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_scenario5(self):
"""
Scenario: Successfully creating a local prediction from an Ensemble:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create an ensemble of <number_of_models> models and <tlp> tlp
And I wait until the ensemble is ready less than <time_3> secs
And I create a local Ensemble
When I create a local ensemble prediction using median with confidence for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | number_of_models | tlp | data_input |prediction |
| ../data/grades.csv | 10 | 10 | 50 | 2 | 1 | {} | 67.5 |
"""
print self.test_scenario5.__doc__
examples = [
['data/grades.csv', '30', '30', '50', '2', '1', '{}', 69.0934]]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
ensemble_create.i_create_an_ensemble(self, example[4], example[5])
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3])
ensemble_create.create_local_ensemble(self)
prediction_create.create_local_ensemble_prediction_using_median_with_confidence(self, example[6])
compare_pred.the_local_prediction_is(self, example[7])
def test_scenario10(self):
"""
Scenario: Successfully comparing predictions for linear regression:
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 linear regression with objective "<objective>" and "<params>"
And I wait until the linear regression is ready less than <time_3> secs
And I create a local linear regression
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/grades.csv', '10', '50', '30000',
'{"000000": 1, "000001": 1, "000002": 1}', '000005', 29.63024,
'{"input_fields": ["000000", "000001", "000002"]}'
],
[
'data/iris.csv', '10', '50', '30000',
'{"000000": 1, "000001": 1, "000004": "Iris-virginica"}',
'000003', 1.21187,
'{"input_fields": ["000000", "000001", "000004"]}'
],
[
'data/movies.csv', '10', '50', '30000', '{"000007": "Action"}',
'000009', 4.33333, '{"input_fields": ["000007"]}'
],
[
'data/movies.csv', '10', '50', '30000', '{"000006": "1999"}',
'000009', 3.28427,
'{"input_fields": ["000006"], "bias": false}'
]
]
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])
linear_create.i_create_a_linear_regression_with_objective_and_params( \
self, example[5], example[7])
linear_create.the_linear_regression_is_finished_in_less_than( \
self, example[3])
prediction_compare.i_create_a_local_linear(self)
prediction_create.i_create_a_linear_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_linear_prediction(
self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario1(self):
"""
Scenario: Successfully creating a local prediction from an Ensemble:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create an ensemble of <number_of_models> models and <tlp> tlp
And I wait until the ensemble is ready less than <time_3> secs
And I create a local Ensemble
When I create a local ensemble prediction with confidence for "<data_input>"
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
And the local probabilities are "<probabilities>"
Examples:
| data | time_1 | time_2 | time_3 | number_of_models | tlp | data_input |prediction | confidence
| ../data/iris.csv | 10 | 10 | 50 | 5 | 1 | {"petal width": 0.5} | Iris-versicolor | 0.3687 | [0.3403, 0.4150, 0.2447]
"""
print self.test_scenario1.__doc__
examples = [
['data/iris.csv', '10', '10', '50', '5', '1', '{"petal width": 0.5}', 'Iris-versicolor', '0.415', '["0.3403", "0.4150", "0.2447"]' ]]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
ensemble_create.i_create_an_ensemble(self, example[4], example[5])
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3])
ensemble_create.create_local_ensemble(self)
prediction_create.create_local_ensemble_prediction_with_confidence(self, example[6])
compare_pred.the_local_prediction_is(self, example[7])
compare_pred.the_local_prediction_confidence_is(self, example[8])
compare_pred.the_local_probabilities_are(self, example[9])
def test_scenario2(self):
"""
Scenario: Successfully creating a 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 prediction from a local model in a json file:
Given I create a local model from a "<model>" file
When I create a local prediction for "<data_input>" with confidence
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
Examples:
| model | data_input | prediction | confidence
| ../data/iris_model.json | {"petal length": 0.5} | Iris-setosa | 0.90594
"""
print self.test_scenario1.__doc__
examples = [[
'data/iris_model.json', '{"petal length": 0.5}', 'Iris-setosa',
'0.90594'
],
[
'data/iris_model.json', '{"petal length": "0.5"}',
'Iris-setosa', '0.90594'
]]
for example in examples:
print "\nTesting with:\n", example
prediction_compare.i_create_a_local_model_from_file(
self, example[0])
prediction_compare.i_create_a_local_prediction_with_confidence(
self, example[1])
prediction_compare.the_local_prediction_is(self, example[2])
prediction_compare.the_local_prediction_confidence_is(
self, example[3])
def test_scenario7(self):
"""
Scenario: Successfully comparing predictions with text options:
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 update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a logistic regression model with objective "<objective>"
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And the logistic regression probability for the prediction is "<probability>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
And the local logistic regression probability for the prediction is "<probability>"
Examples:
| data | time_1 | time_2 | objective | time_3 | options | data_input | prediction | probability
"""
examples = [
[
'data/spam.csv', '20', '20', '180',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}',
'{"Message": "A normal message"}', 'ham', 0.9169, "000000"
],
[
'data/spam.csv', '20', '20', '180',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}',
'{"Message": "mobile"}', 'ham', 0.815, "000000"
],
[
'data/movies.csv', '20', '20', '180',
'{"fields": {"000007": {"optype": "items", "item_analysis": {"separator": "$"}}}}',
'{"gender": "Female", "genres": "Adventure$Action", "timestamp": 993906291, "occupation": "K-12 student", "zipcode": 59583, "rating": 3}',
'Under 18', '0.8393', '000002'
]
]
show_doc(self.test_scenario7, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
source_create.i_update_source_with(self, example[4])
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])
model_create.the_logistic_model_is_finished_in_less_than(
self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[5])
prediction_create.the_logistic_prediction_is(self, example[6])
prediction_create.the_logistic_probability_is(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_compare.the_local_probability_is(self, example[7])
def test_scenario3(self):
"""
Scenario: Successfully comparing predictions with proportional missing strategy:
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 proportional missing strategy prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And the confidence for the prediction is "<confidence>"
And I create a proportional missing strategy local prediction for "<data_input>"
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence |
| ../data/iris.csv | 10 | 10 | 10 | {} | 000004 | Iris-setosa | 0.2629 |
| ../data/grades.csv | 10 | 10 | 10 | {} | 000005 | 68.62224 | 27.5358 |
| ../data/grades.csv | 10 | 10 | 10 | {"Midterm": 20} | 000005 | 46.69889 | 37.27594297134128 |
| ../data/grades.csv | 10 | 10 | 10 | {"Midterm": 20, "Tutorial": 90, "TakeHome": 100} | 000005 | 28.06 | 24.86634 |
"""
print self.test_scenario3.__doc__
examples = [[
'data/iris.csv', '10', '10', '10', '{}', '000004', 'Iris-setosa',
'0.2629'
],
[
'data/grades.csv', '10', '10', '10', '{}', '000005',
'68.62224', '27.5358'
],
[
'data/grades.csv', '10', '10', '10', '{"Midterm": 20}',
'000005', '46.69889', '37.27594297134128'
],
[
'data/grades.csv', '10', '10', '10',
'{"Midterm": 20, "Tutorial": 90, "TakeHome": 100}',
'000005', '28.06', '24.86634'
]]
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])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_proportional_prediction(
self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_proportional_local_prediction(
self, example[4])
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_scenario5(self):
"""
Scenario: Successfully comparing predictions in operating kind for models:
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>" in "<operating_kind>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction for "<data_input>" in "<operating_kind>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | prediction | operating_point
"""
examples = [[
'data/iris.csv', '10', '50', '50',
'{"petal length": 2.46, "sepal length": 5}', 'Iris-versicolor',
"probability", "000004"
],
[
'data/iris.csv', '10', '50', '50',
'{"petal length": 2.46, "sepal length": 5}',
'Iris-versicolor', "confidence", "000004"
],
[
'data/iris.csv', '10', '50', '50',
'{"petal length": 2}', 'Iris-setosa', "probability",
"000004"
],
[
'data/iris.csv', '10', '50', '50',
'{"petal length": 2}', 'Iris-setosa', "confidence",
"000004"
]]
show_doc(self.test_scenario5, 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(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_prediction_op_kind(
self, example[4], example[6])
prediction_create.the_prediction_is(self, example[7], example[5])
prediction_compare.i_create_a_local_prediction_op_kind(
self, example[4], example[6])
prediction_compare.the_local_prediction_is(self, example[5])
def test_scenario11(self):
"""
Scenario: Successfully comparing remote and local predictions
with raw date input for linear regression:
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 linear regression
And I wait until the linear regression is ready
less than <time_3> secs
And I create a local linear regression
When I create a prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
|data|time_1|time_2|time_3|data_input|objective|prediction
"""
examples = [
['data/dates2.csv', '20', '20', '25',
'{"time-1": "1910-05-08T19:10:23.106", "cat-0":"cat2"}',
'000002', -0.01284],
['data/dates2.csv', '20', '20', '25',
'{"time-1": "1920-06-30T20:21:20.320", "cat-0":"cat1"}',
'000002', -0.09459],
['data/dates2.csv', '20', '20', '25',
'{"time-1": "1932-01-30T19:24:11.440", "cat-0":"cat2"}',
'000002', -0.02259],
['data/dates2.csv', '20', '20', '25',
'{"time-1": "1950-11-06T05:34:05.252", "cat-0":"cat1"}',
'000002', -0.06754],
['data/dates2.csv', '20', '20', '25',
'{"time-1": "2001-01-05T23:04:04.693", "cat-0":"cat2"}',
'000002', 0.05204],
['data/dates2.csv', '20', '20', '25',
'{"time-1": "2011-04-01T00:16:45.747", "cat-0":"cat2"}',
'000002', 0.05878]]
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])
linear_create.i_create_a_linear_regression(self)
linear_create.the_linear_regression_is_finished_in_less_than(self,
example[3])
prediction_compare.i_create_a_local_linear(self)
prediction_create.i_create_a_linear_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_linear_prediction(self,
example[4])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario6(self):
"""
Scenario: Successfully comparing predictions for ensembles:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create an ensemble with "<params>"
And I wait until the ensemble is ready less than <time_3> secs
And I create a local ensemble
When I create a prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | params
['data/iris.csv', '10', '10', '120', '{"petal width": 0.5}', '000004', 'Iris-versicolor', '{"number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal length": 6, "petal width": 2}', '000004', 'Iris-virginica', '{"number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal length": 4, "petal width": 1.5}', '000004', 'Iris-versicolor', '{"number_of_models": 5}'],
['data/grades.csv', '10', '10', '120', '{"Midterm": 20}', '000005', 46.261364, '{"number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal width": 0.5}', '000004', 'Iris-setosa', '{"boosting": {"iterations": 5}, "number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal length": 6, "petal width": 2}', '000004', 'Iris-virginica', '{"boosting": {"iterations": 5}, "number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal length": 4, "petal width": 1.5}', '000004', 'Iris-versicolor', '{"boosting": {"iterations": 5}, "number_of_models": 5}'],
"""
examples = [
[
'data/iris_unbalanced.csv', '10', '10', '120',
'{"petal width": 4}', '000004', 'Iris-virginica',
'{"boosting": {"iterations": 5}, "number_of_models": 5}'
],
[
'data/grades.csv', '10', '10', '120', '{"Midterm": 20}',
'000005', 61.61036,
'{"boosting": {"iterations": 5}, "number_of_models": 5}'
]
]
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])
ensemble_create.i_create_an_ensemble_with_params(self, example[7])
ensemble_create.the_ensemble_is_finished_in_less_than(
self, example[3])
ensemble_create.create_local_ensemble(self)
prediction_create.i_create_an_ensemble_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_ensemble_prediction(
self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario2(self):
"""
Scenario: Successfully comparing predictions with text options:
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 update the source with params "<options>"
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 | options | data_input | objective | prediction |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} |{"Message": "Mobile call"} | 000000 | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} |{"Message": "A normal message"} | 000000 | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} |{"Message": "Mobile calls"} | 000000 | spam |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} |{"Message": "A normal message"} | 000000 | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} |{"Message": "Mobile call"} | 000000 | spam |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} |{"Message": "A normal message"} | 000000 | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Message": "FREE for 1st week! No1 Nokia tone 4 ur mob every week just txt NOKIA to 87077 Get txting and tell ur mates. zed POBox 36504 W45WQ norm150p/tone 16+"} | 000000 | spam |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Message": "Ok"} | 000000 | ham |
| ../data/movies.csv | 20 | 20 | 30 | {"fields": {"000007": {"optype": "items", "item_analysis": {"separator": "$"}}}} |{"genres": "Adventure$Action", "timestamp": 993906291, "occupation": "K-12 student"}'| 000009| 3.93064
| ../data/text_missing.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, {"000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}} |{} | 000003 | paperwork |
"""
print self.test_scenario2.__doc__
examples = [
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Message": "Mobile call"}', '000000', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Message": "A normal message"}', '000000', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Message": "Mobile calls"}', '000000', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Message": "A normal message"}', '000000', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Message": "Mobile call"}', '000000', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Message": "A normal message"}', '000000', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Message": "FREE for 1st week! No1 Nokia tone 4 ur mob every week just txt NOKIA to 87077 Get txting and tell ur mates. zed POBox 36504 W45WQ norm150p/tone 16+"}', '000000', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Message": "Ok"}', '000000', 'ham'],
['data/movies.csv', '20', '20', '30', '{"fields": {"000007": {"optype": "items", "item_analysis": {"separator": "$"}}}}', '{"genres": "Adventure$Action", "timestamp": 993906291, "occupation": "K-12 student"}', '000009', '3.93064'],
['data/text_missing.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, "000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}', '{}', "000003", 'swap']]
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])
source_create.i_update_source_with(self, example[4])
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])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_prediction(self, example[5])
prediction_create.the_prediction_is(self, example[6], example[7])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[7])
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_scenario1(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 |
| ../data/iris.csv | 10 | 10 | 10 | {"petal width": 0.5} | 000004 | Iris-setosa |
| ../data/iris.csv | 10 | 10 | 10 | {"petal length": 6, "petal width": 2} | 000004 | Iris-virginica |
| ../data/iris.csv | 10 | 10 | 10 | {"petal length": 4, "petal width": 1.5}| 000004 | Iris-versicolor |
| ../data/iris_sp_chars.csv | 10 | 10 | 10 | {"pétal.length": 4, "pétal&width\u0000": 1.5}| 000004 | Iris-versicolor |
"""
print self.test_scenario1.__doc__
examples = [[
'data/iris.csv', '10', '10', '10', '{"petal width": 0.5}',
'000004', 'Iris-setosa'
],
[
'data/iris.csv', '10', '10', '10',
'{"petal length": 6, "petal width": 2}', '000004',
'Iris-virginica'
],
[
'data/iris.csv', '10', '10', '10',
'{"petal length": 4, "petal width": 1.5}', '000004',
'Iris-versicolor'
],
[
'data/iris_sp_chars.csv', '10', '10', '10',
'{"pétal.length": 4, "pétal&width\u0000": 1.5}',
'000004', '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(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
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])
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_scenario2(self):
"""
Scenario: Successfully comparing predictions with text options:
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 update the source with params "<options>"
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 | options | data_input | objective | prediction |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} |{"Message": "Mobile call"} | 000000 | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} |{"Message": "A normal message"} | 000000 | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} |{"Message": "Mobile calls"} | 000000 | spam |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} |{"Message": "A normal message"} | 000000 | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} |{"Message": "Mobile call"} | 000000 | spam |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} |{"Message": "A normal message"} | 000000 | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Message": "FREE for 1st week! No1 Nokia tone 4 ur mob every week just txt NOKIA to 87077 Get txting and tell ur mates. zed POBox 36504 W45WQ norm150p/tone 16+"} | 000000 | spam |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Message": "Ok"} | 000000 | ham |
| ../data/movies.csv | 20 | 20 | 30 | {"fields": {"000007": {"optype": "items", "item_analysis": {"separator": "$"}}}} |{"genres": "Adventure$Action", "timestamp": 993906291, "occupation": "K-12 student"}'| 000009| 3.93064
| ../data/text_missing.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, {"000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}} |{} | 000003 | paperwork |
"""
print self.test_scenario2.__doc__
examples = [
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Message": "Mobile call"}', '000000', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Message": "A normal message"}', '000000', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Message": "Mobile calls"}', '000000', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Message": "A normal message"}', '000000', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Message": "Mobile call"}', '000000', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Message": "A normal message"}', '000000', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Message": "FREE for 1st week! No1 Nokia tone 4 ur mob every week just txt NOKIA to 87077 Get txting and tell ur mates. zed POBox 36504 W45WQ norm150p/tone 16+"}', '000000', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Message": "Ok"}', '000000', 'ham'],
['data/movies.csv', '20', '20', '30', '{"fields": {"000007": {"optype": "items", "item_analysis": {"separator": "$"}}}}', '{"genres": "Adventure$Action", "timestamp": 993906291, "occupation": "K-12 student"}', '000009', '3.93064'],
['data/text_missing.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, "000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}', '{}', "000003", 'swap']]
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])
source_create.i_update_source_with(self, example[4])
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])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_prediction(self, example[5])
prediction_create.the_prediction_is(self, example[6], example[7])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[7])
def test_scenario10(self):
"""
Scenario: Successfully comparing predictions with proportional missing strategy and balanced models:
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 balanced model
And I wait until the model is ready less than <time_3> secs
And I create a local model
When I create a proportional missing strategy prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And the confidence for the prediction is "<confidence>"
And I create a proportional missing strategy local prediction for "<data_input>"
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
And I create local probabilities for "<data_input>"
Then the local probabilities are "<probabilities>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence |
"""
examples = [
[
'data/iris_unbalanced.csv', '10', '10', '10', '{}', '000004',
'Iris-setosa', '0.25284', '[0.33333, 0.33333, 0.33333]'
],
[
'data/iris_unbalanced.csv', '10', '10', '10',
'{"petal length":1, "sepal length":1, "petal width": 1, "sepal width": 1}',
'000004', 'Iris-setosa', '0.7575', '[1.0, 0.0, 0.0]'
]
]
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_balanced_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_proportional_prediction(
self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_proportional_local_prediction(
self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_create.the_confidence_is(self, example[7])
prediction_compare.the_local_prediction_confidence_is(
self, example[7])
prediction_compare.i_create_local_probabilities(self, example[4])
prediction_compare.the_local_probabilities_are(self, example[8])
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_scenario3(self):
"""
Scenario: Successfully comparing predictions with proportional missing strategy:
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 proportional missing strategy prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And the confidence for the prediction is "<confidence>"
And I create a proportional missing strategy local prediction for "<data_input>"
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence |
| ../data/iris.csv | 10 | 10 | 10 | {} | 000004 | Iris-setosa | 0.2629 |
| ../data/grades.csv | 10 | 10 | 10 | {} | 000005 | 68.62224 | 27.5358 |
| ../data/grades.csv | 10 | 10 | 10 | {"Midterm": 20} | 000005 | 46.69889 | 37.27594297134128 |
| ../data/grades.csv | 10 | 10 | 10 | {"Midterm": 20, "Tutorial": 90, "TakeHome": 100} | 000005 | 28.06 | 24.86634 |
"""
print self.test_scenario3.__doc__
examples = [
["data/iris.csv", "10", "10", "10", "{}", "000004", "Iris-setosa", "0.2629"],
["data/grades.csv", "10", "10", "10", "{}", "000005", "68.62224", "27.5358"],
["data/grades.csv", "10", "10", "10", '{"Midterm": 20}', "000005", "46.69889", "37.27594297134128"],
[
"data/grades.csv",
"10",
"10",
"10",
'{"Midterm": 20, "Tutorial": 90, "TakeHome": 100}',
"000005",
"28.06",
"24.86634",
],
]
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])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_proportional_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_proportional_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario9(self):
"""
Scenario: Successfully comparing predictions with text options:
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 update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a logistic regression model
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | options | data_input | prediction |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} |{"Message": "Mobile call"} | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} |{"Message": "A normal message"} | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} |{"Message": "Mobile calls"} | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} |{"Message": "A normal message"} | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} |{"Message": "Mobile call"} | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} |{"Message": "A normal message"} | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Message": "FREE for 1st week! No1 Nokia tone 4 ur mob every week just txt NOKIA to 87077 Get txting and tell ur mates. zed POBox 36504 W45WQ norm150p/tone 16+"} | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Message": "Ok"} | ham |
"""
print self.test_scenario9.__doc__
examples = [
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Message": "Mobile call"}', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Message": "A normal message"}', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Message": "Mobile calls"}', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Message": "A normal message"}', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Message": "Mobile call"}', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Message": "A normal message"}', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Message": "FREE for 1st week! No1 Nokia tone 4 ur mob every week just txt NOKIA to 87077 Get txting and tell ur mates. zed POBox 36504 W45WQ norm150p/tone 16+"}', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Message": "Ok"}', 'ham']]
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])
source_create.i_update_source_with(self, example[4])
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(self)
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[5])
prediction_create.the_logistic_prediction_is(self, example[6])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario9(self):
"""
Scenario: Successfully comparing predictions with text options:
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 update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a logistic regression model
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | options | data_input | prediction |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} |{"Message": "Mobile call"} | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} |{"Message": "A normal message"} | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} |{"Message": "Mobile calls"} | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} |{"Message": "A normal message"} | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} |{"Message": "Mobile call"} | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} |{"Message": "A normal message"} | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Message": "FREE for 1st week! No1 Nokia tone 4 ur mob every week just txt NOKIA to 87077 Get txting and tell ur mates. zed POBox 36504 W45WQ norm150p/tone 16+"} | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Message": "Ok"} | ham |
"""
print self.test_scenario9.__doc__
examples = [
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Message": "Mobile call"}', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Message": "A normal message"}', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Message": "Mobile calls"}', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Message": "A normal message"}', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Message": "Mobile call"}', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Message": "A normal message"}', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Message": "FREE for 1st week! No1 Nokia tone 4 ur mob every week just txt NOKIA to 87077 Get txting and tell ur mates. zed POBox 36504 W45WQ norm150p/tone 16+"}', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Message": "Ok"}', 'ham']]
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])
source_create.i_update_source_with(self, example[4])
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(self)
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[5])
prediction_create.the_logistic_prediction_is(self, example[6])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[6])
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_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_scenario6(self):
"""
Scenario: Successfully comparing predictions with proportional missing strategy for missing_splits models:
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 missing splits
And I wait until the model is ready less than <time_3> secs
And I create a local model
When I create a proportional missing strategy prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And the confidence for the prediction is "<confidence>"
And I create a proportional missing strategy local prediction for "<data_input>"
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence |
| ../data/iris_missing2.csv | 10 | 10 | 10 | {"petal width": 1} | 000004 | Iris-setosa | 0.8064 |
| ../data/iris_missing2.csv | 10 | 10 | 10 | {"petal width": 1, "petal length": 4} | 000004 | Iris-versicolor | 0.7847 |
"""
print self.test_scenario6.__doc__
examples = [
["data/iris_missing2.csv", "10", "10", "10", '{"petal width": 1}', "000004", "Iris-setosa", "0.8064"],
[
"data/iris_missing2.csv",
"10",
"10",
"10",
'{"petal width": 1, "petal length": 4}',
"000004",
"Iris-versicolor",
"0.7847",
],
]
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_missing_splits(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_proportional_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_create.the_confidence_is(self, example[7])
prediction_compare.i_create_a_proportional_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_compare.the_local_prediction_confidence_is(self, example[7])
def test_scenario6(self):
"""
Scenario: Successfully comparing predictions with proportional missing strategy for missing_splits models:
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 missing splits
And I wait until the model is ready less than <time_3> secs
And I create a local model
When I create a proportional missing strategy prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And the confidence for the prediction is "<confidence>"
And I create a proportional missing strategy local prediction for "<data_input>"
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence |
| ../data/iris_missing2.csv | 10 | 10 | 10 | {"petal width": 1} | 000004 | Iris-setosa | 0.8064 |
| ../data/iris_missing2.csv | 10 | 10 | 10 | {"petal width": 1, "petal length": 4} | 000004 | Iris-versicolor | 0.7847 |
"""
print self.test_scenario6.__doc__
examples = [[
'data/iris_missing2.csv', '10', '10', '10', '{"petal width": 1}',
'000004', 'Iris-setosa', '0.8064'
],
[
'data/iris_missing2.csv', '10', '10', '10',
'{"petal width": 1, "petal length": 4}', '000004',
'Iris-versicolor', '0.7847'
]]
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_missing_splits(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_proportional_prediction(
self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_create.the_confidence_is(self, example[7])
prediction_compare.i_create_a_proportional_local_prediction(
self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_compare.the_local_prediction_confidence_is(
self, example[7])
def test_scenario3(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_missing.csv', '30',
'{"fields": {"000000": {"optype": "numeric"}}, "source_parser": {"missing_tokens": ["foo"]}}',
'30', '{"sepal length": "foo", "petal length": 3}', '000004',
'Iris-versicolor'
],
[
'data/iris_missing.csv', '30',
'{"fields": {"000000": {"optype": "numeric"}}, "source_parser": {"missing_tokens": ["foo"]}}',
'30',
'{"sepal length": "foo", "petal length": 5, "petal width": 1.5}',
'000004', 'Iris-virginica'
]
]
show_doc(self.test_scenario3, 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])
source_create.i_update_source_with(self, example[2])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(
self, example[3])
model_create.i_create_a_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
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])
def test_scenario1(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 |
| ../data/iris.csv | 10 | 10 | 10 | {"petal width": 0.5} | 000004 | Iris-setosa |
| ../data/iris.csv | 10 | 10 | 10 | {"petal length": 6, "petal width": 2} | 000004 | Iris-virginica |
| ../data/iris.csv | 10 | 10 | 10 | {"petal length": 4, "petal width": 1.5}| 000004 | Iris-versicolor |
| ../data/iris_sp_chars.csv | 10 | 10 | 10 | {"pétal.length": 4, "pétal&width\u0000": 1.5}| 000004 | Iris-versicolor |
"""
print self.test_scenario1.__doc__
examples = [
["data/iris.csv", "10", "10", "10", '{"petal width": 0.5}', "000004", "Iris-setosa"],
["data/iris.csv", "10", "10", "10", '{"petal length": 6, "petal width": 2}', "000004", "Iris-virginica"],
["data/iris.csv", "10", "10", "10", '{"petal length": 4, "petal width": 1.5}', "000004", "Iris-versicolor"],
[
"data/iris_sp_chars.csv",
"10",
"10",
"10",
'{"pétal.length": 4, "pétal&width\u0000": 1.5}',
"000004",
"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(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
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])
def test_scenario9(self):
"""
Scenario: Successfully comparing predictions for logistic regressions with operating kind and supervised 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 logistic regression with objective "<objective>"
And I wait until the logistic regression is ready less than <time_3> secs
And I create a local supervised model
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": 5}',
'000004', 'Iris-versicolor', '{}', "probability"
],
[
'data/iris.csv', '10', '50', '30000',
'{"petal length": 2}', '000004', 'Iris-setosa', '{}',
"probability"
]]
show_doc(self.test_scenario9, 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_logistic_model(self)
model_create.the_logistic_model_is_finished_in_less_than(
self, example[3])
prediction_compare.i_create_a_local_supervised_model(
self, model_type="logistic_regression")
prediction_create.i_create_a_logistic_prediction_with_op_kind(
self, example[4], example[8])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_logistic_prediction_op_kind(
self, example[4], example[8])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario8(self):
"""
Scenario: Successfully comparing predictions with text options and proportional missing strategy:
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 update the source with params "<options>"
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 proportional missing strategy prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a proportional missing strategy local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
"""
examples = [
[
'data/text_missing.csv', '20', '20', '30',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, "000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}',
'{}', "000003", 'swap'
],
[
'data/text_missing.csv', '20', '20', '30',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, "000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}',
'{"category1": "a"}', "000003", 'paperwork'
]
]
show_doc(self.test_scenario8, 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])
source_create.i_update_source_with(self, example[4])
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])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_proportional_prediction(
self, example[5])
prediction_create.the_prediction_is(self, example[6], example[7])
prediction_compare.i_create_a_proportional_local_prediction(
self, example[5])
prediction_compare.the_local_prediction_is(self, example[7])
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', '60', '{"petal length": 2.46}',
'000004', 'Iris-setosa', '{}', "probability"
],
[
'data/iris.csv', '10', '50', '60',
'{"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 logistic regression predictions with constant fields:
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 update the dataset with "<params>"
And I wait until the dataset is ready less than <time_4> secs
And I create a logistic regression model
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 |time_4| data_input | prediction | field_id
"""
examples = [[
'data/constant_field.csv', '10', '10', '50', '10',
'{"a": 1, "b": 1, "c": 1}', 'a',
'{"fields": {"000000": {"preferred": true}}}'
]]
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])
dataset_create.i_update_dataset_with(self, example[7])
dataset_create.the_dataset_is_finished_in_less_than(
self, example[4])
model_create.i_create_a_logistic_model(self)
model_create.the_logistic_model_is_finished_in_less_than(
self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[5])
prediction_create.the_logistic_prediction_is(self, example[6])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario6(self):
"""
Scenario: Successfully comparing predictions for ensembles:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create an ensemble with "<params>"
And I wait until the ensemble is ready less than <time_3> secs
And I create a local ensemble
When I create a prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | params
['data/iris.csv', '10', '10', '120', '{"petal width": 0.5}', '000004', 'Iris-versicolor', '{"number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal length": 6, "petal width": 2}', '000004', 'Iris-virginica', '{"number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal length": 4, "petal width": 1.5}', '000004', 'Iris-versicolor', '{"number_of_models": 5}'],
['data/grades.csv', '10', '10', '120', '{"Midterm": 20}', '000005', 46.261364, '{"number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal width": 0.5}', '000004', 'Iris-setosa', '{"boosting": {"iterations": 5}, "number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal length": 6, "petal width": 2}', '000004', 'Iris-virginica', '{"boosting": {"iterations": 5}, "number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal length": 4, "petal width": 1.5}', '000004', 'Iris-versicolor', '{"boosting": {"iterations": 5}, "number_of_models": 5}'],
"""
examples = [
['data/iris_unbalanced.csv', '30', '30', '120', '{"petal width": 4}', '000004', 'Iris-virginica', '{"boosting": {"iterations": 5}, "number_of_models": 5}'],
['data/grades.csv', '30', '30', '120', '{"Midterm": 20}', '000005', 61.61036, '{"boosting": {"iterations": 5}, "number_of_models": 5}']]
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])
ensemble_create.i_create_an_ensemble_with_params(self, example[7])
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3])
ensemble_create.create_local_ensemble(self)
prediction_create.i_create_an_ensemble_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_ensemble_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario15(self):
"""
Scenario: Successfully comparing predictions with text options:
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 logistic regression model with objective "<objective>"
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And the logistic regression probability for the prediction is "<probability>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
And the local logistic regression probability for the prediction is "<probability>"
Examples:
| data | time_1 | time_2 | objective | time_3 | options | data_input | prediction | probability
"""
examples = [[
'data/iris.csv', '20', '20', '180',
'{"weight_field": "000000", "missing_numerics": false}',
'{"petal width": 1.5, "petal length": 2, "sepal width":1}',
'Iris-versicolor', '0.9547', '000004'
]]
show_doc(self.test_scenario15, 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_logistic_model_with_objective_and_parms(
self, example[8], example[4])
model_create.the_logistic_model_is_finished_in_less_than(
self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[5])
prediction_create.the_logistic_prediction_is(self, example[6])
prediction_create.the_logistic_probability_is(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_compare.the_local_probability_is(self, example[7])
def test_scenario8(self):
"""
Scenario: Successfully comparing logistic regression 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 logistic regresssion model
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | prediction |
| ../data/iris.csv | 10 | 10 | 10 | {"petal width": 0.5, "petal length": 0.5, "sepal width": 0.5, "sepal length": 0.5} | 'Iris-virginica' |
| ../data/iris.csv | 10 | 10 | 10 | {"petal width": 2, "petal length": 6, "sepal width": 0.5, "sepal length": 0.5} | Iris-virginica |
| ../data/iris.csv | 10 | 10 | 10 | {"petal width": 1.5, "petal length": 4, "sepal width": 0.5, "sepal length": 0.5} | Iris-virginica |
| ../data/iris.csv | 10 | 10 | 10 | {"petal width": 1} | Iris-versicolor |
| ../data/iris_sp_chars.csv | 10 | 10 | 10 | {"pétal.length": 4, "pétal&width\u0000": 1.5, "sépal&width": 0.5, "sépal.length": 0.5}| Iris-virginica |
| ../data/price.csv | 10 | 10 | 10 | {"Price": 1200}| Product1 |
"""
print self.test_scenario8.__doc__
examples = [
['data/iris.csv', '10', '10', '50', '{"petal width": 0.5, "petal length": 0.5, "sepal width": 0.5, "sepal length": 0.5}', 'Iris-virginica'],
['data/iris.csv', '10', '10', '50', '{"petal width": 2, "petal length": 6, "sepal width": 0.5, "sepal length": 0.5}', 'Iris-virginica'],
['data/iris.csv', '10', '10', '50', '{"petal width": 1.5, "petal length": 4, "sepal width": 0.5, "sepal length": 0.5}', 'Iris-virginica'],
['data/iris.csv', '10', '10', '50', '{"petal length": 1}', 'Iris-virginica'],
['data/iris_sp_chars.csv', '10', '10', '50', '{"pétal.length": 4, "pétal&width\u0000": 1.5, "sépal&width": 0.5, "sépal.length": 0.5}', 'Iris-virginica'],
['data/price.csv', '10', '10', '50', '{"Price": 1200}', 'Product2']]
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(self)
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[4])
prediction_create.the_logistic_prediction_is(self, example[5])
prediction_compare.i_create_a_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[5])
def test_scenario8(self):
"""
Scenario: Successfully comparing logistic regression 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 logistic regresssion model
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | prediction |
| ../data/iris.csv | 10 | 10 | 10 | {"petal width": 0.5, "petal length": 0.5, "sepal width": 0.5, "sepal length": 0.5} | 'Iris-virginica' |
| ../data/iris.csv | 10 | 10 | 10 | {"petal width": 2, "petal length": 6, "sepal width": 0.5, "sepal length": 0.5} | Iris-virginica |
| ../data/iris.csv | 10 | 10 | 10 | {"petal width": 1.5, "petal length": 4, "sepal width": 0.5, "sepal length": 0.5} | Iris-virginica |
| ../data/iris.csv | 10 | 10 | 10 | {"petal width": 1} | Iris-versicolor |
| ../data/iris_sp_chars.csv | 10 | 10 | 10 | {"pétal.length": 4, "pétal&width\u0000": 1.5, "sépal&width": 0.5, "sépal.length": 0.5}| Iris-virginica |
| ../data/price.csv | 10 | 10 | 10 | {"Price": 1200}| Product1 |
"""
print self.test_scenario8.__doc__
examples = [
['data/iris.csv', '10', '10', '50', '{"petal width": 0.5, "petal length": 0.5, "sepal width": 0.5, "sepal length": 0.5}', 'Iris-virginica'],
['data/iris.csv', '10', '10', '50', '{"petal width": 2, "petal length": 6, "sepal width": 0.5, "sepal length": 0.5}', 'Iris-virginica'],
['data/iris.csv', '10', '10', '50', '{"petal width": 1.5, "petal length": 4, "sepal width": 0.5, "sepal length": 0.5}', 'Iris-virginica'],
['data/iris.csv', '10', '10', '50', '{"petal length": 1}', 'Iris-virginica'],
['data/iris_sp_chars.csv', '10', '10', '50', '{"pétal.length": 4, "pétal&width\u0000": 1.5, "sépal&width": 0.5, "sépal.length": 0.5}', 'Iris-virginica'],
['data/price.csv', '10', '10', '50', '{"Price": 1200}', 'Product2']]
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(self)
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[4])
prediction_create.the_logistic_prediction_is(self, example[5])
prediction_compare.i_create_a_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[5])
def test_scenario2(self):
"""
Scenario: Successfully creating a prediction using a shared 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
And I wait until the model is ready less than <time_3> secs
And I make the model shared
And I wait until the model is ready less than <time_3> secs
And I get the model sharing info
And I check the model status using the model's shared url
And I check the model status using the model's shared key
And I create a local model
When I create a local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | prediction |
| ../data/iris.csv | 10 | 10 | 10 | {"petal width": 0.5} | Iris-setosa |
"""
print self.test_scenario2.__doc__
examples = [[
'data/iris.csv', '10', '10', '10', '{"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(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.make_the_model_shared(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.get_sharing_info(self)
model_create.model_from_shared_url(self)
model_create.model_from_shared_key(self)
compare_pred.i_create_a_local_model(self)
compare_pred.i_create_a_local_prediction(self, example[4])
compare_pred.the_local_prediction_is(self, example[5])
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_scenario10(self):
"""
Scenario: Successfully comparing predictions with text options:
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 update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a logistic regression model with objective "<objective>"
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And the logistic regression probability for the prediction is "<probability>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
And the local logistic regression probability for the prediction is "<probability>"
Examples:
| data | time_1 | time_2 | objective | time_3 | options | data_input | prediction | probability
| ../data/spam.csv | 20 | 20 | 000002 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Message": "A normal message"} | ham | 0.7645
"""
print self.test_scenario10.__doc__
examples = [
['data/spam.csv', '20', '20', '80', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Message": "A normal message"}', 'ham', 0.7645, "000000"],
['data/spam.csv', '20', '20', '80', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}', '{"Message": "mobile"}', 'spam', 0.7175, "000000"],
['data/movies.csv', '20', '20', '80', '{"fields": {"000007": {"optype": "items", "item_analysis": {"separator": "$"}}}}', '{"gender": "Female", "genres": "Adventure$Action", "timestamp": 993906291, "occupation": "K-12 student", "zipcode": 59583, "rating": 3}', '25-34', '0.4135', '000002']]
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])
source_create.i_update_source_with(self, example[4])
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])
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[5])
prediction_create.the_logistic_prediction_is(self, example[6])
prediction_create.the_logistic_probability_is(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_compare.the_local_probability_is(self, example[7])
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 with proportional missing strategy and balanced models:
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 balanced model
And I wait until the model is ready less than <time_3> secs
And I create a local model
When I create a proportional missing strategy prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And the confidence for the prediction is "<confidence>"
And I create a proportional missing strategy local prediction for "<data_input>"
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
And I create local probabilities for "<data_input>"
Then the local probabilities are "<probabilities>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence |
"""
examples = [
['data/iris_unbalanced.csv', '10', '10', '10', '{}', '000004', 'Iris-setosa', '0.25284', '[0.33333, 0.33333, 0.33333]'],
['data/iris_unbalanced.csv', '10', '10', '10', '{"petal length":1, "sepal length":1, "petal width": 1, "sepal width": 1}', '000004', 'Iris-setosa', '0.7575', '[1.0, 0.0, 0.0]']]
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_balanced_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_proportional_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_proportional_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_create.the_confidence_is(self, example[7])
prediction_compare.the_local_prediction_confidence_is(self, example[7])
prediction_compare.i_create_local_probabilities(self, example[4])
prediction_compare.the_local_probabilities_are(self, example[8])
def test_scenario10(self):
"""
Scenario: Successfully comparing predictions for linear regression:
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 linear regression with objective "<objective>" and "<params>"
And I wait until the linear regression is ready less than <time_3> secs
And I create a local linear regression
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/grades.csv', '10', '50', '30000', '{"000000": 1, "000001": 1, "000002": 1}', '000005', 29.63024, '{"input_fields": ["000000", "000001", "000002"]}'],
['data/iris.csv', '10', '50', '30000', '{"000000": 1, "000001": 1, "000004": "Iris-virginica"}', '000003', 1.21187, '{"input_fields": ["000000", "000001", "000004"]}'],
['data/movies.csv', '10', '50', '30000', '{"000007": "Action"}', '000009', 4.33333, '{"input_fields": ["000007"]}'],
['data/movies.csv', '10', '50', '30000', '{"000006": "1999"}', '000009', 0, '{"input_fields": ["000006"], "bias": false}']]
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])
linear_create.i_create_a_linear_regression_with_objective_and_params( \
self, example[5], example[7])
linear_create.the_linear_regression_is_finished_in_less_than( \
self, example[3])
prediction_compare.i_create_a_local_linear(self)
prediction_create.i_create_a_linear_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_linear_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario9(self):
"""
Scenario: Successfully comparing predictions with text options:
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 update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a logistic regression model with objective "<objective>" and parms "<parms>"
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And the logistic regression probability for the prediction is "<probability>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
And the local logistic regression probability for the prediction is "<probability>"
"""
examples = [
['data/iris.csv', '20', '20', '130', '{"fields": {"000000": {"optype": "categorical"}}}', '{"species": "Iris-setosa"}', '5.0', 0.0394, "000000", '{"field_codings": [{"field": "species", "coding": "dummy", "dummy_class": "Iris-setosa"}]}'],
['data/iris.csv', '20', '20', '130', '{"fields": {"000000": {"optype": "categorical"}}}', '{"species": "Iris-setosa"}', '5.0', 0.051, "000000", '{"balance_fields": false, "field_codings": [{"field": "species", "coding": "contrast", "coefficients": [[1, 2, -1, -2]]}]}'],
['data/iris.csv', '20', '20', '130', '{"fields": {"000000": {"optype": "categorical"}}}', '{"species": "Iris-setosa"}', '5.0', 0.051, "000000", '{"balance_fields": false, "field_codings": [{"field": "species", "coding": "other", "coefficients": [[1, 2, -1, -2]]}]}'],
['data/iris.csv', '20', '20', '130', '{"fields": {"000000": {"optype": "categorical"}}}', '{"species": "Iris-setosa"}', '5.0', 0.0417, "000000", '{"bias": false}']]
show_doc(self.test_scenario9, 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])
source_create.i_update_source_with(self, example[4])
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[9])
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[5])
prediction_create.the_logistic_prediction_is(self, example[6])
prediction_create.the_logistic_probability_is(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_compare.the_local_probability_is(self, example[7])
def test_scenario2(self):
"""
Scenario: Successfully creating a prediction using a shared 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
And I wait until the model is ready less than <time_3> secs
And I make the model shared
And I wait until the model is ready less than <time_3> secs
And I get the model sharing info
And I check the model status using the model's shared url
And I check the model status using the model's shared key
And I create a local model
When I create a local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | prediction |
| ../data/iris.csv | 10 | 10 | 10 | {"petal width": 0.5} | Iris-setosa |
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '10', '10', '10', '{"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(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.make_the_model_shared(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.get_sharing_info(self)
model_create.model_from_shared_url(self)
model_create.model_from_shared_key(self)
compare_pred.i_create_a_local_model(self)
compare_pred.i_create_a_local_prediction(self, example[4])
compare_pred.the_local_prediction_is(self, example[5])
def test_scenario3(self):
"""
Scenario: Successfully comparing predictions with proportional missing strategy:
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 proportional missing strategy prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And the confidence for the prediction is "<confidence>"
And I create a proportional missing strategy local prediction for "<data_input>"
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence |
"""
examples = [
['data/iris.csv', '10', '10', '10', '{}', '000004', 'Iris-setosa', '0.2629'],
['data/grades.csv', '10', '10', '10', '{}', '000005', '68.62224', '27.5358'],
['data/grades.csv', '10', '10', '10', '{"Midterm": 20}', '000005', '40.46667', '54.89713'],
['data/grades.csv', '10', '10', '10', '{"Midterm": 20, "Tutorial": 90, "TakeHome": 100}', '000005', '28.06', '25.65806']]
show_doc(self.test_scenario3, 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(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_proportional_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_create.the_confidence_is(self, example[7])
prediction_compare.i_create_a_proportional_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_compare.the_local_prediction_confidence_is(self, example[7])
def test_scenario7(self):
"""
Scenario: Successfully comparing predictions in operating points for ensembles:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create an ensemble
And I wait until the ensemble is ready less than <time_3> secs
And I create a local ensemble
When I create a prediction for "<data_input>" in "<operating_kind>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local ensemble prediction for "<data_input>" in "<operating_kind>"
Then the local ensemble prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | prediction | operating_kind
"""
examples = [
['data/iris.csv', '10', '50', '50', '{"petal length": 2.46}', 'Iris-versicolor', "probability", "000004"],
['data/iris.csv', '10', '50', '50', '{"petal length": 2}', 'Iris-setosa', "probability", "000004"],
['data/iris.csv', '10', '50', '50', '{"petal length": 2.46}', 'Iris-versicolor', "confidence", "000004"],
['data/iris.csv', '10', '50', '50', '{"petal length": 2}', 'Iris-setosa', "confidence", "000004"],
['data/iris.csv', '10', '50', '50', '{"petal length": 2.46}', 'Iris-versicolor', "votes", "000004"],
['data/iris.csv', '10', '50', '50', '{"petal length": 1}', 'Iris-setosa', "votes", "000004"]]
show_doc(self.test_scenario7, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
ensemble_create.i_create_an_ensemble(self)
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3])
ensemble_create.create_local_ensemble(self)
prediction_create.i_create_an_ensemble_prediction_op_kind(self, example[4], example[6])
prediction_create.the_prediction_is(self, example[7], example[5])
prediction_compare.i_create_a_local_ensemble_prediction_op_kind(self, example[4], example[6])
prediction_compare.the_local_prediction_is(self, example[5])
def test_scenario9(self):
"""
Scenario: Successfully comparing predictions with text options:
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 update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a logistic regression model with objective "<objective>" and parms "<parms>"
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And the logistic regression probability for the prediction is "<probability>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
And the local logistic regression probability for the prediction is "<probability>"
"""
examples = [
['data/iris.csv', '20', '20', '130', '{"fields": {"000000": {"optype": "categorical"}}}', '{"species": "Iris-setosa"}', '5.0', 0.0394, "000000", '{"field_codings": [{"field": "species", "coding": "dummy", "dummy_class": "Iris-setosa"}]}'],
['data/iris.csv', '20', '20', '130', '{"fields": {"000000": {"optype": "categorical"}}}', '{"species": "Iris-setosa"}', '5.0', 0.0511, "000000", '{"balance_fields": false, "field_codings": [{"field": "species", "coding": "contrast", "coefficients": [[1, 2, -1, -2]]}]}'],
['data/iris.csv', '20', '20', '130', '{"fields": {"000000": {"optype": "categorical"}}}', '{"species": "Iris-setosa"}', '5.0', 0.0511, "000000", '{"balance_fields": false, "field_codings": [{"field": "species", "coding": "other", "coefficients": [[1, 2, -1, -2]]}]}'],
['data/iris.csv', '20', '20', '130', '{"fields": {"000000": {"optype": "categorical"}}}', '{"species": "Iris-setosa"}', '5.0', 0.0417, "000000", '{"bias": false}']]
show_doc(self.test_scenario9, 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])
source_create.i_update_source_with(self, example[4])
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[9])
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[5])
prediction_create.the_logistic_prediction_is(self, example[6])
prediction_create.the_logistic_probability_is(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_compare.the_local_probability_is(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_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_scenario12(self):
"""
Scenario: Successfully comparing logistic regression predictions with constant fields:
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 update the dataset with "<params>"
And I wait until the dataset is ready less than <time_4> secs
And I create a logistic regression model
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 |time_4| data_input | prediction | field_id
"""
examples = [
['data/constant_field.csv', '10', '10', '50', '10','{"a": 1, "b": 1, "c": 1}', 'a', '{"fields": {"000000": {"preferred": true}}}']]
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])
dataset_create.i_update_dataset_with(self, example[7])
dataset_create.the_dataset_is_finished_in_less_than(self, example[4])
model_create.i_create_a_logistic_model(self)
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[5])
prediction_create.the_logistic_prediction_is(self, example[6])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario4(self):
"""
Scenario: Successfully comparing predictions in operating points for ensembles:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create an ensemble
And I wait until the ensemble is ready less than <time_3> secs
And I create a local ensemble
When I create a prediction for "<data_input>" in "<operating_point>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local ensemble prediction for "<data_input>" in "<operating_point>"
Then the local ensemble prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | prediction | operating_point
"""
examples = [
['data/iris.csv', '10', '50', '50', '{"petal width": 4}', 'Iris-setosa', {"kind": "probability", "threshold": 0.1, "positive_class": "Iris-setosa"}, "000004"],
['data/iris.csv', '10', '50', '50', '{"petal width": 4}', 'Iris-virginica', {"kind": "probability", "threshold": 0.9, "positive_class": "Iris-setosa"}, "000004"],
['data/iris.csv', '10', '50', '50', '{"sepal length": 4.1, "sepal width": 2.4}', 'Iris-setosa', {"kind": "confidence", "threshold": 0.1, "positive_class": "Iris-setosa"}, "000004"],
['data/iris.csv', '10', '50', '50', '{"sepal length": 4.1, "sepal width": 2.4}', 'Iris-versicolor', {"kind": "confidence", "threshold": 0.9, "positive_class": "Iris-setosa"}, "000004"]]
show_doc(self.test_scenario4, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
ensemble_create.i_create_an_ensemble(self)
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3])
ensemble_create.create_local_ensemble(self)
prediction_create.i_create_an_ensemble_prediction_op(self, example[4], example[6])
prediction_create.the_prediction_is(self, example[7], example[5])
prediction_compare.i_create_a_local_ensemble_prediction_op(self, example[4], example[6])
prediction_compare.the_local_prediction_is(self, example[5])
def test_scenario2(self):
"""
Scenario: Successfully comparing predictions in operating points for models:
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>" in "<operating_point>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction for "<data_input>" in "<operating_point>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | prediction | operating_point
"""
examples = [
['data/iris.csv', '10', '50', '50', '{"petal width": 4}', 'Iris-setosa', {"kind": "probability", "threshold": 0.1, "positive_class": "Iris-setosa"}, "000004"],
['data/iris.csv', '10', '50', '50', '{"petal width": 4}', 'Iris-versicolor', {"kind": "probability", "threshold": 0.9, "positive_class": "Iris-setosa"}, "000004"],
['data/iris.csv', '10', '50', '50', '{"sepal length": 4.1, "sepal width": 2.4}', 'Iris-setosa', {"kind": "confidence", "threshold": 0.1, "positive_class": "Iris-setosa"}, "000004"],
['data/iris.csv', '10', '50', '50', '{"sepal length": 4.1, "sepal width": 2.4}', 'Iris-versicolor', {"kind": "confidence", "threshold": 0.9, "positive_class": "Iris-setosa"}, "000004"]]
show_doc(self.test_scenario2, 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(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_prediction_op(self, example[4], example[6])
prediction_create.the_prediction_is(self, example[7], example[5])
prediction_compare.i_create_a_local_prediction_op(self, example[4], example[6])
prediction_compare.the_local_prediction_is(self, example[5])
def test_scenario1(self):
"""
Scenario: Successfully creating a prediction from a local model in a json file:
Given I create a local model from a "<model>" file
When I create a local prediction for "<data_input>" with confidence
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
Examples:
| model | data_input | prediction | confidence
| ../data/iris_model.json | {"petal length": 0.5} | Iris-setosa | 0.90594
"""
print self.test_scenario1.__doc__
examples = [
['data/iris_model.json', '{"petal length": 0.5}', 'Iris-setosa', '0.90594']]
for example in examples:
print "\nTesting with:\n", example
prediction_compare.i_create_a_local_model_from_file(self, example[0])
prediction_compare.i_create_a_local_prediction_with_confidence(self, example[1])
prediction_compare.the_local_prediction_is(self, example[2])
prediction_compare.the_local_prediction_confidence_is(self, example[3])
def test_scenario15(self):
"""
Scenario: Successfully comparing predictions with text options:
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 logistic regression model with objective "<objective>"
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And the logistic regression probability for the prediction is "<probability>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
And the local logistic regression probability for the prediction is "<probability>"
Examples:
| data | time_1 | time_2 | objective | time_3 | options | data_input | prediction | probability
"""
examples = [
['data/iris.csv', '20', '20', '180', '{"weight_field": "000000", "missing_numerics": false}', '{"petal width": 1.5, "petal length": 2, "sepal width":1}', 'Iris-versicolor', '0.9547', '000004']]
show_doc(self.test_scenario15, 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_logistic_model_with_objective_and_parms(self, example[8], example[4])
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[5])
prediction_create.the_logistic_prediction_is(self, example[6])
prediction_create.the_logistic_probability_is(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_compare.the_local_probability_is(self, example[7])
def test_scenario9(self):
"""
Scenario: Successfully comparing predictions for logistic regressions with operating kind and supervised 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 logistic regression with objective "<objective>"
And I wait until the logistic regression is ready less than <time_3> secs
And I create a local supervised model
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": 5}', '000004', 'Iris-versicolor', '{}', "probability"],
['data/iris.csv', '10', '50', '30000', '{"petal length": 2}', '000004', 'Iris-setosa', '{}', "probability"]]
show_doc(self.test_scenario9, 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_logistic_model(self)
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_supervised_model(self, model_type="logistic_regression")
prediction_create.i_create_a_logistic_prediction_with_op_kind(self, example[4], example[8])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_logistic_prediction_op_kind(self, example[4], example[8])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario11(self):
"""
Scenario: Successfully comparing predictions with text options and proportional missing strategy:
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 update the source with params "<options>"
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 proportional missing strategy prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a proportional missing strategy local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| ../data/text_missing.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, {"000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}} |{} | paperwork |
"""
print self.test_scenario11.__doc__
examples = [
['data/text_missing.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, "000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}', '{}', "000003",'swap'],
['data/text_missing.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, "000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}', '{"category1": "a"}', "000003",'paperwork']]
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])
source_create.i_update_source_with(self, example[4])
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])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_proportional_prediction(self, example[5])
prediction_create.the_prediction_is(self, example[6], example[7])
prediction_compare.i_create_a_proportional_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[7])
def test_scenario14(self):
"""
Scenario: Successfully comparing predictions with supervised 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
And I wait until the model is ready less than <time_3> secs
And I create a local supervised 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'],
['data/iris.csv', '10', '10', '10', '{"petal length": 6, "petal width": 2}', '000004', 'Iris-virginica'],
['data/iris.csv', '10', '10', '10', '{"petal length": 4, "petal width": 1.5}', '000004', 'Iris-versicolor'],
['data/iris_sp_chars.csv', '10', '10', '10', '{"pétal.length": 4, "pétal&width\u0000": 1.5}', '000004', 'Iris-versicolor']]
show_doc(self.test_scenario14, 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(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_supervised_model(self)
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])
