def test_scenario9(self):
"""
Scenario 9: Successfully creating a local association 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 time series
And I wait until the time series is ready less than <time_3> secs
And I export the time series to "<exported_file>"
When I create a local time series from the file "<exported_file>"
Then the time series ID and the local time series ID match
Examples:
| data | time_1 | time_2 | time_3 | exported_file
| ../data/iris.csv | 10 | 10 | 50 | ./tmp/time_series.json
"""
print self.test_scenario9.__doc__
examples = [
['data/iris.csv', '10', '10', '500', './tmp/time_series.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])
timeseries_create.i_create_a_time_series(self)
timeseries_create.the_time_series_is_finished_in_less_than(self, example[3])
timeseries_create.i_export_time_series(self, example[4])
timeseries_create.i_create_local_time_series_from_file(self, example[4])
timeseries_create.check_time_series_id_local_id(self)
def test_scenario9(self):
"""
Scenario 9: Successfully creating a local association 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 time series
And I wait until the time series is ready less than <time_3> secs
And I export the time series to "<exported_file>"
When I create a local time series from the file "<exported_file>"
Then the time series ID and the local time series ID match
Examples:
| data | time_1 | time_2 | time_3 | exported_file
| ../data/iris.csv | 10 | 10 | 50 | ./tmp/time_series.json
"""
print self.test_scenario9.__doc__
examples = [
['data/iris.csv', '10', '10', '500', './tmp/time_series.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])
timeseries_create.i_create_a_time_series(self)
timeseries_create.the_time_series_is_finished_in_less_than(self, example[3])
timeseries_create.i_export_time_series(self, example[4])
timeseries_create.i_create_local_time_series_from_file(self, example[4])
timeseries_create.check_time_series_id_local_id(self)
def test_scenario2(self):
"""
Scenario: Successfully comparing forecasts from time series with "A" seasonality
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 time series with "<params>"
And I wait until the time series is ready less than <time_3> secs
And I create a local time series
When I create a forecast for "<input_data>"
Then the forecast is "<forecasts>"
And I create a local forecast for "<data_input>"
Then the local forecast is "<forecasts>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | forecasts | params
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["A,Ad,A"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast":[66.16225, 72.17308, 66.65573, 73.09698, 70.51449], "model": "A,Ad,A"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}']
"""
examples = [
[
'data/grades.csv', '30', '30', '120',
'{"000005": {"horizon": 5}}',
'{"000005": [{"point_forecast": [73.96192, 74.04106, 74.12029, 74.1996, 74.27899], "model": "M,M,N"}]}',
'{"objective_fields": ["000001", "000005"], "period": 12}'
],
[
'data/grades.csv', '30', '30', '120',
'{"000005": {"horizon": 5, "ets_models": {"names": ["M,N,A"], "criterion": "aic", "limit": 3}}}',
'{"000005": [{"point_forecast": [67.43222, 68.24468, 64.14437, 67.5662, 67.79028], "model": "M,N,A"}]}',
'{"objective_fields": ["000001", "000005"], "period": 12}'
],
[
'data/grades.csv', '30', '30', '120',
'{"000005": {"horizon": 5, "ets_models": {"names": ["A,A,A"], "criterion": "aic", "limit": 3}}}',
'{"000005": [{"point_forecast": [74.73553, 71.6163, 71.90264, 76.4249, 75.06982], "model": "A,A,A"}]}',
'{"objective_fields": ["000001", "000005"], "period": 12}'
]
]
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])
time_series_create.i_create_a_time_series_with_params(
self, example[6])
time_series_create.the_time_series_is_finished_in_less_than(
self, example[3])
time_series_create.create_local_time_series(self)
forecast_create.i_create_a_forecast(self, example[4])
forecast_create.the_forecast_is(self, example[5])
forecast_compare.i_create_a_local_forecast(self, example[4])
forecast_compare.the_local_forecast_is(self, example[5])
def test_scenario1(self):
"""
Scenario: Successfully creating forecasts 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 time-series from a dataset
And I wait until the time series is ready less than <time_3> secs
And I update the time series name to "<time_series_name>"
When I wait until the time series is ready less than <time_4> secs
Then the time series name is "<time_series_name>"
And I create a forecast for "<input_data>"
Then the forecasts are "<forecast_points>"
Examples:
| data | time_1 | time_2 | time_3 | time_4 | time_series_name |input_data | forecast_points
| ../data/grades.csv | 10 | 10 | 20 | 50 | my new time_series name |
{"000005": {"horizon": 5}], {}}
"""
print self.test_scenario1.__doc__
examples = [[
'data/grades.csv', '30', '30', '50', '50',
'my new time series name', '{"000005": {"horizon": 5}}',
'{"000005": [{"point_forecast": [73.96192, 74.04106, 74.12029, 74.1996, 74.27899], "model": "M,M,N"}]}'
]]
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])
time_series_create.i_create_a_time_series(self)
time_series_create.the_time_series_is_finished_in_less_than(
self, example[3])
time_series_create.i_update_time_series_name(self, example[5])
time_series_create.the_time_series_is_finished_in_less_than(
self, example[4])
time_series_create.i_check_time_series_name(self, example[5])
forecast_create.i_create_a_forecast(self, example[6])
forecast_create.the_forecast_is(self, example[7])
def test_scenario3(self):
"""
Scenario: Successfully comparing forecasts from time series with "M" seasonality
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 time series with "<params>"
And I wait until the time series is ready less than <time_3> secs
And I create a local time series
When I create a forecast for "<input_data>"
Then the forecast is "<forecasts>"
And I create a local forecast for "<data_input>"
Then the local forecast is "<forecasts>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | forecasts | params
,
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,Ad,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [73.75816, 74.60699, 66.71212, 72.49586, 71.76787], "model": "M,Ad,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,Md,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [74.3725, 75.02963, 67.15826, 73.19628, 71.66919], "model": "M,Md,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}']
"""
examples = [
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,N,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [68.99775, 72.76777, 66.5556, 70.90818, 70.92998], "model": "M,N,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,A,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [70.65993, 78.20652, 69.64806, 75.43716, 78.13556], "model": "M,A,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,M,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [71.75055, 80.67195, 70.81368, 79.84999, 78.27634], "model": "M,M,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}']]
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])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
time_series_create.i_create_a_time_series_with_params(self, example[6])
time_series_create.the_time_series_is_finished_in_less_than(self, example[3])
time_series_create.create_local_time_series(self)
forecast_create.i_create_a_forecast(self, example[4])
forecast_create.the_forecast_is(self, example[5])
forecast_compare.i_create_a_local_forecast(self, example[4])
forecast_compare.the_local_forecast_is(self, example[5])
def test_scenario3(self):
"""
Scenario: Successfully comparing forecasts from time series with "M" seasonality
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 time series with "<params>"
And I wait until the time series is ready less than <time_3> secs
And I create a local time series
When I create a forecast for "<input_data>"
Then the forecast is "<forecasts>"
And I create a local forecast for "<data_input>"
Then the local forecast is "<forecasts>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | forecasts | params
"""
examples = [
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,N,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [72.39954, 71.72705, 66.45629, 69.92079, 68.9178], "model": "M,N,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,A,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [75.8422, 76.16165, 68.78198, 74.59676, 73.37048], "model": "M,A,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,M,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [76.18187, 77.95967, 70.45434, 75.35636, 74.66747], "model": "M,M,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,Ad,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [73.75816, 74.60699, 66.71212, 72.49586, 71.76787], "model": "M,Ad,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,Md,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [74.3725, 75.02963, 67.15826, 73.19628, 71.66919], "model": "M,Md,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}']]
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])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
time_series_create.i_create_a_time_series_with_params(self, example[6])
time_series_create.the_time_series_is_finished_in_less_than(self, example[3])
time_series_create.create_local_time_series(self)
forecast_create.i_create_a_forecast(self, example[4])
forecast_create.the_forecast_is(self, example[5])
forecast_compare.i_create_a_local_forecast(self, example[4])
forecast_compare.the_local_forecast_is(self, example[5])
def test_scenario3(self):
"""
Scenario: Successfully comparing forecasts from time series with "M" seasonality
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 time series with "<params>"
And I wait until the time series is ready less than <time_3> secs
And I create a local time series
When I create a forecast for "<input_data>"
Then the forecast is "<forecasts>"
And I create a local forecast for "<data_input>"
Then the local forecast is "<forecasts>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | forecasts | params
,
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,Ad,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [73.75816, 74.60699, 66.71212, 72.49586, 71.76787], "model": "M,Ad,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,Md,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [74.3725, 75.02963, 67.15826, 73.19628, 71.66919], "model": "M,Md,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}']
"""
examples = [
['data/grades.csv', '30', '30', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,N,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [68.99775, 72.76777, 66.5556, 70.90818, 70.92998], "model": "M,N,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '30', '30', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,A,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [70.65993, 78.20652, 69.64806, 75.43716, 78.13556], "model": "M,A,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '30', '30', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,M,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [71.75055, 80.67195, 70.81368, 79.84999, 78.27634], "model": "M,M,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}']]
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])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
time_series_create.i_create_a_time_series_with_params(self, example[6])
time_series_create.the_time_series_is_finished_in_less_than(self, example[3])
time_series_create.create_local_time_series(self)
forecast_create.i_create_a_forecast(self, example[4])
forecast_create.the_forecast_is(self, example[5])
forecast_compare.i_create_a_local_forecast(self, example[4])
forecast_compare.the_local_forecast_is(self, example[5])
def test_scenario3(self):
"""
Scenario: Successfully comparing forecasts from time series with "M" seasonality
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 time series with "<params>"
And I wait until the time series is ready less than <time_3> secs
And I create a local time series
When I create a forecast for "<input_data>"
Then the forecast is "<forecasts>"
And I create a local forecast for "<data_input>"
Then the local forecast is "<forecasts>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | forecasts | params
"""
examples = [
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,N,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [72.17615, 72.03876, 66.47413, 69.65207, 68.69178], "model": "M,N,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,A,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [74.64993, 76.14421, 68.47124, 74.19305, 73.51855], "model": "M,A,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,M,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [74.22947, 78.06875, 69.96644, 75.98277, 75.92554], "model": "M,M,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,Ad,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast":[73.80787, 74.61327, 67.30521, 73.12079, 72.03179], "model": "M,Ad,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,Md,M"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [74.65602, 74.79726, 67.07633, 72.56983, 72.23638], "model": "M,Md,M"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}']]
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])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
time_series_create.i_create_a_time_series_with_params(self, example[6])
time_series_create.the_time_series_is_finished_in_less_than(self, example[3])
time_series_create.create_local_time_series(self)
forecast_create.i_create_a_forecast(self, example[4])
forecast_create.the_forecast_is(self, example[5])
forecast_compare.i_create_a_local_forecast(self, example[4])
forecast_compare.the_local_forecast_is(self, example[5])
def test_scenario1(self):
"""
Scenario: Successfully comparing forecasts from time series:
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 time series with "<params>"
And I wait until the time series is ready less than <time_3> secs
And I create a local time series
When I create a forecast for "<input_data>"
Then the forecast is "<forecasts>"
And I create a local forecast for "<data_input>"
Then the local forecast is "<forecasts>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | forecasts | params
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["A,Ad,N"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [69.90959, 69.92755, 69.94514, 69.96236, 69.97922], "model": "A,Ad,N"}]}', '{"objective_fields": ["000001", "000005"]}'],
"""
examples = [
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5}}', '{"000005": [{"point_forecast": [73.96192, 74.04106, 74.12029, 74.1996, 74.27899], "model": "M,M,N"}]}', '{"objective_fields": ["000001", "000005"]}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,N,N"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [68.39832, 68.39832, 68.39832, 68.39832, 68.39832], "model": "M,N,N"}]}', '{"objective_fields": ["000001", "000005"]}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["A,A,N"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [72.46247, 72.56247, 72.66247, 72.76247, 72.86247], "model": "A,A,N"}]}', '{"objective_fields": ["000001", "000005"]}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5}, "000001": {"horizon": 3, "ets_models": {"criterion": "aic", "limit": 2}}}', '{"000005": [{"point_forecast": [73.96192, 74.04106, 74.12029, 74.1996, 74.27899], "model": "M,M,N"}], "000001": [{"point_forecast": [55.51577, 89.69111, 82.04935], "model": "A,N,A"}, {"point_forecast": [56.67419, 91.89657, 84.70017], "model": "A,A,A"}]}', '{"objective_fields": ["000001", "000005"]}']]
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])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
time_series_create.i_create_a_time_series_with_params(self, example[6])
time_series_create.the_time_series_is_finished_in_less_than(self, example[3])
time_series_create.create_local_time_series(self)
forecast_create.i_create_a_forecast(self, example[4])
forecast_create.the_forecast_is(self, example[5])
forecast_compare.i_create_a_local_forecast(self, example[4])
forecast_compare.the_local_forecast_is(self, example[5])
def test_scenario4(self):
"""
Scenario: Successfully comparing forecasts from time series with trivial models
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 time series with "<params>"
And I wait until the time series is ready less than <time_3> secs
And I create a local time series
When I create a forecast for "<input_data>"
Then the forecast is "<forecasts>"
And I create a local forecast for "<data_input>"
Then the local forecast is "<forecasts>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | forecasts | params
"""
examples = [
['data/grades.csv', '10', '1000', '1000', '{"000005": {"horizon": 5, "ets_models": {"names": ["naive"]}}}', '{"000005": [{"point_forecast": [61.39, 61.39, 61.39, 61.39, 61.39], "model": "naive"}]}', '{"objective_fields": ["000001", "000005"], "period": 1}'],
['data/grades.csv', '10', '1000', '1000', '{"000005": {"horizon": 5, "ets_models": {"names": ["naive"]}}}', '{"000005": [{"point_forecast": [78.89, 61.39, 78.89, 61.39, 78.89], "model": "naive"}]}', '{"objective_fields": ["000001", "000005"], "period": 2}'],
['data/grades.csv', '10', '1000', '1000', '{"000005": {"horizon": 5, "ets_models": {"names": ["mean"]}}}', '{"000005": [{"point_forecast": [68.45974, 68.45974, 68.45974, 68.45974, 68.45974], "model": "mean"}]}', '{"objective_fields": ["000001", "000005"], "period": 1}'],
['data/grades.csv', '10', '1000', '1000', '{"000005": {"horizon": 5, "ets_models": {"names": ["mean"]}}}', '{"000005": [{"point_forecast": [69.79553, 67.15821, 69.79553, 67.15821, 69.79553], "model": "mean"}]}', '{"objective_fields": ["000001", "000005"], "period": 2}'],
['data/grades.csv', '10', '1000', '1000', '{"000005": {"horizon": 5, "ets_models": {"names": ["drift"]}}}', '{"000005": [{"point_forecast": [61.50545, 61.6209, 61.73635, 61.8518, 61.96725], "model": "drift"}]}', '{"objective_fields": ["000001", "000005"], "period": 1}'],
['data/grades.csv', '10', '1000', '1000', '{"000005": {"horizon": 5, "ets_models": {"names": ["drift"]}}}', '{"000005": [{"point_forecast": [61.50545, 61.6209, 61.73635, 61.8518, 61.96725], "model": "drift"}]}', '{"objective_fields": ["000001", "000005"], "period": 2}']]
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])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
time_series_create.i_create_a_time_series_with_params(self, example[6])
time_series_create.the_time_series_is_finished_in_less_than(self, example[3])
time_series_create.create_local_time_series(self)
forecast_create.i_create_a_forecast(self, example[4])
forecast_create.the_forecast_is(self, example[5])
forecast_compare.i_create_a_local_forecast(self, example[4])
forecast_compare.the_local_forecast_is(self, example[5])
def test_scenario1(self):
"""
Scenario: Successfully comparing forecasts from time series:
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 time series with "<params>"
And I wait until the time series is ready less than <time_3> secs
And I create a local time series
When I create a forecast for "<input_data>"
Then the forecast is "<forecasts>"
And I create a local forecast for "<data_input>"
Then the local forecast is "<forecasts>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | forecasts | params
"""
examples = [
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5}}', '{"000005": [{"point_forecast": [68.53181, 68.53181, 68.53181, 68.53181, 68.53181], "model": "A,N,N"}]}', '{"objective_fields": ["000001", "000005"]}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,N,N"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [68.50838, 68.50838, 68.50838, 68.50838, 68.50838], "model": "M,N,N"}]}', '{"objective_fields": ["000001", "000005"]}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["A,A,N"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [75.00015, 75.16225, 75.32435, 75.48645, 75.64855], "model": "A,A,N"}]}', '{"objective_fields": ["000001", "000005"]}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["A,Ad,N"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [73.32521, 73.38823, 73.45, 73.51052, 73.56984], "model": "A,Ad,N"}]}', '{"objective_fields": ["000001", "000005"]}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5}, "000001": {"horizon": 3, "ets_models": {"criterion": "aic", "limit": 2}}}', '{"000005": [{"point_forecast": [68.53181, 68.53181, 68.53181, 68.53181, 68.53181], "model": "A,N,N"}], "000001": [{"point_forecast": [54.77665, 90.00943, 83.59285], "model": "A,N,A"}, {"point_forecast": [55.88282, 90.52555, 83.44909], "model": "A,Ad,A"}]}', '{"objective_fields": ["000001", "000005"]}']]
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])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
time_series_create.i_create_a_time_series_with_params(self, example[6])
time_series_create.the_time_series_is_finished_in_less_than(self, example[3])
time_series_create.create_local_time_series(self)
forecast_create.i_create_a_forecast(self, example[4])
forecast_create.the_forecast_is(self, example[5])
forecast_compare.i_create_a_local_forecast(self, example[4])
forecast_compare.the_local_forecast_is(self, example[5])
def test_scenario1(self):
"""
Scenario: Successfully comparing forecasts from time series:
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 time series with "<params>"
And I wait until the time series is ready less than <time_3> secs
And I create a local time series
When I create a forecast for "<input_data>"
Then the forecast is "<forecasts>"
And I create a local forecast for "<data_input>"
Then the local forecast is "<forecasts>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | forecasts | params
"""
examples = [
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5}}', '{"000005": [{"point_forecast": [68.50243, 68.50243, 68.50243, 68.50243, 68.50243], "model": "A,N,N"}]}', '{"objective_fields": ["000001", "000005"]}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,N,N"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [68.47181, 68.47181, 68.47181, 68.47181, 68.47181], "model": "M,N,N"}]}', '{"objective_fields": ["000001", "000005"]}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["A,A,N"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [72.54131, 72.64131, 72.74131, 72.84131, 72.94131], "model": "A,A,N"}]}', '{"objective_fields": ["000001", "000005"]}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["A,Ad,N"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [69.87368, 69.89291, 69.91175, 69.93022, 69.94831], "model": "A,Ad,N"}]}', '{"objective_fields": ["000001", "000005"]}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5}, "000001": {"horizon": 3, "ets_models": {"criterion": "aic", "limit": 2}}}', '{"000005": [{"point_forecast": [68.50243, 68.50243, 68.50243, 68.50243, 68.50243], "model": "A,N,N"}], "000001": [{"point_forecast": [54.73965, 89.70194, 82.0656], "model": "A,N,A"}, {"point_forecast": [56.84168, 91.91525, 84.8288], "model": "A,A,A"}]}', '{"objective_fields": ["000001", "000005"]}']]
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])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
time_series_create.i_create_a_time_series_with_params(self, example[6])
time_series_create.the_time_series_is_finished_in_less_than(self, example[3])
time_series_create.create_local_time_series(self)
forecast_create.i_create_a_forecast(self, example[4])
forecast_create.the_forecast_is(self, example[5])
forecast_compare.i_create_a_local_forecast(self, example[4])
forecast_compare.the_local_forecast_is(self, example[5])
def test_scenario1(self):
"""
Scenario: Successfully comparing forecasts from time series:
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 time series with "<params>"
And I wait until the time series is ready less than <time_3> secs
And I create a local time series
When I create a forecast for "<input_data>"
Then the forecast is "<forecasts>"
And I create a local forecast for "<data_input>"
Then the local forecast is "<forecasts>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | forecasts | params
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["A,Ad,N"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [69.90959, 69.92755, 69.94514, 69.96236, 69.97922], "model": "A,Ad,N"}]}', '{"objective_fields": ["000001", "000005"]}'],
"""
examples = [
['data/grades.csv', '30', '30', '120', '{"000005": {"horizon": 5}}', '{"000005": [{"point_forecast": [73.96192, 74.04106, 74.12029, 74.1996, 74.27899], "model": "M,M,N"}]}', '{"objective_fields": ["000001", "000005"]}'],
['data/grades.csv', '30', '30', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,N,N"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [68.39832, 68.39832, 68.39832, 68.39832, 68.39832], "model": "M,N,N"}]}', '{"objective_fields": ["000001", "000005"]}'],
['data/grades.csv', '30', '30', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["A,A,N"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [72.46247, 72.56247, 72.66247, 72.76247, 72.86247], "model": "A,A,N"}]}', '{"objective_fields": ["000001", "000005"]}'],
['data/grades.csv', '30', '30', '120', '{"000005": {"horizon": 5}, "000001": {"horizon": 3, "ets_models": {"criterion": "aic", "limit": 2}}}', '{"000005": [{"point_forecast": [73.96192, 74.04106, 74.12029, 74.1996, 74.27899], "model": "M,M,N"}], "000001": [{"point_forecast": [55.51577, 89.69111, 82.04935], "model": "A,N,A"}, {"point_forecast": [56.67419, 91.89657, 84.70017], "model": "A,A,A"}]}', '{"objective_fields": ["000001", "000005"]}']]
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])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
time_series_create.i_create_a_time_series_with_params(self, example[6])
time_series_create.the_time_series_is_finished_in_less_than(self, example[3])
time_series_create.create_local_time_series(self)
forecast_create.i_create_a_forecast(self, example[4])
forecast_create.the_forecast_is(self, example[5])
forecast_compare.i_create_a_local_forecast(self, example[4])
forecast_compare.the_local_forecast_is(self, example[5])
def test_scenario4(self):
"""
Scenario: Successfully comparing forecasts from time series with trivial models
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 time series with "<params>"
And I wait until the time series is ready less than <time_3> secs
And I create a local time series
When I create a forecast for "<input_data>"
Then the forecast is "<forecasts>"
And I create a local forecast for "<data_input>"
Then the local forecast is "<forecasts>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | forecasts | params
"""
examples = [
['data/grades.csv', '10', '1000', '1000', '{"000005": {"horizon": 5, "ets_models": {"names": ["naive"]}}}', '{"000005": [{"point_forecast": [61.39, 61.39, 61.39, 61.39, 61.39], "model": "naive"}]}', '{"objective_fields": ["000001", "000005"], "period": 1}'],
['data/grades.csv', '10', '1000', '1000', '{"000005": {"horizon": 5, "ets_models": {"names": ["naive"]}}}', '{"000005": [{"point_forecast": [78.89, 61.39, 78.89, 61.39, 78.89], "model": "naive"}]}', '{"objective_fields": ["000001", "000005"], "period": 2}'],
['data/grades.csv', '10', '1000', '1000', '{"000005": {"horizon": 5, "ets_models": {"names": ["mean"]}}}', '{"000005": [{"point_forecast": [68.45974, 68.45974, 68.45974, 68.45974, 68.45974], "model": "mean"}]}', '{"objective_fields": ["000001", "000005"], "period": 1}'],
['data/grades.csv', '10', '1000', '1000', '{"000005": {"horizon": 5, "ets_models": {"names": ["mean"]}}}', '{"000005": [{"point_forecast": [69.79553, 67.15821, 69.79553, 67.15821, 69.79553], "model": "mean"}]}', '{"objective_fields": ["000001", "000005"], "period": 2}'],
['data/grades.csv', '10', '1000', '1000', '{"000005": {"horizon": 5, "ets_models": {"names": ["drift"]}}}', '{"000005": [{"point_forecast": [61.50545, 61.6209, 61.73635, 61.8518, 61.96725], "model": "drift"}]}', '{"objective_fields": ["000001", "000005"], "period": 1}'],
['data/grades.csv', '10', '1000', '1000', '{"000005": {"horizon": 5, "ets_models": {"names": ["drift"]}}}', '{"000005": [{"point_forecast": [61.50545, 61.6209, 61.73635, 61.8518, 61.96725], "model": "drift"}]}', '{"objective_fields": ["000001", "000005"], "period": 2}']]
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])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
time_series_create.i_create_a_time_series_with_params(self, example[6])
time_series_create.the_time_series_is_finished_in_less_than(self, example[3])
time_series_create.create_local_time_series(self)
forecast_create.i_create_a_forecast(self, example[4])
forecast_create.the_forecast_is(self, example[5])
forecast_compare.i_create_a_local_forecast(self, example[4])
forecast_compare.the_local_forecast_is(self, example[5])
def test_scenario2(self):
"""
Scenario: Successfully comparing forecasts from time series with "A" seasonality
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 time series with "<params>"
And I wait until the time series is ready less than <time_3> secs
And I create a local time series
When I create a forecast for "<input_data>"
Then the forecast is "<forecasts>"
And I create a local forecast for "<data_input>"
Then the local forecast is "<forecasts>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | forecasts | params
"""
examples = [
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5}}', '{"000005": [{"point_forecast": [68.53181, 68.53181, 68.53181, 68.53181, 68.53181], "model": "A,N,N"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,N,A"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [72.51702, 72.29175, 66.73389, 72.20277, 69.47615], "model": "M,N,A"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["A,A,A"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [69.60446, 74.28786, 67.46402, 75.91042, 72.52807], "model": "A,A,A"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["A,Ad,A"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast":[66.28032, 71.15014, 65.99847, 73.28416, 71.04533], "model": "A,Ad,A"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}']]
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])
time_series_create.i_create_a_time_series_with_params(self, example[6])
time_series_create.the_time_series_is_finished_in_less_than(self, example[3])
time_series_create.create_local_time_series(self)
forecast_create.i_create_a_forecast(self, example[4])
forecast_create.the_forecast_is(self, example[5])
forecast_compare.i_create_a_local_forecast(self, example[4])
forecast_compare.the_local_forecast_is(self, example[5])
def test_scenario2(self):
"""
Scenario: Successfully comparing forecasts from time series with "A" seasonality
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 time series with "<params>"
And I wait until the time series is ready less than <time_3> secs
And I create a local time series
When I create a forecast for "<input_data>"
Then the forecast is "<forecasts>"
And I create a local forecast for "<data_input>"
Then the local forecast is "<forecasts>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | forecasts | params
"""
examples = [
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5}}', '{"000005": [{"point_forecast": [68.50243, 68.50243, 68.50243, 68.50243, 68.50243], "model": "A,N,N"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,N,A"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [72.09715, 72.11611, 66.58929, 71.71928, 69.29861], "model": "M,N,A"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["A,A,A"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [68.56915, 73.55705, 68.07577, 74.99042, 72.51314], "model": "A,A,A"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["A,Ad,A"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast":[66.16225, 72.17308, 66.65573, 73.09698, 70.51449], "model": "A,Ad,A"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}']]
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])
time_series_create.i_create_a_time_series_with_params(self, example[6])
time_series_create.the_time_series_is_finished_in_less_than(self, example[3])
time_series_create.create_local_time_series(self)
forecast_create.i_create_a_forecast(self, example[4])
forecast_create.the_forecast_is(self, example[5])
forecast_compare.i_create_a_local_forecast(self, example[4])
forecast_compare.the_local_forecast_is(self, example[5])
def test_scenario2(self):
"""
Scenario: Successfully comparing forecasts from time series with "A" seasonality
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 time series with "<params>"
And I wait until the time series is ready less than <time_3> secs
And I create a local time series
When I create a forecast for "<input_data>"
Then the forecast is "<forecasts>"
And I create a local forecast for "<data_input>"
Then the local forecast is "<forecasts>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | forecasts | params
['data/grades.csv', '10', '10', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["A,Ad,A"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast":[66.16225, 72.17308, 66.65573, 73.09698, 70.51449], "model": "A,Ad,A"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}']
"""
examples = [
['data/grades.csv', '30', '30', '120', '{"000005": {"horizon": 5}}', '{"000005": [{"point_forecast": [73.96192, 74.04106, 74.12029, 74.1996, 74.27899], "model": "M,M,N"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '30', '30', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["M,N,A"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [67.43222, 68.24468, 64.14437, 67.5662, 67.79028], "model": "M,N,A"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}'],
['data/grades.csv', '30', '30', '120', '{"000005": {"horizon": 5, "ets_models": {"names": ["A,A,A"], "criterion": "aic", "limit": 3}}}', '{"000005": [{"point_forecast": [74.73553, 71.6163, 71.90264, 76.4249, 75.06982], "model": "A,A,A"}]}', '{"objective_fields": ["000001", "000005"], "period": 12}']]
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])
time_series_create.i_create_a_time_series_with_params(self, example[6])
time_series_create.the_time_series_is_finished_in_less_than(self, example[3])
time_series_create.create_local_time_series(self)
forecast_create.i_create_a_forecast(self, example[4])
forecast_create.the_forecast_is(self, example[5])
forecast_compare.i_create_a_local_forecast(self, example[4])
forecast_compare.the_local_forecast_is(self, example[5])
def test_scenario1(self):
"""
Scenario: Successfully creating forecasts 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 time-series from a dataset
And I wait until the time series is ready less than <time_3> secs
And I update the time series name to "<time_series_name>"
When I wait until the time series is ready less than <time_4> secs
Then the time series name is "<time_series_name>"
And I create a forecast for "<input_data>"
Then the forecasts are "<forecast_points>"
Examples:
| data | time_1 | time_2 | time_3 | time_4 | time_series_name |input_data | forecast_points
| ../data/grades.csv | 10 | 10 | 20 | 50 | my new time_series name |
{"000005": {"horizon": 5}], {}}
"""
print self.test_scenario1.__doc__
examples = [
['data/grades.csv', '10', '10', '20', '50', 'my new time series name',
'{"000005": {"horizon": 5}}', '{"000005": [{"point_forecast": [68.53181, 68.53181, 68.53181, 68.53181, 68.53181], "model": "A,N,N"}]}']]
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])
time_series_create.i_create_a_time_series(self)
time_series_create.the_time_series_is_finished_in_less_than(self, example[3])
time_series_create.i_update_time_series_name(self, example[5])
time_series_create.the_time_series_is_finished_in_less_than(self, example[4])
time_series_create.i_check_time_series_name(self, example[5])
forecast_create.i_create_a_forecast(self, example[6])
forecast_create.the_forecast_is(self, example[7])
def test_scenario3(self):
"""
Scenario: Successfully comparing forecasts from time series with "M" seasonality
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 time series with "<params>"
And I wait until the time series is ready less than <time_3> secs
And I create a local time series
When I create a forecast for "<input_data>"
Then the forecast is "<forecasts>"
And I create a local forecast for "<data_input>"
Then the local forecast is "<forecasts>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | forecasts | params
"""
examples = [
[
'data/grades.csv', '10', '10', '120',
'{"000005": {"horizon": 5, "ets_models": {"names": ["M,N,M"], "criterion": "aic", "limit": 3}}}',
'{"000005": [{"point_forecast": [72.17615, 72.03876, 66.47413, 69.65207, 68.69178], "model": "M,N,M"}]}',
'{"objective_fields": ["000001", "000005"], "period": 12}'
],
[
'data/grades.csv', '10', '10', '120',
'{"000005": {"horizon": 5, "ets_models": {"names": ["M,A,M"], "criterion": "aic", "limit": 3}}}',
'{"000005": [{"point_forecast": [74.64993, 76.14421, 68.47124, 74.19305, 73.51855], "model": "M,A,M"}]}',
'{"objective_fields": ["000001", "000005"], "period": 12}'
],
[
'data/grades.csv', '10', '10', '120',
'{"000005": {"horizon": 5, "ets_models": {"names": ["M,M,M"], "criterion": "aic", "limit": 3}}}',
'{"000005": [{"point_forecast": [74.22947, 78.06875, 69.96644, 75.98277, 75.92554], "model": "M,M,M"}]}',
'{"objective_fields": ["000001", "000005"], "period": 12}'
],
[
'data/grades.csv', '10', '10', '120',
'{"000005": {"horizon": 5, "ets_models": {"names": ["M,Ad,M"], "criterion": "aic", "limit": 3}}}',
'{"000005": [{"point_forecast":[73.80787, 74.61327, 67.30521, 73.12079, 72.03179], "model": "M,Ad,M"}]}',
'{"objective_fields": ["000001", "000005"], "period": 12}'
],
[
'data/grades.csv', '10', '10', '120',
'{"000005": {"horizon": 5, "ets_models": {"names": ["M,Md,M"], "criterion": "aic", "limit": 3}}}',
'{"000005": [{"point_forecast": [74.65602, 74.79726, 67.07633, 72.56983, 72.23638], "model": "M,Md,M"}]}',
'{"objective_fields": ["000001", "000005"], "period": 12}'
]
]
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])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(
self, example[2])
time_series_create.i_create_a_time_series_with_params(
self, example[6])
time_series_create.the_time_series_is_finished_in_less_than(
self, example[3])
time_series_create.create_local_time_series(self)
forecast_create.i_create_a_forecast(self, example[4])
forecast_create.the_forecast_is(self, example[5])
forecast_compare.i_create_a_local_forecast(self, example[4])
forecast_compare.the_local_forecast_is(self, example[5])
def test_scenario2(self):
"""
Scenario: Successfully comparing forecasts from time series with "A" seasonality
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 time series with "<params>"
And I wait until the time series is ready less than <time_3> secs
And I create a local time series
When I create a forecast for "<input_data>"
Then the forecast is "<forecasts>"
And I create a local forecast for "<data_input>"
Then the local forecast is "<forecasts>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | forecasts | params
"""
examples = [
[
'data/grades.csv', '10', '10', '120',
'{"000005": {"horizon": 5}}',
'{"000005": [{"point_forecast": [68.53181, 68.53181, 68.53181, 68.53181, 68.53181], "model": "A,N,N"}]}',
'{"objective_fields": ["000001", "000005"], "period": 12}'
],
[
'data/grades.csv', '10', '10', '120',
'{"000005": {"horizon": 5, "ets_models": {"names": ["M,N,A"], "criterion": "aic", "limit": 3}}}',
'{"000005": [{"point_forecast": [72.51702, 72.29175, 66.73389, 72.20277, 69.47615], "model": "M,N,A"}]}',
'{"objective_fields": ["000001", "000005"], "period": 12}'
],
[
'data/grades.csv', '10', '10', '120',
'{"000005": {"horizon": 5, "ets_models": {"names": ["A,A,A"], "criterion": "aic", "limit": 3}}}',
'{"000005": [{"point_forecast": [69.60446, 74.28786, 67.46402, 75.91042, 72.52807], "model": "A,A,A"}]}',
'{"objective_fields": ["000001", "000005"], "period": 12}'
],
[
'data/grades.csv', '10', '10', '120',
'{"000005": {"horizon": 5, "ets_models": {"names": ["A,Ad,A"], "criterion": "aic", "limit": 3}}}',
'{"000005": [{"point_forecast":[66.28032, 71.15014, 65.99847, 73.28416, 71.04533], "model": "A,Ad,A"}]}',
'{"objective_fields": ["000001", "000005"], "period": 12}'
]
]
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])
time_series_create.i_create_a_time_series_with_params(
self, example[6])
time_series_create.the_time_series_is_finished_in_less_than(
self, example[3])
time_series_create.create_local_time_series(self)
forecast_create.i_create_a_forecast(self, example[4])
forecast_create.the_forecast_is(self, example[5])
forecast_compare.i_create_a_local_forecast(self, example[4])
forecast_compare.the_local_forecast_is(self, example[5])
def test_scenario1(self):
"""
Scenario: Successfully comparing forecasts from time series:
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 time series with "<params>"
And I wait until the time series is ready less than <time_3> secs
And I create a local time series
When I create a forecast for "<input_data>"
Then the forecast is "<forecasts>"
And I create a local forecast for "<data_input>"
Then the local forecast is "<forecasts>"
Examples:
| data | time_1 | time_2 | time_3 | input_data | forecasts | params
"""
examples = [
[
'data/grades.csv', '10', '10', '120',
'{"000005": {"horizon": 5}}',
'{"000005": [{"point_forecast": [68.53181, 68.53181, 68.53181, 68.53181, 68.53181], "model": "A,N,N"}]}',
'{"objective_fields": ["000001", "000005"]}'
],
[
'data/grades.csv', '10', '10', '120',
'{"000005": {"horizon": 5, "ets_models": {"names": ["M,N,N"], "criterion": "aic", "limit": 3}}}',
'{"000005": [{"point_forecast": [68.50838, 68.50838, 68.50838, 68.50838, 68.50838], "model": "M,N,N"}]}',
'{"objective_fields": ["000001", "000005"]}'
],
[
'data/grades.csv', '10', '10', '120',
'{"000005": {"horizon": 5, "ets_models": {"names": ["A,A,N"], "criterion": "aic", "limit": 3}}}',
'{"000005": [{"point_forecast": [75.00015, 75.16225, 75.32435, 75.48645, 75.64855], "model": "A,A,N"}]}',
'{"objective_fields": ["000001", "000005"]}'
],
[
'data/grades.csv', '10', '10', '120',
'{"000005": {"horizon": 5, "ets_models": {"names": ["A,Ad,N"], "criterion": "aic", "limit": 3}}}',
'{"000005": [{"point_forecast": [73.32521, 73.38823, 73.45, 73.51052, 73.56984], "model": "A,Ad,N"}]}',
'{"objective_fields": ["000001", "000005"]}'
],
[
'data/grades.csv', '10', '10', '120',
'{"000005": {"horizon": 5}, "000001": {"horizon": 3, "ets_models": {"criterion": "aic", "limit": 2}}}',
'{"000005": [{"point_forecast": [68.53181, 68.53181, 68.53181, 68.53181, 68.53181], "model": "A,N,N"}], "000001": [{"point_forecast": [54.77665, 90.00943, 83.59285], "model": "A,N,A"}, {"point_forecast": [55.88282, 90.52555, 83.44909], "model": "A,Ad,A"}]}',
'{"objective_fields": ["000001", "000005"]}'
]
]
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])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(
self, example[2])
time_series_create.i_create_a_time_series_with_params(
self, example[6])
time_series_create.the_time_series_is_finished_in_less_than(
self, example[3])
time_series_create.create_local_time_series(self)
forecast_create.i_create_a_forecast(self, example[4])
forecast_create.the_forecast_is(self, example[5])
forecast_compare.i_create_a_local_forecast(self, example[4])
forecast_compare.the_local_forecast_is(self, example[5])
