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base.py
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"""
Neuraxle's Base Classes
====================================
This is the core of Neuraxle. Most pipeline steps derive (inherit) from those classes. They are worth noticing.
..
Copyright 2019, Neuraxio Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
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.
"""
import hashlib
import inspect
import logging
import os
import pprint
import traceback
import warnings
from abc import ABC, abstractmethod
from collections import OrderedDict
from copy import copy
from enum import Enum
from typing import List, Union, Any, Iterable, KeysView, ItemsView, ValuesView, Callable, Dict, Tuple, Type
from joblib import dump, load
from neuraxle.data_container import DataContainer
from neuraxle.hyperparams.space import HyperparameterSpace, HyperparameterSamples, RecursiveDict
DEFAULT_CACHE_FOLDER = os.path.join(os.getcwd(), 'cache')
LOGGER_FORMAT = "[%(asctime)s][%(levelname)s][%(module)s][%(lineno)d]: %(message)s"
DATE_FORMAT = "%H:%M:%S"
logging.basicConfig(format=LOGGER_FORMAT, datefmt=DATE_FORMAT, level=logging.INFO)
class BaseHasher(ABC):
"""
Base class to hash hyperparamters, and data input ids together.
The :class:`~neuraxle.data_container.DataContainer` class uses the hashed values for its current ids.
:class:`BaseStep` uses many :class:`BaseHasher` objects
to hash hyperparameters, and data inputs ids together after each transform.
.. seealso::
:class:`~neuraxle.data_container.DataContainer`
"""
@abstractmethod
def single_hash(self, current_id: str, hyperparameters: HyperparameterSamples) -> List[str]:
"""
Hash summary id, and hyperparameters together.
:param current_id: current hashed id
:param hyperparameters: step hyperparameters to hash with current ids
:type hyperparameters: HyperparameterSamples
:return: the new hashed current id
"""
raise NotImplementedError()
@abstractmethod
def hash(self, current_ids: List[str], hyperparameters: HyperparameterSamples, data_inputs: Iterable) -> List[str]:
"""
Hash :class:`~neuraxle.data_container.DataContainer`.current_ids, data inputs, and hyperparameters together.
:param current_ids: current hashed ids (can be None if this function has not been called yet)
:param hyperparameters: step hyperparameters to hash with current ids
:param data_inputs: data inputs to hash current ids for
:return: the new hashed current ids
"""
raise NotImplementedError()
class HashlibMd5Hasher(BaseHasher):
"""
Class to hash hyperparamters, and data input ids together using md5 algorithm from hashlib :
`<https://docs.python.org/3/library/hashlib.html>`_
The :class:`~neuraxle.data_container.DataContainer` class uses the hashed values for its current ids.
:class:`BaseStep` uses many :class:`BaseHasher` objects
to hash hyperparameters, and data inputs ids together after each transform.
.. seealso::
:class:`~neuraxle.base.BaseHasher`,
:class:`~neuraxle.data_container.DataContainer`
"""
def single_hash(self, current_id: str, hyperparameters: HyperparameterSamples) -> List[str]:
"""
Hash summary id, and hyperparameters together.
:param current_id: current hashed id
:param hyperparameters: step hyperparameters to hash with current ids
:return: the new hashed current id
"""
m = hashlib.md5()
current_hyperparameters_hash = hashlib.md5(
str.encode(str(hyperparameters.to_flat_dict()))
).hexdigest()
m.update(str.encode(str(current_id)))
m.update(str.encode(current_hyperparameters_hash))
return m.hexdigest()
def hash(self, current_ids, hyperparameters, data_inputs: Any = None) -> List[str]:
"""
Hash :class:`~neuraxle.data_container.DataContainer`.current_ids, data inputs, and hyperparameters together
using `hashlib.md5 <https://docs.python.org/3/library/hashlib.html>`_
:param current_ids: current hashed ids (can be None if this function has not been called yet)
:param hyperparameters: step hyperparameters to hash with current ids
:param data_inputs: data inputs to hash current ids for
:return: the new hashed current ids
"""
if current_ids is None:
if isinstance(data_inputs, Iterable):
current_ids = [str(i) for i in range(len(data_inputs))]
else:
current_ids = [str(0)]
if len(hyperparameters) == 0:
return current_ids
hyperperams_dict = hyperparameters.to_flat_dict()
current_hyperparameters_hash = hashlib.md5(str.encode(str(hyperperams_dict))).hexdigest()
new_current_ids = []
for current_id in current_ids:
m = hashlib.md5()
m.update(str.encode(current_id))
m.update(str.encode(current_hyperparameters_hash))
new_current_ids.append(m.hexdigest())
return new_current_ids
class HashlibMd5ValueHasher(HashlibMd5Hasher):
def hash(self, current_ids, hyperparameters, data_inputs: Any = None) -> List[str]:
"""
Hash :class:`~neuraxle.data_container.DataContainer`.current_ids, data inputs, and hyperparameters together
using `hashlib.md5 <https://docs.python.org/3/library/hashlib.html>`_
:param current_ids: current hashed ids (can be None if this function has not been called yet)
:param hyperparameters: step hyperparameters to hash with current ids
:param data_inputs: data inputs to hash current ids for
:return: the new hashed current ids
"""
if len(current_ids) != len(data_inputs):
current_ids: List[str] = [str(i) for i in range(len(data_inputs))]
if current_ids is None:
if isinstance(data_inputs, Iterable):
current_ids = [str(i) for i in range(len(data_inputs))]
else:
current_ids = [str(0)]
if len(hyperparameters) == 0:
return current_ids
hyperperams_dict = hyperparameters.to_flat_dict()
current_hyperparameters_hash = hashlib.md5(str.encode(str(hyperperams_dict))).hexdigest()
new_current_ids = []
for current_id, di in zip(current_ids, data_inputs):
m = hashlib.md5()
m.update(str.encode(current_id))
m.update(str.encode(str(di)))
m.update(str.encode(current_hyperparameters_hash))
new_current_ids.append(m.hexdigest())
return new_current_ids
class BaseSaver(ABC):
"""
Any saver must inherit from this one. Some savers just save parts of objects, some save it all or what remains.
Each :class`BaseStep` can potentially have multiple savers to make serialization possible.
.. seealso::
:func:`~neuraxle.base._HasSavers.save`,
:func:`~neuraxle.base._HasSavers.load`
"""
@abstractmethod
def save_step(self, step: 'BaseTransformer', context: 'ExecutionContext') -> 'BaseTransformer':
"""
Save step with execution context.
:param step: step to save
:param context: execution context
:param save_savers:
:return:
"""
raise NotImplementedError()
@abstractmethod
def can_load(self, step: 'BaseTransformer', context: 'ExecutionContext'):
"""
Returns true if we can load the given step with the given execution context.
:param step: step to load
:param context: execution context to load from
:return:
"""
raise NotImplementedError()
@abstractmethod
def load_step(self, step: 'BaseTransformer', context: 'ExecutionContext') -> 'BaseTransformer':
"""
Load step with execution context.
:param step: step to load
:param context: execution context to load from
:return: loaded base step
"""
raise NotImplementedError()
class JoblibStepSaver(BaseSaver):
"""
Saver that can save, or load a step with `joblib.load <https://joblib.readthedocs.io/en/latest/generated/joblib.load.html>`_,
and `joblib.dump <https://joblib.readthedocs.io/en/latest/generated/joblib.dump.html>`_.
This saver is a good default saver when the object is
already stripped out of things that would make it unserializable.
It is the default stripped_saver for the :class:`ExecutionContext`.
The stripped saver is the first to load the step, and the last to save the step.
The saver receives a *stripped* version of the step so that it can be saved by joblib.
.. seealso::
:class:`~neuraxle.base.BaseSaver`,
:class:`~neuraxle.base.ExecutionContext`
"""
def can_load(self, step: 'BaseTransformer', context: 'ExecutionContext') -> bool:
"""
Returns true if the given step has been saved with the given execution context.
:param step: step that might have been saved
:param context: execution context
:return: if we can load the step with the given context
"""
return os.path.exists(
self._create_step_path(context, step)
)
def _create_step_path(self, context, step):
"""
Create step path for the given context.
:param context: execution context
:param step: step to save, or load
:return: path
"""
return os.path.join(context.get_path(), '{0}.joblib'.format(step.name))
def save_step(self, step: 'BaseTransformer', context: 'ExecutionContext') -> 'BaseTransformer':
"""
Saved step stripped out of things that would make it unserializable.
:param step: stripped step to save
:param context: execution context to save from
:return:
"""
context.mkdir()
path = self._create_step_path(context, step)
dump(step, path)
return step
def load_step(self, step: 'BaseTransformer', context: 'ExecutionContext') -> 'BaseTransformer':
"""
Load stripped step.
:param step: stripped step to load
:param context: execution context to load from
:return:
"""
loaded_step = load(self._create_step_path(context, step))
# we need to keep the current steps in memory because they have been deleted before saving...
# the steps that have not been saved yet need to be in memory while loading a truncable steps...
if isinstance(loaded_step, TruncableSteps) and hasattr(step, 'steps'):
loaded_step.steps = step.steps
return loaded_step
class ExecutionMode(Enum):
FIT_OR_FIT_TRANSFORM_OR_TRANSFORM = 'fit_or_fit_transform_or_transform'
FIT_OR_FIT_TRANSFORM = 'fit_or_fit_transform'
TRANSFORM = 'transform'
FIT = 'fit'
FIT_TRANSFORM = 'fit_transform'
INVERSE_TRANSFORM = 'inverse_transform'
class ExecutionPhase(Enum):
UNSPECIFIED = None
PRETRAIN = "pretraining"
TRAIN = "training"
VALIDATION = "validation"
TEST = "test"
PROD = "production"
class ExecutionContext:
"""
Execution context object containing all of the pipeline hierarchy steps.
First item in execution context parents is root, second is nested, and so on. This is like a stack.
The execution context is used for fitted step saving, and caching :
* :func:`~neuraxle.base._HasSavers.save`
* :func:`~neuraxle.base._HasSavers.load`
* :func:`~neuraxle.steps.caching.ValueCachingWrapper.handle_transform`
* :func:`~neuraxle.steps.caching.ValueCachingWrapper.handle_fit_transform`
.. seealso::
:class:`~neuraxle.base.BaseStep`,
:class:`~neuraxle.steps.caching.ValueCachingWrapper`
"""
def __init__(
self,
root: str = DEFAULT_CACHE_FOLDER,
execution_phase: ExecutionPhase = ExecutionPhase.UNSPECIFIED,
execution_mode: ExecutionMode = ExecutionMode.FIT_OR_FIT_TRANSFORM_OR_TRANSFORM,
stripped_saver: BaseSaver = None,
parents: List['BaseStep'] = None,
services: Dict[Type, object] = None,
logger: logging.Logger = None
):
self.execution_mode = execution_mode
self.execution_phase = execution_phase
if stripped_saver is None:
stripped_saver: BaseSaver = JoblibStepSaver()
self.stripped_saver = stripped_saver
self.root: str = root
if parents is None:
parents = []
self.parents: List[BaseStep] = parents
if services is None:
services: Dict[Type, object] = dict()
self.services: Dict[Type, object] = services
if logger is None:
logger = logging.getLogger()
self.logger = logger
def set_execution_phase(self, phase: ExecutionPhase) -> 'ExecutionContext':
"""
Set the instance's execution phase to given phase.
:param phase:
:return:
"""
self.execution_phase: ExecutionPhase = phase
return self
def set_service_locator(self, services: Dict[Type, object]) -> 'ExecutionContext':
"""
Register abstract class type instances.
:param services: instance
:return: self
"""
self.services: Dict[Type, object] = services
return self
def register_service(self, service_abstract_class_type: Type, service_instance: object) -> 'ExecutionContext':
"""
Register base class instance inside the services.
:param service_abstract_class_type: base type
:param service_instance: instance
:return: self
"""
self.services[service_abstract_class_type] = service_instance
return self
def get_service(self, service_abstract_class_type: Type) -> object:
"""
Get the registered instance for the given abstract class type.
:param service_abstract_class_type: base type
:return: self
"""
return self.services[service_abstract_class_type]
def get_services(self) -> object:
"""
Get the registered instances in the services.
:return: self
"""
return self.services
def has_service(self, service_abstract_class_type: Type) -> bool:
"""
Return a bool indicating if the service has been registered.
:param service_abstract_class_type: base type
:return: if the service registered or not
"""
return service_abstract_class_type in self.services
def set_logger(self, logger):
self.logger = logger
return self
def get_execution_mode(self) -> ExecutionMode:
return self.execution_mode
def save(self, full_dump=False):
"""
Save all unsaved steps in the parents of the execution context using :func:`~neuraxle.base._HasSavers.save`.
This method is called from a step checkpointer inside a :class:`Checkpoint`.
:param full_dump: save full pipeline dump to be able to load everything without source code (false by default).
:return:
.. seealso::
:class:`BaseStep`,
:func:`~neuraxle.base._HasSavers.save`
"""
while not self.empty():
should_save_last_step = self.should_save_last_step()
last_step = self.peek()
if full_dump:
should_save_last_step = True
self.pop()
if should_save_last_step:
last_step.save(self, full_dump)
def save_last(self):
"""
Save only the last step in the execution context.
.. seealso::
:func:`~neuraxle.base.ExecutionContext.save`
"""
last_step = self.peek()
self.pop()
last_step.save(self, True)
def should_save_last_step(self) -> bool:
"""
Returns True if the last step should be saved.
:return: if the last step should be saved
"""
if len(self.parents) > 0:
return self.parents[-1].should_save()
return False
def pop_item(self) -> 'BaseTransformer':
"""
Change the execution context to be the same as the latest parent context.
:return:
"""
return self.parents.pop()
def pop(self) -> bool:
"""
Pop the context. Returns True if it successfully popped an item from the parents list.
:return: if an item has been popped
"""
if len(self) == 0:
return False
self.pop_item()
return True
def push(self, step: 'BaseTransformer') -> 'ExecutionContext':
"""
Pushes a step in the parents of the execution context.
:param step: step to add to the execution context
:return: self
"""
return ExecutionContext(
root=self.root,
execution_mode=self.execution_mode,
execution_phase=self.execution_phase,
parents=self.parents + [step],
services=self.services,
logger=self.logger
)
def copy(self):
return ExecutionContext(
root=self.root,
execution_mode=self.execution_mode,
execution_phase=self.execution_phase,
parents=copy(self.parents),
services=self.services,
logger=self.logger
)
def peek(self) -> 'BaseTransformer':
"""
Get last parent.
:return: the last parent base step
"""
return self.parents[-1]
def mkdir(self):
"""
Creates the directory to save the last parent step.
:return:
"""
path = self.get_path()
if not os.path.exists(path):
os.makedirs(path)
def get_path(self, is_absolute: bool = True):
"""
Creates the directory path for the current execution context.
:param is_absolute: bool to say if we want to add root to the path or not
:return: current context path
"""
parents_with_path = [self.root] if is_absolute else []
parents_with_path += [p.name for p in self.parents]
if len(parents_with_path) == 0:
return '.' + os.sep
return os.path.join(*parents_with_path)
def get_names(self):
"""
Returns a list of the parent names.
:return: list of parents step names
"""
return [p.name for p in self.parents]
def empty(self):
"""
Return True if the context doesn't have parent steps.
:return: if parents len is 0
"""
return len(self) == 0
def load(self, path: str) -> 'BaseTransformer':
"""
Load full dump at the given path.
:param path: pipeline step path
:return: loaded step
.. seealso::
:class:`FullDumpLoader`,
:class:`Identity`
"""
context_for_loading = self
context_for_loading = context_for_loading.push(Identity(name=path))
if os.sep in path:
context_for_loading = context_for_loading.to_identity()
path = path.split(os.sep)[-1]
return FullDumpLoader(
name=path,
stripped_saver=self.stripped_saver
).load(context_for_loading, True)
def to_identity(self) -> 'ExecutionContext':
"""
Create a fake execution context containing only identity steps.
Create the parents by using the path of the current execution context.
:return: fake identity execution context
.. seealso::
:class:`FullDumpLoader`,
:class:`Identity`
"""
step_names = self.get_path(False).split(os.sep)
parents = [
Identity(name=name)
for name in step_names
]
return ExecutionContext(
root=self.root,
execution_mode=self.execution_mode,
stripped_saver=self.stripped_saver,
parents=parents
)
def __len__(self):
return len(self.parents)
class _RecursiveArguments:
"""
This class is used by :func:`~neuraxle.base.BaseStep.apply`, and :class:`_HasChildrenMixin` to pass the right arguments to steps with children.
.. seealso::
:class:`_HasChildrenMixin`,
:func:`~neuraxle.base._HasHyperparamsSpace.set_hyperparams_space`,
:func:`~neuraxle.base._HasHyperparamsSpace.get_hyperparams_space`,
:func:`~neuraxle.base._HasHyperparams.get_hyperparams`,
:func:`~neuraxle.base._HasHyperparams.set_hyperparams`,
:func:`~neuraxle.base._HasHyperparams.update_hyperparams`,
:func:`~neuraxle.base._HasHyperparamsSpace.update_hyperparams_space`,
:func:`~neuraxle.base.BaseTransformer.invalidate`
"""
def __init__(self, ra=None, *args, **kwargs):
if ra is not None:
args = ra.args
kwargs = ra.kwargs
self.args = args
self.kwargs = kwargs
def __getitem__(self, child_step_name: str):
"""
Return recursive arguments for the given child step name.
If child step name is None, return the root values.
:param child_step_name: child step name, or None if we want to get root values.
:return: recursive argument for the given child step name
"""
if child_step_name is None:
arguments = list()
keyword_arguments = dict()
for arg in self.args:
if isinstance(arg, RecursiveDict):
arguments.append(arg.get(child_step_name))
else:
arguments.append(arg)
for key, arg in self.kwargs.items():
if isinstance(arg, RecursiveDict):
keyword_arguments[key] = arg.get(child_step_name)
else:
keyword_arguments[key] = arg
return _RecursiveArguments(*arguments, **keyword_arguments)
else:
arguments = list()
keyword_arguments = dict()
for arg in self.args:
if isinstance(arg, RecursiveDict):
arguments.append(arg.get(child_step_name))
else:
arguments.append(arg)
for key, arg in self.kwargs.items():
if isinstance(arg, RecursiveDict):
keyword_arguments[key] = arg.get(child_step_name)
else:
keyword_arguments[key] = arg
return _RecursiveArguments(*arguments, **keyword_arguments)
def __iter__(self):
return self.kwargs
class _HasRecursiveMethods:
"""
An internal class to represent a step that has recursive methods.
The apply :func:`apply` function is used to apply a method to a step and its children.
Example usage :
.. code-block:: python
class _HasHyperparams:
# ...
def set_hyperparams(self, hyperparams: Union[HyperparameterSamples, Dict]) -> HyperparameterSamples:
self.apply(method='_set_hyperparams', hyperparams=HyperparameterSamples(hyperparams))
return self
def _set_hyperparams(self, hyperparams: Union[HyperparameterSamples, Dict]) -> HyperparameterSamples:
self._invalidate()
hyperparams = HyperparameterSamples(hyperparams)
self.hyperparams = hyperparams if len(hyperparams) > 0 else self.hyperparams
return self.hyperparams
pipeline = Pipeline([
SomeStep()
])
pipeline.set_hyperparams(HyperparameterSamples({
'learning_rate': 0.1,
'SomeStep__learning_rate': 0.05
}))
.. seealso::
:class:`BaseStep`,
:class:`BaseTransformer`,
:class:`_HasChildrenMixin`
:class:`_RecursiveArguments`
"""
def apply(self, method: Union[str, Callable], ra: _RecursiveArguments = None, *args, **kwargs) -> RecursiveDict:
"""
Apply a method to a step and its children.
:param method: method name that need to be called on all steps
:param ra: recursive arguments
:param args: any additional arguments to be passed to the method
:param kwargs: any additional positional arguments to be passed to the method
:return: method outputs, or None if no method has been applied
.. seealso::
:class:`_RecursiveArguments`,
:class:`_HasChildrenMixin`
"""
if ra is None:
ra = _RecursiveArguments(*args, **kwargs)
kargs = ra.args
def _return_empty(*args, **kwargs):
return RecursiveDict()
_method = _return_empty
if isinstance(method, str) and hasattr(self, method) and callable(getattr(self, method)):
_method = getattr(self, method)
if not isinstance(method, str):
_method = method
kargs = [self] + list(kargs)
try:
results = _method(*kargs, **ra.kwargs)
if not isinstance(results, RecursiveDict):
raise ValueError(
'Method {} must return a RecursiveDict because it is applied recursively.'.format(method))
return results
except Exception as err:
print('{}: Failed to apply method {}.'.format(self.name, method))
print(traceback.format_stack())
raise err
class _TransformerStep(ABC):
"""
An internal class to represent a step that can be transformed, or inverse transformed.
See :class:`BaseTransformer`, for the complete transformer step that can be used inside a :class:`neuraxle.pipeline.Pipeline`.
See :class:`BaseStep`, for a step that can also be fitted inside a :class:`neuraxle.pipeline.Pipeline`.
Every step must implement :func:`~neuraxle.base._TransformerStep.transform`.
If a step is not transformable, you can inherit from :class:`NonTransformableMixin`.
Every transformer step has handle methods that can be overridden to add side effects or change the execution flow based on the execution context, and the data container :
* :func:`~neuraxle.base._TransformerStep.handle_transform`
* :func:`~neuraxle.base._TransformerStep.handle_fit_transform`
* :func:`~neuraxle.base._TransformerStep.handle_fit`
.. seealso::
:class:`BaseStep`,
:class:`BaseTransformer`,
:class:`_FittableStep`,
:class:`~neuraxle.data_container.DataContainer`
"""
def _will_process(self, data_container: DataContainer, context: ExecutionContext) -> (
DataContainer, ExecutionContext):
"""
Apply side effects before any step method.
:param data_container: data container
:param context: execution context
:return: (data container, execution context)
"""
return data_container, context
def handle_transform(self, data_container: DataContainer, context: ExecutionContext) -> DataContainer:
"""
Override this to add side effects or change the execution flow before (or after) calling * :func:`~neuraxle.base._TransformerStep.transform`.
The default behavior is to rehash current ids with the step hyperparameters.
:param data_container: the data container to transform
:param context: execution context
:return: transformed data container
"""
if not self.is_initialized:
self.setup(context)
data_container, context = self._will_process(data_container, context)
data_container, context = self._will_transform_data_container(data_container, context)
data_container = self._transform_data_container(data_container, context)
data_container = self._did_transform(data_container, context)
data_container = self._did_process(data_container, context)
return data_container
def _will_transform_data_container(self, data_container: DataContainer, context: ExecutionContext) -> \
(DataContainer, ExecutionContext):
"""
Apply side effects before transform.
:param data_container: data container
:param context: execution context
:return: (data container, execution context)
"""
return data_container, context.push(self)
def _transform_data_container(self, data_container: DataContainer, context: ExecutionContext) -> DataContainer:
"""
Transform data container.
:param data_container: data container
:param context: execution context
:return: data container
"""
data_container.set_data_inputs(self(data_container.data_inputs))
return data_container
def __call__(self, *args, **kwargs) -> Any:
return self.transform(*args)
@abstractmethod
def transform(self, data_inputs):
"""
Transform given data inputs.
:param data_inputs: data inputs
:return: transformed data inputs
"""
raise NotImplementedError(
"TODO: Implement this method in {}, or have this class inherit from the NonTransformableMixin.".format(
self.__class__.__name__))
def _did_transform(self, data_container: DataContainer, context: ExecutionContext) -> DataContainer:
"""
Apply side effects after transform.
:param data_container: data container
:param context: execution context
:return: data container
"""
return data_container
def _did_process(self, data_container: DataContainer, context: ExecutionContext) -> DataContainer:
"""
Apply side effects after any step method.
:param data_container: data container
:param context: execution context
:return: (data container, execution context)
"""
data_container = self.hash_data_container(data_container)
return data_container
def handle_fit(self, data_container: DataContainer, context: ExecutionContext) -> 'BaseTransformer':
"""
Override this to add side effects or change the execution flow before (or after) calling :func:`~neuraxle.base._FittableStep.fit`.
The default behavior is to rehash current ids with the step hyperparameters.
:param data_container: the data container to transform
:param context: execution context
:return: tuple(fitted pipeline, data_container)
.. seealso::
:class:`~neuraxle.data_container.DataContainer`,
:class:`~neuraxle.pipeline.Pipeline`
"""
if not self.is_initialized:
self.setup(context)
self._did_process(data_container, context)
return self
def handle_fit_transform(self, data_container: DataContainer, context: ExecutionContext) -> \
('BaseTransformer', DataContainer):
"""
Override this to add side effects or change the execution flow before (or after) calling * :func:`~neuraxle.base._FittableStep.fit_transform`.
The default behavior is to rehash current ids with the step hyperparameters.
:param data_container: the data container to transform
:param context: execution context
:return: tuple(fitted pipeline, data_container)
"""
return self, self.handle_transform(data_container, context)
def fit(self, data_inputs, expected_outputs) -> '_TransformerStep':
"""
Fit given data inputs. By default, a step only transforms in the fit transform method.
To add fitting to your step, see class:`_FittableStep` for more info.
:param data_inputs: data inputs
:param expected_outputs: expected outputs to fit on
:return: transformed data inputs
"""
return self
def fit_transform(self, data_inputs, expected_outputs=None):
"""
Fit transform given data inputs. By default, a step only transforms in the fit transform method.
To add fitting to your step, see class:`_FittableStep` for more info.
:param data_inputs: data inputs
:param expected_outputs: expected outputs to fit on
:return: transformed data inputs
"""
return self, self.transform(data_inputs)
def handle_predict(self, data_container: DataContainer, context: ExecutionContext) -> DataContainer:
"""
Handle_transform in test mode.
:param data_container: the data container to transform
:param context: execution context
:return: transformed data container
"""
was_train: bool = self.is_train
self.set_train(False)
data_container = self.handle_transform(data_container, context)
self.set_train(was_train)
return data_container
def predict(self, data_input):
"""
Predict the expected output in test mode using func:`~neuraxle.base._TransformerStep.transform`, but by setting self to test mode first and then reverting the mode.
:param data_input: data input to predict
:return: prediction
"""
was_train: bool = self.is_train
self.set_train(False)
outputs = self(data_input)
self.set_train(was_train)
return outputs
def handle_inverse_transform(self, data_container: DataContainer, context: ExecutionContext) -> DataContainer:
"""
Override this to add side effects or change the execution flow before (or after) calling :func:`~neuraxle.base._TransformerStep.inverse_transform`.
The default behavior is to rehash current ids with the step hyperparameters.
:param data_container: the data container to inverse transform
:param context: execution context
:return: data_container
.. seealso::
:class:`~neuraxle.data_container.DataContainer`,
:class:`~neuraxle.pipeline.Pipeline`
"""
if not self.is_initialized:
self.setup(context)
data_container, context = self._will_process(data_container, context)
data_container = self._inverse_transform_data_container(data_container, context)
data_container = self._did_process(data_container, context)
return data_container
def _inverse_transform_data_container(self, data_container: DataContainer, context: ExecutionContext) -> \
DataContainer:
processed_outputs = self.inverse_transform(data_container.data_inputs)
data_container.set_data_inputs(processed_outputs)
return data_container
def inverse_transform(self, processed_outputs):
"""
Inverse Transform the given transformed data inputs.
:func:`~neuraxle.base.BaseStep.mutate` or :func:`~neuraxle.base.BaseTransformer.reverse` can be called to change the default transform behavior :
.. code-block:: python