def test_scenario2(self):
"""
Scenario: Successfully obtaining parsing error counts:
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 "<params>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
When I ask for the error counts in the fields
Then the error counts dict is "<error_values>"
Examples:
| data | time_1 | params | time_2 |error_values |
| ../data/iris_missing.csv | 30 | {"fields": {"000000": {"optype": "numeric"}}} |30 |{"000000": 1} |
"""
print self.test_scenario2.__doc__
examples = [
['data/iris_missing.csv', '30', '{"fields": {"000000": {"optype": "numeric"}}}', '30', '{"000000": 1}']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
source_create.i_update_source_with(self, example[2])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self,
example[3])
dataset_read.i_get_the_errors_values(self)
dataset_read.i_get_the_properties_values(
self, 'error counts', example[4])
def test_scenario5(self):
"""
Scenario: Successfully comparing association sets:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a model
And I wait until the association is ready less than <time_3> secs
And I create a local association
When I create an association set for "<data_input>"
Then the association set is like the contents of "<association_set_file>"
And I create a local association set for "<data_input>"
Then the local association set is like the contents of "<association_set_file>"
"""
examples = [
['data/groceries.csv', '20', '20', '30', '{"fields": {"00000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}', 'data/associations/association_set.json', '{"field1": "cat food"}']]
show_doc(self.test_scenario5, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
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])
association_create.i_create_an_association_from_dataset(self)
association_create.the_association_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_association(self)
prediction_create.i_create_an_association_set(self, example[6])
prediction_compare.the_association_set_is_like_file(self, example[5])
prediction_compare.i_create_a_local_association_set(self, example[6])
prediction_compare.the_local_association_set_is_like_file(self, example[5])
def test_scenario5(self):
"""
Scenario: Successfully comparing association sets:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a model
And I wait until the association is ready less than <time_3> secs
And I create a local association
When I create an association set for "<data_input>"
Then the association set is like the contents of "<association_set_file>"
And I create a local association set for "<data_input>"
Then the local association set is like the contents of "<association_set_file>"
"""
examples = [
['data/groceries.csv', '20', '20', '30', '{"fields": {"00000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}', 'data/associations/association_set.json', '{"field1": "cat food"}']]
show_doc(self.test_scenario5, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
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])
association_create.i_create_an_association_from_dataset(self)
association_create.the_association_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_association(self)
prediction_create.i_create_an_association_set(self, example[6])
prediction_compare.the_association_set_is_like_file(self, example[5])
prediction_compare.i_create_a_local_association_set(self, example[6])
prediction_compare.the_local_association_set_is_like_file(self, example[5])
def test_scenario7(self):
"""
Scenario: Successfully comparing predictions with text options:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a logistic regression model with objective "<objective>"
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And the logistic regression probability for the prediction is "<probability>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
And the local logistic regression probability for the prediction is "<probability>"
Examples:
| data | time_1 | time_2 | objective | time_3 | options | data_input | prediction | probability
"""
examples = [
[
'data/spam.csv', '20', '20', '180',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}',
'{"Message": "A normal message"}', 'ham', 0.9169, "000000"
],
[
'data/spam.csv', '20', '20', '180',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}',
'{"Message": "mobile"}', 'ham', 0.815, "000000"
],
[
'data/movies.csv', '20', '20', '180',
'{"fields": {"000007": {"optype": "items", "item_analysis": {"separator": "$"}}}}',
'{"gender": "Female", "genres": "Adventure$Action", "timestamp": 993906291, "occupation": "K-12 student", "zipcode": 59583, "rating": 3}',
'Under 18', '0.8393', '000002'
]
]
show_doc(self.test_scenario7, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
source_create.i_update_source_with(self, example[4])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(
self, example[2])
model_create.i_create_a_logistic_model_with_objective_and_parms(
self, example[8])
model_create.the_logistic_model_is_finished_in_less_than(
self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[5])
prediction_create.the_logistic_prediction_is(self, example[6])
prediction_create.the_logistic_probability_is(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_compare.the_local_probability_is(self, example[7])
def test_scenario8(self):
"""
Scenario 8: Successfully creating a local topic model 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 topic model
And I wait until the topic model is ready less than <time_3> secs
And I export the topic model to "<exported_file>"
When I create a local topic model from the file "<exported_file>"
Then the topic model ID and the local topic model ID match
Examples:
| data | time_1 | time_2 | time_3 | exported_file
| ../data/iris.csv | 10 | 10 | 50 | ./tmp/topic_model.json
"""
print self.test_scenario8.__doc__
examples = [
['data/spam.csv', '10', '10', '500', './tmp/topic_model.json', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}']]
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[5])
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])
topic_create.i_create_a_topic_model(self)
topic_create.the_topic_model_is_finished_in_less_than(self, example[3])
topic_create.i_export_topic_model(self, example[4])
topic_create.i_create_local_topic_model_from_file(self, example[4])
topic_create.check_topic_model_id_local_id(self)
def test_scenario7(self):
"""
Scenario: Successfully creating a Topic 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 update the source with params "<params>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
When I create a Topic Model from a dataset
Then I wait until the Topic Model is ready less than <time_3> secs
Examples:
| data | time_1 | time_2 | time_3 | params
| ../data/movies.csv | 10 | 10 | 100 | {"fields": {"genre": {"optype": "items", "item_analysis": {"separator": "$"}}, "title": {"optype": "text"}}}
"""
print self.test_scenario7.__doc__
examples = [
['data/movies.csv', '10', '10', '100', '{"fields": {"000007": {"optype": "items", "item_analysis": {"separator": "$"}}, "000006": {"optype": "text"}}}']]
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, data=example[4])
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])
topic_create.i_create_a_topic_model(self)
topic_create.the_topic_model_is_finished_in_less_than(self, example[3])
def test_scenario2(self):
"""
Scenario 2: Successfully creating Topic Model from a dataset:
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 topic model from a dataset
And I wait until the topic model is ready less than <time_3> secs
And I update the topic model name to "<topic_model_name>"
When I wait until the topic_model is ready less than <time_4> secs
Then the topic model name is "<topic_model_name>"
Examples:
| data | time_1 | time_2 | time_3 | time_4 | topic_model_name | params
| ../data/spam.csv | 100 | 100 | 200 | 500 | my new topic model name | '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}'
"""
print self.test_scenario2.__doc__
examples = [
['data/spam.csv', '100', '100', '10000', '500', 'my new topic model name', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}']]
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[6])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
topic_create.i_create_a_topic_model(self)
topic_create.the_topic_model_is_finished_in_less_than(self, example[3])
topic_create.i_update_topic_model_name(self, example[5])
topic_create.the_topic_model_is_finished_in_less_than(self, example[4])
topic_create.i_check_topic_model_name(self, example[5])
def test_scenario2(self):
"""
Scenario: Successfully obtaining parsing error counts:
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 "<params>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
When I ask for the error counts in the fields
Then the error counts dict is "<error_values>"
Examples:
| data | time_1 | params | time_2 |error_values |
| ../data/iris_missing.csv | 30 | {"fields": {"000000": {"optype": "numeric"}}} |30 |{"000000": 1} |
"""
print self.test_scenario2.__doc__
examples = [[
'data/iris_missing.csv', '30',
'{"fields": {"000000": {"optype": "numeric"}}}', '30',
'{"000000": 1}'
]]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
source_create.i_update_source_with(self, example[2])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(
self, example[3])
dataset_read.i_get_the_errors_values(self)
dataset_read.i_get_the_properties_values(self, 'error counts',
example[4])
def test_scenario8(self):
"""
Scenario 8: Successfully creating a local topic model 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 topic model
And I wait until the topic model is ready less than <time_3> secs
And I export the topic model to "<exported_file>"
When I create a local topic model from the file "<exported_file>"
Then the topic model ID and the local topic model ID match
Examples:
| data | time_1 | time_2 | time_3 | exported_file
| ../data/iris.csv | 10 | 10 | 50 | ./tmp/topic_model.json
"""
print self.test_scenario8.__doc__
examples = [
['data/spam.csv', '10', '10', '500', './tmp/topic_model.json', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}']]
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[5])
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])
topic_create.i_create_a_topic_model(self)
topic_create.the_topic_model_is_finished_in_less_than(self, example[3])
topic_create.i_export_topic_model(self, example[4])
topic_create.i_create_local_topic_model_from_file(self, example[4])
topic_create.check_topic_model_id_local_id(self)
def test_scenario2(self):
"""
Scenario 2: Successfully creating Topic Model from a dataset:
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 topic model from a dataset
And I wait until the topic model is ready less than <time_3> secs
And I update the topic model name to "<topic_model_name>"
When I wait until the topic_model is ready less than <time_4> secs
Then the topic model name is "<topic_model_name>"
Examples:
| data | time_1 | time_2 | time_3 | time_4 | topic_model_name | params
| ../data/spam.csv | 100 | 100 | 200 | 500 | my new topic model name | '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}'
"""
print self.test_scenario2.__doc__
examples = [
['data/spam.csv', '100', '100', '10000', '500', 'my new topic model name', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}']]
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[6])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
topic_create.i_create_a_topic_model(self)
topic_create.the_topic_model_is_finished_in_less_than(self, example[3])
topic_create.i_update_topic_model_name(self, example[5])
topic_create.the_topic_model_is_finished_in_less_than(self, example[4])
topic_create.i_check_topic_model_name(self, example[5])
def test_scenario7(self):
"""
Scenario: Successfully creating a Topic 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 update the source with params "<params>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
When I create a Topic Model from a dataset
Then I wait until the Topic Model is ready less than <time_3> secs
Examples:
| data | time_1 | time_2 | time_3 | params
| ../data/movies.csv | 10 | 10 | 100 | {"fields": {"genre": {"optype": "items", "item_analysis": {"separator": "$"}}, "title": {"optype": "text"}}}
"""
print self.test_scenario7.__doc__
examples = [[
'data/movies.csv', '10', '10', '100',
'{"fields": {"000007": {"optype": "items", "item_analysis": {"separator": "$"}}, "000006": {"optype": "text"}}}'
]]
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, data=example[4])
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])
topic_create.i_create_a_topic_model(self)
topic_create.the_topic_model_is_finished_in_less_than(
self, example[3])
def test_scenario4(self):
"""
Scenario: Successfully comparing centroids with or without 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 cluster
And I wait until the cluster is ready less than <time_3> secs
And I create a local cluster
When I create a centroid for "<data_input>"
Then the centroid is "<centroid>" with distance "<distance>"
And I create a local centroid for "<data_input>"
Then the local centroid is "<centroid>" with distance "<distance>"
Examples:
| data | time_1 | time_2 | time_3 | options | data_input | centroid | distance |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} |{"Type": "ham", "Message": "Mobile call"} | Cluster 7 | 0.341886116992 |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false}}}} |{"Type": "ham", "Message": "A normal message"} | Cluster 0 | 0.5 |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} |{"Type": "ham", "Message": "Mobile calls"} | Cluster 0 | 0.5 |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} |{"Type": "ham", "Message": "A normal message"} | Cluster 0 | 0.5 |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} |{"Type": "ham", "Message": "Mobile call"} | Cluster 4 | 0.382148869802 |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} |{"Type": "ham", "Message": "A normal message"} | Cluster 4 | 0.382148869802 |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Type": "ham", "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+"} | Cluster 1 | 0.5 |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Type": "ham", "Message": "Ok"} | Cluster 1 | 0.478833312167 |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} |{"Type": "", "Message": ""} | Cluster 0 | 0.707106781187 |
| ../data/diabetes.csv | 20 | 20 | 30 | {"fields": {}} |{"pregnancies": 0, "plasma glucose": 118, "blood pressure": 84, "triceps skin thickness": 47, "insulin": 230, "bmi": 45.8, "diabetes pedigree": 0.551, "age": 31, "diabetes": "true"} | Cluster 6 | 0.486471379368 |
| ../data/iris_sp_chars.csv | 20 | 20 | 30 | {"fields": {}} |{"pétal.length":1, "pétal&width\u0000": 2, "sépal.length":1, "sépal&width": 2, "spécies": "Iris-setosa"} | Cluster 7 | 0.757736964835 |
"""
print self.test_scenario4.__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"}}}}', '{"Type": "ham", "Message": "Mobile call"}', 'Cluster 0', '0.5'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false}}}}', '{"Type": "ham", "Message": "A normal message"}', 'Cluster 5', '0.375'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Type": "ham", "Message": "Mobile calls"}', 'Cluster 0', '0.5'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Type": "ham", "Message": "A normal message"}', 'Cluster 0', '0.5'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Type": "ham", "Message": "Mobile call"}', 'Cluster 1', '0.375'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Type": "ham", "Message": "A normal message"}', 'Cluster 1', '0.375'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Type": "ham", "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+"}', 'Cluster 0', '0.5'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Type": "ham", "Message": "Ok"}', 'Cluster 0', '0.478833312167'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Type": "", "Message": ""}', 'Cluster 0', '0.707106781187'],
['data/diabetes.csv', '20', '20', '30', '{"fields": {}}', '{"pregnancies": 0, "plasma glucose": 118, "blood pressure": 84, "triceps skin thickness": 47, "insulin": 230, "bmi": 45.8, "diabetes pedigree": 0.551, "age": 31, "diabetes": "true"}', 'Cluster 5', '0.4006712471727391'],
['data/iris_sp_chars.csv', '20', '20', '30', '{"fields": {}}', '{"pétal.length":1, "pétal&width\u0000": 2, "sépal.length":1, "sépal&width": 2, "spécies": "Iris-setosa"}', 'Cluster 0', '0.811744494026442']]
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])
cluster_create.i_create_a_cluster(self)
cluster_create.the_cluster_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_cluster(self)
prediction_create.i_create_a_centroid(self, example[5])
prediction_create.the_centroid_is_with_distance(self, example[6], example[7])
prediction_compare.i_create_a_local_centroid(self, example[5])
prediction_compare.the_local_centroid_is(self, example[6], example[7])
def test_scenario2(self):
"""
Scenario: Successfully comparing predictions with text options:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a model
And I wait until the model is ready less than <time_3> secs
And I create a local model
When I create a prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | options | data_input | objective | prediction |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} |{"Message": "Mobile call"} | 000000 | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} |{"Message": "A normal message"} | 000000 | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} |{"Message": "Mobile calls"} | 000000 | spam |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} |{"Message": "A normal message"} | 000000 | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} |{"Message": "Mobile call"} | 000000 | spam |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} |{"Message": "A normal message"} | 000000 | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Message": "FREE for 1st week! No1 Nokia tone 4 ur mob every week just txt NOKIA to 87077 Get txting and tell ur mates. zed POBox 36504 W45WQ norm150p/tone 16+"} | 000000 | spam |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Message": "Ok"} | 000000 | ham |
| ../data/movies.csv | 20 | 20 | 30 | {"fields": {"000007": {"optype": "items", "item_analysis": {"separator": "$"}}}} |{"genres": "Adventure$Action", "timestamp": 993906291, "occupation": "K-12 student"}'| 000009| 3.93064
| ../data/text_missing.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, {"000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}} |{} | 000003 | paperwork |
"""
print self.test_scenario2.__doc__
examples = [
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Message": "Mobile call"}', '000000', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Message": "A normal message"}', '000000', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Message": "Mobile calls"}', '000000', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Message": "A normal message"}', '000000', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Message": "Mobile call"}', '000000', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Message": "A normal message"}', '000000', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Message": "FREE for 1st week! No1 Nokia tone 4 ur mob every week just txt NOKIA to 87077 Get txting and tell ur mates. zed POBox 36504 W45WQ norm150p/tone 16+"}', '000000', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Message": "Ok"}', '000000', 'ham'],
['data/movies.csv', '20', '20', '30', '{"fields": {"000007": {"optype": "items", "item_analysis": {"separator": "$"}}}}', '{"genres": "Adventure$Action", "timestamp": 993906291, "occupation": "K-12 student"}', '000009', '3.93064'],
['data/text_missing.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, "000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}', '{}', "000003", 'swap']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
source_create.i_update_source_with(self, example[4])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
model_create.i_create_a_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_prediction(self, example[5])
prediction_create.the_prediction_is(self, example[6], example[7])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[7])
def test_scenario2(self):
"""
Scenario: Successfully comparing predictions with text options:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a model
And I wait until the model is ready less than <time_3> secs
And I create a local model
When I create a prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | options | data_input | objective | prediction |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} |{"Message": "Mobile call"} | 000000 | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} |{"Message": "A normal message"} | 000000 | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} |{"Message": "Mobile calls"} | 000000 | spam |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} |{"Message": "A normal message"} | 000000 | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} |{"Message": "Mobile call"} | 000000 | spam |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} |{"Message": "A normal message"} | 000000 | ham |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Message": "FREE for 1st week! No1 Nokia tone 4 ur mob every week just txt NOKIA to 87077 Get txting and tell ur mates. zed POBox 36504 W45WQ norm150p/tone 16+"} | 000000 | spam |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Message": "Ok"} | 000000 | ham |
| ../data/movies.csv | 20 | 20 | 30 | {"fields": {"000007": {"optype": "items", "item_analysis": {"separator": "$"}}}} |{"genres": "Adventure$Action", "timestamp": 993906291, "occupation": "K-12 student"}'| 000009| 3.93064
| ../data/text_missing.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, {"000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}} |{} | 000003 | paperwork |
"""
print self.test_scenario2.__doc__
examples = [
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Message": "Mobile call"}', '000000', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Message": "A normal message"}', '000000', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Message": "Mobile calls"}', '000000', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Message": "A normal message"}', '000000', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Message": "Mobile call"}', '000000', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Message": "A normal message"}', '000000', 'ham'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Message": "FREE for 1st week! No1 Nokia tone 4 ur mob every week just txt NOKIA to 87077 Get txting and tell ur mates. zed POBox 36504 W45WQ norm150p/tone 16+"}', '000000', 'spam'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Message": "Ok"}', '000000', 'ham'],
['data/movies.csv', '20', '20', '30', '{"fields": {"000007": {"optype": "items", "item_analysis": {"separator": "$"}}}}', '{"genres": "Adventure$Action", "timestamp": 993906291, "occupation": "K-12 student"}', '000009', '3.93064'],
['data/text_missing.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, "000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}', '{}', "000003", 'swap']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
source_create.i_update_source_with(self, example[4])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
model_create.i_create_a_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_prediction(self, example[5])
prediction_create.the_prediction_is(self, example[6], example[7])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[7])
def test_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_scenario4(self):
"""
Scenario: Successfully comparing topic distributions:
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 topic model
And I wait until the topic model is ready less than <time_3> secs
And I create a local topic model
When I create a topic distribution for "<data_input>"
Then the topic distribution is "<topic_distribution>"
And I create a local topic distribution for "<data_input>"
Then the local topic distribution is "<topic_distribution>"
Examples headers:
| data | time_1 | time_2 | time_3 | options | data_input | topic distribution |
"""
examples = [
[
'data/spam.csv', '20', '20', '30',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}',
'{"Type": "ham", "Message": "Mobile call"}',
'[0.01878, 0.00388, 0.00388, 0.00388, 0.20313, 0.47315, 0.00574, 0.05695, 0.00388, 0.19382, 0.00388, 0.02902]'
],
[
'data/spam.csv', '20', '20', '30',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}',
'{"Type": "ham", "Message": "Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat..."}',
'[0.00263, 0.01083, 0.00831, 0.06004, 0.33701, 0.00263, 0.01209, 0.44553, 0.0531, 0.00326, 0.06193, 0.00263]'
]
]
show_doc(self.test_scenario4, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
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])
topic_create.i_create_a_topic_model(self)
topic_create.the_topic_model_is_finished_in_less_than(
self, example[3])
prediction_compare.i_create_a_local_topic_model(self)
topic_create.i_create_a_local_topic_distribution(self, example[5])
prediction_compare.the_local_topic_distribution_is(
self, example[6])
topic_create.i_create_a_topic_distribution(self, example[5])
prediction_compare.the_topic_distribution_is(self, example[6])
def test_scenario3(self):
"""
Scenario: Successfully comparing predictions:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a model
And I wait until the model is ready less than <time_3> secs
And I create a local model
When I create a prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction |
"""
examples = [
[
'data/iris_missing.csv', '30',
'{"fields": {"000000": {"optype": "numeric"}}, "source_parser": {"missing_tokens": ["foo"]}}',
'30', '{"sepal length": "foo", "petal length": 3}', '000004',
'Iris-versicolor'
],
[
'data/iris_missing.csv', '30',
'{"fields": {"000000": {"optype": "numeric"}}, "source_parser": {"missing_tokens": ["foo"]}}',
'30',
'{"sepal length": "foo", "petal length": 5, "petal width": 1.5}',
'000004', 'Iris-virginica'
]
]
show_doc(self.test_scenario3, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
source_create.i_update_source_with(self, example[2])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(
self, example[3])
model_create.i_create_a_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario6(self):
"""
Scenario: Successfully comparing projections for PCAs:
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 PCA with "<params>"
And I wait until the PCA is ready less than <time_3> secs
And I create a local PCA
When I create a projection for "<input_data>"
Then the projection is "<projection>"
And I create a local projection for "<data_input>"
Then the local projection is "<projection>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | projection | params
"""
examples = [
[
'data/spam_tiny.csv', '30', '30', '30',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all"}}}}',
'{"Message": "early"}', '{}',
'{"PC40": 0.00416, "PC38": 0.08267, "PC39": 0.00033, "PC18": 0.28094, "PC19": -0.15056, "PC14": 0.20643, "PC15": 0.23931, "PC16": 0.03251, "PC17": 0.02776, "PC10": 0.1424, "PC11": 0.4059, "PC12": -0.1238, "PC13": 0.15131, "PC43": 0.29617, "PC42": 1.0091, "PC41": 0, "PC25": 0.07164, "PC24": -0.29904, "PC27": -0.1331, "PC26": -0.18572, "PC21": 0.25616, "PC20": 0.30424, "PC23": -0.45775, "PC22": -0.3362, "PC47": -0.13757, "PC49": 0.01864, "PC48": 0.04742, "PC29": -0.16286, "PC28": 0.42207, "PC32": -0.05917, "PC46": -0.05018, "PC31": -0.13973, "PC45": -0.05015, "PC36": 0.03017, "PC44": 0, "PC37": -0.06093, "PC34": 0.25821, "PC35": -0.22194, "PC33": -0.23398, "PC8": 0.01159, "PC9": -0.16042, "PC2": -0.09202, "PC3": 0.14371, "PC1": 0.65114, "PC6": -0.43034, "PC7": -0.02563, "PC4": -0.04947, "PC5": -0.07796, "PC50": -0.00769, "PC30": 0.07813}'
],
[
'data/spam_tiny.csv', '30', '30', '30',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all"}}}}',
'{"Message": "mobile call"}', '{}',
'{"PC40": 0.31818, "PC38": 0.06912, "PC39": -0.14342, "PC18": 0.22382, "PC19": 0.18518, "PC14": 0.89231, "PC15": 0.05046, "PC16": -0.00241, "PC17": 0.54501, "PC10": -0.26463, "PC11": 0.30251, "PC12": 1.16327, "PC13": 0.16973, "PC43": 0.11952, "PC42": 1.05499, "PC41": 0.51263, "PC25": 0.02467, "PC24": -0.65128, "PC27": 0.48916, "PC26": -0.45228, "PC21": -0.44167, "PC20": 0.76896, "PC23": 0.29398, "PC22": 0.06425, "PC47": 0.70416, "PC49": -0.30313, "PC48": 0.12976, "PC29": -0.34, "PC28": 0.17406, "PC32": -0.06411, "PC46": 0.69257, "PC31": 0.07523, "PC45": -0.03461, "PC36": 0.29732, "PC44": 0.14516, "PC37": -0.19109, "PC34": 0.58399, "PC35": 0.37608, "PC33": -0.00378, "PC8": -0.88156, "PC9": 0.38233, "PC2": -0.56685, "PC3": 0.56321, "PC1": 0.49171, "PC6": -0.09854, "PC7": -1.24639, "PC4": 1.50134, "PC5": -0.03161, "PC50": 0.17349, "PC30": -1.29612}'
]
]
show_doc(self.test_scenario6, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
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])
pca_create.i_create_a_pca_with_params(self, example[6])
pca_create.the_pca_is_finished_in_less_than(self, example[3])
projection_create.i_create_a_projection(self, example[5])
projection_create.the_projection_is(self, example[7])
compare_predictions.create_local_pca(self)
compare_predictions.i_create_a_local_projection(self, example[5])
compare_predictions.the_local_projection_is(self, example[7])
def test_scenario8(self):
"""
Scenario: Successfully comparing predictions with text options and proportional missing strategy:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a model
And I wait until the model is ready less than <time_3> secs
And I create a local model
When I create a proportional missing strategy prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a proportional missing strategy local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
"""
examples = [
[
'data/text_missing.csv', '20', '20', '30',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, "000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}',
'{}', "000003", 'swap'
],
[
'data/text_missing.csv', '20', '20', '30',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, "000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}',
'{"category1": "a"}', "000003", 'paperwork'
]
]
show_doc(self.test_scenario8, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
source_create.i_update_source_with(self, example[4])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(
self, example[2])
model_create.i_create_a_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_proportional_prediction(
self, example[5])
prediction_create.the_prediction_is(self, example[6], example[7])
prediction_compare.i_create_a_proportional_local_prediction(
self, example[5])
prediction_compare.the_local_prediction_is(self, example[7])
def test_scenario1(self):
"""
Scenario: Successfully comparing centroids with or without 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 cluster
And I wait until the cluster is ready less than <time_3> secs
And I create a local cluster
When I create a centroid for "<data_input>"
Then the centroid is "<centroid>" with distance "<distance>"
And I create a local centroid for "<data_input>"
Then the local centroid is "<centroid>" with distance "<distance>"
Examples headers:
| data | time_1 | time_2 | time_3 | options | data_input | centroid | distance |
"""
examples = [
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Type": "ham", "Message": "Mobile call"}', 'Cluster 0', '0.25'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false}}}}', '{"Type": "ham", "Message": "A normal message"}', 'Cluster 0', '0.5'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Type": "ham", "Message": "Mobile calls"}', 'Cluster 0', '0.5'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Type": "ham", "Message": "A normal message"}', 'Cluster 0', '0.5'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Type": "ham", "Message": "Mobile call"}', 'Cluster 0', '0.5'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Type": "ham", "Message": "A normal message"}', 'Cluster 1', '0.36637'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Type": "ham", "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+"}', 'Cluster 0', '0.5'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Type": "ham", "Message": "Ok"}', 'Cluster 0', '0.478833312167'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Type": "", "Message": ""}', 'Cluster 6', '0.5'],
['data/diabetes.csv', '20', '20', '30', '{"fields": {}}', '{"pregnancies": 0, "plasma glucose": 118, "blood pressure": 84, "triceps skin thickness": 47, "insulin": 230, "bmi": 45.8, "diabetes pedigree": 0.551, "age": 31, "diabetes": "true"}', 'Cluster 3', '0.5033378686559257'],
['data/diabetes.csv', '20', '20', '30', '{"fields": {}}', '{"pregnancies": 0, "plasma glucose": 118, "blood pressure": 84, "triceps skin thickness": 47, "insulin": 230, "bmi": 45.8, "diabetes pedigree": 0.551, "age": 31, "diabetes": true}', 'Cluster 3', '0.5033378686559257'],
['data/iris_sp_chars.csv', '20', '20', '30', '{"fields": {}}', '{"pétal.length":1, "pétal&width\u0000": 2, "sépal.length":1, "sépal&width": 2, "spécies": "Iris-setosa"}', 'Cluster 7', '0.8752380218327035'],
['data/movies.csv', '20', '20', '30', '{"fields": {"000007": {"optype": "items", "item_analysis": {"separator": "$"}}}}', '{"gender": "Female", "age_range": "18-24", "genres": "Adventure$Action", "timestamp": 993906291, "occupation": "K-12 student", "zipcode": 59583, "rating": 3}', 'Cluster 1', '0.7294650227133437']]
show_doc(self.test_scenario1, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
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])
cluster_create.i_create_a_cluster(self)
cluster_create.the_cluster_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_cluster(self)
prediction_create.i_create_a_centroid(self, example[5])
prediction_create.the_centroid_is_with_distance(self, example[6], example[7])
prediction_compare.i_create_a_local_centroid(self, example[5])
prediction_compare.the_local_centroid_is(self, example[6], example[7])
def test_scenario1(self):
"""
Scenario: Successfully comparing centroids with or without 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 cluster
And I wait until the cluster is ready less than <time_3> secs
And I create a local cluster
When I create a centroid for "<data_input>"
Then the centroid is "<centroid>" with distance "<distance>"
And I create a local centroid for "<data_input>"
Then the local centroid is "<centroid>" with distance "<distance>"
Examples headers:
| data | time_1 | time_2 | time_3 | options | data_input | centroid | distance |
"""
examples = [
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Type": "ham", "Message": "Mobile call"}', 'Cluster 7', '0.36637'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false}}}}', '{"Type": "ham", "Message": "A normal message"}', 'Cluster 0', '0.5'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Type": "ham", "Message": "Mobile calls"}', 'Cluster 0', '0.5'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', '{"Type": "ham", "Message": "A normal message"}', 'Cluster 0', '0.5'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Type": "ham", "Message": "Mobile call"}', 'Cluster 0', '0.5'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', '{"Type": "ham", "Message": "A normal message"}', 'Cluster 1', '0.36637'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Type": "ham", "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+"}', 'Cluster 0', '0.5'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Type": "ham", "Message": "Ok"}', 'Cluster 0', '0.478833312167'],
['data/spam.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Type": "", "Message": ""}', 'Cluster 1', '0.5'],
['data/diabetes.csv', '20', '20', '30', '{"fields": {}}', '{"pregnancies": 0, "plasma glucose": 118, "blood pressure": 84, "triceps skin thickness": 47, "insulin": 230, "bmi": 45.8, "diabetes pedigree": 0.551, "age": 31, "diabetes": "true"}', 'Cluster 3', '0.5033378686559257'],
['data/diabetes.csv', '20', '20', '30', '{"fields": {}}', '{"pregnancies": 0, "plasma glucose": 118, "blood pressure": 84, "triceps skin thickness": 47, "insulin": 230, "bmi": 45.8, "diabetes pedigree": 0.551, "age": 31, "diabetes": true}', 'Cluster 3', '0.5033378686559257'],
['data/iris_sp_chars.csv', '20', '20', '30', '{"fields": {}}', '{"pétal.length":1, "pétal&width\u0000": 2, "sépal.length":1, "sépal&width": 2, "spécies": "Iris-setosa"}', 'Cluster 7', '0.8752380218327035'],
['data/movies.csv', '20', '20', '30', '{"fields": {"000007": {"optype": "items", "item_analysis": {"separator": "$"}}}}', '{"gender": "Female", "age_range": "18-24", "genres": "Adventure$Action", "timestamp": 993906291, "occupation": "K-12 student", "zipcode": 59583, "rating": 3}', 'Cluster 1', '0.7294650227133437']]
show_doc(self.test_scenario1, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
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])
cluster_create.i_create_a_cluster(self)
cluster_create.the_cluster_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_cluster(self)
prediction_create.i_create_a_centroid(self, example[5])
prediction_create.the_centroid_is_with_distance(self, example[6], example[7])
prediction_compare.i_create_a_local_centroid(self, example[5])
prediction_compare.the_local_centroid_is(self, example[6], example[7])
def test_scenario3(self):
"""
Scenario: Successfully creating a model and translate the tree model into a set of IF-THEN rules:
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 "<options>" waiting less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a model
And I wait until the model is ready less than <time_3> secs
And I create a local model
And I translate the tree into IF_THEN rules
Then I check the output is like "<expected_file>" expected file
Examples:
| data | time_1 | time_2 | time_3 | options | expected_file |
| data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} | data/model/if_then_rules_spam_textanalysis_1.txt |
| data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false}}}} | data/model/if_then_rules_spam_textanalysis_2.txt |
| data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} | data/model/if_then_rules_spam_textanalysis_3.txt |
| data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} | data/model/if_then_rules_spam_textanalysis_4.txt |
| data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} | data/model/if_then_rules_spam_textanalysis_5.txt |
| data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} | data/model/if_then_rules_spam_textanalysis_6.txt |
"""
print self.test_scenario3.__doc__
examples = [
['data/spam.csv', '30', '30', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}','data/model/if_then_rules_spam_textanalysis_1.txt'],
['data/spam.csv', '30', '30', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false}}}}', 'data/model/if_then_rules_spam_textanalysis_2.txt'],
['data/spam.csv', '30', '30', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', 'data/model/if_then_rules_spam_textanalysis_3.txt'],
['data/spam.csv', '30', '30', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', 'data/model/if_then_rules_spam_textanalysis_4.txt'],
['data/spam.csv', '30', '30', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', 'data/model/if_then_rules_spam_textanalysis_5.txt'],
['data/spam.csv', '30', '30', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', 'data/model/if_then_rules_spam_textanalysis_6.txt']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
source_create.i_update_source_with(self, example[4])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
model_create.i_create_a_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
inspect_model.i_translate_the_tree_into_IF_THEN_rules(self)
inspect_model.i_check_if_the_output_is_like_expected_file(self, example[5])
def test_scenario10(self):
"""
Scenario: Successfully comparing predictions with text options:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a logistic regression model with objective "<objective>"
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And the logistic regression probability for the prediction is "<probability>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
And the local logistic regression probability for the prediction is "<probability>"
Examples:
| data | time_1 | time_2 | objective | time_3 | options | data_input | prediction | probability
| ../data/spam.csv | 20 | 20 | 000002 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Message": "A normal message"} | ham | 0.7645
"""
print self.test_scenario10.__doc__
examples = [
['data/spam.csv', '20', '20', '80', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', '{"Message": "A normal message"}', 'ham', 0.7645, "000000"],
['data/spam.csv', '20', '20', '80', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}', '{"Message": "mobile"}', 'spam', 0.7175, "000000"],
['data/movies.csv', '20', '20', '80', '{"fields": {"000007": {"optype": "items", "item_analysis": {"separator": "$"}}}}', '{"gender": "Female", "genres": "Adventure$Action", "timestamp": 993906291, "occupation": "K-12 student", "zipcode": 59583, "rating": 3}', '25-34', '0.4135', '000002']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
source_create.i_update_source_with(self, example[4])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
model_create.i_create_a_logistic_model_with_objective_and_parms(self, example[8])
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[5])
prediction_create.the_logistic_prediction_is(self, example[6])
prediction_create.the_logistic_probability_is(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_compare.the_local_probability_is(self, example[7])
def test_scenario3(self):
"""
Scenario: Successfully creating a model and translate the tree model into a set of IF-THEN rules:
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 "<options>" waiting less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a model
And I wait until the model is ready less than <time_3> secs
And I create a local model
And I translate the tree into IF_THEN rules
Then I check the output is like "<expected_file>" expected file
Examples:
| data | time_1 | time_2 | time_3 | options | expected_file |
| data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} | data/model/if_then_rules_spam_textanalysis_1.txt |
| data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false}}}} | data/model/if_then_rules_spam_textanalysis_2.txt |
| data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} | data/model/if_then_rules_spam_textanalysis_3.txt |
| data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} | data/model/if_then_rules_spam_textanalysis_4.txt |
| data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} | data/model/if_then_rules_spam_textanalysis_5.txt |
| data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} | data/model/if_then_rules_spam_textanalysis_6.txt |
"""
print self.test_scenario3.__doc__
examples = [
['data/spam.csv', '10', '10', '10', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}','data/model/if_then_rules_spam_textanalysis_1.txt'],
['data/spam.csv', '10', '10', '10', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false}}}}', 'data/model/if_then_rules_spam_textanalysis_2.txt'],
['data/spam.csv', '10', '10', '10', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}', 'data/model/if_then_rules_spam_textanalysis_3.txt'],
['data/spam.csv', '10', '10', '10', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}', 'data/model/if_then_rules_spam_textanalysis_4.txt'],
['data/spam.csv', '10', '10', '10', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}', 'data/model/if_then_rules_spam_textanalysis_5.txt'],
['data/spam.csv', '10', '10', '10', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', 'data/model/if_then_rules_spam_textanalysis_6.txt']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
source_create.i_update_source_with(self, example[4])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
model_create.i_create_a_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
inspect_model.i_translate_the_tree_into_IF_THEN_rules(self)
inspect_model.i_check_if_the_output_is_like_expected_file(self, example[5])
def test_scenario9(self):
"""
Scenario: Successfully comparing predictions with text options:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a logistic regression model with objective "<objective>" and parms "<parms>"
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And the logistic regression probability for the prediction is "<probability>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
And the local logistic regression probability for the prediction is "<probability>"
"""
examples = [
['data/iris.csv', '20', '20', '130', '{"fields": {"000000": {"optype": "categorical"}}}', '{"species": "Iris-setosa"}', '5.0', 0.0394, "000000", '{"field_codings": [{"field": "species", "coding": "dummy", "dummy_class": "Iris-setosa"}]}'],
['data/iris.csv', '20', '20', '130', '{"fields": {"000000": {"optype": "categorical"}}}', '{"species": "Iris-setosa"}', '5.0', 0.051, "000000", '{"balance_fields": false, "field_codings": [{"field": "species", "coding": "contrast", "coefficients": [[1, 2, -1, -2]]}]}'],
['data/iris.csv', '20', '20', '130', '{"fields": {"000000": {"optype": "categorical"}}}', '{"species": "Iris-setosa"}', '5.0', 0.051, "000000", '{"balance_fields": false, "field_codings": [{"field": "species", "coding": "other", "coefficients": [[1, 2, -1, -2]]}]}'],
['data/iris.csv', '20', '20', '130', '{"fields": {"000000": {"optype": "categorical"}}}', '{"species": "Iris-setosa"}', '5.0', 0.0417, "000000", '{"bias": false}']]
show_doc(self.test_scenario9, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
source_create.i_update_source_with(self, example[4])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
model_create.i_create_a_logistic_model_with_objective_and_parms(self, example[8], example[9])
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[5])
prediction_create.the_logistic_prediction_is(self, example[6])
prediction_create.the_logistic_probability_is(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_compare.the_local_probability_is(self, example[7])
def test_scenario9(self):
"""
Scenario: Successfully comparing predictions with text options:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a logistic regression model with objective "<objective>" and parms "<parms>"
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And the logistic regression probability for the prediction is "<probability>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
And the local logistic regression probability for the prediction is "<probability>"
"""
examples = [
['data/iris.csv', '20', '20', '130', '{"fields": {"000000": {"optype": "categorical"}}}', '{"species": "Iris-setosa"}', '5.0', 0.0394, "000000", '{"field_codings": [{"field": "species", "coding": "dummy", "dummy_class": "Iris-setosa"}]}'],
['data/iris.csv', '20', '20', '130', '{"fields": {"000000": {"optype": "categorical"}}}', '{"species": "Iris-setosa"}', '5.0', 0.0511, "000000", '{"balance_fields": false, "field_codings": [{"field": "species", "coding": "contrast", "coefficients": [[1, 2, -1, -2]]}]}'],
['data/iris.csv', '20', '20', '130', '{"fields": {"000000": {"optype": "categorical"}}}', '{"species": "Iris-setosa"}', '5.0', 0.0511, "000000", '{"balance_fields": false, "field_codings": [{"field": "species", "coding": "other", "coefficients": [[1, 2, -1, -2]]}]}'],
['data/iris.csv', '20', '20', '130', '{"fields": {"000000": {"optype": "categorical"}}}', '{"species": "Iris-setosa"}', '5.0', 0.0417, "000000", '{"bias": false}']]
show_doc(self.test_scenario9, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
source_create.i_update_source_with(self, example[4])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
model_create.i_create_a_logistic_model_with_objective_and_parms(self, example[8], example[9])
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[5])
prediction_create.the_logistic_prediction_is(self, example[6])
prediction_create.the_logistic_probability_is(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_compare.the_local_probability_is(self, example[7])
def test_scenario6(self):
"""
Scenario: Successfully comparing projections for PCAs:
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 PCA with "<params>"
And I wait until the PCA is ready less than <time_3> secs
And I create a local PCA
When I create a projection for "<input_data>"
Then the projection is "<projection>"
And I create a local projection for "<data_input>"
Then the local projection is "<projection>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | projection | params
"""
examples = [
['data/spam_tiny.csv', '30', '30', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all"}}}}', '{"Message": "early"}', '{}', '{"PC40": 0.00416, "PC38": 0.08267, "PC39": 0.00033, "PC18": 0.28094, "PC19": -0.15056, "PC14": 0.20643, "PC15": 0.23931, "PC16": 0.03251, "PC17": 0.02776, "PC10": 0.1424, "PC11": 0.4059, "PC12": -0.1238, "PC13": 0.15131, "PC43": 0.29617, "PC42": 1.0091, "PC41": 0, "PC25": 0.07164, "PC24": -0.29904, "PC27": -0.1331, "PC26": -0.18572, "PC21": 0.25616, "PC20": 0.30424, "PC23": -0.45775, "PC22": -0.3362, "PC47": -0.13757, "PC49": 0.01864, "PC48": 0.04742, "PC29": -0.16286, "PC28": 0.42207, "PC32": -0.05917, "PC46": -0.05018, "PC31": -0.13973, "PC45": -0.05015, "PC36": 0.03017, "PC44": 0, "PC37": -0.06093, "PC34": 0.25821, "PC35": -0.22194, "PC33": -0.23398, "PC8": 0.01159, "PC9": -0.16042, "PC2": -0.09202, "PC3": 0.14371, "PC1": 0.65114, "PC6": -0.43034, "PC7": -0.02563, "PC4": -0.04947, "PC5": -0.07796, "PC50": -0.00769, "PC30": 0.07813}'],
['data/spam_tiny.csv', '30', '30', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all"}}}}', '{"Message": "mobile call"}', '{}', '{"PC40": 0.31818, "PC38": 0.06912, "PC39": -0.14342, "PC18": 0.22382, "PC19": 0.18518, "PC14": 0.89231, "PC15": 0.05046, "PC16": -0.00241, "PC17": 0.54501, "PC10": -0.26463, "PC11": 0.30251, "PC12": 1.16327, "PC13": 0.16973, "PC43": 0.11952, "PC42": 1.05499, "PC41": 0.51263, "PC25": 0.02467, "PC24": -0.65128, "PC27": 0.48916, "PC26": -0.45228, "PC21": -0.44167, "PC20": 0.76896, "PC23": 0.29398, "PC22": 0.06425, "PC47": 0.70416, "PC49": -0.30313, "PC48": 0.12976, "PC29": -0.34, "PC28": 0.17406, "PC32": -0.06411, "PC46": 0.69257, "PC31": 0.07523, "PC45": -0.03461, "PC36": 0.29732, "PC44": 0.14516, "PC37": -0.19109, "PC34": 0.58399, "PC35": 0.37608, "PC33": -0.00378, "PC8": -0.88156, "PC9": 0.38233, "PC2": -0.56685, "PC3": 0.56321, "PC1": 0.49171, "PC6": -0.09854, "PC7": -1.24639, "PC4": 1.50134, "PC5": -0.03161, "PC50": 0.17349, "PC30": -1.29612}']]
show_doc(self.test_scenario6, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
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])
pca_create.i_create_a_pca_with_params(self, example[6])
pca_create.the_pca_is_finished_in_less_than(self, example[3])
projection_create.i_create_a_projection(self, example[5])
projection_create.the_projection_is(self, example[7])
compare_predictions.create_local_pca(self)
compare_predictions.i_create_a_local_projection(self, example[5])
compare_predictions.the_local_projection_is(self, example[7])
def test_scenario11(self):
"""
Scenario: Successfully comparing predictions with text options and proportional missing strategy:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a model
And I wait until the model is ready less than <time_3> secs
And I create a local model
When I create a proportional missing strategy prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a proportional missing strategy local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| ../data/text_missing.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, {"000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}} |{} | paperwork |
"""
print self.test_scenario11.__doc__
examples = [
['data/text_missing.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, "000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}', '{}', "000003",'swap'],
['data/text_missing.csv', '20', '20', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}, "000000": {"optype": "text", "term_analysis": {"token_mode": "all", "language": "en"}}}}', '{"category1": "a"}', "000003",'paperwork']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
source_create.i_update_source_with(self, example[4])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
model_create.i_create_a_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_proportional_prediction(self, example[5])
prediction_create.the_prediction_is(self, example[6], example[7])
prediction_compare.i_create_a_proportional_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[7])
def test_scenario4(self):
"""
Scenario: Successfully comparing topic distributions:
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 topic model
And I wait until the topic model is ready less than <time_3> secs
And I create a local topic model
When I create a topic distribution for "<data_input>"
Then the topic distribution is "<topic_distribution>"
And I create a local topic distribution for "<data_input>"
Then the local topic distribution is "<topic_distribution>"
Examples headers:
| data | time_1 | time_2 | time_3 | options | data_input | topic distribution |
"""
examples = [
['data/spam.csv', '30', '30', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Type": "ham", "Message": "Mobile call"}', '[0.51133, 0.00388, 0.00574, 0.00388, 0.00388, 0.00388, 0.00388, 0.00388, 0.00388, 0.00388, 0.00388, 0.44801]'],
['data/spam.csv', '30', '30', '30', '{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}', '{"Type": "ham", "Message": "Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat..."}', '[0.39188, 0.00643, 0.00264, 0.00643, 0.08112, 0.00264, 0.37352, 0.0115, 0.00707, 0.00327, 0.00264, 0.11086]']]
show_doc(self.test_scenario4, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
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])
topic_create.i_create_a_topic_model(self)
topic_create.the_topic_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_topic_model(self)
topic_create.i_create_a_topic_distribution(self, example[5])
prediction_compare.the_topic_distribution_is(self, example[6])
topic_create.i_create_a_local_topic_distribution(self, example[5])
prediction_compare.the_local_topic_distribution_is(self, example[6])
def test_scenario11(self):
"""
Scenario: Successfully comparing predictions for logistic regression with balance_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 update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a logistic regression model with objective "<objective>" and flags
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And the logistic regression probability for the prediction is "<probability>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
And the local logistic regression probability for the prediction is "<probability>"
Examples:
| data | time_1 | time_2 | objective | time_3 | options | data_input | prediction | probability
"""
examples = [
[
'data/movies.csv', '20', '20', '180',
'{"fields": {"000000": {"name": "user_id", "optype": "numeric"},'
' "000001": {"name": "gender", "optype": "categorical"},'
' "000002": {"name": "age_range", "optype": "categorical"},'
' "000003": {"name": "occupation", "optype": "categorical"},'
' "000004": {"name": "zipcode", "optype": "numeric"},'
' "000005": {"name": "movie_id", "optype": "numeric"},'
' "000006": {"name": "title", "optype": "text"},'
' "000007": {"name": "genres", "optype": "items",'
'"item_analysis": {"separator": "$"}},'
'"000008": {"name": "timestamp", "optype": "numeric"},'
'"000009": {"name": "rating", "optype": "categorical"}},'
'"source_parser": {"separator": ";"}}',
'{"timestamp": "999999999"}', '4', 0.4028, "000009",
'{"balance_fields": false}'
],
[
'data/movies.csv', '20', '20', '180',
'{"fields": {"000000": {"name": "user_id", "optype": "numeric"},'
' "000001": {"name": "gender", "optype": "categorical"},'
' "000002": {"name": "age_range", "optype": "categorical"},'
' "000003": {"name": "occupation", "optype": "categorical"},'
' "000004": {"name": "zipcode", "optype": "numeric"},'
' "000005": {"name": "movie_id", "optype": "numeric"},'
' "000006": {"name": "title", "optype": "text"},'
' "000007": {"name": "genres", "optype": "items",'
'"item_analysis": {"separator": "$"}},'
'"000008": {"name": "timestamp", "optype": "numeric"},'
'"000009": {"name": "rating", "optype": "categorical"}},'
'"source_parser": {"separator": ";"}}',
'{"timestamp": "999999999"}', '4', 0.2622, "000009",
'{"normalize": true}'
],
[
'data/movies.csv', '20', '20', '180',
'{"fields": {"000000": {"name": "user_id", "optype": "numeric"},'
' "000001": {"name": "gender", "optype": "categorical"},'
' "000002": {"name": "age_range", "optype": "categorical"},'
' "000003": {"name": "occupation", "optype": "categorical"},'
' "000004": {"name": "zipcode", "optype": "numeric"},'
' "000005": {"name": "movie_id", "optype": "numeric"},'
' "000006": {"name": "title", "optype": "text"},'
' "000007": {"name": "genres", "optype": "items",'
'"item_analysis": {"separator": "$"}},'
'"000008": {"name": "timestamp", "optype": "numeric"},'
'"000009": {"name": "rating", "optype": "categorical"}},'
'"source_parser": {"separator": ";"}}',
'{"timestamp": "999999999"}', '4', 0.2622, "000009",
'{"balance_fields": true, "normalize": true}'
]
]
show_doc(self.test_scenario11, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
source_create.i_update_source_with(self, example[4])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(
self, example[2])
model_create.i_create_a_logistic_model_with_objective_and_parms(
self, example[8], example[9])
model_create.the_logistic_model_is_finished_in_less_than(
self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[5])
prediction_create.the_logistic_prediction_is(self, example[6])
prediction_create.the_logistic_probability_is(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_compare.the_local_probability_is(self, example[7])
def test_scenario4(self):
"""
Scenario: Successfully comparing centroids with or without 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 cluster
And I wait until the cluster is ready less than <time_3> secs
And I create a local cluster
When I create a centroid for "<data_input>"
Then the centroid is "<centroid>" with distance "<distance>"
And I create a local centroid for "<data_input>"
Then the local centroid is "<centroid>" with distance "<distance>"
Examples:
| data | time_1 | time_2 | time_3 | options | data_input | centroid | distance |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} |{"Type": "ham", "Message": "Mobile call"} | Cluster 7 | 0.341886116992 |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false}}}} |{"Type": "ham", "Message": "A normal message"} | Cluster 0 | 0.5 |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} |{"Type": "ham", "Message": "Mobile calls"} | Cluster 0 | 0.5 |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}} |{"Type": "ham", "Message": "A normal message"} | Cluster 0 | 0.5 |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} |{"Type": "ham", "Message": "Mobile call"} | Cluster 4 | 0.382148869802 |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}} |{"Type": "ham", "Message": "A normal message"} | Cluster 4 | 0.382148869802 |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Type": "ham", "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+"} | Cluster 1 | 0.5 |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}} |{"Type": "ham", "Message": "Ok"} | Cluster 1 | 0.478833312167 |
| ../data/spam.csv | 20 | 20 | 30 | {"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}} |{"Type": "", "Message": ""} | Cluster 0 | 0.707106781187 |
| ../data/diabetes.csv | 20 | 20 | 30 | {"fields": {}} |{"pregnancies": 0, "plasma glucose": 118, "blood pressure": 84, "triceps skin thickness": 47, "insulin": 230, "bmi": 45.8, "diabetes pedigree": 0.551, "age": 31, "diabetes": "true"} | Cluster 6 | 0.486471379368 |
| ../data/iris_sp_chars.csv | 20 | 20 | 30 | {"fields": {}} |{"pétal.length":1, "pétal&width\u0000": 2, "sépal.length":1, "sépal&width": 2, "spécies": "Iris-setosa"} | Cluster 7 | 0.757736964835 |
"""
print self.test_scenario4.__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"}}}}',
'{"Type": "ham", "Message": "Mobile call"}', 'Cluster 7',
'0.341886116992'
],
[
'data/spam.csv', '20', '20', '30',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false}}}}',
'{"Type": "ham", "Message": "A normal message"}', 'Cluster 0',
'0.5'
],
[
'data/spam.csv', '20', '20', '30',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}',
'{"Type": "ham", "Message": "Mobile calls"}', 'Cluster 0',
'0.5'
],
[
'data/spam.csv', '20', '20', '30',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": false, "use_stopwords": false, "language": "en"}}}}',
'{"Type": "ham", "Message": "A normal message"}', 'Cluster 0',
'0.5'
],
[
'data/spam.csv', '20', '20', '30',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}',
'{"Type": "ham", "Message": "Mobile call"}', 'Cluster 4',
'0.382148869802'
],
[
'data/spam.csv', '20', '20', '30',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": false, "stem_words": true, "use_stopwords": true, "language": "en"}}}}',
'{"Type": "ham", "Message": "A normal message"}', 'Cluster 4',
'0.382148869802'
],
[
'data/spam.csv', '20', '20', '30',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}',
'{"Type": "ham", "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+"}',
'Cluster 1', '0.5'
],
[
'data/spam.csv', '20', '20', '30',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"token_mode": "full_terms_only", "language": "en"}}}}',
'{"Type": "ham", "Message": "Ok"}', 'Cluster 1',
'0.478833312167'
],
[
'data/spam.csv', '20', '20', '30',
'{"fields": {"000001": {"optype": "text", "term_analysis": {"case_sensitive": true, "stem_words": true, "use_stopwords": false, "language": "en"}}}}',
'{"Type": "", "Message": ""}', 'Cluster 0', '0.707106781187'
],
[
'data/diabetes.csv', '20', '20', '30', '{"fields": {}}',
'{"pregnancies": 0, "plasma glucose": 118, "blood pressure": 84, "triceps skin thickness": 47, "insulin": 230, "bmi": 45.8, "diabetes pedigree": 0.551, "age": 31, "diabetes": "true"}',
'Cluster 6', '0.486471379368'
],
[
'data/iris_sp_chars.csv', '20', '20', '30', '{"fields": {}}',
'{"pétal.length":1, "pétal&width\u0000": 2, "sépal.length":1, "sépal&width": 2, "spécies": "Iris-setosa"}',
'Cluster 7', '0.757736964835'
]
]
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])
cluster_create.i_create_a_cluster(self)
cluster_create.the_cluster_is_finished_in_less_than(
self, example[3])
prediction_compare.i_create_a_local_cluster(self)
prediction_create.i_create_a_centroid(self, example[5])
prediction_create.the_centroid_is_with_distance(
self, example[6], example[7])
prediction_compare.i_create_a_local_centroid(self, example[5])
prediction_compare.the_local_centroid_is(self, example[6],
example[7])
def test_scenario11(self):
"""
Scenario: Successfully comparing predictions for logistic regression with balance_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 update the source with params "<options>"
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a logistic regression model with objective "<objective>" and flags
And I wait until the logistic regression model is ready less than <time_3> secs
And I create a local logistic regression model
When I create a logistic regression prediction for "<data_input>"
Then the logistic regression prediction is "<prediction>"
And the logistic regression probability for the prediction is "<probability>"
And I create a local logistic regression prediction for "<data_input>"
Then the local logistic regression prediction is "<prediction>"
And the local logistic regression probability for the prediction is "<probability>"
Examples:
| data | time_1 | time_2 | objective | time_3 | options | data_input | prediction | probability
"""
examples = [
['data/movies.csv', '20', '20', '180', '{"fields": {"000000": {"name": "user_id", "optype": "numeric"},'
' "000001": {"name": "gender", "optype": "categorical"},'
' "000002": {"name": "age_range", "optype": "categorical"},'
' "000003": {"name": "occupation", "optype": "categorical"},'
' "000004": {"name": "zipcode", "optype": "numeric"},'
' "000005": {"name": "movie_id", "optype": "numeric"},'
' "000006": {"name": "title", "optype": "text"},'
' "000007": {"name": "genres", "optype": "items",'
'"item_analysis": {"separator": "$"}},'
'"000008": {"name": "timestamp", "optype": "numeric"},'
'"000009": {"name": "rating", "optype": "categorical"}},'
'"source_parser": {"separator": ";"}}', '{"timestamp": "999999999"}', '4', 0.3231, "000009", '{"balance_fields": false}'],
['data/movies.csv', '20', '20', '180', '{"fields": {"000000": {"name": "user_id", "optype": "numeric"},'
' "000001": {"name": "gender", "optype": "categorical"},'
' "000002": {"name": "age_range", "optype": "categorical"},'
' "000003": {"name": "occupation", "optype": "categorical"},'
' "000004": {"name": "zipcode", "optype": "numeric"},'
' "000005": {"name": "movie_id", "optype": "numeric"},'
' "000006": {"name": "title", "optype": "text"},'
' "000007": {"name": "genres", "optype": "items",'
'"item_analysis": {"separator": "$"}},'
'"000008": {"name": "timestamp", "optype": "numeric"},'
'"000009": {"name": "rating", "optype": "categorical"}},'
'"source_parser": {"separator": ";"}}', '{"timestamp": "999999999"}', '4', 0.2622, "000009", '{"normalize": true}'],
['data/movies.csv', '20', '20', '180', '{"fields": {"000000": {"name": "user_id", "optype": "numeric"},'
' "000001": {"name": "gender", "optype": "categorical"},'
' "000002": {"name": "age_range", "optype": "categorical"},'
' "000003": {"name": "occupation", "optype": "categorical"},'
' "000004": {"name": "zipcode", "optype": "numeric"},'
' "000005": {"name": "movie_id", "optype": "numeric"},'
' "000006": {"name": "title", "optype": "text"},'
' "000007": {"name": "genres", "optype": "items",'
'"item_analysis": {"separator": "$"}},'
'"000008": {"name": "timestamp", "optype": "numeric"},'
'"000009": {"name": "rating", "optype": "categorical"}},'
'"source_parser": {"separator": ";"}}', '{"timestamp": "999999999"}', '4', 0.2622, "000009", '{"balance_fields": true, "normalize": true}']]
show_doc(self.test_scenario11, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
source_create.i_update_source_with(self, example[4])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
model_create.i_create_a_logistic_model_with_objective_and_parms(self, example[8], example[9])
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_logistic_model(self)
prediction_create.i_create_a_logistic_prediction(self, example[5])
prediction_create.the_logistic_prediction_is(self, example[6])
prediction_create.the_logistic_probability_is(self, example[7])
prediction_compare.i_create_a_local_prediction(self, example[5])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_compare.the_local_probability_is(self, example[7])
