def test_scenario10(self):
"""
Scenario: Successfully comparing predictions for fusions:
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 fusion from a list of models
And I wait until the fusion is ready less than <time_4> secs
And I create a local fusion
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 | params| tag | data_input | objective | prediction | params
"""
examples = [[
'data/iris_unbalanced.csv', '30', '30', '120', '120',
'{"tags":["my_fusion_tag"]}', 'my_fusion_tag',
'{"petal width": 4}', '000004', 'Iris-virginica'
],
[
'data/grades.csv', '30', '30', '120', '120',
'{"tags":["my_fusion_tag_reg"]}', 'my_fusion_tag_reg',
'{"Midterm": 20}', '000005', 43.65286
]]
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_model_with(self, example[5])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[5])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[5])
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_retrieve_a_list_of_remote_models(
self, example[6])
model_create.i_create_a_fusion(self)
model_create.the_fusion_is_finished_in_less_than(self, example[4])
prediction_compare.i_create_a_local_fusion(self)
prediction_create.i_create_a_fusion_prediction(self, example[7])
prediction_create.the_prediction_is(self, example[8], example[9])
prediction_compare.i_create_a_local_prediction(self, example[7])
prediction_compare.the_local_prediction_is(self, example[9])
def test_scenario2(self):
"""
Scenario 2: Successfully creating a fusion:
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 fusion from a list of models
And I wait until the fusion is ready less than <time_4> secs
And I update the fusion name to "<fusion_name>"
When I wait until the fusion is ready less than <time_5> secs
And I create a prediction for "<data_input>"
Then the fusion name is "<fusion_name>"
And the prediction for "<objective>" is "<prediction>"
And I create an evaluation for the fusion 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 | fusion_name | data_input | objective | prediction
| ../data/iris.csv | 10 | 10 | 20 | 20 | my new fusion name | {"petal length": 1, "petal width": 1} | "000004" | "Iris-setosa"
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '10', '10', '20', '20', 'my new fusion name',
'{"tags":["my_fusion_2_tag"]}', 'my_fusion_2_tag',
'{"petal width": 1.75, "petal length": 2.45}', "000004",
"Iris-setosa", 'average_phi', '1.0']]
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[6])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[6])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[6])
model_create.the_model_is_finished_in_less_than(self, example[3])
compare_pred.i_retrieve_a_list_of_remote_models(self, example[7])
model_create.i_create_a_fusion(self)
model_create.the_fusion_is_finished_in_less_than(self, example[3])
model_create.i_update_fusion_name(self, example[5])
model_create.the_fusion_is_finished_in_less_than(self, example[4])
model_create.i_check_fusion_name(self, example[5])
prediction_create.i_create_a_fusion_prediction(self, example[8])
prediction_create.the_prediction_is(self, example[9], example[10])
evaluation_create.i_create_an_evaluation_fusion(self)
evaluation_create.the_evaluation_is_finished_in_less_than(self, example[3])
evaluation_create.the_measured_measure_is_value(self, example[11], example[12])
def test_scenario2(self):
"""
Scenario 2: Successfully creating a fusion:
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 fusion from a list of models
And I wait until the fusion is ready less than <time_4> secs
And I update the fusion name to "<fusion_name>"
When I wait until the fusion is ready less than <time_5> secs
And I create a prediction for "<data_input>"
Then the fusion name is "<fusion_name>"
And the prediction for "<objective>" is "<prediction>"
And I create an evaluation for the fusion 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 | fusion_name | data_input | objective | prediction
| ../data/iris.csv | 10 | 10 | 20 | 20 | my new fusion name | {"petal length": 1, "petal width": 1} | "000004" | "Iris-setosa"
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '10', '10', '20', '20', 'my new fusion name',
'{"tags":["my_fusion_2_tag"]}', 'my_fusion_2_tag',
'{"petal width": 1.75, "petal length": 2.45}', "000004",
"Iris-setosa", 'average_phi', '1.0']]
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[6])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[6])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[6])
model_create.the_model_is_finished_in_less_than(self, example[3])
compare_pred.i_retrieve_a_list_of_remote_models(self, example[7])
model_create.i_create_a_fusion(self)
model_create.the_fusion_is_finished_in_less_than(self, example[3])
model_create.i_update_fusion_name(self, example[5])
model_create.the_fusion_is_finished_in_less_than(self, example[4])
model_create.i_check_fusion_name(self, example[5])
prediction_create.i_create_a_fusion_prediction(self, example[8])
prediction_create.the_prediction_is(self, example[9], example[10])
evaluation_create.i_create_an_evaluation_fusion(self)
evaluation_create.the_evaluation_is_finished_in_less_than(self, example[3])
evaluation_create.the_measured_measure_is_value(self, example[11], example[12])
def test_scenario6(self):
"""
Scenario 6: Successfully creating a fusion:
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 logistic regression with "<params>"
And I wait until the logistic regression is ready less than <time_3> secs
And I create a logistic regression with "<params>"
And I wait until the logistic regression is ready less than <time_3> secs
And I retrieve a list of remote logistic regression tagged with "<tag>"
And I create a fusion from a list of models and weights
And I wait until the fusion is ready less than <time_4> secs
When I create a prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And the fusion probability for the prediction is "<probability>"
And I create a local fusion prediction for "<data_input>"
Then the local fusion prediction is "<prediction>"
And the local fusion probability for the prediction is "<probability>"
Examples:
| data | time_1 | time_2 | time_3 | time_4 | data_input | objective | prediction
| ../data/iris.csv | 10 | 10 | 20 | 20 | {"petal length": 1, "petal width": 1} | "000004" | "Iris-setosa"
"""
print self.test_scenario6.__doc__
examples = [
['data/iris.csv', '10', '10', '20', '20',
'{"tags":["my_fusion_6_tag"], "missing_numerics": true}',
'my_fusion_6_tag',
'{"petal width": 1.75, "petal length": 2.45}',
"000004",
"Iris-setosa",
'0.4727',
'{"tags":["my_fusion_6_tag"], "missing_numerics": false, "balance_fields": false }', '[1, 2]']]
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_logistic_model_with_objective_and_parms(self, example[8], example[5])
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_logistic_model_with_objective_and_parms(self, example[8], example[11])
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
compare_pred.i_retrieve_a_list_of_remote_logistic_regressions(self, example[6])
model_create.i_create_a_fusion_with_weights(self, example[12])
model_create.the_fusion_is_finished_in_less_than(self, example[3])
compare_pred.i_create_a_local_fusion(self)
prediction_create.i_create_a_fusion_prediction(self, example[7])
prediction_create.the_prediction_is(self, example[8], example[9])
prediction_create.the_fusion_probability_is(self, example[10])
compare_pred.i_create_a_local_prediction(self, example[7])
compare_pred.the_local_prediction_is(self, example[9])
compare_pred.the_local_probability_is(self, example[10])
def test_scenario6(self):
"""
Scenario 6: Successfully creating a fusion:
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 logistic regression with "<params>"
And I wait until the logistic regression is ready less than <time_3> secs
And I create a logistic regression with "<params>"
And I wait until the logistic regression is ready less than <time_3> secs
And I retrieve a list of remote logistic regression tagged with "<tag>"
And I create a fusion from a list of models and weights
And I wait until the fusion is ready less than <time_4> secs
When I create a prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And the fusion probability for the prediction is "<probability>"
And I create a local fusion prediction for "<data_input>"
Then the local fusion prediction is "<prediction>"
And the local fusion probability for the prediction is "<probability>"
Examples:
| data | time_1 | time_2 | time_3 | time_4 | data_input | objective | prediction
| ../data/iris.csv | 10 | 10 | 20 | 20 | {"petal length": 1, "petal width": 1} | "000004" | "Iris-setosa"
"""
print self.test_scenario6.__doc__
examples = [
['data/iris.csv', '10', '10', '20', '20',
'{"tags":["my_fusion_6_tag"], "missing_numerics": true}',
'my_fusion_6_tag',
'{"petal width": 1.75, "petal length": 2.45}',
"000004",
"Iris-setosa",
'0.4727',
'{"tags":["my_fusion_6_tag"], "missing_numerics": false, "balance_fields": false }', '[1, 2]']]
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_logistic_model_with_objective_and_parms(self, example[8], example[5])
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_logistic_model_with_objective_and_parms(self, example[8], example[11])
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
compare_pred.i_retrieve_a_list_of_remote_logistic_regressions(self, example[6])
model_create.i_create_a_fusion_with_weights(self, example[12])
model_create.the_fusion_is_finished_in_less_than(self, example[3])
compare_pred.i_create_a_local_fusion(self)
prediction_create.i_create_a_fusion_prediction(self, example[7])
prediction_create.the_prediction_is(self, example[8], example[9])
prediction_create.the_fusion_probability_is(self, example[10])
compare_pred.i_create_a_local_prediction(self, example[7])
compare_pred.the_local_prediction_is(self, example[9])
compare_pred.the_local_probability_is(self, example[10])
def test_scenario13(self):
"""
Scenario: Successfully comparing predictions for fusions:
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 fusion from a list of models
And I wait until the fusion is ready less than <time_4> secs
And I create a local fusion
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 | params| tag | data_input | objective | prediction | params
"""
examples = [[
'data/grades.csv', '30', '30', '120', '120', 'my_fusion_tag_lreg',
'{"000000": 10, "000001": 10, "000002": 10, "000003": 10, "000004": 10}',
'000005', 21.01712
]]
show_doc(self.test_scenario13, examples)
for example in examples:
print "\nTesting with:\n", example
tag = "%s_%s" % (example[5], PY3)
tag_args = '{"tags":["%s"]}' % tag
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])
linear_create.i_create_a_linear_regression_with_params(
self, tag_args)
linear_create.the_linear_regression_is_finished_in_less_than(
self, example[3])
prediction_compare.i_retrieve_a_list_of_remote_linear_regressions(
self, tag)
model_create.i_create_a_fusion(self)
model_create.the_fusion_is_finished_in_less_than(self, example[4])
prediction_compare.i_create_a_local_fusion(self)
prediction_create.i_create_a_fusion_prediction(self, example[6])
prediction_create.the_prediction_is(self, example[7], example[8])
prediction_compare.i_create_a_local_prediction(self, example[6])
prediction_compare.the_local_prediction_is(self, example[8])
def test_scenario12(self):
"""
Scenario: Successfully comparing predictions for fusions:
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 fusion from a list of models
And I wait until the fusion is ready less than <time_4> secs
And I create a local fusion
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 | params| tag | data_input | objective | prediction | params
"""
tag = "my_fusion_tag_12_%s" % PY3
tag_reg = "my_fusion_tag_12_reg_%s" % PY3
examples = [
['data/iris_unbalanced.csv', '30', '30', '120', '120', '{"tags":["%s"], "sample_rate": 0.8, "seed": "bigml"}' % tag, tag, '{"petal width": 4}', '000004', 'Iris-virginica'],
['data/grades.csv', '30', '30', '120', '120', '{"tags":["%s"], "sample_rate": 0.8, "seed": "bigml"}' % tag_reg, tag_reg, '{"Midterm": 20}', '000005', 44.37625]]
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_model_with(self, example[5])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[5])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[5])
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_retrieve_a_list_of_remote_models(self, example[6])
model_create.i_create_a_fusion_with_weights(self)
model_create.the_fusion_is_finished_in_less_than(self, example[4])
prediction_compare.i_create_a_local_fusion(self)
prediction_create.i_create_a_fusion_prediction(self, example[7])
prediction_create.the_prediction_is(self, example[8], example[9])
prediction_compare.i_create_a_local_prediction(self, example[7])
prediction_compare.the_local_prediction_is(self, example[9])
