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dante-verification
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Alejandro Moreo Fernandez 2019-06-04 09:05:25 +02:00
parent 13d9a5ed57
commit 67adaa441c
5 changed files with 393 additions and 25 deletions
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95
src/author_attribution.py Normal file → Executable file
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@ -1,3 +1,4 @@
from mpl_toolkits.axes_grid1 import make_axes_locatable
from sklearn.linear_model import LogisticRegression from sklearn.linear_model import LogisticRegression
from data.dante_loader import load_texts from data.dante_loader import load_texts
from data.features import * from data.features import *
@ -6,60 +7,104 @@ import numpy as np
import matplotlib import matplotlib
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
def plot_attribution(path, authors, attributions, paragraph_offset=1): def plot_attribution(path, authors, attributions, paragraph_offset=1, figsize=(5,5), label_offset=0.3):
paragraphs = ["Full"] + [f'{paragraph_offset+i}' for i in range(attributions.shape[1]-1)] attributions = attributions.T
print(attributions.shape)
# attributions=attributions>0.5
paragraphs = ["Full"] + [f'{paragraph_offset+i}' for i in range(attributions.shape[0]-1)]
fig, ax = plt.subplots() fig, ax = plt.subplots(figsize=figsize)
im = ax.imshow(attributions)
# im = ax.imshow(attributions, vmin=0, vmax=1, cmap='Greens')
im = ax.imshow(attributions, vmin=0, vmax=1, cmap='Greys')
# Create colorbar
# cbar = fig.colorbar(im, ax=ax, orientation="horizontal", fraction=0.1, pad=0.04)
# ax.figure.colorbar(im, ax=ax, orientation="horizontal", fraction=0.1)
# ax.figure.colorbar(im, ax=ax, orientation="horizontal", pad=0.05)
# cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom")
# We want to show all ticks... # We want to show all ticks...
ax.set_xticks(np.arange(len(paragraphs))) # ax.set_xticks(np.arange(len(authors)))
ax.set_yticks(np.arange(len(authors))) ax.set_xticks(np.arange(len(authors) + 0) + label_offset)
ax.set_yticks(np.arange(len(paragraphs)))
# ... and label them with the respective list entries # ... and label them with the respective list entries
ax.set_xticklabels(paragraphs) ax.set_xticklabels(authors)
ax.set_yticklabels(authors) ax.set_yticklabels(paragraphs)
ax.tick_params(top=False, bottom=False,
labeltop=True, labelbottom=False)
# Rotate the tick labels and set their alignment. # Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right", plt.setp(ax.get_xticklabels(), rotation=90, ha="left", rotation_mode="anchor")
rotation_mode="anchor")
for edge, spine in ax.spines.items():
spine.set_visible(False)
ax.set_xticks(np.arange(len(authors)+1) - .5, minor=True)
ax.set_yticks(np.arange(len(paragraphs)+1) - .5, minor=True)
ax.grid(which="minor", color="k", linestyle='-', linewidth=1)
ax.tick_params(which="minor", bottom=False, left=False)
# Loop over data dimensions and create text annotations. # Loop over data dimensions and create text annotations.
for i in range(len(authors)): # for i in range(len(authors)):
for j in range(len(paragraphs)): # for j in range(len(paragraphs)):
text = ax.text(j, i, f'{attributions[i, j]:.2f}', ha="center", va="center", color="w") # text = ax.text(j, i, f'{attributions[i, j]:.2f}', ha="center", va="center", color="w")
ax.set_title("Attribution matrix") # ax.set_title("Attribution matrix")
fig.tight_layout() fig.tight_layout()
# plt.show() # plt.show()
plt.savefig(path) plt.savefig(path)
import sys import sys
authors = ['Dante', 'ClaraAssisiensis', 'GiovanniBoccaccio', 'GuidoFaba', 'PierDellaVigna'] for epistola in [1]:
attributions = np.load('attribution_ep1.npy') if epistola == 1:
plot_attribution('plot1.pdf', authors, attributions) authors = ['Dante', 'ClaraAssisiensis', 'GiovanniBoccaccio', 'GuidoFaba', 'PierDellaVigna']
paragraph_offset = 1
figsize=(3,9)
label_offset=0.2
else:
authors = ['Dante', 'BeneFlorentinus', 'BenvenutoDaImola', 'BoncompagnoDaSigna',
'FilippoVillani', 'GiovanniBoccaccio', 'GiovanniDelVirgilio',
'GrazioloBambaglioli', 'GuidoDaPisa',
'GuidoDeColumnis', 'GuidoFaba', 'IacobusDeVaragine', 'IohannesDeAppia',
'IohannesDePlanoCarpini', 'IulianusDeSpira', 'NicolaTrevet',
'PietroAlighieri', 'RaimundusLullus',
'RyccardusDeSanctoGermano', 'ZonoDeMagnalis']
paragraph_offset = 14
figsize = (6,20)
label_offset=0.3
attributions = np.load(f'attribution_ep{epistola}.npy')
plot_attribution(f'plot{epistola}.png', authors, attributions, paragraph_offset=paragraph_offset, figsize=figsize, label_offset=label_offset)
sys.exit(0) sys.exit(0)
author_attribution = []
for epistola in [1]: for epistola in [1]:
author_attribution = []
print(f'Epistola {epistola}') print(f'Epistola {epistola}')
print('='*80) print('='*80)
path = f'../testi_{epistola}' path = f'../testi_{epistola}'
if epistola == 1: if epistola == 1:
authors = ['Dante', 'ClaraAssisiensis', 'GiovanniBoccaccio', 'GuidoFaba', 'PierDellaVigna'] authors = ['Dante', 'ClaraAssisiensis', 'GiovanniBoccaccio', 'GuidoFaba', 'PierDellaVigna']
paragraphs = range(1,3) paragraphs = range(1,14)
else: else:
authors = ['Dante', 'BeneFlorentinus', 'BenvenutoDaImola', 'BoncompagnoDaSigna', 'ClaraAssisiensis', authors = ['Dante', 'BeneFlorentinus', 'BenvenutoDaImola', 'BoncompagnoDaSigna',
'FilippoVillani', 'GiovanniBoccaccio', 'GiovanniDelVirgilio', 'FilippoVillani', 'GiovanniBoccaccio', 'GiovanniDelVirgilio',
'GrazioloBambaglioli', 'GuidoDaPisa', 'GrazioloBambaglioli', 'GuidoDaPisa',
'GuidoDeColumnis', 'GuidoFaba', 'IacobusDeVaragine', 'IohannesDeAppia', 'GuidoDeColumnis', 'GuidoFaba', 'IacobusDeVaragine', 'IohannesDeAppia',
'IohannesDePlanoCarpini', 'IulianusDeSpira', 'NicolaTrevet', 'PierDellaVigna', 'IohannesDePlanoCarpini', 'IulianusDeSpira', 'NicolaTrevet',
'PietroAlighieri', 'RaimundusLullus', 'PietroAlighieri', 'RaimundusLullus',
'RyccardusDeSanctoGermano', 'ZonoDeMagnalis'] 'RyccardusDeSanctoGermano', 'ZonoDeMagnalis']
paragraphs = range(13, 90) paragraphs = range(14, 91)
assert len(authors)==20, f'unexpected number of authors ({len(authors)})'
path+='_tutti'
discarded = 0 discarded = 0
f1_scores = [] f1_scores = []
@ -72,9 +117,9 @@ for epistola in [1]:
target = [f'EpistolaXIII_{epistola}.txt'] + [f'EpistolaXIII_{epistola}_{paragraph}.txt' for paragraph in paragraphs] target = [f'EpistolaXIII_{epistola}.txt'] + [f'EpistolaXIII_{epistola}_{paragraph}.txt' for paragraph in paragraphs]
positive, negative, ep_texts = load_texts(path, positive_author=author, unknown_target=target) positive, negative, ep_texts = load_texts(path, positive_author=author, unknown_target=target)
if len(positive) < 2: # if len(positive) < 2:
discarded += 1 # discarded += 1
continue # continue
n_full_docs = len(positive) + len(negative) n_full_docs = len(positive) + len(negative)

152
src/author_attribution_XIV.py Executable file
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@ -0,0 +1,152 @@
from mpl_toolkits.axes_grid1 import make_axes_locatable
from sklearn.linear_model import LogisticRegression
from data.dante_loader import load_texts
from data.features import *
from model import AuthorshipVerificator, f1_from_counters
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
def plot_attribution(path, authors, attributions, paragraph_offset=1, figsize=(5,5), label_offset=0.3):
attributions = attributions.T
print(attributions.shape)
# attributions=attributions>0.5
paragraphs = ["Full"] + [f'{paragraph_offset+i}' for i in range(attributions.shape[0]-1)]
fig, ax = plt.subplots(figsize=figsize)
# im = ax.imshow(attributions, vmin=0, vmax=1, cmap='Greens')
im = ax.imshow(attributions, vmin=0, vmax=1, cmap='Greys')
# Create colorbar
# cbar = fig.colorbar(im, ax=ax, orientation="horizontal", fraction=0.1, pad=0.04)
# ax.figure.colorbar(im, ax=ax, orientation="horizontal", fraction=0.1)
# ax.figure.colorbar(im, ax=ax, orientation="horizontal", pad=0.05)
# cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom")
# We want to show all ticks...
# ax.set_xticks(np.arange(len(authors)))
ax.set_xticks(np.arange(len(authors) + 0) + label_offset)
ax.set_yticks(np.arange(len(paragraphs)))
# ... and label them with the respective list entries
ax.set_xticklabels(authors)
ax.set_yticklabels(paragraphs)
ax.tick_params(top=False, bottom=False,
labeltop=True, labelbottom=False)
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=90, ha="left", rotation_mode="anchor")
for edge, spine in ax.spines.items():
spine.set_visible(False)
ax.set_xticks(np.arange(len(authors)+1) - .5, minor=True)
ax.set_yticks(np.arange(len(paragraphs)+1) - .5, minor=True)
ax.grid(which="minor", color="k", linestyle='-', linewidth=1)
ax.tick_params(which="minor", bottom=False, left=False)
# Loop over data dimensions and create text annotations.
# for i in range(len(authors)):
# for j in range(len(paragraphs)):
# text = ax.text(j, i, f'{attributions[i, j]:.2f}', ha="center", va="center", color="w")
# ax.set_title("Attribution matrix")
fig.tight_layout()
# plt.show()
plt.savefig(path)
import sys
authors1 = ['ClaraAssisiensis', 'GiovanniBoccaccio', 'GuidoFaba', 'PierDellaVigna']
authors2 = ['BeneFlorentinus', 'BenvenutoDaImola', 'BoncompagnoDaSigna',
'FilippoVillani', 'GiovanniBoccaccio', 'GiovanniDelVirgilio',
'GrazioloBambaglioli', 'GuidoDaPisa',
'GuidoDeColumnis', 'GuidoFaba', 'IacobusDeVaragine', 'IohannesDeAppia',
'IohannesDePlanoCarpini', 'IulianusDeSpira', 'NicolaTrevet',
'PietroAlighieri', 'RaimundusLullus',
'RyccardusDeSanctoGermano', 'ZonoDeMagnalis']
authors3 = sorted(np.unique(authors1 + authors2).tolist())
for epistola in [1]:
paragraph_offset = 1
label_offset = 0.2
if epistola == 1:
authors = ['Dante'] + authors1
figsize = (4, 4)
elif epistola == 2:
authors = ['Dante'] + authors2
figsize = (6, 4)
else:
authors = ['Dante'] + authors3
attributions = np.load(f'attribution_ep{epistola}_xiv.npy')
plot_attribution(f'plot{epistola}_xiv.png', authors, attributions, paragraph_offset=paragraph_offset, figsize=figsize, label_offset=label_offset)
sys.exit(0)
for epistola in [1]:
author_attribution = []
print(f'Epistola {epistola}')
print('='*80)
path = f'../testiXIV_{epistola}'
if epistola == 1:
authors = ['Dante'] + authors1
elif epistola == 2:
authors = ['Dante'] + authors2
path += '_tutti'
else:
authors = ['Dante'] + authors3
discarded = 0
f1_scores = []
counters = []
for i, author in enumerate(authors):
print('=' * 80)
print('Authorship Identification for {} (complete {}/{})'.format(author, i, len(authors)))
print('Corpus of Epistola {}'.format(epistola))
print('=' * 80)
target = [f'Epistola_ArigoVII.txt'] + [f'Epistola_ArigoVII_{paragraph}.txt' for paragraph in range(1,6)]
positive, negative, ep_texts = load_texts(path, positive_author=author, unknown_target=target, train_skip_prefix='Epistola_ArigoVII')
n_full_docs = len(positive) + len(negative)
feature_extractor = FeatureExtractor(function_words_freq='latin',
conjugations_freq='latin',
features_Mendenhall=True,
features_sentenceLengths=True,
tfidf_feat_selection_ratio=0.1,
wordngrams=True, n_wordngrams=(1, 2),
charngrams=True, n_charngrams=(3, 4, 5),
preserve_punctuation=False,
split_documents=True, split_policy=split_by_sentences, window_size=3,
normalize_features=True)
Xtr, ytr, groups = feature_extractor.fit_transform(positive, negative)
print('Fitting the Verificator')
av = AuthorshipVerificator(nfolds=10, estimator=LogisticRegression, author_name=author)
av.fit(Xtr, ytr, groups)
attributions=[]
for i,target_text in enumerate(ep_texts):
ep = feature_extractor.transform(target_text, avoid_splitting=True)
prob,_ = av.predict_proba(ep, epistola_name=target[i])
attributions.append(prob)
author_attribution.append(attributions)
author_attribution = np.asarray(author_attribution)
attribution_path = f'attribution_ep{epistola}_xiv.npy'
print(f'saving attribution matrix of shape {author_attribution.shape} in {attribution_path}')
np.save(attribution_path, author_attribution)

77
src/author_verification_XIV.py Executable file
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@ -0,0 +1,77 @@
from sklearn.linear_model import LogisticRegression
from data.dante_loader import load_texts
from data.features import *
from model import AuthorshipVerificator, f1_from_counters
from sklearn.svm import LinearSVC, SVC
from util.color_visualization import color
import pickle
import os
for epistola in [1,2,3]: #3 means "both Ep1 and Ep2 corpora"
print('Epistola {}'.format(epistola))
print('='*80)
path = '../testiXIV_{}'.format(epistola)
paragraphs = range(1, 6)
if epistola==2:
path+='_tutti'
target = [f'Epistola_ArigoVII.txt'] + [f'Epistola_ArigoVII_{paragraph}.txt' for paragraph in paragraphs]
positive, negative, ep_texts = load_texts(path, positive_author='Dante', unknown_target=target, train_skip_prefix='Epistola_ArigoVII')
pickle_file = f'../dante_color/epistola{epistola}_xiv.pkl'
if os.path.exists(pickle_file):
print(f'loading pickle file {pickle_file}')
probabilities = pickle.load(open(pickle_file, 'rb'))
else:
print(f'generating pickle file')
n_full_docs = len(positive) + len(negative)
feature_extractor = FeatureExtractor(function_words_freq='latin',
conjugations_freq='latin',
features_Mendenhall=True,
features_sentenceLengths=True,
tfidf_feat_selection_ratio=0.1,
wordngrams=True, n_wordngrams=(1, 2),
charngrams=True, n_charngrams=(3, 4, 5),
preserve_punctuation=False,
split_documents=True, split_policy=split_by_sentences, window_size=3,
normalize_features=True)
Xtr,ytr,groups = feature_extractor.fit_transform(positive, negative)
print(ytr)
print('Fitting the Verificator')
av = AuthorshipVerificator(nfolds=10, estimator=LogisticRegression, author_name='Dante')
av.fit(Xtr,ytr,groups)
probabilities = []
for i, target_text in enumerate(ep_texts):
ep = feature_extractor.transform(target_text, avoid_splitting=True)
prob, _ = av.predict_proba(ep, epistola_name=target[i])
probabilities.append(prob)
pickle.dump(probabilities, open(pickle_file, 'wb'), pickle.HIGHEST_PROTOCOL)
color(path=f'../dante_color/epistola{epistola}_xiv.html', texts=ep_texts,
probabilities=probabilities, title=f'Epistola {epistola}',
paragraph_offset=paragraphs[0])
# print('Predicting the Epistola {}'.format(epistola))
# title = 'Epistola {}'.format('I' if epistola==1 else 'II')
# av.predict(ep, title)
# fulldoc_prob, fragment_probs = av.predict_proba(ep, title)
# color(path='../dante_color/epistola{}.html'.format(epistola), texts=ep_fragments, probabilities=fragment_probs, title=title)
# score_ave, score_std = av.leave_one_out(Xtr, ytr, groups, test_lowest_index_only=False)
# print('LOO[full-and-fragments]={:.3f} +-{:.5f}'.format(score_ave, score_std))
# score_ave, score_std, tp, fp, fn, tn = av.leave_one_out(Xtr, ytr, groups, test_lowest_index_only=True, counters=True)
# print('LOO[full-docs]={:.3f} +-{:.5f}'.format(score_ave, score_std))
# f1_ = f1_from_counters(tp, fp, fn, tn)
# print('F1 = {:.3f}'.format(f1_))
# score_ave, score_std = av.leave_one_out(Xtr, ytr, None)
# print('LOO[w/o groups]={:.3f} +-{:.5f}'.format(score_ave, score_std))

35
src/util/colormap.py Executable file
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import numpy as np
import matplotlib.pyplot as plt
# Have colormaps separated into categories:
# http://matplotlib.org/examples/color/colormaps_reference.html
cmaps = [('Diverging', ['RdYlGn','RdYlGn']),]
nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps)
gradient = np.linspace(0.25, 0.75, 256)
gradient = np.vstack((gradient, gradient))
def plot_color_gradients(cmap_category, cmap_list, nrows):
fig, axes = plt.subplots(nrows=nrows)
fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)
axes[0].set_title(cmap_category + ' colormaps', fontsize=14)
for ax, name in zip(axes, cmap_list):
ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))
pos = list(ax.get_position().bounds)
x_text = pos[0] - 0.01
y_text = pos[1] + pos[3]/2.
fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10)
# Turn off *all* ticks & spines, not just the ones with colormaps.
for ax in axes:
ax.set_axis_off()
for cmap_category, cmap_list in cmaps:
plot_color_gradients(cmap_category, cmap_list, nrows)
plt.show()

59
src/weight_inspection.py Executable file
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@ -0,0 +1,59 @@
from sklearn.linear_model import LogisticRegression
from data.dante_loader import load_texts
from data.features import *
from model import AuthorshipVerificator, f1_from_counters
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
for epistola in [2]:
author_attribution = []
print(f'Epistola {epistola}')
print('='*80)
path = f'../testi_{epistola}'
if epistola==2: path+='_tutti'
author = 'Dante'
print('=' * 80)
print('Corpus of Epistola {}'.format(epistola))
print('=' * 80)
positive, negative, ep_texts = load_texts(path, positive_author=author, unknown_target=f'EpistolaXIII_{epistola}.txt')
n_full_docs = len(positive) + len(negative)
feature_extractor = FeatureExtractor(function_words_freq='latin',
conjugations_freq='latin',
features_Mendenhall=True,
features_sentenceLengths=True,
tfidf_feat_selection_ratio=0.1,
wordngrams=True, n_wordngrams=(1, 2),
charngrams=True, n_charngrams=(3, 4, 5),
preserve_punctuation=False,
split_documents=True, split_policy=split_by_sentences, window_size=3,
normalize_features=True)
Xtr, ytr, groups = feature_extractor.fit_transform(positive, negative)
print('Fitting the Verificator')
av = AuthorshipVerificator(nfolds=10, estimator=LogisticRegression, author_name=author)
av.fit(Xtr, ytr, groups)
feat_rank = np.argsort(av.estimator.coef_[0])
coef_ordered = av.estimator.coef_[0][feat_rank]
feat_name_ordered = feature_extractor.feature_names[feat_rank]
print('Most Dantesque features::')
for i in range(100):
print(f'{i}: {feat_name_ordered[::-1][i]} {coef_ordered[::-1][i]:.3f}')
print('\nMost Non-Dantesque features::')
for i in range(100):
print(f'{i}: {feat_name_ordered[i]} {coef_ordered[i]:.3f}')
print('done')