def analyze(results, out_path, name="analysis", plot=False):
"""Analyze results.
Given a results dataframe, generates a folder with
results and a dataframe of the exact data used to generate those results,
aswell as introspection to return information on the computer
parameters
----------
out_path: location to store analysis folder
results: Dataframe generated from Results object
path: string/None
plot: whether to plot results
Either path or results is necessary
"""
# input checks #
if not isinstance(out_path, str):
raise ValueError("Given out_path argument is not string")
elif not os.path.isdir(out_path):
raise IOError("Given directory does not exist")
else:
analysis_path = os.path.join(out_path, name)
unique_ids = [plt._simplify_names(x) for x in results.pipeline.unique()]
simplify = True
print(unique_ids)
print(set(unique_ids))
if len(unique_ids) != len(set(unique_ids)):
log.warning(
"Pipeline names are too similar, turning off name shortening")
simplify = False
os.makedirs(analysis_path, exist_ok=True)
# TODO: no good cross-platform way of recording CPU info?
with open(os.path.join(analysis_path, "info.txt"), "a") as f:
dt = datetime.now()
f.write("Date: {:%Y-%m-%d}\n Time: {:%H:%M}\n".format(dt, dt))
f.write("System: {}\n".format(platform.system()))
f.write("CPU: {}\n".format(platform.processor()))
results.to_csv(os.path.join(analysis_path, "data.csv"))
stats = compute_dataset_statistics(results)
stats.to_csv(os.path.join(analysis_path, "stats.csv"))
P, T = find_significant_differences(stats)
if plot:
fig, color_dict = plt.score_plot(results)
fig.savefig(os.path.join(analysis_path, "scores.pdf"))
fig = plt.summary_plot(P, T, simplify=simplify)
fig.savefig(os.path.join(analysis_path, "ordering.pdf"))
def analyze(results, out_path, name='analysis', suffix=''):
'''Given a results object (or the location for one), generates a folder with
results and a dataframe of the exact data used to generate those results, as
well as introspection to return information on the computer
In:
out_path: location to store analysis folder
results: Obj/tuple;
path: string/None
Either path or results is necessary
'''
# input checks #
if type(results) is not Results:
res = Results(*results, suffix=suffix)
if type(out_path) is not str:
raise ValueError('Given out_path argument is not string')
elif not os.path.isdir(out_path):
raise IOError('Given directory does not exist')
else:
analysis_path = os.path.join(out_path, name)
os.makedirs(analysis_path, exist_ok=True)
# TODO: no good cross-platform way of recording CPU info?
with open(os.path.join(analysis_path, 'info.txt'), 'a') as f:
f.write('Date: {:%Y-%m-%d}\n Time: {:%H:%M}\n'.format(
datetime.now(), datetime.now()))
f.write('System: {}\n'.format(platform.system()))
f.write('CPU: {}\n'.format(platform.processor()))
res = results
data = res.to_dataframe()
data.to_csv(os.path.join(analysis_path, 'data.csv'))
plt.score_plot(data).savefig(os.path.join(analysis_path, 'scores.pdf'))
plt.time_line_plot(data).savefig(os.path.join(analysis_path, 'time2d.pdf'))
def analyze(results, out_path, name='analysis', plot=False):
'''Analyze results.
Given a results dataframe, generates a folder with
results and a dataframe of the exact data used to generate those results,
aswell as introspection to return information on the computer
parameters
----------
out_path: location to store analysis folder
results: Dataframe generated from Results object
path: string/None
plot: whether to plot results
Either path or results is necessary
'''
# input checks #
if type(out_path) is not str:
raise ValueError('Given out_path argument is not string')
elif not os.path.isdir(out_path):
raise IOError('Given directory does not exist')
else:
analysis_path = os.path.join(out_path, name)
os.makedirs(analysis_path, exist_ok=True)
# TODO: no good cross-platform way of recording CPU info?
with open(os.path.join(analysis_path, 'info.txt'), 'a') as f:
dt = datetime.now()
f.write('Date: {:%Y-%m-%d}\n Time: {:%H:%M}\n'.format(dt, dt))
f.write('System: {}\n'.format(platform.system()))
f.write('CPU: {}\n'.format(platform.processor()))
results.to_csv(os.path.join(analysis_path, 'data.csv'))
stats = compute_dataset_statistics(results)
stats.to_csv(os.path.join(analysis_path, 'stats.csv'))
P, T = find_significant_differences(stats)
if plot:
fig, color_dict = plt.score_plot(results)
fig.savefig(os.path.join(analysis_path, 'scores.pdf'))
fig = plt.summary_plot(P, T)
fig.savefig(os.path.join(analysis_path, 'ordering.pdf'))
evaluation = CrossSessionEvaluation(paradigm=paradigm,
datasets=datasets,
suffix="stats",
overwrite=overwrite)
results = evaluation.process(pipelines)
##############################################################################
# MOABB plotting
# ----------------
#
# Here we plot the results using some of the convenience methods within the
# toolkit. The score_plot visualizes all the data with one score per subject
# for every dataset and pipeline.
fig = moabb_plt.score_plot(results)
plt.show()
###############################################################################
# For a comparison of two algorithms, there is the paired_plot, which plots
# performance in one versus the performance in the other over all chosen
# datasets. Note that there is only one score per subject, regardless of the
# number of sessions.
fig = moabb_plt.paired_plot(results, "CSP+LDA", "RG+LDA")
plt.show()
###############################################################################
# Statistical testing and further plots
# ----------------------------------------
#
