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running_predictions.py
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running_predictions.py
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# coding: utf-8
# In[ ]:
import predict
import os
import subprocess
# In[ ]:
#!ls ../resources/models/best_model/ -asl
# In[ ]:
from collections import namedtuple
# In[ ]:
dir_ = "../resources/WSD_Evaluation_Framework/Evaluation_Datasets"
eval_datasets = sorted([i for i in os.listdir(dir_) if i.startswith("se")])
resources_path = '../resources'
del eval_datasets[1]
eval_datasets
# In[ ]:
bashCommand = "sudo javac ../resources/WSD_Evaluation_Framework/Evaluation_Datasets/Scorer.java"
process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)
output, error = process.communicate()
output,error
# In[ ]:
scores = {"babelnet": {}, 'wordnet_domains': {}, 'lexicographer': {}}
for name in eval_datasets:
for key in list(scores.keys()):
scores[key].update({name:None})
scores
# In[ ]:
record = namedtuple("predictions", "build perform")
Basic_model = record(True, True) # task determines if its basic or Multitask
MFS_baseline = record(False, False)
task = 'Multitask'
#assert not (perform_predictions and MFS_baseline)
# In[ ]:
for name in eval_datasets:
print("Dataset: {}\n".format(name))
path = os.path.join(dir_, name)
xml_file = [i for i in os.listdir(path) if i.endswith('.xml')][0]
xml_file = os.path.join(path, xml_file)
print(xml_file)
print("_"*50)
if Basic_model.build:
predict.predict_babelnet(input_path = '../resources/WSD_Evaluation_Framework/Evaluation_Datasets/{}/{}.data.xml'.format(name, name),
output_path = '../resources/WSD_Evaluation_Framework/Evaluation_Datasets/{}/{}.pred.babelnet.{}.txt'.format(name, name, task),
resources_path = resources_path)
if Basic_model.perform:
bashCommand = "sudo java Scorer {}/{}.gold.babelnet.txt {}/{}.pred.babelnet2.txt".format(name, name, name, name)
if MFS_baseline.build:
predict.MFS_predict_writer(input_path = '../resources/WSD_Evaluation_Framework/Evaluation_Datasets/{}/{}.data.xml'.format(name, name),
output_path = '../resources/WSD_Evaluation_Framework/Evaluation_Datasets/{}/MFS.{}.pred.babelnet.{}.txt'.format(name, name, task),
resources_path = resources_path,
prediction_type = 'babelnet')
if MFS_baseline.perform:
bashCommand = "sudo java Scorer {}/{}.gold.babelnet.txt {}/MFS.{}.pred.babelnet.{}.txt".format(name, name, name, name, task)
########################################################
os.chdir("../resources/WSD_Evaluation_Framework/Evaluation_Datasets/")
process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)
output, error = process.communicate()
scores['babelnet'][name] = float(output.decode("UTF-8").split("\n")[0].split("\t")[1].split("%")[0])
print("babelnet: {}".format(name))
for i in output.decode("UTF-8").split("\n"):
print(i)
os.chdir("../../../code")
########################################################
########################################################
########################################################
print("_"*50)
if Basic_model.build:
predict.predict_wordnet_domains(input_path = '../resources/WSD_Evaluation_Framework/Evaluation_Datasets/{}/{}.data.xml'.format(name, name),
output_path = '../resources/WSD_Evaluation_Framework/Evaluation_Datasets/{}/{}.pred.wordnet_domains.{}..txt'.format(name, name, task),
resources_path = resources_path)
if Basic_model.perform:
bashCommand = "sudo java Scorer {}/{}.gold.wordnet_domains.txt {}/{}.pred.wordnet_domains2.txt".format(name, name, name, name)
if MFS_baseline.build:
predict.MFS_predict_writer(input_path = '../resources/WSD_Evaluation_Framework/Evaluation_Datasets/{}/{}.data.xml'.format(name, name),
output_path = '../resources/WSD_Evaluation_Framework/Evaluation_Datasets/{}/MFS.{}.pred.wordnet_domain.{}.txt'.format(name, name, task),
resources_path = resources_path,
prediction_type = 'wordnet_domains')
if MFS_baseline.perform:
bashCommand = "sudo java Scorer {}/{}.gold.wordnet_domains.txt {}/MFS.{}.pred.wordnet_domains.{}.txt".format(name, name, name, name, task)
########################################################
os.chdir("../resources/WSD_Evaluation_Framework/Evaluation_Datasets/")
process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)
output, error = process.communicate()
scores['wordnet_domains'][name] = float(output.decode("UTF-8").split("\n")[0].split("\t")[1].split("%")[0])
print("wordnet_domains: {}".format(name))
for i in output.decode("UTF-8").split("\n"):
print(i)
os.chdir("../../../code")
########################################################
########################################################
########################################################
print("_"*50)
if Basic_model.build:
predict.predict_lexicographer(input_path = '../resources/WSD_Evaluation_Framework/Evaluation_Datasets/{}/{}.data.xml'.format(name, name),
output_path = '../resources/WSD_Evaluation_Framework/Evaluation_Datasets/{}/{}.pred.lexicographer.{}..txt'.format(name, name, task),
resources_path = resources_path)
if Basic_model.perform:
bashCommand = "sudo java Scorer {}/{}.gold.lexicographer.txt {}/{}.pred.lexicographer2.txt".format(name, name, name, name)
if MFS_baseline.build:
predict.MFS_predict_writer(input_path = '../resources/WSD_Evaluation_Framework/Evaluation_Datasets/{}/{}.data.xml'.format(name, name),
output_path = '../resources/WSD_Evaluation_Framework/Evaluation_Datasets/{}/MFS.{}.pred.lexicographer.{}..txt'.format(name, name, task),
resources_path = resources_path,
prediction_type = 'lexicographer')
if MFS_baseline.perform:
bashCommand = "sudo java Scorer {}/{}.gold.lexicographer.txt {}/MFS.{}.pred.lexicographer.{}..txt".format(name, name, name, name, task)
########################################################
os.chdir("../resources/WSD_Evaluation_Framework/Evaluation_Datasets/")
process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)
output, error = process.communicate()
scores['lexicographer'][name] = float(output.decode("UTF-8").split("\n")[0].split("\t")[1].split("%")[0])
print("lexicographer: {}".format(name))
for i in output.decode("UTF-8").split("\n"):
print(i)
os.chdir("../../../code")
########################################################
print("_"*50)
print("_"*50)
print("_"*50)
# In[ ]:
import pandas as pd
# In[ ]:
# MFS_scores = pd.DataFrame(scores)
# MFS_scores
# In[ ]:
# basicModel_scores = pd.DataFrame(scores)
# basicModel_scores
# In[ ]:
MultiTask_scores = pd.DataFrame(scores)
MultiTask_scores