def test_fit_ko_not_found(self):
"""
Tests the execution failure when the population is not readable from the processdata in DB.
"""
with self.assertRaises(IkatsNotFoundError):
fit(population="no_table",
target_column_name='"Species"',
identifier_column_name="Id",
max_depth=0,
balanced_class_weight=False)
def test_fit_population_sklearn_ko(self):
"""
Tests the unexpected error, raised by algorithm
"""
with self.assertRaises(IkatsException):
fit(population="iris",
target_column_name='"Species"',
identifier_column_name="Id",
max_depth=0,
balanced_class_weight=False)
def test_fit_nominal_from_id(self):
"""
Tests the nominal execution based upon mock data: IRIS data
"""
_, mdl, dot = fit(population="iris",
target_column_name='"Species"',
identifier_column_name="Id",
max_depth=0,
balanced_class_weight=False)
# trained: 71,5.9,3.2,4.8,1.8,I. versicolor
# => test below should obtain same class:
#
# mdl.predict([[5.9, 3.2, 4.8, 1.8]]) returns a numpy.ndarray
# so we have to convert predicted value to str:
for x_val, ref_predict in [[[5.9, 3.2, 4.8, 1.8], "I. versicolor"],
[[
5.7,
2.8,
4.1,
1.3,
], "I. versicolor"],
[[5.9, 3.0, 5.1, 1.8], "I. virginica"]]:
predicted = "{}".format(mdl.predict([x_val])[0])
self.assertEqual(
predicted, ref_predict,
"Failed to use the computed model: predict x={}".format(x_val))
self.assertEqual(type(dot), str, "Bad type for the dot returned")
def test_predict_nominal(self):
"""
Tests the nominal execution based upon mock data: IRIS data
tests data originated from mocked iris dataset => accuracy = 100%
"""
_, mdl, _ = fit(population="iris",
target_column_name='"Species"',
identifier_column_name="Id",
max_depth=0,
balanced_class_weight=False)
test_table = {
"table_desc": {},
"headers": {
"col": {
"data": [
"Id", "Sepal length", "Sepal width", "Petal length",
"Petal width", "Species"
]
},
"row": {
"data": [None, "1", "5", "8", "128"]
}
},
"content": {
"cells": [["5.9", "3.2", "4.8", "1.8", "I. versicolor"],
["5.0", "3.5", "1.6", "0.6", "I. setosa"],
["5.9", "3.0", "5.1", "1.8", "I. virginica"],
["6.8", "3.0", "5.5", "2.1", "I. virginica"]]
}
}
attempted_matrix = [[1, 0, 0], [0, 1, 0], [0, 0, 2]]
# Create the table used for this test
create_table(name="pop_table", data=test_table)
table_name, accuracy = predict(model=mdl,
population_name="pop_table",
target_column_name='Species',
identifier_column_name='Id',
table_name='my_table')
matrix = IkatsApi.table.read(table_name)
self.assertListEqual(matrix['content']['cells'], attempted_matrix)
self.assertEqual(accuracy, 1)
def test_fit_with_depth(self):
"""
Tests the nominal execution based upon mock data: IRIS data
"""
_, mdl, dot = fit(population="iris",
target_column_name='"Species"',
identifier_column_name="Id",
max_depth=4,
balanced_class_weight=True)
# trained: 71,5.9,3.2,4.8,1.8,I. versicolor
# => test below should obtain same class:
#
# mdl.predict([[5.9, 3.2, 4.8, 1.8]]) retuns a numpy.ndarray
# so we have to convert predicted value to str:
predicted = "{}".format(mdl.predict([[5.9, 3.2, 4.8, 1.8]])[0])
self.assertEqual(predicted, "I. versicolor",
"Failed to use the computed model")
self.assertEqual(type(dot), str, "Bad type for the dot returned")