target_name,
results,
participant,
experiment,
dot_dir,
window=n_back)
c = pd.DataFrame(results) # tansform a dictionary object to a data frame
c.to_csv('../results/Pos_6_features.csv', index=False) # save as a csv
c = pd.read_csv('../results/Pos_6_features.csv')
# now it is the nonparametric t test with random resampling
ttest = dict(model=[], window=[], ps_mean=[], ps_std=[])
for (model, window), c_sub in c.groupby(['model', 'window']):
ps = resample_ttest(
c_sub['score'].values, # numpy-array
baseline=0.5, # the value we want to compare against to
n_ps=500, # estimate the p value 500 times
n_permutation=int(5e4) # use 50000 resamplings to estimate 1 p value
)
ttest['model'].append(model)
ttest['window'].append(window)
ttest['ps_mean'].append(np.mean(ps))
ttest['ps_std'].append(np.std(ps))
print('{} window {} {:.3f}'.format(model, window, np.mean(ps)))
d = pd.DataFrame(ttest) # transform a dictionary object to data frame
# now it is the p value correction for multiple comparison
# note that the correction is done within each model along the number of windows
# , and we have 3 models
temp = []
for model, d_ in d.groupby('model'): # for each model
idx_sort = np.argsort(d_['ps_mean'].values)
for name in d_.columns:
value_vars = ['mean_variance','best_variance'],
var_name = 'Variance Explained',
value_name = 'Differences of Variance Explained')
df_stat = dict(
condition = [],
roi = [],
model = [],
diff_mean = [],
diff_std = [],
ps_mean = [],
ps_std = [],)
col = 'mean_variance'
for (condition,roi,model),df_sub in df_difference.groupby(['condition','roi','Model']):
df_sub
ps = utils.resample_ttest(df_sub[col].values,baseline = 0,
n_ps = 100, n_permutation = int(5e4))
df_stat['condition'].append(condition)
df_stat['roi'].append(roi)
df_stat['model'].append(model)
df_stat['diff_mean'].append(df_sub[col].values.mean())
df_stat['diff_std'].append(df_sub[col].values.std())
df_stat['ps_mean'].append(ps.mean())
df_stat['ps_std'].append(ps.std())
df_stat = pd.DataFrame(df_stat)
temp = []
for (condition),df_sub in df_stat.groupby(['condition']):
df_sub = df_sub.sort_values(['ps_mean'])
converter = utils.MCPConverter(pvals = df_sub['ps_mean'].values)
d = converter.adjust_many()
df_sub['ps_corrected'] = d['bonferroni'].values
att.to_csv('../results/ATT_6_features.csv', index=False)
df = pos.copy()
######################### compared against chance level ###############
df = df[(0 < df['window']) & (df['window'] < 5)]
results = dict(
model=[],
window=[],
ps_mean=[],
ps_std=[],
)
for (model, window), df_sub in df.groupby(['model', 'window']):
df_sub = df_sub.sort_values('sub')
ps = resample_ttest(df_sub['score'].values,
0.5,
one_tail=False,
n_ps=1000,
n_permutation=10000)
results['model'].append(model)
results['window'].append(window)
results['ps_mean'].append(ps.mean())
results['ps_std'].append(ps.std())
results = pd.DataFrame(results)
temp = []
for model, df_sub in results.groupby('model'):
idx_sort = np.argsort(df_sub['ps_mean'])
for name in results.columns:
df_sub[name] = df_sub[name].values[idx_sort]
convert = MCPConverter(pvals=df_sub['ps_mean'].values)
df_pvals = convert.adjust_many()
df_sub['ps_corrected'] = df_pvals['bonferroni'].values
window=[],
attribute=[],
ps_mean=[],
ps_std=[],
value_mean=[],
value_std=[],
baseline=[],
)
for (model_name, window, attribute), df_sub in df_pos_plot.groupby(
['model_name', 'window', 'attr']):
print(model_name, window, attribute, df_sub['value'].values.mean())
if model_name == 'LogisticRegression':
baseline = 1
ps = resample_ttest(df_sub['value'].values,
baseline=baseline,
n_ps=100,
n_permutation=int(1e6),
one_tail=False)
elif model_name == 'RandomForest':
baseline = 0
ps = resample_ttest(df_sub['value'].values,
baseline=baseline,
n_ps=100,
n_permutation=int(1e6),
one_tail=False)
ttest_results['model_name'].append(model_name)
ttest_results['window'].append(window)
ttest_results['attribute'].append(attribute)
ttest_results['ps_mean'].append(ps.mean())
ttest_results['ps_std'].append(ps.std())
ttest_results['value_mean'].append(df_sub['value'].values.mean())
window=[],
attribute=[],
ps_mean=[],
ps_std=[],
value_mean=[],
value_std=[],
baseline=[],
)
for (model_name, attribute,
window), df_sub in c_test.groupby(['Models', 'Attributes', 'Window']):
if model_name == "RandomForestClassifier":
baseline = 1 / 3.
ps = resample_ttest(
df_sub['Values'].values,
baseline=baseline,
n_ps=n_ps,
n_permutation=n_permutation,
one_tail=True,
)
elif model_name == "LogisticRegression":
baseline = 1
ps = resample_ttest(
df_sub['Values'].values,
baseline=baseline,
n_ps=n_ps,
n_permutation=n_permutation,
one_tail=True,
)
ttest_results['model_name'].append(model_name)
ttest_results['window'].append(window)
ttest_results['attribute'].append(attribute)
