parser.add_argument('-v', '--validate', dest='validate', default=None,
nargs=1, required=False,
help="Validate a given model")
parser.add_argument('-k', '--classify', dest='classify', default=None,
nargs=1, required=False,
help="Save classification into given output file")
return parser.parse_args()
if __name__ == '__main__':
app_args = get_args()
load_dataset(app_args.num_samples)
pyvotune.set_debug(app_args.debug_mode)
rng = random.Random()
gene_pool = get_gene_pool(rng)
if app_args.classify:
if not app_args.validate:
log.error("Need path to model -v")
sys.exit(0)
classify_models(app_args.validate[0], app_args.classify[0])
sys.exit(1)
elif app_args.validate:
validate_models(app_args.validate[0])
sys.exit(1)
log = pyvotune.log.logger()
def reproduce(offspring_cs, variator, rng, args):
if isinstance(variator, collections.Iterable):
for op in variator:
offspring_cs = op(random=rng, candidates=offspring_cs, args=args)
return offspring_cs
else:
return [variator(random=rng, candidates=offspring_cs, args=args)]
if __name__ == '__main__':
pyvotune.set_debug(True)
# Dummy data
n_features = 28 * 28
rng = random.Random()
#################################
# Initialize PyvoTune Generator #
#################################
gen = pyvotune.Generate(
initial_state={
'sparse': False
},
gene_pool=pyvotune.sklearn.get_classifiers(n_features, rng) +
pyvotune.sklearn.get_decomposers(n_features, rng) +
def setUp(self):
pyvotune.set_debug(False)
'--classify',
dest='classify',
default=None,
nargs=1,
required=False,
help="Save classification into given output file")
return parser.parse_args()
if __name__ == '__main__':
app_args = get_args()
load_dataset(app_args.num_samples)
pyvotune.set_debug(app_args.debug_mode)
rng = random.Random()
gene_pool = get_gene_pool(rng)
if app_args.classify:
if not app_args.validate:
log.error("Need path to model -v")
sys.exit(0)
classify_models(app_args.validate[0], app_args.classify[0])
sys.exit(1)
elif app_args.validate:
validate_models(app_args.validate[0])
sys.exit(1)
return pipeline
def test_individual(pipeline, test_X, test_y, display=False):
observed_y = pipeline.predict(test_X)
f1 = sklearn.metrics.f1_score(test_y, observed_y)
if display:
print sklearn.metrics.classification_report(test_y, observed_y)
return round(f1 * 100., 2)
if __name__ == '__main__':
pyvotune.set_debug(False)
############################
# Load the initial dataset #
############################
data = sklearn.datasets.load_digits()
X = data['data']
y = data['target']
print X.shape
# Split the dataset into training, testing and then validation parts
train_X, temp_X, train_y, temp_y = train_test_split(X, y, test_size=0.25)
test_X, validate_X, test_y, validate_y = train_test_split(
temp_X, temp_y, test_size=0.5)
comma_me(err_pct) + "%"
print ""
print "Actual Solution:" "E = m *", SPEED_OF_LIGHT, "*", SPEED_OF_LIGHT
print "Best Solution:", best_eq
print "Actual C:", SPEED_OF_LIGHT**2
print "Our C:", best_eq(1)
print "Diff:", abs(best_eq(1) - SPEED_OF_LIGHT**2)
print "Diff Pct:", round(
abs(best_eq(1) - SPEED_OF_LIGHT**2) / (SPEED_OF_LIGHT**2.) * 100, 2)
print "Fitness", best.fitness
print "MSE", sum_errs / samps
if __name__ == '__main__':
pyvotune.set_debug(False)
gen = pyvotune.Generate(gene_pool=[mass, someconst, oper],
max_length=10,
noop_frequency=0.2)
ea = inspyred.ec.GA(random.Random())
ea.terminator = [
inspyred.ec.terminators.time_termination,
inspyred.ec.terminators.average_fitness_termination
]
ea.observer = inspyred.ec.observers.stats_observer
ea.variator = [
pyvotune.variators.param_reset_mutation,
