def get_algorithm_result(algorithm_class, algorithm_args):
runner = create_runner(
algorithm_class,
num_workers=1,
algorithm_args=algorithm_args,
)
result = capture_stdout(runner.run)()
result = json.loads(result)
return result
def get_algorithm_result(algorithm_class, test_input, num_workers=1):
alg_args = sum([["-" + p["name"], p["value"]] for p in test_input], [])
runner = create_runner(
algorithm_class,
num_workers=num_workers,
algorithm_args=alg_args,
)
result = capture_stdout(runner.run)()
result = json.loads(result)["result"][0]["data"]
return result
if __name__ == "__main__":
import time
algorithm_args = [
"-x",
"lefthippocampus",
"-y",
"alzheimerbroadcategory",
"-pathology",
"dementia",
"-dataset",
"adni",
"-filter",
"",
"-formula",
"",
"-positive_level",
"AD",
"-negative_level",
"CN",
]
runner = create_runner(
LogisticRegression,
num_workers=10,
algorithm_args=algorithm_args,
)
start = time.time()
runner.run()
end = time.time()
print("Completed in ", end - start)
continue
counter = self.fetch("model__" + "counter_" + categorical +
"_" + dataset)
raw_out["model"][dataset]["data"][categorical] = dict(counter)
self.result = AlgorithmResult(raw_data=raw_out)
if __name__ == "__main__":
import time
from mipframework import create_runner
algorithm_args = [
"-y",
"rightphgparahippocampalgyrus, gender, alzheimerbroadcategory, rs10498633_t",
"-pathology",
"dementia",
"-dataset",
"lille_simulation, lille_simulation1",
"-filter",
"",
]
runner = create_runner(
DescriptiveStats,
algorithm_args=algorithm_args,
num_workers=2,
)
start = time.time()
runner.run()
end = time.time()
print("Completed in ", end - start)
import time
from mipframework import create_runner
algorithm_args = [
"-x",
# "lefthippocampus,righthippocampus,leftaccumbensarea",
# "gender,apoe4,agegroup",
"lefthippocampus,righthippocampus,leftaccumbensarea,gender,apoe4,agegroup",
"-y",
"alzheimerbroadcategory",
"-alpha",
"1",
"-k",
"10",
"-pathology",
"dementia",
"-dataset",
"adni",
"-filter",
"",
]
runner = create_runner(
NaiveBayes,
algorithm_args=algorithm_args,
num_workers=3,
)
start = time.time()
runner.run()
end = time.time()
# print("Completed in ", end - start)
if __name__ == "__main__":
import time
from mipframework import create_runner
algorithm_args = [
"-x",
"rightioginferioroccipitalgyrus,rightmfcmedialfrontalcortex",
"-y",
"subjectage,rightventraldc,rightaccumbensarea",
"-pathology",
"dementia, leftaccumbensarea",
"-dataset",
"adni",
"-filter",
"",
"-formula",
"",
"-coding",
"",
]
runner = create_runner(
Pearson,
algorithm_args=algorithm_args,
num_workers=3,
)
start = time.time()
runner.run()
end = time.time()
print("Completed in ", end - start)
"-y",
"lefthippocampus, righthippocampus, leftcaudate",
"-x",
"gender, agegroup",
"-pathology",
"dementia",
"-dataset",
"edsd, ppmi",
"-filter",
"",
"-dx",
"alzheimerbroadcategory",
"-c2_feature_selection_method",
"RF",
"-c2_num_clusters_method",
"Euclidean",
"-c2_num_clusters",
"6",
"-c2_clustering_method",
"Euclidean",
"-c3_feature_selection_method",
"RF",
"-c3_classification_method",
"RF",
]
runner = create_runner(ThreeC, algorithm_args=algorithm_args, num_workers=1,)
start = time.time()
runner.run()
end = time.time()
print("Completed in ", end - start)
"-y",
"hospOutcomeLatest_RIC10",
"-devel",
"internal",
"-max_deg",
"4",
"-confLevels",
"0.80, 0.95",
"-thres",
"0.95",
"-num_points",
"200",
"-pathology",
"dementia",
"-dataset",
"cb_data",
"-filter",
"",
"-formula",
"",
]
runner = create_runner(
algorithm_class=CalibrationBelt,
num_workers=1,
algorithm_args=algorithm_args,
)
start = time.time()
runner.run()
end = time.time()
print("Completed in ", end - start)
tukey_row["t_stat"] = row["t value"]
tukey_row["p_tukey"] = row["Pr(>|t|)"]
tukey_dict.append(tukey_row)
return tukey_dict
if __name__ == "__main__":
import time
from mipframework import create_runner
algorithm_args = [
"-y",
"rightmprgprecentralgyrusmedialsegment",
"-x",
"alzheimerbroadcategory",
"-pathology",
"dementia",
"-dataset",
"ppmi,adni",
"-filter",
"",
]
runner = create_runner(
Anova,
algorithm_args=algorithm_args,
num_workers=3,
)
start = time.time()
runner.run()
end = time.time()
"field":"alzheimerbroadcategory",
"type":"string",
"input":"select",
"operator":"equal",
"value":"AD"
},
{
"id":"alzheimerbroadcategory",
"field":"alzheimerbroadcategory",
"type":"string",
"input":"select",
"operator":"equal",
"value":"MCI"
}
],
"valid":true
}
""",
"-outcome_pos",
"MCI",
"-outcome_neg",
"AD",
"-total_duration",
"1100",
]
runner = create_runner(KaplanMeier, algorithm_args=algorithm_args, num_workers=1,)
start = time.time()
runner.run()
end = time.time()
print("Completed in ", end - start)
idx = eigenvalues.argsort()[::-1]
eigenvalues = eigenvalues[idx]
eigenvectors = eigenvectors[:, idx]
eigenvectors = eigenvectors.T
return eigenvalues, eigenvectors
if __name__ == "__main__":
import time
from mipframework import create_runner
algorithm_args = [
"-y",
"pib",
"-pathology",
"dementia",
"-dataset",
"adni",
"-filter",
"",
]
runner = create_runner(
PCA,
algorithm_args=algorithm_args,
num_workers=1,
)
start = time.time()
runner.run()
end = time.time()
print("Completed in ", end - start)