def test_EvoMSA_regression():
from EvoMSA.base import LabelEncoderWrapper
from EvoMSA.model import EmoSpaceEs
import os
dirname = os.path.join(get_dirname(), 'models')
if not os.path.isdir(dirname):
os.mkdir(dirname)
output = os.path.join(dirname, EmoSpaceEs.model_fname())
if not os.path.isfile(output):
EmoSpaceEs.create_space(TWEETS, output=output)
X, y = get_data()
X = [dict(text=x) for x in X]
l = LabelEncoderWrapper().fit(y)
y = l.transform(y) - 1.5
evo = EvoMSA(evodag_args=dict(popsize=10,
early_stopping_rounds=10,
time_limit=5,
n_estimators=2),
classifier=False,
models=[['EvoMSA.model.Identity', 'EvoMSA.model.EmoSpaceEs']],
TR=False,
n_jobs=1).fit(X, y)
assert evo
df = evo.decision_function(X)
print(df.shape, df.ndim)
assert df.shape[0] == len(X) and df.ndim == 1
def test_EvoMSA_regression():
from EvoMSA.base import LabelEncoderWrapper
from EvoMSA.utils import download
X, y = get_data()
X = [dict(text=x) for x in X]
l = LabelEncoderWrapper().fit(y)
y = l.transform(y) - 1.5
evo = EvoMSA(stacked_method_args=dict(popsize=10,
early_stopping_rounds=10,
time_limit=5,
n_estimators=2),
classifier=False,
models=[[download("emo_Es.tm"), 'EvoMSA.model.Identity']],
TR=False,
n_jobs=1).fit(X, y)
assert evo
df = evo.decision_function(X)
print(df.shape, df.ndim)
assert df.shape[0] == len(X) and df.ndim == 1
df = evo.predict(X)
assert df.shape[0] == len(X) and df.ndim == 1
