def test_scenario2(self):
"""
Scenario: Successfully creating a model with missing values and translate the tree model into a set of IF-THEN rules:
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
And I translate the tree into IF_THEN rules
Then I check the output is like "<expected_file>" expected file
Examples:
| data | time_1 | time_2 | time_3 | expected_file |
| data/iris_missing2.csv | 10 | 10 | 10 | data/model/if_then_rules_iris_missing2_MISSINGS.txt |
"""
print self.test_scenario2.__doc__
examples = [["data/iris_missing2.csv", "10", "10", "10", "data/model/if_then_rules_iris_missing2_MISSINGS.txt"]]
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_missing_splits(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
inspect_model.i_translate_the_tree_into_IF_THEN_rules(self)
inspect_model.i_check_if_the_output_is_like_expected_file(self, example[4])
def test_scenario2(self):
"""
Scenario: Successfully creating a model with missing values and translate the tree model into a set of IF-THEN rules:
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
And I translate the tree into IF_THEN rules
Then I check the output is like "<expected_file>" expected file
Examples:
| data | time_1 | time_2 | time_3 | expected_file |
| data/iris_missing2.csv | 10 | 10 | 10 | data/model/if_then_rules_iris_missing2_MISSINGS.txt |
"""
print self.test_scenario2.__doc__
examples = [
['data/iris_missing2.csv', '10', '10', '10', 'data/model/if_then_rules_iris_missing2_MISSINGS.txt']]
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_missing_splits(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
inspect_model.i_translate_the_tree_into_IF_THEN_rules(self)
inspect_model.i_check_if_the_output_is_like_expected_file(self, example[4])
def test_scenario6(self):
"""
Scenario: Successfully comparing predictions with proportional missing strategy for missing_splits 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 with missing splits
And I wait until the model is ready less than <time_3> secs
And I create a local model
When I create a proportional missing strategy prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And the confidence for the prediction is "<confidence>"
And I create a proportional missing strategy local prediction for "<data_input>"
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence |
| ../data/iris_missing2.csv | 10 | 10 | 10 | {"petal width": 1} | 000004 | Iris-setosa | 0.8064 |
| ../data/iris_missing2.csv | 10 | 10 | 10 | {"petal width": 1, "petal length": 4} | 000004 | Iris-versicolor | 0.7847 |
"""
print self.test_scenario6.__doc__
examples = [[
'data/iris_missing2.csv', '10', '10', '10', '{"petal width": 1}',
'000004', 'Iris-setosa', '0.8064'
],
[
'data/iris_missing2.csv', '10', '10', '10',
'{"petal width": 1, "petal length": 4}', '000004',
'Iris-versicolor', '0.7847'
]]
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_missing_splits(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_proportional_prediction(
self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_create.the_confidence_is(self, example[7])
prediction_compare.i_create_a_proportional_local_prediction(
self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_compare.the_local_prediction_confidence_is(
self, example[7])
def test_scenario6(self):
"""
Scenario: Successfully comparing predictions with proportional missing strategy for missing_splits 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 with missing splits
And I wait until the model is ready less than <time_3> secs
And I create a local model
When I create a proportional missing strategy prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And the confidence for the prediction is "<confidence>"
And I create a proportional missing strategy local prediction for "<data_input>"
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence |
| ../data/iris_missing2.csv | 10 | 10 | 10 | {"petal width": 1} | 000004 | Iris-setosa | 0.8064 |
| ../data/iris_missing2.csv | 10 | 10 | 10 | {"petal width": 1, "petal length": 4} | 000004 | Iris-versicolor | 0.7847 |
"""
print self.test_scenario6.__doc__
examples = [
["data/iris_missing2.csv", "10", "10", "10", '{"petal width": 1}', "000004", "Iris-setosa", "0.8064"],
[
"data/iris_missing2.csv",
"10",
"10",
"10",
'{"petal width": 1, "petal length": 4}',
"000004",
"Iris-versicolor",
"0.7847",
],
]
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_missing_splits(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_proportional_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_create.the_confidence_is(self, example[7])
prediction_compare.i_create_a_proportional_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_compare.the_local_prediction_confidence_is(self, example[7])
