notebooks/IA717_ProjectCHAI-fairness_AMR_Student.ipynb → notebooks/IA717_ProjectCHAI-fairness_Student_02_AMR.ipynb

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" /></center>
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 <h1><center><strong><font color="green">IA 717: CHAI & fairness: linguistics of AI ethics charters & manifestos<br/>PART4 : AMR</font></strong></center></h1>
 <h3><center><font color="blue"><strong>Student Version</strong></font></center></h3>
 <center>
 <h3> Project Supervisor</h3>
 <a>Maria Boritchev</a> <email>maria.boritchev@telecom-paris.fr</email>
 <h3> Project student</h3>
 <a>Josephine Bernard</a> <email>josephine.bernard@telecom-paris.fr</email><br/>
 <a>Laury Magne</a> <email>laury.magne@telecom-paris.fr</email><br/>
 <a>Dan Hayoun</a> <email>dan.hayoun@telecom-paris.fr</email><br/>
 <a>Nicolas Allègre</a> <email>nicolas.allegre@telecom-paris.fr</email><br/>
 <br/>
 Year 2024-2025
 </center>
 ------------------------
 %% Cell type:markdown id: tags:
 ### Table of Contents
 * [Chapter 0 initialisation Python](#0---code-python-initial)
 * [Chapter 4 - AMR](#4---amr)
     * [Section 4.0 - intro et explication](#40-intro-et-explication)
     * [Section 4.1 - initialisation d'utilisation](#41-initialisation-utilisation)
     * [Section 4.2 - analyse AMR](#42-analyse-fearness-à-partir-amr)
         * [Part 4.2.2 - analyse des parents AMR ( **P** )](#422-recherche-des-parents-amr--p-)
         * [Part 4.2.3 - analyse des liens parents AMR ( **F_tag** )](#423-analyse-des-liens-parents-amr--f_tag-)
         * [Part 4.2.4 - analyse des voisins AMR ( **V** )](#424-recherche-des-voisins-amr--v-)
 <!-- <a class="anchor" id="0---code-python-initial"></a> -->
 %% Cell type:markdown id: tags:
 # <font color="green">**0 - Code Python initial**</font>
 %% Cell type:markdown id: tags:
 ### 0.1) Première partie imports et fonctions globales
 %% Cell type:code id: tags:
 ``` python
-# python -m pip install matplotlib numpy scipy sklearn tabulate penman
+# python -m pip install matplotlib numpy scipy sklearn tabulate penman wordcloud
 # Global import
 import importlib
 import itertools
 import math
 import os
 import re
 import shutil
 import sys
 import string
 import typing
 from collections import Counter
 from collections.abc import Iterable
 from itertools import islice
 from pathlib import Path
 from pprint import pprint
 import matplotlib.pyplot as plt
 import numpy as np
 import numpy.typing as npt
 import penman
 from scipy.stats import norm  # type: ignore[import-untyped]
 from sklearn.feature_extraction.text import CountVectorizer
 from tabulate import tabulate
 from wordcloud import WordCloud
 import metamorphosed.amrdoc as amrdoc
 import metamorphosed.amreditor as amreditor
 # from Corpus import Corpus
 PATH_DATA_FOLDER = '../data'
 PATH_LOG_FOLDER = '../log'
 PATH_DATA_TXT = '../data/txts'
 PATH_DATA_DOCS = '../data/docs'
 PATH_DATA_PREPROCESSED = '../data/preprocessed'
 PATH_DATA_CORTEX = '../data/corpus_cortext'
 PATH_DATA_IRAMUTEQ = '../data/corpus_iramuteq'
 PATH_DATA_AMR = '../data/AMR'
 FILENAME_DATA_IRAMUTEQ = 'corpus.txt'
 FILENAME_DATA_LANG = 'corpus_lang.csv'
 FILENAME_DATA_LANG_PREPRO = 'corpus_lang_preprocessing.csv'
 FILENAME_DATA_AMR = 'key_penmans.amr'
 PATH_DATA_FILE_LANG = os.path.join(PATH_DATA_FOLDER, FILENAME_DATA_LANG)
 PATH_DATA_FILE_LANG_PREPRO = os.path.join(PATH_DATA_FOLDER, FILENAME_DATA_LANG_PREPRO)
 PATH_DATA_FILE_IRAMUTEQ = os.path.join(PATH_DATA_IRAMUTEQ, FILENAME_DATA_IRAMUTEQ)
 PATH_DATA_FILE_AMR = os.path.join('..', FILENAME_DATA_AMR)
 TYPE_METHOD = ['cortex', 'iramuteq', 'txt']
 CHARSET = 'UTF-8'
 sys.path.append('..')
 def take(n: int, iterable: Iterable) -> list[any]:
     """Return the first n items of the iterable as a list."""
     return list(islice(iterable, n))
 #end def take
+
+def dict_revert(x: dict[str, any], sort: bool=False) -> list[list[str, any]]:
+    tmp = x
+    if sorted is True:
+        tmp = dict(sorted(x.items(), key=lambda item: item[1], reverse=True))
+
+    return [[list(x.keys())[i], list(x.values())[i]] for i in range(len(x))]
+
+
 # Chargement de tout les corpus
 # list_corpus = {method: Corpus(method) for method in TYPE_METHOD}
 ```
 %% Cell type:markdown id: tags:
 ### 0.2) Deuxième partie chargement des données
 %% Cell type:code id: tags:
 ``` python
 amr_mapaie = amrdoc.AMRdoc(PATH_DATA_FILE_AMR)
 ```
 %% Output
     1201 sentences read from ..\key_penmans.amr
 %% Cell type:markdown id: tags:
 # <font color="green">**4 - AMR**</font>
 %% Cell type:markdown id: tags:
 ## **4.0) Intro et explication**
 %% Cell type:markdown id: tags:
 ### 4.0.1) Mémo d'utilisation
 %% Cell type:markdown id: tags:
 ##### **Variable Notebook `amr_mapaie` :**
 - **`amr_mapaie`** : variable contenant le chargement du fichier AMR (classe amrdoc.AMRdoc)
   - `amr_mapaie.sentences` : liste contenant toutes les phrases en AMR (classe amrdoc.AMRsentence)
   - `amr_mapaie.sentences.tsv()` : liste de l'AMR au format Graph Penman (triplet)
     - ATTENTION : le format Graph n'est pas unique à partir d'un AMR !!!
     > *Conversion from a PENMAN string to a Tree, and vice versa, is straightforward and lossless. Conversion to a Graph, however, is potentially lossy as the same graph can be represented by different trees.*
   - `amrdoc.relations_between_concepts([amr_mapaie])` : Permet d'avoir un comptage et des stats sommaires.
     - Avec *, depth=1* comme argument, seulement les concepts
     - Avec *, depth=2* (défaut) comme argument, les stats concepts et relation associés
 ##### **La classe `amrdoc.AMRsentence` :**
  - `amr_mapaie.sentences[0].amr` : l'AMR de la phrase (non parsé)
  - `amr_mapaie.sentences[0].text` : la phrase de l'AMR
  - `amr_mapaie.sentences[0].comments[0]` : le numéro du fichier (Format : 'File xxx')
  - `amr_mapaie.sentences[0].tsv()` : Graph de l'AMR (même remarque)
    - égale à *penman.decode(amr_mapaie.sentences[0].amr)*
  - `amr_mapaie.sentences[0].getconceptlist()` : Retourne la liste des concept de la phrase
 ##### **Le module `Penman` :**
 À utiliser pour plus de précision sur le parcours des graphes/arbres de la syntaxe AMR.
 - https://penman.readthedocs.io/en/latest/api/penman.html
 - https://penman.readthedocs.io/en/latest/api/penman.graph.html
 ```python
 amr = amr_mapaie.sentences[0].amr
 g = penman.decode(amr)
 top = g.top  # Sommet de l'arbre
 concepts = {}
 # for s, p, o in amr_mapaie.sentences[0].tsv():
 for s, p, o in g.instances():
   # s = source – the source variable of the triple (g.top est le sommet)
   # p = role – the edge label between the source and target
   # o = target – the target variable or constant => C'est le concept pour les noeuds
   if p == ":instance":
     concepts[s] = o
 ...
 ```
 %% Cell type:markdown id: tags:
 ### 4.0.2) Aide analyse AMR
 %% Cell type:markdown id: tags:
 Le fichier AMR de mapaie, `key_penmans.amr` est un fichier brut pouvant être lu en texte.
 - La ligne *::snt* est la phrase originelle
 - Suivie du numéro de fichier*
 - Suivie de la syntaxe AMR (Penman)
 Normalement, il n'y a pas besoin de lire directement ce fichier, juste à utiliser la variable `amr_mapaie`.
 Aide sur la syntaxe AMR : https://github.com/amrisi/amr-guidelines/blob/master/amr.md
 %% Cell type:markdown id: tags:
 ### 4.0.3) Exemple d'utilisation dans amrdoc :
 %% Cell type:code id: tags:
 ``` python
 def relations_between_concepts(ads, depth=2):
     output = []
     concepts = {} # {concept: {relation: {concept: freq}}}
     for ad in ads:
         for sent in ad.sentences:
             instances = {} # inst: concept
             for s, p, o in sent.tsv():
                 if p == ":instance":
                     instances[s] = o
             for s, p, o in sent.tsv():
                 if p != ":instance":
                     sclass = instances[s]
                     oclass = instances.get(o, "lit:" + o)
                     if depth == 1:
                         if sclass in concepts:
                             concepts[sclass] += 1
                         else:
                             concepts[sclass] = 1
                         continue
                     if sclass in concepts:
                         relations = concepts[sclass]
                     else:
                         relations = {}
                         concepts[sclass] = relations
                     if depth == 2:
                         if p in relations:
                             relations[p] += 1
                         else:
                             relations[p] = 1
                         continue
                     if p in relations:
                         objectconcepts = relations[p]
                     else:
                         objectconcepts = {}
                         relations[p] = objectconcepts
                     if oclass in objectconcepts:
                         objectconcepts[oclass] += 1
                     else:
                         objectconcepts[oclass] = 1
     for c in sorted(concepts):
         if depth == 1:
             #print(c, concepts[c], sep="\t")
             output.append("%s\t%s" % (c, concepts[c]))
             continue
         #print(c)
         output.append(c)
         for r in sorted(concepts[c]):
             if depth == 2:
                 #print("   %s\t%s" % (r, concepts[c][r]))
                 output.append("   %s\t%s" % (r, concepts[c][r]))
                 continue
             #print("   ", r)
             output.append("   " + r)
             for oc in sorted(concepts[c][r]):
                 #print("     %s\t%s" % (oc, concepts[c][r][oc]))
                 output.append("     %s\t%s" % (oc, concepts[c][r][oc]))
     return output
 ```
 %% Cell type:markdown id: tags:
 ## **4.1) Initialisation utilisation**
 %% Cell type:code id: tags:
 ``` python
 c1 = [x.split('\t') for x in amrdoc.relations_between_concepts([amr_mapaie], depth=1)]
 c2 = [x.split('\t') for x in amrdoc.relations_between_concepts([amr_mapaie], depth=2)]
 # Concepts présents dans les AMR de mapaie
 concepts = set(x[0] for x in c1)
 # Type de relation présents dans les AMR de mapaie
 relations = set(x[0] for x in c2) - concepts
 ```
 %% Cell type:markdown id: tags:
 Il faut chercher dans cette liste ceux qui se rapporte à **fairness** pour pourvoir effectuer les analyses à faire.
 - Une première méthode simple est de regarder cette liste `concepts` dans la lettre 'f' :
   >'face-01', 'facet', 'facilitate-01', 'facility', 'fact', 'factor', 'factor-01', 'fail-01', 'fair-01', 'fairness', 'fairwash-01', 'fall-01', 'fall-04', 'fallacy', 'familiarize-01', 'fatality', 'feasibility', 'feature', 'feature-01', 'federate-01', 'feed-01', 'feed-02', 'feedback', 'feel-01', 'few', 'fidelity', 'field', 'fight-01', 'figure', 'fill-01', 'fill-in-05', 'find-01', 'find-02', 'fine-04', 'fire-02', 'firm', 'fit-01', 'fit-03', 'fit-06', 'fix-03', 'flaw-01', 'flexibility', 'flow-01', 'focus-01', 'follow-01', 'follow-02', 'follow-04', 'follow-through-07', 'foresee-01', 'foreword', 'forgive-01', 'form', 'form-01', 'formalize-01', 'formula', 'formulate-01', 'foster-01', 'found-01', 'foundation', 'frame', 'frame-06', 'framework', 'free-04', 'frequent-02', 'friendly-01', 'from', 'fulfill-01', 'full-09', 'fun-01', 'function', 'function-01', 'functional-03', 'fund', 'fund-01', 'furnish-01', 'fuse-01', 'future'
   - **'fair-01', 'fairness', 'fairwash-01'** pourrait nous interresser.
   - *À noter que 'fairness' n'existe pas dans les concepts de probBank*
 - Une deuxième méthode serait pour chaque phrase qui ne contient pas les concepts AMR 'fair-01', 'fairness', regarder manuellement comment le mot fairness a été transcrit.
 Ensuite, on pourrait se renseigner au sens des mots choisis dans les fichiers XML de probBank [lien](https://github.com/propbank/propbank-frames/tree/main/frames) (ou sur l'application metamorphosed).
 %% Cell type:code id: tags:
 ``` python
 stat_concept = dict.fromkeys(sorted(concepts), 0)
 for x in c1:
     stat_concept[x[0]] = int(x[1])
 stat_relation = dict.fromkeys(sorted(relations), 0)
 stat_concept_relation = dict.fromkeys(sorted(concepts), {})
 tmp_concept = list(stat_concept_relation.keys())[0]
 for x in c2:
     if len(x) == 1 and x[0] in concepts:  # C'est un concept
         tmp_concept = x[0]
         stat_concept_relation[tmp_concept] = dict.fromkeys(sorted(relations), 0)
         continue
     if len(x) > 1:  # C'est une relation associé au concept précédant
         stat_relation[x[0]] += int(x[1])
         stat_concept_relation[tmp_concept][x[0]] = int(x[1])
 # stat_concept : contient les stats d'utilisation des concepts
 # stat_relation : contient les stats d'utilisation du type de relation
 # stat_concept_relation : contient les stats d'utilisation des type de relation par concept
 ```
 %% Cell type:code id: tags:
 ``` python
 tmp = dict(sorted(stat_concept.items(), key=lambda item: item[1], reverse=True))
 pprint({k: tmp[k] for i, k in enumerate(tmp) if i < 20}, sort_dicts=False)
 # tmp = dict(sorted(stat_relation.items(), key=lambda item: item[1], reverse=True))
 # pprint({k: tmp[k] for i, k in enumerate(tmp) if i < 20}, sort_dicts=False)
 ```
 %% Output
     {'and': 5658,
      'name': 2995,
      'multi-sentence': 1020,
      'or': 572,
      'possible-01': 569,
      'publication-91': 395,
      'person': 379,
      'publication': 362,
      'intelligent-01': 354,
      'use-01': 354,
      'have-degree-91': 334,
      'mean-01': 305,
      'date-entity': 300,
      'principle': 278,
      'recommend-01': 257,
      'fair-01': 256,
      'develop-02': 249,
      'system': 246,
      'cause-01': 234,
      'ensure-01': 233}
 %% Cell type:code id: tags:
 ``` python
 # Première initialisation des concepts AMR liés à FAIRNESS :
 list_concept_tosee = ['fair-01', 'fairness', 'fairwash-01']
 # Filtre pour l'affichage
 list_relation_tosee = set([y for x in list_concept_tosee for y in stat_concept_relation[x] if stat_concept_relation[x][y] != 0])
 # tmp = {x: {y.strip(): stat_concept_relation[x][y] for y in list_relation_tosee} for x in list_concept_tosee}
 # print(tabulate(tmp.values(), showindex=list_concept_tosee, headers='keys', tablefmt='pipe'))
 tmpbis = {y.strip(): {x: stat_concept_relation[x][y] for x in list_concept_tosee} for y in list_relation_tosee}
 print(tabulate(tmpbis.values(), showindex=list_relation_tosee, headers='keys', tablefmt='pipe'))
 ```
 %% Output
     |              |   fair-01 |   fairness |   fairwash-01 |
     |:-------------|----------:|-----------:|--------------:|
     | :mod         |        13 |        105 |             0 |
     | :polarity    |        58 |         42 |             0 |
     | :topic       |         0 |         10 |             0 |
     | :li          |         0 |          2 |             0 |
     | :ARG0        |         5 |          0 |             1 |
     | :ARG1        |       156 |          0 |             1 |
     | :degree      |         1 |          0 |             0 |
     | :source      |         0 |          1 |             0 |
     | :prep-on     |         0 |          1 |             0 |
     | :beneficiary |         0 |          2 |             0 |
     | :domain      |         1 |          5 |             0 |
     | :ARG3        |         1 |          1 |             0 |
     | :quant       |         0 |          1 |             0 |
     | :manner      |         5 |          6 |             1 |
     | :location    |         3 |          5 |             0 |
     | :poss        |         0 |         15 |             0 |
     | :ARG2        |         6 |          0 |             0 |
     | :ARG4        |         1 |          0 |             0 |
     | :prep-in     |         0 |          9 |             0 |
     | :example     |         0 |          1 |             0 |
     | :condition   |         6 |          0 |             0 |
     | :time        |         0 |          1 |             0 |
 %% Cell type:markdown id: tags:
 ## **4.2) Analyse *Fearness* à partir AMR**
 %% Cell type:markdown id: tags:
 Nous nous interressons au voisinage de l'utilisation du mot fairness dans le graph AMR :
 <pre style='font=font-family:Courier New, Courier, monospace;'>
         =====
         = P =
         =====
          / \
   V_tag /   \ F_tag
        /     \
    =====     =====
    = V =     = F =
    =====     =====
                |
                | E_tag
                |
              -------
              |  E  |
              | ... |
              -------
 </pre>
 **Explications :**
 - F : Le noeud où le concept de *fairness* apparait
 - P : le concept parent
 - V : le concept voisin, lié à *fairness* par le parent
 - E : les concepts enfants
 - x_tag : les tags AMR associés
 #### Recherche à faire :
 <font color="red">Faire les statistiques puis analyser avec les précédents constats de notre analyse PosTag :
 - P( P )
 - P( F_tag )
 - P( V )
 - P( V \ P)
 - P( V_tag, F_tag )
 - P( E_tag )
 - P( {E}=∅ ) : Fairness n'a pas d'enfant
 - P( rang(E)=1 ) : Fairness n'a qu'un enfant
 - P( rang(E)>1 ) : Fairness à plusieurs enfants
 </font>
 %% Cell type:code id: tags:
 ``` python
 # TEST TO SAVE IMAGE des graph AMR (pour les profs) :
 # dataformat = 'png'
 # self.aps = {} # parsed and possibly modified PENMAN AMRs
 #  # initial version of Penman graph
 # for sentnum, cursentence in enumerate(self.amrdoc.sentences, start=1):
 #     if sentnum % 10 == 0:
 #         print("%d initialized" % sentnum, end="\r")
 #     ap = amreditor.AMRProcessor()
 #     self.aps[sentnum] = ap
 #     ap.lastpm = cursentence.amr
 #     ap.comments = cursentence.comments
 # dataformat = 'png'
 # for ix, x in enumerate(self.aps, 1):
 #     ap = self.aps[x]
 #     if not ap.isparsed:
 #         ap.readpenman(ap.lastpm)
 #     pm, svg, svg_canon = ap.show(format=dataformat)
 #     if svg:
 #         print("%d.%s" % (ix, dataformat), svg)
 # # show()
 # try:
 #     pm = penman.encode(penman.Graph(self.triples, top=self.top))
 #     self.readpenman(pm)
 #     self.lastsvg = self.dot(highlightinstances, highlightrelations, format=dataformat)
 #     self.lastsvg_canonised = self.dot(highlightinstances, highlightrelations, format=dataformat, inverse_of=True)
 #     self.isDisconnected = False
 # except penman.exceptions.LayoutError:
 #     print("not yet correct")
 #     print(self.triples)
 ```
 %% Cell type:markdown id: tags:
 ### 4.2.0) Exemple d'utilisation de la syntaxe penman AMR
 %% Cell type:code id: tags:
 ``` python
 sentnum = 1
 cursentence = amr_mapaie.sentences[0]
 # for sentnum, cursentence in enumerate(amr_mapaie.sentences, start=1):
 #     pass
 # Initialisation du parsing :
 ap = amreditor.AMRProcessor()
 ap.lastpm = cursentence.amr
 ap.comments = cursentence.comments
 if not ap.isparsed:
     ap.readpenman(ap.lastpm)
 # Utilisation : 1- liste des concepts
 ap.vars
 ```
 %% Output
     {'p': 'possible-01',
      'f': 'facilitate-01',
      'o': 'or',
      'r': 'regulate-01',
      'e': 'exist-01',
      'r2': 'regulate-01',
      'n': 'new-01',
      'o2': 'or',
      'a': 'accountable-02',
      'a2': 'algorithm',
      'f2': 'fair-01',
      'a3': 'amr-unknown',
      'f3': 'first-of-all'}
 %% Cell type:code id: tags:
 ``` python
 # Utilisation : 2- arbre AMR entier sous une liste de triplet
 ap.triples
 # 1- Vérifier values ap.vars == fairness et récupérer la clé (id)
 # 2- Récupérer triplet F_tag (ap.triples[2]==id) (id_P, type_F_tag, id)
 # 3- Sortir le parent P (ap.vars[id_P] => concept)
 # 4- Sortir triplet V_tag (ap.triples[0]==id_P and ap.triples[1]!=':instance') (id_p, type_V_tag,id_V)
 # 5- Sortir le voisin V (ap.vars[id_V] => concept)
     # enfants_F = [x for x in ap.triples if x[0]==id and x[2]!=fearness]
     # ids_E=[x[2] for x in enfants_F]
 # 6- Nombre branche fairness : len(enfants_F)
 # 7- concept enfant : [ap.vars[x] for x in ids_E]
 # 8- Nb petit-enfant : len(ap.triples if x[0]==ids_E and != ap.vars[ids_E]|concept)
 ```
 %% Output
     [('p', ':instance', 'possible-01'),
      ('p', ':ARG1', 'f'),
      ('p', ':mod', 'f3'),
      ('f', ':instance', 'facilitate-01'),
      ('f', ':ARG0', 'o'),
      ('f', ':ARG1', 'o2'),
      ('f', ':manner', 'a3'),
      ('o', ':instance', 'or'),
      ('o', ':op1', 'r'),
      ('o', ':op2', 'r2'),
      ('r', ':instance', 'regulate-01'),
      ('r', ':ARG1-of', 'e'),
      ('e', ':instance', 'exist-01'),
      ('r2', ':instance', 'regulate-01'),
      ('r2', ':ARG1-of', 'n'),
      ('n', ':instance', 'new-01'),
      ('o2', ':instance', 'or'),
      ('o2', ':op1', 'a'),
      ('o2', ':op2', 'f2'),
      ('a', ':instance', 'accountable-02'),
      ('a', ':mod', 'a2'),
      ('a2', ':instance', 'algorithm'),
      ('f2', ':instance', 'fair-01'),
      ('a3', ':instance', 'amr-unknown'),
      ('f3', ':instance', 'first-of-all')]
 %% Cell type:markdown id: tags:
 ### 4.2.1) Fonctions permettant d'extraire le voisinage
 %% Cell type:code id: tags:
 ``` python
 # Fonctions récupèrant le parent
 def get_parent_infos(targets, amr_sentence):
     """Recherche les parents d'un concept (noeud AMR) pour une phrase
     :param list[str] targets: liste de concept à rechercher
     :param amrdoc.AMRsentence amr_sentence: phrase à analyser
     :return results, racines:
         results[concept_target] = [parent_id, parent_type, relation, amr_sentence]
         racines: list = list des phrases dont le concept et le sommet de l'arbre
     """
     # Initialisation de l'AMRProcessor
     ap = amreditor.AMRProcessor()
     ap.lastpm = amr_sentence.amr
     ap.comments = amr_sentence.comments
     if not ap.isparsed:
         ap.readpenman(ap.lastpm)
     results = {}
     racines = []
     for target in targets:
         target_vars = ap.getvars(target)
         if target_vars:
             infos = []
             for target_var in target_vars:  # Parcourir toutes les occurrences du mot
                 has_parent = False
                 for triple in ap.triples:
                     if triple[2] == target_var:
                         parent_var = triple[0]
                         relation = triple[1]
                         parent_type = ap.vars[parent_var]
                         infos.append((parent_var, parent_type,
                                      relation, amr_sentence))
                         has_parent = True
                 if not has_parent:
                     infos.append(("NA", "NA", "NA", amr_sentence))
                     racines.append((amr_sentence, amr_sentence))
             results[target] = infos
     return results, racines
 def get_all_parents(targetlist, amr_sentences):
     """Recherche les parents d'un concept (noeud AMR) dans tout le corpus
     :param list[str] targetlist: liste de concept à rechercher
     :param list[amrdoc.AMRsentence] amr_sentences: list des phrase du corpus
     :return results, racines:
         results[concept_target] = [parent_id, parent_type, relation, amr_sentence]
         racines: list[list] = list des phrases dont le concept et le sommet de l'arbre
     """
     results = {}
     racines = []
     for i, sentence in enumerate(amr_sentences):
         results[i], racine = get_parent_infos(targetlist, sentence)
         if racine:
             racines.append(racine)
     return results, racines
 # Fonctions récupèrant les enfants
 def get_node_children(node_id, amr_sentence, list_concept_tosee):
     # Initialisation de l'AMRProcessor
     ap = amreditor.AMRProcessor()
     ap.lastpm = amr_sentence.amr
     ap.comments = amr_sentence.comments
     if not ap.isparsed:
         ap.readpenman(ap.lastpm)
     children = []  # Liste des enfants avec leurs liens
     # Parcourir les triples pour trouver les enfants du noeud donné
     for triple in ap.triples:
         if triple[0] == node_id:  # Si le noeud est le sujet
             child_id = triple[2]  # Identifiant de l'enfant
             relation = triple[1]  # Relation entre le noeud et l'enfant
             # Type de l'enfant (s'il existe)
             child_type = ap.vars.get(child_id, "NA")
             if child_type in list_concept_tosee:
                 continue
             children.append((child_id, child_type, relation,
                             amr_sentence.comments[0]))
     return children
 def get_all_node_children(parents, list_concept_tosee):
     childrens = []
     for index, list_parents in parents.items():
         for word_fair, list_parents in list_parents.items():
             for parent in list_parents:
                 children = get_node_children(
                     parent[0], parent[3], list_concept_tosee)
                 childrens.append(children)
     return childrens
 ```
 %% Cell type:markdown id: tags:
 ### 4.2.2) Recherche des parents AMR ( **P** )
 %% Cell type:code id: tags:
 ``` python
 list_concept_tosee = ['fair-01', 'fairness', 'fairwash-01']
 parents, racines = get_all_parents(list_concept_tosee, amr_mapaie.sentences)
 # comptage des parents
 count_words_fair = {}
 for fair in list_concept_tosee:
     count_words_fair[fair] = 0
 count_parents_type = {}
 count_parents_link = {}
 for index, list_parents in parents.items():
     for word_fair, list_parents in list_parents.items():
         for parent in list_parents:
             count_words_fair[word_fair] += 1
             if parent[1] in count_parents_type:
                 count_parents_type[parent[1]] += 1
             else:
                 count_parents_type[parent[1]] = 1
             if parent[2] in count_parents_link:
                 count_parents_link[parent[2]] += 1
             else:
                 count_parents_link[parent[2]] = 1
 ```
 %% Cell type:markdown id: tags:
 #### 4.2.2.1) Analyse des noeuds parents AMR
 %% Cell type:markdown id: tags:
 Maintenant, nous pouvons effecter une représentation en nuage de mot de parent du concept fair* :
 %% Cell type:code id: tags:
 ``` python
 print("type de parent :")
-top_10 = sorted(count_parents_type.items(),
-                key=lambda x: x[1], reverse=True)[:10]
+top_10 = sorted(count_parents_type.items(), key=lambda x: x[1], reverse=True)[:10]
 # Afficher le Top 10
 for key, value in top_10:
     print(f"{key}: {value}")
 # nuage de mots
 # Créer le nuage de mots
 wordcloud = WordCloud(
     width=800,
     height=400,
     background_color="white",
     colormap="viridis"
 ).generate_from_frequencies(count_parents_type)
 # Afficher le nuage de mots
 plt.figure(figsize=(10, 5))
 plt.imshow(wordcloud, interpolation="bilinear")
 plt.axis("off")  # Supprime les axes
 plt.show()
 ```
 %% Output
     type de parent :
     and: 476
     define-01: 57
     principle: 51
     or: 44
     ensure-01: 32
     multi-sentence: 28
     realize-01: 24
     assess-01: 16
     criteria: 15
     concept: 14
 %% Cell type:markdown id: tags:
 **Analyses :**
 - Plus d'un tiers des parents sont une jonction de 2 concepts.
 - L'analyse AMR confirme notre analyse précédente avec les postags
 - Fairness est donc souvent associé à un autre concept.
 %% Cell type:markdown id: tags:
 #### 4.2.2.2) Analyse du cas où il lui-même parent
 %% Cell type:markdown id: tags:
 Il est maintenant interessant d'étudier les phrases dont le concept fair* est racine de l'arbre AMR
 %% Cell type:code id: tags:
 ``` python
 for sentence in racines:
     print(sentence[0][0].comments[0] + " : " + sentence[0][0].text)
 ```
 %% Output
     File 89 : Fairness.
     File 157 : Fairness through awareness.
     File 157 : Counterfactual fairness.
     File 157 : https://doi.org/10.1007/s12599 -017-0487 -z      Outcome fairness
     File 157 : Path -specific counterfactual fairness.
     File 157 : Fairness through awareness.
     File 157 : Counterfactual fairness.
     File 157 : Fairness beyond disparate treatment &  disparate impact: Learning classification without disparate mistreatment.
     File 157 : Fairness in decision -making the causal explanation formula.
     File 18 : Fairness.
     File 18 : Fairness.
     File 78 : 'Fairness through  awareness. '
     File 128 : They fairwash a model by taking a closed-box model and produce an ensemble of interpretable models that approximate the original model but are much fairer, which then hide the unfairness of the original model.
 %% Cell type:markdown id: tags:
 **Analyse :**
 - Seules 13 occurences en tant que racine de l'arbre AMR
 - Concerne majoritairement des titres ou des occurences seules
 - Donc aucune signification sématique pour nous
 %% Cell type:markdown id: tags:
 #### 4.2.2.3) Analyse particulière pour *fearwash*
 %% Cell type:markdown id: tags:
 **Focus sur le concept *fairwash* :**
 Ce concept n'existe pas dans PropBank et a donc été inventé par le NN. Cependant, le réseau de neurones a considéré ce concept particulier et donc de distance dimensionnelle plus éloignée que les concepts de fair/fairness. D'où, l'intérêt pour nous d'effectuer une analyse plus poussée sur ce résultat de la sortie du NN.
 %% Cell type:code id: tags:
 ``` python
 # Total des occurrences
 total = sum(count_parents_type.values())
 # Proportion de "and"
 proportion_and = count_parents_type['and'] / \
     total if 'and' in count_parents_type else 0
 # Affichage
 print(f"Proportion de 'and': {proportion_and:.2%}")
 ```
 %% Output
     Proportion de 'and': 36.25%
 %% Cell type:markdown id: tags:
 ### 4.2.3) Analyse des liens parents AMR ( **F_tag** )
 %% Cell type:code id: tags:
 ``` python
 print("lien avec le parent :")
-top_10_2 = sorted(count_parents_link.items(),
-                key=lambda x: x[1], reverse=True)[:10]
+top_10_2 = sorted(count_parents_link.items(), key=lambda x: x[1], reverse=True)[:10]
 # Afficher le Top 10
 for key, value in top_10_2:
     print(f"{key}: {value}")
 ```
 %% Output
     lien avec le parent :
     :ARG1: 407
     :op1: 263
     :op2: 175
     :topic: 113
     :op3: 61
     :mod: 55
     :ARG2: 49
     :ARG1-of: 40
     :ARG0: 35
     :op4: 23
 %% Cell type:code id: tags:
 ``` python
 # Total des occurrences
 total = sum(count_parents_link.values())
 # Proportion de "ARG1"
 proportion_ARG1 = count_parents_link[":ARG1"] / \
     total if ":ARG1" in count_parents_link else 0
 # Proportion de ":opX"
 proportion_opX = (count_parents_link[":op1"] + count_parents_link[":op2"] + count_parents_link[":op3"]) / \
     total if ":op1" in count_parents_link or ":op2" in count_parents_link or ":op3" in count_parents_link  else 0
 print(f"Proportion de ':ARG1': {proportion_ARG1:.2%}")
 print(f"Proportion de ':opX': {proportion_opX:.2%}")
 ```
 %% Output
     Proportion de ':ARG1': 31.00%
     Proportion de ':opX': 38.00%
 %% Cell type:markdown id: tags:
 **Analyses :**
 - Le lien avec le parent peut aussi porter du sens.
 - opx sont en grande majorité liés à un jonction (and) ou un dijonction (or)
 - ARG1 est autant présent que les liens précedement. Il représente que "fair" est l'objet affecté par le parent.
 Lorsque l'on recherchera les voisins AMR, il faudrait donc effectuer une apparté vis-à-vis de ce lien.
 %% Cell type:markdown id: tags:
 ### 4.2.4) Recherche des voisins AMR ( **V** )
 %% Cell type:markdown id: tags:
 #### 4.2.4.1) Analyse générale des voisins AMR
 %% Cell type:markdown id: tags:
 Ainsi, nous pouvons aller rechercher les concepts associés :
 %% Cell type:code id: tags:
 ``` python
 neighbors = get_all_node_children(results, list_concept_tosee)
 # dans neighbors[2]:
 # [('and', 'NA', ':instance', 'File 150'), => parent
 # ('t', 'transparency', ':op1', 'File 150')] => enfant
 # exemple, dans le fichier 150 le mot fairness est joint avec transparency
 # Cherchons toutes ces jonctions
 neighbor_concepts = []
 for neighbor in neighbors:
     if neighbor:
         parent = neighbor[0]  # Le premier élément est le parent
         children = neighbor[1:]  # Les éléments suivants sont les enfants
         if parent[0] == "and":
             for child in children:
                 neighbor_concepts.append(child[1])
 # Compter les occurrences
 word_counts = Counter(neighbor_concepts)
 pprint(word_counts)
 ```
 %% Output
     Counter({'transparency': 131,
              'accountable-02': 111,
              'safe-01': 50,
              'explain-01': 37,
              'privacy': 36,
              'and': 35,
              'justice': 30,
              'discriminate-02': 29,
              'bias-01': 26,
              'security': 26,
              'possible-01': 21,
              'discriminate-01': 18,
              'govern-01': 17,
              'include-01': 17,
              'right-05': 17,
              'value': 15,
              'publication': 15,
              'private-02': 14,
              'equal-01': 14,
              'ethics': 14,
              'respect-01': 13,
              'accurate': 13,
              'accuracy': 13,
              'learn-01': 12,
              'protect-01': 11,
              'prevent-01': 11,
              'impact-01': 11,
              'robustness': 11,
              'law': 10,
              'limit-01': 10,
              'diversity': 9,
              'NA': 8,
              'deserve-01': 7,
              'sustain-01': 7,
              'et-cetera': 7,
              'assess-01': 7,
              'interpret-01': 7,
              'name': 6,
              'dignity': 6,
              'minimize-01': 6,
              'rely-01': 6,
              'effective-04': 6,
              'lawfulness': 6,
              'system': 5,
              'equitable': 5,
              'control-01': 5,
              'free-04': 5,
              'autonomy': 5,
              'thing': 5,
              'responsible-02': 5,
              'deliver-01': 5,
              'algorithm': 4,
              'future': 4,
              'intelligibility': 4,
              'equity': 4,
              'democracy': 4,
              'humanity': 4,
              'solidarity': 4,
              'person': 4,
              'oversee-01': 4,
              'confidentiality': 4,
              'recommend-01': 4,
              'base-02': 4,
              'transparent': 4,
              'predict-01': 4,
              'due-process': 3,
              'agency': 3,
              'capable-01': 3,
              'integrity': 3,
              'lawful': 3,
              'principle': 3,
              'infer-01': 3,
              'interact-01': 3,
              'design-01': 3,
              'mitigate-01': 3,
              'just-02': 3,
              'permit-01': 3,
              'slash': 3,
              'attend-02': 3,
              'vigilant-01': 3,
              'organization': 3,
              'mean-01': 3,
              'consequence-03': 3,
              'redress-01': 3,
              'reliability': 3,
              'important-01': 2,
              'collaborate-01': 2,
              'intelligent-01': 2,
              'well-09': 2,
              'audit-01': 2,
              'fidelity': 2,
              'trust-01': 2,
              'entitlement': 2,
              'equalize-01': 2,
              'authority': 2,
              'maleficiary': 2,
              'inclusiveness': 2,
              'absent-01': 2,
              'number': 2,
              'define-01': 2,
              'privilege-01': 2,
              'access-01': 2,
              'lack-01': 2,
              'pipeline': 2,
              'quality': 2,
              'implement-01': 2,
              'dimension': 2,
              'abstract-02': 2,
              'harm-01': 2,
              'rule-03': 2,
              'metric': 2,
              'contest-02': 2,
              'suitable-04': 2,
              'perform-02': 2,
              'domain': 2,
              'liable-01': 2,
              'preserve-01': 2,
              'plurality': 2,
              'cooperate-01': 2,
              'risk-01': 2,
              'corporation': 2,
              'productive-03': 1,
              'truthfulness': 1,
              'injure-01': 1,
              'value-01': 1,
              'clarity': 1,
              'certainty': 1,
              'reciprocity': 1,
              'have-degree-91': 1,
              'impartiality': 1,
              'need-01': 1,
              'clear-06': 1,
              'shar-e': 1,
              'enforce-01': 1,
              'transpar': 1,
              'openness': 1,
              'share-01': 1,
              'oversight-02': 1,
              'organize-01': 1,
              'rob-01': 1,
              'responsible-03': 1,
              'knowledge': 1,
              'pluralism': 1,
              'work-01': 1,
              'ability': 1,
              'subject': 1,
              'situa-tional': 1,
              'opportunity': 1,
              'rency': 1,
              'distribution-range-91': 1,
              'devote-01': 1,
              'term': 1,
              'find-01': 1,
              'misrepresent-01': 1,
              'available-02': 1,
              'maximum': 1,
              'explainabi': 1,
              'honesty': 1,
              'consistent-02': 1,
              'transpare-ncy': 1,
              'timely': 1,
              'classify-01': 1,
              'cite-01': 1,
              'precision': 1,
              'substitute-01': 1,
              'age-01': 1,
              'score-on-scale-91': 1,
              'obligate-01': 1,
              'vulnerable-01': 1,
              'summarize-01': 1,
              'have-li-91': 1,
              'ice': 1,
              'amr-unknown': 1,
              'responsible-01': 1,
              'justifiability': 1,
              'project': 1,
              'you': 1,
              'reasonableness': 1,
              'understand-01': 1,
              'formulate-01': 1,
              'randomness': 1,
              'make-01': 1,
              'emotion': 1,
              'respond-01': 1,
              'efficient-01': 1,
              'found-01': 1,
              'explicate-01': 1,
              'comply-01': 1,
              'exemplify-01': 1,
              'affect-01': 1,
              'factuality': 1,
              'anthropomorphize-01': 1,
              'zugutekommen': 1,
              'calibrate-01': 1,
              'decide-01': 1,
              'fire-02': 1,
              'use-01': 1,
              'do-02': 1,
              'describe-01': 1,
              'surveil-01': 1,
              'intelligible': 1,
              'align-01': 1,
              'page': 1,
              'likelihood': 1,
              'significant-02': 1,
              'data': 1,
              'society': 1,
              'identify-01': 1,
              'inclusive': 1,
              'foster-01': 1,
              'other': 1,
              'artificial': 1,
              'section': 1,
              'cybersecurity': 1,
              'malice': 1,
              'ensure-01': 1,
              'prosper-01': 1,
              'economy': 1,
              'information': 1,
              'matter': 1,
              'transgender': 1,
              'accountability': 1,
              'social-03': 1,
              'benefit-01': 1,
              'practice-01': 1,
              'increase-01': 1,
              'consent-01': 1,
              'instead-of-91': 1,
              'empower-01': 1,
              'engage-01': 1,
              'reduce-01': 1,
              'power': 1,
              'analyze-01': 1,
              'legal-02': 1,
              'secure-02': 1})
 %% Cell type:markdown id: tags:
 **Analyses :**
 Plus de la moitié des concepts associés portent sur la possibilité de comprendre :
 - 'transparency': 131
 - 'accountable-02': 111
 - 'explain-01': 37
 On peut en déduire que fairness est en effet un but mais qu'on ne peut y parvenir sans comprendre le fonctionnement (explicabilité).
 %% Cell type:markdown id: tags:
 #### 4.2.4.2) Analyse des voisins AMR important pour le lien ARG1
 %% Cell type:markdown id: tags:
 Si opx a pû être facilement ressortir lors de l'analyse du corpus via le POS tagging, nous allons étudier plus attentivement le lien ARG1.
 Ce qui signifie que "fair" est l'objet affecté par le parent.
 %% Cell type:code id: tags:
 ``` python
 countwordsarg1 = dict()
 for index, list_parents in parents.items():
     for word_fair, list_parents in list_parents.items():
         for parent in list_parents:
             if parent[2] == ":ARG1":
                 if parent[1] in countwordsarg1:
                     countwordsarg1[parent[1]] += 1
                 else:
                     countwordsarg1[parent[1]] = 1
 print("concept utilisé avec :ARG1 :")
-top_10_arg1 = sorted(countwordsarg1.items(),
-                key=lambda x: x[1], reverse=True)[:10]
+top_10_arg1 = sorted(countwordsarg1.items(), key=lambda x: x[1], reverse=True)[:10]
 # Afficher le Top 10
 for key, value in top_10_arg1:
     print(f"{key}: {value}")
 cleaned_countwordsarg1 = {}
 for key, value in countwordsarg1.items():
     # Utiliser une regex pour enlever `-01`, `-02`, etc.
     base_key = re.sub(r"-\d+$", "", key)
     cleaned_countwordsarg1[base_key] = cleaned_countwordsarg1.get(
         base_key, 0) + value
 # nuage de mots
 wordcloud = WordCloud(
     width=800,
     height=400,
     background_color="white",
     colormap="viridis"
 ).generate_from_frequencies(cleaned_countwordsarg1)
 # Afficher le nuage de mots
 plt.figure(figsize=(10, 5))
 plt.imshow(wordcloud, interpolation="bilinear")
 plt.axis("off")  # Supprime les axes
 plt.show()
 ```
 %% Output
     concept utilisé avec :ARG1 :
     define-01: 57
     ensure-01: 31
     realize-01: 23
     assess-01: 16
     source-02: 12
     evaluate-01: 10
     measure-01: 10
     discuss-01: 8
     consider-02: 8
     achieve-01: 8
 %% Cell type:markdown id: tags:
 **Analyses :**
 Des mots ressortent forcement ; notamment : "define", "realize", "ensure".
 Une traduction possible par nous serait :
 - "Définir l'équité" (ou "definition de l'équité" [trouver fair-01 et define-01])
 - "garantir l'équité" (même remarque)
 - etc.

notebooks/NOTESTECH_AMR.txt

+### INSTALLATION
+#1- Graphviz
+#** Installer Graphviz
+#** Mettre Graphviz dans le PATH
+PATH=C:\Logiciel\_prog\Graphviz\bin;%PATH%
+
+#2- metamorphosed
+#** Préparation dépôt GIT
+git clone https://github.com/Orange-OpenSource/metamorphosed.git
+virtualenv venv
+.\venv\Scripts\activate
+cd metamorphosed
+python -m pip install --upgrade pip setuptools wheel
+
+#** Installation prérequis
+# python -m pip install -r requirements.txt
+#Pb install de smatchcpp, installation manuelle :
+python -m pip install pytest werkzeug Flask Flask-Cors graphviz Penman GitPython PyYAML requests rdflib oxrdflib
+
+#** Installation module smatchpp
+cd ..
+git clone https://github.com/flipz357/smatchpp.git
+cd smatchpp
+# Edit setup .py
+# =>long_description = (this_directory / "README.md").read_text(encoding='utf-8')
+python setup.py install
+
+#** Récupération AMR concept pour PropBank
+cd propbank-frames\frames
+curl https://raw.githubusercontent.com/propbank/propbank-frames/development/frames/AMR-UMR-91-rolesets.xml -o AMR-UMR-91-rolesets.xml
+cd ../..
+
+#** Installation metamorphosed
+python metamorphosed\installJQ.py
+
+#** Correction metamorphosed pour Windows :
+python -m pip install pyreadline3
+# Remplacer "import readline" dans les fichiers par :
+# => amreditor.py
+# => relations_constraints.py
+from pyreadline3 import Readline
+readline = Readline()
+
+#** Sécurité :
+# Remplacer '0.0.0.0' par '127.0.0.1' dans le fichier metamorphosed/__init__.py
+
+
+### EXECUTION :
+python metamorphosed_server.py --file amr-test.txt --pbframes propbank-frames/frames --readonly
+
+# exemple :
+metamorphosed_server.py --file amr-test.txt \
+--pbframes propbank-frames/frames \
+--reification reification-table.txt \
+--relations amr-relation-list.txt \
+--concepts amr-concept-list.txt \
+--constraints constraints.yml
+
+
+### TEST utilisation en local
+import metamorphosed.amrdoc as amrdoc
+filename = 'key_penmans.amr'
+a = amrdoc.AMRdoc(filename)
+a.getsentencelist()
+
+a.sentences[0].amr
+a.sentences[0].tsv()
+=>penman.decode(self.amr)
+
+>>> a.sentences[0].text
+'First of all, how can existing regulations or new ones facilitate algorithmic accountability  or fairness?'
+>>> a.sentences[0].comments
+['File 167']
+>>> a.sentences[0].amr
+
+amrdoc.stats([a], args.conceptlist)
+
+amrdoc.relations_between_concepts([a], depth=1)
+
+amrdoc.stats([a], ['fearness'])
+
+
+####
+armdoc.py
+Remplacer ServerException par Exception
+
+amrdoc.stats([a], ['fearness'])
+# sentences: 1201
+# relations: 55 mean: 431.07 median: 43
+# concepts: 2289 mean: 12.26 median: 2
+  # verbs:  1228 mean: 10.40 median: 3
+  # others: 1061 mean: 14.42 median: 2
+
+'face-01', 'facet', 'facilitate-01', 'facility', 'fact', 'factor', 'factor-01', 'fail-01', 'fair-01', 'fairness', 'fairwash-01', 'fall-01', 'fall-04', 'fallacy', 'familiarize-01', 'fatality', 'feasibility', 'feature', 'feature-01', 'federate-01', 'feed-01', 'feed-02', 'feedback', 'feel-01', 'few', 'fidelity', 'field', 'fight-01', 'figure', 'fill-01', 'fill-in-05', 'find-01', 'find-02', 'fine-04', 'fire-02', 'firm', 'fit-01', 'fit-03', 'fit-06', 'fix-03', 'flaw-01', 'flexibility', 'flow-01', 'focus-01', 'follow-01', 'follow-02', 'follow-04', 'follow-through-07', 'foresee-01', 'foreword', 'forgive-01', 'form', 'form-01', 'formalize-01', 'formula', 'formulate-01', 'foster-01', 'found-01', 'foundation', 'frame', 'frame-06', 'framework', 'free-04', 'frequent-02', 'friendly-01', 'from', 'fulfill-01', 'full-09', 'fun-01', 'function', 'function-01', 'functional-03', 'fund', 'fund-01', 'furnish-01', 'fuse-01', 'future',
+
+c=amrdoc.relations_between_concepts([a])
+d=[x.split('\t') for x in c]
+print(tabulate([x.split('\t') for x in c], tablefmt='pipe'))
+
+print(tabulate([x.split('\t') for x in amrdoc.relations_between_concepts([a], depth=2)], tablefmt='pipe'))
+
+c1 = [x.split('\t') for x in amrdoc.relations_between_concepts([a], depth=1)]
+concepts = set(x[0] for x in c1)
+stat_concept = dict.fromkeys(sorted(concepts), 0)
+for x in c1:
+    stat_concept[x[0]] = int(x[1])
+
+c2=[x.split('\t') for x in amrdoc.relations_between_concepts([a], depth=2)]
+relations = set(x[0] for x in c2) - concepts
+stat_relation = dict.fromkeys(sorted(relations), 0)
+stat_concept_relation = dict.fromkeys(sorted(concepts), {})
+tmp_concept = list(stat_concept_relation.keys())[0]
+for x in c2:
+    if len(x) == 1 and x[0] in concepts:  # C'est un concept
+        tmp_concept = x[0]
+        stat_concept_relation[tmp_concept] = dict.fromkeys(sorted(relations), 0)
+        continue
+    
+    if len(x) > 1:  # C'est une relation associé au concept précédant
+        stat_relation[x[0]] += int(x[1])
+        stat_concept_relation[tmp_concept][x[0]] = int(x[1])
+
+# print(tabulate(stat_concept_relation.values(), showindex=list(stat_concept_relation.keys()), headers='keys', tablefmt='pipe'))
+list_concept_tosee = ['fair-01', 'fairness', 'fairwash-01']
+list_relation_tosee = set([y for x in list_concept_tosee for y in stat_concept_relation[x] if stat_concept_relation[x][y] != 0])
+filtre = dict.fromkeys(list_concept_tosee, {})
+tmp = {x: {y.strip(): stat_concept_relation[x][y] for y in list_relation_tosee} for x in list_concept_tosee}
+print(tabulate(tmp.values(), showindex=list_concept_tosee, headers='keys', tablefmt='pipe'))
+
+
+print(tabulate([x.split('\t') for x in c], tablefmt='pipe'))
+
+['fair-01']
+['   :ARG0', '5']
+['   :ARG1', '156']
+['   :ARG2', '6']
+['   :ARG3', '1']
+['   :ARG4', '1']
+['   :condition', '6']
+['   :degree', '1']
+['   :domain', '1']
+['   :location', '3']
+['   :manner', '5']
+['   :mod', '13']
+['   :polarity', '58']
+['fairness']
+['   :ARG3', '1']
+['   :beneficiary', '2']
+['   :domain', '5']
+['   :example', '1']
+['   :li', '2']
+['   :location', '5']
+['   :manner', '6']
+['   :mod', '105']
+['   :polarity', '42']
+['   :poss', '15']
+['   :prep-in', '9']
+['   :prep-on', '1']
+['   :quant', '1']
+['   :source', '1']
+['   :time', '1']
+['   :topic', '10']
+['fairwash-01']
+['   :ARG0', '1']
+['   :ARG1', '1']
+['   :manner', '1']
+
+
+PATH=C:\Logiciel\_prog\Graphviz\bin;%PATH%
+cd Projet_NLP_AMR\AMR\
+venv\Scripts\activate
+
+
+import metamorphosed.propbank_frames as propbank_frames
+import metamorphosed.amrdoc as amrdoc
+
+v = propbank_frames.PropBankFrames('propbank-frames/frames')
+
+filename = 'key_penmans.amr'
+a = amrdoc.AMRdoc(filename)
+l = a.validate([v])
+# Ou avec la version corrigé sur le GIT mapaie :
+l = a.validate_dict([v])

notebooks/metamorphosed/rapport_AMR/_STATS.txt

+amrdoc.stats([a], True)
+# sentences: 1201
+# relations: 55 mean: 431.07 median: 43
+# concepts: 2289 mean: 12.26 median: 2
+  # verbs:  1228 mean: 10.40 median: 3
+  # others: 1061 mean: 14.42 median: 2
\ No newline at end of file

notebooks/metamorphosed/rapport_AMR/nonverbalconcepts-list.txt

+or	231
+algorithm	217
+amr-unknown	91
+first-of-all	2
+organization	177
+name	1164
+and	2017
+fairness	917
+person	659
+human	166
+individual	76
+section	38
+multi-sentence	288
+program	10
+enterprise	2
+country	56
+mechanism	17
+transparency	216
+principle	207
+common	17
+ordinal-entity	27
+way	29
+many	31
+gender	13
+example	8
+diverse	9
+opportunity	18
+demography	5
+outcome	60
+distinctive	1
+very	8
+we	107
+everyone	2
+thing	308
+privacy	103
+beyond	13
+physician	1
+professional	10
+medicine	6
+data	268
+security	70
+considerable	2
+already	7
+another	8
+team	34
+robot	7
+paper	10
+some	29
+machine	96
+insucient	2
+also	86
+context	60
+accurate	51
+always	4
+benevolence	1
+inherent	6
+metric	18
+other	77
+strategy	12
+task	9
+ethics	141
+artificial	385
+corporation	5
+large	10
+state	18
+current	18
+initiative	7
+method	20
+district	2
+system	306
+partisanship	1
+map	3
+aspect	18
+technical	68
+evolution	1
+multiple	16
+space	5
+this	185
+adequate	6
+framework	46
+too	3
+more	87
+notion	16
+algorithmic	12
+big	15
+due-process	4
+time	8
+role	9
+it	43
+tension	6
+any	26
+between	17
+threshold	4
+criteria	33
+project	26
+publication	325
+part	18
+table	9
+subject	18
+contingency	6
+confidential	2
+diversity	27
+result	9
+robust	9
+solution	8
+resilience	3
+business	21
+large-scale	3
+yes	2
+no	2
+purpose	22
+dataset	21
+database	3
+body	7
+in-house	2
+slash	24
+origin	1
+culture	13
+et-cetera	27
+model	64
+process	2
+research-institute	11
+variable	14
+government-organization	52
+date-entity	204
+tutorial	1
+culinary	1
+you	78
+further	8
+party	9
+case	10
+matrix	2
+operation	1
+final	4
+random	4
+accessibility	1
+audience	1
+all	46
+from	3
+outset	2
+customer	1
+law	119
+baseline	1
+core	5
+set	32
+perspective	19
+interdisciplinary	9
+science	8
+computer	13
+fundamental	14
+director	1
+expertise	6
+concept	27
+just	5
+outside	5
+field	9
+society	88
+value-interval	31
+url-entity	71
+such	19
+information	39
+certain	20
+input	12
+tool	40
+they	40
+behind	2
+secrecy	2
+in-turn	2
+federal	11
+consideration	1
+application	23
+tandem	1
+so	8
+justice	54
+phase	9
+scope	9
+future	15
+artificial-intelligence	8
+intelligibility	5
+mathematics	10
+statistics	11
+facility	2
+community	23
+continent	8
+value	86
+truthfulness	1
+upfront	1
+i	8
+around	3
+world	19
+of-course	2
+imperative	1
+technology	53
+place	7
+industry	18
+academia	10
+technique	10
+potential	44
+systematic	5
+underway	1
+vital	2
+conference	58
+temporal-quantity	11
+year	11
+product	69
+digital	9
+broad	2
+era	1
+clarity	1
+certainty	4
+both	3
+sector	27
+especially	8
+device	1
+problem	23
+contract	1
+situation	14
+symmetric	1
+gap	3
+market	5
+public	22
+prior	4
+heuristics	1
+reciprocity	1
+equity	11
+clause	1
+duty	4
+environment	17
+great	12
+constant	1
+several	12
+asymmetry	3
+interest	3
+guideline	13
+over	2
+well	4
+to-date	1
+here	5
+extent	3
+page	14
+classic	1
+term	9
+often	20
+most	33
+sense	3
+transparent	17
+label	4
+sensor	2
+interface	2
+implicit	2
+social	14
+indicator	1
+identifier	1
+level	33
+robustness	30
+minimum	3
+member	21
+she	4
+company	80
+condition	5
+previous	7
+history	5
+group	72
+mechanics	1
+reason	6
+architect	2
+rule	6
+parameter	6
+area	20
+journal	17
+planet	8
+democracy	14
+entity	5
+vertrauenswrdige	1
+ethnic-group	5
+zugute	1
+male	4
+Werte	1
+verstndlich	1
+stellen	1
+verantwortlichen	1
+trustworthy	11
+objective	9
+various	14
+literature	7
+much	9
+instrumental	1
+date-interval	4
+event	9
+virtual	2
+immediate	3
+philosophy	5
+basic	8
+dignity	18
+inclusion	1
+background	5
+impartiality	1
+agency	29
+anxiety	1
+full	11
+particular	27
+transgender	2
+manual	3
+theme	14
+entitlement	3
+transpar	7
+document	10
+percentage-entity	43
+generosity	1
+bene	2
+autonomy	36
+solidarity	8
+keyword	1
+humanity	4
+shar-e	1
+parity	6
+generative	4
+prominence	1
+each	16
+backgr	1
+documentumen	1
+top	2
+equitable	11
+documen	2
+openness	1
+cyber	1
+race	4
+sexuality	1
+bo	1
+fact	8
+central	4
+number	23
+coefficient	2
+fidelity	3
+corpus	1
+entire	5
+knowledge	1
+pluralism	2
+globe	9
+mass	1
+emana	1
+piece	2
+next	4
+chap	2
+through	3
+checklis	4
+under	7
+stand	1
+among	1
+alone	3
+together	9
+within	1
+criterion	1
+stage	12
+dang	1
+there	3
+difficult	13
+richness	1
+word	1
+string-entity	11
+thr	1
+article	11
+enough	3
+piano	1
+affirmative-action	2
+lot	1
+resource	8
+treatman	1
+line	3
+extreme	2
+ability	1
+deplo-ymen	1
+explana-	1
+transpar-ent	1
+consen-t	1
+situa-tional	1
+across	15
+platform	13
+that	12
+profile	3
+political-party	4
+minutia	1
+only	7
+overview	3
+vel	1
+code	7
+again	12
+norm	10
+heading	1
+severe	1
+ecosystem	3
+as-well	1
+measur-e	1
+drawback	1
+truth-value	10
+measur	1
+one	3
+authority	8
+collective	4
+emotion	3
+religion	2
+status	3
+economy	14
+ques	1
+frame	2
+today	2
+gamut	1
+storage	1
+integrity	9
+confidentiality	8
+consensus	4
+report	10
+chapter	11
+end	6
+rigorous	1
+in-place	4
+force	1
+vironmental	1
+series	4
+course	1
+workshop	5
+stake	14
+civil	4
+international	13
+essential	5
+power	5
+circuit	2
+committee	3
+domain	12
+lawful	8
+rency	1
+transpa	1
+external	11
+stewardship	1
+theory	1
+top-down	1
+utilitarianism	1
+contractarianism	1
+Kantianism	1
+unique	1
+toolkit	10
+index	12
+sovereign	1
+lifecycle	11
+inadvertent	2
+maleficiary	2
+skill	3
+whole	4
+constraint	7
+accuracy	24
+module	1
+inclusiveness	2
+intrinsic	3
+property	8
+form	11
+type	22
+politics	10
+compatible	7
+ion	1
+odology	1
+meth	1
+cost	6
+deficiency	2
+in-fact	4
+absolute	1
+less	5
+past	4
+flagship	1
+variety	2
+simultaneous	5
+even	8
+procedure	15
+logic	2
+additional	5
+category	6
+main	4
+output	14
+proper	8
+sometimes	8
+version	4
+alternative	2
+documentation	2
+health	5
+before	13
+content	10
+feature	15
+rhetoric	1
+al	1
+rigor	3
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+comprehensive	2
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+criminal	8
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+false	5
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+error	9
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+awareness	4
+classifier	1
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+therefore	1
+statistician	1
+scientist	9
+sociology	3
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+honesty	1
+left-handed	1
+language	5
+foreign	1
+movie	1
+few	5
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+institution	7
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+complex	7
+verifiable	1
+after	4
+colleague	2
+recent	5
+bias	8
+subjective	2
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+Proceedings	5
+court	2
+standard	27
+disproportionate	1
+idea	8
+population	13
+architecture	4
+nuances	3
+scale	4
+speed	2
+plain	2
+success	2
+weight	4
+income	2
+likelihood	3
+rate	11
+scenario	2
+citizen	11
+smartphone	2
+e-commerce	2
+media	2
+detail	2
+network	3
+structure	7
+every	9
+throughout	5
+element	14
+emergency	2
+pathway	4
+art	6
+insight	5
+blatant	4
+dynamic	6
+function	4
+loop	4
+automatic	3
+even-when	3
+and_11	2
+rapid	4
+bill	2
+positive	10
+negative	2
+third-party	2
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+prerequisite	2
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+medium	1
+welfare	2
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+onus	1
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+craft	2
+air	2
+barrier	7
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+ethicist	3
+bureaucracy	2
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+aircraft	2
+equipment	2
+physical	3
+complexity	9
+evidence	4
+fund	2
+cybersecurity	3
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+still	3
+factor	2
+original	4
+above	8
+at-least	5
+lawfulness	6
+finance	9
+accountability	3
+monetary	1
+jurisdiction	1
+domestic	1
+work-of-art	1
+ice	1
+pipeline	6
+causality	3
+instance	3
+book	3
+analytics	1
+messiness	1
+systemic	2
+reality	1
+entry	1
+outlier	1
+format	1
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+university	12
+boundary	3
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+safeguard	4
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+quality	9
+certificate	5
+kite-mark	1
+competitiveness	2
+ever	2
+woman	2
+imbalance	1
+champion	1
+job	3
+degree	4
+more-than	2
+state-of-the-art	2
+service	9
+intermediation	1
+online	1
+preliminary	2
+first	2
+reliability	8
+trace	1
+opaqueness	1
+block	2
+ground-up	1
+means	10
+goal	8
+justifiability	1
+reasonableness	2
+juncture	1
+plane	1
+equivalent	1
+initial	5
+anyone	1
+crucial	6
+being	8
+bullet	1
+silver	1
+strict	3
+self	3
+component	3
+pattern	1
+life	5
+workflow	3
+target	2
+raw	1
+due	1
+proxy	3
+forethought	1
+below	3
+feasibility	3
+heavy	1
+multidisciplinary	1
+formal	7
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+agent	1
+picture	1
+statistic	1
+proportion	2
+odds	2
+disadvantage	2
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+probability	2
+counterfactual	6
+circumstantial	1
+out	1
+disparate	10
+thorough	2
+association	2
+source	6
+rationale	4
+confidence	2
+path	3
+end-to-end	7
+peer	2
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+downstream	1
+proactive	3
+dimension	11
+column	2
+department	1
+point	5
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+location	3
+criter	2
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+stakeholder	2
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+protocol	2
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+formula	1
+cloud	1
+road	2
+opaque	1
+substantial	5
+hand	1
+mental	1
+search-engine	1
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+he	1
+indeed	1
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+nation	9
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+age	1
+roundtable	1
+school	3
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+arm	2
+substantive	2
+proportionality	2
+careful	4
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+cycle	2
+ethnicity	2
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+panacea	1
+now	4
+early	4
+spectrum	1
+tone	4
+skin	7
+doctor	3
+feedback	1
+mind	1
+disease	1
+region	3
+text	2
+image	2
+artifact	1
+card	3
+factuality	1
+trait	1
+animate	1
+adversarial	1
+library	2
+synthetic	3
+top-of-mind	1
+chain	1
+zugutekommen	1
+geschtzt	1
+vertrauenswrdig	1
+personality	1
+unconscious	2
+energy	1
+technologist	1
+independent	1
+strand	1
+impartial	1
+subgroup	2
+off	1
+tradition	1
+versus	6
+truth	1
+ground	1
+ratio-of	2
+paradigm	3
+productio	2
+prospective	1
+figure	1
+heart	4
+right	1
+optimal	1
+priority	5
+forward	4
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+site	2
+ahead	1
+day	4
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+auxiliary	1
+healthcare	6
+overall	3
+universal	3
+statistical	3
+qualitative	2
+blackbox	1
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+face	2
+benchmark	1
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+sledgehammer	1
+pillar	2
+ity12	1
+radiologist	1
+patient	17
+note	3
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+candidate	1
+artificial-physical	1
+crowd	1
+and-or	3
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+liberty	1
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+computational	2
+sophisticated	2
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+accident	3
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+subsequent	1
+holistic	1
+up-to	2
+viewpoint	1
+step	1
+minority	4
+marketplace	1
+direction	1
+so-called	2
+bioethic	1
+deep-learning	1
+readmission	1
+violence	1
+situational	1
+et-al	1
+equal	1
+scoping	1
+checklist	1
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+period	4
+firm	4
+oversight	1
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+integral	1
+center	2
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+monetary-quantity	3
+dollar	2
+pound	1
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+gas	1
+greenhouse	1
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+by	4
+capital	1
+healthy	2
+y	1
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+incident	2
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+chief	1
+de-facto	1
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+then	5
+personnel	2
+opera	1
+ring	1
+fruitful	1
+adequacy	1
+young	2
+old	1
+sand	1
+disagr-eemen	1
+stem	1
+ethic	1
+directive	2
+questionnaire	1
+principal	1
+mark	1
+joint	1
+sandbox	1
+at-scale	1
+conti-nuous	1
+sound	1
+foundation	2
+immense	2
+collateral	3
+upon	1
+subsystem	1
+demographics	1
+intuitive	2
+facet	1
+controversy	4
+contemporary	1
+mutual	2
+good	1
+penalty	1
+cookie	1
+kind	1
+since	1
+definitive	1
+gravity	1
+recidivism	3
+pact	1
+im	1
+tactic	1
+subjec	1
+once	1
+ex-post-	1
+bear	1
+wide-scale	1
+order	1
+caveat	2
+al-so	1
+sole	1
+amr-choice	1
+ideal	1
+domai	2
+brief	1
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+yet	1
+skewer	1
+newspaper	1
+fallacy	1
+distributive	1
+vol	1
+magazine	1
+adversary	1
+no-longer	1
+cluster	1
+infrastructure	1
+externality	2
+border	2
+plurality	2
+research	1
+fatality	1
+traffic	1
+artificial-physical-intelligence	1
+charter	1
+thereby	1
+everybody	1
+meaningful	1
+nature	1
+husband	2
+ensemble	1
+sex	1
+color	1
+genetics	2
+membership	1
+disability	1
+unseemly	1
+statute	1
+ethical	1
+unscrupulous	1
+bus	1
+penumbra	1
+startup	1
+spirit	1
+entrepreneurial	1
+fiduciary	1
+quantity	1
+massive	1
+near	1
+precedent	1
+workforce	1
+at-large	1
+partnership	1
+availability	1
+sovereignty	1
+summer	1
+benefit	1
+paragraph	1
+anonymity	1
+stock	2
+website	1
+sort	1
+exhaustive	1
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+le	1
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+attitude	1
+technocratic	1
+forefront	1
+definite	1
+at-all	1
+primer	1
+clinician	1
+trust	1
+financial	1

notebooks/metamorphosed/rapport_AMR/relations-list.txt

+:ARG1	8939
+:ARG0	3511
+:mod	3017
+:manner	334
+:name	1171
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+:extent	3
+:prep-on	2
+:accompanier	2
+:ARG8	2
+:season	1

notebooks/metamorphosed/rapport_AMR/verbalconcepts-list.txt

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