def test_scenario3(self):
"""
Scenario: Successfully creating a Fields object and a modified fields structure from a file:
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 Fields object from the dataset with objective column "<objective_column>"
And I import a summary fields file "<summary_file>" as a fields structure
Then I check the new field structure has field "<field_id>" as "<optype>"
Examples:
| data | time_1 | objective_column | summary_file| field_id | optype | time_2
| ../data/iris.csv | 10 | 0 | fields_summary_modified.csv | 000000 | categorical | 10
"""
print self.test_scenario3.__doc__
examples = [
['data/iris.csv', '10', '0', 'data/fields/fields_summary_modified.csv', '000000', 'categorical', '10']]
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[6])
fields_steps.create_fields_from_dataset(self, example[2])
fields_steps.import_summary_file(self, example[3])
fields_steps.check_field_type(self, example[4], example[5])
def test_scenario3(self):
"""
Scenario: Successfully creating a Fields object and a modified fields structure from a file:
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 Fields object from the dataset with objective column "<objective_column>"
And I import a summary fields file "<summary_file>" as a fields structure
Then I check the new field structure has field "<field_id>" as "<optype>"
Examples:
| data | time_1 | objective_column | summary_file| field_id | optype | time_2
| ../data/iris.csv | 10 | 0 | fields_summary_modified.csv | 000000 | categorical | 10
"""
print self.test_scenario3.__doc__
examples = [[
'data/iris.csv', '10', '0',
'data/fields/fields_summary_modified.csv', '000000', 'categorical',
'10'
]]
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[6])
fields_steps.create_fields_from_dataset(self, example[2])
fields_steps.import_summary_file(self, example[3])
fields_steps.check_field_type(self, example[4], example[5])
def test_scenario2(self):
"""
Scenario: Successfully creating a Fields object and a summary fields file:
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 Fields object from the dataset with objective column "<objective_column>"
And I export a summary fields file "<summary_file>"
Then I check that the file "<summary_file>" is like "<expected_file>"
Examples:
| data | time_1 | objective_column | summary_file| expected_file | time_2
| ../data/iris.csv | 10 | 0 | fields_summary.csv | data/fields/fields_summary.csv | 10
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '10', '0', 'fields_summary.csv', 'data/fields/fields_summary.csv', '10']]
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[5])
fields_steps.create_fields_from_dataset(self, example[2])
fields_steps.generate_summary(self, example[3])
fields_steps.check_summary_like_expected(self, example[3], example[4])
def test_scenario2(self):
"""
Scenario: Successfully creating a Fields object and a summary fields file:
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 Fields object from the dataset with objective column "<objective_column>"
And I export a summary fields file "<summary_file>"
Then I check that the file "<summary_file>" is like "<expected_file>"
Examples:
| data | time_1 | objective_column | summary_file| expected_file | time_2
| ../data/iris.csv | 10 | 0 | fields_summary.csv | data/fields/fields_summary.csv | 10
"""
print self.test_scenario2.__doc__
examples = [[
'data/iris.csv', '10', '0', 'fields_summary.csv',
'data/fields/fields_summary.csv', '10'
]]
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[5])
fields_steps.create_fields_from_dataset(self, example[2])
fields_steps.generate_summary(self, example[3])
fields_steps.check_summary_like_expected(self, example[3],
example[4])
