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abigailt 2022-03-07 19:09:31 +02:00
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tests/test_model.py
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@ -1,44 +1,36 @@
import pytest
from apt.utils.models import SklearnClassifier, SklearnRegressor
from apt.utils.datasets import ArrayDataset
from apt.utils import dataset_utils
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestClassifier
def test_sklearn_classifier():
(x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset()
underlying_model = RandomForestClassifier()
model = SklearnClassifier(underlying_model)
model.fit(x_train, y_train)
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test, y_test)
model.fit(train)
pred = model.predict(x_test)
assert(pred.shape[0] == x_test.shape[0])
score = model.score(x_test, y_test)
score = model.score(test)
assert(0.0 <= score <= 1.0)
def test_sklearn_regressor():
(x_train, y_train), (x_test, y_test) = dataset_utils.get_diabetes_dataset()
underlying_model = DecisionTreeRegressor()
model = SklearnRegressor(underlying_model)
model.fit(x_train, y_train)
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test, y_test)
model.fit(train)
pred = model.predict(x_test)
assert (pred.shape[0] == x_test.shape[0])
score = model.score(x_test, y_test)
losses = model.loss(x_test, y_test)
assert (losses.shape[0] == x_test.shape[0])
# Probably not needed for now, as we will not be using these wrappers directly in ART.
# def test_sklearn_decision_tree():
# (x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset()
# underlying_model = DecisionTreeClassifier()
# model = SklearnDecisionTreeClassifier(underlying_model)
# model.fit(x_train, y_train)
# pred = model.predict(x_test)
# assert(pred.shape[0] == x_test.shape[0])
#
# score = model.score(x_test, y_test)
# assert(0.0 <= score <= 1.0)
score = model.score(test)
assert (0 <= score <= 1)