from pyitab.results import get_results_bids, filter_dataframe, apply_function
import seaborn as sns
import h5py
path = "/media/robbis/DATA/meg/viviana-hcp/derivatives/pipeline-blp-dexterity-trial"
dataframe = get_results_bids(path,
field_list=['sample_slicer','estimator__clf', 'feature_slicer'],
pipeline=['raw'],
)
dataframe['networks'] = dataframe['nodes_1']+'+'+dataframe['nodes_2']
average_df = apply_function(dataframe,
keys=['nodes_1', 'nodes_2', 'band', 'task', 'estimator__clf'],
attr='score_accuracy',
fx=lambda x:np.vstack(x).mean())
grid = sns.FacetGrid(dataframe, col="band", row="task", palette="tab20c",
height=1.5)
grid.map(sns.barplot, "networks", 'score_accuracy')
def plot_heatmap(nodes1, nodes2, accuracy, **kwargs):
df = dict(n1=nodes1, n2=nodes2, a=accuracy)
df = pd.DataFrame(df)
pdf = df.pivot("n1", "n2", "a")
nz = np.nonzero(np.isnan(pdf.values))
pdf.values[nz] = pdf.values[nz[::-1]]
sns.heatmap(pdf, annot=True, cmap="RdBu", fmt=".2f", center=.5, vmax=.8)
from pyitab.results import get_results_bids, filter_dataframe, apply_function
import seaborn as sns
import h5py
tasks_dict = dict(
'task1': 'left_hand',
'task2': 'left_foot',
'task4': 'right_hand',
'task5': 'right_foot',
)
path = "/media/robbis/DATA/meg/viviana-hcp/derivatives/pipeline-dexterity+full/"
dataframe = get_results_bids(path,
field_list=['sample_slicer','estimator__clf', 'feature_slicer'],
pipeline=['dexterity+full'],
scores=['accuracy']
)
dataframe['networks'] = dataframe['nodes_1']+'+'+dataframe['nodes_2']
average_df = apply_function(dataframe,
keys=['nodes_1', 'nodes_2', 'band', 'task', 'estimator__clf'],
attr='score_accuracy',
fx=lambda x:np.vstack(x).mean())
grid = sns.FacetGrid(dataframe, col="band", row="task", palette="tab20c",
height=1.5)
grid.map(sns.barplot, "networks", 'score_accuracy')
def plot_heatmap(nodes1, nodes2, accuracy, **kwargs):
'feature_slicer',
'estimator__clf',
'estimator__fsel__k',
#'estimator__pca__n_components',
'target_transformer__attr'
]
scores = ['r2', 'explained_variance', 'neg_mean_squared_error']
pipeline = 'dexterity+prediction'
pipeline = 'dexterity+prediction+featureselection'
#pipeline = 'dexterity+prediction+pca'
path = "/media/robbis/DATA/meg/viviana-hcp/derivatives/pipeline-%s/" % (
pipeline)
dataframe = get_results_bids(path,
field_list=loaded_keywords,
pipeline=[pipeline],
scores=scores)
for score in scores:
score_key = 'score_' + score
for clf in np.unique(dataframe['estimator__clf']):
#for task in np.unique(dataframe['task']):
df = filter_dataframe(dataframe, estimator__clf=[clf], fx=[score])
#print(clf)
grid = sns.relplot(
data=df,
x="estimator__fsel__k",
#x='estimator__pca__n_components',
row='band',
y=score_key,
sns.heatmap(pdf, annot=False, cmap="RdBu_r", fmt=".2f", center=0, vmax=1)
loaded_keywords = [
'sample_slicer', 'feature_slicer', 'estimator__clf',
'target_transformer__attr'
]
scores = ['r2', 'explained_variance', 'neg_mean_squared_error']
pipeline = 'dexterity+prediction+network'
path = "/media/robbis/DATA/meg/viviana-hcp/derivatives/pipeline-%s/" % (
pipeline)
dataframe = get_results_bids(path,
field_list=loaded_keywords,
pipeline=[pipeline],
scores=scores)
for score in scores:
score_key = 'score_' + score
for clf in np.unique(dataframe['estimator__clf']):
for t in np.unique(dataframe['task']):
df = filter_dataframe(dataframe,
estimator__clf=[clf],
fx=[score],
task=[t])
df = apply_function(
df,
keys=['nodes_1', 'nodes_2', 'band', 'estimator__clf', 'fx'],
attr=score_key,
from pyitab.results import get_results_bids, filter_dataframe, apply_function
import seaborn as sns
import h5py
path = "/media/robbis/DATA/meg/viviana-hcp/derivatives/pipeline-fingerprint+taskband"
dataframe = get_results_bids(
path,
field_list=['sample_slicer', 'estimator__clf', 'feature_slicer'],
pipeline=['fingerprint+taskband'],
scores=['accuracy'])
dataframe['nodes_1'] = [v.split('+')[0] for v in dataframe['value'].values]
dataframe['nodes_2'] = [v.split('+')[1] for v in dataframe['value'].values]
task = ['rest ', 'task1 ', 'task14', 'task2 ', 'task25', 'task4 ', 'task5 ']
band = [
'alpha ', 'betahigh ', 'betalow ', 'gammahigh', 'gammalow ',
'gammamid '
]
def plot_heatmap(nodes1, nodes2, accuracy, **kwargs):
df = dict(n1=nodes1, n2=nodes2, a=accuracy)
df = pd.DataFrame(df)
pdf = df.pivot("n1", "n2", "a")
nz = np.nonzero(np.isnan(pdf.values))
pdf.values[nz] = pdf.values[nz[::-1]]