Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.
'learning_rate': 0.1,
'l2_weight_reg': 0.001,
'hidden_size': 32,
'num_layers': 3,
'num_lstm_layers': 1,
'use_xavier_init': True,
'use_max_pool_else_avg_pool': True,
'dropout_drop_proba': 0.5,
'momentum': 0.1
})
AN_INPUT = "I am an input"
AN_EXPECTED_OUTPUT = "I am an expected output"
class SomeStep(NonFittableMixin, BaseStep):
def __init__(self, hyperparams_space: HyperparameterSpace = None, output=AN_EXPECTED_OUTPUT):
BaseStep.__init__(self, hyperparams=None, hyperparams_space=hyperparams_space)
NonFittableMixin.__init__(self)
self.output = output
def transform(self, data_inputs):
return [self.output] * len(data_inputs)
class SomeStepWithHyperparams(BaseStep):
def __init__(self):
BaseStep.__init__(self,
hyperparams=HYPERPARAMETERS,
hyperparams_space=HYPERPARAMETERS_SPACE,
name="MockStep"
)
def __init__(self, seed, increment_seed_after_each_fit=True):
InputAndOutputTransformerMixin.__init__(self)
BaseStep.__init__(self)
self.seed = seed
self.increment_seed_after_each_fit = increment_seed_after_each_fit
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
..
Thanks to Umaneo Technologies Inc. for their contributions to this Machine Learning
project, visit https://www.umaneo.com/ for more information on Umaneo Technologies Inc.
"""
from neuraxle.base import ExecutionContext, BaseStep, MetaStepMixin
from neuraxle.data_container import DataContainer
class OutputTransformerWrapper(MetaStepMixin, BaseStep):
"""
Transform expected output wrapper step that can sends the expected_outputs to the wrapped step
so that it can transform the expected outputs.
"""
def __init__(self, wrapped):
MetaStepMixin.__init__(self, wrapped)
BaseStep.__init__(self)
def handle_transform(self, data_container: DataContainer, context: ExecutionContext) -> DataContainer:
new_expected_outputs_data_container = self.wrapped.handle_transform(
DataContainer(
current_ids=data_container.current_ids,
data_inputs=data_container.expected_outputs,
expected_outputs=None
),
from typing import List, Tuple, Union
import numpy as np
from neuraxle.base import BaseStep, NonFittableMixin, MetaStepMixin
from neuraxle.pipeline import Pipeline
from neuraxle.steps.loop import ForEachDataInput
from neuraxle.union import FeatureUnion
ColumnSelectionType = Union[Tuple[int, BaseStep], Tuple[List[int], BaseStep], Tuple[slice, BaseStep]]
ColumnChooserTupleList = List[ColumnSelectionType]
class ColumnSelector2D(NonFittableMixin, BaseStep):
"""
A ColumnSelector2D selects column in a sequence.
"""
def __init__(self, columns_selection: ColumnSelectionType):
super().__init__()
self.column_selection = columns_selection
def transform(self, data_inputs):
if isinstance(self.column_selection, range):
self.column_selection = slice(
self.column_selection.start,
from neuraxle.base import MetaStepMixin, BaseStep, ExecutionContext, DataContainer, NonTransformableMixin, \
ExecutionMode, NonFittableMixin, ForceHandleMixin
class TransformOnlyWrapper(
NonTransformableMixin,
NonFittableMixin,
MetaStepMixin,
BaseStep
):
"""
A wrapper step that makes its wrapped step only executes in the transform execution mode.
.. seealso:: :class:`ExecutionMode`,
:class:`neuraxle.base.DataContainer`,
:class:`neuraxle.base.NonTransformableMixin`,
:class:`neuraxle.base.NonFittableMixin`,
:class:`neuraxle.base.MetaStepMixin`,
:class:`neuraxle.base.BaseStep`
"""
def __init__(self, wrapped: BaseStep):
NonTransformableMixin.__init__(self)
NonFittableMixin.__init__(self)
MetaStepMixin.__init__(self, wrapped=wrapped)
..
Thanks to Umaneo Technologies Inc. for their contributions to this Machine Learning
project, visit https://www.umaneo.com/ for more information on Umaneo Technologies Inc.
"""
from abc import ABC, abstractmethod
import numpy as np
from flask import Response
from neuraxle.base import BaseStep, NonFittableMixin
from neuraxle.pipeline import Pipeline
class JSONDataBodyDecoder(NonFittableMixin, BaseStep, ABC):
"""
Class to be used within a FlaskRESTApiWrapper to convert input json to actual data (e.g.: arrays)
"""
def transform(self, data_inputs):
return self.decode(data_inputs)
@abstractmethod
def decode(self, data_inputs: dict):
"""
Will convert data_inputs to a dict or a compatible data structure for jsonification
:param encoded data_inputs (dict parsed from json):
:return: data_inputs (as a data structure compatible with pipeline's data inputs)
"""
raise NotImplementedError("TODO: inherit from the `JSONDataBodyDecoder` class and implement this method.")
def __init__(self, wrapped: BaseStep, copy_op=copy.deepcopy):
BaseStep.__init__(self)
MetaStepMixin.__init__(self, wrapped)
self.set_step(wrapped)
self.steps: List[BaseStep] = []
self.copy_op = copy_op
def __init__(self):
BaseStep.__init__(self)
NonFittableMixin.__init__(self)