code stringlengths 3 6.57k |
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time.sleep(.1) |
get_rule(rid) |
get_cids_of_rule(rid) |
time.sleep(.1) |
get_cids_of_rule(rid) |
wait_cmdlets(cids) |
time.time() |
stop_rule(rid) |
open(log, 'a') |
f.write(str(int(end_time - start_time) |
f.close() |
chemplot (Chemical Space Visualization) |
get_mordred_descriptors(smiles_list) |
generate_mordred_descriptors(smiles_list, Chem.MolFromSmiles, 'SMILES') |
get_mordred_descriptors_from_inchi(inchi_list) |
generate_mordred_descriptors(inchi_list, Chem.MolFromInchi, 'InChi') |
generate_mordred_descriptors(encoding_list, encoding_function, encoding_name) |
mordred.Calculator() |
calc.register(mordred.AtomCount) |
calc.register(mordred.RingCount) |
calc.register(mordred.BondCount) |
calc.register(mordred.HydrogenBond) |
calc.register(mordred.CarbonTypes) |
calc.register(mordred.SLogP) |
calc.register(mordred.Constitutional) |
calc.register(mordred.TopoPSA) |
calc.register(mordred.Weight) |
calc.register(mordred.Polarizability) |
calc.register(mordred.McGowanVolume) |
name_list.append(str(desc_name) |
encoding_function(encoding) |
descriptors_list.append([None]*len(name_list) |
erroneous_encodings.append(encoding) |
Chem.AddHs(mol) |
calc(mol) |
range(len(calculated_descriptors._values) |
math.isnan(calculated_descriptors._values[i]) |
len(name_list) |
encodings_none_descriptors.append(encoding) |
descriptors_list.append(calculated_descriptors._values) |
len(erroneous_encodings) |
print("The following erroneous {} have been found in the data:\n{}.\nThe erroneous {} will be removed from the data.".format(encoding_name, '\n'.join(map(str, erroneous_encodings) |
len(encodings_none_descriptors) |
print("For the following {} not all descriptors can be computed:\n{}.\nThese {} will be removed from the data.".format(encoding_name, '\n'.join(map(str, encodings_none_descriptors) |
pd.DataFrame(descriptors_list,columns=name_list) |
df_descriptors.select_dtypes(exclude=['object']) |
select_descriptors_lasso(df_descriptors,target_list, R_select=0.05, C_select=0.05, kind="R") |
df_descriptors.assign(target=target_list.values) |
df_descriptors.dropna(how='any') |
to_list() |
df_descriptors.drop(columns=['target']) |
StandardScaler() |
fit_transform(df_descriptors) |
if(kind=="C") |
LogisticRegression(C=C_select,penalty='l1', solver='liblinear',random_state=1) |
fit(df_descriptors_scaled, target_list) |
Lasso(alpha=R_select,max_iter=10000,random_state=1) |
fit(df_descriptors_scaled, target_list) |
SelectFromModel(model, prefit=True) |
selected.transform(df_descriptors) |
pd.DataFrame(selected.inverse_transform(X_new_lasso) |
selected_features.var() |
get_ecfp(smiles_list, target_list, radius=2, nBits=2048) |
generate_ecfp(smiles_list, Chem.MolFromSmiles, 'SMILES', target_list, radius=2, nBits=2048) |
get_ecfp_from_inchi(inchi_list, target_list, radius=2, nBits=2048) |
generate_ecfp(inchi_list, Chem.MolFromInchi, 'InChi', target_list, radius=2, nBits=2048) |
generate_ecfp(encoding_list, encoding_function, encoding_name, target_list, radius=2, nBits=2048) |
encoding_function(encoding) |
ecfp_fingerprints.append([None]*nBits) |
erroneous_encodings.append(encoding) |
Chem.AddHs(mol) |
AllChem.GetMorganFingerprintAsBitVect(mol, radius, nBits) |
ToBitString() |
ecfp_fingerprints.append(list_bits_fingerprint) |
pd.DataFrame(data = ecfp_fingerprints, index = encoding_list) |
len(erroneous_encodings) |
print("The following erroneous {} have been found in the data:\n{}.\nThe erroneous {} will be removed from the data.".format(encoding_name, '\n'.join(map(str, erroneous_encodings) |
len(target_list) |
df_ecfp_fingerprints.assign(target=target_list.values) |
df_ecfp_fingerprints.dropna(how='any') |
len(target_list) |
to_list() |
df_ecfp_fingerprints.drop(columns=['target']) |
variablity (all "0" or all "1") |
any(axis=0) |
any(axis=0) |
Copyright (c) |
os.path.join(base.ROOTDIR, 'unit/extensions') |
APIv2TestBase(base.BaseTestCase) |
setUp(self) |
super(APIv2TestBase, self) |
setUp() |
self.config_parse() |
self.setup_coreplugin(plugin, load_plugins=False) |
mock.patch(plugin, autospec=True) |
self._plugin_patcher.start() |
tools.make_mock_plugin_json_encodable(instance) |
router.APIRouter() |
webtest.TestApp(api) |
policy.init() |
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