mirror of
https://github.com/explosion/spaCy.git
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180 lines
7.6 KiB
Python
180 lines
7.6 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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from collections import OrderedDict
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from wasabi import Printer
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from .tokens import Doc, Token, Span
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from .errors import Errors, Warnings, user_warning
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def analyze_pipes(pipeline, name, pipe, index, warn=True):
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"""Analyze a pipeline component with respect to its position in the current
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pipeline and the other components. Will check whether requirements are
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fulfilled (e.g. if previous components assign the attributes).
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pipeline (list): A list of (name, pipe) tuples e.g. nlp.pipeline.
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name (unicode): The name of the pipeline component to analyze.
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pipe (callable): The pipeline component function to analyze.
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index (int): The index of the component in the pipeline.
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warn (bool): Show user warning if problem is found.
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RETURNS (list): The problems found for the given pipeline component.
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"""
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assert pipeline[index][0] == name
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prev_pipes = pipeline[:index]
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pipe_requires = getattr(pipe, "requires", [])
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requires = OrderedDict([(annot, False) for annot in pipe_requires])
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if requires:
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for prev_name, prev_pipe in prev_pipes:
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prev_assigns = getattr(prev_pipe, "assigns", [])
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for annot in prev_assigns:
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requires[annot] = True
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problems = []
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for annot, fulfilled in requires.items():
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if not fulfilled:
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problems.append(annot)
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if warn:
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user_warning(Warnings.W025.format(name=name, attr=annot))
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return problems
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def analyze_all_pipes(pipeline, warn=True):
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"""Analyze all pipes in the pipeline in order.
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pipeline (list): A list of (name, pipe) tuples e.g. nlp.pipeline.
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warn (bool): Show user warning if problem is found.
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RETURNS (dict): The problems found, keyed by component name.
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"""
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problems = {}
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for i, (name, pipe) in enumerate(pipeline):
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problems[name] = analyze_pipes(pipeline, name, pipe, i, warn=warn)
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return problems
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def dot_to_dict(values):
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"""Convert dot notation to a dict. For example: ["token.pos", "token._.xyz"]
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become {"token": {"pos": True, "_": {"xyz": True }}}.
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values (iterable): The values to convert.
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RETURNS (dict): The converted values.
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"""
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result = {}
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for value in values:
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path = result
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parts = value.lower().split(".")
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for i, item in enumerate(parts):
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is_last = i == len(parts) - 1
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path = path.setdefault(item, True if is_last else {})
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return result
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def validate_attrs(values):
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"""Validate component attributes provided to "assigns", "requires" etc.
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Raises error for invalid attributes and formatting. Doesn't check if
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custom extension attributes are registered, since this is something the
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user might want to do themselves later in the component.
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values (iterable): The string attributes to check, e.g. `["token.pos"]`.
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RETURNS (iterable): The checked attributes.
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"""
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data = dot_to_dict(values)
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objs = {"doc": Doc, "token": Token, "span": Span}
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for obj_key, attrs in data.items():
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if obj_key == "span":
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# Support Span only for custom extension attributes
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span_attrs = [attr for attr in values if attr.startswith("span.")]
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span_attrs = [attr for attr in span_attrs if not attr.startswith("span._.")]
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if span_attrs:
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raise ValueError(Errors.E180.format(attrs=", ".join(span_attrs)))
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if obj_key not in objs: # first element is not doc/token/span
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invalid_attrs = ", ".join(a for a in values if a.startswith(obj_key))
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raise ValueError(Errors.E181.format(obj=obj_key, attrs=invalid_attrs))
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if not isinstance(attrs, dict): # attr is something like "doc"
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raise ValueError(Errors.E182.format(attr=obj_key))
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for attr, value in attrs.items():
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if attr == "_":
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if value is True: # attr is something like "doc._"
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raise ValueError(Errors.E182.format(attr="{}._".format(obj_key)))
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for ext_attr, ext_value in value.items():
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# We don't check whether the attribute actually exists
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if ext_value is not True: # attr is something like doc._.x.y
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good = "{}._.{}".format(obj_key, ext_attr)
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bad = "{}.{}".format(good, ".".join(ext_value))
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raise ValueError(Errors.E183.format(attr=bad, solution=good))
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continue # we can't validate those further
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if attr.endswith("_"): # attr is something like "token.pos_"
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raise ValueError(Errors.E184.format(attr=attr, solution=attr[:-1]))
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if value is not True: # attr is something like doc.x.y
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good = "{}.{}".format(obj_key, attr)
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bad = "{}.{}".format(good, ".".join(value))
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raise ValueError(Errors.E183.format(attr=bad, solution=good))
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obj = objs[obj_key]
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if not hasattr(obj, attr):
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raise ValueError(Errors.E185.format(obj=obj_key, attr=attr))
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return values
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def _get_feature_for_attr(pipeline, attr, feature):
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assert feature in ["assigns", "requires"]
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result = []
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for pipe_name, pipe in pipeline:
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pipe_assigns = getattr(pipe, feature, [])
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if attr in pipe_assigns:
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result.append((pipe_name, pipe))
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return result
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def get_assigns_for_attr(pipeline, attr):
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"""Get all pipeline components that assign an attr, e.g. "doc.tensor".
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pipeline (list): A list of (name, pipe) tuples e.g. nlp.pipeline.
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attr (unicode): The attribute to check.
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RETURNS (list): (name, pipeline) tuples of components that assign the attr.
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"""
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return _get_feature_for_attr(pipeline, attr, "assigns")
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def get_requires_for_attr(pipeline, attr):
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"""Get all pipeline components that require an attr, e.g. "doc.tensor".
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pipeline (list): A list of (name, pipe) tuples e.g. nlp.pipeline.
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attr (unicode): The attribute to check.
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RETURNS (list): (name, pipeline) tuples of components that require the attr.
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"""
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return _get_feature_for_attr(pipeline, attr, "requires")
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def print_summary(nlp, pretty=True, no_print=False):
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"""Print a formatted summary for the current nlp object's pipeline. Shows
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a table with the pipeline components and why they assign and require, as
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well as any problems if available.
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nlp (Language): The nlp object.
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pretty (bool): Pretty-print the results (color etc).
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no_print (bool): Don't print anything, just return the data.
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RETURNS (dict): A dict with "overview" and "problems".
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"""
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msg = Printer(pretty=pretty, no_print=no_print)
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overview = []
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problems = {}
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for i, (name, pipe) in enumerate(nlp.pipeline):
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requires = getattr(pipe, "requires", [])
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assigns = getattr(pipe, "assigns", [])
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retok = getattr(pipe, "retokenizes", False)
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overview.append((i, name, requires, assigns, retok))
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problems[name] = analyze_pipes(nlp.pipeline, name, pipe, i, warn=False)
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msg.divider("Pipeline Overview")
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header = ("#", "Component", "Requires", "Assigns", "Retokenizes")
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msg.table(overview, header=header, divider=True, multiline=True)
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n_problems = sum(len(p) for p in problems.values())
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if any(p for p in problems.values()):
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msg.divider("Problems ({})".format(n_problems))
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for name, problem in problems.items():
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if problem:
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problem = ", ".join(problem)
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msg.warn("'{}' requirements not met: {}".format(name, problem))
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else:
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msg.good("No problems found.")
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if no_print:
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return {"overview": overview, "problems": problems}
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