def i_create_a_proportional_local_prediction(step, data=None):
if data is None:
data = "{}"
data = json.loads(data)
world.local_prediction = world.local_model.predict(
data, missing_strategy=1, full=True)
world.local_prediction = cast_prediction(world.local_prediction,
to="list",
confidence=True)
def batch_predict(self,
input_data_list,
output_file_path=None,
reuse=False,
missing_strategy=LAST_PREDICTION,
headers=None,
to_file=True,
use_median=False):
"""Makes predictions for a list of input data.
When the to_file argument is set to True, the predictions
generated for each model are stored in an output
file. The name of the file will use the following syntax:
model_[id of the model]__predictions.csv
For instance, when using model/50c0de043b563519830001c2 to predict,
the output file name will be
model_50c0de043b563519830001c2__predictions.csv
On the contrary, if it is False, the function returns a list
of MultiVote objects with the model's predictions.
"""
add_headers = (isinstance(input_data_list[0], list)
and headers is not None
and len(headers) == len(input_data_list[0]))
if not add_headers and not isinstance(input_data_list[0], dict):
raise ValueError("Input data list is not a dictionary or the"
" headers and input data information are not"
" consistent.")
order = 0
if not to_file:
votes = []
for model in self.models:
order += 1
out = None
if to_file:
output_file = get_predictions_file_name(
model.resource_id, output_file_path)
if reuse:
try:
predictions_file = open(output_file)
predictions_file.close()
continue
except IOError:
pass
try:
out = UnicodeWriter(output_file)
except IOError:
raise Exception("Cannot find %s directory." %
output_file_path)
if out:
out.open_writer()
for index, input_data in enumerate(input_data_list):
if add_headers:
input_data = dict(list(zip(headers, input_data)))
prediction = model.predict(input_data,
missing_strategy=missing_strategy,
full=True)
if model.regression:
# if median is to be used, we just replace the prediction
if use_median:
prediction["prediction"] = prediction["median"]
if to_file:
prediction = cast_prediction(prediction,
to="list",
confidence=True,
distribution=True,
count=True)
out.writerow(prediction)
else:
if len(votes) <= index:
votes.append(MultiVote([]))
votes[index].append(prediction)
if out:
out.close_writer()
if not to_file:
return votes
