def test_classifier_iris():
import os
csv_file = os.path.join(os.path.dirname(__file__), "iris.csv")
data = np.genfromtxt(csv_file, dtype=float, delimiter=',', names=True)
X = np.array([data["sepallength"], data["sepalwidth"], data["petallength"], data["petalwidth"]]).T
y = data["class"]
from sklearn.preprocessing import MinMaxScaler
X = MinMaxScaler().fit_transform(X)
l = fpcga.FuzzyPatternClassifierGA(iterations=100)
from sklearn import cross_validation
scores = cross_validation.cross_val_score(l, X, y, cv=10)
mean = np.mean(scores)
print("mean", mean)
assert 0.90 < mean
def test_classifier_single():
l = fpcga.FuzzyPatternClassifierGA(iterations=25, epsilon=None)
X = np.array([
[0.1, 0.2, 0.4],
[0.11, 0.3, 0.5],
[0.2, 0.4, 0.8],
[0.18, 0.42, 0.88]
])
y = np.array([
3,
3,
10,
10
])
l.fit(X, y)
y_pred = l.predict([0.17, 0.45, 0.85])
assert 10 == y_pred
def test_classifier_iris():
iris = load_iris()
X = iris.data
y = iris.target
from sklearn.preprocessing import MinMaxScaler
X = MinMaxScaler().fit_transform(X)
l = fpcga.FuzzyPatternClassifierGA(iterations=100, random_state=1)
from sklearn.model_selection import cross_val_score
scores = cross_val_score(l, X, y, cv=10)
assert len(scores) == 10
assert np.mean(scores) > 0.6
mean = np.mean(scores)
print("mean", mean)
assert 0.92 == pytest.approx(mean, 0.01)