def test_extract_features(self):
# todo: implement more methods and test more aspects
df = self.create_test_data_sample()
extracted_features = extract_features(df, column_id="id", column_sort="sort", column_kind="kind",
column_value="val",
n_jobs=self.n_jobs)
self.assertIsInstance(extracted_features, pd.DataFrame)
self.assertTrue(np.all(extracted_features.a__maximum == np.array([71, 77])))
self.assertTrue(np.all(extracted_features.a__sum_values == np.array([691, 1017])))
self.assertTrue(np.all(extracted_features.a__abs_energy == np.array([32211, 63167])))
self.assertTrue(np.all(extracted_features.b__sum_values == np.array([757, 695])))
self.assertTrue(np.all(extracted_features.b__minimum == np.array([3, 1])))
self.assertTrue(np.all(extracted_features.b__abs_energy == np.array([36619, 35483])))
self.assertTrue(np.all(extracted_features.b__mean == np.array([37.85, 34.75])))
self.assertTrue(np.all(extracted_features.b__median == np.array([39.5, 28.0])))
df_sts = self.create_one_valued_time_series()
extracted_features_sts = extract_features(df_sts, column_id="id", column_sort="sort", column_kind="kind",
column_value="val",
n_jobs=self.n_jobs)
self.assertIsInstance(extracted_features_sts, pd.DataFrame)
self.assertTrue(np.all(extracted_features_sts.a__maximum == np.array([1.0, 6.0])))
self.assertTrue(np.all(extracted_features_sts.a__sum_values == np.array([1.0, 11.0])))
self.assertTrue(np.all(extracted_features_sts.a__count_above_mean == np.array([0, 1])))
def test_extract_features_for_one_time_series(self):
# todo: implement more methods and test more aspects
df = self.create_test_data_sample()
settings = ComprehensiveFCParameters()
extracted_features = extract_features(df, default_fc_parameters=settings,
column_value="val", column_id="id", column_kind="kind",
column_sort="sort")
self.assertIsInstance(extracted_features, pd.DataFrame)
self.assertTrue(np.all(extracted_features.b__sum_values == np.array([757, 695])))
self.assertTrue(np.all(extracted_features.b__minimum == np.array([3, 1])))
self.assertTrue(np.all(extracted_features.b__abs_energy == np.array([36619, 35483])))
self.assertTrue(np.all(extracted_features.b__mean == np.array([37.85, 34.75])))
self.assertTrue(np.all(extracted_features.b__median == np.array([39.5, 28.0])))
df_sts = self.create_one_valued_time_series()
extracted_features_sts = extract_features(df_sts, default_fc_parameters=settings,
column_value="val", column_id="id", column_kind="kind",
column_sort="sort")
self.assertIsInstance(extracted_features_sts, pd.DataFrame)
self.assertTrue(np.all(extracted_features_sts.a__maximum == np.array([1.0, 6.0])))
self.assertTrue(np.all(extracted_features_sts.a__sum_values == np.array([1.0, 11.0])))
self.assertTrue(np.all(extracted_features_sts.a__count_above_mean == np.array([0, 1])))
def test_profiling_cumulative_file_written_out(self):
PROFILING_FILENAME = os.path.join(self.directory, "test_profiling_cumulative.txt")
PROFILING_SORTING = "cumulative"
df = pd.DataFrame(data={"id": np.repeat([1, 2], 10), "val": np.random.normal(0, 1, 20)})
extract_features(df, column_id="id",
column_value="val", n_jobs=self.n_jobs,
profile=True, profiling_filename=PROFILING_FILENAME, profiling_sorting=PROFILING_SORTING)
self.assertTrue(os.path.isfile(PROFILING_FILENAME))
os.remove(PROFILING_FILENAME)
def test_extract_features_with_and_without_parallelization(self):
df = self.create_test_data_sample()
features_parallel = extract_features(df, column_id="id", column_sort="sort", column_kind="kind",
column_value="val",
n_jobs=self.n_jobs)
features_serial = extract_features(df, column_id="id", column_sort="sort", column_kind="kind",
column_value="val", n_jobs=0)
six.assertCountEqual(self, features_parallel.columns, features_serial.columns)
for col in features_parallel.columns:
np.testing.assert_array_almost_equal(features_parallel[col], features_serial[col])
def test_extract_features_after_randomisation(self):
df = self.create_test_data_sample()
df_random = df.copy().sample(frac=1)
extracted_features = extract_features(df, column_id="id", column_sort="sort", column_kind="kind",
column_value="val",
n_jobs=self.n_jobs).sort_index()
extracted_features_from_random = extract_features(df_random, column_id="id", column_sort="sort",
column_kind="kind",
column_value="val",
n_jobs=self.n_jobs).sort_index()
six.assertCountEqual(self, extracted_features.columns, extracted_features_from_random.columns)
for col in extracted_features:
self.assertIsNone(np.testing.assert_array_almost_equal(extracted_features[col],
extracted_features_from_random[col]))
def test_extract_index_preservation(self):
df = self.create_test_data_nearly_numerical_indices()
extracted_features = extract_features(df, column_id="id", column_sort="sort", column_kind="kind",
column_value="val",
n_jobs=self.n_jobs)
self.assertIsInstance(extracted_features, pd.DataFrame)
self.assertTrue(set(df.index) == set(extracted_features.index))
def test_extract_features_without_settings(self):
df = pd.DataFrame(data={"id": np.repeat([1, 2], 10),
"value1": np.random.normal(0, 1, 20),
"value2": np.random.normal(0, 1, 20)})
X = extract_features(df, column_id="id",
n_jobs=self.n_jobs)
self.assertIn("value1__maximum", list(X.columns))
self.assertIn("value2__maximum", list(X.columns))
def test_profiling_file_written_out(self):
df = pd.DataFrame(data={"id": np.repeat([1, 2], 10), "val": np.random.normal(0, 1, 20)})
profiling_filename = os.path.join(self.directory, "test_profiling.txt")
X = extract_features(df, column_id="id",
column_value="val", n_jobs=self.n_jobs,
profile=True, profiling_filename=profiling_filename)
self.assertTrue(os.path.isfile(profiling_filename))
os.remove(profiling_filename)
def test_from_column_correct_for_comprehensive_fc_parameters(self):
fset = ComprehensiveFCParameters()
X_org = extract_features(pd.DataFrame({
"value": [1, 2, 3],
"id": [1, 1, 1]
}),
default_fc_parameters=fset,
column_id="id",
column_value="value",
n_jobs=0)
inferred_fset = from_columns(X_org)
X_new = extract_features(pd.DataFrame({
"value": [1, 2, 3],
"id": [1, 1, 1]
}),
kind_to_fc_parameters=inferred_fset,
column_id="id",
column_value="value",
n_jobs=0)
assert_frame_equal(X_org.sort_index(), X_new.sort_index())
def test_distributor_map_reduce_and_close_are_called(self):
df = self.create_test_data_sample()
mock = Mock(spec=DistributorBaseClass)
mock.close.return_value = None
mock.map_reduce.return_value = []
X = extract_features(timeseries_container=df, column_id="id", column_sort="sort", column_kind="kind",
column_value="val", default_fc_parameters=self.name_to_param,
distributor=mock)
self.assertTrue(mock.close.called)
self.assertTrue(mock.map_reduce.called)
def test_extraction_runs_through(self):
rfs = EfficientFCParameters()
data = pd.DataFrame([[0, 0, 0, 0], [1, 0, 0, 0]],
columns=["id", "time", "kind", "value"])
extracted_features = extract_features(data,
default_fc_parameters=rfs,
column_kind="kind",
column_value="value",
column_sort="time",
column_id="id")
six.assertCountEqual(self, extracted_features.index, [0, 1])
def test_extraction_runs_through(self):
mfs = MinimalFCParameters()
data = pd.DataFrame([[0, 0, 0, 0], [1, 0, 0, 0]],
columns=["id", "time", "kind", "value"])
extracted_features = extract_features(data,
default_fc_parameters=mfs,
column_kind="kind",
column_value="value",
column_sort="time",
column_id="id")
six.assertCountEqual(self, extracted_features.columns, [
"0__median", "0__standard_deviation", "0__sum_values",
"0__maximum", "0__variance", "0__minimum", "0__mean", "0__length"
])
six.assertCountEqual(self, extracted_features.index, [0, 1])