def run_sims_in_parallel(outlier_score_file, feature_dir, output_file):
pool = Pool(8)
mahal_timeseries, c_timeseries = readOutlierScores(outlier_score_file)
global_pace_timeseries = readGlobalPace(feature_dir)
sim_function = partial(run_many_simulations, mahal_timeseries = mahal_timeseries,
c_timeseries = c_timeseries, global_pace_timeseries=global_pace_timeseries)
sim_sizes = [1250]*8
result = pool.map(sim_function, sim_sizes)
with open(output_file, 'w') as f:
w = csv.writer(f)
w.writerow(['event','start_date', 'end_date', 'duration', 'max_pace_dev', 'min_pace_dev'])
for chunk in result:
w.writerows(chunk)
def run_random_sims(outlier_score_file, feature_dir):
mahal_timeseries, c_timeseries = readOutlierScores(outlier_score_file)
global_pace_timeseries = readGlobalPace(feature_dir)
for p in range(50):
print ("Sim %d" % p)
initial_state, trans_matrix, emission_matrix = randomly_draw_parameters()
events, predictions = detect_events_hmm(mahal_timeseries, c_timeseries,
global_pace_timeseries, threshold_quant=.95,
trans_matrix = trans_matrix,
emission_matrix=emission_matrix)
new_scores_file = 'tmp_results/coarse_events_k%d_scores.csv'%p
augment_outlier_scores(outlier_score_file, new_scores_file, predictions)
