def test_numpy(self):
try:
import numpy as np
model = Model.load('./resources/models/single_iris_dectree.xml')
# Data in 1-D
result = model.predict(np.array([5.1, 3.5, 1.4, 0.2]))
self.assertEqual(result[0], 'Iris-setosa')
self.assertEqual(result[1], 1.0)
self.assertEqual(result[2], 1.0)
self.assertEqual(result[3], 0.0)
self.assertEqual(result[4], 0.0)
self.assertEqual(result[5], '1')
# Data in 2-D
result = model.predict(
np.array([[5.1, 3.5, 1.4, 0.2], [7, 3.2, 4.7, 1.4]]))
self.assertEqual(len(result), 2)
self.assertEqual(result[0][0], 'Iris-setosa')
self.assertEqual(result[0][1], 1.0)
self.assertEqual(result[0][2], 1.0)
self.assertEqual(result[0][3], 0.0)
self.assertEqual(result[0][4], 0.0)
self.assertEqual(result[0][5], '1')
self.assertEqual(result[1][0], 'Iris-versicolor')
self.assertEqual(result[1][1], 0.9074074074074074)
self.assertEqual(result[1][2], 0.0)
self.assertEqual(result[1][3], 0.9074074074074074)
self.assertEqual(result[1][4], 0.09259259259259259)
self.assertEqual(result[1][5], '3')
except ImportError:
pass
def test_pandas(self):
try:
import pandas as pd
model = Model.load('./resources/models/single_iris_dectree.xml')
# Data in Series
result = model.predict(
pd.Series({
'sepal_length': 5.1,
'sepal_width': 3.5,
'petal_length': 1.4,
'petal_width': 0.2
}))
self.assertEqual(result.get('predicted_class'), 'Iris-setosa')
self.assertEqual(result.get('probability'), 1.0)
self.assertEqual(result.get('node_id'), '1')
# Data in DataFrame
data = pd.read_csv('./resources/data/Iris.csv')
result = model.predict(data)
self.assertEqual(result.iloc[0].get('predicted_class'),
'Iris-setosa')
self.assertEqual(result.iloc[0].get('probability'), 1.0)
self.assertEqual(result.iloc[0].get('node_id'), '1')
except ImportError:
pass
def load(self) -> bool:
model_path = kfserving.Storage.download(self.model_dir)
paths = [os.path.join(model_path, MODEL_BASENAME + model_extension)
for model_extension in MODEL_EXTENSIONS]
for path in paths:
if os.path.exists(path):
self._model = Model.load(path)
self.ready = True
break
return self.ready
def test_load(self):
file_path = './resources/models/single_iris_dectree.xml'
self.assertTrue(Model.load(file_path) is not None)
with open(file_path, 'rb') as f:
self.assertTrue(Model.load(f) is not None)
with open(file_path) as f:
self.assertTrue(Model.load(f) is not None)
with open(file_path, 'rb') as f:
s = f.read()
self.assertTrue(Model.load(s) is not None)
s = bytearray(s)
self.assertTrue(Model.load(s) is not None)
with open(file_path) as f:
s = f.read()
self.assertTrue(Model.load(s) is not None)
def load_model_from_file(path):
return Model.load(path)
def load_model():
"""Returns the pmml model"""
model_path = pkg_resources.resource_stream(
__name__, "models/pydamage_glm_model.pmml")
model = Model.load(model_path)
return model
def load_model_from_artifact(self, artifact_path):
from pypmml import Model
model = Model.load(artifact_path)
return model
