def test_scenario2(self):
"""
Scenario: Successfully comparing centroids with configuration 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 create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a cluster with options "<options>"
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 | full_data_input
"""
examples = [
['data/iris.csv', '30', '30', '30', '{"summary_fields": ["sepal width"]}', '{"petal length": 1, "petal width": 1, "sepal length": 1, "species": "Iris-setosa"}', 'Cluster 2', '1.16436', '{"petal length": 1, "petal width": 1, "sepal length": 1, "species": "Iris-setosa"}'],
['data/iris.csv', '20', '20', '30', '{"default_numeric_value": "zero"}', '{"petal length": 1}', 'Cluster 4', '1.41215', '{"petal length": 1, "petal width": 0, "sepal length": 0, "sepal width": 0, "species": ""}']]
show_doc(self.test_scenario2, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
cluster_create.i_create_a_cluster_with_options(self, example[4])
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[8])
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 centroids with configuration 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 create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a cluster with options "<options>"
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 | full_data_input
"""
examples = [
['data/iris.csv', '20', '20', '30', '{"summary_fields": ["sepal width"]}', '{"petal length": 1, "petal width": 1, "sepal length": 1, "species": "Iris-setosa"}', 'Cluster 2', '1.16436', '{"petal length": 1, "petal width": 1, "sepal length": 1, "species": "Iris-setosa"}'],
['data/iris.csv', '20', '20', '30', '{"default_numeric_value": "zero"}', '{"petal length": 1}', 'Cluster 4', '1.41215', '{"petal length": 1, "petal width": 0, "sepal length": 0, "sepal width": 0, "species": ""}']]
show_doc(self.test_scenario2, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
cluster_create.i_create_a_cluster_with_options(self, example[4])
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[8])
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_scenario5(self):
"""
Scenario: Successfully comparing centroids with summary fields:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a cluster with options "<options>"
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/iris.csv | 20 | 20 | 30 | {"summary_fields": ["sepal width"]} |{"petal length": 1, "petal width": 1, "sepal length": 1, "species": "Iris-setosa"} | Cluster 6 | 0.7310939266123302 |
"""
print self.test_scenario5.__doc__
examples = [
['data/iris.csv', '20', '20', '30', '{"summary_fields": ["sepal width"]}', '{"petal length": 1, "petal width": 1, "sepal length": 1, "species": "Iris-setosa"}', 'Cluster 0', '0.7310939266123302']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
cluster_create.i_create_a_cluster_with_options(self, example[4])
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 creating models for first centroid of a cluster:
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 cluster with options "<options>"
And I wait until the cluster is ready less than <time_3> secs
When I create a model associated to centroid "<centroid_id>"
And I wait until the model is ready less than <time_4> secs
Then the model is associated to the centroid "<centroid_id>" of the cluster
Examples:
| data | time_1 | time_2 | time_3 | centroid_id | time_4 |
| ../data/iris.csv | 10 | 10 | 40 | 000001 | 10 |
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '10', '10', '40', '000001', '10', '{"model_clusters": true}']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
cluster_create.i_create_a_cluster_with_options(self, example[6])
cluster_create.the_cluster_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_from_cluster(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[5])
model_create.is_associated_to_centroid_id(self, example[4])
def test_scenario5(self):
"""
Scenario: Successfully comparing centroids with summary fields:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create a cluster with options "<options>"
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/iris.csv | 20 | 20 | 30 | {"summary_fields": ["sepal width"]} |{"petal length": 1, "petal width": 1, "sepal length": 1, "species": "Iris-setosa"} | Cluster 2 | 1.1643644909783857 |
"""
print self.test_scenario5.__doc__
examples = [
['data/iris.csv', '20', '20', '30', '{"summary_fields": ["sepal width"]}', '{"petal length": 1, "petal width": 1, "sepal length": 1, "species": "Iris-setosa"}', 'Cluster 2', '1.1643644909783857']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
cluster_create.i_create_a_cluster_with_options(self, example[4])
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 creating models for first centroid of a cluster:
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 cluster with options "<options>"
And I wait until the cluster is ready less than <time_3> secs
When I create a model associated to centroid "<centroid_id>"
And I wait until the model is ready less than <time_4> secs
Then the model is associated to the centroid "<centroid_id>" of the cluster
Examples:
| data | time_1 | time_2 | time_3 | centroid_id | time_4 |
| ../data/iris.csv | 10 | 10 | 40 | 000001 | 10 |
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '10', '10', '40', '000001', '10', '{"model_clusters": true}']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
cluster_create.i_create_a_cluster_with_options(self, example[6])
cluster_create.the_cluster_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_from_cluster(self, example[4])
model_create.the_model_is_finished_in_less_than(self, example[5])
model_create.is_associated_to_centroid_id(self, example[4])
