def test_scenario13(self):
"""
Scenario: Successfully comparing predictions:
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 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/iris.csv', '10', '10', '10', '{"petal width": 0.5}', '000004', 'Iris-setosa', "tmp/my_model.json", "my_test"],
['data/iris.csv', '10', '10', '10', '{"petal length": 6, "petal width": 2}', '000004', 'Iris-virginica', "tmp/my_model.json", "my_test"],
['data/iris.csv', '10', '10', '10', '{"petal length": 4, "petal width": 1.5}', '000004', 'Iris-versicolor', "tmp/my_model.json", "my_test"],
['data/iris_sp_chars.csv', '10', '10', '10', '{"pétal.length": 4, "pétal&width\u0000": 1.5}', '000004', 'Iris-versicolor', "tmp/my_model.json", "my_test"]]
show_doc(self.test_scenario13, 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])
args = '{"tags": ["%s"]}' % example[8]
model_create.i_create_a_model_with(self, data=args)
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_export_model(self, False, example[7]) # no pmml
prediction_compare.i_create_a_local_model_from_file(self, example[7])
prediction_create.i_create_a_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
model_create.i_export_tags_model(self, example[7], example[8])
prediction_compare.i_create_a_local_model_from_file(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario13(self):
"""
Scenario: Successfully comparing predictions:
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 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/iris.csv', '10', '10', '10', '{"petal width": 0.5}', '000004', 'Iris-setosa', "tmp/my_model.json", "my_test"],
['data/iris.csv', '10', '10', '10', '{"petal length": 6, "petal width": 2}', '000004', 'Iris-virginica', "tmp/my_model.json", "my_test"],
['data/iris.csv', '10', '10', '10', '{"petal length": 4, "petal width": 1.5}', '000004', 'Iris-versicolor', "tmp/my_model.json", "my_test"],
['data/iris_sp_chars.csv', '10', '10', '10', '{"pétal.length": 4, "pétal&width\u0000": 1.5}', '000004', 'Iris-versicolor', "tmp/my_model.json", "my_test"]]
show_doc(self.test_scenario13, 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])
args = '{"tags": ["%s"]}' % example[8]
model_create.i_create_a_model_with(self, data=args)
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_export_model(self, False, example[7]) # no pmml
prediction_compare.i_create_a_local_model_from_file(self, example[7])
prediction_create.i_create_a_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
model_create.i_export_tags_model(self, example[7], example[8])
prediction_compare.i_create_a_local_model_from_file(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
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_scenario11(self):
"""
Scenario: Successfully comparing predictions in operating points for fusions:
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>" in "<operating_point>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local fusion prediction for "<data_input>" in "<operating_point>"
Then the local ensemble prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | params| tag | data_input | objective | prediction | params | operating_point
"""
examples = [
['data/iris_unbalanced.csv', '30', '30', '120', '120', '{"tags":["my_fusion_tag_11"]}', 'my_fusion_tag_11', '{"petal width": 4}', '000004', 'Iris-virginica', {"kind": "probability", "threshold": 0.1, "positive_class": "Iris-setosa"}],
['data/iris_unbalanced.csv', '30', '30', '120', '120', '{"tags":["my_fusion_tag_11_b"]}', 'my_fusion_tag_11_b', '{"petal width": 4}', '000004', 'Iris-virginica', {"kind": "probability", "threshold": 0.9, "positive_class": "Iris-setosa"}]]
show_doc(self.test_scenario11, 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_op(self, example[7], example[10])
prediction_create.the_prediction_is(self, example[8], example[9])
prediction_compare.i_create_a_local_prediction_op(self, example[7], example[10])
prediction_compare.the_local_prediction_is(self, example[9])
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])
def test_scenario3(self):
"""
Scenario 3: Successfully creating a fusion from a dataset:
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
When I create a batch prediction for the dataset with the fusion
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 | time_3 | time_4 | tag | local_file | predictions_file |
| ../data/iris.csv | 10 | 10 | 20 | 20 | mytag | ./tmp/batch_predictions.csv | ./data/batch_predictions_fs.csv |
"""
print self.test_scenario3.__doc__
examples = [[
'data/iris.csv', '10', '10', '20', '20',
'{"tags":["my_fusion_3_tag"]}', 'my_fusion_3_tag',
'tmp/batch_predictions.csv', 'data/batch_predictions_fs.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])
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])
compare_pred.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[3])
batch_pred_create.i_create_a_batch_prediction_fusion(self)
batch_pred_create.the_batch_prediction_is_finished_in_less_than(
self, example[4])
batch_pred_create.i_download_predictions_file(self, example[7])
batch_pred_create.i_check_predictions(self, example[8])
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_scenario10(self):
"""
Scenario 10: Successfully creating a local fusion 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 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_3> secs
And I export the fusion to "<exported_file>"
When I create a local fusion from the file "<exported_file>"
Then the fusion ID and the local fusion ID match
Examples:
| data | time_1 | time_2 | time_3 | exported_file | params | tag
| ../data/iris.csv | 10 | 10 | 50 | ./tmp/fusion.json
"""
print self.test_scenario10.__doc__
examples = [[
'data/iris.csv', '10', '10', '50', './tmp/fusion.json',
'my_fusion_tag'
]]
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])
model_create.i_create_a_model_with(self, tag_args)
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, tag_args)
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, tag_args)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_retrieve_a_list_of_remote_models(self, tag)
model_create.i_create_a_fusion(self)
model_create.the_fusion_is_finished_in_less_than(self, example[3])
model_create.i_export_fusion(self, example[4])
model_create.i_create_local_fusion_from_file(self, example[4])
model_create.check_fusion_id_local_id(self)
def test_scenario3(self):
"""
Scenario 3: 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
When I create a batch prediction for the dataset with the fusion
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 | time_3 | time_4 | tag | local_file | predictions_file |
| ../data/iris.csv | 10 | 10 | 20 | 20 | mytag | ./tmp/batch_predictions.csv | ./data/batch_predictions_fs.csv |
"""
print self.test_scenario3.__doc__
examples = [
['data/iris.csv', '10', '10', '20', '20',
'{"tags":["my_fusion_3_tag"]}', 'my_fusion_3_tag',
'tmp/batch_predictions.csv', 'data/batch_predictions_fs.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])
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])
compare_pred.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[3])
batch_pred_create.i_create_a_batch_prediction_fusion(self)
batch_pred_create.the_batch_prediction_is_finished_in_less_than(self, example[4])
batch_pred_create.i_download_predictions_file(self, example[7])
batch_pred_create.i_check_predictions(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_scenario2(self):
"""
Scenario: Successfully obtaining field importance 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 a model with "<parms1>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<parms2>"
And I wait until the model is ready less than <time_4> secs
And I create a model with "<parms3>"
And I wait until the model is ready less than <time_5> secs
When I create a local Ensemble with the last <number_of_models> models
Then the field importance text is <field_importance>
Examples:
| data | time_1 | time_2 |parms1 | time_3 |parms2 | time_4 |parms3| time_5 |number_of_models |field_importance
| ../data/iris.csv | 10 | 10 |{"input_fields": ["000000", "000001","000003", "000004"]} |20 |{"input_fields": ["000000", "000001","000002", "000004"]} | 20 |{"input_fields": ["000000", "000001","000002", "000003", "000004"]} | 20 | 3 |[["000002", 0.5269933333333333], ["000003", 0.38936], ["000000", 0.04662333333333333], ["000001", 0.037026666666666666]]
"""
print self.test_scenario2.__doc__
examples = [[
'data/iris.csv', '10', '10',
'{"input_fields": ["000000", "000001","000003", "000004"]}', '20',
'{"input_fields": ["000000", "000001","000002", "000004"]}', '20',
'{"input_fields": ["000000", "000001","000002", "000003", "000004"]}',
'20', '3',
'[["000002", 0.5269933333333333], ["000003", 0.38936], ["000000", 0.04662333333333333], ["000001", 0.037026666666666666]]'
]]
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[3])
model_create.the_model_is_finished_in_less_than(self, example[4])
model_create.i_create_a_model_with(self, example[5])
model_create.the_model_is_finished_in_less_than(self, example[6])
model_create.i_create_a_model_with(self, example[7])
model_create.the_model_is_finished_in_less_than(self, example[8])
ensemble_create.create_local_ensemble_with_list(self, example[9])
ensemble_create.field_importance_print(self, example[10])
def test_scenario10(self):
"""
Scenario 10: Successfully creating a local fusion 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 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_3> secs
And I export the fusion to "<exported_file>"
When I create a local fusion from the file "<exported_file>"
Then the fusion ID and the local fusion ID match
Examples:
| data | time_1 | time_2 | time_3 | exported_file | params | tag
| ../data/iris.csv | 10 | 10 | 50 | ./tmp/fusion.json
"""
print self.test_scenario10.__doc__
examples = [
['data/iris.csv', '10', '10', '50', './tmp/fusion.json', '{"tags":["my_fusion_tag"]}', 'my_fusion_tag']]
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[3])
model_create.i_export_fusion(self, example[4])
model_create.i_create_local_fusion_from_file(self, example[4])
model_create.check_fusion_id_local_id(self)
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 obtaining field importance 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 a model with "<parms1>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<parms2>"
And I wait until the model is ready less than <time_4> secs
And I create a model with "<parms3>"
And I wait until the model is ready less than <time_5> secs
When I create a local Ensemble with the last <number_of_models> models
Then the field importance text is <field_importance>
Examples:
| data | time_1 | time_2 |parms1 | time_3 |parms2 | time_4 |parms3| time_5 |number_of_models |field_importance
| ../data/iris.csv | 10 | 10 |{"input_fields": ["000000", "000001","000003", "000004"]} |20 |{"input_fields": ["000000", "000001","000002", "000004"]} | 20 |{"input_fields": ["000000", "000001","000002", "000003", "000004"]} | 20 | 3 |[["000002", 0.5269933333333333], ["000003", 0.38936], ["000000", 0.04662333333333333], ["000001", 0.037026666666666666]]
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '10', '10', '{"input_fields": ["000000", "000001","000003", "000004"]}', '20', '{"input_fields": ["000000", "000001","000002", "000004"]}', '20', '{"input_fields": ["000000", "000001","000002", "000003", "000004"]}', '20', '3', '[["000002", 0.5269933333333333], ["000003", 0.38936], ["000000", 0.04662333333333333], ["000001", 0.037026666666666666]]']]
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[3])
model_create.the_model_is_finished_in_less_than(self, example[4])
model_create.i_create_a_model_with(self, example[5])
model_create.the_model_is_finished_in_less_than(self, example[6])
model_create.i_create_a_model_with(self, example[7])
model_create.the_model_is_finished_in_less_than(self, example[8])
ensemble_create.create_local_ensemble_with_list(self, example[9])
ensemble_create.field_importance_print(self, example[10])
def test_scenario11(self):
"""
Scenario: Successfully comparing predictions in operating points for fusions:
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>" in "<operating_point>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local fusion prediction for "<data_input>" in "<operating_point>"
Then the local ensemble prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | params| tag | data_input | objective | prediction | params | operating_point
"""
examples = [[
'data/iris_unbalanced.csv', '30', '30', '120', '120',
'{"tags":["my_fusion_tag_11"]}', 'my_fusion_tag_11',
'{"petal width": 4}', '000004', 'Iris-virginica', {
"kind": "probability",
"threshold": 0.1,
"positive_class": "Iris-setosa"
}
],
[
'data/iris_unbalanced.csv', '30', '30', '120', '120',
'{"tags":["my_fusion_tag_11_b"]}',
'my_fusion_tag_11_b', '{"petal width": 4}', '000004',
'Iris-virginica', {
"kind": "probability",
"threshold": 0.9,
"positive_class": "Iris-setosa"
}
]]
show_doc(self.test_scenario11, 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_op(
self, example[7], example[10])
prediction_create.the_prediction_is(self, example[8], example[9])
prediction_compare.i_create_a_local_prediction_op(
self, example[7], example[10])
prediction_compare.the_local_prediction_is(self, example[9])
