"""This module contains the _EstimatorPrettyPrinter class used in BaseEstimator.__repr__ for pretty-printing estimators""" # Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, # 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018 Python Software Foundation; # All Rights Reserved # Authors: Fred L. Drake, Jr. (built-in CPython pprint module) # Nicolas Hug (scikit-learn specific changes) # License: PSF License version 2 (see below) # PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2 # -------------------------------------------- # 1. This LICENSE AGREEMENT is between the Python Software Foundation ("PSF"), # and the Individual or Organization ("Licensee") accessing and otherwise # using this software ("Python") in source or binary form and its associated # documentation. # 2. Subject to the terms and conditions of this License Agreement, PSF hereby # grants Licensee a nonexclusive, royalty-free, world-wide license to # reproduce, analyze, test, perform and/or display publicly, prepare # derivative works, distribute, and otherwise use Python alone or in any # derivative version, provided, however, that PSF's License Agreement and # PSF's notice of copyright, i.e., "Copyright (c) 2001, 2002, 2003, 2004, # 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, # 2017, 2018 Python Software Foundation; All Rights Reserved" are retained in # Python alone or in any derivative version prepared by Licensee. # 3. In the event Licensee prepares a derivative work that is based on or # incorporates Python or any part thereof, and wants to make the derivative # work available to others as provided herein, then Licensee hereby agrees to # include in any such work a brief summary of the changes made to Python. # 4. 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This License Agreement does not grant permission to use PSF # trademarks or trade name in a trademark sense to endorse or promote products # or services of Licensee, or any third party. # 8. By copying, installing or otherwise using Python, Licensee agrees to be # bound by the terms and conditions of this License Agreement. # Brief summary of changes to original code: # - "compact" parameter is supported for dicts, not just lists or tuples # - estimators have a custom handler, they're not just treated as objects # - long sequences (lists, tuples, dict items) with more than N elements are # shortened using ellipsis (', ...') at the end. import inspect import pprint from collections import OrderedDict from .._config import get_config from ..base import BaseEstimator from . import is_scalar_nan class KeyValTuple(tuple): """Dummy class for correctly rendering key-value tuples from dicts.""" def __repr__(self): # needed for _dispatch[tuple.__repr__] not to be overridden return super().__repr__() class KeyValTupleParam(KeyValTuple): """Dummy class for correctly rendering key-value tuples from parameters.""" pass def _changed_params(estimator): """Return dict (param_name: value) of parameters that were given to estimator with non-default values.""" params = estimator.get_params(deep=False) init_func = getattr(estimator.__init__, "deprecated_original", estimator.__init__) init_params = inspect.signature(init_func).parameters init_params = {name: param.default for name, param in init_params.items()} def has_changed(k, v): if k not in init_params: # happens if k is part of a **kwargs return True if init_params[k] == inspect._empty: # k has no default value return True # try to avoid calling repr on nested estimators if isinstance(v, BaseEstimator) and v.__class__ != init_params[k].__class__: return True # Use repr as a last resort. It may be expensive. if repr(v) != repr(init_params[k]) and not ( is_scalar_nan(init_params[k]) and is_scalar_nan(v) ): return True return False return {k: v for k, v in params.items() if has_changed(k, v)} class _EstimatorPrettyPrinter(pprint.PrettyPrinter): """Pretty Printer class for estimator objects. This extends the pprint.PrettyPrinter class, because: - we need estimators to be printed with their parameters, e.g. Estimator(param1=value1, ...) which is not supported by default. - the 'compact' parameter of PrettyPrinter is ignored for dicts, which may lead to very long representations that we want to avoid. Quick overview of pprint.PrettyPrinter (see also https://stackoverflow.com/questions/49565047/pprint-with-hex-numbers): - the entry point is the _format() method which calls format() (overridden here) - format() directly calls _safe_repr() for a first try at rendering the object - _safe_repr formats the whole object recursively, only calling itself, not caring about line length or anything - back to _format(), if the output string is too long, _format() then calls the appropriate _pprint_TYPE() method (e.g. _pprint_list()) depending on the type of the object. This where the line length and the compact parameters are taken into account. - those _pprint_TYPE() methods will internally use the format() method for rendering the nested objects of an object (e.g. the elements of a list) In the end, everything has to be implemented twice: in _safe_repr and in the custom _pprint_TYPE methods. Unfortunately PrettyPrinter is really not straightforward to extend (especially when we want a compact output), so the code is a bit convoluted. This class overrides: - format() to support the changed_only parameter - _safe_repr to support printing of estimators (for when they fit on a single line) - _format_dict_items so that dict are correctly 'compacted' - _format_items so that ellipsis is used on long lists and tuples When estimators cannot be printed on a single line, the builtin _format() will call _pprint_estimator() because it was registered to do so (see _dispatch[BaseEstimator.__repr__] = _pprint_estimator). both _format_dict_items() and _pprint_estimator() use the _format_params_or_dict_items() method that will format parameters and key-value pairs respecting the compact parameter. This method needs another subroutine _pprint_key_val_tuple() used when a parameter or a key-value pair is too long to fit on a single line. This subroutine is called in _format() and is registered as well in the _dispatch dict (just like _pprint_estimator). We had to create the two classes KeyValTuple and KeyValTupleParam for this. """ def __init__( self, indent=1, width=80, depth=None, stream=None, *, compact=False, indent_at_name=True, n_max_elements_to_show=None, ): super().__init__(indent, width, depth, stream, compact=compact) self._indent_at_name = indent_at_name if self._indent_at_name: self._indent_per_level = 1 # ignore indent param self._changed_only = get_config()["print_changed_only"] # Max number of elements in a list, dict, tuple until we start using # ellipsis. This also affects the number of arguments of an estimators # (they are treated as dicts) self.n_max_elements_to_show = n_max_elements_to_show def format(self, object, context, maxlevels, level): return _safe_repr( object, context, maxlevels, level, changed_only=self._changed_only ) def _pprint_estimator(self, object, stream, indent, allowance, context, level): stream.write(object.__class__.__name__ + "(") if self._indent_at_name: indent += len(object.__class__.__name__) if self._changed_only: params = _changed_params(object) else: params = object.get_params(deep=False) params = OrderedDict((name, val) for (name, val) in sorted(params.items())) self._format_params( params.items(), stream, indent, allowance + 1, context, level ) stream.write(")") def _format_dict_items(self, items, stream, indent, allowance, context, level): return self._format_params_or_dict_items( items, stream, indent, allowance, context, level, is_dict=True ) def _format_params(self, items, stream, indent, allowance, context, level): return self._format_params_or_dict_items( items, stream, indent, allowance, context, level, is_dict=False ) def _format_params_or_dict_items( self, object, stream, indent, allowance, context, level, is_dict ): """Format dict items or parameters respecting the compact=True parameter. For some reason, the builtin rendering of dict items doesn't respect compact=True and will use one line per key-value if all cannot fit in a single line. Dict items will be rendered as <'key': value> while params will be rendered as . The implementation is mostly copy/pasting from the builtin _format_items(). This also adds ellipsis if the number of items is greater than self.n_max_elements_to_show. """ write = stream.write indent += self._indent_per_level delimnl = ",\n" + " " * indent delim = "" width = max_width = self._width - indent + 1 it = iter(object) try: next_ent = next(it) except StopIteration: return last = False n_items = 0 while not last: if n_items == self.n_max_elements_to_show: write(", ...") break n_items += 1 ent = next_ent try: next_ent = next(it) except StopIteration: last = True max_width -= allowance width -= allowance if self._compact: k, v = ent krepr = self._repr(k, context, level) vrepr = self._repr(v, context, level) if not is_dict: krepr = krepr.strip("'") middle = ": " if is_dict else "=" rep = krepr + middle + vrepr w = len(rep) + 2 if width < w: width = max_width if delim: delim = delimnl if width >= w: width -= w write(delim) delim = ", " write(rep) continue write(delim) delim = delimnl class_ = KeyValTuple if is_dict else KeyValTupleParam self._format( class_(ent), stream, indent, allowance if last else 1, context, level ) def _format_items(self, items, stream, indent, allowance, context, level): """Format the items of an iterable (list, tuple...). Same as the built-in _format_items, with support for ellipsis if the number of elements is greater than self.n_max_elements_to_show. """ write = stream.write indent += self._indent_per_level if self._indent_per_level > 1: write((self._indent_per_level - 1) * " ") delimnl = ",\n" + " " * indent delim = "" width = max_width = self._width - indent + 1 it = iter(items) try: next_ent = next(it) except StopIteration: return last = False n_items = 0 while not last: if n_items == self.n_max_elements_to_show: write(", ...") break n_items += 1 ent = next_ent try: next_ent = next(it) except StopIteration: last = True max_width -= allowance width -= allowance if self._compact: rep = self._repr(ent, context, level) w = len(rep) + 2 if width < w: width = max_width if delim: delim = delimnl if width >= w: width -= w write(delim) delim = ", " write(rep) continue write(delim) delim = delimnl self._format(ent, stream, indent, allowance if last else 1, context, level) def _pprint_key_val_tuple(self, object, stream, indent, allowance, context, level): """Pretty printing for key-value tuples from dict or parameters.""" k, v = object rep = self._repr(k, context, level) if isinstance(object, KeyValTupleParam): rep = rep.strip("'") middle = "=" else: middle = ": " stream.write(rep) stream.write(middle) self._format( v, stream, indent + len(rep) + len(middle), allowance, context, level ) # Note: need to copy _dispatch to prevent instances of the builtin # PrettyPrinter class to call methods of _EstimatorPrettyPrinter (see issue # 12906) # mypy error: "Type[PrettyPrinter]" has no attribute "_dispatch" _dispatch = pprint.PrettyPrinter._dispatch.copy() # type: ignore _dispatch[BaseEstimator.__repr__] = _pprint_estimator _dispatch[KeyValTuple.__repr__] = _pprint_key_val_tuple def _safe_repr(object, context, maxlevels, level, changed_only=False): """Same as the builtin _safe_repr, with added support for Estimator objects.""" typ = type(object) if typ in pprint._builtin_scalars: return repr(object), True, False r = getattr(typ, "__repr__", None) if issubclass(typ, dict) and r is dict.__repr__: if not object: return "{}", True, False objid = id(object) if maxlevels and level >= maxlevels: return "{...}", False, objid in context if objid in context: return pprint._recursion(object), False, True context[objid] = 1 readable = True recursive = False components = [] append = components.append level += 1 saferepr = _safe_repr items = sorted(object.items(), key=pprint._safe_tuple) for k, v in items: krepr, kreadable, krecur = saferepr( k, context, maxlevels, level, changed_only=changed_only ) vrepr, vreadable, vrecur = saferepr( v, context, maxlevels, level, changed_only=changed_only ) append("%s: %s" % (krepr, vrepr)) readable = readable and kreadable and vreadable if krecur or vrecur: recursive = True del context[objid] return "{%s}" % ", ".join(components), readable, recursive if (issubclass(typ, list) and r is list.__repr__) or ( issubclass(typ, tuple) and r is tuple.__repr__ ): if issubclass(typ, list): if not object: return "[]", True, False format = "[%s]" elif len(object) == 1: format = "(%s,)" else: if not object: return "()", True, False format = "(%s)" objid = id(object) if maxlevels and level >= maxlevels: return format % "...", False, objid in context if objid in context: return pprint._recursion(object), False, True context[objid] = 1 readable = True recursive = False components = [] append = components.append level += 1 for o in object: orepr, oreadable, orecur = _safe_repr( o, context, maxlevels, level, changed_only=changed_only ) append(orepr) if not oreadable: readable = False if orecur: recursive = True del context[objid] return format % ", ".join(components), readable, recursive if issubclass(typ, BaseEstimator): objid = id(object) if maxlevels and level >= maxlevels: return "{...}", False, objid in context if objid in context: return pprint._recursion(object), False, True context[objid] = 1 readable = True recursive = False if changed_only: params = _changed_params(object) else: params = object.get_params(deep=False) components = [] append = components.append level += 1 saferepr = _safe_repr items = sorted(params.items(), key=pprint._safe_tuple) for k, v in items: krepr, kreadable, krecur = saferepr( k, context, maxlevels, level, changed_only=changed_only ) vrepr, vreadable, vrecur = saferepr( v, context, maxlevels, level, changed_only=changed_only ) append("%s=%s" % (krepr.strip("'"), vrepr)) readable = readable and kreadable and vreadable if krecur or vrecur: recursive = True del context[objid] return ("%s(%s)" % (typ.__name__, ", ".join(components)), readable, recursive) rep = repr(object) return rep, (rep and not rep.startswith("<")), False