def test_scenario5(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 using median with confidence for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | number_of_models | tlp | data_input |prediction |
| ../data/grades.csv | 10 | 10 | 50 | 2 | 1 | {} | 67.5 |
"""
print self.test_scenario5.__doc__
examples = [
['data/grades.csv', '30', '30', '50', '2', '1', '{}', 69.0934]]
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_using_median_with_confidence(self, example[6])
compare_pred.the_local_prediction_is(self, example[7])
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_scenario2(self):
"""
Scenario: Successfully creating a batch prediction for 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
When I create a batch prediction for the dataset with the ensemble
And I wait until the batch prediction is ready less than <time_4> secs
And I download the created predictions file to "<local_file>"
Then the batch prediction file is like "<predictions_file>"
Examples:
| data | time_1 | time_2 | number_of_models | tlp | time_3 | time_4 | local_file | predictions_file |
| ../data/iris.csv | 30 | 30 | 5 | 1 | 80 | 50 | ./tmp/batch_predictions.csv | ./data/batch_predictions_e.csv |
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '30', '30', '5', '1', '80', '50', 'tmp/batch_predictions.csv', 'data/batch_predictions_e.csv']]
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[3], example[4])
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[5])
batch_pred_create.i_create_a_batch_prediction_ensemble(self)
batch_pred_create.the_batch_prediction_is_finished_in_less_than(self, example[6])
batch_pred_create.i_download_predictions_file(self, example[7])
batch_pred_create.i_check_predictions(self, example[8])
def test_scenario2(self):
"""
Scenario 2: Successfully creating a local ensemble from an exported 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 an ensemble
And I wait until the ensemble is ready less than <time_3> secs
And I export the ensemble to "<exported_file>"
When I create a local ensemble from the file "<exported_file>"
Then the ensemble ID and the local ensemble ID match
Examples:
| data | time_1 | time_2 | time_3 | exported_file
| ../data/iris.csv | 10 | 10 | 50 | ./tmp/ensemble.json
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '10', '10', '50', './tmp/ensemble.json']]
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)
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3])
ensemble_create.i_export_ensemble(self, example[4])
ensemble_create.i_create_local_ensemble_from_file(self, example[4])
ensemble_create.check_ensemble_id_local_id(self)
def test_scenario2(self):
"""
Scenario: Successfully creating an evaluation for 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
When I create an evaluation for the ensemble with the dataset
And I wait until the evaluation is ready less than <time_4> secs
Then the measured "<measure>" is <value>
Examples:
| data | time_1 | time_2 | number_of_models | tlp | time_3 | time_4 | measure | value |
| ../data/iris.csv | 30 | 30 | 5 | 1 | 50 | 30 | average_phi | 0.98029 |
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '50', '50', '5', '1', '80', '80', 'average_phi', '0.98029']]
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[3], example[4])
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[5])
evaluation_create.i_create_an_evaluation_ensemble(self)
evaluation_create.the_evaluation_is_finished_in_less_than(self, example[6])
evaluation_create.the_measured_measure_is_value(self, example[7], example[8])
def test_scenario2(self):
"""
Scenario: Successfully creating a batch prediction for 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
When I create a batch prediction for the dataset with the ensemble
And I wait until the batch prediction is ready less than <time_4> secs
And I download the created predictions file to "<local_file>"
Then the batch prediction file is like "<predictions_file>"
Examples:
| data | time_1 | time_2 | number_of_models | tlp | time_3 | time_4 | local_file | predictions_file |
| ../data/iris.csv | 30 | 30 | 5 | 1 | 80 | 50 | ./tmp/batch_predictions.csv | ./data/batch_predictions_e.csv |
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '30', '30', '5', '1', '80', '50', 'tmp/batch_predictions.csv', 'data/batch_predictions_e.csv']]
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[3], example[4])
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[5])
batch_pred_create.i_create_a_batch_prediction_ensemble(self)
batch_pred_create.the_batch_prediction_is_finished_in_less_than(self, example[6])
batch_pred_create.i_download_predictions_file(self, example[7])
batch_pred_create.i_check_predictions(self, example[8])
def test_scenario2(self):
"""
Scenario 2: Successfully creating a local ensemble from an exported 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 an ensemble
And I wait until the ensemble is ready less than <time_3> secs
And I export the ensemble to "<exported_file>"
When I create a local ensemble from the file "<exported_file>"
Then the ensemble ID and the local ensemble ID match
Examples:
| data | time_1 | time_2 | time_3 | exported_file
| ../data/iris.csv | 10 | 10 | 50 | ./tmp/ensemble.json
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '10', '10', '50', './tmp/ensemble.json']]
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)
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3])
ensemble_create.i_export_ensemble(self, example[4])
ensemble_create.i_create_local_ensemble_from_file(self, example[4])
ensemble_create.check_ensemble_id_local_id(self)
def test_scenario1(self):
"""
Scenario: Successfully creating a 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
When I create an ensemble prediction for "<data_input>"
And I wait until the prediction is ready less than <time_4> secs
Then the prediction for "<objective>" is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | time_4 | number_of_models | tlp | data_input | objective | prediction |
| ../data/iris.csv | 10 | 10 | 50 | 20 | 5 | 1 | {"petal width": 0.5} | 000004 | Iris-versicolor |
| ../data/iris_sp_chars.csv | 10 | 10 | 50 | 20 | 5 | 1 | {"pétal&width\u0000": 0.5} | 000004 | Iris-versicolor |
| ../data/grades.csv | 10 | 10 | 150 | 20 | 10 | 1 | {"Assignment": 81.22, "Tutorial": 91.95, "Midterm": 79.38, "TakeHome": 105.93} | 000005 | 88.205575 |
| ../data/grades.csv | 10 | 10 | 150 | 20 | 10 | 1 | {"Assignment": 97.33, "Tutorial": 106.74, "Midterm": 76.88, "TakeHome": 108.89} | 000005 | 84.29401 |
"""
print self.test_scenario1.__doc__
examples = [
[
'data/iris.csv', '30', '30', '50', '20', '5', '1',
'{"petal width": 0.5}', '000004', 'Iris-versicolor'
],
[
'data/iris_sp_chars.csv', '30', '30', '50', '20', '5', '1',
'{"pétal&width\u0000": 0.5}', '000004', 'Iris-versicolor'
],
[
'data/grades.csv', '30', '30', '150', '20', '10', '1',
'{"Assignment": 81.22, "Tutorial": 91.95, "Midterm": 79.38, "TakeHome": 105.93}',
'000005', '84.556'
],
[
'data/grades.csv', '30', '30', '150', '20', '10', '1',
'{"Assignment": 97.33, "Tutorial": 106.74, "Midterm": 76.88, "TakeHome": 108.89}',
'000005', '73.13558'
]
]
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[5], example[6])
ensemble_create.the_ensemble_is_finished_in_less_than(
self, example[3])
prediction_create.i_create_an_ensemble_prediction(self, example[7])
prediction_create.the_prediction_is_finished_in_less_than(
self, example[4])
prediction_create.the_prediction_is(self, example[8], example[9])
def test_scenario7(self):
"""
Scenario: Successfully comparing predictions in operating points for ensembles:
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
And I wait until the ensemble is ready less than <time_3> secs
And I create a local ensemble
When I create a prediction for "<data_input>" in "<operating_kind>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local ensemble prediction for "<data_input>" in "<operating_kind>"
Then the local ensemble prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | prediction | operating_kind
"""
examples = [
['data/iris.csv', '10', '50', '50', '{"petal length": 2.46}', 'Iris-versicolor', "probability", "000004"],
['data/iris.csv', '10', '50', '50', '{"petal length": 2}', 'Iris-setosa', "probability", "000004"],
['data/iris.csv', '10', '50', '50', '{"petal length": 2.46}', 'Iris-versicolor', "confidence", "000004"],
['data/iris.csv', '10', '50', '50', '{"petal length": 2}', 'Iris-setosa', "confidence", "000004"],
['data/iris.csv', '10', '50', '50', '{"petal length": 2.46}', 'Iris-versicolor', "votes", "000004"],
['data/iris.csv', '10', '50', '50', '{"petal length": 1}', 'Iris-setosa', "votes", "000004"]]
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(self)
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3])
ensemble_create.create_local_ensemble(self)
prediction_create.i_create_an_ensemble_prediction_op_kind(self, example[4], example[6])
prediction_create.the_prediction_is(self, example[7], example[5])
prediction_compare.i_create_a_local_ensemble_prediction_op_kind(self, example[4], example[6])
prediction_compare.the_local_prediction_is(self, example[5])
def test_scenario7(self):
"""
Scenario: Successfully comparing predictions in operating points for ensembles:
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
And I wait until the ensemble is ready less than <time_3> secs
And I create a local ensemble
When I create a prediction for "<data_input>" in "<operating_kind>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local ensemble prediction for "<data_input>" in "<operating_kind>"
Then the local ensemble prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | prediction | operating_kind
"""
examples = [
['data/iris.csv', '10', '50', '50', '{"petal length": 2.46}', 'Iris-versicolor', "probability", "000004"],
['data/iris.csv', '10', '50', '50', '{"petal length": 2}', 'Iris-setosa', "probability", "000004"],
['data/iris.csv', '10', '50', '50', '{"petal length": 2.46}', 'Iris-versicolor', "confidence", "000004"],
['data/iris.csv', '10', '50', '50', '{"petal length": 2}', 'Iris-setosa', "confidence", "000004"],
['data/iris.csv', '10', '50', '50', '{"petal length": 2.46}', 'Iris-versicolor', "votes", "000004"],
['data/iris.csv', '10', '50', '50', '{"petal length": 1}', 'Iris-setosa', "votes", "000004"]]
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(self)
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3])
ensemble_create.create_local_ensemble(self)
prediction_create.i_create_an_ensemble_prediction_op_kind(self, example[4], example[6])
prediction_create.the_prediction_is(self, example[7], example[5])
prediction_compare.i_create_a_local_ensemble_prediction_op_kind(self, example[4], example[6])
prediction_compare.the_local_prediction_is(self, example[5])
def test_scenario2(self):
"""
Scenario: Successfully creating an evaluation for 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
When I create an evaluation for the ensemble with the dataset
And I wait until the evaluation is ready less than <time_4> secs
Then the measured "<measure>" is <value>
Examples:
| data | time_1 | time_2 | number_of_models | tlp | time_3 | time_4 | measure | value |
| ../data/iris.csv | 30 | 30 | 5 | 1 | 50 | 30 | average_phi | 0.98029 |
"""
print self.test_scenario2.__doc__
examples = [[
'data/iris.csv', '50', '50', '5', '1', '80', '80', 'average_phi',
'0.98029'
]]
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[3], example[4])
ensemble_create.the_ensemble_is_finished_in_less_than(
self, example[5])
evaluation_create.i_create_an_evaluation_ensemble(self)
evaluation_create.the_evaluation_is_finished_in_less_than(
self, example[6])
evaluation_create.the_measured_measure_is_value(
self, example[7], example[8])
def test_scenario1(self):
"""
Scenario: Successfully creating a 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
When I create an ensemble prediction for "<data_input>"
And I wait until the prediction is ready less than <time_4> secs
Then the prediction for "<objective>" is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | time_4 | number_of_models | tlp | data_input | objective | prediction |
| ../data/iris.csv | 10 | 10 | 50 | 20 | 5 | 1 | {"petal width": 0.5} | 000004 | Iris-versicolor |
| ../data/iris_sp_chars.csv | 10 | 10 | 50 | 20 | 5 | 1 | {"pétal&width\u0000": 0.5} | 000004 | Iris-versicolor |
| ../data/grades.csv | 10 | 10 | 150 | 20 | 10 | 1 | {"Assignment": 81.22, "Tutorial": 91.95, "Midterm": 79.38, "TakeHome": 105.93} | 000005 | 88.205575 |
| ../data/grades.csv | 10 | 10 | 150 | 20 | 10 | 1 | {"Assignment": 97.33, "Tutorial": 106.74, "Midterm": 76.88, "TakeHome": 108.89} | 000005 | 84.29401 |
"""
print self.test_scenario1.__doc__
examples = [
['data/iris.csv', '10', '10', '50', '20', '5', '1', '{"petal width": 0.5}', '000004', 'Iris-versicolor'],
['data/iris_sp_chars.csv', '10', '10', '50', '20', '5', '1', '{"pétal&width\u0000": 0.5}', '000004', 'Iris-versicolor'],
['data/grades.csv', '10', '10', '150', '20', '10', '1', '{"Assignment": 81.22, "Tutorial": 91.95, "Midterm": 79.38, "TakeHome": 105.93}', '000005', '88.205575'],
['data/grades.csv', '10', '10', '150', '20', '10', '1', '{"Assignment": 97.33, "Tutorial": 106.74, "Midterm": 76.88, "TakeHome": 108.89}', '000005', '84.29401']]
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[5], example[6])
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3])
prediction_create.i_create_an_ensemble_prediction(self, example[7])
prediction_create.the_prediction_is_finished_in_less_than(self, example[4])
prediction_create.the_prediction_is(self, example[8], example[9])
def test_scenario6(self):
"""
Scenario: Successfully comparing predictions with raw date input:
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
And I wait until the ensemble is ready less than <time_3> secs
And I create a local ensemble
When I create a prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
|data|time_1|time_2|time_3|data_input|objective|prediction|
"""
examples = [
[
'data/dates2.csv', 10, 10, 50,
'{"time-1": "1910-05-08T19:10:23.106", "cat-0":"cat2"}',
'000002', -0.11052
],
[
'data/dates2.csv', 10, 10, 50,
'{"time-1": "1920-06-30T20:21:20.320", "cat-0":"cat1"}',
'000002', 0.79179
],
[
'data/dates2.csv', 10, 10, 50,
'{"time-1": "1932-01-30T19:24:11.450", "cat-0":"cat2"}',
'000002', -1.00834
],
[
'data/dates2.csv', 10, 10, 50,
'{"time-1": "1950-11-06T05:34:05.252", "cat-0":"cat1"}',
'000002', -0.14442
],
[
'data/dates2.csv', 10, 10, 50,
'{"time-1": "1969-7-14 17:36", "cat-0":"cat2"}', '000002',
-0.05469
],
[
'data/dates2.csv', 10, 10, 50,
'{"time-1": "2001-01-05T23:04:04.693", "cat-0":"cat2"}',
'000002', -0.23387
],
[
'data/dates2.csv', 10, 10, 50,
'{"time-1": "1969-W29-1T17:36:39Z", "cat-0":"cat1"}', '000002',
-0.05469
],
[
'data/dates2.csv', 10, 10, 50,
'{"time-1": "Mon Jul 14 17:36 +0000 1969", "cat-0":"cat1"}',
'000002', -0.05469
]
]
print self.test_scenario6.__doc__
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)
ensemble_create.the_ensemble_is_finished_in_less_than(
self, example[3])
ensemble_create.create_local_ensemble(self)
prediction_create.i_create_an_ensemble_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_create.create_local_ensemble_prediction_using_median_with_confidence(
self, example[4])
compare_pred.the_local_prediction_is(self, example[6])
