Source code for kedro.pipeline.pipeline

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"""A ``Pipeline`` is a collection of ``Node`` objects which can be executed as
a Directed Acyclic Graph, sequentially or in parallel. The ``Pipeline`` class
offers quick access to input dependencies,
produced outputs and execution order.
"""
import copy
import json
from collections import Counter, defaultdict
from itertools import chain
from typing import Callable, Dict, Iterable, List, Set, Tuple, Union

from toposort import CircularDependencyError as ToposortCircleError
from toposort import toposort

import kedro
from kedro.pipeline.node import Node, _to_list

TRANSCODING_SEPARATOR = "@"


def _transcode_split(element: str) -> Tuple[str, str]:
    """Split the name by the transcoding separator.
    If the transcoding part is missing, empty string will be put in.

    Returns:
        Node input/output name before the transcoding separator, if present.
    Raises:
        ValueError: Raised if more than one transcoding separator
        is present in the name.
    """
    split_name = element.split(TRANSCODING_SEPARATOR)

    if len(split_name) > 2:
        raise ValueError(
            "Expected maximum 1 transcoding separator, found {} instead: '{}'.".format(
                len(split_name) - 1, element
            )
        )
    if len(split_name) == 1:
        split_name.append("")

    return tuple(split_name)  # type: ignore


def _strip_transcoding(element: str) -> str:
    """Strip out the transcoding separator and anything that follows.

    Returns:
        Node input/output name before the transcoding separator, if present.
    Raises:
        ValueError: Raised if more than one transcoding separator
        is present in the name.
    """
    return _transcode_split(element)[0]


class OutputNotUniqueError(Exception):
    """Raised when two or more nodes that are part of the same pipeline
    produce outputs with the same name.
    """

    pass


class ConfirmNotUniqueError(Exception):
    """Raised when two or more nodes that are part of the same pipeline
    attempt to confirm the same dataset.
    """

    pass


[docs]class Pipeline: # pylint: disable=too-many-public-methods """A ``Pipeline`` defined as a collection of ``Node`` objects. This class treats nodes as part of a graph representation and provides inputs, outputs and execution order. """
[docs] def __init__( self, nodes: Iterable[Union[Node, "Pipeline"]], *, tags: Union[str, Iterable[str]] = None, ): """Initialise ``Pipeline`` with a list of ``Node`` instances. Args: nodes: The iterable of nodes the ``Pipeline`` will be made of. If you provide pipelines among the list of nodes, those pipelines will be expanded and all their nodes will become part of this new pipeline. tags: Optional set of tags to be applied to all the pipeline nodes. Raises: ValueError: When an empty list of nodes is provided, or when not all nodes have unique names. CircularDependencyError: When visiting all the nodes is not possible due to the existence of a circular dependency. OutputNotUniqueError: When multiple ``Node`` instances produce the same output. ConfirmNotUniqueError: When multiple ``Node`` instances attempt to confirm the same dataset. Example: :: >>> from kedro.pipeline import Pipeline >>> from kedro.pipeline import node >>> >>> # In the following scenario first_ds and second_ds >>> # are data sets provided by io. Pipeline will pass these >>> # data sets to first_node function and provides the result >>> # to the second_node as input. >>> >>> def first_node(first_ds, second_ds): >>> return dict(third_ds=first_ds+second_ds) >>> >>> def second_node(third_ds): >>> return third_ds >>> >>> pipeline = Pipeline([ >>> node(first_node, ['first_ds', 'second_ds'], ['third_ds']), >>> node(second_node, dict(third_ds='third_ds'), 'fourth_ds')]) >>> >>> pipeline.describe() >>> """ if nodes is None: raise ValueError( "`nodes` argument of `Pipeline` is None. It must be an " "iterable of nodes and/or pipelines instead." ) nodes = list(nodes) # in case it's a generator _validate_duplicate_nodes(nodes) nodes = list( chain.from_iterable( [[n] if isinstance(n, Node) else n.nodes for n in nodes] ) ) _validate_transcoded_inputs_outputs(nodes) _tags = set(_to_list(tags)) nodes = [n.tag(_tags) for n in nodes] self._nodes_by_name = {node.name: node for node in nodes} _validate_unique_outputs(nodes) _validate_unique_confirms(nodes) # input -> nodes with input self._nodes_by_input = defaultdict(set) # type: Dict[str, Set[Node]] for node in nodes: for input_ in node.inputs: self._nodes_by_input[_strip_transcoding(input_)].add(node) # output -> node with output self._nodes_by_output = {} # type: Dict[str, Node] for node in nodes: for output in node.outputs: self._nodes_by_output[_strip_transcoding(output)] = node self._nodes = nodes self._topo_sorted_nodes = _topologically_sorted(self.node_dependencies)
def __repr__(self): # pragma: no cover """Pipeline ([node1, ..., node10 ...], name='pipeline_name')""" max_nodes_to_display = 10 nodes_reprs = [repr(node) for node in self.nodes[:max_nodes_to_display]] if len(self.nodes) > max_nodes_to_display: nodes_reprs.append("...") nodes_reprs_str = ( "[\n{}\n]".format(",\n".join(nodes_reprs)) if nodes_reprs else "[]" ) constructor_repr = "({})".format(nodes_reprs_str) return "{}{}".format(self.__class__.__name__, constructor_repr) def __add__(self, other): if not isinstance(other, Pipeline): return NotImplemented return Pipeline(set(self.nodes + other.nodes)) def __sub__(self, other): if not isinstance(other, Pipeline): return NotImplemented return Pipeline(set(self.nodes) - set(other.nodes)) def __and__(self, other): if not isinstance(other, Pipeline): return NotImplemented return Pipeline(set(self.nodes) & set(other.nodes)) def __or__(self, other): if not isinstance(other, Pipeline): return NotImplemented return Pipeline(set(self.nodes + other.nodes))
[docs] def all_inputs(self) -> Set[str]: """All inputs for all nodes in the pipeline. Returns: All node input names as a Set. """ return set.union(set(), *[node.inputs for node in self.nodes])
[docs] def all_outputs(self) -> Set[str]: """All outputs of all nodes in the pipeline. Returns: All node outputs. """ return set.union(set(), *[node.outputs for node in self.nodes])
def _remove_intermediates(self, datasets: Set[str]) -> Set[str]: intermediate = {_strip_transcoding(i) for i in self.all_inputs()} & { _strip_transcoding(o) for o in self.all_outputs() } return {d for d in datasets if _strip_transcoding(d) not in intermediate}
[docs] def inputs(self) -> Set[str]: """The names of free inputs that must be provided at runtime so that the pipeline is runnable. Does not include intermediate inputs which are produced and consumed by the inner pipeline nodes. Resolves transcoded names where necessary. Returns: The set of free input names needed by the pipeline. """ return self._remove_intermediates(self.all_inputs())
[docs] def outputs(self) -> Set[str]: """The names of outputs produced when the whole pipeline is run. Does not include intermediate outputs that are consumed by other pipeline nodes. Resolves transcoded names where necessary. Returns: The set of final pipeline outputs. """ return self._remove_intermediates(self.all_outputs())
[docs] def data_sets(self) -> Set[str]: """The names of all data sets used by the ``Pipeline``, including inputs and outputs. Returns: The set of all pipeline data sets. """ return self.all_outputs() | self.all_inputs()
def _transcode_compatible_names(self): return {_strip_transcoding(ds) for ds in self.data_sets()}
[docs] def describe(self, names_only: bool = True) -> str: """Obtain the order of execution and expected free input variables in a loggable pre-formatted string. The order of nodes matches the order of execution given by the topological sort. Args: names_only: The flag to describe names_only pipeline with just node names. Example: :: >>> pipeline = Pipeline([ ... ]) >>> >>> logger = logging.getLogger(__name__) >>> >>> logger.info(pipeline.describe()) After invocation the following will be printed as an info level log statement: :: #### Pipeline execution order #### Inputs: C, D func1([C]) -> [A] func2([D]) -> [B] func3([A, D]) -> [E] Outputs: B, E ################################## Returns: The pipeline description as a formatted string. """ def set_to_string(set_of_strings): """Convert set to a string but return 'None' in case of an empty set. """ return ", ".join(sorted(set_of_strings)) if set_of_strings else "None" nodes_as_string = "\n".join( node.name if names_only else str(node) for node in self.nodes ) str_representation = ( "#### Pipeline execution order ####\n" "Inputs: {0}\n\n" "{1}\n\n" "Outputs: {2}\n" "##################################" ) return str_representation.format( set_to_string(self.inputs()), nodes_as_string, set_to_string(self.outputs()) )
@property def node_dependencies(self) -> Dict[Node, Set[Node]]: """All dependencies of nodes where the first Node has a direct dependency on the second Node. Returns: Dictionary where keys are nodes and values are sets made up of their parent nodes. Independent nodes have this as empty sets. """ dependencies = { node: set() for node in self._nodes } # type: Dict[Node, Set[Node]] for parent in self._nodes: for output in parent.outputs: for child in self._nodes_by_input[_strip_transcoding(output)]: dependencies[child].add(parent) return dependencies @property def nodes(self) -> List[Node]: """Return a list of the pipeline nodes in topological order, i.e. if node A needs to be run before node B, it will appear earlier in the list. Returns: The list of all pipeline nodes in topological order. """ return list(chain.from_iterable(self._topo_sorted_nodes)) @property def grouped_nodes(self) -> List[Set[Node]]: """Return a list of the pipeline nodes in topologically ordered groups, i.e. if node A needs to be run before node B, it will appear in an earlier group. Returns: The pipeline nodes in topologically ordered groups. """ return copy.copy(self._topo_sorted_nodes)
[docs] def only_nodes(self, *node_names: str) -> "Pipeline": """Create a new ``Pipeline`` which will contain only the specified nodes by name. Args: node_names: One or more node names. The returned ``Pipeline`` will only contain these nodes. Raises: ValueError: When some invalid node name is given. Returns: A new ``Pipeline``, containing only ``nodes``. """ unregistered_nodes = set(node_names) - set(self._nodes_by_name.keys()) if unregistered_nodes: raise ValueError( "Pipeline does not contain nodes named {}.".format( list(unregistered_nodes) ) ) nodes = [self._nodes_by_name[name] for name in node_names] return Pipeline(nodes)
[docs] def only_nodes_with_namespace(self, node_namespace: str) -> "Pipeline": """Create a new ``Pipeline`` which will contain only the specified nodes by namespace. Args: node_namespace: One node namespace. Raises: ValueError: When pipeline contains no pipeline with the specified namespace. Returns: A new ``Pipeline`` with nodes starting with a specified namespace. """ nodes = [ n for n in self.nodes if n.namespace and n.namespace.startswith(node_namespace) ] if not nodes: raise ValueError( "Pipeline does not contain nodes with namespace(s) {}.".format( list(node_namespace) ) ) return Pipeline(nodes)
def _get_nodes_with_inputs_transcode_compatible( self, datasets: Set[str] ) -> Set[Node]: """Retrieves nodes that use the given `datasets` as inputs. If provided a name, but no format, for a transcoded dataset, it includes all nodes that use inputs with that name, otherwise it matches to the fully-qualified name only (i.e. name@format). Raises: ValueError: if any of the given datasets do not exist in the ``Pipeline`` object Returns: Set of ``Nodes`` that use the given datasets as inputs. """ missing = sorted( datasets - self.data_sets() - self._transcode_compatible_names() ) if missing: raise ValueError( "Pipeline does not contain data_sets named {}".format(missing) ) relevant_nodes = set() for input_ in datasets: if _strip_transcoding(input_) == input_: relevant_nodes.update(self._nodes_by_input[_strip_transcoding(input_)]) else: for node_ in self._nodes_by_input[_strip_transcoding(input_)]: if input_ in node_.inputs: relevant_nodes.add(node_) return relevant_nodes def _get_nodes_with_outputs_transcode_compatible( self, datasets: Set[str] ) -> Set[Node]: """Retrieves nodes that output to the given `datasets`. If provided a name, but no format, for a transcoded dataset, it includes the node that outputs to that name, otherwise it matches to the fully-qualified name only (i.e. name@format). Raises: ValueError: if any of the given datasets do not exist in the ``Pipeline`` object Returns: Set of ``Nodes`` that output to the given datasets. """ missing = sorted( datasets - self.data_sets() - self._transcode_compatible_names() ) if missing: raise ValueError( "Pipeline does not contain data_sets named {}".format(missing) ) relevant_nodes = set() for output in datasets: if _strip_transcoding(output) in self._nodes_by_output: node_with_output = self._nodes_by_output[_strip_transcoding(output)] if ( _strip_transcoding(output) == output or output in node_with_output.outputs ): relevant_nodes.add(node_with_output) return relevant_nodes
[docs] def only_nodes_with_inputs(self, *inputs: str) -> "Pipeline": """Create a new ``Pipeline`` object with the nodes which depend directly on the provided inputs. If provided a name, but no format, for a transcoded input, it includes all the nodes that use inputs with that name, otherwise it matches to the fully-qualified name only (i.e. name@format). Args: inputs: A list of inputs which should be used as a starting point of the new ``Pipeline``. Raises: ValueError: Raised when any of the given inputs do not exist in the ``Pipeline`` object. Returns: A new ``Pipeline`` object, containing a subset of the nodes of the current one such that only nodes depending directly on the provided inputs are being copied. """ starting = set(inputs) nodes = self._get_nodes_with_inputs_transcode_compatible(starting) return Pipeline(nodes)
[docs] def from_inputs(self, *inputs: str) -> "Pipeline": """Create a new ``Pipeline`` object with the nodes which depend directly or transitively on the provided inputs. If provided a name, but no format, for a transcoded input, it includes all the nodes that use inputs with that name, otherwise it matches to the fully-qualified name only (i.e. name@format). Args: inputs: A list of inputs which should be used as a starting point of the new ``Pipeline`` Raises: ValueError: Raised when any of the given inputs do not exist in the ``Pipeline`` object. Returns: A new ``Pipeline`` object, containing a subset of the nodes of the current one such that only nodes depending directly or transitively on the provided inputs are being copied. """ starting = set(inputs) result = set() # type: Set[Node] next_nodes = self._get_nodes_with_inputs_transcode_compatible(starting) while next_nodes: result |= next_nodes outputs = set(chain.from_iterable(node.outputs for node in next_nodes)) starting = outputs next_nodes = set( chain.from_iterable( self._nodes_by_input[_strip_transcoding(input_)] for input_ in starting ) ) return Pipeline(result)
[docs] def only_nodes_with_outputs(self, *outputs: str) -> "Pipeline": """Create a new ``Pipeline`` object with the nodes which are directly required to produce the provided outputs. If provided a name, but no format, for a transcoded dataset, it includes all the nodes that output to that name, otherwise it matches to the fully-qualified name only (i.e. name@format). Args: outputs: A list of outputs which should be the final outputs of the new ``Pipeline``. Raises: ValueError: Raised when any of the given outputs do not exist in the ``Pipeline`` object. Returns: A new ``Pipeline`` object, containing a subset of the nodes of the current one such that only nodes which are directly required to produce the provided outputs are being copied. """ starting = set(outputs) nodes = self._get_nodes_with_outputs_transcode_compatible(starting) return Pipeline(nodes)
[docs] def to_outputs(self, *outputs: str) -> "Pipeline": """Create a new ``Pipeline`` object with the nodes which are directly or transitively required to produce the provided outputs. If provided a name, but no format, for a transcoded dataset, it includes all the nodes that output to that name, otherwise it matches to the fully-qualified name only (i.e. name@format). Args: outputs: A list of outputs which should be the final outputs of the new ``Pipeline``. Raises: ValueError: Raised when any of the given outputs do not exist in the ``Pipeline`` object. Returns: A new ``Pipeline`` object, containing a subset of the nodes of the current one such that only nodes which are directly or transitively required to produce the provided outputs are being copied. """ starting = set(outputs) result = set() # type: Set[Node] next_nodes = self._get_nodes_with_outputs_transcode_compatible(starting) while next_nodes: result |= next_nodes inputs = set(chain.from_iterable(node.inputs for node in next_nodes)) starting = inputs next_nodes = { self._nodes_by_output[_strip_transcoding(output)] for output in starting if _strip_transcoding(output) in self._nodes_by_output } return Pipeline(result)
[docs] def from_nodes(self, *node_names: str) -> "Pipeline": """Create a new ``Pipeline`` object with the nodes which depend directly or transitively on the provided nodes. Args: node_names: A list of node_names which should be used as a starting point of the new ``Pipeline``. Raises: ValueError: Raised when any of the given names do not exist in the ``Pipeline`` object. Returns: A new ``Pipeline`` object, containing a subset of the nodes of the current one such that only nodes depending directly or transitively on the provided nodes are being copied. """ res = self.only_nodes(*node_names) res += self.from_inputs(*map(_strip_transcoding, res.all_outputs())) return res
[docs] def to_nodes(self, *node_names: str) -> "Pipeline": """Create a new ``Pipeline`` object with the nodes required directly or transitively by the provided nodes. Args: node_names: A list of node_names which should be used as an end point of the new ``Pipeline``. Raises: ValueError: Raised when any of the given names do not exist in the ``Pipeline`` object. Returns: A new ``Pipeline`` object, containing a subset of the nodes of the current one such that only nodes required directly or transitively by the provided nodes are being copied. """ res = self.only_nodes(*node_names) res += self.to_outputs(*map(_strip_transcoding, res.all_inputs())) return res
[docs] def only_nodes_with_tags(self, *tags: str) -> "Pipeline": """Create a new ``Pipeline`` object with the nodes which contain *any* of the provided tags. The resulting ``Pipeline`` is empty if no tags are provided. Args: tags: A list of node tags which should be used to lookup the nodes of the new ``Pipeline``. Returns: Pipeline: A new ``Pipeline`` object, containing a subset of the nodes of the current one such that only nodes containing *any* of the tags provided are being copied. """ tags = set(tags) nodes = [node for node in self.nodes if tags & node.tags] return Pipeline(nodes)
[docs] def decorate(self, *decorators: Callable) -> "Pipeline": """Create a new ``Pipeline`` by applying the provided decorators to all the nodes in the pipeline. If no decorators are passed, it will return a copy of the current ``Pipeline`` object. Args: decorators: Decorators to be applied on all node functions in the pipeline. Decorators will be applied from right to left. Returns: A new ``Pipeline`` object with all nodes decorated with the provided decorators. """ nodes = [node.decorate(*decorators) for node in self.nodes] return Pipeline(nodes)
[docs] def tag(self, tags: Union[str, Iterable[str]]) -> "Pipeline": """ Return a copy of the pipeline, with each node tagged accordingly. :param tags: The tags to be added to the nodes. :return: New `Pipeline` object. """ nodes = [n.tag(tags) for n in self.nodes] return Pipeline(nodes)
[docs] def to_json(self): """Return a json representation of the pipeline.""" transformed = [ { "name": n.name, "inputs": list(n.inputs), "outputs": list(n.outputs), "tags": list(n.tags), } for n in self.nodes ] pipeline_versioned = { "kedro_version": kedro.__version__, "pipeline": transformed, } return json.dumps(pipeline_versioned)
def _validate_duplicate_nodes(nodes_or_pipes: Iterable[Union[Node, Pipeline]]): seen_nodes = set() # type: Set[str] duplicates = defaultdict(set) # type: Dict[Union[Pipeline, None], Set[str]] def _check_node(node_: Node, pipeline_: Pipeline = None): name = node_.name if name in seen_nodes: duplicates[pipeline_].add(name) else: seen_nodes.add(name) for each in nodes_or_pipes: if isinstance(each, Node): _check_node(each) elif isinstance(each, Pipeline): for node in each.nodes: _check_node(node, pipeline_=each) if duplicates: duplicates_info = "" for pipeline, names in duplicates.items(): pipe_repr = ( "Free nodes" if pipeline is None else repr(pipeline).replace("\n", "") ) nodes_repr = "\n".join(f" - {name}" for name in sorted(names)) duplicates_info += f"{pipe_repr}:\n{nodes_repr}\n" raise ValueError( f"Pipeline nodes must have unique names. The following node names " f"appear more than once:\n\n{duplicates_info}\nYou can name your " f"nodes using the last argument of `node()`." ) def _validate_unique_outputs(nodes: List[Node]) -> None: outputs = chain.from_iterable(node.outputs for node in nodes) outputs = map(_strip_transcoding, outputs) duplicates = [key for key, value in Counter(outputs).items() if value > 1] if duplicates: raise OutputNotUniqueError( "Output(s) {} are returned by more than one nodes. Node " "outputs must be unique.".format(sorted(duplicates)) ) def _validate_unique_confirms(nodes: List[Node]) -> None: confirms = chain.from_iterable(node.confirms for node in nodes) confirms = map(_strip_transcoding, confirms) duplicates = [key for key, value in Counter(confirms).items() if value > 1] if duplicates: raise ConfirmNotUniqueError( "{} datasets are confirmed by more than one node. Node " "confirms must be unique.".format(sorted(duplicates)) ) def _validate_transcoded_inputs_outputs(nodes: List[Node]) -> None: """Users should not be allowed to refer to a transcoded dataset both with and without the separator. """ all_inputs_outputs = set( chain( chain.from_iterable(node.inputs for node in nodes), chain.from_iterable(node.outputs for node in nodes), ) ) invalid = set() for dataset_name in all_inputs_outputs: name = _strip_transcoding(dataset_name) if name != dataset_name and name in all_inputs_outputs: invalid.add(name) if invalid: raise ValueError( "The following datasets are used with transcoding, but " "were referenced without the separator: {}.\n" "Please specify a transcoding option or " "rename the datasets.".format(", ".join(invalid)) ) def _topologically_sorted(node_dependencies) -> List[Set[Node]]: """Topologically group and sort (order) nodes such that no node depends on a node that appears in the same or a later group. Raises: CircularDependencyError: When it is not possible to topologically order provided nodes. Returns: The list of node sets in order of execution. First set is nodes that should be executed first (no dependencies), second set are nodes that should be executed on the second step, etc. """ def _circle_error_message(error_data: Dict[str, str]) -> str: """Error messages provided by the toposort library will refer to indices that are used as an intermediate step. This method can be used to replace that message with one that refers to the nodes' string representations. """ circular = [str(node) for node in error_data.keys()] return "Circular dependencies exist among these items: {}".format(circular) try: return list(toposort(node_dependencies)) except ToposortCircleError as error: message = _circle_error_message(error.data) raise CircularDependencyError(message) from error class CircularDependencyError(Exception): """Raised when it is not possible to provide a topological execution order for nodes, due to a circular dependency existing in the node definition. """ pass