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support_functions.py
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support_functions.py
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# -*- coding: utf-8 -*-
"""
Support functions for reference parsing
"""
__author__ = """Giovanni Colavizza"""
from collections import OrderedDict
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import learning_curve
from sklearn.metrics import make_scorer, accuracy_score, classification_report
from sklearn_crfsuite import metrics
import statistics
#
# BALANCED ERROR RATE calculations (a cost function for skewed datasets)
#
def BER(yn, ynhat):
"""
Implementation of Balanced Error Rate
:param yn: ground truth
:param ynhat: predicted values
:return: error score
"""
y = list()
for z in yn:
y.extend(z)
yhat = list()
for z in ynhat:
yhat.extend(z)
yn = np.array(y)
ynhat = np.array(yhat)
c = set(list(yn) + list(ynhat)) # set of unique classes
error = 0.0
numClasses = 0
for C in c:
if(len(np.array(yn == C)) != 0):
error += np.sum(np.array(yn == C) * np.array(yn != ynhat))/float(np.sum(np.array(yn == C)))
numClasses += 1
if numClasses == 0: return 1.0
error = error/numClasses
return error
def BER_vector(yn, ynhat):
"""
Implementation of Balanced Error Rate, returns a vector with errors for each class
:param yn: ground truth
:param ynhat: predicted values
:return: error score vector, scores for each class
"""
y = list()
for z in yn:
y.extend(z)
yhat = list()
for z in ynhat:
yhat.extend(z)
yn = np.array(y)
ynhat = np.array(yhat)
c = set(list(yn) + list(ynhat)) # set of unique classes
error = list()
classes = list()
for C in c:
if(np.sum(np.array(yn == C)) != 0):
error.append(np.sum(np.array(yn == C) * np.array(yn != ynhat))/float(np.sum(np.array(yn == C))))
classes.append(C)
return error, classes
def error_report(yn, ynhat):
"""
Helper function for error report.
:param yn: ground truth labels
:param ynhat: predicted labels
:return: Print error report
"""
print("Accuracy: ", accuracy_score(yn, ynhat))
print("Report: ", classification_report(yn, ynhat))
print("BER error: ", BER(yn,ynhat))
print("BER class error: ", BER_vector(yn,ynhat))
def flatten_predictions(y):
# Takes a list of list and returns a list
y_n = list()
for i in y:
y_n.extend(i)
return y_n
#
# PLOTTING
#
def plot_learning_curve(estimator, title, X, y, labels, ylim=None, cv=None,
n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5), message=""):
"""
FROM: https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html
Generate a simple plot of the test and traning learning curve.
Parameters
----------
estimator : object type that implements the "fit" and "predict" methods
An object of that type which is cloned for each validation.
title : string
Title for the chart.
X : array-like, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape (n_samples) or (n_samples, n_features), optional
Target relative to X for classification or regression;
None for unsupervised learning.
ylim : tuple, shape (ymin, ymax), optional
Defines minimum and maximum yvalues plotted.
cv : integer, cross-validation generator, optional
If an integer is passed, it is the number of folds (defaults to 3).
Specific cross-validation objects can be passed, see
sklearn.cross_validation module for the list of possible objects
n_jobs : integer, optional
Number of jobs to run in parallel (default 1).
"""
plt.figure()
plt.title(title)
if ylim is not None:
plt.ylim(*ylim)
plt.xlabel("Training examples")
plt.ylabel("Score")
# Define scorer
scorer = make_scorer(metrics.flat_f1_score,
average='weighted', labels=labels)
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, scoring=scorer)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
plt.grid()
plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
train_scores_mean + train_scores_std, alpha=0.1,
color="r")
plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
test_scores_mean + test_scores_std, alpha=0.1, color="g")
plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
label="Training score")
plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
label="Testing score")
plt.legend(loc="best")
#plt.savefig("plots/learning_curve_%s.pdf"%message)
return plt
#
# EXPORTS
#
# Takes an annotated or not page and dumps it for the lookup
def parse_page(page):
"""
Consolidates all the references parsed for a single page
:param page: A page in a doc, with specific and BET tags
:return: A list of consolidated references
"""
refs = list()
ref = dict()
list_of_generic_tags = list()
tag = ""
tag_start = 0
previous_end = 0
surface = ""
in_ref = False
counter = 1
continuation_candidate_out = False
continuation_candidate_in = False
first_ref = True
for n,token in enumerate(page["offsets"]):
if in_ref and n == len(page["offsets"]) -1:
continuation_candidate_out = True # side end, page 1
# check generic tag
if (page["BET_tags"][n].startswith("b") or page["BET_tags"][n].startswith("i")) and not in_ref:
in_ref = True
if first_ref and page["BET_tags"][n].startswith("i"):
continuation_candidate_in = True # side in, page 2
first_ref = False
else:
continuation_candidate_in = False
first_ref = False
generic = page["BET_tags"][n][2:]
list_of_generic_tags = [generic]
ref = {"reference_string": "", "continuation_candidate_out": continuation_candidate_out, "continuation_candidate_in": continuation_candidate_in,
"in_golden": page["is_annotated"], "ref_type": generic, "contents": OrderedDict(), "order_in_page": counter, "continuation": False}
continuation_candidate_in = False
surface = token[0][0]
tag = page["specific_tags"][n]
tag_start = token[0][1]
elif page["BET_tags"][n].startswith("b") and in_ref: # maybe incorrect boundaries, we split nevertheless
first_ref = False
ref["contents"].update({str(len(ref["contents"].keys())+1): {"tag":tag, "surface":surface,
"start":tag_start,"end":previous_end,
"single_page_file_number":int(page["single_page_file_number"]),
"page_id":page["page_id"],
"page_mongo_id": page["page_mongo_id"]}})
ref["continuation_candidate_out"] = continuation_candidate_out
try:
ref["ref_type"] = statistics.mode(list_of_generic_tags) # vote for most frequent generic tag
except:
pass # if mode not unique, just pick one
# add final global surface
ref["reference_string"] = " ".join([x["surface"] for x in ref["contents"].values()])
refs.append(ref)
counter += 1 # dumping
# new ref starting
generic = page["BET_tags"][n][2:]
list_of_generic_tags = [generic]
ref = {"reference_string": "", "continuation_candidate_out": continuation_candidate_out, "continuation_candidate_in": continuation_candidate_in,
"in_golden": page["is_annotated"], "ref_type": generic, "contents": OrderedDict(), "order_in_page": counter, "continuation": False}
continuation_candidate_in = False
surface = token[0][0]
tag = page["specific_tags"][n]
tag_start = token[0][1]
elif page["BET_tags"][n].startswith("o") and in_ref: # DUMP if outside
ref["contents"].update({str(len(ref["contents"].keys())+1): {"tag": tag, "surface": surface,
"start": tag_start, "end": previous_end,
"single_page_file_number": int(
page["single_page_file_number"]),
"page_id": page["page_id"],
"page_mongo_id": page["page_mongo_id"]}})
ref["continuation_candidate_out"] = continuation_candidate_out
try:
ref["ref_type"] = statistics.mode(list_of_generic_tags) # vote for most frequent generic tag
except:
pass # if mode not unique, just pick one
# add final global surface
ref["reference_string"] = " ".join([x["surface"] for x in ref["contents"].values()])
refs.append(ref)
counter += 1 # dumping
in_ref = False
elif page["BET_tags"][n].startswith("e") and in_ref: #DUMP if end
list_of_generic_tags.append(page["BET_tags"][n][2:])
if page["specific_tags"][n] != tag:
# dump ref before and create a new one
ref["contents"].update({str(len(ref["contents"].keys())+1): {"tag": tag, "surface": surface,
"start": tag_start, "end": previous_end,
"single_page_file_number": int(page["single_page_file_number"]),
"page_id": page["page_id"],
"page_mongo_id": page["page_mongo_id"]}})
ref["continuation_candidate_out"] = continuation_candidate_out
tag = page["specific_tags"][n]
surface = token[0][0]
tag_start = token[0][1]
ref["contents"].update({str(len(ref["contents"].keys())+1): {"tag": tag, "surface": surface,
"start": tag_start, "end": token[0][2],
"single_page_file_number": int(page["single_page_file_number"]),
"page_id": page["page_id"],
"page_mongo_id": page["page_mongo_id"]}})
ref["continuation_candidate_out"] = continuation_candidate_out
else:
# update ref before and dump it
surface = surface+" "+token[0][0]
ref["contents"].update({str(len(ref["contents"].keys())+1): {"tag": tag, "surface": surface,
"start": tag_start, "end": token[0][2],
"single_page_file_number": int(
page["single_page_file_number"]),
"page_id": page["page_id"],
"page_mongo_id": page["page_mongo_id"]}})
ref["continuation_candidate_out"] = continuation_candidate_out
# dump full reference
try:
ref["ref_type"] = statistics.mode(list_of_generic_tags) # vote for most frequent generic tag
except:
pass # if mode not unique, just pick one
# add final global surface
ref["reference_string"] = " ".join([x["surface"] for x in ref["contents"].values()])
refs.append(ref)
counter += 1 # dumping
in_ref = False
elif in_ref:
list_of_generic_tags.append(page["BET_tags"][n][2:])
if page["specific_tags"][n] != tag and len(page["specific_tags"][n]) > 0:
# dump ref before and create a new one
ref["contents"].update({str(len(ref["contents"].keys())+1): {"tag": tag, "surface": surface,
"start": tag_start, "end": previous_end,
"single_page_file_number": int(
page["single_page_file_number"]),
"page_id": page["page_id"],
"page_mongo_id": page["page_mongo_id"]}})
ref["continuation_candidate_out"] = continuation_candidate_out
tag = page["specific_tags"][n]
surface = token[0][0]
tag_start = token[0][1]
else:
# update surface before
surface = surface+" "+token[0][0]
previous_end = token[0][2]
return refs
# dump all annotated references to json, consolidating continuations
def process_doc(doc):
"""
Process a single doc
:param doc: A doc from parsing
:return: The same doc with all its references consolidated, and a list of possible continuations for that doc. A doc is an issue or a bid/monograph (i.e. single volume)
"""
doc["references"] = list()
possible_continuations = list()
for page in doc["pages"].values():
doc["references"].extend(parse_page(page))
# process continuations
cont_refs_out = [(n,x) for n,x in enumerate(doc["references"]) if x["continuation_candidate_out"]]
cont_refs_in = [(n,x) for n,x in enumerate(doc["references"]) if x["continuation_candidate_in"]]
for r_out in cont_refs_out:
if len(r_out[1]["contents"].keys()) < 1:
continue
for r_in in cont_refs_in:
if len(r_in[1]["contents"].keys()) < 1:
continue
try:
if (r_out[1]["contents"]['1']["single_page_file_number"] == r_in[1]["contents"]['1']["single_page_file_number"] -1) and r_out[1]["ref_type"] == r_in[1]["ref_type"] and not r_out[1]["contents"][str(len(r_out[1]["contents"].keys()))]["tag"] == "pagination":
# MERGE
prev_len = len(r_out[1]["contents"].keys())
for n,token in list(r_in[1]["contents"].items()):
doc["references"][r_out[0]]["contents"].update({str(prev_len+int(n)+1): token})
doc["references"][r_out[0]]["continuation"] = True
del doc["references"][r_in[0]]
possible_continuations.append((r_out,r_in))
except:
pass # skip merging if problems arise
return doc, possible_continuations
from multiprocessing import Pool
from datetime import datetime
def json_outputter(data,threads=7):
"""
Exports a json-like version of the consolidated, parsed references
:param data: parsed data
:param threads: number of threads to use
:return: list of refs per doc, list of refs, list of consolidations
"""
threads = Pool(threads)
data_json = list()
data_json_references = list()
pc_list = list()
for doc, pc in threads.imap_unordered(process_doc, data):
data_json.append(doc)
pc_list.extend(pc)
for reference in doc["references"]:
reference["bid"] = doc["bid"]
reference["issue"] = doc["doc_number"]
reference["updated_at"] = datetime.now()
data_json_references.append(reference)
return data_json, data_json_references, pc_list