def test_get_ordered_weights(self):
expected_results = {
'additional_indemnity_money': {
'additional_facts': ['asker_is_tenant', 'bothers_others'],
'important_facts': [
'asker_is_landlord', 'tenant_monthly_payment',
'tenant_not_paid_lease_timespan'
]
}
}
class MockClassifierModel:
def __init__(self):
self.estimators_ = []
class MockEstimator:
def __init__(self):
self.coef_ = np.array([[
-1.52808095e-05, 1.99961642e+00, 1.85962826e-04,
3.77475828e-15, -3.83904518e-05, -4.15205874e-04
]])
mock_classifier_labels = {
0: ('additional_indemnity_money', 'int'),
}
mock_label_column_index = {
'outcomes_vector':
[(0, 'additional_indemnity_money', 'int'),
(1, 'declares_housing_inhabitable', 'bool'),
(2, 'declares_resiliation_is_correct', 'bool')],
'facts_vector': [(0, 'apartment_dirty', 'bool'),
(1, 'asker_is_landlord', 'bool'),
(2, 'asker_is_tenant', 'bool'),
(3, 'bothers_others', 'bool'),
(4, 'disrespect_previous_judgement', 'bool'),
(5, 'landlord_inspector_fees', 'int')]
}
mock_estimator = MockEstimator()
mock_classifier_model = MockClassifierModel()
mock_classifier_model.estimators_.append(mock_estimator)
linear_svc = MultiClassSVM(None)
linear_svc.model = mock_classifier_model
linear_svc.classifier_labels = mock_classifier_labels
linear_svc.label_column_index = mock_label_column_index
MlController.classifier_model = linear_svc
result = MlController.get_weighted_facts()
self.assertEqual(result, expected_results)
def test_fact_dict_to_vector(self):
# Test data
input_json = json.loads(self.test_json)
# Execute
result = MlController.fact_dict_to_vector(input_json['facts'])
# Verify
assert_array_equal(result, [
0., 0., 0., 1., 0., 0., 0., 0., 48., 0., 1., 0., 0., 0., 31., 0.,
0., 540., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 674., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0.
])
def test_probability_vector_to_dict(self):
probability_vector = np.array([0, 1, 0, 0, 0, 1, 1, 0, 1, 0])
MlController.classifier_labels = self.mock_classifier_index
result = MlController.probability_vector_to_dict(probability_vector)
expected_dict = {
'rejects_tenant_demand': '1',
'orders_expulsion': '0',
'declares_resiliation_is_correct': '0',
'tenant_ordered_to_pay_landlord': '0',
'orders_tenant_pay_first_of_month': '1',
'orders_immediate_execution': '0',
'rejects_landlord_demand': '0',
'declares_housing_inhabitable': '1',
'orders_resiliation': '1',
'additional_indemnity_money': '0'
}
self.assertEqual(expected_dict, result)
def test_get_ml_statistics(self):
result = MlController.get_ml_statistics()
self.assertTrue(isinstance(result, dict))
def test_get_anti_fact(self):
anti_facts = MlController.get_anti_facts()
self.assertTrue(isinstance(anti_facts, dict))
def classify_claim_category():
input_json = request.get_json()
return jsonify(MlController.predict_outcome(input_json))
def get_ml_metrics():
return jsonify(MlController.get_ml_statistics())
def get_anti_facts():
return jsonify(MlController.get_anti_facts())
def get_ordered_weights():
return jsonify(MlController.get_weighted_facts())