def local_prediction(logistic_regressions, test_reader, output, args,
exclude=None):
"""Get local logistic_regression and issue prediction
"""
# Only one logistic_regression at present
local_logistic = LogisticRegression(logistic_regressions[0])
test_set_header = test_reader.has_headers()
for input_data in test_reader:
input_data_dict = test_reader.dict(input_data, filtering=False)
prediction_info = local_logistic.predict(
input_data_dict, by_name=test_set_header)
write_prediction(prediction_info, output,
args.prediction_info, input_data, exclude)
def local_prediction(logistic_regressions, test_reader, output, args,
exclude=None):
"""Get local logistic_regression and issue prediction
"""
# Only one logistic_regression at present
local_logistic = LogisticRegression(logistic_regressions[0],
api=args.retrieve_api_)
kwargs = {"full": True}
if args.operating_point_:
kwargs.update({"operating_point": args.operating_point_})
for input_data in test_reader:
input_data_dict = test_reader.dict(input_data, filtering=False)
prediction_info = local_logistic.predict(
input_data_dict, **kwargs)
write_prediction(prediction_info, output,
args.prediction_info, input_data, exclude)
def local_prediction(logistic_regressions,
test_reader,
output,
args,
exclude=None):
"""Get local logistic_regression and issue prediction
"""
# Only one logistic_regression at present
local_logistic = LogisticRegression(logistic_regressions[0])
test_set_header = test_reader.has_headers()
for input_data in test_reader:
input_data_dict = test_reader.dict(input_data, filtering=False)
prediction_info = local_logistic.predict(input_data_dict,
by_name=test_set_header)
write_prediction(prediction_info, output, args.prediction_info,
input_data, exclude)
def local_prediction(logistic_regressions,
test_reader,
output,
args,
exclude=None):
"""Get local logistic_regression and issue prediction
"""
# Only one logistic_regression at present
local_logistic = LogisticRegression(logistic_regressions[0],
api=args.retrieve_api_)
test_set_header = test_reader.has_headers()
kwargs = {"by_name": test_set_header}
if args.operating_point_:
kwargs.update({"operating_point": args.operating_point_})
for input_data in test_reader:
input_data_dict = test_reader.dict(input_data, filtering=False)
prediction_info = local_logistic.predict(input_data_dict, **kwargs)
write_prediction(prediction_info, output, args.prediction_info,
input_data, exclude)
"YEAR": 1,
"DATE": 1,
"Index_": 1,
"ACCNUM": 1,
"X": 1,
"Y": 1,
"AUTOMOBILE": yes,
"MOTORCYCLE": yes,
"CYCLIST": yes,
"STREET1": ave,
"STREET2": ave,
"DRIVACT": Driving Properly,
"MANOEUVER": Going Ahead,
"DRIVCOND": Normal,
"FATAL_NO": 1,
"VEHTYPE": automobile,
"INITDIR": West,
"DATE.hour": 1,
"DATE.day-of-month": 1,
"DATE.day-of-week": 1,
"DATE.year": 1,
"DATE.month": 1
}
logisticregression.predict(input_data, full=True)
#
# input_data: dict for the input values
# (e.g. {"petal length": 1, "sepal length": 3})
# full: if set to True, the output will be a dictionary that includes the
# distribution of each class in the objective field, the predicted class and
# its probability. Please check:
# https://bigml.readthedocs.io/en/latest/#local-logistic-regression-predictions
# model = api.get_model('model/563a1c7a3cd25747430023ce')
# prediction = api.create_prediction(model, {'petal length': 4.07, 'sepal width': 3.15, 'petal width': 1.51})
# local_model = Model('model/56430eb8636e1c79b0001f90', api=api)
# prediction = local_model.predict({'petal length': 0.96, 'sepal width': 4.1, 'petal width': 2.52}, 2, add_confidence=True, multiple=3)
#local_model = Ensemble('ensemble/563219b8636e1c5eca006d38', api=api)
# local_model = Ensemble('ensemble/564a081bc6c19b6cf3011c60', api=api)
#prediction = local_model.predict({'petal length': 0.96, 'sepal width': 2.25, 'petal width': 1.51, 'sepal length': 6.02}, method=2, add_confidence=True)
#local_model = Ensemble('ensemble/5666fb621d55051209009f0f', api=api)
#prediction = local_model.predict({'Salary': 18000000, 'Team' : 'Atlanta Braves'}, method=0, add_confidence=True)
#local_model = Ensemble('ensemble/566954af1d5505120900bf69', api=api)
#prediction = local_model.predict({'Price' : 5.8, 'Grape' : 'Pinot Grigio', 'Rating' : 89, 'Country' : 'Italy'}, method=1, add_confidence=True, add_distribution=True)
# local_ensemble = Ensemble('ensemble/564623d4636e1c79b00051f7', api=api)
# prediction = local_ensemble.predict({'Price' : 5.8, 'Grape' : 'Pinot Grigio', 'Country' : 'Italy', 'Rating' : 92}, True)
# local_anomaly = Anomaly('anomaly/564c5a76636e1c3d52000007', api=api)
# prediction = local_anomaly.anomaly_score({'petal length': 4.07, 'sepal width': 3.15, 'petal width': 1.51, 'sepal length': 6.02, 'species': 'Iris-setosa'}, True)
# prediction = local_anomaly.anomaly_score({'petal length': 0.96, 'sepal width': 4.1, 'petal width': 2.51, 'sepal length': 6.02, 'species': 'Iris-setosa'}, True)
# prediction = local_anomaly.anomaly_score({'petal length': 0.96, 'sepal width': 4.1, 'petal width': 2.51}, True)
logistic_regression = LogisticRegression(
'logisticregression/5697c1179ed2334090003217')
prediction = logistic_regression.predict({"petal length": 4.07, "petal width": 14.07,
"sepal length": 6.02, "sepal width": 3.15})
api.pprint(prediction)
# local_model = Model('model/56430eb8636e1c79b0001f90', api=api)
# prediction = local_model.predict({'petal length': 0.96, 'sepal width': 4.1, 'petal width': 2.52}, 2, add_confidence=True, multiple=3)
#local_model = Ensemble('ensemble/563219b8636e1c5eca006d38', api=api)
# local_model = Ensemble('ensemble/564a081bc6c19b6cf3011c60', api=api)
#prediction = local_model.predict({'petal length': 0.96, 'sepal width': 2.25, 'petal width': 1.51, 'sepal length': 6.02}, method=2, add_confidence=True)
#local_model = Ensemble('ensemble/5666fb621d55051209009f0f', api=api)
#prediction = local_model.predict({'Salary': 18000000, 'Team' : 'Atlanta Braves'}, method=0, add_confidence=True)
#local_model = Ensemble('ensemble/566954af1d5505120900bf69', api=api)
#prediction = local_model.predict({'Price' : 5.8, 'Grape' : 'Pinot Grigio', 'Rating' : 89, 'Country' : 'Italy'}, method=1, add_confidence=True, add_distribution=True)
# local_ensemble = Ensemble('ensemble/564623d4636e1c79b00051f7', api=api)
# prediction = local_ensemble.predict({'Price' : 5.8, 'Grape' : 'Pinot Grigio', 'Country' : 'Italy', 'Rating' : 92}, True)
# local_anomaly = Anomaly('anomaly/564c5a76636e1c3d52000007', api=api)
# prediction = local_anomaly.anomaly_score({'petal length': 4.07, 'sepal width': 3.15, 'petal width': 1.51, 'sepal length': 6.02, 'species': 'Iris-setosa'}, True)
# prediction = local_anomaly.anomaly_score({'petal length': 0.96, 'sepal width': 4.1, 'petal width': 2.51, 'sepal length': 6.02, 'species': 'Iris-setosa'}, True)
# prediction = local_anomaly.anomaly_score({'petal length': 0.96, 'sepal width': 4.1, 'petal width': 2.51}, True)
logistic_regression = LogisticRegression(
'logisticregression/5697c1179ed2334090003217')
prediction = logistic_regression.predict({
"petal length": 4.07,
"petal width": 14.07,
"sepal length": 6.02,
"sepal width": 3.15
})
api.pprint(prediction)
