def testDeleteNonexistentAttribute(self):
# Deletes nonexistent attribute
dataPath = os.path.join(THIS_DIR, "test/DataSets/Tests/StringData.csv")
se = StringEnumerator(dataPath, "phenotype")
with self.assertRaises(Exception) as context:
se.delete_attribute("N")
self.assertTrue("Header Doesn't Exist" in str(context.exception))
def testget_paramsFail(self):
# Get params when not all features/class have been enumerated
dataPath = os.path.join(THIS_DIR, "test/DataSets/Tests/StringData.csv")
se = StringEnumerator(dataPath, "phenotype")
with self.assertRaises(Exception) as context:
se.get_params()
self.assertTrue("Features and Phenotypes must be fully numeric" in str(
context.exception))
def testchange_header_nameInvalid2(self):
# Changes non existing header name should raise exception
dataPath = os.path.join(THIS_DIR, "test/DataSets/Tests/StringData.csv")
se = StringEnumerator(dataPath, "phenotype")
with self.assertRaises(Exception) as context:
se.change_header_name("N", "N5")
self.assertTrue(
"Current Header Doesn't Exist" in str(context.exception))
def testchange_header_nameInvalid(self):
# Changes header name to an existing header or class name should raise exception
dataPath = os.path.join(THIS_DIR, "test/DataSets/Tests/StringData.csv")
se = StringEnumerator(dataPath, "phenotype")
with self.assertRaises(Exception) as context:
se.change_header_name("N1", "N2")
self.assertTrue(
"New Class Name Cannot Be An Already Existing Data Header or Phenotype Name"
in str(context.exception))
def testdelete_attribute(self):
# Deletes attributes and checks map, headers, and arrays for correctness
dataPath = os.path.join(THIS_DIR, "test/DataSets/Tests/StringData.csv")
se = StringEnumerator(dataPath, "phenotype")
se.change_header_name("N1", "gender")
se.add_attribute_converter_random("gender")
se.add_attribute_converter_random("N3")
se.delete_attribute("gender")
cHeaders = np.array(["N2", "N3"])
cMap = {"N3": {"young": "0", "old": "1"}}
self.assertTrue(np.array_equal(cHeaders, se.dataHeaders))
self.assertTrue(np.array_equal("phenotype", se.classLabel))
self.assertTrue(se.map == cMap)
def testInitHeaders(self):
# Tests if init gets the headers correct
dataPath = os.path.join(THIS_DIR,
"test/DataSets/Tests/MissingFeatureData.csv")
se = StringEnumerator(dataPath, "phenotype")
cHeaders = np.array(["N1", "N2", "N3", "N4"])
self.assertTrue(np.array_equal(cHeaders, se.dataHeaders))
def testInitFeaturesAndClassRemoval(self):
# Tests if init gets the features and class arrays correct given missing phenotype data
dataPath = os.path.join(
THIS_DIR, "test/DataSets/Tests/MissingFeatureAndPhenotypeData.csv")
se = StringEnumerator(dataPath, "phenotype")
cFeatures = np.array([["1.0", "NA", "1.0", "4.0"],
["NA", "1.0", "NA", "1.0"],
["6.0", "NA", "1.0", "1.0"]])
cClasses = np.array(["1.0", "0.0", "1.0"])
self.assertTrue(np.array_equal(cFeatures, se.dataFeatures))
self.assertTrue(np.array_equal(cClasses, se.dataPhenotypes))
def testInitMissingData(self):
# Tests if init filters missing data into NAs
dataPath = os.path.join(THIS_DIR,
"test/DataSets/Tests/MissingFeatureData.csv")
se = StringEnumerator(dataPath, "phenotype")
cFeatures = np.array([["1.0", "NA", "1.0", "4.0"],
["2.0", "0.0", "1.0", "NA"],
["4.0", "NA", "1.0", "2.0"],
["NA", "1.0", "NA", "1.0"],
["6.0", "NA", "1.0", "1.0"]])
self.assertTrue(np.array_equal(cFeatures, se.dataFeatures))
def testChangeClassAndHeaderNames2(self):
# Changes header and class names. Checks map, and classLabel/dataHeaders correctness
dataPath = os.path.join(THIS_DIR, "test/DataSets/Tests/StringData.csv")
se = StringEnumerator(dataPath, "phenotype")
se.add_class_converter_random()
se.change_header_name("N1", "gender")
se.add_attribute_converter_random("gender")
se.change_header_name("gender", "Gender")
se.add_attribute_converter_random("Gender")
se.add_attribute_converter_random("Gender")
se.add_attribute_converter_random("gender")
se.add_attribute_converter_random("N3")
se.change_header_name("N3", "Age")
cHeaders = np.array(["Gender", "N2", "Age"])
cMap = {
"phenotype": {
"china": "0",
"japan": "1",
"russia": "2"
},
"Gender": {
"male": "0",
"female": "1"
},
"Age": {
"young": "0",
"old": "1"
}
}
self.assertTrue(np.array_equal(cHeaders, se.dataHeaders))
self.assertTrue(np.array_equal("phenotype", se.classLabel))
self.assertTrue(se.map == cMap)
def testChangeClassAndHeaderNames(self):
# Changes header and class names. Checks map, and classLabel/dataHeaders correctness
dataPath = os.path.join(THIS_DIR, "test/DataSets/Tests/StringData.csv")
se = StringEnumerator(dataPath, "phenotype")
se.change_class_name("country")
se.change_header_name("N1", "gender")
se.change_header_name("N2", "N1")
se.change_header_name("N1", "floats")
se.change_header_name("N3", "phenotype")
se.change_header_name("phenotype", "age")
cHeaders = np.array(["gender", "floats", "age"])
self.assertTrue(np.array_equal(cHeaders, se.dataHeaders))
self.assertTrue(np.array_equal("country", se.classLabel))
def testget_params2(self):
# Get Params Test
dataPath = os.path.join(THIS_DIR, "test/DataSets/Tests/StringData.csv")
se = StringEnumerator(dataPath, "phenotype")
se.change_header_name("N1", "gender")
se.change_header_name("N2", "floats")
se.change_header_name("N3", "age")
se.change_class_name("country")
se.add_attribute_converter("gender",
np.array(["female", "male", "NA", "other"]))
se.add_attribute_converter("age", np.array(["old", "young"]))
se.add_class_converter_random()
se.convert_all_attributes()
dataHeaders, classLabel, dataFeatures, dataPhenotypes = se.get_params()
cHeaders = np.array(["gender", "floats", "age"])
cFeatures = np.array([[1, 1.2, 1], [0, 0.3, np.nan], [0, -0.4, 0],
[np.nan, 0, 1]])
cPhenotypes = np.array([0, 1, 0, 2])
self.assertEqual("country", classLabel)
self.assertTrue(np.array_equal(cHeaders, dataHeaders))
self.assertTrue(np.allclose(cFeatures, dataFeatures, equal_nan=True))
self.assertTrue(
np.allclose(cPhenotypes, dataPhenotypes, equal_nan=True))
def testNumericCheck(self):
# Checks non missing numeric
dataPath = os.path.join(THIS_DIR, "test/DataSets/Tests/StringData.csv")
se = StringEnumerator(dataPath, "phenotype")
self.assertFalse(se.check_is_full_numeric())
se.add_attribute_converter_random("N1")
se.convert_all_attributes()
self.assertFalse(se.check_is_full_numeric())
se.add_attribute_converter_random("N3")
se.add_class_converter_random()
se.convert_all_attributes()
self.assertTrue(se.check_is_full_numeric())
dataPath = os.path.join(THIS_DIR,
"test/DataSets/Tests/MissingFeatureData.csv")
se2 = StringEnumerator(dataPath, "phenotype")
self.assertTrue(se2.check_is_full_numeric())
def testDeleteInstancesWithMissing2(self):
# Deletes instances and checks arrays for correctness
dataPath = os.path.join(THIS_DIR, "test/DataSets/Tests/StringData.csv")
se = StringEnumerator(dataPath, "phenotype")
se.change_header_name("N1", "gender")
se.delete_all_instances_without_header_data("gender")
se.delete_all_instances_without_header_data("N2")
se.delete_all_instances_without_header_data("N3")
se.add_attribute_converter_random("gender")
se.add_attribute_converter_random("N3")
se.add_class_converter_random()
se.convert_all_attributes()
cHeaders = np.array(["gender", "N2", "N3"])
cMap = {
"phenotype": {
"china": "0"
},
"gender": {
"male": "0",
"female": "1"
},
"N3": {
"young": "0",
"old": "1"
}
}
cArray = np.array([["0", "1.2", "0"], ["1", "-0.4", "1"]])
cPArray = np.array(["0", "0"])
self.assertTrue(np.array_equal(cHeaders, se.dataHeaders))
self.assertTrue(np.array_equal("phenotype", se.classLabel))
self.assertTrue(np.array_equal(cArray, se.dataFeatures))
self.assertTrue(np.array_equal(cPArray, se.dataPhenotypes))
self.assertTrue(se.map == cMap)