def test_scenario5(self):
"""
Scenario 5: Successfully creating a local cluster 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 cluster
And I wait until the cluster is ready less than <time_3> secs
And I export the cluster to "<exported_file>"
When I create a local cluster from the file "<exported_file>"
Then the cluster ID and the local cluster ID match
Examples:
| data | time_1 | time_2 | time_3 | exported_file
| ../data/iris.csv | 10 | 10 | 50 | ./tmp/cluster.json
"""
print self.test_scenario5.__doc__
examples = [
['data/iris.csv', '10', '10', '500', './tmp/cluster.json']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
cluster_create.i_create_a_cluster(self)
cluster_create.the_cluster_is_finished_in_less_than(self, example[3])
cluster_create.i_export_cluster(self, example[4])
cluster_create.i_create_local_cluster_from_file(self, example[4])
cluster_create.check_cluster_id_local_id(self)
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 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 remote and local predictions
with raw date input for cluster
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
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|data_input|centroid|distance|
"""
examples = [
[
'data/dates2.csv', '20', '30', '60',
'{"time-1":"1910-05-08T19:10:23.106","cat-0":"cat2","target-2":0.4}',
"Cluster 2", 0.92112
],
[
'data/dates2.csv', '20', '30', '60',
'{"time-1":"1920-06-30T20:21:20.320","cat-0":"cat1","target-2":0.2}',
"Cluster 3", 0.77389
],
[
'data/dates2.csv', '20', '30', '60',
'{"time-1":"1932-01-30T19:24:11.440","cat-0":"cat2","target-2":0.1}',
"Cluster 0", 0.87855
],
[
'data/dates2.csv', '20', '30', '60',
'{"time-1":"1950-11-06T05:34:05.602","cat-0":"cat1" ,"target-2":0.9}',
"Cluster 6", 0.83506
]
]
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(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[4])
prediction_create.the_centroid_is_with_distance(
self, example[5], example[6])
prediction_compare.i_create_a_local_centroid(self, example[4])
prediction_compare.the_local_centroid_is(self, example[5],
example[6])
def test_scenario1(self):
"""
Scenario: Successfully creating datasets 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
And I wait until the cluster is ready less than <time_3> secs
When I create a dataset associated to centroid "<centroid_id>"
And I wait until the dataset is ready less than <time_4> secs
Then the dataset 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_scenario1.__doc__
examples = [['data/iris.csv', '10', '10', '40', '000001', '10']]
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(self)
cluster_create.the_cluster_is_finished_in_less_than(
self, example[3])
dataset_create.i_create_a_dataset_from_cluster(self, example[4])
dataset_create.the_dataset_is_finished_in_less_than(
self, example[5])
dataset_create.is_associated_to_centroid_id(self, example[4])
def test_scenario5(self):
"""
Scenario 5: Successfully creating a local cluster 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 cluster
And I wait until the cluster is ready less than <time_3> secs
And I export the cluster to "<exported_file>"
When I create a local cluster from the file "<exported_file>"
Then the cluster ID and the local cluster ID match
Examples:
| data | time_1 | time_2 | time_3 | exported_file
| ../data/iris.csv | 10 | 10 | 50 | ./tmp/cluster.json
"""
print self.test_scenario5.__doc__
examples = [
['data/iris.csv', '10', '10', '500', './tmp/cluster.json']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
cluster_create.i_create_a_cluster(self)
cluster_create.the_cluster_is_finished_in_less_than(self, example[3])
cluster_create.i_export_cluster(self, example[4])
cluster_create.i_create_local_cluster_from_file(self, example[4])
cluster_create.check_cluster_id_local_id(self)
def test_scenario4(self):
"""
Scenario: Successfully getting the closest centroid in 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
And I wait until the cluster is ready less than <time_3> secs
And I create a local cluster
Then the centroid in the cluster closest to "<reference>" is "<closest>"
Examples:
| data | time_1 | time_2 | time_3 | reference | closest |
"""
print self.test_scenario4.__doc__
examples = [[
'data/spam_4w.csv', '10', '10', '40', '{"Message": "free"}',
'000005'
]]
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(self)
cluster_create.the_cluster_is_finished_in_less_than(
self, example[3])
prediction_compare.i_create_a_local_cluster(self)
cluster_create.closest_centroid_in_cluster(self, example[4],
example[5])
def test_scenario5(self):
"""
Scenario: Successfully creating a centroid and the associated 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 a cluster
And I wait until the cluster is ready less than <time_3> secs
When I create a centroid for "<data_input>"
And I check the centroid is ok
Then the centroid is "<centroid>"
And I create a dataset from the cluster and the centroid
And I wait until the dataset is ready less than <time_2> secs
And I check that the dataset is created for the cluster and the centroid
Examples:
| data | time_1 | time_2 | time_3 | data_input | centroid |
| ../data/diabetes.csv | 10 | 20 | 20 | {"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 |
"""
print self.test_scenario5.__doc__
examples = [
['data/diabetes.csv', '10', '20', '20', '{"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']]
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(self)
cluster_create.the_cluster_is_finished_in_less_than(self, example[3])
prediction_create.i_create_a_centroid(self, example[4])
prediction_create.the_centroid_is(self, example[5])
def test_scenario5(self):
"""
Scenario: Successfully creating a centroid and the associated 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 a cluster
And I wait until the cluster is ready less than <time_3> secs
When I create a centroid for "<data_input>"
And I check the centroid is ok
Then the centroid is "<centroid>"
And I create a dataset from the cluster and the centroid
And I wait until the dataset is ready less than <time_2> secs
And I check that the dataset is created for the cluster and the centroid
Examples:
| data | time_1 | time_2 | time_3 | data_input | centroid |
| ../data/diabetes.csv | 10 | 20 | 20 | {"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 |
"""
print self.test_scenario5.__doc__
examples = [
['data/diabetes.csv', '10', '20', '20', '{"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']]
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(self)
cluster_create.the_cluster_is_finished_in_less_than(self, example[3])
prediction_create.i_create_a_centroid(self, example[4])
prediction_create.the_centroid_is(self, example[5])
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_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_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 getting the closest point in 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
And I wait until the cluster is ready less than <time_3> secs
And I create a local cluster
Then the data point in the cluster closest to "<reference>" is "<closest>"
Examples:
| data | time_1 | time_2 | time_3 | reference | closest |
"""
print self.test_scenario3.__doc__
examples = [[
'data/iris.csv', '10', '10', '40',
'{"petal length": 1.4, "petal width": 0.2,'
' "sepal width": 3.0, "sepal length": 4.89,'
' "species": "Iris-setosa"}',
'{"distance": 0.001894153207990619, "data":'
' {"petal length": "1.4", "petal width": "0.2",'
' "sepal width": "3.0", "sepal length": "4.9",'
' "species": "Iris-setosa"}}'
],
[
'data/spam_4w.csv', '10', '10', '40',
'{"Message": "mobile"}', '{"distance": 0.0, "data":'
' {"Message": "mobile", "Type": "spam"}}'
]]
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(self)
cluster_create.the_cluster_is_finished_in_less_than(
self, example[3])
prediction_compare.i_create_a_local_cluster(self)
cluster_create.closest_in_cluster(self, example[4], example[5])
def test_scenario3(self):
"""
Scenario: Successfully creating a batch centroid from 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
And I wait until the cluster is ready less than <time_3> secs
When I create a batch centroid for the dataset
And I check the batch centroid is ok
And I wait until the batch centroid is ready less than <time_4> secs
And I download the created centroid file to "<local_file>"
Then the batch centroid file is like "<predictions_file>"
Examples:
| data | time_1 | time_2 | time_3 | time_4 | local_file | predictions_file |
| ../data/diabetes.csv | 50 | 50 | 50 | 50 | ./tmp/batch_predictions.csv |./data/batch_predictions_c.csv |
"""
print self.test_scenario3.__doc__
examples = [[
'data/diabetes.csv', '50', '50', '50', '50',
'tmp/batch_predictions.csv', 'data/batch_predictions_c.csv'
]]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(
self, example[2])
cluster_create.i_create_a_cluster(self)
cluster_create.the_cluster_is_finished_in_less_than(
self, example[3])
batch_pred_create.i_create_a_batch_prediction_with_cluster(self)
batch_pred_create.the_batch_centroid_is_finished_in_less_than(
self, example[4])
batch_pred_create.i_download_centroid_file(self, example[5])
batch_pred_create.i_check_predictions(self, example[6])
def test_scenario3(self):
"""
Scenario: Successfully creating a batch centroid from 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
And I wait until the cluster is ready less than <time_3> secs
When I create a batch centroid for the dataset
And I check the batch centroid is ok
And I wait until the batch centroid is ready less than <time_4> secs
And I download the created centroid file to "<local_file>"
Then the batch centroid file is like "<predictions_file>"
Examples:
| data | time_1 | time_2 | time_3 | time_4 | local_file | predictions_file |
| ../data/diabetes.csv | 50 | 50 | 50 | 50 | ./tmp/batch_predictions.csv |./data/batch_predictions_c.csv |
"""
print self.test_scenario3.__doc__
examples = [
["data/diabetes.csv", "50", "50", "50", "50", "tmp/batch_predictions.csv", "data/batch_predictions_c.csv"]
]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
cluster_create.i_create_a_cluster(self)
cluster_create.the_cluster_is_finished_in_less_than(self, example[3])
batch_pred_create.i_create_a_batch_prediction_with_cluster(self)
batch_pred_create.the_batch_centroid_is_finished_in_less_than(self, example[4])
batch_pred_create.i_download_centroid_file(self, example[5])
batch_pred_create.i_check_predictions(self, example[6])
def test_scenario3(self):
"""
Scenario: Successfully getting the closest point in 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
And I wait until the cluster is ready less than <time_3> secs
And I create a local cluster
Then the data point in the cluster closest to "<reference>" is "<closest>"
Examples:
| data | time_1 | time_2 | time_3 | reference | closest |
"""
print self.test_scenario3.__doc__
examples = [
['data/iris.csv', '10', '10', '40',
'{"petal length": 1.4, "petal width": 0.2,'
' "sepal width": 3.0, "sepal length": 4.89,'
' "species": "Iris-setosa"}',
'{"distance": 0.001894153207990619, "data":'
' {"petal length": "1.4", "petal width": "0.2",'
' "sepal width": "3.0", "sepal length": "4.9",'
' "species": "Iris-setosa"}}']]
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(self)
cluster_create.the_cluster_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_cluster(self)
cluster_create.closest_in_cluster(self, example[4], example[5])
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])
