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Add support for xgboost XGBClassifier (#53)

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abigailt 2022-07-28 16:31:08 +03:00 • committed by abigailgold
parent a13415ad67
commit a9e2a35e18
3 changed files with 106 additions and 1 deletions
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19
tests/test_model.py
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@ -2,12 +2,13 @@ import pytest
import numpy as np
from apt.utils.models import SklearnClassifier, SklearnRegressor, ModelOutputType, KerasClassifier, KerasRegressor, \
BlackboxClassifierPredictions, BlackboxClassifierPredictFunction, is_one_hot, get_nb_classes
BlackboxClassifierPredictions, BlackboxClassifierPredictFunction, is_one_hot, get_nb_classes, XGBoostClassifier
from apt.utils.datasets import ArrayDataset, Data, DatasetWithPredictions
from apt.utils import dataset_utils
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Input
@ -90,6 +91,22 @@ def test_keras_regressor():
score = model.score(test)
def test_xgboost_classifier():
(x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset_np()
underlying_model = XGBClassifier()
underlying_model.fit(x_train, y_train)
model = XGBoostClassifier(underlying_model, ModelOutputType.CLASSIFIER_PROBABILITIES, input_shape=(4,), nb_classes=3)
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test, y_test)
pred = model.predict(test)
assert(pred.shape[0] == x_test.shape[0])
score = model.score(test)
assert(0.0 <= score <= 1.0)
model.fit(train)
def test_blackbox_classifier():
(x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset_np()