581 lines
53 KiB
Plaintext
581 lines
53 KiB
Plaintext
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d ee d ee de ee f fd!„« Z ed ee d"ee de ee f fd#„« Z eed$e e« ed%œd ee dedeeege f d&ed'eeegef d(ee dee de ee f fd)„« Z y)*ÚScorerzCompute evaluation scores.NÚxxÚnlpr Ú default_langÚdefault_pipeliner$ c ó„ — || _ |r|| _ y t |« « }|D ] }|j |« Œ || _ y)zOInitialize the Scorer.
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DOCS: https://spacy.io/api/scorer#init
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N)Úcfgrn r Úadd_pipe)r' rn ro rp rr Úpipes r( r) zScorer.__init__r sA € 𠈌٠؈D�Hà.”. Ó.Ó0ˆCÛ(�Ø— ‘ ˜TÕ"ð )àˆD�Hr* F)Ú
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per_componentÚexamplesru c ó, — i }t | j j d« rw|r5 | j j j |fi | j ¤Ž|d< n@|j | j j j |fi | j ¤Ž« | j j D ]a \ }}t |d« sŒ|r! |j |fi | j ¤Ž||< Œ6|j |j |fi | j ¤Ž« Œc |S )aQ Evaluate a list of Examples.
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examples (Iterable[Example]): The predicted annotations + correct annotations.
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per_component (bool): Whether to return the scores keyed by component
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name. Defaults to False.
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RETURNS (Dict): A dictionary of scores.
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DOCS: https://spacy.io/api/scorer#score
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rj Ú tokenizer)Úhasattrrn rx rj rr ÚupdateÚpipeline)r' rv ru ÚscoresÚnameÚ components r( rj z Scorer.score† sà € ð ˆÜ �4—8‘8×%Ñ% wÔ /Ù Ø&> d§h¡h×&8Ñ&8×&>Ñ&>¸xÑ&TÈ4Ï8É8Ñ&T��{Ò#à—
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c_ Œ£ |j! | |« �ŒX t |« dkD r/|j" |j" |j$ |j& dœS dddddœS )a~ Returns accuracy and PRF scores for tokenization.
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* token_acc: # correct tokens / # gold tokens
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* token_p/r/f: PRF for token character spans
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examples (Iterable[Example]): Examples to score
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RETURNS (Dict[str, Any]): A dictionary containing the scores
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token_acc/p/r/f.
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DOCS: https://spacy.io/api/scorer#score_tokenization
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r )Ú token_accÚtoken_pÚtoken_rÚtoken_fN)r Ú referenceÚ predictedÚhas_unknown_spacesÚ alignmentrO Úorth_ÚisspaceÚaddÚidxr7 Úx2yÚlengthsÚir" r! r: r> rA rF ) rv rr Ú acc_scoreÚ prf_scoreÚexampleÚgold_docÚpred_docÚalignÚ
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pred_spansÚtokens r( Úscore_tokenizationzScorer.score_tokenization st € ô “Jˆ Ü “Jˆ Ü ˆGØ ×(Ñ(ˆHØ ×(Ñ(ˆHØ×*Ò*ØØ×%Ñ%ˆEÜ ›ˆJÜ ›ˆJÛ!�Ø—;‘;×&Ñ&Ô(Ø Ø—‘ § ¡ ¨5¯9©9´s¸5³zÑ+AÐ BÕCð "ó "�Ø—;‘;×&Ñ&Ô(Ø Ø—‘ § ¡ ¨5¯9©9´s¸5³zÑ+AÐ BÔCØ—9‘9×$Ñ$ U§W¡WÑ-°Ò2Ø —L’L AÑ%–Là —L’L AÑ%–Lð "ð
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« �Œ* |› d�}t |« dk( r|diS ||j iS )a‹ Returns an accuracy score for a token-level attribute.
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examples (Iterable[Example]): Examples to score
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attr (str): The attribute to score.
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getter (Callable[[Token, str], Any]): Defaults to getattr. If provided,
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getter(token, attr) should return the value of the attribute for an
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individual token.
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missing_values (Set[Any]): Attribute values to treat as missing annotation
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in the reference annotation.
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RETURNS (Dict[str, Any]): A dictionary containing the accuracy score
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under the key attr_acc.
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DOCS: https://spacy.io/api/scorer#score_token_attr
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r Ú_accN)r r„ r… r‡ rO Ú enumeraterŠ rˆ r‰ rŒ r� rŽ r: r7 rF )rv r› r™ rš rr Ú tag_scorer‘ r’ r“ r” Ú gold_tagsÚmissing_indicesÚgold_ir— ÚvalueÚ pred_tagsÚ score_keys r( Úscore_token_attrzScorer.score_token_attrÒ sP € ô. “Jˆ Ü ˆGØ ×(Ñ(ˆHØ ×(Ñ(ˆHØ×%Ñ%ˆEÜ›ˆIÜ !›eˆOÜ!*¨8Ö!4‘
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|f« Œ^ �Œ" |D ]‰ }|j' |j) |t « « | j) |t « « « || j' |j) |t « « | j) |t « « « Œ‹ �ŒÇ i }t+ |« dkD rp|j, ||› d�< |j. ||› d�< |j0 ||› d�< |j3 « D ��ci c] \ }}||j5 « “Œ c}}||› d�< |S d||› d�< d||› d�< d||› d�< d||› d�< |S c c}}w )a Return micro PRF and PRF scores per feat for a token attribute in
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examples (Iterable[Example]): Examples to score
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attr (str): The attribute to score.
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getter (Callable[[Token, str], Any]): Defaults to getattr. If provided,
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getter(token, attr) should return the value of the attribute for an
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individual token.
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missing_values (Set[Any]): Attribute values to treat as missing
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annotation in the reference annotation.
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RETURNS (dict): A dictionary containing the micro PRF scores under the
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key attr_micro_p/r/f and the per-feat PRF scores under
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attr_per_feat.
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j |« Œw |r5|j « D ]" \ } }| |v sŒ |j || || « Œ$ |j ||« �ŒP |› d�d|› d�d|› d�di}|rd||› d�< t |« dkD rp|j ||› d�< |j ||› d�< |j ||› d�< |r8|j « D � �ci c] \ } }| |j! « “Œ c}} ||› d�< |S c c} w c c} w c c}w c c}w c c}} w )ab Returns PRF scores for labeled spans.
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examples (Iterable[Example]): Examples to score
|
|||
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attr (str): The attribute to score.
|
|||
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getter (Callable[[Doc, str], Iterable[Span]]): Defaults to getattr. If
|
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provided, getter(doc, attr) should return the spans for the
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individual doc.
|
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has_annotation (Optional[Callable[[Doc], bool]]) should return whether a `Doc`
|
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has annotation for this `attr`. Docs without annotation are skipped for
|
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scoring purposes.
|
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labeled (bool): Whether or not to include label information in
|
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the evaluation. If set to 'False', two spans will be considered
|
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equal if their start and end match, irrespective of their label.
|
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allow_overlap (bool): Whether or not to allow overlapping spans.
|
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|
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If set to 'False', the alignment will automatically resolve conflicts.
|
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RETURNS (Dict[str, Any]): A dictionary containing the PRF scores under
|
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|
|
the keys attr_p/r/f and the per-type PRF scores under attr_per_type.
|
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DOCS: https://spacy.io/api/scorer#score_spans
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w )!a• Returns PRF and ROC AUC scores for a doc-level attribute with a
|
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dict with scores for each label like Doc.cats. The reported overall
|
|||
|
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score depends on the scorer settings.
|
|||
|
|
|
|||
|
|
examples (Iterable[Example]): Examples to score
|
|||
|
|
attr (str): The attribute to score.
|
|||
|
|
getter (Callable[[Doc, str], Any]): Defaults to getattr. If provided,
|
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|
|
getter(doc, attr) should return the values for the individual doc.
|
|||
|
|
labels (Iterable[str]): The set of possible labels. Defaults to [].
|
|||
|
|
multi_label (bool): Whether the attribute allows multiple labels.
|
|||
|
|
Defaults to True. When set to False (exclusive labels), missing
|
|||
|
|
gold labels are interpreted as 0.0 and the threshold is set to 0.0.
|
|||
|
|
positive_label (str): The positive label for a binary task with
|
|||
|
|
exclusive classes. Defaults to None.
|
|||
|
|
threshold (float): Cutoff to consider a prediction "positive". Defaults
|
|||
|
|
to 0.5 for multi-label, and 0.0 (i.e. whatever's highest scoring)
|
|||
|
|
otherwise.
|
|||
|
|
RETURNS (Dict[str, Any]): A dictionary containing the scores, with
|
|||
|
|
inapplicable scores as None:
|
|||
|
|
for all:
|
|||
|
|
attr_score (one of attr_micro_f / attr_macro_f / attr_macro_auc),
|
|||
|
|
attr_score_desc (text description of the overall score),
|
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|
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attr_micro_p,
|
|||
|
|
attr_micro_r,
|
|||
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attr_micro_f,
|
|||
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attr_macro_p,
|
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attr_macro_r,
|
|||
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attr_macro_f,
|
|||
|
|
attr_macro_auc,
|
|||
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attr_f_per_type,
|
|||
|
|
attr_auc_per_type
|
|||
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|
|||
|
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DOCS: https://spacy.io/api/scorer#score_cats
|
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Ng à?rW r r
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rS ©Úits r( ú<lambda>z#Scorer.score_cats.<locals>.<lambda> ó € ÈrÐRSÊur* )Úkeyc ó — | d S rÛ rS rÜ s r( rÞ z#Scorer.score_cats.<locals>.<lambda> rß r* c ó — | d S rÛ rS rÜ s r( rÞ z#Scorer.score_cats.<locals>.<lambda> s € ÀrÈ!Âur* c ó — | d S rÛ rS rÜ s r( rÞ z#Scorer.score_cats.<locals>.<lambda> rß r* r= c 3 ó4 K — | ] }|j –— Œ y wr& ©r> ©Ú.0Úprfs r( ú <genexpr>z$Scorer.score_cats.<locals>.<genexpr>" ó è ø€ ÐCÑ/B¨�c—m•mÑ/Bùó ‚c 3 ó4 K — | ] }|j –— Œ y wr& ©rA ræ s r( ré z$Scorer.score_cats.<locals>.<genexpr># ó è ø€ Ð@Ñ,? S�c—j•jÑ,?ùrë c 3 ó4 K — | ] }|j –— Œ y wr& ©rF ræ s r( ré z$Scorer.score_cats.<locals>.<genexpr>$ rî rë c 3 óX K — | ]" }|j « r |j nd –— Œ$ yw)rW N)rb rj )rç Úaucs r( ré z$Scorer.score_cats.<locals>.<genexpr>( s$ è ø€ ÐWÑAV¸#˜SŸ]™]œ_�— ’ °#Ó5ÑAVùs ‚(*Ú_scoreÚ _score_descr¨ r© rª Ú_macro_pÚ_macro_rÚ_macro_fÚ
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f_per_typeÚ auc_per_typer‘ Ú pred_catsr½ r¾ Ú gold_catsÚ
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|d|d|d|j% « D ��ci c] \ }}||j' « “Œ c}}i }|S c c}}w )a Returns PRF for predicted links on the entity level.
|
|||
|
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To disentangle the performance of the NEL from the NER,
|
|||
|
|
this method only evaluates NEL links for entities that overlap
|
|||
|
|
between the gold reference and the predictions.
|
|||
|
|
|
|||
|
|
examples (Iterable[Example]): Examples to score
|
|||
|
|
negative_labels (Iterable[str]): The string values that refer to no annotation (e.g. "NIL")
|
|||
|
|
RETURNS (Dict[str, Any]): A dictionary containing the scores.
|
|||
|
|
|
|||
|
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DOCS: https://spacy.io/api/scorer#score_links
|
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Nr
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5Ñ)Ø$×+Ñ+�Dð Ñ'Ø 'Ÿ™˜Ø ?Ñ2°t¸Ñ7Nà Ø ! Tš\Ø & uÑ -× 0Ò 0°AÑ 5Ö 0Ø ! _Ñ 4Ø & uÑ -× 0Ò 0°AÑ 5Ö 0Ø ! _Ñ 4Ø & uÑ -× 0Ò 0°AÑ 5Ö 0ð ' uÑ -× 0Ò 0°AÑ 5Õ 0Ø & uÑ -× 0Ò 0°AÑ 5Ö 0ò3 3ð ô> “Jˆ Ø#×*Ñ*Ö,ˆIØ �LŠL˜IŸL™LÑ (�LØ �LŠL˜IŸL™LÑ (�LØ �LŠL˜IŸL™LÑ (ŽLð -ô �z“? VÑ+ˆÜÑC¨z×/@Ñ/@Ô/BÓCÓCÀhÑNˆÜÑ@¨J×,=Ñ,=Ô,?Ó@Ó@À8ÑKˆÜÑ@¨J×,=Ñ,=Ô,?Ó@Ó@À8ÑKˆà˜)×*Ñ*Ø ˜yؘI× /Ñ /ؘI× ,Ñ ,ؘI× ,Ñ ,ؘGؘGؘGØ ¸:×;KÑ;KÔ;MÔ NÑ;M±4°1°a 1§9¡9£;¡Ð;MÒ Nð
|
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ˆð ˆùó Os ÊJ4(Úhead)r™ Ú head_attrÚ head_getterÚ
|
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ignore_labelsrš r) r* r+ c óö — t « }t « } t « }
|
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t « } | D �]Ò } | j }
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|
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c_ | xj dz
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vr
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| j |j! |t « « |j! |t « « « ŒI |j t d„ |D « « t d„ |D « « « �ŒÕ t# |« dkD rU|› d�|j$ |› d�| j$ |› d�|
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j' « D ��ci c] \ }}||j) « “Œ c}}iS |› d�d|› d�d|› d�diS c c}}w ) a2 Returns the UAS, LAS, and LAS per type scores for dependency
|
|||
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parses.
|
|||
|
|
|
|||
|
|
examples (Iterable[Example]): Examples to score
|
|||
|
|
attr (str): The attribute containing the dependency label.
|
|||
|
|
getter (Callable[[Token, str], Any]): Defaults to getattr. If provided,
|
|||
|
|
getter(token, attr) should return the value of the attribute for an
|
|||
|
|
individual token.
|
|||
|
|
head_attr (str): The attribute containing the head token. Defaults to
|
|||
|
|
'head'.
|
|||
|
|
head_getter (Callable[[Token, str], Token]): Defaults to getattr. If provided,
|
|||
|
|
head_getter(token, attr) should return the value of the head for an
|
|||
|
|
individual token.
|
|||
|
|
ignore_labels (Tuple): Labels to ignore while scoring (e.g., punct).
|
|||
|
|
missing_values (Set[Any]): Attribute values to treat as missing annotation
|
|||
|
|
in the reference annotation.
|
|||
|
|
RETURNS (Dict[str, Any]): A dictionary containing the scores:
|
|||
|
|
attr_uas, attr_las, and attr_las_per_type.
|
|||
|
|
|
|||
|
|
DOCS: https://spacy.io/api/scorer#score_deps
|
|||
|
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r
|
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Nr c 3 ó& K — | ] }|d d –— Œ y wr_ rS ©rç Úitems r( ré z$Scorer.score_deps.<locals>.<genexpr>æ s è ø€ Ð3© �D˜˜!”H©ùó ‚c 3 ó& K — | ] }|d d –— Œ y wr_ rS r. s r( ré z$Scorer.score_deps.<locals>.<genexpr>æ s è ø€ Ð8XÉiÀd¸¸b¸q¼Éiùr0 Ú_uasÚ_lasÚ
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_las_per_type)r rÈ rO r„ r… r‡ rž rŠ rŽ rˆ r‰ rŒ r� Ústripr" r: r² r7 rF r³ rI )rv r› r™ r) r* r+ rš rr Ú
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unlabelledÚlabelledÚlabelled_per_depr¡ r‘ r’ r“ r” Ú gold_depsÚgold_deps_per_depr¢ r— Údepr( Ú pred_depsÚpred_deps_per_depÚ gold_headr½ r¾ s r( Ú
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score_depszScorer.score_deps‹ s| € ôB “Zˆ
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�˜Ù ˜U DÓ)�Ù" 5¨)Ó4�ؘnÑ,Ø -Ò/Ø!Ÿ
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v¨t¯v©v°sÐ&;Ô<Ø Ð&6Ñ6Ü4<³JÐ ,¨SÑ 1Ø Ð&7Ñ7Ü58³UÐ -¨cÑ 2Ø)¨#Ñ.×2Ñ2°F¸D¿F¹FÀCÐ3HÕIà#×'Ñ'¨Õ/ð "5ô ›ˆIØ02Ð Ü!�Ø—;‘;×&Ñ&Ô(Ø Ø—9‘9×$Ñ$ U§W¡WÑ-°Ò2Ø !‘Fà "ŸY™Y u§w¡wÑ /°Ñ 2�FØ Ò0Ù ¨Ó-�CÙ& u¨iÓ8�DØ -Ò/°E·K±K×4EÑ4EÕ4GØ Ÿ9™9×,Ñ,¨T¯V©VÑ4¸Ò9Ø(-¯ © °$·&±&Ñ(9¸!Ñ(<™Ià(,˜Ið "˜>¨YÐ->Ø &ŸMšM¨QÑ .�MØ $ŸKšK¨1Ñ ,ŸKà %ŸM™M¨6°9¸cÐ*BÔ CØ "Ð*:Ñ :Ü8@»
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Ð 0°Ñ 5Ø "Ð*;Ñ ;Ü9<»Ð 1°#Ñ 6Ø -¨cÑ 2× 6Ñ 6¸À È3Ð7OÖ Pð5 "ð6
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× Ñ ˜y¨)Ô 4Û'�Ø Ñ%×/Ñ/Ø%×)Ñ)¨#¬s«uÓ5Ð7H×7LÑ7LÈSÔRUÓRWÓ7Xõð (ð
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rR r r) r Úboolr r rj Ú staticmethodr˜ ÚgetattrÚMISSING_VALUESr r r r¦ r¿ r r rÕ r rQ r r' r? rS r* r( rl rl o s´ „ Ù$ð %)Ø Ø*:ñ à
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}Œ t |« dkD rU|j$ |j& |j( |j+ « D ��ci c] \ }}||j- « “Œ c}}dœS dddddœS c c}w c c}}w )zGCompute micro-PRF and per-entity PRF scores for a sequence of examples.ÚENT_IOBr éÿÿÿÿr
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c 3 ó: K — | ] }|j d k7 –— Œ yw)r N)Úent_iob)rç r— s r( ré z get_ner_prf.<locals>.<genexpr> s è ø€ Ð>±v¨e�u—}‘}¨Õ)±vùs ‚)Úents_pÚents_rÚents_fÚ
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ents_per_typeN)r r ÚyrÀ r rÉ rÊ rË r‡ rŒ Úxr7 Úallr! Úremover" r# rº r> rA rF r³ rI )rv ÚkwargsrÍ ÚegÚerX Ú align_x2yr$ ÚindicesÚg_spanrà re rÊ rË Útotalsrè r½ r¾ s r( Ú get_ner_prfrY ø s
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€ ä ¤Ó*€N܈Ø�t‰t×"Ñ" 9Ô-Ø Ø57·T±T·Y²YÓ?±Y°�!—(‘(˜AŸG™G Q§U¡UÒ+°YˆÐ?Ø—L‘L×$Ñ$ˆ ØŸ™Ÿ œ ˆHØ�‰ nÑ4Ü2:³*�˜xŸ™Ñ/Ø §¡°· ± Ð>ˆGÜ�7�|ØŸ™˜g a™j¨7°2©;¸©?Ð;�ô Ñ>±vÓ>Õ>Ø#Ÿ?™?¨G°A©J¸À¹ Àa¹ÐH�Cؘe‘|Ø& x§¡Ñ7×:Ò:¸aÑ?Õ:Ø Ÿ ™ SÕ)à& x§¡Ñ7×:Ò:¸aÑ?Ö:ð "ó "'Ñ ˆE�5˜#Ø ˜5Ñ !× $Ò $¨Ñ )Ö $ò "'ð+ ô. ‹Z€FØ ×$Ñ$Ö&ˆØ�#‰
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|
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ùò? @ùó8 Qs Á
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*IÈI c ó. — t j | « } |€|}n„t j |« }|j dk( rIt j |« }dg| j z }|j d ||< |j |« }nt j ||¬« }| j }t
|
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|
d« g|z }t
|
|||
|
|
d« g|z }t
|
|||
|
|
dd« ||< t
|
|||
|
|
dd« ||< || t |« | t |« z z dz j |« } | S # t $ ro t j |« }t j | « } t j j || t |« | t |« z z dz |« } Y | S w xY w)a›
|
|||
|
|
Integrate along the given axis using the composite trapezoidal rule.
|
|||
|
|
|
|||
|
|
If `x` is provided, the integration happens in sequence along its
|
|||
|
|
elements - they are not sorted.
|
|||
|
|
|
|||
|
|
Integrate `y` (`x`) along each 1d slice on the given axis, compute
|
|||
|
|
:math:`\int y(x) dx`.
|
|||
|
|
When `x` is specified, this integrates along the parametric curve,
|
|||
|
|
computing :math:`\int_t y(t) dt =
|
|||
|
|
\int_t y(t) \left.\frac{dx}{dt}\right|_{x=x(t)} dt`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
y : array_like
|
|||
|
|
Input array to integrate.
|
|||
|
|
x : array_like, optional
|
|||
|
|
The sample points corresponding to the `y` values. If `x` is None,
|
|||
|
|
the sample points are assumed to be evenly spaced `dx` apart. The
|
|||
|
|
default is None.
|
|||
|
|
dx : scalar, optional
|
|||
|
|
The spacing between sample points when `x` is None. The default is 1.
|
|||
|
|
axis : int, optional
|
|||
|
|
The axis along which to integrate.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
trapezoid : float or ndarray
|
|||
|
|
Definite integral of `y` = n-dimensional array as approximated along
|
|||
|
|
a single axis by the trapezoidal rule. If `y` is a 1-dimensional array,
|
|||
|
|
then the result is a float. If `n` is greater than 1, then the result
|
|||
|
|
is an `n`-1 dimensional array.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
cumulative_trapezoid, simpson, romb
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
Image [2]_ illustrates trapezoidal rule -- y-axis locations of points
|
|||
|
|
will be taken from `y` array, by default x-axis distances between
|
|||
|
|
points will be 1.0, alternatively they can be provided with `x` array
|
|||
|
|
or with `dx` scalar. Return value will be equal to combined area under
|
|||
|
|
the red lines.
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
.. [1] Wikipedia page: https://en.wikipedia.org/wiki/Trapezoidal_rule
|
|||
|
|
|
|||
|
|
.. [2] Illustration image:
|
|||
|
|
https://en.wikipedia.org/wiki/File:Composite_trapezoidal_rule_illustration.png
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
Use the trapezoidal rule on evenly spaced points:
|
|||
|
|
|
|||
|
|
>>> import numpy as np
|
|||
|
|
>>> from scipy import integrate
|
|||
|
|
>>> integrate.trapezoid([1, 2, 3])
|
|||
|
|
4.0
|
|||
|
|
|
|||
|
|
The spacing between sample points can be selected by either the
|
|||
|
|
``x`` or ``dx`` arguments:
|
|||
|
|
|
|||
|
|
>>> integrate.trapezoid([1, 2, 3], x=[4, 6, 8])
|
|||
|
|
8.0
|
|||
|
|
>>> integrate.trapezoid([1, 2, 3], dx=2)
|
|||
|
|
8.0
|
|||
|
|
|
|||
|
|
Using a decreasing ``x`` corresponds to integrating in reverse:
|
|||
|
|
|
|||
|
|
>>> integrate.trapezoid([1, 2, 3], x=[8, 6, 4])
|
|||
|
|
-8.0
|
|||
|
|
|
|||
|
|
More generally ``x`` is used to integrate along a parametric curve. We can
|
|||
|
|
estimate the integral :math:`\int_0^1 x^2 = 1/3` using:
|
|||
|
|
|
|||
|
|
>>> x = np.linspace(0, 1, num=50)
|
|||
|
|
>>> y = x**2
|
|||
|
|
>>> integrate.trapezoid(y, x)
|