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)
for t in np.unique(average_df['task']):
task_df = filter_dataframe(average_df, task=[t])
grid = sns.FacetGrid(task_df, col="band", row="task")
grid.map(plot_heatmap, "nodes_1", "nodes_2", "score_accuracy")
fname = "/home/robbis/Dropbox/PhD/experiments/blp-hcp-viviana/Figure-%s.png" % (t)
grid.savefig(fname, dpi=150)
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")
mask = np.triu(np.ones_like(corr, dtype=bool), k=1)
nz = np.nonzero(np.isnan(pdf.values))
pdf.values[nz] = pdf.values[nz[::-1]]
sns.heatmap(pdf, annot=True, cmap="RdBu_r", fmt=".2f", center=.5, vmax=.7, mask=mask)
for clf in np.unique(average_df['estimator__clf']):
for t in np.unique(average_df['task']):
task_df = filter_dataframe(average_df, task=[t], estimator__clf=[clf])
grid = sns.FacetGrid(task_df, col="band", row="task")
grid.map(plot_heatmap, "nodes_1", "nodes_2", "score_accuracy")
fname = "/home/robbis/Dropbox/PhD/experiments/blp-hcp-viviana/Figure-%s.png" % (t)
grid.savefig(fname, dpi=150)
##############################################
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")
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,
col="target_transformer__attr",
hue='task',
kind="line",
marker='o',
markersize=10,
markeredgecolor=None,
palette='Dark2')
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,
fx=lambda x: np.vstack(x).mean())
grid = sns.FacetGrid(df, col="band")
grid.map(plot_heatmap, "nodes_1", "nodes_2", score_key)
fname = "/home/robbis/Dropbox/PhD/experiments/blp-hcp-viviana/figure-regression-%s.png" % (
t)
grid.savefig(fname, dpi=150)
]
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")
band_df = filter_dataframe(dataframe, num=['2'])
band_df['band'] = [band[f - 1] for f in band_df['fold'].values]
task_df = filter_dataframe(dataframe, num=['1'])
task_df['task'] = [task[t - 1] for t in task_df['fold'].values]
grid = sns.FacetGrid(band_df, col="band", col_wrap=3)
grid.map(plot_heatmap, "nodes_1", "nodes_2", "score_accuracy")
grid = sns.FacetGrid(task_df, col="task", col_wrap=3)
grid.map(plot_heatmap, "nodes_1", "nodes_2", "score_accuracy")
####################
path = "/media/robbis/DATA/meg/viviana-hcp/derivatives/analysis-roi+decoding/"
dataframe = get_results_bids(
path,