def runScikiteLCS(dataFile,
classLabel,
learningIterations,
randomSeed,
cv=False):
data = pd.read_csv(dataFile)
dataFeatures = data.drop(classLabel, axis=1).values
dataPhenotypes = data[classLabel].values
model = skeLCS.eLCS(learningIterations=learningIterations,
randomSeed=randomSeed)
random.seed(randomSeed)
if cv == False:
model.fit(dataFeatures, dataPhenotypes)
score = model.score(dataFeatures, dataPhenotypes)
return np.array([
score, model.timer.globalDeletion, model.timer.globalEvaluation,
model.timer.globalMatching, model.timer.globalSelection,
model.timer.globalSubsumption, model.timer.globalTime
])
else:
formatted = np.insert(dataFeatures, dataFeatures.shape[1],
dataPhenotypes, 1)
random.shuffle(formatted)
dataFeatures = np.delete(formatted, -1, axis=1)
dataPhenotypes = formatted[:, -1]
return np.mean(
cross_val_score(model, dataFeatures, dataPhenotypes, cv=cv))
def testContSpec(self):
dataPath = os.path.join(THIS_DIR, "test/DataSets/Tests/SpecificityTests/Specifics.csv")
converter = StringEnumerator(dataPath, "class")
headers, classLabel, dataFeatures, dataPhenotypes = converter.get_params()
clf = eLCS(learning_iterations=0,discrete_attribute_limit="c",specified_attributes=np.array([0,2,3]))
clf.fit(dataFeatures,dataPhenotypes)
self.assertEqual(clf.env.formatData.attributeInfoType,[True,False,True])
self.assertTrue(clf.env.formatData.discretePhenotype)
def test11BitMultiplexer5000Iterations(self):
dataPath = os.path.join(THIS_DIR, "test/DataSets/Real/Multiplexer11.csv")
converter = StringEnumerator(dataPath, "class")
headers, classLabel, dataFeatures, dataPhenotypes = converter.get_params()
clf = eLCS(learning_iterations=5000)
clf.fit(dataFeatures,dataPhenotypes)
answerKey = 1
self.assertTrue(self.approxEqualOrBetter(0.2, clf.get_final_accuracy(), answerKey,True))
def testContinuous1000Iterations(self):
dataPath = os.path.join(THIS_DIR, "test/DataSets/Real/ContinuousAndNonBinaryDiscreteAttributes.csv")
converter = StringEnumerator(dataPath, "Class")
headers, classLabel, dataFeatures, dataPhenotypes = converter.get_params()
clf = eLCS(learning_iterations=1000)
clf.fit(dataFeatures,dataPhenotypes)
answerKey = 0.61
self.assertTrue(self.approxEqualOrBetter(0.2, clf.get_final_accuracy(), answerKey,True))
def testFullFloatsMissing(self):
dataPath = os.path.join(THIS_DIR, "test/DataSets/Tests/NumericTests/numericFloatsMissing.csv")
converter = StringEnumerator(dataPath, "class")
#converter = StringEnumerator("Datasets/Tests/NumericTests/numericFloatsMissing.csv", "class")
headers, classLabel, dataFeatures, dataPhenotypes = converter.get_params()
clf = eLCS(learning_iterations=2)
clf.fit(dataFeatures, dataPhenotypes)
self.assertTrue(clf.explorIter, 2)
def testMPCV(self):
dataPath = os.path.join(THIS_DIR, "test/DataSets/Real/Multiplexer6.csv")
converter = StringEnumerator(dataPath, "class")
headers, classLabel, dataFeatures, dataPhenotypes = converter.get_params()
clf = eLCS(learning_iterations=2000)
formatted = np.insert(dataFeatures, dataFeatures.shape[1], dataPhenotypes, 1)
np.random.shuffle(formatted)
dataFeatures = np.delete(formatted, -1, axis=1)
dataPhenotypes = formatted[:, -1]
score = np.mean(cross_val_score(clf, dataFeatures, dataPhenotypes,cv=3))
self.assertTrue(self.approxEqual(0.3,score,0.8452))
def testContMissing(self):
dataPath = os.path.join(THIS_DIR, "test/DataSets/Real/ContinuousAndNonBinaryDiscreteAttributesMissing.csv")
converter = StringEnumerator(dataPath, "Class")
headers, classLabel, dataFeatures, dataPhenotypes = converter.get_params()
clf = eLCS(learning_iterations=5000)
formatted = np.insert(dataFeatures, dataFeatures.shape[1], dataPhenotypes, 1)
np.random.shuffle(formatted)
dataFeatures = np.delete(formatted, -1, axis=1)
dataPhenotypes = formatted[:, -1]
score = np.mean(cross_val_score(clf, dataFeatures, dataPhenotypes))
self.assertTrue(self.approxEqual(0.2, score, 0.6355))
def testNANPhenotypeExists(self):
dataPath = os.path.join(THIS_DIR, "test/DataSets/Tests/NumericTests/numericFloatsNAN.csv")
data = pd.read_csv(dataPath, sep=',') # Puts data from csv into indexable np arrays
data = data.fillna("NA")
dataFeatures = data.drop("class", axis=1).values # splits into an array of instances
dataPhenotypes = data["class"].values
clf = eLCS(learning_iterations=2)
with self.assertRaises(Exception) as context:
clf.fit(dataFeatures,dataPhenotypes)
self.assertTrue("X and y must be fully numeric" in str(context.exception))
def testParamThetaSub2(self):
clf = eLCS(theta_sub=5)
self.assertEqual(clf.theta_sub,5)
def testParamN(self):
clf = eLCS(N=2000)
self.assertEqual(clf.N,2000)
def testDoSub(self):
clf = eLCS(do_correct_set_subsumption=True)
self.assertEqual(clf.do_correct_set_subsumption,True)
def testParamNInvalidNumeric2(self):
with self.assertRaises(Exception) as context:
clf = eLCS(N=-2)
self.assertTrue("N param must be nonnegative integer" in str(context.exception))
def testDoSub2Invalid(self):
with self.assertRaises(Exception) as context:
clf = eLCS(do_GA_subsumption=2)
self.assertTrue("do_GA_subsumption param must be boolean" in str(context.exception))
def testDoSub2(self):
clf = eLCS(do_GA_subsumption=True)
self.assertEqual(clf.do_GA_subsumption,True)
def testDiscreteAttributeLimitInv2(self):
with self.assertRaises(Exception) as context:
clf = eLCS(discrete_attribute_limit=-10)
self.assertTrue("discrete_attribute_limit param must be nonnegative integer or 'c' or 'd'" in str(context.exception))
def testParamP_Spec2(self):
clf = eLCS(p_spec=0.3)
self.assertEqual(clf.p_spec,0.3)
def testRandomSeedInv2(self):
with self.assertRaises(Exception) as context:
clf = eLCS(random_state=1.2)
self.assertTrue("random_state param must be integer or None" in str(context.exception))
def testSelection2(self):
clf = eLCS(selection_method="roulette")
self.assertEqual(clf.selection_method,"roulette")
def testSelection1(self):
clf = eLCS(selection_method="tournament")
self.assertEqual(clf.selection_method,"tournament")
def testSelectionInvalid(self):
with self.assertRaises(Exception) as context:
clf = eLCS(selection_method="hello")
self.assertTrue("selection_method param must be 'tournament' or 'roulette'" in str(context.exception))
def testParamThetaSub1(self):
clf = eLCS(theta_sub=0)
self.assertEqual(clf.theta_sub,0)
def testParamThetaSubInv4(self):
with self.assertRaises(Exception) as context:
clf = eLCS(theta_sub=-5)
self.assertTrue("theta_sub param must be nonnegative integer" in str(context.exception))
def testRandomSeed2(self):
clf = eLCS(random_state=200)
self.assertEqual(clf.random_state,200)
def testParamNNonnumeric(self):
with self.assertRaises(Exception) as context:
clf = eLCS(N="hello")
self.assertTrue("N param must be nonnegative integer" in str(context.exception))
def testParamP_SpecInv3(self):
with self.assertRaises(Exception) as context:
clf = eLCS(p_spec=-1.2)
self.assertTrue("p_spec param must be float from 0 - 1" in str(context.exception))
def testRandomSeed3(self):
clf = eLCS(random_state=None)
self.assertEqual(clf.random_state,None)
def testParamP_Spec3(self):
clf = eLCS(p_spec=1)
self.assertEqual(clf.p_spec,1)
def testRebootFilenameInv2(self):
with self.assertRaises(Exception) as context:
clf = eLCS(reboot_filename=True)
self.assertTrue("reboot_filename param must be None or String from pickle" in str(context.exception))
def testRebootFilename2(self):
clf = eLCS(reboot_filename=None)
self.assertEqual(clf.reboot_filename, None)
def testParamThetaDel2(self):
clf = eLCS(theta_del=5)
self.assertEqual(clf.theta_del,5)
