for sector in val_matrix:
print("sector: ", sector)
for row in sector:
print("row: ", row)
writer.writerow(row)
writer.writerow([])
q_matrix = pcmci.get_corrected_pvalues(p_matrix=results['p_matrix'],
fdr_method='fdr_bh')
pcmci.print_significant_links(p_matrix=results['p_matrix'],
q_matrix=q_matrix,
val_matrix=results['val_matrix'],
alpha_level=0.01)
link_matrix = pcmci.return_significant_parents(
pq_matrix=q_matrix, val_matrix=results['val_matrix'],
alpha_level=0.01)['link_matrix']
graph = tp.plot_graph(
val_matrix=results['val_matrix'],
link_matrix=link_matrix,
var_names=headers,
link_colorbar_label='cross-MCI',
node_colorbar_label='auto-MCI',
)
if verbose > 1:
if display_images:
graph[0].show()
if save_images:
graph[0].savefig("causal.png")
def test(dataframes,max_lags=[4],alpha=[None],tests=['ParCorr'],limit=1):
''' This function performs the PCMCI algorithm for all the dataframes received as parameters, given the hyper-parameters of the conditional
independence test
Args:
dataframes: A list of TIGRAMITE dataframes
max_lags: Maximum number of lags to consider for the laggd time series
alpha: Significance level to perform the parent test
tests: A list of conditional independence test to be performed
limit: A limit for the instances to be considered
Returns:
'''
test_results = []
random.shuffle(dataframes)
total = limit*len(max_lags)*len(alpha)*len(tests)
data_frame_iter = iter(dataframes)
tests_to_evaluate=[]
if 'RCOT' in tests:
rcot = RCOT()
tests_to_evaluate.append(['RCOT',rcot])
if 'GPDC' in tests:
gpdc = GPDC()
tests_to_evaluate.append(['GPDC', gpdc])
if 'ParCorr' in tests:
parcorr = ParCorr(significance='analytic')
tests_to_evaluate.append(['ParCorr',parcorr])
if 'CMIknn' in tests:
cmiknn = CMIknn()
tests_to_evaluate.append(['CMIknn',cmiknn])
unique_complexities = list(set(l[1] for l in dataframes))
counts = {}
for i in unique_complexities:
counts[i] = 0
for test in tests_to_evaluate:
stop = False
for l in max_lags:
for a in alpha:
while not stop:
try:
i = random.sample(dataframes,1)[0]
if counts[i[1]] < limit:
print('evaluating: ' + str(i[3]))
start = time.time()
pcmci = PCMCI(
dataframe=i[2],
cond_ind_test=test[1],
verbosity=0)
# correlations = pcmci.get_lagged_dependencies(tau_max=20)
pcmci.verbosity = 1
results = pcmci.run_pcmci(tau_max=l, pc_alpha=a)
time_lapse = round(time.time() - start, 2)
q_matrix = pcmci.get_corrected_pvalues(p_matrix=results['p_matrix'], fdr_method='fdr_bh')
valid_parents = list(pcmci.return_significant_parents(pq_matrix=q_matrix,
val_matrix=results['val_matrix'],
alpha_level=a)['parents'].values())
flat_list = []
for sublist in valid_parents:
for item in sublist:
flat_list.append(item)
valid_links = len(flat_list)
test_results.append([i[3], i[0], i[1], l,test[0],a,valid_links,time_lapse])
results_df = pd.DataFrame(test_results,
columns=['representation', 'complexity', 'sample_size', 'max_lag','test','alpha','valid_links_at_alpha',
'learning_time'])
print('results ready to be saved')
results_df.to_csv(
'results/performance_sample_sizes.csv',
index=False)
counts[i[1]] += 1
if all(value == limit for value in counts.values()):
stop = True
except:
print('Hoopla!')
pass
for i in unique_complexities:
counts[i] = 0
