def test_iqspr_1(data):
np.random.seed(0)
ecfp = ECFP(n_jobs=1, input_type='smiles')
bre = GaussianLogLikelihood(descriptor=ecfp)
ngram = NGram()
iqspr = IQSPR(estimator=bre, modifier=ngram)
X, y = data['pg']
bre.fit(X, y)
ngram.fit(data['pg'][0][0:20], train_order=10)
beta = np.linspace(0.05, 1, 10)
for s, ll, p, f in iqspr(data['pg'][0][:5], beta, yield_lpf=True, bandgap=(0.1, 0.2), density=(0.9, 1.2)):
assert np.abs(np.sum(p) - 1.0) < 1e-5
assert np.sum(f) == 5, print(f)
def test_gaussian_ll_4(data):
# check if training of NaN data and pd.Series input are ok
ecfp = data['ecfp']
bre = GaussianLogLikelihood(descriptor=ecfp)
train_data = pd.DataFrame({'x': ['C','CC','CCC','CCCC','CCCCC'], 'a': [np.nan, np.nan, 3, 4, 5], 'b': [1, 2, 3, np.nan, np.nan]})
bre.fit(train_data['x'], train_data['a'])
bre.remove_estimator()
bre.fit(train_data['x'], train_data[['a','b']])
def data():
# ignore numpy warning
import warnings
print('ignore NumPy RuntimeWarning\n')
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
pwd = Path(__file__).parent
pg_data = pd.read_csv(str(pwd / 'polymer_test_data.csv'))
X = pg_data['smiles']
y = pg_data.drop(['smiles', 'Unnamed: 0'], axis=1)
ecfp = ECFP(n_jobs=1, input_type='smiles')
bre = GaussianLogLikelihood(descriptor=ecfp)
ngram = NGram()
iqspr = IQSPR(estimator=bre, modifier=ngram)
# prepare test data
yield dict(ecfp=ecfp, bre=bre, ngram=ngram, iqspr=iqspr, pg=(X, y))
print('test over')
def data():
# ignore numpy warning
import warnings
print('ignore NumPy RuntimeWarning\n')
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
pwd = Path(__file__).parent
pg_data = pd.read_csv(str(pwd / 'polymer_test_data.csv'))
X = pg_data['smiles']
y = pg_data.drop(['smiles', 'Unnamed: 0'], axis=1)
ecfp = ECFP(n_jobs=1, input_type='smiles', target_col=0)
rdkitfp = RDKitFP(n_jobs=1, input_type='smiles', target_col=0)
bre = GaussianLogLikelihood(descriptor=ecfp)
bre2 = GaussianLogLikelihood(descriptor=rdkitfp)
bre.fit(X, y[['bandgap', 'glass_transition_temperature']])
bre2.fit(X, y[['density', 'refractive_index']])
bre.update_targets(bandgap=(1, 2), glass_transition_temperature=(200, 300))
bre2.update_targets(refractive_index=(2, 3), density=(0.9, 1.2))
class MyLogLikelihood(BaseLogLikelihoodSet):
def __init__(self):
super().__init__()
self.loglike = bre
self.loglike = bre2
like_mdl = MyLogLikelihood()
ngram = NGram()
ngram.fit(X[0:20], train_order=5)
iqspr = IQSPR(estimator=bre, modifier=ngram)
# prepare test data
yield dict(ecfp=ecfp, rdkitfp=rdkitfp, bre=bre, bre2=bre2, like_mdl=like_mdl, ngram=ngram, iqspr=iqspr, pg=(X, y))
print('test over')