def band(self):
all_tasks = []
my_config = SomeConfig()
for i, d in enumerate(period_dates(self.task_target_date,
self.period)):
source = TDataSource(task_target_date=d)
complicated = TComplicatedTask(
specific_input=source,
some_param=my_config.some_param,
task_input=source.logs,
# empty_input=None,
task_target_date=d,
)
all_tasks.append(complicated)
self.combined_output = data_combine(
[t.simplest_output for t in all_tasks])
self.list_output = [t.simplest_output for t in all_tasks]
self.list_tasks_output = [t for t in all_tasks]
self.nested = [
TNestedPipeline1(task_name="MyNewPipe").some_output,
TNestedPipeline1(task_name="custom_task_name").some_output,
]
self.nested2 = TNestedPipeline2().some_output
self.nested3 = TSuperNestedPipeline(
list_parameter=self.list_param).some_output
def fetch_data(task_target_date, period=datetime.timedelta(days=7)):
all_data = []
for d in period_dates(task_target_date, period):
data = fetch_wine_quality(task_target_date=d)
all_data.append(data)
return data_combine(all_data, sort=True)
def band(self):
projected_logs = []
for i, d in enumerate(period_dates(self.task_target_date,
self.period)):
raw_logs = RawDeviceLog(task_target_date=d).logs
projected = DeviceLogProjection(raw_logs=raw_logs,
task_target_date=d)
projected_logs.append(projected.projected_logs)
self.projected = data_combine(projected_logs)
def top_artists_report(task_target_date, period=timedelta(days=2)):
logging.info("top_artists_report")
streams = [
stream(task_name="Stream_%s" % i, task_target_date=d)
for i, d in enumerate(period_dates(task_target_date, period))
]
artists = aggregate_artists(stream=data_combine(streams))
top_n = top_n_artists(artists=artists)
return top_n
def band(self):
all_ids, all_data = {}, {}
for i, d in enumerate(period_dates(self.task_target_date,
self.period)):
# if self.task_env == TaskEnv.prod and not self.run_on_prod:
# ids = cb_data_dump_path(task_target_date=d, name="ids")
# data = cb_data_dump_path(task_target_date=d, name="data")
# else:
ids = FetchIds(task_target_date=d, period=one_day).ids
data = FetchData(task_target_date=d, ids=ids).data
d_key = d.strftime("%Y-%m-%d")
all_ids[d_key] = ids
all_data[d_key] = data
self.ids = data_combine(all_ids.values(), sort=True)
self.data = data_combine(all_data.values(), sort=True)
def fetch_partner_data(
task_target_date,
selected_partners: List[str],
period=datetime.timedelta(days=7)
) -> List[pd.DataFrame]:
partner_data = []
for partner in selected_partners:
all_data = []
for d in period_dates(task_target_date, period):
if partner == "a":
data = ingest_partner_a(task_target_date=d)
elif partner == "b":
data = ingest_partner_b(task_target_date=d)
elif partner == "c":
data = ingest_partner_c(task_target_date=d)
else:
raise Exception("Partner not found!")
all_data.append(data)
partner_data.append(data_combine(all_data, sort=True))
return partner_data
def generate_partner_data(
seed: pd.DataFrame = demo_data_repo.seed,
task_target_date=datetime.datetime.now().date(),
period=datetime.timedelta(days=7),
bad_labels_date=datetime.datetime.strptime("2018-01-01",
"%Y-%m-%d").date(),
):
data = rename_columns(data=seed)
data = calculate_target_variable(data=data)
results = {}
for d in period_dates(task_target_date, period):
r = generate_for_date(
task_target_date=d,
data=data,
a_out=demo_data_repo.partner_a_file(d),
b_out=demo_data_repo.partner_b_file(d),
c_out=demo_data_repo.partner_c_file(d),
)
results[str(d)] = r
noisy_data = generate_for_date(
task_target_date=bad_labels_date,
data=data,
noise=True,
a_out=demo_data_repo.partner_a_file(bad_labels_date),
b_out=demo_data_repo.partner_b_file(bad_labels_date),
c_out=demo_data_repo.partner_c_file(bad_labels_date),
)
results[str(bad_labels_date)] = noisy_data
customers = create_customer_files(
seed=data,
a_out=partner_data_file("customer_a.csv"),
b_out=partner_data_file("customer_b.csv"),
)
results["customers"] = customers
return results