class PythonPredictor(BaseLanguagePredictor):
def __init__(self):
super(PythonPredictor, self).__init__()
self._model_adapter = None
def configure(self, params):
super(PythonPredictor, self).configure(params)
self._model_adapter = PythonModelAdapter(
model_dir=self._custom_model_path)
sys.path.append(self._custom_model_path)
self._model_adapter.load_custom_hooks()
self._model = self._model_adapter.load_model_from_artifact()
if self._model is None:
raise Exception("Failed to load model")
def predict(self, df):
kwargs = {}
if self._positive_class_label and self._negative_class_label:
kwargs[
POSITIVE_CLASS_LABEL_ARG_KEYWORD] = self._positive_class_label
kwargs[
NEGATIVE_CLASS_LABEL_ARG_KEYWORD] = self._negative_class_label
predictions = self._model_adapter.predict(data=df,
model=self._model,
**kwargs)
return predictions
class PythonPredictor(BaseLanguagePredictor):
def __init__(self):
super(PythonPredictor, self).__init__()
self._model_adapter = None
self._mlops = None
def configure(self, params):
super(PythonPredictor, self).configure(params)
self._model_adapter = PythonModelAdapter(model_dir=self._custom_model_path)
sys.path.append(self._custom_model_path)
self._model_adapter.load_custom_hooks()
self._model = self._model_adapter.load_model_from_artifact()
if self._model is None:
raise Exception("Failed to load model")
def predict(self, input_filename):
kwargs = {}
if self._positive_class_label and self._negative_class_label:
kwargs[POSITIVE_CLASS_LABEL_ARG_KEYWORD] = self._positive_class_label
kwargs[NEGATIVE_CLASS_LABEL_ARG_KEYWORD] = self._negative_class_label
start_predict = time.time()
predictions = self._model_adapter.predict(
input_filename, model=self._model, unstructured_mode=self._unstructured_mode, **kwargs
)
end_predict = time.time()
execution_time_ms = (end_predict - start_predict) * 1000
# TODO: call monitor only if we are in structured mode
self.monitor(input_filename, predictions, execution_time_ms)
return predictions
class PythonPredictor(BaseLanguagePredictor):
def __init__(self):
super(PythonPredictor, self).__init__()
self._model_adapter = None
self._mlops = None
def configure(self, params):
super(PythonPredictor, self).configure(params)
self._model_adapter = PythonModelAdapter(
model_dir=self._custom_model_path, target_type=self._target_type)
sys.path.append(self._custom_model_path)
self._model_adapter.load_custom_hooks()
self._model = self._model_adapter.load_model_from_artifact()
if self._model is None:
raise Exception("Failed to load model")
@property
def supported_payload_formats(self):
return self._model_adapter.supported_payload_formats
def predict(self, input_filename):
kwargs = {}
kwargs[TARGET_TYPE_ARG_KEYWORD] = self._target_type
if self._positive_class_label and self._negative_class_label:
kwargs[
POSITIVE_CLASS_LABEL_ARG_KEYWORD] = self._positive_class_label
kwargs[
NEGATIVE_CLASS_LABEL_ARG_KEYWORD] = self._negative_class_label
if self._class_labels:
kwargs[CLASS_LABELS_ARG_KEYWORD] = self._class_labels
start_predict = time.time()
predictions = self._model_adapter.predict(input_filename,
model=self._model,
**kwargs)
end_predict = time.time()
execution_time_ms = (end_predict - start_predict) * 1000
self.monitor(input_filename, predictions, execution_time_ms)
return predictions
def transform(self, input_filename):
return self._model_adapter.transform(input_filename, model=self._model)
def predict_unstructured(self, data, **kwargs):
str_or_tuple = self._model_adapter.predict_unstructured(
model=self._model, data=data, **kwargs)
if isinstance(str_or_tuple, (str, bytes, type(None))):
ret = str_or_tuple, None
elif isinstance(str_or_tuple, tuple):
ret = str_or_tuple
else:
raise DrumCommonException(
"Wrong type returned in unstructured mode: {}".format(
type(str_or_tuple)))
return ret
class PythonPredictor(BaseLanguagePredictor):
def __init__(self):
super(PythonPredictor, self).__init__()
self._model_adapter = None
self._mlops = None
def configure(self, params):
super(PythonPredictor, self).configure(params)
self._model_adapter = PythonModelAdapter(model_dir=self._code_dir,
target_type=self._target_type)
sys.path.append(self._code_dir)
self._model_adapter.load_custom_hooks()
self._model = self._model_adapter.load_model_from_artifact()
if self._model is None:
raise Exception("Failed to load model")
@property
def supported_payload_formats(self):
return self._model_adapter.supported_payload_formats
def model_info(self):
model_info = super(PythonPredictor, self).model_info()
model_info.update(self._model_adapter.model_info())
return model_info
def has_read_input_data_hook(self):
return self._model_adapter.has_read_input_data_hook()
def _predict(self, **kwargs):
kwargs[TARGET_TYPE_ARG_KEYWORD] = self._target_type
if self._positive_class_label is not None and self._negative_class_label is not None:
kwargs[
POSITIVE_CLASS_LABEL_ARG_KEYWORD] = self._positive_class_label
kwargs[
NEGATIVE_CLASS_LABEL_ARG_KEYWORD] = self._negative_class_label
if self._class_labels:
kwargs[CLASS_LABELS_ARG_KEYWORD] = self._class_labels
predictions = self._model_adapter.predict(model=self._model, **kwargs)
return predictions
def transform(self, **kwargs):
return self._model_adapter.transform(model=self._model, **kwargs)
def predict_unstructured(self, data, **kwargs):
str_or_tuple = self._model_adapter.predict_unstructured(
model=self._model, data=data, **kwargs)
if isinstance(str_or_tuple, (str, bytes, type(None))):
ret = str_or_tuple, None
elif isinstance(str_or_tuple, tuple):
ret = str_or_tuple
else:
raise DrumCommonException(
"Wrong type returned in unstructured mode: {}".format(
type(str_or_tuple)))
return ret
class PythonFit(ConnectableComponent):
def __init__(self, engine):
super(PythonFit, self).__init__(engine)
self.target_name = None
self.output_dir = None
self.estimator = None
self.positive_class_label = None
self.negative_class_label = None
self.class_labels = None
self.custom_model_path = None
self.input_filename = None
self.weights = None
self.sparse_column_file = None
self.weights_filename = None
self.target_filename = None
self._model_adapter = None
self.num_rows = None
self.parameter_file = None
def configure(self, params):
super(PythonFit, self).configure(params)
self.custom_model_path = self._params["__custom_model_path__"]
self.input_filename = self._params["inputFilename"]
self.sparse_column_file = self._params["sparse_column_file"]
self.target_name = self._params.get("targetColumn")
self.output_dir = self._params["outputDir"]
self.positive_class_label = self._params.get("positiveClassLabel")
self.negative_class_label = self._params.get("negativeClassLabel")
self.class_labels = self._params.get("classLabels")
self.weights = self._params["weights"]
self.weights_filename = self._params["weightsFilename"]
self.target_filename = self._params.get("targetFilename")
self.num_rows = self._params["numRows"]
self.parameter_file = self._params.get("parameterFile")
self._model_adapter = PythonModelAdapter(self.custom_model_path)
sys.path.append(self.custom_model_path)
self._model_adapter.load_custom_hooks()
def _materialize(self, parent_data_objs, user_data):
X, y, class_order, row_weights, parameters = shared_fit_preprocessing(
self)
self._model_adapter.fit(
X,
y,
output_dir=self.output_dir,
class_order=class_order,
row_weights=row_weights,
parameters=parameters,
)
make_sure_artifact_is_small(self.output_dir)
return []
class PythonPredictor(ConnectableComponent):
def __init__(self, engine):
super(PythonPredictor, self).__init__(engine)
self.model = None
self.positive_class_label = None
self.negative_class_label = None
self.custom_model_path = None
self._model_adapter = None
def configure(self, params):
super(PythonPredictor, self).configure(params)
self.custom_model_path = self._params["__custom_model_path__"]
self.positive_class_label = self._params.get("positiveClassLabel")
self.negative_class_label = self._params.get("negativeClassLabel")
self._model_adapter = PythonModelAdapter(
model_dir=self.custom_model_path)
sys.path.append(self.custom_model_path)
self._model_adapter.load_custom_hooks()
self.model = self._model_adapter.load_model_from_artifact()
if self.model is None:
raise Exception("Failed to load model")
def _materialize(self, parent_data_objs, user_data):
df = parent_data_objs[0]
kwargs = {}
if self.positive_class_label and self.negative_class_label:
kwargs[
POSITIVE_CLASS_LABEL_ARG_KEYWORD] = self.positive_class_label
kwargs[
NEGATIVE_CLASS_LABEL_ARG_KEYWORD] = self.negative_class_label
predictions = self._model_adapter.predict(data=df,
model=self.model,
**kwargs)
return [predictions]
class PythonFit(ConnectableComponent):
def __init__(self, engine):
super(PythonFit, self).__init__(engine)
self.target_name = None
self.output_dir = None
self.estimator = None
self.positive_class_label = None
self.negative_class_label = None
self.custom_model_path = None
self.input_filename = None
self.weights = None
self.weights_filename = None
self.target_filename = None
self._model_adapter = None
self.num_rows = None
def configure(self, params):
super(PythonFit, self).configure(params)
self.custom_model_path = self._params["__custom_model_path__"]
self.input_filename = self._params["inputFilename"]
self.target_name = self._params.get("targetColumn")
self.output_dir = self._params["outputDir"]
self.positive_class_label = self._params.get("positiveClassLabel")
self.negative_class_label = self._params.get("negativeClassLabel")
self.weights = self._params["weights"]
self.weights_filename = self._params["weightsFilename"]
self.target_filename = self._params.get("targetFilename")
self.num_rows = self._params["numRows"]
self._model_adapter = PythonModelAdapter(self.custom_model_path)
sys.path.append(self.custom_model_path)
self._model_adapter.load_custom_hooks()
def _materialize(self, parent_data_objs, user_data):
df = pd.read_csv(self.input_filename)
if self.num_rows == "ALL":
self.num_rows = len(df)
else:
self.num_rows = int(self.num_rows)
if self.target_filename:
X = df.sample(self.num_rows, random_state=1)
y = pd.read_csv(self.target_filename,
index_col=False).sample(self.num_rows,
random_state=1)
assert len(y.columns) == 1
assert len(X) == len(y)
y = y.iloc[:, 0]
else:
X = df.drop(self.target_name, axis=1).sample(self.num_rows,
random_state=1,
replace=True)
y = df[self.target_name].sample(self.num_rows,
random_state=1,
replace=True)
if self.weights_filename:
row_weights = pd.read_csv(self.weights_filename).sample(
self.num_rows, random_state=1, replace=True)
elif self.weights:
if self.weights not in X.columns:
raise ValueError(
"The column name {} is not one of the columns in "
"your training data".format(self.weights))
row_weights = X[self.weights]
else:
row_weights = None
class_order = ([self.negative_class_label, self.positive_class_label]
if self.negative_class_label else None)
self._model_adapter.fit(X,
y,
output_dir=self.output_dir,
class_order=class_order,
row_weights=row_weights)
make_sure_artifact_is_small(self.output_dir)
return []