def test_scenario2(self):
"""
Scenario: Successfully building test predictions from source using datasets with max categories
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources with <max_categories> as categories limit and <objective> as objective field using source to test "<test>" and log predictions in "<output>"
And I check that the dataset has been created
And I check that the max_categories datasets have been created
And I check that the models have been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs |max_categories| objective | test | output |predictions_file |
| scenario_mc_1| {"data": "../data/iris.csv", "max_categories": "1", "objective": "species", "output": "./scenario_mc_1/predictions.csv", "test": "../data/test_iris.csv"} |1| species | ../data/test_iris.csv | ./scenario_mc_2/predictions.csv | ./check_files/predictions_mc.csv |
"""
print self.test_scenario2.__doc__
examples = [
['scenario_mc_1', '{"data": "data/iris.csv", "max_categories": "1", "objective": "species", "output": "scenario_mc_1/predictions.csv", "test": "data/test_iris.csv"}', '1', 'species', 'data/test_iris.csv', 'scenario_mc_2/predictions.csv', 'check_files/predictions_mc.csv']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
max_cat.i_create_all_mc_resources_from_source(self, max_categories=example[2], objective=example[3], test=example[4], output=example[5])
test_pred.i_check_create_dataset(self, suffix=None)
max_cat.i_check_create_max_categories_datasets(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[6])
def setup_scenario1(self):
"""
Scenario: Successfully building test predictions from training data using datasets with max categories
Given I create BigML resources from "<data>" with <max_categories> as categories limit and <objective> as objective field to test "<test>" and log predictions in "<output>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the max_categories datasets have been created
And I check that the models have been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|data |max_categories | objective | test | output |predictions_file |
|../data/iris.csv |1| species |../data/test_iris.csv | ./scenario_mc_1/predictions.csv | ./check_files/predictions_mc.csv |
"""
print self.setup_scenario1.__doc__
examples = [
['data/iris.csv', '1', 'species', 'data/test_iris.csv', 'scenario_mc_1/predictions.csv', 'check_files/predictions_mc.csv']]
for example in examples:
print "\nTesting with:\n", example
max_cat.i_create_all_mc_resources(self, example[0], max_categories=example[1], objective=example[2], test=example[3], output=example[4])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
max_cat.i_check_create_max_categories_datasets(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[5])
def setup_scenario1(self):
"""
Scenario: Successfully building multi-label test predictions from start:
Given I create BigML multi-label resources tagged as "<tag>" with "<label_separator>" label separator and <number_of_labels> labels uploading train "<data>" file with "<training_separator>" field separator to test "<test>" and log predictions in "<output>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the models have been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|tag |label_separator |number_of_labels | data |training_separator | test | output |predictions_file |
|my_multilabel_1|:|7| ../data/multilabel.csv |,| ../data/test_multilabel.csv | ./scenario_ml_1/predictions.csv | ./check_files/predictions_ml.csv |
"""
print self.setup_scenario1.__doc__
examples = [[
'my_multilabel_1', ':', '7', 'data/multilabel.csv', ',',
'data/test_multilabel.csv', 'scenario_ml_1/predictions.csv',
'check_files/predictions_ml.csv'
]]
for example in examples:
print "\nTesting with:\n", example
ml_pred.i_create_all_ml_resources(self,
tag=example[0],
label_separator=example[1],
number_of_labels=example[2],
data=example[3],
training_separator=example[4],
test=example[5],
output=example[6])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[7])
def setup_scenario1(self):
"""
Scenario: Successfully building multi-label test predictions from start:
Given I create BigML multi-label resources tagged as "<tag>" with "<label_separator>" label separator and <number_of_labels> labels uploading train "<data>" file with "<training_separator>" field separator and "<ml_fields>" as multi-label fields using model_fields "<model_fields>" and objective "<objective>" to test "<test>" and log predictions in "<output>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the models have been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|tag |label_separator |number_of_labels | data |training_separator | ml_fields | model_fields | objective | test | output |predictions_file |
|my_multilabelm_1|:|7| ../data/multilabel_multi.csv |, | type,class | -type,-type - W,-type - A,-type - C,-type - S,-type - R,-type - T,-type - P | class |../data/test_multilabel.csv | ./scenario_mlm_1/predictions.csv | ./check_files/predictions_ml.csv |
"""
print self.setup_scenario1.__doc__
examples = [
['my_multilabelm_1', ':', '7', 'data/multilabel_multi.csv', ',', 'type,class', '-type,-type - W,-type - A,-type - C,-type - S,-type - R,-type - T,-type - P', 'class', 'data/test_multilabel.csv', 'scenario_mlm_1/predictions.csv', 'check_files/predictions_ml.csv']]
for example in examples:
print "\nTesting with:\n", example
ml_pred.i_create_all_mlm_resources(self, tag=("%s_%s" % (example[0], PY3)), label_separator=example[1], number_of_labels=example[2], data=example[3], training_separator=example[4], ml_fields=example[5], model_fields=example[6], objective=example[7], test=example[8], output=example[9])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[10])
def test_scenario2(self):
"""
Scenario: Successfully building test predictions from source
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML multi-label resources using source with objective "<objective>" and model fields "<model_fields>" to test "<test>" and log predictions in "<output>"
And I check that the dataset has been created
And I check that the models have been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs | objective | model_fields | test | output |predictions_file |
| scenario_mlm_1| {"tag": "my_multilabelm_1", "data": "../data/multilabel_multi.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "./scenario_mlm_1/predictions.csv", "test": "../data/test_multilabel.csv", "ml_fields": "type,class", "model_fields": "-type,-type - W,-type - A,-type - C,-type - S,-type - R,-type - T,-type - P", "objective": "class"} | class | -type,-type - W,-type - A,-type - C,-type - S,-type - R,-type - T,-type - P |../data/test_multilabel.csv | ./scenario_mlm_2/predictions.csv | ./check_files/predictions_ml_comma.csv |
"""
print self.test_scenario2.__doc__
examples = [
['scenario_mlm_1', '{"tag": "my_multilabelm_1", "data": "data/multilabel_multi.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "scenario_mlm_1/predictions.csv", "test": "data/test_multilabel.csv", "ml_fields": "type,class", "model_fields": "-type,-type - W,-type - A,-type - C,-type - S,-type - R,-type - T,-type - P", "objective": "class"}', 'class', '-type,-type - W,-type - A,-type - C,-type - S,-type - R,-type - T,-type - P', 'data/test_multilabel.csv', 'scenario_mlm_2/predictions.csv', 'check_files/predictions_ml_comma.csv']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
test_pred.i_create_resources_from_source_with_objective(self, multi_label='multi-label ', objective=example[2], model_fields=example[3], test=example[4], output=example[5])
test_pred.i_check_create_dataset(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[6])
def test_scenario2(self):
"""
Scenario: Successfully building multi-label evaluations from source
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML multi-label resources using source to evaluate and log evaluation in "<output>"
And I check that the dataset has been created
And I check that the models have been created
And I check that the <number_of_labels> evaluations have been created
And I check that the evaluation is ready
Then the evaluation file is like "<json_evaluation_file>"
Examples:
|scenario | kwargs | number_of_labels | output |json_evaluation_file |
| scenario_ml_e1| {"tag": "my_multilabel_e_1", "data": "../data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "./scenario_ml_e1/evaluation"} | 7 | ./scenario_ml_e2/evaluation | ./check_files/evaluation_ml.json |
"""
print self.test_scenario2.__doc__
examples = [[
'scenario_ml_e1',
'{"tag": "my_multilabel_e_1", "data": "data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "scenario_ml_e1/evaluation"}',
'7', 'scenario_ml_e2/evaluation', 'check_files/evaluation_ml.json'
]]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(
self, example[0], example[1])
ml_eval.i_create_ml_evaluations_from_source(self,
output=example[3])
test_pred.i_check_create_dataset(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_evaluations(
self, number_of_evaluations=example[2])
ml_eval.i_check_evaluation_ready(self)
evaluation.then_the_evaluation_file_is_like(self, example[4])
def setup_scenario1(self):
"""
Scenario: Successfully building multi-label test predictions from start:
Given I create BigML multi-label resources tagged as "<tag>" with "<label_separator>" label separator and <number_of_labels> labels uploading train "<data>" file with "<training_separator>" field separator and "<ml_fields>" as multi-label fields using model_fields "<model_fields>" and objective "<objective>" to test "<test>" and log predictions in "<output>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the models have been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|tag |label_separator |number_of_labels | data |training_separator | ml_fields | model_fields | objective | test | output |predictions_file |
|my_multilabelm_1|:|7| ../data/multilabel_multi.csv |, | type,class | -type,-type - W,-type - A,-type - C,-type - S,-type - R,-type - T,-type - P | class |../data/test_multilabel.csv | ./scenario_mlm_1/predictions.csv | ./check_files/predictions_ml.csv |
"""
print self.setup_scenario1.__doc__
examples = [
['my_multilabelm_1', ':', '7', 'data/multilabel_multi.csv', ',', 'type,class', '-type,-type - W,-type - A,-type - C,-type - S,-type - R,-type - T,-type - P', 'class', 'data/test_multilabel.csv', 'scenario_mlm_1/predictions.csv', 'check_files/predictions_ml.csv']]
for example in examples:
print "\nTesting with:\n", example
ml_pred.i_create_all_mlm_resources(self, tag=example[0], label_separator=example[1], number_of_labels=example[2], data=example[3], training_separator=example[4], ml_fields=example[5], model_fields=example[6], objective=example[7], test=example[8], output=example[9])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[10])
def test_scenario2(self):
"""
Scenario: Successfully building test predictions from source using datasets with max categories
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources with <max_categories> as categories limit and <objective> as objective field using source to test "<test>" and log predictions in "<output>"
And I check that the dataset has been created
And I check that the max_categories datasets have been created
And I check that the models have been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs |max_categories| objective | test | output |predictions_file |
| scenario_mc_1| {"data": "../data/iris.csv", "max_categories": "1", "objective": "species", "output": "./scenario_mc_1/predictions.csv", "test": "../data/test_iris.csv"} |1| species | ../data/test_iris.csv | ./scenario_mc_2/predictions.csv | ./check_files/predictions_mc.csv |
"""
print self.test_scenario2.__doc__
examples = [
['scenario_mc_1', '{"data": "data/iris.csv", "max_categories": "1", "objective": "species", "output": "scenario_mc_1/predictions.csv", "test": "data/test_iris.csv"}', '1', 'species', 'data/test_iris.csv', 'scenario_mc_2/predictions.csv', 'check_files/predictions_mc.csv']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
max_cat.i_create_all_mc_resources_from_source(self, max_categories=example[2], objective=example[3], test=example[4], output=example[5])
test_pred.i_check_create_dataset(self, suffix=None)
max_cat.i_check_create_max_categories_datasets(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[6])
def test_scenario12(self):
"""
Scenario: Successfully building cross-validation from dataset
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create a BigML cross-validation with rate <rate> using the dataset in file "<dataset_file>" and log results in "<output>"
And I check that the models have been created
And I check that the evaluations have been created
Then the cross-validation json model info is like the one in "<cv_file>"
Examples:
|scenario | kwargs | rate | dataset_file | output |cv_file |
| scenario1| {"data": "../data/iris.csv", "output": "./scenario1/predictions.csv", "test": "../data/test_iris.csv"} | 0.05 | ./scenario1/dataset | ./scenario12/cross-validation | ./check_files/cross_validation.json |
"""
print self.test_scenario12.__doc__
examples = [
['scenario1', '{"data": "data/iris.csv", "output": "scenario1/predictions.csv", "test": "data/test_iris.csv"}', '0.05', 'scenario1/dataset', 'scenario12/cross-validation', 'check_files/cross_validation.json']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
test_pred.i_create_cross_validation_from_dataset(self, rate=example[2], dataset_file=example[3], output=example[4])
test_pred.i_check_create_models(self)
test_pred.i_check_create_evaluations(self, number_of_evaluations=None)
test_pred.i_check_cross_validation(self, example[5])
def test_scenario2(self):
"""
Scenario: Successfully building test predictions from source
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML multi-label resources using source to test "<test>" and log predictions in "<output>"
And I check that the dataset has been created
And I check that the models have been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs | test | output |predictions_file |
| scenario_ml_1| {"tag": "my_multilabel_1", "data": "../data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "./scenario_ml_1/predictions.csv", "test": "../data/test_multilabel.csv"} | ../data/test_multilabel.csv | ./scenario_ml_2/predictions.csv | ./check_files/predictions_ml_comma.csv |
"""
print self.test_scenario2.__doc__
examples = [[
'scenario_ml_1',
'{"tag": "my_multilabel_1", "data": "data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "scenario_ml_1/predictions.csv", "test": "data/test_multilabel.csv"}',
'data/test_multilabel.csv', 'scenario_ml_2/predictions.csv',
'check_files/predictions_ml_comma.csv'
]]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(
self, example[0], example[1])
test_pred.i_create_resources_from_source(self,
multi_label='multi-label',
test=example[2],
output=example[3])
test_pred.i_check_create_dataset(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
def setup_scenario1(self):
"""
Scenario: Successfully building test predictions from training data using datasets with max categories
Given I create BigML resources from "<data>" with <max_categories> as categories limit and <objective> as objective field to test "<test>" and log predictions in "<output>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the max_categories datasets have been created
And I check that the models have been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|data |max_categories | objective | test | output |predictions_file |
|../data/iris.csv |1| species |../data/test_iris.csv | ./scenario_mc_1/predictions.csv | ./check_files/predictions_mc.csv |
"""
print self.setup_scenario1.__doc__
examples = [
['data/iris.csv', '1', 'species', 'data/test_iris.csv', 'scenario_mc_1/predictions.csv', 'check_files/predictions_mc.csv']]
for example in examples:
print "\nTesting with:\n", example
max_cat.i_create_all_mc_resources(self, example[0], max_categories=example[1], objective=example[2], test=example[3], output=example[4])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
max_cat.i_check_create_max_categories_datasets(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[5])
def test_scenario2(self):
"""
Scenario: Successfully building multi-label evaluations from source
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML multi-label resources using source to evaluate and log evaluation in "<output>"
And I check that the dataset has been created
And I check that the models have been created
And I check that the <number_of_labels> evaluations have been created
And I check that the evaluation is ready
Then the evaluation file is like "<json_evaluation_file>"
Examples:
|scenario | kwargs | number_of_labels | output |json_evaluation_file |
| scenario_ml_e1| {"tag": "my_multilabel_e_1", "data": "../data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "./scenario_ml_e1/evaluation"} | 7 | ./scenario_ml_e2/evaluation | ./check_files/evaluation_ml.json |
"""
print self.test_scenario2.__doc__
examples = [
['scenario_ml_e1', '{"tag": "my_multilabel_e_1", "data": "data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "scenario_ml_e1/evaluation"}', '7', 'scenario_ml_e2/evaluation', 'check_files/evaluation_ml.json']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
ml_eval.i_create_ml_evaluations_from_source(self, output=example[3])
test_pred.i_check_create_dataset(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_evaluations(self, number_of_evaluations=example[2])
ml_eval.i_check_evaluation_ready(self)
evaluation.then_the_evaluation_file_is_like(self, example[4])
def setup_scenario1(self):
"""
Scenario: Successfully building multi-label evaluations from scratch
Given I create BigML multi-label resources tagged as "<tag>" with "<label_separator>" label separator and "<number_of_labels>" labels uploading train "<data>" file with "<training_separator>" field separator to evaluate and log evaluation in "<output>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the models have been created
And I check that the <number_of_labels> evaluations have been created
And I check that the evaluation is ready
Then the evaluation file is like "<json_evaluation_file>"
Examples:
|tag |label_separator |number_of_labels| data |training_separator | output |json_evaluation_file
|my_multilabel_e_1|:|7| ../data/multilabel.csv |,| ./scenario_ml_e1/evaluation | ./check_files/evaluation_ml.json
"""
print self.setup_scenario1.__doc__
examples = [
['my_multilabel_e_1', ':', '7', 'data/multilabel.csv', ',', 'scenario_ml_e1/evaluation', 'check_files/evaluation_ml.json']]
for example in examples:
print "\nTesting with:\n", example
ml_eval.i_create_all_ml_evaluations(self, tag=example[0], label_separator=example[1], number_of_labels=example[2], data=example[3], training_separator=example[4], output=example[5])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_evaluations(self, number_of_evaluations=example[2])
ml_eval.i_check_evaluation_ready(self)
evaluation.then_the_evaluation_file_is_like(self, example[6])
def test_scenario12(self):
"""
Scenario: Successfully building cross-validation from dataset
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create a BigML cross-validation with rate <rate> using the dataset in file "<dataset_file>" and log results in "<output>"
And I check that the models have been created
And I check that the evaluations have been created
Then the cross-validation json model info is like the one in "<cv_file>"
Examples:
|scenario | kwargs | rate | dataset_file | output |cv_file |
| scenario1| {"data": "../data/iris.csv", "output": "./scenario1/predictions.csv", "test": "../data/test_iris.csv"} | 0.05 | ./scenario1/dataset | ./scenario12/cross-validation | ./check_files/cross_validation.json |
"""
print self.test_scenario12.__doc__
examples = [[
'scenario1',
'{"data": "data/iris.csv", "output": "scenario1/predictions.csv", "test": "data/test_iris.csv"}',
'0.05', 'scenario1/dataset', 'scenario12/cross-validation',
'check_files/cross_validation.json'
]]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(
self, example[0], example[1])
test_pred.i_create_cross_validation_from_dataset(
self,
rate=example[2],
dataset_file=example[3],
output=example[4])
test_pred.i_check_create_models(self)
test_pred.i_check_create_evaluations(self,
number_of_evaluations=None)
test_pred.i_check_cross_validation(self, example[5])
def test_scenario5(self):
"""
Scenario: Successfully building test predictions from dataset using datasets and model fields with max categories
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources with <max_categories> as categories limit and <objective> as objective field and model fields "<model_fields>" using dataset to test "<test>" and log predictions in "<output>"
And I check that the max_categories datasets have been created
And I check that the models have been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs |max_categories|objective | model_fields | test | output |predictions_file |
| scenario_mc_1| {"data": "../data/iris.csv", "max_categories": "1", "objective": "species", "output": "./scenario_mc_1/predictions.csv", "test": "../data/test_iris.csv"} |1| species |sepal length,sepal width |../data/test_iris.csv | ./scenario_mc_5/predictions.csv | ./check_files/predictions_mc2.csv |
"""
print self.test_scenario5.__doc__
examples = [
[
"scenario_mc_1",
'{"data": "data/iris.csv", "max_categories": "1", "objective": "species", "output": "scenario_mc_1/predictions.csv", "test": "data/test_iris.csv"}',
"1",
"species",
"sepal length,sepal width",
"data/test_iris.csv",
"scenario_mc_5/predictions.csv",
"check_files/predictions_mc2.csv",
]
]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
max_cat.i_create_all_mc_resources_from_dataset_with_model_fields(
self,
max_categories=example[2],
objective=example[3],
model_fields=example[4],
test=example[5],
output=example[6],
)
max_cat.i_check_create_max_categories_datasets(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[7])
def test_scenario3(self):
"""
Scenario: Successfully building test predictions from dataset
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML multi-label resources using dataset with objective "<objective>" and model fields "<model_fields>" to test "<test>" and log predictions in "<output>"
And I check that the models have been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs | objective | model_fields | test | output |predictions_file |
| scenario_mlm_1| {"tag": "my_multilabelm_1", "data": "../data/multilabel_multi.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "./scenario_mlm_1/predictions.csv", "test": "../data/test_multilabel.csv", "ml_fields": "type,class", "model_fields": "-type,-type - W,-type - A,-type - C,-type - S,-type - R,-type - T,-type - P", "objective": "class"} | class | -type,-type - W,-type - A,-type - C,-type - S,-type - R,-type - T,-type - P |../data/test_multilabel.csv | ./scenario_mlm_3/predictions.csv | ./check_files/predictions_ml_comma.csv |
"""
print self.test_scenario3.__doc__
examples = [
['scenario_mlm_1', '{"tag": "my_multilabelm_1", "data": "data/multilabel_multi.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "scenario_mlm_1/predictions.csv", "test": "data/test_multilabel.csv", "ml_fields": "type,class", "model_fields": "-type,-type - W,-type - A,-type - C,-type - S,-type - R,-type - T,-type - P", "objective": "class"}', 'class', '-type,-type - W,-type - A,-type - C,-type - S,-type - R,-type - T,-type - P', 'data/test_multilabel.csv', 'scenario_mlm_3/predictions.csv', 'check_files/predictions_ml_comma.csv']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
test_pred.i_create_resources_from_dataset_with_objective(self, multi_label='multi-label ', objective=example[2], model_fields=example[3], test=example[4], output=example[5])
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[6])
def test_scenario3(self):
"""
Scenario: Successfully building test predictions from dataset
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML multi-label resources using dataset to test "<test>" and log predictions in "<output>"
And I check that the models have been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs | test | output |predictions_file |
| scenario_ml_1| {"tag": "my_multilabel_1", "data": "../data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "./scenario_ml_1/predictions.csv", "test": "../data/test_multilabel.csv"} | ../data/test_multilabel.csv | ./scenario_ml_3/predictions.csv | ./check_files/predictions_ml_comma.csv |
"""
print self.test_scenario3.__doc__
examples = [
['scenario_ml_1', '{"tag": "my_multilabel_1", "data": "data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "scenario_ml_1/predictions.csv", "test": "data/test_multilabel.csv"}', 'data/test_multilabel.csv', 'scenario_ml_3/predictions.csv', 'check_files/predictions_ml_comma.csv']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
test_pred.i_create_resources_from_dataset(self, multi_label='multi-label', test=example[2], output=example[3])
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
