def test_ensemble(self,test_file):
assert self.authenticated, 'Not authenticated!'
# download a local copy of the ensemble
self.logger.info('Creating local ensemble')
local_ensemble = Ensemble(self.ensemble_res,api=self.api)
# make the Fields object
source = self.api.get_source(self.source_res)
fields = Fields(source['object']['fields'])
self.logger.info('Reading test data and generating predictions')
true_labels = []
predict_labels = []
pr = Profile()
pr.enable()
with open(test_file) as fid:
test_reader = csv.reader(fid)
# skip the header line
test_reader.next()
for row in test_reader:
row_list = [val for val in row]
true_labels.append(row_list.pop())
instance = fields.pair(row_list)
predict_labels.append(local_ensemble.predict(instance,
by_name=False,
method=1))
pr.disable()
ps = Stats(pr)
self.predict_time = ps.total_tt
# eval_args = {'combiner':1}
# evaluation = self.api.create_evaluation(self.ensemble_res,test_data,eval_args)
# check_resource(evaluation['resource'],self.api.get_evaluation)
# evaluation = self.api.get_evaluation(evaluation['resource'])
# matrix = evaluation['object']['result']['model']['confusion_matrix']
# self.predict_time = evaluation['object']['status']['elapsed']/1000
if self.regression:
self.results = (predict_labels,true_labels)
else:
self.results = make_confusion_matrix(true_labels,predict_labels)
from bigml.ensemble import Ensemble
# Downloads and generates a local version of the ensemble, if it
# hasn't been downloaded previously.
from bigml.api import BigML
ensemble = Ensemble('ensemble/5ddd1a3f1efc925827001f7a',
api=BigML("deven96",
"81795cceca568fff4115d5c047071728a0700673",
domain="bigml.io"))
# To make predictions fill the desired input_data in next line.
input_data = {}
ret = ensemble.predict({'comment_text': "f**k you c**t"}, full=True)
print(ret)
# Requires BigML Python bindings
#
# Install via: pip install bigml
#
# or clone it:
# git clone https://github.com/bigmlcom/python.git
from bigml.ensemble import Ensemble
# Downloads and generates a local version of the ensemble, if it
# hasn't been downloaded previously.
from bigml.api import BigML
ensemble = Ensemble('ensemble/5cacf3dceba31d30ba000d60',
api=BigML("rshelton",
"adabd734dd2a2af5cb4e49176f0eb472cfa8ce5a",
domain="bigml.io"))
# To make predictions fill the desired input_data in next line.
input_data = {}
ensemble.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 all the
# available information in the predicted node. The attributes vary depending
# on the ensemble type. Please check:
# https://bigml.readthedocs.io/en/latest/#local-ensemble-s-predictions
api = BigML(dev_mode=True)
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/564a02d5636e1c79b5006e13", api=api)
local_model = Ensemble("ensemble/564a081bc6c19b6cf3011c60", api=api)
prediction = local_model.predict(
{"petal length": 0.95, "sepal width": 3.9, "petal width": 1.51, "sepal length": 7.0}, method=2, add_confidence=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)
api.pprint(prediction)
def _predict_nba(request, context):
"""
:param request: iterable sequence of bundled rows
:return: string
"""
# Disable caching by uncomment the following two lines
#md = (('qlik-cache', 'no-store'),)
#context.send_initial_metadata(md)
params = []
# Iterate over bundled rows to retrieve data
for request_rows in request:
# Iterate over rows
for row in request_rows.rows:
# Retrieve string value of parameter and append to the params variable
# Length of param is 1 since one column is received, the [0] collects the first value in the list
param = [d.strData for d in row.duals][0]
print('param:', param)
params.append(param)
print('params:', params)
# Possible selections to predict
opt_selections = ['Kevin Durant',
'Allen Iverson',
'Carmelo Anthony',
'Isaiah Thomas',
'Cory Jefferson',
'Robbie Hummel',
'Wesley Johnson']
# Check selections
if len(params) == 1 and any([selection in params for selection in opt_selections]):
selection = params[0].split(' ') # list of first name and last name
file = 'NBA_data/Demo_predictPPGPk_{}_{}'.format(selection[0], selection[1])
with open(file, 'rb') as f:
data = pickle.load(f)
print('data:', data)
correct_res = data['NBA PPG']
del data['NBA PPG']
try:
# Use pre-trained ensemble
ensemble_link = 'ensemble/5727212049c4a15ca1004b77'
ensemble = Ensemble(ensemble_link, api=BigML(dev_mode=True, domain='bigml.io')) # saves locally
except:
err = sys.exc_info()
logging.error('Unexpected error: {}, {}, {}'.format(err[2].tb_frame.f_code.co_filename,
err[2].tb_lineno, err[1]))
# Predict data using the trained ensemble
res = ensemble.predict(data, with_confidence=True)
print('res:', res)
result = 'Predicted number of PPG: {} <br> ' \
'Correct number of PPG: {} <br>' \
'Confidence: {}'.format(round(res[0], 1), correct_res, round(res[1], 1))
else:
result = 'Not possible to predict.'
# Create an iterable of dual with the result
duals = iter([SSE.Dual(strData=result)])
# Yield the row data as bundled rows
yield SSE.BundledRows(rows=[SSE.Row(duals=duals)])
