def setUp(self) -> None:
np.random.seed(1)
self.sdf = StandardDataFormat(
labels=['a', 'b'],
timestamps=np.array([
datetime(2019, 7, 2, 12, 0),
datetime(2019, 7, 2, 12, 1),
datetime(2019, 7, 2, 12, 2),
datetime(2019, 7, 2, 12, 3),
datetime(2019, 7, 2, 12, 4),
]),
data=np.round(np.random.random((5, 2)) * 100)
)
def _process2d(self,
processor_input: StandardDataFormat) -> StandardDataFormat:
self.state = cast(skpp.OneHotEncoder, self.state) # just for ide
fields_input = [f['inputField'] for f in self.generate]
fields_output = [f['outputField'] for f in self.generate]
np_partial_data = ColumnSelector(columns=fields_input).process(
processor_input=processor_input).data
np_partial_data = _ensure_2d(np_partial_data)
self._init_encoder()
np_partial_data = self.state.transform(np_partial_data).toarray()
partial_data_labels = []
for ix, category in enumerate(self.state.categories_):
for v in category.tolist():
partial_data_labels.append(f"{fields_output[ix]}${v}")
partial_data = StandardDataFormat(
timestamps=processor_input.timestamps,
data=np_partial_data,
labels=partial_data_labels)
merged_data = ColumnDropper(columns=fields_input).process(
processor_input=processor_input)
return merged_data.add_cols(partial_data)
def get(self) -> StandardDataFormat:
if not AppConfig.get_config_or_default('general.enable_cache',
default=True):
self.logger.info("caching disabled")
return self._get()
import json
import os
logger = self.logger
cache_id = hashlib.sha256(
json.dumps(self.source_description,
sort_keys=True).encode('utf-8')).hexdigest()
logger.info(
"caching source is enabled. Cache-Id is {0}".format(cache_id))
path_to_cache = os.path.join(AppConfig['general.dir_cache'],
"cache_{0}".format(cache_id))
logger.info(f"path for cache is: {path_to_cache}")
if os.path.isfile(path_to_cache):
logger.info(MSG_CACHED_VERSION_FOUND.format(path_to_cache))
df = pd.read_pickle(path_to_cache)
return StandardDataFormat.from_dataframe(df)
else:
os.makedirs(AppConfig['general.dir_cache'], exist_ok=True)
logger.info(MSG_CACHED_VERSION_NOT_FOUND)
data = self._get()
data.to_dataframe().to_pickle(path_to_cache)
return data
def _fetch(self, _fields: List[Field]) -> StandardDataFormat:
self.logger.info(
f"getting data from time period {self.date_from.isoformat()} - {self.date_to.isoformat()}"
)
self.logger.info(
f"using username for authentification {self.session.auth.username}"
)
series = []
for field in _fields:
self.logger.info(
f"getting data for field: {field.name} aka. {field.alias}")
url = self._get_url(sensor_id=field.name,
date_from=self.date_from,
date_to=self.date_to)
self.logger.debug(f"download data from: {url}")
serie = pd.read_json(self._get_data(url, field))
serie = serie.set_index('timestamp')
serie.index = serie.index.round("T")
serie = serie.rename({"value": field.name}, axis=1)
dups = serie.index.duplicated()
if len(dups):
self.logger.info(
f"dropping {len(dups)} duplicated (index) measurements from {field}"
)
serie = serie[~dups]
series.append(serie)
self.logger.debug(f"received rows: {len(serie)}")
df = pd.concat(series, axis=1)
return StandardDataFormat.from_dataframe(df)
def test_aggregation_call(self):
data = np.random.random((10, 4))
data[:, 0] = np.arange(0, 10)
data[:, 1] = np.arange(10, 20)
data.flags.writeable = False
expected_param = np.zeros((2, 9, 2))
expected_param[0, :, 0] = np.arange(0, 9)
expected_param[0, :, 1] = np.arange(10, 19)
expected_param[1, :, 0] = np.arange(1, 10)
expected_param[1, :, 1] = np.arange(11, 20)
expected_data = np.full((10, 4 + 2), fill_value=np.nan)
expected_data[:, :4] = data
expected_data[-2:, 4:] = 9
agg = DummyAggreagor(sequence=9,
generate=[
InputOutputField(inputField='abc',
outputField='hello'),
InputOutputField(inputField='xyz',
outputField='world'),
])
input_format = StandardDataFormat(
timestamps=helper_data.generate_timestamps(10,
samples=data.shape[0]),
labels=['abc', 'xyz', 'aaa', 'bbb'],
data=data)
result = agg.process(input_format)
assert_array_equal(agg.grouped_data, expected_param)
self.assertEqual(result.labels,
['abc', 'xyz', 'aaa', 'bbb', 'hello', 'world'])
assert_array_equal(result.data, expected_data)
def _process2d(self, processor_input: StandardDataFormat) -> StandardDataFormat:
ix = np.arange(processor_input.data.shape[0])
np.random.shuffle(ix)
return processor_input.modify_copy(
timestamps=processor_input.timestamps[ix],
data=processor_input.data[ix])
def _process2d(self, processor_input: StandardDataFormat) -> StandardDataFormat:
selector = LabelSelector(processor_input.labels).select(self.fields)
data = processor_input.data.copy()
for col_id in selector.indexes:
selection = data[:, col_id]
ix_nans = np.isnan(selection)
data[ix_nans, col_id] = self.replacement
return processor_input.modify_copy(data=data)
def _process2d(self,
processor_input: StandardDataFormat) -> StandardDataFormat:
time_extracted = self._extractor(
pd.Series(processor_input.timestamps.copy())).reshape(-1, 1)
data = np.hstack((processor_input.data, time_extracted))
return StandardDataFormat(labels=processor_input.labels +
[self.output_field],
timestamps=processor_input.timestamps,
data=data)
def _process2d(self,
processor_input: StandardDataFormat) -> StandardDataFormat:
df = pd.DataFrame(data=processor_input.data)
data = df.interpolate(method=self.method,
limit_direction='forward',
limit=self.threshold).values
return StandardDataFormat(labels=processor_input.labels,
timestamps=processor_input.timestamps,
data=data)
def _process2d(self,
processor_input: StandardDataFormat) -> StandardDataFormat:
# index having zero nan in row
ix_valid = np.sum(np.isnan(processor_input.data), axis=1) == 0
return StandardDataFormat(
labels=processor_input.labels,
timestamps=processor_input.timestamps[ix_valid],
data=processor_input.data[ix_valid])
def test_no_outliers(self):
data = np.array([[10, 30], [12, 25]], dtype="float64")
data.flags.writeable = False
input_data = StandardDataFormat(
labels=['a', 'b'],
data=data,
timestamps=helper_data.generate_timestamps(2, 2))
# noinspection PyArgumentList
configs: List[InputOutputLimits] = [InputOutputLimits(inputField="a")]
result = OutlierRemover(generate=configs)._process2d(input_data)
assert_array_equal(data, result.data)
def test_min_outlier(self):
data = np.array([[10, 30], [12, 25]], dtype="float64")
data.flags.writeable = False
input_data = StandardDataFormat(
labels=['a', 'b'],
data=data,
timestamps=helper_data.generate_timestamps(2, 2))
limits = [{'inputField': 'b', 'min': 28}]
result_expected = data.copy()
result_expected[1, 1] = np.nan
result = OutlierRemover(generate=limits)._process2d(input_data)
assert_array_equal(result_expected, result.data)
def test_column_dropper(self):
timestamps = np.arange(datetime(2019, 7, 2, 12, 0),
datetime(2019, 7, 2, 20, 0),
timedelta(minutes=15)).astype(datetime)
timestamps.flags.writeable = False
data = np.random.random((5, 3))
data.flags.writeable = False
processor_data = StandardDataFormat(
labels=["preis", "temperatur", "feuchtigkeit"],
timestamps=timestamps[:5],
data=data)
result_expected = StandardDataFormat(labels=["preis", "feuchtigkeit"],
timestamps=timestamps[:5],
data=processor_data.data[:,
[0, 2]])
result = ColumnDropper(
columns=["temperatur"])._process2d(processor_data)
self.assertListEqual(result_expected.labels, result.labels)
assert_array_equal(result_expected.timestamps, result.timestamps)
assert_array_equal(result_expected.data, result.data)
def test_standard_case(self):
data = np.array([
[23, 55],
[21, 52],
[np.nan, 52],
[23, np.nan],
])
data.flags.writeable = False
result_excepted = data[:2]
process_data = StandardDataFormat(
data=data,
labels=['a', 'b'],
timestamps=helper_data.generate_timestamps(2, samples=4))
result = NanRemover()._process2d(process_data)
assert_array_equal(result_excepted, result.data)
def test_standard_case(self):
data = np.array([[11, 20], [12, 21], [13, 22], [14, 20], [15, 20],
[16, 20], [17, 20], [18, 20]],
dtype='float')
result_expected = np.array(
[[11, 20], [12, 21], [13, 22], [14, 20], [15, 20], [16, 20],
[17, np.nan], [18, np.nan]],
dtype='float')
processor_data = StandardDataFormat(
timestamps=helper_data.generate_timestamps(samples=data.shape[0]),
labels=helper_data.get_labels(2),
data=data)
processor_data_result = FreezedValueRemover(
max_freezed_values=3)._process2d(processor_data)
assert_array_equal(result_expected, processor_data_result.data)
def test_range_encoder(self):
generates = [{'inputField': 'hour', 'outputField': 'hourOneHot'}]
encoder = RangeEncoder(generate=generates, value_from=0, value_to=3)
data = transform_to_2d_matrix(np.array([0, 1, 1, 2]))
result_expected = np.zeros((4, 3))
result_expected[0, 0] = 1
result_expected[1, 1] = 1
result_expected[2, 1] = 1
result_expected[3, 2] = 1
result = encoder._process2d(
StandardDataFormat(data=data,
labels=['hour'],
timestamps=generate_timestamps(
samples=result_expected.shape[0])))
assert_array_equal(result_expected, result.data)
class TestStandardDataFormat(unittest.TestCase):
def setUp(self) -> None:
np.random.seed(1)
self.sdf = StandardDataFormat(
labels=['a', 'b'],
timestamps=np.array([
datetime(2019, 7, 2, 12, 0),
datetime(2019, 7, 2, 12, 1),
datetime(2019, 7, 2, 12, 2),
datetime(2019, 7, 2, 12, 3),
datetime(2019, 7, 2, 12, 4),
]),
data=np.round(np.random.random((5, 2)) * 100)
)
def test_to_dataframe(self):
df = self.sdf.to_dataframe()
self.assertEqual(self.sdf.labels, df.columns.values.tolist())
assert_array_equal(self.sdf.timestamps, df.index.values)
assert_array_equal(self.sdf.data, df.values)
def _process2d(self,
processor_input: StandardDataFormat) -> StandardDataFormat:
partial_data = ColumnSelector(
self.fields).process(processor_input).data
transformer_restored = self.state
if transformer_restored:
partial_data = transformer_restored.transform(partial_data)
else:
transformer = create_instance(qualified_name=self.scaler,
kwargs=self.kwargs)
partial_data = transformer.fit_transform(partial_data)
# note: state will be saved only during training process
# each training has it unique identificator which is associated with state data
self.state = transformer
label_selection = LabelSelector(
elements=processor_input.labels).select(self.fields)
data = processor_input.data.copy()
data[:, label_selection.indexes] = partial_data
return processor_input.modify_copy(data=data)
def get(self) -> StandardDataFormat:
required_fields = self._get_fields()
raw = self._fetch(required_fields)
AbstractDatasourceAdapter._check_fields_availability(
raw, required_fields, using_alias=self.source_returns_alias)
labels_new = [f.alias for f in required_fields]
if self.source_returns_alias:
ix_selection = LabelSelector(elements=raw.labels).select(
selection=[f.alias for f in required_fields]).indexes
else:
ix_selection = LabelSelector(elements=raw.labels).select(
selection=[f.name for f in required_fields]).indexes
# ensure ordering of columns are correct
data = raw.data[:, ix_selection]
return StandardDataFormat(timestamps=raw.timestamps,
labels=labels_new,
data=data)
def test_multi_aggregation(self):
delta = timedelta(minutes=5)
start_date = datetime(2019, 7, 1, 12, 1)
end_date = start_date + 10 * delta
data = StandardDataFormat(
labels=["temp1", "temp2"],
timestamps=np.arange(start_date, end_date, delta).astype(datetime),
data=np.array([np.arange(0, 10),
np.arange(10, 20)]).T)
data.timestamps.flags.writeable = False
data.data.flags.writeable = False
multi = MultiAggregation(
sequence=5, # todo: use sequence instead minutes
instances=[
Max(sequence=5,
generate=[{
"inputField": "temp1",
"outputField": "temp1Max"
}]),
Min(sequence=5,
generate=[{
"inputField": "temp1",
"outputField": "temp1Min"
}])
])
multi.instances[0].id = "0"
multi.instances[1].id = "1"
result = PipelineExecutor(pipeline=[multi]).execute(data=data)
self.assertEqual(['temp1', 'temp2', 'temp1Max', 'temp1Min'],
result.labels)
assert_array_equal(data.timestamps, result.timestamps)
assert_array_equal(np.full((4, 2), fill_value=np.nan),
result.data[:4, [2, 3]])
assert_array_equal(np.arange(4, 10), result.data[4:, 2])
assert_array_equal(np.arange(6), result.data[4:, 3])
def test_range_encoder_two_cols(self):
generates = [{'inputField': 'hour', 'outputField': 'hourOneHot'}]
encoder = RangeEncoder(generate=generates, value_from=0, value_to=3)
data = np.array([[0, 1, 1, 2], [1, 2, 3, 4]]).T
result_expected = np.zeros((4, 4))
result_expected[0, 1 + 0] = 1
result_expected[1, 1 + 1] = 1
result_expected[2, 1 + 1] = 1
result_expected[3, 1 + 2] = 1
result_expected[:, 0] = np.array([1, 2, 3, 4])
result = encoder._process2d(
StandardDataFormat(data=data,
labels=['hour', 'abc'],
timestamps=generate_timestamps(
samples=result_expected.shape[0])))
assert_array_equal(result_expected, result.data)
self.assertEqual("abc", result.labels[0])
self.assertEqual("hourOneHot$0", result.labels[1])
self.assertEqual("hourOneHot$1", result.labels[2])
self.assertEqual("hourOneHot$2", result.labels[3])
def test_resampling(self):
rows = 2
timestamps = np.array([
datetime(2019, 7, 2, 12, 0),
datetime(2019, 7, 2, 12, 3),
])
timestamps.flags.writeable = False
data = np.random.random((rows, 3)) * 100
data.flags.writeable = False
processor_data = StandardDataFormat(
labels=["temperatur", "feuchtigkeit", "preis"],
data=data,
timestamps=timestamps)
result = Resampler(freq="1min")._process2d(processor_data)
# expected rows: 0, 1, 2, 3 => 4 rows
expected_timestamps = np.arange(datetime(2019, 7, 2, 12, 0),
datetime(2019, 7, 2, 12, 4),
timedelta(minutes=1))
self.assertListEqual(result.labels,
["temperatur", "feuchtigkeit", "preis"])
self.assertTupleEqual(result.data.shape,
(expected_timestamps.shape[0], 3))
for i in range(expected_timestamps.shape[0]):
self.assertEqual(expected_timestamps[i], result.timestamps[i])
assert_array_equal(data[0], result.data[0])
assert_array_equal(data[-1], result.data[-1])
assert_array_equal(result.data[1:-1], np.full((2, 3),
fill_value=np.nan))
def _process2d(self,
processor_input: StandardDataFormat) -> StandardDataFormat:
fields_in = [
ConfigReader.from_dict(g).get_or_error(
key="inputField", context="OutlierRemover Config")
for g in self.generate
]
cols_selected = LabelSelector(elements=processor_input.labels).select(
selection=fields_in).indexes
# we expend feature-array to have a 3D-array with only one entry (of 2D-Array)
grouped_data = np.ma.array(
np.expand_dims(processor_input.data[:, cols_selected], axis=0))
affected_index = Outlier(sequence=np.nan, generate=self.generate)\
.affected_index(grouped_data=grouped_data)
affected_index = np.squeeze(affected_index, axis=0)
data = processor_input.data.copy()
t = data[:, cols_selected]
t[affected_index] = np.nan
data[:, cols_selected] = t
return processor_input.modify_copy(data=data)
def _process2d(self, processor_input: StandardDataFormat) -> StandardDataFormat:
mask = get_freezed_value_mask(processor_input.data, self.max_freezed_values)
data = processor_input.data.copy()
data[mask] = np.nan
return processor_input.modify_copy(data=data)
import datetime
import unittest
import numpy as np
from numpy.testing import assert_array_equal
from mlpipe.processors.standard_data_format import StandardDataFormat
from mlpipe.processors.time_extractor import TimeExtractor
test_data = StandardDataFormat(
timestamps=np.array([
datetime.datetime(2019, 7, 2, 12, 25), # tuesday
datetime.datetime(2019, 8, 3, 20, 45), # wednesday
]),
data=np.arange(2).reshape(-1, 1),
labels=["example_data"])
class TestTimeExtractor(unittest.TestCase):
def test_extract_hours(self):
result_expected = np.hstack(
(test_data.data, np.array([12, 21]).reshape(-1, 1)))
result = TimeExtractor(
extract="hour", outputField="example_hour")._process2d(test_data)
assert_array_equal(result_expected, result.data)
def test_extract_weekday(self):
# note weekday start from monday. 0 = monday
result_expected = np.hstack(
(test_data.data, np.array([1, 5]).reshape(-1, 1)))
result = TimeExtractor(