def test_scenario1(self):
"""
Scenario: Successfully building a new dataset from an existing one
Given I create a BigML dataset from "<data>" and store logs in "<output_dir>"
And I check that the source has been created
And I check that the dataset has been created
And I create a new BigML dataset using the specs in JSON file "<new_fields>" and a model with "<model_fields>"
And I check that the new dataset has been created
And I check that the model has been created
Then I check that the new dataset has field "<field>"
Examples:
|data |output_dir |new_fields | field | model_fields
|../data/iris.csv | ./scenario_d_1 |../data/new_fields.json| outlier? |petal length,outlier?,species
"""
print self.test_scenario1.__doc__
examples = [[
'data/iris.csv', 'scenario_d_1', 'data/new_fields.json',
u'outlier?', u'petal length,outlier?,species'
]]
for example in examples:
print "\nTesting with:\n", example
dataset_adv.i_create_dataset(self,
data=example[0],
output_dir=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
dataset_adv.i_create_dataset_new_fields(self,
json_file=example[2],
model_fields=example[4])
test_pred.i_check_create_new_dataset(self)
test_pred.i_check_create_model(self)
dataset_adv.i_check_dataset_has_field(self, example[3])
def test_scenario8(self):
"""
Scenario: Successfully building a new dataset from an existing one and analyzing it
Given I create a BigML dataset from "<data>" and store logs in "<output_dir>"
And I check that the source has been created
And I check that the dataset has been created
And I create a new BigML dataset using the specs in JSON file "<new_fields>" and a model with "<model_fields>"
And I check that the new dataset has been created
And I check that the model has been created
And I create BigML nodes analysis from <min_nodes> to <max_nodes> by <nodes_step> with <kfold>-cross-validation improving "<metric>"
And I check that the <kfold>-datasets have been created
And I check that the <kfold>-models have been created
And I check that all the <kfold>-fold cross-validations have been created
Then the best node threshold is "<node_threshold>", with "<metric>" of <metric_value>
Examples:
|data |output_dir |new_fields | field | model_fields| min_nodes | max_nodes | nodes_step | kfold | metric | node_threshold | metric_value |
|../data/iris.csv | ./scenario_a_10 |../data/new_fields.json| outlier? |petal length,outlier?,species| 3 | 14 | 2 |2 | precision | 9 | 94.71% |
"""
print self.test_scenario8.__doc__
examples = [
['data/iris.csv', 'scenario_a_10', 'data/new_fields2.json', u'outlier?', u'outlier?,species', '3', '14', '2', '2', 'precision', '5', '98.21%']]
for example in examples:
print "\nTesting with:\n", example
dataset_adv.i_create_dataset(self, data=example[0], output_dir=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
dataset_adv.i_create_dataset_new_fields(self, json_file=example[2], model_fields=example[4])
test_pred.i_check_create_new_dataset(self)
test_pred.i_check_create_model(self)
test_pred.i_create_nodes_analysis(self, min_nodes=example[5], max_nodes=example[6], nodes_step=example[7], k_fold=example[8], metric=example[9])
test_pred.i_check_create_kfold_datasets(self, example[8])
test_pred.i_check_create_kfold_models(self, example[8])
test_pred.i_check_create_all_kfold_cross_validations(self, example[8])
test_pred.i_check_node_threshold(self, example[10], example[9], example[11])
def test_scenario4(self):
"""
Scenario: Successfully building feature selection from filtered dataset setting objective:
Given I create BigML dataset uploading train "<data>" file in "<output>"
And I check that the source has been created
And I check that the dataset has been created
And I filter out field "<field>" from dataset and log to "<output_dir>"
And I check that the new dataset has been created
And I create BigML feature selection <kfold>-fold cross-validations for "<objective>" improving "<metric>"
And I check that the <kfold>-datasets have been created
And I check that the <kfold>-models have been created
And I check that all the <kfold>-fold cross-validations have been created
Then the best feature selection is "<selection>", with "<metric>" of <metric_value>
Examples:
| data | field | objective |output | output_dir | kfold | metric | selection | metric_value |
| ../data/iris_2fd.csv | sepal length | species |./scenario_a_6/evaluation |./scenario_a_6 | 2 | recall | petal width | 100.00% |
"""
print self.test_scenario4.__doc__
examples = [
['data/iris_2fd.csv', 'sepal length', 'species', 'scenario_a_6/evaluation', 'scenario_a_6', '2', 'recall', 'petal width', '100.00%']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_dataset(self, data=example[0], output=example[3])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
dataset.i_filter_field_from_dataset(self, field=example[1], output_dir=example[4])
test_pred.i_check_create_new_dataset(self)
test_pred.i_create_kfold_cross_validation_objective(self, k_folds=example[5], objective=example[2], metric=example[6])
test_pred.i_check_create_kfold_datasets(self, example[5])
test_pred.i_check_create_kfold_models(self, example[5])
test_pred.i_check_create_all_kfold_cross_validations(self, example[5])
test_pred.i_check_feature_selection(self, example[7], example[6], example[8])
def test_scenario8(self):
"""
Scenario: Successfully building a new dataset from an existing one and analyzing it
Given I create a BigML dataset from "<data>" and store logs in "<output_dir>"
And I check that the source has been created
And I check that the dataset has been created
And I create a new BigML dataset using the specs in JSON file "<new_fields>" and a model with "<model_fields>"
And I check that the new dataset has been created
And I check that the model has been created
And I create BigML nodes analysis from <min_nodes> to <max_nodes> by <nodes_step> with <kfold>-cross-validation improving "<metric>"
And I check that the <kfold>-datasets have been created
And I check that the <kfold>-models have been created
And I check that all the <kfold>-fold cross-validations have been created
Then the best node threshold is "<node_threshold>", with "<metric>" of <metric_value>
Examples:
|data |output_dir |new_fields | field | model_fields| min_nodes | max_nodes | nodes_step | kfold | metric | node_threshold | metric_value |
|../data/iris.csv | ./scenario_a_10 |../data/new_fields.json| outlier? |petal length,outlier?,species| 3 | 14 | 2 |2 | precision | 9 | 94.71% |
"""
print self.test_scenario8.__doc__
examples = [
['data/iris.csv', 'scenario_a_10', 'data/new_fields2.json', u'outlier?', u'outlier?,species', '3', '14', '2', '2', 'precision', '5', '98.21%']]
for example in examples:
print "\nTesting with:\n", example
dataset_adv.i_create_dataset(self, data=example[0], output_dir=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
dataset_adv.i_create_dataset_new_fields(self, json_file=example[2], model_fields=example[4])
test_pred.i_check_create_new_dataset(self)
test_pred.i_check_create_model(self)
test_pred.i_create_nodes_analysis(self, min_nodes=example[5], max_nodes=example[6], nodes_step=example[7], k_fold=example[8], metric=example[9])
test_pred.i_check_create_kfold_datasets(self, example[8])
test_pred.i_check_create_kfold_models(self, example[8])
test_pred.i_check_create_all_kfold_cross_validations(self, example[8])
test_pred.i_check_node_threshold(self, example[10], example[9], example[11])
def test_scenario4(self):
"""
Scenario: Successfully building feature selection from filtered dataset setting objective:
Given I create BigML dataset uploading train "<data>" file in "<output>"
And I check that the source has been created
And I check that the dataset has been created
And I filter out field "<field>" from dataset and log to "<output_dir>"
And I check that the new dataset has been created
And I create BigML feature selection <kfold>-fold cross-validations for "<objective>" improving "<metric>"
And I check that the <kfold>-datasets have been created
And I check that the <kfold>-models have been created
And I check that all the <kfold>-fold cross-validations have been created
Then the best feature selection is "<selection>", with "<metric>" of <metric_value>
Examples:
| data | field | objective |output | output_dir | kfold | metric | selection | metric_value |
| ../data/iris_2fd.csv | sepal length | species |./scenario_a_6/evaluation |./scenario_a_6 | 2 | recall | petal width | 100.00% |
"""
print self.test_scenario4.__doc__
examples = [
['data/iris_2fd.csv', 'sepal length', 'species', 'scenario_a_6/evaluation', 'scenario_a_6', '2', 'recall', 'petal width', '100.00%']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_dataset(self, data=example[0], output=example[3])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
dataset.i_filter_field_from_dataset(self, field=example[1], output_dir=example[4])
test_pred.i_check_create_new_dataset(self)
test_pred.i_create_kfold_cross_validation_objective(self, k_folds=example[5], objective=example[2], metric=example[6])
test_pred.i_check_create_kfold_datasets(self, example[5])
test_pred.i_check_create_kfold_models(self, example[5])
test_pred.i_check_create_all_kfold_cross_validations(self, example[5])
test_pred.i_check_feature_selection(self, example[7], example[6], example[8])
def test_scenario1(self):
"""
Scenario: Successfully building a new dataset from an existing one
Given I create a BigML dataset from "<data>" and store logs in "<output_dir>"
And I check that the source has been created
And I check that the dataset has been created
And I create a new BigML dataset using the specs in JSON file "<new_fields>" and a model with "<model_fields>"
And I check that the new dataset has been created
And I check that the model has been created
Then I check that the new dataset has field "<field>"
Examples:
|data |output_dir |new_fields | field | model_fields
|../data/iris.csv | ./scenario_d_1 |../data/new_fields.json| outlier? |petal length,outlier?,species
"""
print self.test_scenario1.__doc__
examples = [
['data/iris.csv', 'scenario_d_1', 'data/new_fields.json', u'outlier?', u'petal length,outlier?,species']]
for example in examples:
print "\nTesting with:\n", example
dataset_adv.i_create_dataset(self, data=example[0], output_dir=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
dataset_adv.i_create_dataset_new_fields(self, json_file=example[2], model_fields=example[4])
test_pred.i_check_create_new_dataset(self)
test_pred.i_check_create_model(self)
dataset_adv.i_check_dataset_has_field(self, example[3])
