def __load_peripheral_tables(self, peripheral_tables, s):
peripheral_data_frames = []
for i, peripheral_table in enumerate(peripheral_tables):
if type(peripheral_table) == engine.DataFrame:
peripheral_data_frames.append(peripheral_table)
else:
categorical_peripheral = [
per.thisptr["categorical_"]
for per in self.params["peripheral"]
]
discrete_peripheral = [
per.thisptr["discrete_"]
for per in self.params["peripheral"]
]
numerical_peripheral = [
per.thisptr["numerical_"]
for per in self.params["peripheral"]
]
join_keys_peripheral = [
per.thisptr["join_keys_"]
for per in self.params["peripheral"]
]
names_peripheral = [
per.thisptr["name_"] for per in self.params["peripheral"]
]
time_stamps_peripheral = [
per.thisptr["time_stamps_"]
for per in self.params["peripheral"]
]
peripheral_data_frames.append(
engine.DataFrame(name=self.__make_random_name(),
join_keys=join_keys_peripheral[i],
time_stamps=time_stamps_peripheral[i],
categorical=categorical_peripheral[i],
discrete=discrete_peripheral[i],
numerical=numerical_peripheral[i],
targets=[],
units=self.params['units']))
peripheral_data_frames[i].send(data_frame=peripheral_table,
sock=s)
return peripheral_data_frames
def __load_population_table(self, population_table, targets, s):
if type(population_table) == engine.DataFrame:
population_data_frame = population_table
else:
population_data_frame = engine.DataFrame(
name=self.__make_random_name(),
join_keys=self.params["population"].thisptr["join_keys_"],
time_stamps=self.params["population"].thisptr["time_stamps_"],
categorical=self.params["population"].thisptr["categorical_"],
discrete=self.params["population"].thisptr["discrete_"],
numerical=self.params["population"].thisptr["numerical_"],
targets=targets,
units=self.params["units"])
population_data_frame.send(data_frame=population_table, sock=s)
return population_data_frame
# With this additional information in place we can construct
# the `DataFrame`s, which will serve as our handles for the tables
# stored in the engine. Using the `.send()` method we upload the
# provided data to the engine and `.save()` ensures the `DataFrame` will
# persist.
# In[ ]:
df_population_training = engine.DataFrame(
"POPULATION_TRAINING",
join_keys=JOIN_KEYS,
time_stamps=TIME_STAMPS,
categorical=CATEGORICAL,
discrete=DISCRETE,
numerical=NUMERICAL,
targets=TARGETS,
units=units
).send(CE_population_training)
df_population_training.save()
df_population_validation = engine.DataFrame(
"POPULATION_VALIDATION",
join_keys=JOIN_KEYS,
time_stamps=TIME_STAMPS,
categorical=CATEGORICAL,
discrete=DISCRETE,
numerical=NUMERICAL,
targets=TARGETS,
units=units