def test_unequal_split(self):
dataset = DataSet([[1, 2], [3, 4], [5, 6], [7, 8]])
first, second = dataset.split(0.3)
self.assertEqual(first.num_samples(), 1)
assert_that(first, equals_dataset([[1, 2]]))
self.assertEqual(second.num_samples(), 3)
assert_that(second, equals_dataset([[3, 4], [5, 6], [7, 8]]))
def test_split_0(self):
dataset = DataSet([[1, 2], [3, 4], [5, 6], [7, 8]])
first, second = dataset.split(0)
self.assertEqual(first.num_samples(), 0)
assert_that(first, equals_dataset([]))
self.assertEqual(second.num_samples(), 4)
assert_that(second, equals_dataset([[1, 2], [3, 4], [5, 6], [7, 8]]))
def test_as_dataset(self):
original = DataSet([[1, 2], [3, 4], [5, 6]])
dataset = as_dataset(original)
self.assertTrue(dataset is original)
dataset._dataframe.ix[1] = 1
assert_that(dataset, equals_dataset([[1, 2], [1, 1], [5, 6]]))
assert_that(original, equals_dataset([[1, 2], [1, 1], [5, 6]]))
def test_contruct_dataset_from_dataset(self):
original = DataSet([[1, 2], [3, 4], [5, 6]])
new = DataSet(original)
self.assertFalse(new is original)
new._dataframe.ix[1] = 1
assert_that(new, equals_dataset([[1, 2], [1, 1], [5, 6]]))
assert_that(original, equals_dataset([[1, 2], [3, 4], [5, 6]]))
def test_copy(self):
dataset1 = DataSet([[1, 2], [3, 4]], labels=pd.Series(["a", "b"]))
dataset2 = dataset1.copy()
dataset2.set_column(1, pd.Series([4, 5]))
assert_that(dataset2, equals_dataset([[1, 4], [3, 5]]))
assert_that(dataset2.get_labels(), equals_series({0: "a", 1: "b"}))
assert_that(dataset1, equals_dataset([[1, 2], [3, 4]]))
assert_that(dataset2.get_labels(), equals_series({0: "a", 1: "b"}))
def test_copy_no_labels(self):
dataset1 = DataSet([[1, 2], [3, 4]])
dataset2 = dataset1.copy()
dataset2.set_column(1, pd.Series([4, 5]))
assert_that(dataset2, equals_dataset([[1, 4], [3, 5]]))
self.assertFalse(dataset2.is_labelled())
assert_that(dataset1, equals_dataset([[1, 2], [3, 4]]))
self.assertFalse(dataset1.is_labelled())
def test_drop_column(self):
original = DataSet([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
self.assertEqual(original.num_features(), 3)
filtered = original.drop_column(1)
self.assertEqual(filtered.num_features(), 2)
assert_that(filtered, equals_dataset([[1, 3], [4, 6], [7, 9]]))
# make sure original unchanged
self.assertEqual(original.num_features(), 3)
assert_that(original, equals_dataset([[1, 2, 3], [4, 5, 6],
[7, 8, 9]]))
def test_otago_example(self):
dataset = self.create_otago_dataset()
transformed = self.get_transformed_otago_data()
principal_components = pca.pca(dataset, 2)
assert_that(principal_components, equals_dataset(transformed,
places=2))
def test_slice_features_list_indices(self):
df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
labels = ["m", "f", "m"]
dataset = DataSet(df, labels=labels)
sliced = dataset.slice_features([1, 2])
assert_that(sliced, equals_dataset([[2, 3], [5, 6], [8, 9]]))
assert_that(sliced.feature_list(), contains(1, 2))
assert_that(sliced.get_labels(), contains(*labels))
def test_bin_feature(self):
df = pd.DataFrame([[0, 1], [7, 2], [6, 3]],
columns=["MATH100", "PHYS125"])
dataset = DataSet(df)
dataset.bin("MATH100", [4, 7])
assert_that(dataset, equals_dataset([[0, 1], [2, 2], [1, 3]]))
def test_bin_feature_floats(self):
df = pd.DataFrame([[3.5, 1], [9.1, 2], [6.2, 3]],
columns=["MATH100", "PHYS125"])
dataset = DataSet(df)
dataset.bin("MATH100", [3.9, 7], bin_names=["low", "mid", "high"])
assert_that(dataset, equals_dataset([["low", 1], ["high", 2],
["mid", 3]]))
def test_bin_feature_1_boundary(self):
df = pd.DataFrame([[0, 1], [9, 2], [6, 3]],
columns=["MATH100", "PHYS125"])
dataset = DataSet(df)
dataset.bin("MATH100", [3], bin_names=["low", "high"])
assert_that(dataset, equals_dataset([["low", 1], ["high", 2],
["high", 3]]))
def test_bin_all(self):
df = pd.DataFrame([[0, 6], [9, 2], [6, 4]],
columns=["MATH100", "PHYS125"])
dataset = DataSet(df)
dataset.bin("*", [4, 7], bin_names=["low", "mid", "high"])
assert_that(dataset, equals_dataset([["low", "mid"], ["high", "low"],
["mid", "mid"]]))
def test_slice_features_list_string(self):
df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
columns=["weight", "height", "age"])
labels = ["m", "f", "m"]
dataset = DataSet(df, labels=labels)
sliced = dataset.slice_features(["weight", "height"])
assert_that(sliced, equals_dataset([[1, 2], [4, 5], [7, 8]]))
assert_that(sliced.feature_list(), contains("weight", "height"))
assert_that(sliced.get_labels(), contains(*labels))
def test_remove_means(self):
dataset = DataSet([[4, 1, 9], [2, 3, 0], [5, 1, 3]])
# column means are: 3.6667, 1.6667, 4
pca.remove_means(dataset)
assert_that(
dataset,
equals_dataset(
[[0.33, -0.67, 5], [-1.67, 1.33, -4], [1.33, -0.67, -1]],
places=2))
def test_drop_empty_samples(self):
df = pd.DataFrame([[1, 2, np.NAN], [np.NAN, np.NAN, np.NAN], [7, 8, 9]])
original = DataSet(df, labels=["a", "b", "c"])
filtered = original.drop_empty_samples()
assert_that(filtered.feature_list(), has_length(3))
assert_that(filtered.num_samples(), equal_to(2))
assert_that(filtered, equals_dataset([[1, 2, np.NAN], [7, 8, 9]]))
assert_that(filtered.get_labels(), contains("a", "c"))
def test_filter_by_label(self):
features = ["name", "hair colour"]
df = pd.DataFrame([["Bill", "brown"], ["Bob", "black"],
["Jim", "brown"]], columns=features)
dataset = DataSet(df, labels=["SENG", "SENG", "CENG"])
filtered = dataset.label_filter("SENG")
assert_that(filtered, equals_dataset([["Bill", "brown"],
["Bob", "black"]]))
assert_that(filtered.get_labels(),
equals_series({0: "SENG", 1: "SENG"}))
def test_filter_by_feature_value(self):
features = ["name", "hair colour"]
df = pd.DataFrame([["Bill", "brown"], ["Bob", "black"],
["Jim", "brown"]], columns=features)
dataset = DataSet(df)
filtered = dataset.value_filter("hair colour", "brown")
self.assertEqual(filtered.feature_list(), features)
assert_that(filtered.get_sample_ids(), contains(0, 2))
assert_that(filtered, equals_dataset([["Bill", "brown"],
["Jim", "brown"]]))
def test_normalize_features(self):
# Note that last column is the training target, so we omitt it by
# loading it as labels.
dataset = loader.load(self.relative_to_base("datasets/ex1data2.txt"),
has_ids=False, has_header=False, has_labels=True)
dataset.normalize_features()
expected = np.loadtxt(self.relative_to_base("datasets/ex1data2norm.txt"),
delimiter=",")
assert_that(dataset, equals_dataset(expected.tolist(), places=15))
def test_get_labelled_rows(self):
dataset = DataSet([[1, 2], [3, 4], [5, 6], [7, 8]],
labels=["a", "a", "b", "b"])
selection = dataset.get_rows([1, 3])
self.assertEqual(selection.num_samples(), 2)
self.assertTrue(selection.is_labelled())
# TODO incorporate labels equals_series into DataSet matcher?
assert_that(selection, equals_dataset([[3, 4], [7, 8]]))
assert_that(selection.get_labels(), equals_series({1: "a", 3: "b"}))
def test_drop_empty_samples_original_unchanged(self):
data_list = [[1, 2, np.NAN], [np.NAN, np.NAN, np.NAN], [7, 8, 9]]
label_list = ["a", "b", "c"]
original = DataSet(pd.DataFrame(data_list), labels=label_list)
filtered = original.drop_empty_samples()
filtered.set_column(0, [-1, -1])
filtered.labels[0] = "z"
assert_that(original, equals_dataset(data_list))
assert_that(original.get_labels(), contains(*label_list))
def test_filter_by_multiple_labels(self):
features = ["name", "hair colour"]
df = pd.DataFrame([["Will", "black"], ["Rob", "blonde"],
["Bill", "brown"], ["Bob", "black"],
["Jim", "brown"]], columns=features)
dataset = DataSet(df, labels=["ELEC", "SENG", "ELEC", "CENG", "SENG"])
filtered = dataset.label_filter(["SENG", "CENG"])
assert_that(filtered, equals_dataset([["Rob", "blonde"],
["Bob", "black"],
["Jim", "brown"]]))
assert_that(filtered.get_labels(),
equals_series({1: "SENG", 3: "CENG", 4: "SENG"}))
def test_fill_missing_with_feature_means(self):
dataset = DataSet([[2, np.NaN, np.NaN], [np.NaN, 6, 10],
[5, 4, np.NaN]])
dataset.fill_missing_with_feature_means()
assert_that(dataset, equals_dataset([[2, 5, 10], [3.5, 6, 10],
[5, 4, 10]]))
def test_get_rows(self):
dataset = DataSet([[1, 2], [3, 4], [5, 6], [7, 8]])
selection = dataset.get_rows([1, 3])
self.assertEqual(selection.num_samples(), 2)
assert_that(selection, equals_dataset([[3, 4], [7, 8]]))
def test_fill_missing(self):
dataset = DataSet([[1, np.NaN, 3], [np.NaN, 5, np.NaN]])
dataset.fill_missing(0)
assert_that(dataset, equals_dataset([[1, 0, 3], [0, 5, 0]]))
def test_fill_missing_with_feature_means_feature_all_empty(self):
dataset = DataSet([[2, np.NaN, np.NaN], [7, np.NaN, 10],
[5, np.NaN, np.NaN]])
dataset.fill_missing_with_feature_means()
assert_that(dataset, equals_dataset([[2, 0, 10], [7, 0, 10],
[5, 0, 10]]))
def test_create_dataset_from_numpy_array(self):
as_list = [[0, 1], [2, 3]]
np_array = np.array(as_list)
dataset = DataSet(np_array)
assert_that(dataset, equals_dataset(as_list))
def test_set_new_column(self):
dataset = DataSet([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dataset.set_column(3, [11, 11, 11])
assert_that(dataset, equals_dataset([[1, 2, 3, 11], [4, 5, 6, 11],
[7, 8, 9, 11]]))
