def test_scenario2(self):
"""
Scenario: Successfully building feature selection from dataset:
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 create BigML feature selection <kfold>-fold cross-validations 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>
And I generate a report from the output directory
And a symlink file is generated in the reports directory
Examples:
| data | output | kfold | metric | selection | metric_value
| ../data/iris_2f.csv | ./scenario_a_2/evaluation | 2 | accuracy | petal width | 100.00%
| ../data/iris_2f.csv | ./scenario_a_3/evaluation | 2 | phi | petal width | 1
"""
print self.test_scenario2.__doc__
examples = [
['data/iris_2f.csv', 'scenario_a_2/evaluation', '2', 'accuracy', 'petal width', '100.00%'],
['data/iris_2f.csv', 'scenario_a_3/evaluation', '2', 'phi', 'petal width', '1']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_dataset(self, data=example[0], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_create_kfold_cross_validation_metric(self, k_folds=example[2], metric=example[3])
test_pred.i_check_create_kfold_datasets(self, example[2])
test_pred.i_check_create_kfold_models(self, example[2])
test_pred.i_check_create_all_kfold_cross_validations(self, example[2])
test_pred.i_check_feature_selection(self, example[4], example[3], example[5])
test_pred.i_generate_report(self)
test_pred.is_symlink(self)
def test_scenario9(self):
"""
Scenario: Successfully building random fields analysis from dataset:
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 create BigML random fields analysis with <kfold>-cross-validation improving "<metric>"
And I check that the <kfold>-datasets have been created
And I check that the <kfold>-random trees have been created
And I check that all the <kfold>-fold cross-validations have been created
Then the best random candidates number is "<random_candidates>", with "<metric>" of <metric_value>
Examples:
| data | output | kfold | metric | random_candidates | metric_value |
| ../data/iris.csv | ./scenario_a_11/evaluation |2 | precision | 4 | 96.09% |
"""
print self.test_scenario9.__doc__
examples = [
['data/iris.csv', 'scenario_a_11/evaluation', '2', 'precision', '4', '96.09%']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_dataset(self, data=example[0], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_create_random_analysis(self, k_fold=example[2], metric=example[3])
test_pred.i_check_create_kfold_datasets(self, example[2])
test_pred.i_check_create_kfold_random_forest(self, example[2])
test_pred.i_check_create_all_kfold_cross_validations(self, example[2])
test_pred.i_check_random_candidates(self, example[4], example[3], example[5])
def test_scenario1(self):
"""
Scenario: Successfully building k-fold cross-validation from dataset:
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 create BigML <kfold>-fold cross-validation
And I check that the <kfold>-datasets have been created
And I check that the <kfold>-models have been created
And I check that the <kfold>-fold cross-validation has been created
Then the evaluation file is like "<json_evaluation_file>"
Examples:
| data | output | kfold | json_evaluation_file |
| ../data/iris.csv | ./scenario_a_1/evaluation | 2 | ./check_files/evaluation_kfold.json |
"""
print self.test_scenario1.__doc__
examples = [
['data/iris.csv', 'scenario_a_1/evaluation', '2', 'check_files/evaluation_kfold.json']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_dataset(self, data=example[0], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_create_kfold_cross_validation(self, k_folds=example[2])
test_pred.i_check_create_kfold_datasets(self, example[2])
test_pred.i_check_create_kfold_models(self, example[2])
test_pred.i_check_create_kfold_cross_validation(self, example[2])
evaluation.then_the_evaluation_file_is_like(self, example[3])
def test_scenario5(self):
"""
Scenario: Successfully building nodes threshold analysis from dataset file:
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 create BigML nodes analysis from dataset file 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 | min_nodes | max_nodes | nodes_step | kfold | metric | node_threshold | metric_value
| ../data/iris.csv | ./scenario_a_4/evaluation | 3 | 14 | 2 |2 | precision | 9 | 94.71%
"""
print self.test_scenario5.__doc__
examples = [
['data/iris.csv', 'scenario_a_4/evaluation', '3', '14', '2', '2', 'precision', '9', '94.71%']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_dataset(self, data=example[0], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_create_nodes_analysis_from_dataset_file(
self, min_nodes=example[2], max_nodes=example[3],
nodes_step=example[4], k_fold=example[5], 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_node_threshold(self, example[7], example[6], example[8])
def test_scenario6(self):
"""
Scenario: Successfully building feature selection from dataset excluding features:
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 create BigML feature selection <kfold>-fold cross-validations excluding "<features>" with separator "<args_separator>" 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 | output | kfold | features | args_separator | metric | selection | metric_value |
| ../data/iris.csv | ./scenario_a_7/evaluation | 2 | petal length!sepal width | ! | accuracy | petal width | 95.33% |
"""
print self.test_scenario6.__doc__
examples = [
['data/iris.csv', 'scenario_a_7/evaluation', '2', 'petal length!sepal width', '!', 'accuracy', 'petal width', '95.33%']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_dataset(self, data=example[0], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_create_kfold_cross_validation_separator_metric_no_fields(self, k_folds=example[2], features=example[3], args_separator=example[4], metric=example[5])
test_pred.i_check_create_kfold_datasets(self, example[2])
test_pred.i_check_create_kfold_models(self, example[2])
test_pred.i_check_create_all_kfold_cross_validations(self, example[2])
test_pred.i_check_feature_selection(self, example[6], example[5], example[7])
def test_scenario3(self):
"""
Scenario: Successfully building feature selection from 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 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 | objective |output | kfold | metric | selection | metric_value |
| ../data/iris_2f.csv | 0 |./scenario_a_5/evaluation | 2 | r_squared| species | 0.352845 |
| ../data/iris_2f.csv | 0 |./scenario_a_8/evaluation | 2 | mean_squared_error| species | 0.475200 |
"""
print self.test_scenario3.__doc__
examples = [
['data/iris_2f.csv', '0', 'scenario_a_5/evaluation', '2', 'r_squared', 'species', '0.352845'],
['data/iris_2f.csv', '0', 'scenario_a_8/evaluation', '2', 'mean_squared_error', 'species', '0.475200']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_dataset(self, data=example[0], output=example[2])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_create_kfold_cross_validation_objective(self, k_folds=example[3], objective=example[1], metric=example[4])
test_pred.i_check_create_kfold_datasets(self, example[3])
test_pred.i_check_create_kfold_models(self, example[3])
test_pred.i_check_create_all_kfold_cross_validations(self, example[3])
test_pred.i_check_feature_selection(self, example[5], example[4], example[6])
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_scenario2(self):
"""
Scenario: Successfully building feature selection from dataset:
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 create BigML feature selection <kfold>-fold cross-validations 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>
And I generate a report from the output directory
And a symlink file is generated in the reports directory
Examples:
| data | output | kfold | metric | selection | metric_value
| ../data/iris_2f.csv | ./scenario_a_2/evaluation | 2 | accuracy | petal width | 100.00%
| ../data/iris_2f.csv | ./scenario_a_3/evaluation | 2 | phi | petal width | 1
"""
print self.test_scenario2.__doc__
examples = [
['data/iris_2f.csv', 'scenario_a_2/evaluation', '2', 'accuracy', 'petal width', '100.00%'],
['data/iris_2f.csv', 'scenario_a_3/evaluation', '2', 'phi', 'petal width', '1']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_dataset(self, data=example[0], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_create_kfold_cross_validation_metric(self, k_folds=example[2], metric=example[3])
test_pred.i_check_create_kfold_datasets(self, example[2])
test_pred.i_check_create_kfold_models(self, example[2])
test_pred.i_check_create_all_kfold_cross_validations(self, example[2])
test_pred.i_check_feature_selection(self, example[4], example[3], example[5])
test_pred.i_generate_report(self)
test_pred.is_symlink(self)
def test_scenario11(self):
"""
Scenario: Successfully building feature selection from 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 create BigML feature selection <kfold>-fold cross-validation with options "<options>"
And I check that the <kfold>-datasets have been created
And I check that the <kfold>-ensembles have been created
And I check that all the <kfold>-fold cross-validations have been created
Then the predictions file "<predictions_file>" is like "<estimated_file>"
Examples:
| data |output | kfold | options | predictions_file | estimated_file
| ../data/iris.csv |./scenario_a_14/evaluation | 2 | --exclude-features="species,petal length" --predictions.csv --number-of-models 2| scenario_a_14/kfold2_pred/predictions.csv | check_files/analyze_predictions_iris_e.csv
"""
print self.test_scenario11.__doc__
examples = [
['data/iris.csv', 'scenario_a_14/evaluation', '2', ' --exclude-features="petal length,sepal length" --predictions-csv --number-of-models 2','scenario_a_14/test/kfold2_pred/predictions.csv', 'check_files/analyze_predictions_iris_e.csv']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_dataset(self, data=example[0], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_create_kfold_cross_validation_options(self, k_folds=example[2], options=example[3])
test_pred.i_check_create_kfold_datasets(self, example[2])
test_pred.i_check_create_kfold_ensembles(self, example[2])
test_pred.i_check_create_all_kfold_cross_validations(self, example[2])
test_pred.i_check_predictions_file(self, example[4], example[5])
def test_scenario11(self):
"""
Scenario: Successfully building feature selection from 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 create BigML feature selection <kfold>-fold cross-validation with options "<options>"
And I check that the <kfold>-datasets have been created
And I check that the <kfold>-ensembles have been created
And I check that all the <kfold>-fold cross-validations have been created
Then the predictions file "<predictions_file>" is like "<estimated_file>"
Examples:
| data |output | kfold | options | predictions_file | estimated_file
| ../data/iris.csv |./scenario_a_14/evaluation | 2 | --exclude-features="species,petal length" --predictions.csv --number-of-models 2| scenario_a_14/kfold2_pred/predictions.csv | check_files/analyze_predictions_iris_e.csv
"""
print self.test_scenario11.__doc__
examples = [
['data/iris.csv', 'scenario_a_14/evaluation', '2', ' --exclude-features="petal length,sepal length" --predictions-csv --number-of-models 2','scenario_a_14/test/kfold2_pred/predictions.csv', 'check_files/analyze_predictions_iris_e.csv']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_dataset(self, data=example[0], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_create_kfold_cross_validation_options(self, k_folds=example[2], options=example[3])
test_pred.i_check_create_kfold_datasets(self, example[2])
test_pred.i_check_create_kfold_ensembles(self, example[2])
test_pred.i_check_create_all_kfold_cross_validations(self, example[2])
test_pred.i_check_predictions_file(self, example[4], example[5])
def test_scenario1(self):
"""
Scenario: Successfully building k-fold cross-validation from dataset:
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 create BigML <kfold>-fold cross-validation
And I check that the <kfold>-datasets have been created
And I check that the <kfold>-models have been created
And I check that the <kfold>-fold cross-validation has been created
Then the evaluation file is like "<json_evaluation_file>"
Examples:
| data | output | kfold | json_evaluation_file |
| ../data/iris.csv | ./scenario_a_1/evaluation | 2 | ./check_files/evaluation_kfold.json |
"""
print self.test_scenario1.__doc__
examples = [
['data/iris.csv', 'scenario_a_1/evaluation', '2', 'check_files/evaluation_kfold.json']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_dataset(self, data=example[0], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_create_kfold_cross_validation(self, k_folds=example[2])
test_pred.i_check_create_kfold_datasets(self, example[2])
test_pred.i_check_create_kfold_models(self, example[2])
test_pred.i_check_create_kfold_cross_validation(self, example[2])
evaluation.then_the_evaluation_file_is_like(self, example[3])
def test_scenario9(self):
"""
Scenario: Successfully building random fields analysis from dataset:
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 create BigML random fields analysis with <kfold>-cross-validation improving "<metric>"
And I check that the <kfold>-datasets have been created
And I check that the <kfold>-random trees have been created
And I check that all the <kfold>-fold cross-validations have been created
Then the best random candidates number is "<random_candidates>", with "<metric>" of <metric_value>
Examples:
| data | output | kfold | metric | random_candidates | metric_value |
| ../data/iris.csv | ./scenario_a_11/evaluation |2 | precision | 4 | 96.09% |
"""
print self.test_scenario9.__doc__
examples = [
['data/iris.csv', 'scenario_a_11/evaluation', '2', 'precision', '4', '96.09%']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_dataset(self, data=example[0], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_create_random_analysis(self, k_fold=example[2], metric=example[3])
test_pred.i_check_create_kfold_datasets(self, example[2])
test_pred.i_check_create_kfold_random_forest(self, example[2])
test_pred.i_check_create_all_kfold_cross_validations(self, example[2])
test_pred.i_check_random_candidates(self, example[4], example[3], example[5])
def test_scenario6(self):
"""
Scenario: Successfully building feature selection from dataset excluding features:
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 create BigML feature selection <kfold>-fold cross-validations excluding "<features>" with separator "<args_separator>" 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 | output | kfold | features | args_separator | metric | selection | metric_value |
| ../data/iris.csv | ./scenario_a_7/evaluation | 2 | petal length!sepal width | ! | accuracy | petal width | 95.33% |
"""
print self.test_scenario6.__doc__
examples = [
['data/iris.csv', 'scenario_a_7/evaluation', '2', 'petal length!sepal width', '!', 'accuracy', 'petal width', '95.33%']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_dataset(self, data=example[0], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_create_kfold_cross_validation_separator_metric_no_fields(self, k_folds=example[2], features=example[3], args_separator=example[4], metric=example[5])
test_pred.i_check_create_kfold_datasets(self, example[2])
test_pred.i_check_create_kfold_models(self, example[2])
test_pred.i_check_create_all_kfold_cross_validations(self, example[2])
test_pred.i_check_feature_selection(self, example[6], example[5], example[7])
def test_scenario5(self):
"""
Scenario: Successfully building nodes threshold analysis from dataset file:
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 create BigML nodes analysis from dataset file 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 | min_nodes | max_nodes | nodes_step | kfold | metric | node_threshold | metric_value
| ../data/iris.csv | ./scenario_a_4/evaluation | 3 | 14 | 2 |2 | precision | 9 | 94.71%
"""
print self.test_scenario5.__doc__
examples = [
['data/iris.csv', 'scenario_a_4/evaluation', '3', '14', '2', '2', 'precision', '9', '94.71%']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_dataset(self, data=example[0], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_create_nodes_analysis_from_dataset_file(
self, min_nodes=example[2], max_nodes=example[3],
nodes_step=example[4], k_fold=example[5], 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_node_threshold(self, example[7], example[6], example[8])
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_scenario3(self):
"""
Scenario: Successfully building feature selection from 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 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 | objective |output | kfold | metric | selection | metric_value |
| ../data/iris_2f.csv | 0 |./scenario_a_5/evaluation | 2 | r_squared| species | 0.352845 |
| ../data/iris_2f.csv | 0 |./scenario_a_8/evaluation | 2 | mean_squared_error| species | 0.475200 |
"""
print self.test_scenario3.__doc__
examples = [
['data/iris_2f.csv', '0', 'scenario_a_5/evaluation', '2', 'r_squared', 'species', '0.352845'],
['data/iris_2f.csv', '0', 'scenario_a_8/evaluation', '2', 'mean_squared_error', 'species', '0.475200']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_dataset(self, data=example[0], output=example[2])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_create_kfold_cross_validation_objective(self, k_folds=example[3], objective=example[1], metric=example[4])
test_pred.i_check_create_kfold_datasets(self, example[3])
test_pred.i_check_create_kfold_models(self, example[3])
test_pred.i_check_create_all_kfold_cross_validations(self, example[3])
test_pred.i_check_feature_selection(self, example[5], example[4], example[6])
