def test_split_2(data):
sp = Splitter(10, test_size=0.1)
with pytest.raises(
ValueError,
match='parameters <arrays> must have size 10 for dim 0'):
sp.split(data[1][1:])
_, test = sp.split(data[1])
assert test[0] in data[1]
def test_split_4(data):
sp = Splitter(10)
x, x_, y, y_, z, z_ = sp.split(data[1], data[2], data[3])
assert isinstance(x, np.ndarray)
assert isinstance(x_, np.ndarray)
assert isinstance(y, pd.DataFrame)
assert isinstance(y_, pd.DataFrame)
assert isinstance(z, pd.Series)
assert isinstance(z_, pd.Series)
def test_cv_5(data):
sp = Splitter(10, test_size=0, k_fold=5)
for _, x_, _, y_, _, z_ in sp.cv(data[1], data[2], data[3]):
assert isinstance(x_, np.ndarray)
assert isinstance(y_, pd.DataFrame)
assert isinstance(z_, pd.Series)
assert x_.size == 20
assert y_.size == 20
assert z_.size == 2
def make_forward_model(data_ss, RDKit_FPs):
# forward model library from scikit-learn
from sklearn.linear_model import BayesianRidge
# xenonpy library for data splitting (cross-validation)
from xenonpy.datatools import Splitter
# property name will be used as a reference for calling models
prop = ['E', 'H**O-LUMO gap']
# prepare indices for cross-validation data sets
sp = Splitter(data_ss.shape[0], test_size=0, cv=5)
# initialize output variables
y_trues, y_preds = [[] for i in range(len(prop))], [[] for i in range(len(prop))]
y_trues_fit, y_preds_fit = [[] for i in range(len(prop))], [[] for i in range(len(prop))]
y_preds_std, y_preds_std_fit = [[] for i in range(len(prop))], [[] for i in range(len(prop))]
# cross-validation test
for iTr, iTe in sp.cv():
x_train = data_ss['SMILES'].iloc[iTr]
x_test = data_ss['SMILES'].iloc[iTe]
fps_train = RDKit_FPs.transform(x_train)
fps_test = RDKit_FPs.transform(x_test)
y_train = data_ss[prop].iloc[iTr]
y_test = data_ss[prop].iloc[iTe]
for i in range(len(prop)):
mdl = BayesianRidge(compute_score=True)
mdl.fit(fps_train, y_train.iloc[:, i])
prd_train, std_train = mdl.predict(fps_train, return_std=True)
prd_test, std_test = mdl.predict(fps_test, return_std=True)
y_trues[i].append(y_test.iloc[:, i].values)
y_trues_fit[i].append(y_train.iloc[:, i].values)
y_preds[i].append(prd_test)
y_preds_fit[i].append(prd_train)
y_preds_std[i].append(std_test)
y_preds_std_fit[i].append(std_train)
# write down list of property name(s) for forward models
prop = ['E', 'H**O-LUMO gap'] # match with data table for convenience
# calculate descriptor values for all SMILES in the data subset
fps_train = RDKit_FPs.transform(data_ss['SMILES'])
# initialize a dictionary for model storage
mdls = {}
# fill in and train the models
for x in prop:
mdls[x] = BayesianRidge()
mdls[x].fit(fps_train, data_ss[x])
# import descriptor calculator and forward model to iQSPR
prd_mdls = BayesianRidgeEstimator(descriptor=RDKit_FPs, **mdls)
return prd_mdls, mdls
def test_split_3(data):
sp = Splitter(10)
sp.split(data[0])
sp.split(data[1])
sp.split(data[2])
sp.split(data[3])
sp.split(data[5])
with pytest.raises(
TypeError,
match=
"<arrays> must be list, numpy.ndarray, pandas.DataFrame, or pandas.Series but got <class 'str'>"
):
sp.split('illegal data')
def test_cv_3(data):
np.random.seed(123456)
sp = Splitter(10, test_size=0, k_fold=data[4])
tmp = []
for x, x_ in sp.cv():
assert isinstance(x, np.ndarray)
assert isinstance(x_, np.ndarray)
assert x.size + x_.size == 10
tmp.append(x_)
sizes = np.sort([x.size for x in tmp])
assert np.array_equal(sizes, [2, 4, 4])
tmp = np.concatenate(tmp)
tmp = np.sort(tmp)
assert np.array_equal(tmp, data[0])
def test_split_1(data):
sp = Splitter(10)
with pytest.raises(RuntimeError, match='parameter <cv> must be set'):
for _ in sp.cv():
pass
assert sp.size == 10
train, test = sp.split()
assert train.size == 8
assert test.size == 2
train, test = sp.split(data[0])
for d in train:
assert d in data[0]
for d in test:
assert d in data[0]
def test_cv_2(data):
sp = Splitter(10, test_size=0.2, k_fold=4)
tmp = []
tmp_x_ = []
for x, x_, _x_ in sp.cv():
assert x.size == 6
assert x_.size == 2
assert _x_.size == 2
assert isinstance(x, np.ndarray)
assert isinstance(x_, np.ndarray)
assert isinstance(_x_, np.ndarray)
tmp_x_.append(_x_)
tmp.append(x_)
assert np.array_equal(tmp_x_[0], tmp_x_[1])
assert np.array_equal(tmp_x_[0], tmp_x_[2])
assert np.array_equal(tmp_x_[0], tmp_x_[3])
tmp = np.concatenate(tmp)
assert tmp.size == 8
tmp = np.concatenate([tmp, tmp_x_[0]])
tmp = np.sort(tmp)
assert np.array_equal(tmp, data[0])
def test_cv_1(data):
sp = Splitter(10, test_size=0, k_fold=5, random_state=123456)
with pytest.raises(
RuntimeError,
match='split action is illegal because `test_size` is none'):
sp.split()
tmp = []
for i, (x, x_) in enumerate(sp.cv()):
assert x.size == 8
assert x_.size == 2
assert isinstance(x, np.ndarray)
assert isinstance(x_, np.ndarray)
tmp.append(x_)
assert i == 4
tmp = np.concatenate(tmp)
assert not np.array_equal(tmp, data[0])
tmp = np.sort(tmp)
assert np.array_equal(tmp, data[0])
tmp = []
for x, x_ in sp.cv(less_for_train=True):
assert x.size == 2
assert x_.size == 8
tmp.append(x)
tmp = np.concatenate(tmp)
tmp = np.sort(tmp)
assert np.array_equal(tmp, data[0])
def test_roll_1():
sp = Splitter(10, test_size=0.3, random_state=123456)
train, test = sp.split()
assert train.size == 7
assert test.size == 3
train_, test_ = sp.split()
assert train_.size == 7
assert test_.size == 3
assert np.array_equal(train_, train)
assert np.array_equal(test_, test)
sp.roll(random_state=123456)
train_, test_ = sp.split()
assert train_.size == 7
assert test_.size == 3
assert np.array_equal(train_, train)
assert np.array_equal(test_, test)
sp.roll()
train_, test_ = sp.split()
assert not np.array_equal(train_, train)
assert not np.array_equal(test_, test)
def test_cv_4(data):
sp = Splitter(10, test_size=0, k_fold=5, random_state=123456)
tmp = []
for _, x_ in sp.cv():
tmp.append(x_)
tmp = np.concatenate(tmp)
tmp_ = []
for _, x_ in sp.cv():
tmp_.append(x_)
tmp_ = np.concatenate(tmp_)
assert np.array_equal(tmp, tmp_)
tmp_ = []
sp.roll()
for _, x_ in sp.cv():
tmp_.append(x_)
tmp_ = np.concatenate(tmp_)
assert not np.array_equal(tmp, tmp_)
def test_init_1():
with pytest.raises(
RuntimeError,
match='<test_size> can be zero only if <cv> is not none'):
Splitter(10, test_size=0, k_fold=None)