def test_scenario11(self):
"""
Scenario: Successfully comparing predictions in operating points for fusions:
Scenario: Successfully comparing predictions for fusions:
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 with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I retrieve a list of remote models tagged with "<tag>"
And I create a fusion from a list of models
And I wait until the fusion is ready less than <time_4> secs
And I create a local fusion
When I create a prediction for "<data_input>" in "<operating_point>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local fusion prediction for "<data_input>" in "<operating_point>"
Then the local ensemble prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | params| tag | data_input | objective | prediction | params | operating_point
"""
examples = [
['data/iris_unbalanced.csv', '30', '30', '120', '120', '{"tags":["my_fusion_tag_11"]}', 'my_fusion_tag_11', '{"petal width": 4}', '000004', 'Iris-virginica', {"kind": "probability", "threshold": 0.1, "positive_class": "Iris-setosa"}],
['data/iris_unbalanced.csv', '30', '30', '120', '120', '{"tags":["my_fusion_tag_11_b"]}', 'my_fusion_tag_11_b', '{"petal width": 4}', '000004', 'Iris-virginica', {"kind": "probability", "threshold": 0.9, "positive_class": "Iris-setosa"}]]
show_doc(self.test_scenario11, 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])
model_create.i_create_a_model_with(self, example[5])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[5])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[5])
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_retrieve_a_list_of_remote_models(self, example[6])
model_create.i_create_a_fusion(self)
model_create.the_fusion_is_finished_in_less_than(self, example[4])
prediction_compare.i_create_a_local_fusion(self)
prediction_create.i_create_a_fusion_prediction_op(self, example[7], example[10])
prediction_create.the_prediction_is(self, example[8], example[9])
prediction_compare.i_create_a_local_prediction_op(self, example[7], example[10])
prediction_compare.the_local_prediction_is(self, example[9])
def test_scenario2(self):
"""
Scenario: Successfully comparing predictions in operating points for models:
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>" in "<operating_point>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction for "<data_input>" in "<operating_point>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | prediction | operating_point
"""
examples = [
['data/iris.csv', '10', '50', '50', '{"petal width": 4}', 'Iris-setosa', {"kind": "probability", "threshold": 0.1, "positive_class": "Iris-setosa"}, "000004"],
['data/iris.csv', '10', '50', '50', '{"petal width": 4}', 'Iris-versicolor', {"kind": "probability", "threshold": 0.9, "positive_class": "Iris-setosa"}, "000004"],
['data/iris.csv', '10', '50', '50', '{"sepal length": 4.1, "sepal width": 2.4}', 'Iris-setosa', {"kind": "confidence", "threshold": 0.1, "positive_class": "Iris-setosa"}, "000004"],
['data/iris.csv', '10', '50', '50', '{"sepal length": 4.1, "sepal width": 2.4}', 'Iris-versicolor', {"kind": "confidence", "threshold": 0.9, "positive_class": "Iris-setosa"}, "000004"]]
show_doc(self.test_scenario2, 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])
model_create.i_create_a_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_prediction_op(self, example[4], example[6])
prediction_create.the_prediction_is(self, example[7], example[5])
prediction_compare.i_create_a_local_prediction_op(self, example[4], example[6])
prediction_compare.the_local_prediction_is(self, example[5])
def test_scenario2(self):
"""
Scenario: Successfully comparing predictions in operating points for models:
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>" in "<operating_point>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction for "<data_input>" in "<operating_point>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | prediction | operating_point
"""
examples = [
['data/iris.csv', '10', '50', '50', '{"petal width": 4}', 'Iris-setosa', {"kind": "probability", "threshold": 0.1, "positive_class": "Iris-setosa"}, "000004"],
['data/iris.csv', '10', '50', '50', '{"petal width": 4}', 'Iris-versicolor', {"kind": "probability", "threshold": 0.9, "positive_class": "Iris-setosa"}, "000004"],
['data/iris.csv', '10', '50', '50', '{"sepal length": 4.1, "sepal width": 2.4}', 'Iris-setosa', {"kind": "confidence", "threshold": 0.1, "positive_class": "Iris-setosa"}, "000004"],
['data/iris.csv', '10', '50', '50', '{"sepal length": 4.1, "sepal width": 2.4}', 'Iris-versicolor', {"kind": "confidence", "threshold": 0.9, "positive_class": "Iris-setosa"}, "000004"]]
show_doc(self.test_scenario2, 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])
model_create.i_create_a_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_prediction_op(self, example[4], example[6])
prediction_create.the_prediction_is(self, example[7], example[5])
prediction_compare.i_create_a_local_prediction_op(self, example[4], example[6])
prediction_compare.the_local_prediction_is(self, example[5])
def test_scenario11(self):
"""
Scenario: Successfully comparing predictions in operating points for fusions:
Scenario: Successfully comparing predictions for fusions:
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 with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I retrieve a list of remote models tagged with "<tag>"
And I create a fusion from a list of models
And I wait until the fusion is ready less than <time_4> secs
And I create a local fusion
When I create a prediction for "<data_input>" in "<operating_point>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local fusion prediction for "<data_input>" in "<operating_point>"
Then the local ensemble prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | params| tag | data_input | objective | prediction | params | operating_point
"""
examples = [[
'data/iris_unbalanced.csv', '30', '30', '120', '120',
'{"tags":["my_fusion_tag_11"]}', 'my_fusion_tag_11',
'{"petal width": 4}', '000004', 'Iris-virginica', {
"kind": "probability",
"threshold": 0.1,
"positive_class": "Iris-setosa"
}
],
[
'data/iris_unbalanced.csv', '30', '30', '120', '120',
'{"tags":["my_fusion_tag_11_b"]}',
'my_fusion_tag_11_b', '{"petal width": 4}', '000004',
'Iris-virginica', {
"kind": "probability",
"threshold": 0.9,
"positive_class": "Iris-setosa"
}
]]
show_doc(self.test_scenario11, 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])
model_create.i_create_a_model_with(self, example[5])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[5])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[5])
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_retrieve_a_list_of_remote_models(
self, example[6])
model_create.i_create_a_fusion(self)
model_create.the_fusion_is_finished_in_less_than(self, example[4])
prediction_compare.i_create_a_local_fusion(self)
prediction_create.i_create_a_fusion_prediction_op(
self, example[7], example[10])
prediction_create.the_prediction_is(self, example[8], example[9])
prediction_compare.i_create_a_local_prediction_op(
self, example[7], example[10])
prediction_compare.the_local_prediction_is(self, example[9])
