def test_scenario2(self):
"""
Scenario: Successfully creating a model from a dataset list and predicting with it using median:
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 multi model
When I create a local multimodel batch prediction using median for <input_data>
Then the local prediction is <prediction>
Examples:
| data | time_1 | time_2 | time_3 | input_data | prediction
| ../data/grades.csv | 10 | 10 | 10 | {'Tutorial': 99.47, 'Midterm': 53.12, 'TakeHome': 87.96} | 50
"""
print self.test_scenario2.__doc__
examples = [
["data/grades.csv", "10", "10", "10", '{"Tutorial": 99.47, "Midterm": 53.12, "TakeHome": 87.96}', 50]
]
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])
world.list_of_models = [world.model]
compare_pred.i_create_a_local_multi_model(self)
compare_pred.i_create_a_local_mm_median_batch_prediction(self, example[4])
compare_pred.the_local_prediction_is(self, example[5])
def test_scenario1(self):
"""
Scenario: Successfully creating a batch prediction from a multi 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 a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I retrieve a list of remote models tagged with "<tag>"
And I create a local multi model
When I create a batch prediction for "<data_input>" and save it in "<path>"
And I combine the votes in "<path>"
Then the plurality combined predictions are "<predictions>"
And the confidence weighted predictions are "<predictions>"
Examples:
| data | time_1 | time_2 | time_3 | params | tag | data_input | path | predictions |
| ../data/iris.csv | 10 | 10 | 10 | {"tags":["mytag"]} | mytag | [{"petal width": 0.5}, {"petal length": 6, "petal width": 2}, {"petal length": 4, "petal width": 1.5}] | ./tmp | ["Iris-setosa", "Iris-virginica", "Iris-versicolor"] |
"""
print self.test_scenario1.__doc__
examples = [[
'data/iris.csv', '10', '10', '10', '{"tags":["mytag"]}', 'mytag',
'[{"petal width": 0.5}, {"petal length": 6, "petal width": 2}, {"petal length": 4, "petal width": 1.5}]',
'./tmp', '["Iris-setosa", "Iris-virginica", "Iris-versicolor"]'
]]
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(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
compare_pred.i_retrieve_a_list_of_remote_models(self, example[5])
compare_pred.i_create_a_local_multi_model(self)
compare_pred.i_create_a_batch_prediction(self, example[6],
example[7])
compare_pred.i_combine_the_votes(self, example[7])
compare_pred.the_plurality_combined_prediction(self, example[8])
compare_pred.the_confidence_weighted_prediction(self, example[8])
def test_scenario2(self):
"""
Scenario: Successfully creating a local batch prediction from a multi 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 a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I retrieve a list of remote models tagged with "<tag>"
And I create a local multi model
When I create a batch multimodel prediction for "<data_inputs>"
Then the predictions are "<predictions>"
Examples:
| data | time_1 | time_2 | time_3 | params | tag | data_inputs | predictions |
| ../data/iris.csv | 10 | 10 | 10 | {"tags":["mytag"]} | mytag | [{"petal width": 0.5}, {"petal length": 6, "petal width": 2}] | ["Iris-setosa", "Iris-virginica"] |
"""
print self.test_scenario2.__doc__
examples = [[
'data/iris.csv', '10', '10', '10', '{"tags":["mytag"]}', 'mytag',
'[{"petal width": 0.5}, {"petal length": 6, "petal width": 2}]',
'["Iris-setosa", "Iris-virginica"]'
]]
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(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
compare_pred.i_retrieve_a_list_of_remote_models(self, example[5])
compare_pred.i_create_a_local_multi_model(self)
compare_pred.i_create_a_batch_prediction_from_a_multi_model(
self, example[6])
compare_pred.the_batch_mm_predictions_are(self, example[7])
def test_scenario1(self):
"""
Scenario: Successfully creating a batch prediction from a multi 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 a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I retrieve a list of remote models tagged with "<tag>"
And I create a local multi model
When I create a batch prediction for "<data_input>" and save it in "<path>"
And I combine the votes in "<path>"
Then the plurality combined predictions are "<predictions>"
And the confidence weighted predictions are "<predictions>"
Examples:
| data | time_1 | time_2 | time_3 | params | tag | data_input | path | predictions |
| ../data/iris.csv | 10 | 10 | 10 | {"tags":["mytag"]} | mytag | [{"petal width": 0.5}, {"petal length": 6, "petal width": 2}, {"petal length": 4, "petal width": 1.5}] | ./tmp | ["Iris-setosa", "Iris-virginica", "Iris-versicolor"] |
"""
print self.test_scenario1.__doc__
examples = [
['data/iris.csv', '10', '10', '10', '{"tags":["mytag"]}', 'mytag', '[{"petal width": 0.5}, {"petal length": 6, "petal width": 2}, {"petal length": 4, "petal width": 1.5}]', './tmp', '["Iris-setosa", "Iris-virginica", "Iris-versicolor"]']]
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(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
compare_pred.i_retrieve_a_list_of_remote_models(self, example[5])
compare_pred.i_create_a_local_multi_model(self)
compare_pred.i_create_a_batch_prediction(self, example[6], example[7])
compare_pred.i_combine_the_votes(self, example[7])
compare_pred.the_plurality_combined_prediction(self, example[8])
compare_pred.the_confidence_weighted_prediction(self, example[8])
def test_scenario1(self):
"""
Scenario: Successfully creating a prediction from a multi 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 a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I retrieve a list of remote models tagged with "<tag>"
And I create a local multi model
When I create a local prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | params | tag | data_input | prediction |
| ../data/iris.csv | 10 | 10 | 10 | {"tags":["mytag"]} | mytag | {"petal width": 0.5} | Iris-setosa |
"""
print self.test_scenario1.__doc__
examples = [[
'data/iris.csv', '10', '10', '10', '{"tags":["mytag"]}', 'mytag',
'{"petal width": 0.5}', 'Iris-setosa'
]]
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(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
compare_pred.i_retrieve_a_list_of_remote_models(self, example[5])
compare_pred.i_create_a_local_multi_model(self)
compare_pred.i_create_a_local_prediction(self, example[6])
compare_pred.the_local_prediction_is(self, example[7])
def test_scenario2(self):
"""
Scenario: Successfully creating a local batch prediction from a multi 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 a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I retrieve a list of remote models tagged with "<tag>"
And I create a local multi model
When I create a batch multimodel prediction for "<data_inputs>"
Then the predictions are "<predictions>"
Examples:
| data | time_1 | time_2 | time_3 | params | tag | data_inputs | predictions |
| ../data/iris.csv | 10 | 10 | 10 | {"tags":["mytag"]} | mytag | [{"petal width": 0.5}, {"petal length": 6, "petal width": 2}] | ["Iris-setosa", "Iris-virginica"] |
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '10', '10', '10', '{"tags":["mytag"]}', 'mytag', '[{"petal width": 0.5}, {"petal length": 6, "petal width": 2}]', '["Iris-setosa", "Iris-virginica"]']]
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(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
compare_pred.i_retrieve_a_list_of_remote_models(self, example[5])
compare_pred.i_create_a_local_multi_model(self)
compare_pred.i_create_a_batch_prediction_from_a_multi_model(self, example[6])
compare_pred.the_batch_mm_predictions_are(self, example[7])
def test_scenario1(self):
"""
Scenario: Successfully creating a prediction from a multi 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 a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I retrieve a list of remote models tagged with "<tag>"
And I create a local multi model
When I create a local prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | params | tag | data_input | prediction |
| ../data/iris.csv | 10 | 10 | 10 | {"tags":["mytag"]} | mytag | {"petal width": 0.5} | Iris-setosa |
"""
print self.test_scenario1.__doc__
examples = [
['data/iris.csv', '10', '10', '10', '{"tags":["mytag"]}', 'mytag', '{"petal width": 0.5}', 'Iris-setosa']]
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(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[3])
compare_pred.i_retrieve_a_list_of_remote_models(self, example[5])
compare_pred.i_create_a_local_multi_model(self)
compare_pred.i_create_a_local_prediction(self, example[6])
compare_pred.the_local_prediction_is(self, example[7])
def test_scenario2(self):
"""
Scenario: Successfully creating a model from a dataset list and predicting with it using median:
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 multi model
When I create a local multimodel batch prediction using median for <input_data>
Then the local prediction is <prediction>
Examples:
| data | time_1 | time_2 | time_3 | input_data | prediction
| ../data/grades.csv | 10 | 10 | 10 | {'Tutorial': 99.47, 'Midterm': 53.12, 'TakeHome': 87.96} | 50
"""
print self.test_scenario2.__doc__
examples = [[
'data/grades.csv', '30', '30', '30',
'{"Tutorial": 99.47, "Midterm": 53.12, "TakeHome": 87.96}', 63.33
]]
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])
world.list_of_models = [world.model]
compare_pred.i_create_a_local_multi_model(self)
compare_pred.i_create_a_local_mm_median_batch_prediction(
self, example[4])
compare_pred.the_local_prediction_is(self, example[5])
