class NameMatching(TransformerMixin):
def __init__(self):
super().__init__()
self.naming = NamingService('recon')
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
return self.naming.to(X)
def hmdb_disease_normalization():
dataset = DataReader().read_hmdb_diseases()
naming = NamingService('hmdb')
nor_data = dict()
for dis, categories in dataset.items():
for cat, measurements in categories.items():
named_measurements = naming.to(dict(measurements))
if len(named_measurements) >= 10:
nor_data['%s %s' % (dis, cat)] = {
k: round(
min(v - 1, 100) if v >= 1 else max(1 - v**-1, -100), 3)
for k, v in named_measurements.items()
}
DataWriter('normalization_hmdb').write_json(nor_data)
class MetabolicSolutionScaler(TransformerMixin):
"""Scaler for converting change level data to pathway level"""
def __init__(self, vectorizer: DictVectorizer):
self.vectorizer = vectorizer
self.solution_service = SolutionService()
self.naming = NamingService('ecolin')
def fit(self, X, y):
return self
def transform(self, X, y=[]):
ecolin_X = self.naming.to(X)
solutions = self.solution_service.get_solutions(ecolin_X)
return solutions
def fit_transform(self, X, y):
return self.fit(X, y).transform(X, y)