Diff of
/edsnlp/conjugator.py
[000000]
..
[cad161]
Switch to unified view
| a | b/edsnlp/conjugator.py | ||
|---|---|---|---|
| 1 | from typing import Dict, List, Union |
||
| 2 | |||
| 3 | import mlconjug3 |
||
| 4 | import pandas as pd |
||
| 5 | |||
| 6 | |||
| 7 | def conjugate_verb( |
||
| 8 | verb: str, |
||
| 9 | conjugator: mlconjug3.Conjugator, |
||
| 10 | ) -> pd.DataFrame: |
||
| 11 | """ |
||
| 12 | Conjugates the verb using an instance of mlconjug3, |
||
| 13 | and formats the results in a pandas `DataFrame`. |
||
| 14 | |||
| 15 | Parameters |
||
| 16 | ---------- |
||
| 17 | verb : str |
||
| 18 | Verb to conjugate. |
||
| 19 | conjugator : mlconjug3.Conjugator |
||
| 20 | mlconjug3 instance for conjugating. |
||
| 21 | |||
| 22 | Returns |
||
| 23 | ------- |
||
| 24 | pd.DataFrame |
||
| 25 | Normalized dataframe containing all conjugated forms |
||
| 26 | for the verb. |
||
| 27 | """ |
||
| 28 | |||
| 29 | df = pd.DataFrame( |
||
| 30 | conjugator.conjugate(verb).iterate(), |
||
| 31 | columns=["mode", "tense", "person", "term"], |
||
| 32 | ) |
||
| 33 | |||
| 34 | df.term = df.term.fillna(df.person) |
||
| 35 | df.loc[df.person == df.term, "person"] = None |
||
| 36 | |||
| 37 | df.insert(0, "verb", verb) |
||
| 38 | |||
| 39 | return df |
||
| 40 | |||
| 41 | |||
| 42 | def conjugate( |
||
| 43 | verbs: Union[str, List[str]], |
||
| 44 | language: str = "fr", |
||
| 45 | ) -> pd.DataFrame: |
||
| 46 | """ |
||
| 47 | Conjugate a list of verbs. |
||
| 48 | |||
| 49 | Parameters |
||
| 50 | ---------- |
||
| 51 | verbs : Union[str, List[str]] |
||
| 52 | List of verbs to conjugate |
||
| 53 | language: str |
||
| 54 | Language to conjugate. Defaults to French (`fr`). |
||
| 55 | |||
| 56 | Returns |
||
| 57 | ------- |
||
| 58 | pd.DataFrame |
||
| 59 | Dataframe containing the conjugations for the provided verbs. |
||
| 60 | Columns: `verb`, `mode`, `tense`, `person`, `term` |
||
| 61 | """ |
||
| 62 | if isinstance(verbs, str): |
||
| 63 | verbs = [verbs] |
||
| 64 | |||
| 65 | conjugator = mlconjug3.Conjugator(language=language) |
||
| 66 | |||
| 67 | df = pd.concat([conjugate_verb(verb, conjugator=conjugator) for verb in verbs]) |
||
| 68 | |||
| 69 | df = df.reset_index(drop=True) |
||
| 70 | |||
| 71 | return df |
||
| 72 | |||
| 73 | |||
| 74 | def get_conjugated_verbs( |
||
| 75 | verbs: Union[str, List[str]], |
||
| 76 | matches: Union[List[Dict[str, str]], Dict[str, str]], |
||
| 77 | language: str = "fr", |
||
| 78 | ) -> List[str]: |
||
| 79 | """ |
||
| 80 | Get a list of conjugated verbs. |
||
| 81 | |||
| 82 | Parameters |
||
| 83 | ---------- |
||
| 84 | verbs : Union[str, List[str]] |
||
| 85 | List of verbs to conjugate. |
||
| 86 | matches : Union[List[Dict[str, str]], Dict[str, str]] |
||
| 87 | List of dictionary describing the mode/tense/persons to keep. |
||
| 88 | language : str, optional |
||
| 89 | [description], by default "fr" (French) |
||
| 90 | |||
| 91 | Returns |
||
| 92 | ------- |
||
| 93 | List[str] |
||
| 94 | List of terms to look for. |
||
| 95 | |||
| 96 | Examples |
||
| 97 | -------- |
||
| 98 | >>> get_conjugated_verbs( |
||
| 99 | "aimer", |
||
| 100 | dict(mode="Indicatif", tense="Présent", person="1p"), |
||
| 101 | ) |
||
| 102 | ['aimons'] |
||
| 103 | """ |
||
| 104 | |||
| 105 | if isinstance(matches, dict): |
||
| 106 | matches = [matches] |
||
| 107 | |||
| 108 | terms = [] |
||
| 109 | |||
| 110 | df = conjugate( |
||
| 111 | verbs=verbs, |
||
| 112 | language=language, |
||
| 113 | ) |
||
| 114 | |||
| 115 | for match in matches: |
||
| 116 | q = " & ".join([f'{k} == "{v}"' for k, v in match.items()]) |
||
| 117 | terms.extend(df.query(q).term.unique()) |
||
| 118 | |||
| 119 | return list(set(terms)) |