def test_iqspr_1(data): np.random.seed(0) ecfp = data['ecfp'] bre = GaussianLogLikelihood(descriptor=ecfp) ngram = NGram() iqspr = IQSPR(estimator=bre, modifier=ngram) X, y = data['pg'] bre.fit(X, y) bre.update_targets(reset=True, bandgap=(0.1, 0.2), density=(0.9, 1.2)) 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): assert np.abs(np.sum(p) - 1.0) < 1e-5 assert np.sum(f) == 5
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')