def test_scenario2(self):
"""
Scenario: Successfully obtaining field importance from an Ensemble:
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 "<parms1>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<parms2>"
And I wait until the model is ready less than <time_4> secs
And I create a model with "<parms3>"
And I wait until the model is ready less than <time_5> secs
When I create a local Ensemble with the last <number_of_models> models
Then the field importance text is <field_importance>
Examples:
| data | time_1 | time_2 |parms1 | time_3 |parms2 | time_4 |parms3| time_5 |number_of_models |field_importance
| ../data/iris.csv | 10 | 10 |{"input_fields": ["000000", "000001","000003", "000004"]} |20 |{"input_fields": ["000000", "000001","000002", "000004"]} | 20 |{"input_fields": ["000000", "000001","000002", "000003", "000004"]} | 20 | 3 |[["000002", 0.5269933333333333], ["000003", 0.38936], ["000000", 0.04662333333333333], ["000001", 0.037026666666666666]]
"""
print self.test_scenario2.__doc__
examples = [[
'data/iris.csv', '10', '10',
'{"input_fields": ["000000", "000001","000003", "000004"]}', '20',
'{"input_fields": ["000000", "000001","000002", "000004"]}', '20',
'{"input_fields": ["000000", "000001","000002", "000003", "000004"]}',
'20', '3',
'[["000002", 0.5269933333333333], ["000003", 0.38936], ["000000", 0.04662333333333333], ["000001", 0.037026666666666666]]'
]]
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[3])
model_create.the_model_is_finished_in_less_than(self, example[4])
model_create.i_create_a_model_with(self, example[5])
model_create.the_model_is_finished_in_less_than(self, example[6])
model_create.i_create_a_model_with(self, example[7])
model_create.the_model_is_finished_in_less_than(self, example[8])
ensemble_create.create_local_ensemble_with_list(self, example[9])
ensemble_create.field_importance_print(self, example[10])
def test_scenario2(self):
"""
Scenario: Successfully obtaining field importance from an Ensemble:
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 "<parms1>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<parms2>"
And I wait until the model is ready less than <time_4> secs
And I create a model with "<parms3>"
And I wait until the model is ready less than <time_5> secs
When I create a local Ensemble with the last <number_of_models> models
Then the field importance text is <field_importance>
Examples:
| data | time_1 | time_2 |parms1 | time_3 |parms2 | time_4 |parms3| time_5 |number_of_models |field_importance
| ../data/iris.csv | 10 | 10 |{"input_fields": ["000000", "000001","000003", "000004"]} |20 |{"input_fields": ["000000", "000001","000002", "000004"]} | 20 |{"input_fields": ["000000", "000001","000002", "000003", "000004"]} | 20 | 3 |[["000002", 0.5269933333333333], ["000003", 0.38936], ["000000", 0.04662333333333333], ["000001", 0.037026666666666666]]
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '10', '10', '{"input_fields": ["000000", "000001","000003", "000004"]}', '20', '{"input_fields": ["000000", "000001","000002", "000004"]}', '20', '{"input_fields": ["000000", "000001","000002", "000003", "000004"]}', '20', '3', '[["000002", 0.5269933333333333], ["000003", 0.38936], ["000000", 0.04662333333333333], ["000001", 0.037026666666666666]]']]
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[3])
model_create.the_model_is_finished_in_less_than(self, example[4])
model_create.i_create_a_model_with(self, example[5])
model_create.the_model_is_finished_in_less_than(self, example[6])
model_create.i_create_a_model_with(self, example[7])
model_create.the_model_is_finished_in_less_than(self, example[8])
ensemble_create.create_local_ensemble_with_list(self, example[9])
ensemble_create.field_importance_print(self, example[10])