def learn_lexical_entries(input_dir, cat_path, dict_path, verbose='none', \
parse_mode='given'):
from IPython.display import display
from src.utl.turtle import html_table
from src.utl.read_files import check_dir, check_dir_files, check_corpus
from src.utl.write_files import list2file
from src.link_grammar.turtle import \
files2disjuncts, lexical_entries, entries2clusters, entries2categories, \
disjuncts2clusters, entries2rules, save_link_grammar
if check_dir(input_dir, create=False, verbose=verbose):
files = check_dir_files(input_dir, verbose=verbose)
if len(files) > 0:
if verbose == 'max': print(files)
for i, file in enumerate(files):
if check_corpus(file, verbose=verbose):
if verbose == 'max':
print('File #' + str(i), file, 'checked')
else:
print('File #' + str(i), file, 'check failed')
else:
print('Input directory', input_dir, 'is empty')
else:
print('No input directory', input_dir)
log = {'project': 'Grammar Learner -- Lexical entries'} # use OR DEL?
disjuncts = files2disjuncts(files, 'LEFT-WALL', True, verbose)
#TODO: parse_mode?
entries = lexical_entries(disjuncts)
category_list = entries2categories(entries)
if verbose == 'max':
display(
html_table(
[['Parent', 'Category', 'Quality', 'Words', 'Relevance']] +
category_list))
if cat_path[-1] != '/': cat_path += '/'
cat_file = cat_path + 'categories.txt'
categories = list2file(category_list, cat_file)
if verbose == 'max':
for line in categories.splitlines()[:3]:
print(line)
print('<...>\nTotal', len(categories.splitlines()), 'lines, saved to',
cat_file)
lg_rule_list = entries2rules(disjuncts2clusters(entries2clusters(entries)))
if verbose == 'max':
display(
html_table([['Cluster', 'Germs', 'L', 'R', 'Disjuncts']] +
lg_rule_list))
lg_rules_str = save_link_grammar(lg_rule_list, dict_path)
if verbose == 'max':
for line in lg_rules_str.splitlines():
print(line)
#-return categories, lg_rules_dict
#TODO: return paths to categories and dict?
s = lg_rules_str.splitlines()[-1]
lg_file = s[s.find(': ') + 2:]
response = {'categories_file': cat_file, 'grammar_file': lg_file}
return response
def save_cat_tree(cats, output_categories, verbose='none'): #80706 0.5
#80611 ~ cats2list without 'djs', children'...
# cats: {'cluster':[], 'words':[], ...} #80609
from copy import deepcopy
from src.utl.write_files import list2file
from src.utl.utl import UTC
tree_file = output_categories
if '.' not in tree_file: #auto file name
if tree_file[-1] != '/': tree_file += '/'
#-tree_file += (str(len(set([x[0] for x in cats_list]))) + '_cat_tree.txt')
n_cats = len(
[x for i, x in enumerate(cats['parent']) if i > 0 and x < 1])
tree_file += (str(n_cats) + '_cat_tree.txt')
categories = []
for i, cluster in enumerate(cats['cluster']):
if i == 0: continue
category = []
if cats['cluster'][i] is not None:
category.append(cats['cluster'][i])
else:
category.append('')
category.append(cats['parent'][i])
category.append(i)
category.append(round(cats['quality'][i], 2))
#!category.append(sorted(cats['words'][i])) #80704+06 tmp hack FIXME
wordz = deepcopy(sorted(cats['words'][i]))
#-80704 word@1, word@2 ⇒ word.a, word.b:
#-wordz = [x.replace('@1','.a') for x in wordz]
#-wordz = [x.replace('@2','.b') for x in wordz]
#-wordz = [x.replace('@3','.c') for x in wordz]
wordz = [x.replace('@', '.')
for x in wordz] #80706 WSD: word@1 ⇒ word.1
category.append(wordz) #80704+06 tmp hack FIXME
#80704+06 end
category.append(cats['similarities'][i])
#-category.append(cats['children'][i])
categories.append(category)
string = list2file(categories, tree_file)
if verbose in ['max', 'debug']:
print(UTC(),':: src/utl.writefiles.py save_cat_tree:', \
len(cats['cluster']) - 1, 'categories')
if verbose == 'debug':
from src.utl.widgets import html_table
from IPython.display import display
display(
html_table(
[['Code', 'Parent', 'Id', 'Sim', 'Words', 'Similarities']] +
categories))
return {'cat_tree_file': tree_file}
def save_category_tree(category_list, tree_file, verbose='none'): #80522
import os
cats = category_list
clusters = {}
m = 0
for i, x in enumerate(cats):
if x[0] not in clusters: clusters[x[0]] = []
clusters[x[0]].append(i)
if x[2] > m: m = x[2]
tree = []
for k, v in clusters.items():
if len(v) == 1:
tree.append(cats[v[0]])
elif len(v) > 1:
words = []
similarities = []
for j in v:
words.extend(cats[j][4])
similarities.extend(cats[j][5])
tree.append(
[cats[v[0]][0], 0, m + 1, cats[v[0]][3], words, similarities])
for j in v:
tree.append([
'', m + 1, cats[j][2], cats[j][3], cats[j][4], cats[j][5]
])
else:
print('WTF?', k, v)
if verbose in ['max', 'debug']:
from src.utl.widgets import html_table
from IPython.display import display
display(
html_table(
[['Code', 'Parent', 'Id', 'Sim', 'Words', 'Similarities']] +
tree))
from src.utl.write_files import list2file
#-tree_file = os.path.dirname(cat_file) + '/cat_tree.txt'
string = list2file(tree, tree_file)
return {'tree_file': tree_file}
def category_learner(links, \
cat_path, dict_path, tmpath = '', verbose = 'none', \
parse_mode = 'given', left_wall = '', period = False, \
context = 1, window = 'mst', weighting = 'ppmi', group = True, \
word_space = 'vectors', dim_max = 100, sv_min = 0.1,
dim_reduction = 'svm', \
clustering = 'kmeans', cluster_range = (2,48,1), \
cluster_criteria = 'silhouette', cluster_level = 0.9): #, \
#-generalization = 'off', merge = 0.8, aggregate = 0.2, grammar_rules = 1):
from src.utl.utl import UTC, round1, round2 #, round3, round4, round5
from src.space.hyperwords import vector_space_dim, pmisvd
from src.clustering.kmeans import cluster_words_kmeans
from src.clustering.poc03 import number_of_clusters, clusters2list #80422
from src.utl.turtle import html_table, plot2d
from src.utl.write_files import list2file
from src.link_grammar.poc import save_link_grammar
log = {'project': 'Grammar Learner v.0.3 2018-04-11', \
'date': str(UTC()), 'project_dir': dict_path}
'''TODO: log: dict ⇒ list [[]]? / OrderedDict?'''
if tmpath == '': tmpath = dict_path # temporary files path
if verbose == 'debug':
print('category_learner: word_space:', word_space, '/ clustering:',
clustering)
if word_space == 'vectors':
#^from src.space.hyperwords import vector_space_dim, pmisvd
#-dim = vector_space_dim(links, dict_path, tmpath, dim_max, sv_min, verbose)
#-80420 dict_path ⇒ tmpath :: dir to save vectors.txt
dim = vector_space_dim(links, tmpath, tmpath, dim_max, sv_min, verbose)
log.update({'vector_space_dim': dim})
if verbose == 'min': print('Optimal vector space dimensionality:', dim)
#-vdf, sv, res3 = pmisvd(links, dict_path, tmpath, dim)
#-80420 dict_path ⇒ tmpath :: dir to save vectors.txt
vdf, sv, res3 = pmisvd(links, tmpath, tmpath, dim)
log.update(res3)
elif verbose in ['max', 'debug']:
print('category_learner: word_space:', word_space, '/ clustering:',
clustering)
if clustering == 'kmeans':
#^from src.clustering.kmeans import cluster_words_kmeans
#^from src.clustering.poc03 import number_of_clusters, clusters2list
n_clusters = number_of_clusters(vdf, cluster_range ,clustering, \
criteria=cluster_criteria, level=cluster_level, verbose=verbose)
clusters, silhouette, inertia = cluster_words_kmeans(vdf, n_clusters)
if verbose not in ['min', 'none']:
print('/poc03/category_learner: number of clusters =', n_clusters)
if verbose in ['max', 'debug']: print(clusters.applymap(round2))
if verbose in ['max', 'debug']: #80412 hack: plots for AGI-2018 :(
if context == 1: #FIXME:DEL?
plot2d(1, 2, clusters, 'cluster_words', 10)
else:
if len(clusters) < 6:
plot2d(1, 3, clusters, 'cluster_words', 10)
else:
plot2d(1, 4, clusters, 'cluster_words', 10)
elif clustering[:5] in ['group', 'ident']:
if verbose in ['max', 'debug']: print('clustering:', clustering)
#TODO: from src.clustering.grouping import group_links
clusters = group_links(links, verbose)
if verbose not in ['min', 'none']:
print('Total', len(clusters), \
'clusters of identical lexical entries', type(clusters))
if verbose in ['max', 'debug']:
print('\n', clusters[['cluster_words', 'disjuncts']]) #.head(12))
# Generalization #TODO next week
# Save categories
#^from src.clustering.poc import clusters2list
#^from src.utl.write_files import list2file
category_list = clusters2list(clusters)
if verbose not in ['min', 'none']:
display(html_table([['Parent','Category','Quality','Words','Relevance']] \
+ category_list))
'''TODO: categories file name'''
if cat_path[-1] != '/': cat_path += '/'
cat_file = cat_path + str(len(clusters)) + '_categories.txt'
categories = list2file(category_list, cat_file)
'''TODO: category file path ⇒ log'''
if verbose in ['debug']:
print('\nWord categories:\n')
for line in categories.splitlines()[:3]:
print(line)
if verbose not in ['min', 'none']:
print('\nCategory list -', len(categories.splitlines()),
'lines, saved to', cat_file)
return category_list, log
def learn_connectors(input_dir, cat_path, dict_path, verbose='none', \
parse_mode='given', \
word_space = 'hyperwords', dim_max = 100, sv_min = 0.1, \
clustering = 'kmeans', cluster_range = (2,48,1), \
cluster_criteria = 'silhouette', cluster_level = 0.9, tmpath = ''):
from src.utl.utl import UTC, round1, round2 #, round3, round4, round5
from src.utl.read_files import check_mst_files
from src.space.poc import files2links
from src.space.hyperwords import vector_space_dim, pmisvd
from src.clustering.kmeans import cluster_words_kmeans
from src.clustering.poc import number_of_clusters, clusters2list
from src.utl.turtle import html_table, plot2d
from src.utl.write_files import list2file
from src.link_grammar.poc import save_link_grammar
log = {'project': 'Unified Grammar Learner: Clustering words', \
'date': str(UTC()), 'project_dir': dict_path, 'input_dir': input_dir }
"""TODO: dict ⇒ list [[]] / OrderedDict?"""
files, response = check_mst_files(input_dir, verbose='none')
links = files2links(files, parse_mode='given', context=1, group = True, \
left_wall='LEFT-WALL', period=True, verbose='none')
# vector_space_dim(links, path, tmpath, dim_max=100, sv_min=0.9, 'max')
if tmpath == '': tmpath = dict_path
dim = vector_space_dim(links, dict_path, tmpath, dim_max, sv_min, verbose)
log.update({'vector_space_dim': dim})
if verbose not in ['none', 'min']:
print('Optimal vector space dimensionality:', dim)
vdf, sv, res2 = pmisvd(links, dict_path, tmpath, dim)
log.update(res2)
#-vdf.applymap(round2).sort_values(by=[1,2,3], ascending=[False,False,False])
n_clusters = number_of_clusters(vdf, cluster_range ,clustering, \
criteria=cluster_criteria, level=cluster_level, verbose=verbose)
if verbose not in ['none', 'min']:
print('Optimal number of clusters:', n_clusters)
clusters, silhouette, inertia = cluster_words_kmeans(vdf, n_clusters)
if verbose in ['max', 'debug']: plot2d(1, 2, clusters, 'cluster_words', 10)
# Generalisation - just histogram? - Grammar-Learner-Clustering-Words 2.6
import numpy as np
from src.clustering.similarity import cluster_similarity
sim_df, res3 = cluster_similarity(clusters, 'max')
log.update(res3)
if verbose in ['max', 'debug']:
count, division = np.histogram(sim_df['similarity'])
sim_df['similarity'].hist(bins=division)
print('Cluster similarities: absolute values > 0.1:')
sim_df.sort_values(by='similarity',
ascending=False).loc[(sim_df['similarity']) > 0.1]
# Save categories
category_list = clusters2list(clusters)
if cat_path[-1] != '/': cat_path += '/'
cat_file = cat_path + 'categories.txt'
categories = list2file(category_list, cat_file)
if verbose in ['max', 'debug']:
for line in categories.splitlines():
print(line)
print('<...>\nTotal', len(categories.splitlines()), \
'lines, saved to', cat_file)
#-print(len(categories.splitlines()), 'categories saved to', cat_file)
# Grammar Learner
lg_rule_list = grammar_learner(clusters, links, verbose)
if verbose == 'max':
display(
html_table([['Cluster', 'Germs', 'L', 'R', 'Disjuncts']] +
lg_rule_list))
lg_rules_str = save_link_grammar(lg_rule_list, dict_path)
if verbose == 'max':
for line in lg_rules_str.splitlines():
print(line)
#-return categories, lg_rules_dict
#TODO: return paths to categories and dict?
s = lg_rules_str.splitlines()[-1]
lg_file = s[s.find(': ') + 2:]
response = {'categories_file': cat_file, 'grammar_file': lg_file}
return response
def category_learner(links, \
cat_path, dict_path, verbose='none', \
parse_mode='given', \
word_space = 'vectors', dim_max = 100, sv_min = 0.1, \
dim_reduction = 'svm', \
clustering = 'kmeans', cluster_range = (2,48,1), \
cluster_criteria = 'silhouette', cluster_level = 0.9, tmpath = '',
generalization = 'off',
grammar_rules = 'connectors'): # no actual need need for grammar rules here?
from src.utl.utl import UTC, round1, round2 #, round3, round4, round5
from src.space.hyperwords import vector_space_dim, pmisvd
from src.clustering.kmeans import cluster_words_kmeans
from src.clustering.poc import number_of_clusters, clusters2list
from src.utl.turtle import html_table, plot2d
from src.utl.write_files import list2file
from src.link_grammar.poc import save_link_grammar
log = {'project': 'Grammar Learner v.0.2 2018-04-06', \
'date': str(UTC()), 'project_dir': dict_path}
'''TODO: log: dict ⇒ list [[]]? / OrderedDict?'''
if word_space == 'vectors':
if tmpath == '': tmpath = dict_path
#^from src.space.hyperwords import vector_space_dim, pmisvd
dim = vector_space_dim(links, dict_path, tmpath, dim_max, sv_min,
verbose)
log.update({'vector_space_dim': dim})
if verbose == 'min': print('Optimal vector space dimensionality:', dim)
vdf, sv, res3 = pmisvd(links, dict_path, tmpath, dim)
log.update(res3)
else:
#TODO: word_space = 'discrete'...
if tmpath == '': tmpath = dict_path
dim = vector_space_dim(links, dict_path, tmpath, dim_max, sv_min,
verbose)
log.update({'vector_space_dim': dim})
if verbose == 'min': print('Optimal vector space dimensionality:', dim)
vdf, sv, res3 = pmisvd(links, dict_path, tmpath, dim)
log.update(res3)
# Clustering
#-clustering = 'group'
if clustering == 'kmeans':
#^from src.clustering.poc import number_of_clusters, clusters2list
n_clusters = number_of_clusters(vdf, cluster_range ,clustering, \
criteria=cluster_criteria, level=cluster_level, verbose=verbose)
clusters, silhouette, inertia = cluster_words_kmeans(vdf, n_clusters)
if verbose not in ['min', 'none']:
print('Optimal number of clusters:', n_clusters)
if verbose == 'max': plot2d(1, 2, clusters, 'cluster_words', 10)
elif clustering[:5] in ['group', 'ident']: #80606 test ~OK
def group_links(links):
#+TODO: old code ⇒ here ⇒ src.clustering.group_links.py
#-Old way:
from src.link_grammar.turtle import lexical_entries, entries2clusters
djs = links.rename(columns={'link': 'disjunct'})
#-clusters = entries2clusters(lexical_entries(djs))
entries = lexical_entries(djs)
clusters = entries2clusters(entries).rename(
columns={'germs': 'cluster_words'})
return clusters
#+from ... import group links
clusters = group_links(links)
if verbose not in ['min', 'none']:
print('Total', len(clusters), \
'clusters of identical lexical entries', type(clusters))
if verbose == 'max':
print('\n', clusters[['cluster_words', 'disjuncts']]) #.head(12))
# Generalization #TODO next week
# Save categories
#^from src.clustering.poc import clusters2list
#^from src.utl.write_files import list2file
category_list = clusters2list(clusters)
if verbose not in ['min', 'none']:
display(html_table([['Parent','Category','Quality','Words','Relevance']] \
+ category_list))
'''TODO: categories file name'''
if cat_path[-1] != '/': cat_path += '/'
cat_file = cat_path + 'categories.txt'
categories = list2file(category_list, cat_file)
'''TODO: category file path ⇒ log'''
if verbose == 'max':
for line in categories.splitlines()[:3]:
print(line)
if verbose != 'none':
print('<...>\nTotal', len(categories.splitlines()), 'lines, saved to',
cat_file)
return category_list, log
def learn_grammar(input_parses, output_categories, output_grammar, **kwargs):
# input_parses - dir with .txt files
# output_categories - path/file.ext / dir ⇒ auto file name
# output_grammar - path/file.ext / dir ⇒ auto file name
def kwa(v, k):
return kwargs[k] if k in kwargs else v
tmpath = kwa('', 'tmpath')
parse_mode = kwa('given', 'parse_mode')
left_wall = kwa('', 'left_wall')
period = kwa(False, 'period')
context = kwa(1, 'context')
window = kwa('mst', 'window')
weighting = kwa('ppmi', 'weighting')
#? distance = kwa(??, 'distance')
group = kwa(True, 'group')
word_space = kwa('vectors', 'word_space')
dim_max = kwa(100, 'dim_max')
sv_min = kwa(0.1, 'sv_min')
dim_reduction = kwa('svm', 'dim_reduction')
clustering = kwa('kmeans', 'clustering')
#-cluster_range = kwa((2,48,1), 'cluster_range')
#-cluster_criteria = kwa('silhouette', 'cluster_criteria')
#-cluster_level = kwa(0.9, 'cluster_level')
cats_gen = kwa('off', 'categories_generalization')
#-cats_merge = kwa(0.8, 'categories_merge')
#-cats_aggr = kwa(0.2, 'categories_aggregation')
grammar_rules = kwa(1, 'grammar_rules')
rules_gen = kwa('off',
'rules_generalization') # 'off', 'cosine', 'jaccard'
#-rules_merge = kwa(0.8, 'rules_merge'), # merge rules with similarity > this 'merge' criteria
#-rules_aggr = kwa(0.3, 'rules_aggregation'), # aggregate rules with similarity > this criteria
verbose = kwa('none', 'verbose')
print('learn_grammar: grammar_rules:', grammar_rules)
#80509 TODO: renamed parameters ⇒ update code
kwargs['input_parses'] = input_parses
kwargs['output_categories'] = output_categories
kwargs['output_grammar'] = output_grammar
#TODO: if parameter != file: auto file name
input_dir = input_parses
#cat_path = output_categories
#-dict_path = output_grammar
import os, pickle #, collections
from collections import OrderedDict
import pandas as pd
from shutil import copy2 as copy
from src.utl.utl import UTC
from src.utl.read_files import check_dir, check_mst_files
from src.space.poc05 import files2links #80528 .poc05
from src.clustering.poc05 import clusters2dict
#+from src.link_grammar.poc05 import category_learner
#+from src.link_grammar.poc05 import induce_grammar
from src.utl.write_files import list2file, save_link_grammar, save_cat_tree
from src.utl.widgets import html_table, plot2d
from src.grammar_learner.generalization import generalize_categories, \
reorder, cats2list, generalize_rules #, aggregate, aggregate_word_categories\
log = OrderedDict({'start': str(UTC()), 'learn_grammar': '80605'})
#TODO: save kwargs?
files, re01 = check_mst_files(input_parses, verbose)
log.update(re01)
if os.path.isdir(output_categories):
prj_dir = output_categories
else:
prj_dir = os.path.dirname(output_categories)
log.update({'project_directory': prj_dir})
#-Save a copy of input parses to prj_dir + '/parses/' #FIXME:DEL? #80704
#-parse_dir = prj_dir + '/parses/'
#-if check_dir(parse_dir, True, verbose):
#- for file in files: copy(file, os.path.dirname(parse_dir))
#-else: raise FileNotFoundError('File not found', input_parses)
# group = True #? always? False option for context = 0 (words)?
kwargs['input_files'] = files
# files ⇒ links:
links, re02 = files2links(**kwargs)
log.update(re02)
# corpus_stats - implanted in files2links 80605
list2file(re02['corpus_stats'], prj_dir + '/corpus_stats.txt')
log.update({'corpus_stats_file': prj_dir + '/corpus_stats.txt'})
if verbose in ['max', 'debug']:
print('\nfiles2links returns links', type(links), ':\n')
with pd.option_context('display.max_rows', 6):
print(links, '\n')
print('learn_grammar: word_space:', word_space, '/ clustering:',
clustering)
# Learn categories: new 80619
categories, re03 = category_learner(links, **kwargs) #v.0.5 categories: {}
log.update(re03)
# Generalize categories #TODO? "gen_cats" ⇒ "categories"? no new name
if cats_gen == 'jaccard' or (cats_gen == 'auto' and clustering == 'group'):
if verbose in ['max', 'debug']:
print(UTC(), ':: learn_grammar ⇒ generalize_categories (jaccard)')
gen_cats, re04 = generalize_categories(categories, **kwargs)
log.update(re04)
elif cats_gen == 'cosine' or (cats_gen == 'auto'
and clustering == 'kmeans'):
#TODO: vectors g12n
gen_cats = categories
log.update({'generalization': 'vector-similarity based - #TODO'})
if verbose == 'debug':
print(
'#TODO: categories generalization based on cosine similarity')
else:
gen_cats = categories
log.update({'generalization': 'error: cats_gen = ' + str(cats_gen)})
if verbose in ['max', 'debug']:
print(UTC(),':: learn_grammar: generalization: else: cats_gen =', \
cats_gen, '⇒ gen_cats = categories')
# Save 1st cats_file = to control 2-step generalization #FIXME:DEL? #80704
#-re05 = save_cat_tree(gen_cats, output_categories, verbose)
#-log.update({'category_tree_file': re05['cat_tree_file']})
# Save cats.pkl
#-with open(re05['cat_tree_file'][:-3]+'pkl', 'wb') as f: #FIXME:DEL? #80704
#- pickle.dump(gen_cats, f)
#-if verbose in ['max','debug']:
#- print(UTC(),':: learn_grammar: 1st cat_tree saved')
# Learn grammar #80623
if grammar_rules != context:
context = kwargs['context']
kwargs['context'] = kwargs['grammar_rules']
if verbose in ['max', 'debug']:
print(UTC(), ':: learn_grammar ⇒ files2links(**kwargs)')
links, re06 = files2links(**kwargs)
kwargs['context'] = context
# add disjuncts to categories {} after k-means clustering #TOEO: speed!
def add_disjuncts(cats, links, verbose='none'):
if verbose in ['max', 'debug']:
print(UTC(), ':: add_disjuncts: cats:', len(cats['cluster']),
'clusters')
from copy import deepcopy
top_clusters = [i for i,x in enumerate(cats['cluster']) \
if i > 0 and x is not None]
word_clusters = dict()
for i in top_clusters:
for word in cats['words'][i]:
word_clusters[word] = i
if verbose in ['max', 'debug']:
print(UTC(), ':: add_disjuncts: word_clusters:',
len(word_clusters), 'words')
df = links.copy()
if verbose in ['max', 'debug']:
print(UTC(),
':: add_disjuncts: df[links] = [[x] for x in df[link]]')
df['links'] = [[x] for x in df['link']]
if verbose in ['max', 'debug']:
print(
UTC(),
':: add_disjuncts: df[cluster] = df[word].apply(lambda x: word_clusters[x])'
)
df['cluster'] = df['word'].apply(lambda x: word_clusters[x])
if verbose in ['max', 'debug']:
print(UTC(),
':: add_disjuncts: cdf = df.groupby("cluster").agg(...')
cdf = df.groupby('cluster').agg({
'links': 'sum',
'count': 'sum'
}).reset_index()
#TODO? del df[...] to free RAM?
disjuncts = [[]] + cdf['links'].tolist()
counts = [0] + cdf['count'].tolist()
if verbose in ['max', 'debug']:
print(UTC(),':: add_disjuncts: len(cluster, disjuncts):', \
len(rules['cluster']), len(disjuncts), '\ncounts:', counts)
fat_cats = deepcopy(cats)
fat_cats['counts'] = [0] + cdf['count'].tolist()
fat_cats['disjuncts'] = [[]] + cdf['links'].tolist()
#['djs']
djset = set()
[[djset.add(y) for y in x] for x in fat_cats['disjuncts']]
djlist = sorted(djset)
fat_cats['djs'] = [set([djlist.index(x) for x in y if x in djlist]) \
for y in fat_cats['disjuncts']]
if verbose in ['max', 'debug']:
print(UTC(), ':: add_disjuncts: return fat_cats')
return fat_cats
#TODO: def djs? vectors(disjuncts, **kwargs)
#if context < 2 and grammar_rules > 1:
if word_space == 'vectors' or clustering == 'kmeans':
if verbose in ['max', 'debug']:
print(UTC(), ':: learn_grammar ⇒ add_disjuncts')
#with open(re05['cat_tree_file'][:-9]+'s.pkl', 'wb') as f: #FIXME:DEL tmp 80601
# pickle.dump(gen_cats, f)
fat_cats = add_disjuncts(gen_cats, links)
if verbose in ['max', 'debug']:
print(UTC(), ':: learn_grammar: back from add_disjuncts')
#TODO: fat_cats['djs'] = djs(fat_cats[disjuncts], **kwargs) #TODO:
else:
fat_cats = gen_cats
# Learn Grammar
#+from src.grammar_learner.poc05 import induce_grammar
rules, re07 = induce_grammar(fat_cats, links)
if verbose == 'debug':
print('induce_grammar ⇒ rules:')
display(html_table([['Code','Parent','Id','Quality','Words', 'Disjuncts', 'djs','Relevance','Children']] \
+ [x for i,x in enumerate(cats2list(rules))]))
# Generalize grammar rules
gen_rules = rules
if 'rules_generalization' in kwargs:
if kwargs['rules_generalization'] not in ['', 'off']:
#-word_clusters, re06 = generalize_rules(rule_list, **kwargs)
from src.grammar_learner.generalization import generalize_rules
gen_rules, re08 = generalize_rules(rules, **kwargs)
log.update(re08)
if verbose == 'debug':
print('generalize_rules ⇒ gen_rules:')
display(html_table([['Code','Parent','Id','Quality','Words', 'Disjuncts', 'djs','Relevance','Children']] \
+ [x for i,x in enumerate(cats2list(gen_rules))]))
# Save cat_tree.txt file
#^from src.utl.write_files import save_cat_tree
re09 = save_cat_tree(gen_rules, output_categories,
verbose='none') #FIXME: verbose?
#TODO: check file save error?
log.update(re09)
# Save Link Grammar .dict
re10 = save_link_grammar(gen_rules, output_grammar, grammar_rules)
log.update(re10)
log.update({'finish': str(UTC())})
#TODO: elapsed execution time? Save log?
return log