def crf(test_loc, train_loc):
test_sents = convertCONLLFormJustExtractionSemEval(test_loc)
train_sents = convertCONLLFormJustExtractionSemEval(train_loc)
#pprint(train_sents[0])
#pprint(test_sents[0])
X_train = [sent2features(s) for s in train_sents]
y_train = [sent2labels(s) for s in train_sents]
X_test = [sent2features(s) for s in test_sents]
y_test = [sent2labels(s) for s in test_sents]
crf = sklearn_crfsuite.CRF(\
algorithm='lbfgs',\
c1=0.1,\
c2=0.1,\
max_iterations=100,\
all_possible_transitions=True
)
crf.fit(X_train, y_train)
labels = list(crf.classes_)
labels.remove('O')
#print(labels)
pickle.dump(crf,
open("/data/xwang/models_origin/linear-chain-crf.model.pickle",
"wb"),
protocol=0,
fix_imports=True)
y_pred = crf.predict(X_test)
sorted_labels = sorted(labels, key=lambda name: (name[1:], name[0]))
f1_score = metrics.flat_f1_score(y_test,
y_pred,
average='weighted',
labels=sorted_labels)
recall = metrics.flat_recall_score(y_test,
y_pred,
average='weighted',
labels=sorted_labels)
precision = metrics.flat_precision_score(y_test,
y_pred,
average='weighted',
labels=sorted_labels)
#print(metrics.flat_classification_report(y_test, y_pred, labels=sorted_labels, digits=3))
return (f1_score, recall, precision)
import bs4 as bs
import json
import re
import nltk
import heapq
import pickle
import sys
from pprint import pprint
import os
import codecs
import string
from sklearn_crfsuite import metrics
from DataExtraction import convertCONLLFormJustExtractionSemEvalPerfile
from FeatureExtraction import sent2labels, sent2features
from PhraseEval import phrasesFromTestSenJustExtractionWithIndex
from nltk import word_tokenize, pos_tag, ne_chunk
from nltk import conlltags2tree, tree2conlltags
#Swapped hardcoded link with system arguement
file_inLoc = sys.argv[1]
file_outLoc = sys.argv[1].split(".")[0] + "-DKE.txt"
file_outLoc = file_outLoc[15:]
with open(file_inLoc, 'r', encoding='utf-8-sig') as f:
article_text = json.load(f)
pprint(article_text)
'''
scraped_data = urllib.request.Request(file_inLoc, headers={'User-Agent' : "Magic Browser"})
scraped_data=urllib.request.urlopen(scraped_data)
parsed_article = bs.BeautifulSoup(article,'lxml')
paragraphs = parsed_article.find_all('p')
article_text = ""
import sys
from pprint import pprint
from sklearn_crfsuite import metrics
from DataExtraction import convertCONLLFormJustExtractionSemEvalPerfile
from FeatureExtraction import sent2labels,sent2features
from PhraseEval import phrasesFromTestSenJustExtractionWithIndex
fileinLoc = sys.argv[1]
fileoutLoc = sys.argv[1].split("-")[0]+"-predicted.ann"
crf = pickle.load(open("linear-chain-crf.model.pickle"))
(test_sents,test_sents_indices) = convertCONLLFormJustExtractionSemEvalPerfile(fileinLoc)
X_test = [sent2features(s) for s in test_sents]
y_test = [sent2labels(s) for s in test_sents]
y_pred = crf.predict(X_test)
labels = list(crf.classes_)
labels.remove('O')
print labels
sorted_labels = sorted(labels,key=lambda name: (name[1:], name[0]))
print(metrics.flat_classification_report(y_test, y_pred, labels=sorted_labels, digits=3))
test_sents_pls = [] #test sentences with predicted labels
for index,testsent in enumerate(test_sents):
sent=[]
pls = y_pred[index]
for i in range(len(iob_tags2)):
#iob_tags3=[]
c = iob_tags2[i][2]
if c[0] == 'B':
iob_tags1 = (iob_tags2[i][0], iob_tags2[i][1], 'B')
elif c[0] == 'I':
iob_tags1 = (iob_tags2[i][0], iob_tags2[i][1], 'I')
else:
iob_tags1 = (iob_tags2[i][0], iob_tags2[i][1], 'o')
iob_tags3.append(iob_tags1)
iob_tags4.append(iob_tags3)
#print(' **** Preprocessed Text given as input to model *******')
#print(iob_tags4)
X_test = [sent2features(s) for s in iob_tags4]
crf = pickle.load(open("linear-chain-crf.model.pickle"))
y_pred = crf.predict(X_test)
labels = list(crf.classes_)
labels.remove('O')
#print labels
#print y_pred
test_sents_pls = [] #test sentences with predicted labels
for index, testsent in enumerate(iob_tags4):
sent = []
pls = y_pred[index]
for (token, pl) in zip(testsent, pls):
def main():
train_sents = convertCONLLFormJustExtractionSemEval(
"medicalData/convertedBIO/combinedTrain.txt")
test_sents = convertCONLLFormJustExtractionSemEval(
"medicalData/convertedBIO/combinedTest.txt")
pprint(train_sents[0])
pprint(test_sents[0])
X_train = [sent2features(s) for s in train_sents]
y_train = [sent2labels(s) for s in train_sents]
X_test = [sent2features(s) for s in test_sents]
y_test = [sent2labels(s) for s in test_sents]
crf = sklearn_crfsuite.CRF(\
algorithm='lbfgs',\
c1=0.1,\
c2=0.1,\
max_iterations=100,\
all_possible_transitions=True
)
crf.fit(X_train, y_train)
labels = list(crf.classes_)
labels.remove('O')
print(labels)
pickle.dump(crf,
open("medicalData/linear-chain-crf.model.pickle", "wb"),
protocol=0,
fix_imports=True)
y_pred = crf.predict(X_test)
# Use this if you need to do grid search on training data for parameter optimization.
'''
crf = sklearn_crfsuite.CRF(
algorithm='lbfgs',
max_iterations=100,
all_possible_transitions=True
)
params_space = {
'c1': scipy.stats.expon(scale=0.5),
'c2': scipy.stats.expon(scale=0.05),
}
# use the same metric for evaluation
f1_scorer = make_scorer(metrics.flat_f1_score,
average='weighted', labels=labels)
# search
rs = RandomizedSearchCV(crf, params_space,
cv=3,
verbose=1,
n_jobs=-1,
n_iter=50,
scoring=f1_scorer)
rs.fit(X_train, y_train)
print("classification done")
crf = rs.best_estimator_
y_pred = crf.predict(X_test)
'''
sorted_labels = sorted(labels, key=lambda name: (name[1:], name[0]))
print(
metrics.flat_classification_report(y_test,
y_pred,
labels=sorted_labels,
digits=3))
# Use this if you want to see how the phrase extraction works. This does NOT produce the ann files.
'''
def main(trainingCorpus):
# trainingCorpus = 'larger'
# trainingCorpus = 'original'
trainFile = "medicalData/convertedBIO/" + trainingCorpus + "/combinedTrain.txt"
testFile = "medicalData/convertedBIO/" + trainingCorpus + "/combinedTest.txt"
train_sents = convertCONLLFormJustExtractionSemEval(trainFile)
test_sents = convertCONLLFormJustExtractionSemEval(testFile)
# pprint(train_sents[0])
# print('\n')
# pprint(test_sents[0])
# print('\n')
X_train = [sent2features(s) for s in train_sents]
y_train = [sent2labels(s) for s in train_sents]
X_test = [sent2features(s) for s in test_sents]
y_test = [sent2labels(s) for s in test_sents]
#%%
c1 = 0.1
c2 = 0.1
crf = sklearn_crfsuite.CRF(\
algorithm='lbfgs',\
c1=c1,\
c2=c2,\
max_iterations=100,\
all_possible_transitions=True
)
crf.fit(X_train, y_train)
if trainingCorpus == 'larger':
labels = list(crf.classes_)
labels.remove('O')
labels.remove('1')
labels.remove('3')
labels.remove('8')
labels.remove('9')
pickle.dump(
crf,
open(
"medicalData/larger/unoptimized/linear-chain-crf.model.pickle",
"wb"),
protocol=0,
fix_imports=True)
elif trainingCorpus == 'original':
labels = list(crf.classes_)
labels.remove('O')
pickle.dump(
crf,
open(
"medicalData/original/unoptimized/linear-chain-crf.model.pickle",
"wb"),
protocol=0,
fix_imports=True)
# print(labels)
print('\n')
y_pred = crf.predict(X_test)
sorted_labels = sorted(labels, key=lambda name: (name[1:], name[0]))
print('\nTest Results (Original Model, Training Corpus: ' +
trainingCorpus + ')')
print('c1 = %s, c2 = %s' % (c1, c2))
print(
metrics.flat_classification_report(y_test,
y_pred,
labels=sorted_labels,
digits=3))
# y_predT = crf.predict(X_train)
# print('\nTrain Results (Original Model, Training Corpus: ' + trainingCorpus + ')')
# print('c1 = %s, c2 = %s' %(c1, c2))
# print(metrics.flat_classification_report(y_train, y_predT, labels=sorted_labels, digits=3))
#%%
# '''
# define fixed parameters and parameters to search
def report(results, n_top=15):
print("")
for i in range(1, n_top + 1):
candidates = np.flatnonzero(results['rank_test_score'] == i)
print(candidates)
for candidate in candidates:
print("Model with rank: {0}".format(i))
print("Mean validation score: {0:.3f} (std: {1:.3f})".format(
results['mean_test_score'][candidate],
results['std_test_score'][candidate]))
print("Parameters: {0}".format(results['params'][candidate]))
print("")
crf = sklearn_crfsuite.CRF(algorithm='lbfgs',
max_iterations=100,
all_possible_transitions=True)
# use the same metric for evaluation
f1_scorer = make_scorer(metrics.flat_f1_score,
average='weighted',
labels=labels)
# params_space = {
# 'c1': scipy.stats.expon(scale=0.5),
# 'c2': scipy.stats.expon(scale=0.5),
# }
# search
# rs = RandomizedSearchCV(crf, params_space,
# cv=3,
# verbose=1,
# n_jobs=-1,
# n_iter=10,
# return_train_score=True,
# scoring=f1_scorer)
param_grid = {
'c1': [0.10, 0.20, 0.30, 0.40],
'c2': [0.10, 0.20, 0.30, 0.40],
}
numSplits = 5
rs = GridSearchCV(crf,
param_grid,
cv=numSplits,
verbose=-1,
n_jobs=-1,
return_train_score=True,
scoring=f1_scorer)
rs.fit(X_train, y_train)
report(rs.cv_results_)
# print(rs.cv_results_['params'])
# print(rs.cv_results_['split0_test_score'])
# print(rs.cv_results_['split1_test_score'])
# print(rs.cv_results_['split2_test_score'])
# print(rs.cv_results_['split3_test_score'])
# print(rs.cv_results_['split4_test_score'])
split1 = (np.array([rs.cv_results_['split0_test_score']])).T
split2 = (np.array([rs.cv_results_['split0_test_score']])).T
split3 = (np.array([rs.cv_results_['split0_test_score']])).T
split4 = (np.array([rs.cv_results_['split0_test_score']])).T
split5 = (np.array([rs.cv_results_['split0_test_score']])).T
splitCompiled = np.hstack((split1, split2, split3, split4, split5))
_x = [s['c1'] for s in rs.cv_results_['params']]
_y = [s['c2'] for s in rs.cv_results_['params']]
_c = [s for s in rs.cv_results_['mean_test_score']]
fig = plt.figure()
fig.set_size_inches(3, 3)
ax = plt.gca()
# ax.set_yscale('log')
# ax.set_xscale('log')
ax.set_xlabel('C1')
ax.set_ylabel('C2')
# ax.set_title("Randomized Hyperparameter Search CV Results (min={:0.3}, max={:0.3})".format(min(_c), max(_c)))
ax.set_title(
"Hyperparameter Gridsearch CV Results \n (min={:0.3}, max={:0.3})".
format(min(_c), max(_c)))
sc = ax.scatter(_x, _y, c=_c, s=60, alpha=0.9)
# plt.text(0.1, 0.11,'Unoptimized Model', fontsize=10, horizontalalignment='left',verticalalignment='bottom')
# if trainingCorpus == 'larger':
# plt.text(0.1, 0.39,'Optimized Model', fontsize=10, horizontalalignment='left',verticalalignment='top')
# elif trainingCorpus == 'original':
# plt.text(0.1, 0.2,'Optimized Model', fontsize=10, horizontalalignment='left',verticalalignment='top')
plt.colorbar(sc)
if trainingCorpus == 'larger':
plt.savefig('medicalData/larger/optimized/OptimizedLarger.png')
elif trainingCorpus == 'original':
plt.savefig('medicalData/original/optimized/OptimizedOriginal.png')
# '''
crf_best = rs.best_estimator_
if trainingCorpus == 'larger':
pickle.dump(
crf_best,
open("medicalData/larger/optimized/linear-chain-crf.model.pickle",
"wb"),
protocol=0,
fix_imports=True)
elif trainingCorpus == 'original':
pickle.dump(
crf_best,
open(
"medicalData/original/optimized/linear-chain-crf.model.pickle",
"wb"),
protocol=0,
fix_imports=True)
# print('best params:', rs.best_params_)
# print('best CV score:', rs.best_score_)
# print('model size: {:0.2f}M'.format(rs.best_estimator_.size_ / 1000000))
y_pred = crf_best.predict(X_test)
print('\nTest Results (Optimized Model, Training Corpus: ' +
trainingCorpus + ')')
# print('c1 = %s, c2 = %s' %(c1, c2))
print(
metrics.flat_classification_report(y_test,
y_pred,
labels=sorted_labels,
digits=3))
# y_predT = crf_best.predict(X_train)
# print('\nTrain Results (Optimized Model, Training Corpus: ' + trainingCorpus + ')')
# # print('c1 = %s, c2 = %s' %(c1, c2))
# print(metrics.flat_classification_report(y_train, y_predT, labels=sorted_labels, digits=3))
#%%
'''
x = np.linspace(1,16,num=16)
fig = plt.figure()
fig.set_size_inches(10,6)
ax = plt.gca()
# ax.set_yscale('log')
# ax.set_xscale('log')
ax.set_xlabel('Regularization Coefficients')
ax.set_ylabel('Split Test Score')
ax.set_title("Randomized Hyperparameter Search CV Results (min={:0.3}, max={:0.3})".format(min(_c), max(_c)))
ax.set_title("Hyperparameter Gridsearch CV Results (min={:0.3}, max={:0.3})".format(min(_c), max(_c)))
# plt.xticks([1, 2])
ax.set_xticklabels(['{0.1, 0.1}','{0.1, 0.2}','{0.1, 0.3}','{0.1, 0.4}',
'{0.2, 0.1}','{0.2, 0.2}','{0.2, 0.3}','{0.2, 0.4}',
'{0.3, 0.1}','{0.3, 0.2}','{0.3, 0.3}','{0.3, 0.4}',
'{0.4, 0.1}','{0.4, 0.2}','{0.4, 0.3}','{0.4, 0.4}'],rotation = 45)
# ax.scatter(_x, _y, c=_c, s=60, alpha=0.9, edgecolors=[0,0,0])
ax.scatter(x, split1, c='midnightblue', s=60, alpha=0.9, marker = 'o', label = 'Split 1')
ax.scatter(x, split2, c='blue', s=60, alpha=0.9, marker = '*', label = 'Split 2')
ax.scatter(x, split3, c='steelblue', s=60, alpha=0.9, marker = 's', label = 'Split 3')
ax.scatter(x, split4, c='skyblue', s=60, alpha=0.9, marker = '+', label = 'Split 4')
ax.scatter(x, split5, c='turquoise', s=60, alpha=0.9, marker = 'D', label = 'Split 5')
# ax.scatter(x, split1, c= split1, s=60, alpha=0.9, marker = 'o', label = 'Split 1')
# ax.scatter(x, split2, c= split2, s=60, alpha=0.9, marker = '*', label = 'Split 2')
# ax.scatter(x, split3, c= split3, s=60, alpha=0.9, marker = 's', label = 'Split 3')
# ax.scatter(x, split4, c= split4, s=60, alpha=0.9, marker = '+', label = 'Split 4')
# ax.scatter(x, split5, c= split5, s=60, alpha=0.9, marker = 'D', label = 'Split 5')
ax.legend()
ax.grid(True)
# sc = plt.scatter(xy, xy, c=z, vmin=0, vmax=20, s=35, cmap=cm)
# plt.colorbar(sc)
'''
return splitCompiled