def test_scenario1(self):
"""
Scenario: Successfully creating a local prediction 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 an ensemble of <number_of_models> models and <tlp> tlp
And I wait until the ensemble is ready less than <time_3> secs
And I create a local Ensemble
When I create a local ensemble prediction with confidence for "<data_input>"
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
And the local probabilities are "<probabilities>"
Examples:
| data | time_1 | time_2 | time_3 | number_of_models | tlp | data_input |prediction | confidence
| ../data/iris.csv | 10 | 10 | 50 | 5 | 1 | {"petal width": 0.5} | Iris-versicolor | 0.3687 | [0.3403, 0.4150, 0.2447]
"""
print self.test_scenario1.__doc__
examples = [
['data/iris.csv', '10', '10', '50', '5', '1', '{"petal width": 0.5}', 'Iris-versicolor', '0.415', '["0.3403", "0.4150", "0.2447"]' ]]
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])
ensemble_create.i_create_an_ensemble(self, example[4], example[5])
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3])
ensemble_create.create_local_ensemble(self)
prediction_create.create_local_ensemble_prediction_with_confidence(self, example[6])
compare_pred.the_local_prediction_is(self, example[7])
compare_pred.the_local_prediction_confidence_is(self, example[8])
compare_pred.the_local_probabilities_are(self, example[9])
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_scenario3(self):
"""
Scenario: Successfully comparing predictions with proportional missing strategy:
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 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 |
"""
examples = [[
'data/iris.csv', '10', '10', '10', '{}', '000004', 'Iris-setosa',
'0.2629'
],
[
'data/grades.csv', '10', '10', '10', '{}', '000005',
'68.62224', '27.5358'
],
[
'data/grades.csv', '10', '10', '10', '{"Midterm": 20}',
'000005', '40.46667', '54.89713'
],
[
'data/grades.csv', '10', '10', '10',
'{"Midterm": 20, "Tutorial": 90, "TakeHome": 100}',
'000005', '28.06', '25.65806'
]]
show_doc(self.test_scenario3, 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_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_scenario9(self):
"""
Scenario: Successfully comparing predictions for ensembles with proportional missing strategy in a supervised model:
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 an esemble with "<params>"
And I wait until the ensemble is ready less than <time_3> secs
And I create a local ensemble
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 | params
['data/iris.csv', '10', '10', '50', '{}', '000004', 'Iris-virginica', '0.33784', '{"boosting": {"iterations": 5}}'],
"""
examples = [[
'data/iris.csv', '10', '10', '50', '{}', '000004',
'Iris-virginica', '0.33784', '{"boosting": {"iterations": 5}}', {}
],
[
'data/iris.csv', '10', '10', '50', '{}', '000004',
'Iris-versicolor', '0.27261',
'{"number_of_models": 5"}', {
"operating_kind": "confidence"
}
]]
show_doc(self.test_scenario7, 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])
ensemble_create.i_create_an_ensemble_with_params(self, example[8])
ensemble_create.the_ensemble_is_finished_in_less_than(
self, example[3])
ensemble_create.create_local_supervised_ensemble(self)
prediction_create.i_create_an_ensemble_proportional_prediction(
self, example[4], example[9])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_create.the_confidence_is(self, example[7])
prediction_create.create_local_ensemble_proportional_prediction_with_confidence(
self, example[4], example[9])
prediction_compare.the_local_ensemble_prediction_is(
self, example[6])
prediction_compare.the_local_prediction_confidence_is(
self, example[7])
def test_scenario10(self):
"""
Scenario: Successfully comparing predictions with proportional missing strategy and balanced 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 balanced model
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>"
And I create local probabilities for "<data_input>"
Then the local probabilities are "<probabilities>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence |
"""
examples = [
[
'data/iris_unbalanced.csv', '10', '10', '10', '{}', '000004',
'Iris-setosa', '0.25284', '[0.33333, 0.33333, 0.33333]'
],
[
'data/iris_unbalanced.csv', '10', '10', '10',
'{"petal length":1, "sepal length":1, "petal width": 1, "sepal width": 1}',
'000004', 'Iris-setosa', '0.7575', '[1.0, 0.0, 0.0]'
]
]
show_doc(self.test_scenario10, 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_balanced_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_proportional_prediction(
self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_proportional_local_prediction(
self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_create.the_confidence_is(self, example[7])
prediction_compare.the_local_prediction_confidence_is(
self, example[7])
prediction_compare.i_create_local_probabilities(self, example[4])
prediction_compare.the_local_probabilities_are(self, example[8])
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])
def test_scenario7(self):
"""
Scenario: Successfully comparing predictions for ensembles with proportional missing strategy:
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 an esemble with "<params>"
And I wait until the ensemble is ready less than <time_3> secs
And I create a local ensemble
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 | params
['data/iris.csv', '10', '10', '50', '{}', '000004', 'Iris-virginica', '0.33784', '{"boosting": {"iterations": 5}}'],
"""
examples = [
['data/iris.csv', '30', '30', '50', '{}', '000004', 'Iris-virginica', '0.33784', '{"boosting": {"iterations": 5}}', {}],
['data/iris.csv', '30', '30', '50', '{}', '000004', 'Iris-versicolor', '0.27261', '{"number_of_models": 5"}', {"operating_kind": "confidence"}],
['data/grades.csv', '30', '30', '50', '{}', '000005', '70.505792', '30.7161', '{"number_of_models": 5}', {}],
['data/grades.csv', '30', '30', '50', '{"Midterm": 20}', '000005', '54.82214', '25.89672', '{"number_of_models": 5}', {"operating_kind": "confidence"}],
['data/grades.csv', '30', '30', '50', '{"Midterm": 20}', '000005', '45.4573', '29.58403', '{"number_of_models": 5}', {}],
['data/grades.csv', '30', '30', '50', '{"Midterm": 20, "Tutorial": 90, "TakeHome": 100}', '000005', '42.814', '31.51804', '{"number_of_models": 5}', {}]]
show_doc(self.test_scenario7, 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])
ensemble_create.i_create_an_ensemble_with_params(self, example[8])
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3])
ensemble_create.create_local_ensemble(self)
prediction_create.i_create_an_ensemble_proportional_prediction(self, example[4], example[9])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_create.the_confidence_is(self, example[7])
prediction_create.create_local_ensemble_proportional_prediction_with_confidence(self, example[4], example[9])
prediction_compare.the_local_ensemble_prediction_is(self, example[6])
prediction_compare.the_local_prediction_confidence_is(self, example[7])
def test_scenario10(self):
"""
Scenario: Successfully comparing predictions with proportional missing strategy and balanced 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 balanced model
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>"
And I create local probabilities for "<data_input>"
Then the local probabilities are "<probabilities>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence |
"""
examples = [
['data/iris_unbalanced.csv', '10', '10', '10', '{}', '000004', 'Iris-setosa', '0.25284', '[0.33333, 0.33333, 0.33333]'],
['data/iris_unbalanced.csv', '10', '10', '10', '{"petal length":1, "sepal length":1, "petal width": 1, "sepal width": 1}', '000004', 'Iris-setosa', '0.7575', '[1.0, 0.0, 0.0]']]
show_doc(self.test_scenario10, 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_balanced_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_proportional_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_proportional_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_create.the_confidence_is(self, example[7])
prediction_compare.the_local_prediction_confidence_is(self, example[7])
prediction_compare.i_create_local_probabilities(self, example[4])
prediction_compare.the_local_probabilities_are(self, example[8])
def test_scenario7(self):
"""
Scenario: Successfully comparing predictions for ensembles with proportional missing strategy:
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 an esemble with "<params>"
And I wait until the ensemble is ready less than <time_3> secs
And I create a local ensemble
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 | params
"""
examples = [
['data/iris.csv', '10', '10', '50', '{}', '000004', 'Iris-virginica', '0.33784', '{"boosting": {"iterations": 5}}'],
['data/iris.csv', '10', '10', '50', '{}', '000004', 'Iris-versicolor', '0.3174', '{"number_of_models": 5}'],
['data/grades.csv', '10', '10', '50', '{}', '000005', '70.505792', '30.7161', '{"number_of_models": 5}'],
['data/grades.csv', '10', '10', '50', '{"Midterm": 20}', '000005', '45.4573', '29.58403', '{"number_of_models": 5}'],
['data/grades.csv', '10', '10', '50', '{"Midterm": 20, "Tutorial": 90, "TakeHome": 100}', '000005', '42.814', '31.51804', '{"number_of_models": 5}']]
show_doc(self.test_scenario7, 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])
ensemble_create.i_create_an_ensemble_with_params(self, example[8])
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3])
ensemble_create.create_local_ensemble(self)
prediction_create.i_create_an_ensemble_proportional_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_create.the_confidence_is(self, example[7])
prediction_create.create_local_ensemble_proportional_prediction_with_confidence(self, example[4])
prediction_compare.the_local_ensemble_prediction_is(self, example[6])
prediction_compare.the_local_prediction_confidence_is(self, example[7])
def test_scenario3(self):
"""
Scenario: Successfully comparing predictions with proportional missing strategy:
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 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 |
"""
examples = [
['data/iris.csv', '10', '10', '10', '{}', '000004', 'Iris-setosa', '0.2629'],
['data/grades.csv', '10', '10', '10', '{}', '000005', '68.62224', '27.5358'],
['data/grades.csv', '10', '10', '10', '{"Midterm": 20}', '000005', '40.46667', '54.89713'],
['data/grades.csv', '10', '10', '10', '{"Midterm": 20, "Tutorial": 90, "TakeHome": 100}', '000005', '28.06', '25.65806']]
show_doc(self.test_scenario3, 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_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_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])
