Make it possible to pick the number of samples used for SVM.

Change-Id: I72b625fcefd4b8fa87cc49f71a847e8223395305
This commit is contained in:
Joan Varvenne 2016-09-14 11:38:33 +01:00
parent 5812bd8429
commit 4c05018883
2 changed files with 17 additions and 5 deletions

View File

@ -20,6 +20,8 @@ import numpy as np
from sklearn import svm
import voluptuous
import monasca_analytics.banana.typeck.type_util as type_util
import monasca_analytics.component.params as params
from monasca_analytics.sml import base
from monasca_analytics.util import validation_utils as vu
@ -36,24 +38,31 @@ class SvmOneClass(base.BaseSML):
def __init__(self, _id, _config):
super(SvmOneClass, self).__init__(_id, _config)
self._nb_samples = int(_config["nb_samples"])
@staticmethod
def validate_config(_config):
svm_schema = voluptuous.Schema({
"module": voluptuous.And(basestring, vu.NoSpaceCharacter())
"module": voluptuous.And(basestring, vu.NoSpaceCharacter()),
"nb_samples": voluptuous.Or(float, int)
}, required=True)
return svm_schema(_config)
@staticmethod
def get_default_config():
return {"module": SvmOneClass.__name__}
return {
"module": SvmOneClass.__name__,
"nb_samples": N_SAMPLES
}
@staticmethod
def get_params():
return []
return [
params.ParamDescriptor("nb_samples", type_util.Number(), N_SAMPLES)
]
def number_of_samples_required(self):
return N_SAMPLES
return self._nb_samples
def _generate_train_test_sets(self, samples, ratio_train):
num_samples_train = int(len(samples) * ratio_train)

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@ -29,7 +29,10 @@ class TestSvmOneClass(MonanasTestCase):
def setUp(self):
super(TestSvmOneClass, self).setUp()
self.svm = svm_one_class.SvmOneClass("fakeid", {"module": "fake"})
self.svm = svm_one_class.SvmOneClass("fakeid", {
"module": "fake",
"nb_samples": 1000
})
def tearDown(self):
super(TestSvmOneClass, self).tearDown()