def make_X(molecular_data,kinds,target_dilution=None,threshold=None,bad=None,
good1=None,good2=None,means=None,stds=None,raw=False):
if type(kinds) is str:
kinds = [kinds]
if threshold is None:
threshold = NAN_PURGE_THRESHOLD
#print("Getting CIDs and dilutions...")
CID_dilutions = []
for kind in kinds:
assert kind in KINDS, "No such kind %s" % kind
CID_dilutions += loading.get_CID_dilutions(kind,target_dilution=target_dilution)
#print("Getting basic molecular data...")
molecular_vectors = get_molecular_vectors(molecular_data,CID_dilutions)
#print("Adding dilution data...")
molecular_vectors = add_dilutions(molecular_vectors,CID_dilutions)
#print("Building a matrix...")
X = build_X(molecular_vectors,CID_dilutions)
if not raw:
if bad:
good0 = list(set(range(X.shape[1])).difference(bad))
X = X[:,good0]
#print("Purging data with too many NaNs...")
X,good1 = purge1_X(X,threshold=NAN_PURGE_THRESHOLD,good_molecular_descriptors=good1)
#print("Imputing remaining NaN data...")
X,imputer = impute_X(X)
#print("Purging data that is still bad, if any...")
X,good2 = purge2_X(X,good_molecular_descriptors=good2)
#print("Normalizing data for fitting...")
X,means,stds = normalize_X(X,means=means,stds=stds,target_dilution=target_dilution)
else:
good1,good2 = list(range(X.shape[1])),list(range(X.shape[1]))
means,stds,imputer = None,None,None
print("The X matrix now has shape (%dx%d) molecules by " % X.shape +\
"non-NaN good molecular descriptors")
return X,good1,good2,means,stds,imputer
def make_X(molecular_data,kinds,target_dilution=None,threshold=None,
good1=None,good2=None,means=None,stds=None):
if type(kinds) is str:
kinds = [kinds]
if threshold is None:
threshold = NAN_PURGE_THRESHOLD
print("Getting CIDs and dilutions...")
CID_dilutions = []
for kind in kinds:
assert kind in ['training','leaderboard','testset'], \
"No such kind %s" % kind
CID_dilutions += loading.get_CID_dilutions(kind,target_dilution=target_dilution)
print("Getting basic molecular data...")
molecular_vectors = get_molecular_vectors(molecular_data,CID_dilutions)
print("Adding dilution data...")
molecular_vectors = add_dilutions(molecular_vectors,CID_dilutions)
print("Building a matrix...")
X = build_X(molecular_vectors,CID_dilutions)
print("Purging data with too many NaNs...")
X,good1 = purge1_X(X,threshold=NAN_PURGE_THRESHOLD,good_molecular_descriptors=good1)
print("Imputing remaining NaN data...")
X,imputer = impute_X(X)
print("Purging data that is still bad, if any...")
X,good2 = purge2_X(X,good_molecular_descriptors=good2)
print("Normalizing data for fitting...")
X,means,stds = normalize_X(X,means=means,stds=stds,target_dilution=target_dilution)
print("The X matrix now has shape (%dx%d) molecules by " % X.shape +\
"non-NaN good molecular descriptors")
return X,good1,good2,means,stds,imputer
def make_X(molecular_data,
kinds,
target_dilution=None,
threshold=None,
good1=None,
good2=None,
means=None,
stds=None):
if type(kinds) is str:
kinds = [kinds]
if threshold is None:
threshold = NAN_PURGE_THRESHOLD
print("Getting CIDs and dilutions...")
CID_dilutions = []
for kind in kinds:
assert kind in ['training','leaderboard','testset'], \
"No such kind %s" % kind
CID_dilutions += loading.get_CID_dilutions(
kind, target_dilution=target_dilution)
print("Getting basic molecular data...")
molecular_vectors = get_molecular_vectors(molecular_data, CID_dilutions)
print("Adding dilution data...")
molecular_vectors = add_dilutions(molecular_vectors, CID_dilutions)
print("Building a matrix...")
X = build_X(molecular_vectors, CID_dilutions)
print("Purging data with too many NaNs...")
X, good1 = purge1_X(X,
threshold=NAN_PURGE_THRESHOLD,
good_molecular_descriptors=good1)
print("Imputing remaining NaN data...")
X, imputer = impute_X(X)
print("Purging data that is still bad, if any...")
X, good2 = purge2_X(X, good_molecular_descriptors=good2)
print("Normalizing data for fitting...")
X, means, stds = normalize_X(X,
means=means,
stds=stds,
target_dilution=target_dilution)
print("The X matrix now has shape (%dx%d) molecules by " % X.shape +\
"non-NaN good molecular descriptors")
return X, good1, good2, means, stds, imputer
