def test_scenario3(self):
"""
Scenario3: Successfully creating an evaluation for a logistic 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 logistic regression
And I wait until the logistic regression is ready less than <time_3> secs
When I create an evaluation for the logistic regression with the dataset
And I wait until the evaluation is ready less than <time_4> secs
Then the measured "<measure>" is <value>
Examples:
| data | time_1 | time_2 | time_3 | time_4 | measure | value |
| ../data/iris.csv | 30 | 30 | 50 | 30 | average_phi | 0.94107 |
"""
print self.test_scenario3.__doc__
examples = [
['data/iris.csv', '50', '50', '800', '80', 'average_phi', '0.89054']]
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])
evaluation_create.i_create_an_evaluation_logistic(self)
evaluation_create.the_evaluation_is_finished_in_less_than(self, example[4])
evaluation_create.the_measured_measure_is_value(self, example[5], example[6])
def test_scenario3(self):
"""
Scenario 3: Successfully creating a local logistic regression from an exported file:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a logistic regression
And I wait until the logistic regression is ready less than <time_3> secs
And I export the logistic regression to "<exported_file>"
When I create a local logistic regression from the file "<exported_file>"
Then the logistic regression ID and the local logistic regression ID match
Examples:
| data | time_1 | time_2 | time_3 | exported_file
| ../data/iris.csv | 10 | 10 | 50 | ./tmp/logistic.json
"""
print self.test_scenario3.__doc__
examples = [
['data/iris.csv', '10', '10', '50', './tmp/logistic.json']]
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])
model_create.i_export_logistic_regression(self, example[4])
model_create.i_create_local_logistic_regression_from_file(self, example[4])
model_create.check_logistic_regression_id_local_id(self)
def test_scenario3(self):
"""
Scenario3: Successfully creating an evaluation for a logistic 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 logistic regression
And I wait until the logistic regression is ready less than <time_3> secs
When I create an evaluation for the logistic regression with the dataset
And I wait until the evaluation is ready less than <time_4> secs
Then the measured "<measure>" is <value>
Examples:
| data | time_1 | time_2 | time_3 | time_4 | measure | value |
| ../data/iris.csv | 30 | 30 | 50 | 30 | average_phi | 0.94107 |
"""
print self.test_scenario3.__doc__
examples = [
['data/iris.csv', '50', '50', '800', '80', 'average_phi', '0.89054']]
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])
evaluation_create.i_create_an_evaluation_logistic(self)
evaluation_create.the_evaluation_is_finished_in_less_than(self, example[4])
evaluation_create.the_measured_measure_is_value(self, example[5], example[6])
def test_scenario3(self):
"""
Scenario 3: Successfully creating a local logistic regression from an exported file:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a logistic regression
And I wait until the logistic regression is ready less than <time_3> secs
And I export the logistic regression to "<exported_file>"
When I create a local logistic regression from the file "<exported_file>"
Then the logistic regression ID and the local logistic regression ID match
Examples:
| data | time_1 | time_2 | time_3 | exported_file
| ../data/iris.csv | 10 | 10 | 50 | ./tmp/logistic.json
"""
print self.test_scenario3.__doc__
examples = [
['data/iris.csv', '10', '10', '50', './tmp/logistic.json']]
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])
model_create.i_export_logistic_regression(self, example[4])
model_create.i_create_local_logistic_regression_from_file(self, example[4])
model_create.check_logistic_regression_id_local_id(self)
def test_scenario6(self):
"""
Scenario: Successfully creating a batch prediction for a logistic 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 logistic regression
And I wait until the logistic regression is ready less than <time_3> secs
When I create a batch prediction for the dataset with the logistic regression
And I wait until the batch prediction is ready less than <time_4> secs
And I download the created predictions file to "<local_file>"
Then the batch prediction file is like "<predictions_file>"
Examples:
| data | time_1 | time_2 | time_3 | time_4 | local_file | predictions_file |
| ../data/iris.csv | 30 | 30 | 80 | 50 | ./tmp/batch_predictions.csv | ./data/batch_predictions_lr.csv |
"""
print self.test_scenario6.__doc__
examples = [
['data/iris.csv', '30', '30', '80', '50', 'tmp/batch_predictions.csv', 'data/batch_predictions_lr.csv']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
model_create.i_create_a_logistic_model(self)
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
batch_pred_create.i_create_a_batch_prediction_logistic_model(self)
batch_pred_create.the_batch_prediction_is_finished_in_less_than(self, example[4])
batch_pred_create.i_download_predictions_file(self, example[5])
batch_pred_create.i_check_predictions(self, example[6])
def test_scenario6(self):
"""
Scenario: Successfully creating a batch prediction for a logistic 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 logistic regression
And I wait until the logistic regression is ready less than <time_3> secs
When I create a batch prediction for the dataset with the logistic regression
And I wait until the batch prediction is ready less than <time_4> secs
And I download the created predictions file to "<local_file>"
Then the batch prediction file is like "<predictions_file>"
Examples:
| data | time_1 | time_2 | time_3 | time_4 | local_file | predictions_file |
| ../data/iris.csv | 30 | 30 | 80 | 50 | ./tmp/batch_predictions.csv | ./data/batch_predictions_lr.csv |
"""
print self.test_scenario6.__doc__
examples = [
['data/iris.csv', '30', '30', '80', '50', 'tmp/batch_predictions.csv', 'data/batch_predictions_lr.csv']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
model_create.i_create_a_logistic_model(self)
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
batch_pred_create.i_create_a_batch_prediction_logistic_model(self)
batch_pred_create.the_batch_prediction_is_finished_in_less_than(self, example[4])
batch_pred_create.i_download_predictions_file(self, example[5])
batch_pred_create.i_check_predictions(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_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_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_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_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_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])
