def test_scenario1(self):
"""
Scenario: Successfully creating a prediction from a local model in a json file:
Given I create a local model from a "<model>" file
When I create a local prediction for "<data_input>" with confidence
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
Examples:
| model | data_input | prediction | confidence
| ../data/iris_model.json | {"petal length": 0.5} | Iris-setosa | 0.90594
"""
print self.test_scenario1.__doc__
examples = [[
'data/iris_model.json', '{"petal length": 0.5}', 'Iris-setosa',
'0.90594'
],
[
'data/iris_model.json', '{"petal length": "0.5"}',
'Iris-setosa', '0.90594'
]]
for example in examples:
print "\nTesting with:\n", example
prediction_compare.i_create_a_local_model_from_file(
self, example[0])
prediction_compare.i_create_a_local_prediction_with_confidence(
self, example[1])
prediction_compare.the_local_prediction_is(self, example[2])
prediction_compare.the_local_prediction_confidence_is(
self, example[3])
def test_scenario13(self):
"""
Scenario: Successfully comparing predictions:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a model
And I wait until the model is ready less than <time_3> secs
And I create a local model
When I create a prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction |
"""
examples = [
['data/iris.csv', '10', '10', '10', '{"petal width": 0.5}', '000004', 'Iris-setosa', "tmp/my_model.json", "my_test"],
['data/iris.csv', '10', '10', '10', '{"petal length": 6, "petal width": 2}', '000004', 'Iris-virginica', "tmp/my_model.json", "my_test"],
['data/iris.csv', '10', '10', '10', '{"petal length": 4, "petal width": 1.5}', '000004', 'Iris-versicolor', "tmp/my_model.json", "my_test"],
['data/iris_sp_chars.csv', '10', '10', '10', '{"pétal.length": 4, "pétal&width\u0000": 1.5}', '000004', 'Iris-versicolor', "tmp/my_model.json", "my_test"]]
show_doc(self.test_scenario13, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
args = '{"tags": ["%s"]}' % example[8]
model_create.i_create_a_model_with(self, data=args)
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_export_model(self, False, example[7]) # no pmml
prediction_compare.i_create_a_local_model_from_file(self, example[7])
prediction_create.i_create_a_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
model_create.i_export_tags_model(self, example[7], example[8])
prediction_compare.i_create_a_local_model_from_file(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario13(self):
"""
Scenario: Successfully comparing predictions:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a model
And I wait until the model is ready less than <time_3> secs
And I create a local model
When I create a prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction |
"""
examples = [
['data/iris.csv', '10', '10', '10', '{"petal width": 0.5}', '000004', 'Iris-setosa', "tmp/my_model.json", "my_test"],
['data/iris.csv', '10', '10', '10', '{"petal length": 6, "petal width": 2}', '000004', 'Iris-virginica', "tmp/my_model.json", "my_test"],
['data/iris.csv', '10', '10', '10', '{"petal length": 4, "petal width": 1.5}', '000004', 'Iris-versicolor', "tmp/my_model.json", "my_test"],
['data/iris_sp_chars.csv', '10', '10', '10', '{"pétal.length": 4, "pétal&width\u0000": 1.5}', '000004', 'Iris-versicolor', "tmp/my_model.json", "my_test"]]
show_doc(self.test_scenario13, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
args = '{"tags": ["%s"]}' % example[8]
model_create.i_create_a_model_with(self, data=args)
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_export_model(self, False, example[7]) # no pmml
prediction_compare.i_create_a_local_model_from_file(self, example[7])
prediction_create.i_create_a_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
model_create.i_export_tags_model(self, example[7], example[8])
prediction_compare.i_create_a_local_model_from_file(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario2(self):
"""
Scenario: Successfully creating a multiple prediction from a local model in a json file:
Given I create a local model from a "<model>" file
When I create a multiple local prediction for "<data_input>"
Then the multiple local prediction is "<prediction>"
Examples:
| model | data_input | prediction
| ../data/iris_model.json | {"petal length": 3} | [{"count": 42, "confidence": 0.4006020980792863, "prediction": "Iris-versicolor", "probability": 0.5060240963855421}, {"count": 41, "confidence": 0.3890868795664999, "prediction": "Iris-virginica", "probability": 0.4939759036144578}]
"""
print self.test_scenario1.__doc__
examples = [
['data/iris_model.json', '{"petal length": 3}', '[{"count": 42, "confidence": 0.4006020980792863, "prediction": "Iris-versicolor", "probability": 0.5060240963855421}, {"count": 41, "confidence": 0.3890868795664999, "prediction": "Iris-virginica", "probability": 0.4939759036144578}]']]
for example in examples:
print "\nTesting with:\n", example
prediction_compare.i_create_a_local_model_from_file(self, example[0])
prediction_compare.i_create_a_multiple_local_prediction(self, example[1])
prediction_compare.the_multiple_local_prediction_is(self, example[2])
def test_scenario1(self):
"""
Scenario: Successfully creating a prediction from a local model in a json file:
Given I create a local model from a "<model>" file
When I create a local prediction for "<data_input>" with confidence
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
Examples:
| model | data_input | prediction | confidence
| ../data/iris_model.json | {"petal length": 0.5} | Iris-setosa | 0.90594
"""
print self.test_scenario1.__doc__
examples = [
['data/iris_model.json', '{"petal length": 0.5}', 'Iris-setosa', '0.90594']]
for example in examples:
print "\nTesting with:\n", example
prediction_compare.i_create_a_local_model_from_file(self, example[0])
prediction_compare.i_create_a_local_prediction_with_confidence(self, example[1])
prediction_compare.the_local_prediction_is(self, example[2])
prediction_compare.the_local_prediction_confidence_is(self, example[3])
