def test_get_dataset(self):
dataset_id = 'us30'
ds_by_id = Dataset(api=self.api, id=dataset_id)
filters = ds_by_id.filters
assert isinstance(filters, dict)
ds_via_api = self.api.get_dataset(dataset_id)
ds_by_uuid = Dataset(api=self.api, uuid=dataset_id)
assert ds_via_api.filters == ds_by_id.filters == ds_by_uuid.filters
class TestDeleteAllByName(object):
api = RPApi()
base_dataset = Dataset(
name='testing_api_delete_all',
filters={}, # a dataset without filters
)
def test_delete_all_by_name(self):
dataset_name = self.base_dataset.name
delete_all_datasets_by_name(self.api, dataset_name)
assert len(get_datasets_by_name(self.api, dataset_name)
) == 0, "Seems we have datasets that should be deleted"
ds1 = self.api.create_dataset(self.base_dataset) # create 1...
ds2 = self.api.create_dataset(self.base_dataset) # create 2...
assert len(get_datasets_by_name(
self.api,
dataset_name)) == 2, "We should have just created 2 datasets"
# we can also check the new ones are in the owned
owned_dataset = self.api.list_datasets()
assert ds1 in owned_dataset
assert ds2 in owned_dataset
delete_all_datasets_by_name(self.api, dataset_name)
assert len(get_datasets_by_name(self.api, dataset_name)
) == 0, "Seems we have datasets that should be deleted"
def test_create_and_update(self):
delete_all_datasets_by_name(self.api, self.dataset_name)
filters = {"rp_entity_id": {"$in": ['AAAAAA']}}
dataset = Dataset(
name=self.dataset_name,
filters=filters, # a dataset with a filter
)
dataset = self.api.create_dataset(dataset)
assert dataset.id is not None
dataset_id = dataset.id
# change the dataset
new_filters = {"rp_entity_id": {"$in": ['BBBBBB']}}
dataset.filters = new_filters
dataset.save()
# get the dataset again
dataset = self.api.get_dataset(dataset_id)
assert dataset.filters == new_filters
new_filters = {"rp_entity_id": {"$in": ['CCCCCC']}}
dataset.filters = new_filters
dataset.save()
dataset.delete()
assert delete_all_datasets_by_name(self.api, self.dataset_name) == 0
def setup_class(cls):
cls.ds = cls.api.create_dataset(
Dataset(
name='testing_encoding',
filters={
"rp_entity_id": '9BFEB5' # this entity has non-ascii name
},
))
def setup_class(cls):
cls.ds = cls.api.create_dataset(
Dataset(
name='test_job_cancel',
filters={
"rp_entity_id": 'D8442A'
},
)
)
def test_dataset_copy_updated(self):
source_dataset = Dataset(api=self.api, id='us30')
new_dataset = Dataset(
api=self.api,
name="copy of the us30 dataset",
filters=source_dataset.filters,
fields=['timestamp_utc', 'rp_entity_id', 'avg_sentiment'],
custom_fields=[{
"avg_sentiment": {
"avg": {
"field": "EVENT_SENTIMENT_SCORE",
}
}
}],
frequency='daily',
tags=['copy', 'test'])
new_dataset.save()
new_dataset.delete()
def create_dataset(self, dataset):
# be sure to create a copy
new_dataset_data = dataset.as_dict()
new_dataset = Dataset(api=self, **new_dataset_data)
if 'uuid' in new_dataset_data:
del new_dataset['uuid']
new_dataset.save()
dataset_id = new_dataset.id
logger.info("Created dataset %s" % dataset_id)
return new_dataset
def test_granular_dataset(self):
self.api.log_curl_commands = True
granular_dataset = Dataset(
name='Test-granular-dataset',
filters={"$and": [{"rp_entity_id": {"$in": ["D8442A"]}}, {"relevance": 90}]},
)
granular_dataset = self.api.create_dataset(granular_dataset)
try:
granular_dataset.json('2018-01-01 00:00', '2018-01-02 00:00')
finally:
granular_dataset.delete()
def daily_download(dataset_id):
end_date = datetime.datetime.utcnow().replace(hour=0,
minute=0,
second=0,
microsecond=0)
start_date = end_date - datetime.timedelta(days=1)
print("Date range: %s-%s" % (start_date, end_date))
dataset = Dataset(api=api, id=dataset_id)
job = dataset.request_datafile(start_date, end_date)
print("Waiting for the job to complete")
job.wait_for_completion()
output_filename = "dailydata-%s.csv" % start_date.strftime("%Y-%m-%d")
job.save_to_file(output_filename)
print("Daily download saved on:", output_filename)
def get_counts_universe(universe, ltgt, start_date, end_date, thresh,
filename):
label = "count_pos"
if ltgt == "lt":
label = "count_neg"
global api
custom_dataset = Dataset(name="Test set",
frequency="daily",
filters={
"and": [
{
"rp_entity_id": {
"in": universe
}
},
{
"event_relevance": {
"gte": thresh
}
},
{
"event_sentiment_score": {
ltgt: 0.5
}
},
]
},
custom_fields=[
{
label: {
"count": {
"field": "rp_entity_id"
}
}
},
])
ds = api.create_dataset(custom_dataset)
req_data = ds.request_datafile(start_date=start_date, end_date=end_date)
fname = filename + ".csv"
with open(fname, "w") as fp:
req_data.save_to_file(filename=fp.name)
fp.close()
print("Done importing data for {}".format(fname))
def test_simple_update(self):
filters = {"rp_entity_id": {"$in": ['D8442A']}}
ds = self.api.create_dataset(
Dataset(
name=self.dataset_name,
filters=filters, # a dataset with a filter
)
)
assert ds._lazy_retrieve_on_get is False
dataset_id = ds.id
ds = self.api.get_dataset(dataset_id) # retrieve the dataset
assert ds._lazy_retrieve_on_get is True # it still have to be lazy loaded here
ds.filters = {"rp_entity_id": {"$in": ["228D42"]}} # update the dataset ***
ds.save()
for r in ds.json('2019-01-01', '2019-01-02'):
assert r['rp_entity_id'] == '228D42', "Expecting entity to be 228D42 - got %s" % r['rp_entity_id']
break
def test_indicator_dataset(self):
indicator_dataset = Dataset(
name='Test-indicator-dataset',
filters={"$and": [{
"rp_entity_id": {
"$in": ["D8442A"]
}
}]},
fields=[{
"average": {
"avg": {
"field": "EVENT_SENTIMENT_SCORE"
}
}
}],
frequency='daily',
)
indicator_dataset = self.api.create_dataset(indicator_dataset)
try:
# ask the indicator dataset for its data
response = indicator_dataset.json('2018-01-01 00:00',
'2018-01-02 00:00')
assert len(response) == 2 # we should get 2 rows
assert {r['rp_entity_id']
for r in response} == {'D8442A', 'ROLLUP'}
# do a request overriding fields and frequency to see the underlying data
response = indicator_dataset.json(
'2018-01-01 00:00',
'2018-01-02 00:00',
fields=['rp_story_id', 'rp_entity_id'],
frequency='granular')
assert len(
response) > 200, "We should have many granular analytics rows"
assert {r['rp_entity_id']
for r in response} == {'D8442A'
}, "All rows should be D8442A"
finally:
indicator_dataset.delete()
def test_create_and_delete(self):
# the test dataset is already there, let's delete it first
# we can have multiple dataset with same name, deleting all of them
delete_all_datasets_by_name(self.api, self.dataset_name)
# create the dataset
filters = {"rp_entity_id": {"$in": ['D8442A']}}
dataset = Dataset(
name=self.dataset_name,
filters=filters, # a dataset with a filter
)
new_dataset = self.api.create_dataset(dataset)
assert new_dataset.filters == dataset.filters, "Created dataset filters are not as expected"
assert new_dataset.id is not None, "We should have a dataset id"
owned_dataset = self.api.list_datasets()
assert new_dataset.id in owned_dataset, "We should own the new dataset"
new_dataset.delete()
owned_dataset = self.api.list_datasets()
assert new_dataset.id not in owned_dataset, "The new dataset should be deleted"
Dataset(
**{
"product":
"rpa",
"product_version":
"1.0",
"name":
"Events in UK - example",
"fields": [
"timestamp_utc", "rp_story_id", "rp_entity_id", "entity_type",
"entity_name", "country_code", "relevance",
"event_sentiment_score", "topic", "group", "headline"
],
"filters": {
"$and": [{
"relevance": {
"$gte": 90
}
}, {
"country_code": {
"$in": ["GB"]
}
}, {
"event_sentiment_score": {
"$nbetween": [-0.5, 0.5]
}
}]
},
"frequency":
"granular",
}))
from ravenpackapi import RPApi, Dataset
import logging
logging.basicConfig(level=logging.INFO)
# initialize the API (here we use the RP_API_KEY in os.environ)
api = RPApi()
# get the us30 dataset (its filters contain the top 30 US companies)
us30 = Dataset(api=api, id='us30')
print(us30.filters)
# creating a new dataset with modified filters and fields
# the filters are an aggregation of the us30 with some additional rule
new_filters = {
"$and": [
us30.filters, {
"relevance": {
"$gte": 90
}
}, {
"event_similarity_days": {
"$gte": 1
}
}
]
}
new_fields = [{
"daily_average_ess_1d": {
"avg": {
"field": "EVENT_SENTIMENT_SCORE",
from ravenpackapi import RPApi, Dataset
from ravenpackapi.models.job import Job
logging.basicConfig(level=logging.INFO)
# initialize the API (here we use the RP_API_KEY in os.environ)
api = RPApi()
dataset_id = None # put here a dataset_id if you have it already
if dataset_id is None:
dataset = Dataset(api=api,
filters={},
name='Average sentiment',
frequency='daily',
fields=[{
'average_ess': {
'avg': {
'field': 'EVENT_SENTIMENT_SCORE'
}
}
}])
dataset_id = dataset.save()
else:
dataset = api.get_dataset(dataset_id)
# job = Job(api=api,
# token='xxx') # if you already have a job you can use this
# ... or request a new one
job = dataset.request_datafile(
start_date='2018-01-01 00:00:00',
def test_invalid_additional_fields(self):
dt = '2020-01-01'
with pytest.raises(ValueError):
Dataset(id='us30', creation_time=dt, last_modified=dt, invalid_field=1)
def test_valid_uuid(self):
d = Dataset(uuid='us30')
assert d.id == 'us30'
def test_valid_additional_fields(self):
dt = '2020-01-01'
d = Dataset(id='us30', creation_time=dt, last_modified=dt)
assert d.id == 'us30'
assert d.creation_time == d.last_modified == dt
def get_dataset(self, dataset_id):
return Dataset(
api=self,
uuid=dataset_id,
)
from ravenpackapi import RPApi, Dataset
from ravenpackapi.utils.helpers import delete_all_datasets_by_name
api = RPApi()
# Begin creating a dataset with your desired filters (see the RPA user guide for syntax)
# You can then add functions (https://app.ravenpack.com/api-documentation/#indicator-syntax)
# Alternatively you can also create the dataset via the query builder and just use the dataset_uuid
dataset = Dataset(api,
name='My Indicator dataset',
filters={"relevance": {"$gt": 90}},
frequency='daily',
fields=[{"avg_1d": {"avg": {"field": "EVENT_SENTIMENT_SCORE", "lookback": 1,
"mode": "granular"}}},
{"avg_7d": {
"avg": {"field": "avg_1d", "lookback": 1, "mode": "granular"}}},
{"buzz_365d": {"buzz": {"field": "RP_ENTITY_ID", "lookback": 365}}},
{"newsvolume_1d": {"count": {"field": "RP_ENTITY_ID", "lookback": 1}}},
{"newsvolume_365d": {"avg": {"field": "newsvolume_1d", "lookback": 365,
"mode": "granular"}}}]
)
dataset.save()
# you can also change the fields, (remember to save afterward)
dataset.fields = [
{"avg": {"avg": {"field": "EVENT_SENTIMENT_SCORE", "lookback": 365}}},
]
dataset.save()
# Following this, you can then generate a datafile (for your desired date range)
job = dataset.request_datafile(