def test_y_log_transformer_select(self):
"""Tests logarithmic data transformer with selection."""
multitask_dataset = self.load_feat_multitask_data()
dfe = pd.read_csv(os.path.join(self.current_dir,
"../../models/tests/feat_multitask_example.csv"))
tid = []
tasklist = ["task0", "task3", "task4", "task5"]
first_task = "task0"
for task in tasklist:
tiid = dfe.columns.get_loc(task)-dfe.columns.get_loc(first_task)
tid = np.concatenate((tid, np.array([tiid])))
tasks = tid.astype(int)
log_transformer = LogTransformer(
transform_y=True, tasks=tasks,
dataset=multitask_dataset)
X, y, w, ids = (multitask_dataset.X, multitask_dataset.y, multitask_dataset.w, multitask_dataset.ids)
log_transformer.transform(multitask_dataset)
X_t, y_t, w_t, ids_t = (multitask_dataset.X, multitask_dataset.y, multitask_dataset.w, multitask_dataset.ids)
# Check ids are unchanged.
for id_elt, id_t_elt in zip(ids, ids_t):
assert id_elt == id_t_elt
# Check X is unchanged since this is a y transformer
np.testing.assert_allclose(X, X_t)
# Check w is unchanged since this is a y transformer
np.testing.assert_allclose(w, w_t)
# Check y is now a logarithmic version of itself
np.testing.assert_allclose(y_t[:,tasks], np.log(y[:,tasks]+1))
# Check that untransform does the right thing.
np.testing.assert_allclose(log_transformer.untransform(y_t), y)
def test_X_log_transformer_select(self):
#Tests logarithmic data transformer with selection.
multitask_dataset = self.load_feat_multitask_data()
dfe = pd.read_csv(os.path.join(self.current_dir,
"../../models/tests/feat_multitask_example.csv"))
fid = []
featurelist = ["feat0", "feat1", "feat2","feat3", "feat5"]
first_feature = "feat0"
for feature in featurelist:
fiid = dfe.columns.get_loc(feature)-dfe.columns.get_loc(first_feature)
fid = np.concatenate((fid, np.array([fiid])))
features = fid.astype(int)
log_transformer = LogTransformer(
transform_X=True, features=features,
dataset=multitask_dataset)
X, y, w, ids = (multitask_dataset.X, multitask_dataset.y, multitask_dataset.w, multitask_dataset.ids)
log_transformer.transform(multitask_dataset)
X_t, y_t, w_t, ids_t = (multitask_dataset.X, multitask_dataset.y, multitask_dataset.w, multitask_dataset.ids)
# Check ids are unchanged.
for id_elt, id_t_elt in zip(ids, ids_t):
assert id_elt == id_t_elt
# Check y is unchanged since this is a X transformer
np.testing.assert_allclose(y, y_t)
# Check w is unchanged since this is a y transformer
np.testing.assert_allclose(w, w_t)
# Check y is now a logarithmic version of itself
np.testing.assert_allclose(X_t[:,features], np.log(X[:,features]+1))
# Check that untransform does the right thing.
np.testing.assert_allclose(log_transformer.untransform(X_t), X)
def test_X_log_transformer_select(self):
#Tests logarithmic data transformer with selection.
multitask_dataset = self.load_feat_multitask_data()
dfe = pd.read_csv(os.path.join(self.current_dir,
"../../models/tests/feat_multitask_example.csv"))
fid = []
featurelist = ["feat0", "feat1", "feat2","feat3", "feat5"]
first_feature = "feat0"
for feature in featurelist:
fiid = dfe.columns.get_loc(feature)-dfe.columns.get_loc(first_feature)
fid = np.concatenate((fid, np.array([fiid])))
features = fid.astype(int)
log_transformer = LogTransformer(
transform_X=True, features=features,
dataset=multitask_dataset)
X, y, w, ids = multitask_dataset.to_numpy()
log_transformer.transform(multitask_dataset)
X_t, y_t, w_t, ids_t = multitask_dataset.to_numpy()
# Check ids are unchanged.
for id_elt, id_t_elt in zip(ids, ids_t):
assert id_elt == id_t_elt
# Check y is unchanged since this is a X transformer
np.testing.assert_allclose(y, y_t)
# Check w is unchanged since this is a y transformer
np.testing.assert_allclose(w, w_t)
# Check y is now a logarithmic version of itself
np.testing.assert_allclose(X_t[:,features], np.log(X[:,features]+1))
# Check that untransform does the right thing.
np.testing.assert_allclose(log_transformer.untransform(X_t), X)
def test_y_log_transformer_select(self):
"""Tests logarithmic data transformer with selection."""
multitask_dataset = self.load_feat_multitask_data()
dfe = pd.read_csv(os.path.join(self.current_dir,
"../../models/tests/feat_multitask_example.csv"))
tid = []
tasklist = ["task0", "task3", "task4", "task5"]
first_task = "task0"
for task in tasklist:
tiid = dfe.columns.get_loc(task)-dfe.columns.get_loc(first_task)
tid = np.concatenate((tid, np.array([tiid])))
tasks = tid.astype(int)
log_transformer = LogTransformer(
transform_y=True, tasks=tasks,
dataset=multitask_dataset)
X, y, w, ids = multitask_dataset.to_numpy()
log_transformer.transform(multitask_dataset)
X_t, y_t, w_t, ids_t = multitask_dataset.to_numpy()
# Check ids are unchanged.
for id_elt, id_t_elt in zip(ids, ids_t):
assert id_elt == id_t_elt
# Check X is unchanged since this is a y transformer
np.testing.assert_allclose(X, X_t)
# Check w is unchanged since this is a y transformer
np.testing.assert_allclose(w, w_t)
# Check y is now a logarithmic version of itself
np.testing.assert_allclose(y_t[:,tasks], np.log(y[:,tasks]+1))
# Check that untransform does the right thing.
np.testing.assert_allclose(log_transformer.untransform(y_t), y)
def test_X_log_transformer(self):
"""Tests logarithmic data transformer."""
solubility_dataset = self.load_solubility_data()
log_transformer = LogTransformer(
transform_X=True, dataset=solubility_dataset)
X, y, w, ids = (solubility_dataset.X, solubility_dataset.y, solubility_dataset.w, solubility_dataset.ids)
log_transformer.transform(solubility_dataset)
X_t, y_t, w_t, ids_t = (solubility_dataset.X, solubility_dataset.y, solubility_dataset.w, solubility_dataset.ids)
# Check ids are unchanged.
for id_elt, id_t_elt in zip(ids, ids_t):
assert id_elt == id_t_elt
# Check y is unchanged since this is a X transformer
np.testing.assert_allclose(y, y_t)
# Check w is unchanged since this is a y transformer
np.testing.assert_allclose(w, w_t)
# Check y is now a logarithmic version of itself
np.testing.assert_allclose(X_t, np.log(X+1))
# Check that untransform does the right thing.
np.testing.assert_allclose(log_transformer.untransform(X_t), X)
def test_X_log_transformer(self):
"""Tests logarithmic data transformer."""
solubility_dataset = self.load_solubility_data()
log_transformer = LogTransformer(
transform_X=True, dataset=solubility_dataset)
X, y, w, ids = solubility_dataset.to_numpy()
log_transformer.transform(solubility_dataset)
X_t, y_t, w_t, ids_t = solubility_dataset.to_numpy()
# Check ids are unchanged.
for id_elt, id_t_elt in zip(ids, ids_t):
assert id_elt == id_t_elt
# Check y is unchanged since this is a X transformer
np.testing.assert_allclose(y, y_t)
# Check w is unchanged since this is a y transformer
np.testing.assert_allclose(w, w_t)
# Check y is now a logarithmic version of itself
np.testing.assert_allclose(X_t, np.log(X+1))
# Check that untransform does the right thing.
np.testing.assert_allclose(log_transformer.untransform(X_t), X)