def test_scenario4(self):
"""
Scenario 4: Successfully creating a local deepnet 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 a deepnet
And I wait until the deepnet is ready less than <time_3> secs
And I export the deepnet to "<exported_file>"
When I create a local deepnet from the file "<exported_file>"
Then the deepnet ID and the local deepnet ID match
Examples:
| data | time_1 | time_2 | time_3 | exported_file
| ../data/iris.csv | 10 | 10 | 50 | ./tmp/deepnet.json
"""
print self.test_scenario4.__doc__
examples = [
['data/iris.csv', '10', '10', '500', './tmp/deepnet.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])
model_create.i_create_a_deepnet(self)
model_create.the_deepnet_is_finished_in_less_than(self, example[3])
model_create.i_export_deepnet(self, example[4])
model_create.i_create_local_deepnet_from_file(self, example[4])
model_create.check_deepnet_id_local_id(self)
def test_scenario4(self):
"""
Scenario4: Successfully creating an evaluation for a deepnet:
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 deepnet
And I wait until the deepnet is ready less than <time_3> secs
When I create an evaluation for the deepnet 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 | time_3 | time_4 | measure | value |
| ../data/iris.csv | 30 | 30 | 50 | 30 | average_phi | 0.95007 |
"""
print self.test_scenario4.__doc__
examples = [
['data/iris.csv', '50', '50', '800', '80', 'average_phi', '0.95007']]
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_deepnet(self)
model_create.the_deepnet_is_finished_in_less_than(self, example[3])
evaluation_create.i_create_an_evaluation_deepnet(self)
evaluation_create.the_evaluation_is_finished_in_less_than(self, example[4])
evaluation_create.the_measured_measure_is_value(self, example[5], example[6])
def test_scenario4(self):
"""
Scenario 4: Successfully creating a local deepnet 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 a deepnet
And I wait until the deepnet is ready less than <time_3> secs
And I export the deepnet to "<exported_file>"
When I create a local deepnet from the file "<exported_file>"
Then the deepnet ID and the local deepnet ID match
Examples:
| data | time_1 | time_2 | time_3 | exported_file
| ../data/iris.csv | 10 | 10 | 50 | ./tmp/deepnet.json
"""
print self.test_scenario4.__doc__
examples = [
['data/iris.csv', '10', '10', '500', './tmp/deepnet.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])
model_create.i_create_a_deepnet(self)
model_create.the_deepnet_is_finished_in_less_than(self, example[3])
model_create.i_export_deepnet(self, example[4])
model_create.i_create_local_deepnet_from_file(self, example[4])
model_create.check_deepnet_id_local_id(self)
def test_scenario1(self):
"""
Scenario: Successfully comparing predictions for deepnets:
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 deepnet with objective "<objective>" and "<params>"
And I wait until the deepnet is ready less than <time_3> secs
And I create a local deepnet
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 | params,
"""
examples = [[
'data/iris.csv', '30', '50', '30000', '{"petal width": 4}',
'000004', 'Iris-virginica', '{}'
],
[
'data/iris.csv', '30', '50', '30000',
'{"sepal length": 4.1, "sepal width": 2.4}', '000004',
'Iris-setosa', '{}'
],
[
'data/iris_missing2.csv', '30', '50', '30000', '{}',
'000004', 'Iris-setosa', '{}'
],
[
'data/grades.csv', '30', '50', '30000', '{}', '000005',
42.15473, '{}'
],
[
'data/spam.csv', '30', '50', '30000', '{}', '000000',
'ham', '{}'
]]
show_doc(self.test_scenario1, 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_deepnet_with_objective_and_params(
self, example[5], example[7])
model_create.the_deepnet_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_deepnet(self)
prediction_create.i_create_a_deepnet_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_deepnet_prediction(
self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario6(self):
"""
Scenario: Successfully comparing predictions for deepnets with operating kind:
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 deepnet with objective "<objective>" and "<params>"
And I wait until the deepnet is ready less than <time_3> secs
And I create a local deepnet
When I create a prediction with operating kind "<operating_kind>" for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction with operating point "<operating_kind>" for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | params | operating_point,
"""
examples = [[
'data/iris.csv', '10', '50', '30000', '{"petal length": 2.46}',
'000004', 'Iris-setosa', '{}', "probability"
],
[
'data/iris.csv', '10', '50', '30000',
'{"petal length": 2}', '000004', 'Iris-setosa', '{}',
"probability"
]]
show_doc(self.test_scenario6, 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_deepnet_with_objective_and_params(
self, example[5], example[7])
model_create.the_deepnet_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_deepnet(self)
prediction_create.i_create_a_deepnet_prediction_op_kind(
self, example[4], example[8])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_deepnet_prediction_op_kind(
self, example[4], example[8])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario1(self):
"""
Scenario: Successfully comparing predictions for deepnets:
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 deepnet with objective "<objective>" and "<params>"
And I wait until the deepnet is ready less than <time_3> secs
And I create a local deepnet
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 | params,
"""
examples = [
['data/iris.csv', '30', '50', '30000', '{"petal width": 4}', '000004', 'Iris-virginica', '{}'],
['data/iris.csv', '30', '50', '30000', '{"sepal length": 4.1, "sepal width": 2.4}', '000004', 'Iris-setosa', '{}'],
['data/iris_missing2.csv', '30', '50', '30000', '{}', '000004', 'Iris-setosa', '{}'],
['data/grades.csv', '30', '50', '30000', '{}', '000005', 42.15473, '{}'],
['data/spam.csv', '30', '50', '30000', '{}', '000000', 'ham', '{}']]
show_doc(self.test_scenario1, 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_deepnet_with_objective_and_params(self, example[5], example[7])
model_create.the_deepnet_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_deepnet(self)
prediction_create.i_create_a_deepnet_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_deepnet_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario6(self):
"""
Scenario: Successfully comparing predictions for deepnets with operating kind:
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 deepnet with objective "<objective>" and "<params>"
And I wait until the deepnet is ready less than <time_3> secs
And I create a local deepnet
When I create a prediction with operating kind "<operating_kind>" for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction with operating point "<operating_kind>" for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | params | operating_point,
"""
examples = [
['data/iris.csv', '10', '50', '30000', '{"petal length": 2.46}', '000004', 'Iris-setosa', '{}', "probability"],
['data/iris.csv', '10', '50', '30000', '{"petal length": 2}', '000004', 'Iris-setosa', '{}', "probability"]]
show_doc(self.test_scenario6, 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_deepnet_with_objective_and_params(self, example[5], example[7])
model_create.the_deepnet_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_deepnet(self)
prediction_create.i_create_a_deepnet_prediction_op_kind(self, example[4], example[8])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_deepnet_prediction_op_kind(self, example[4], example[8])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario12(self):
"""
Scenario: Successfully comparing remote and local predictions
with raw date input for deepnet:
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 deepnet
And I wait until the deepnet is ready
less than <time_3> secs
And I create a local deepnet
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
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1910-05-08T19:10:23.106", "cat-0":"cat2"}',
'000002', 0.04082],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "2011-04-01T00:16:45.747", "cat-0":"cat2"}',
'000002', 0.02919],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1969-W29-1T17:36:39Z", "cat-0":"cat1"}',
'000002', 0.0199],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1969-W29-1T17:36:39Z", "cat-0":"cat1"}',
'000002', 0.0199],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1969-W29-1T17:36:39Z", "cat-0":"cat1"}',
'000002', 0.0199],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1969-W29-1T17:36:39Z", "cat-0":"cat1"}',
'000002', 0.0199],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1920-06-45T20:21:20.320", "cat-0":"cat1"}',
'000002', 0.0199],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "2001-01-05T23:04:04.693", "cat-0":"cat2"}',
'000002', 0.28517],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1950-11-06T05:34:05.602", "cat-0":"cat1"}',
'000002', -0.05673],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1950-11-06T05:34:05.602", "cat-0":"cat1"}',
'000002', -0.05673],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1950-11-06T05:34:05.602", "cat-0":"cat1"}',
'000002', -0.05673],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1950-11-06T05:34:05.602", "cat-0":"cat1"}',
'000002', -0.05673],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1932-01-30T19:24:11.440", "cat-0":"cat2"}',
'000002', 0.16183],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "Mon Jul 14 17:36 +0000 1969", "cat-0":"cat1"}',
'000002', 0.0199]
"""
examples = [
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1910-05-08T19:10:23.106", "cat-0":"cat2"}',
'000002', 0.04082],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "2011-04-01T00:16:45.747", "cat-0":"cat2"}',
'000002', 0.02919],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1969-W29-1T17:36:39Z", "cat-0":"cat1"}',
'000002', 0.0199],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1969-W29-1T17:36:39Z", "cat-0":"cat1"}',
'000002', 0.0199],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1969-W29-1T17:36:39Z", "cat-0":"cat1"}',
'000002', 0.0199],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1969-W29-1T17:36:39Z", "cat-0":"cat1"}',
'000002', 0.0199],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1920-06-45T20:21:20.320", "cat-0":"cat1"}',
'000002', 0.0199],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "2001-01-05T23:04:04.693", "cat-0":"cat2"}',
'000002', 0.28517],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1950-11-06T05:34:05.602", "cat-0":"cat1"}',
'000002', -0.05673],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1950-11-06T05:34:05.602", "cat-0":"cat1"}',
'000002', -0.05673],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1950-11-06T05:34:05.602", "cat-0":"cat1"}',
'000002', -0.05673],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1950-11-06T05:34:05.602", "cat-0":"cat1"}',
'000002', -0.05673],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "1932-01-30T19:24:11.440", "cat-0":"cat2"}',
'000002', 0.16183],
['data/dates2.csv', '20', '45', '60',
'{"time-1": "Mon Jul 14 17:36 +0000 1969", "cat-0":"cat1"}',
'000002', 0.0199]
]
show_doc(self.test_scenario12, 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_no_suggest_deepnet(self)
model_create.the_deepnet_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_deepnet(self)
prediction_create.i_create_a_deepnet_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_deepnet_prediction(self,
example[4])
prediction_compare.the_local_prediction_is(self, example[6])
