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lexent.py
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lexent.py
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#!/usr/bin/env python
import os
import os.path
import argparse
import logging
from collections import defaultdict
import numpy as np
import pandas as pd
from sklearn import metrics
from scipy.stats import linregress
from utdeftvs import load_numpy
import fold
import models
N_FOLDS = 20
# before anything else, configure the logger
logger = logging.getLogger()
handler = logging.StreamHandler()
formatter = logging.Formatter("%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
def main():
parser = argparse.ArgumentParser('Lexical Entailment Classifier')
parser.add_argument('--data', '-d', help='Input file')
parser.add_argument('--space', '-s', help='Distributional space')
parser.add_argument('--seed', '-S', default=1, type=int, help='Random seed')
parser.add_argument('--model', '-m', help='Model setup', choices=models.SETUPS.keys())
parser.add_argument('--experiment', '-e', default='standard', choices=('standard', 'match_error'))
parser.add_argument('--output', '-o', default='results')
args = parser.parse_args()
logger.debug('Lexent Arguments: ')
logger.debug(args)
rng = np.random.RandomState(args.seed)
logger.debug("Loading space")
space = load_numpy(args.space).normalize()
# Handle vocabulary issues
logger.debug("Reading data")
data = pd.read_table("data/%s/data.tsv" % args.data, header=None, names=('word1', 'word2', 'entails'))
data['word1'] = data['word1'].apply(lambda x: x.lower() + '/NN')
data['word2'] = data['word2'].apply(lambda x: x.lower() + '/NN')
mask1 = data.word1.apply(lambda x: x in space.lookup)
mask2 = data.word2.apply(lambda x: x in space.lookup)
T = len(data)
M1T = np.sum(mask1)
M2T = np.sum(mask2)
logger.debug("")
logger.debug("Total Data: %6d" % T)
logger.debug(" LHS OOV: %6d ( %4.1f%% )" % (M1T, M1T*100./T))
logger.debug(" RHS OOV: %6d ( %4.1f%% )" % (M2T, M2T*100./T))
data = data[mask1 & mask2].reset_index(drop=True)
F = len(data)
logger.debug(" Final: %6d ( %4.1f%% )" % (F, F*100./T))
logger.debug("")
logger.debug("Generating %d folds..." % N_FOLDS)
# need our folds for cross validation
folds = fold.generate_folds_lhs(rng, data, n_folds=N_FOLDS)
train_sizes = np.array([len(f[0]) for f in folds], dtype=np.float)
test_sizes = np.array([len(f[1]) for f in folds], dtype=np.float)
logger.debug("Training sizes: %.1f" % np.mean(train_sizes))
logger.debug(" Test sizes: %.1f" % np.mean(test_sizes))
logger.debug(" Test-Tr ratio: %.1f%%" % np.mean(test_sizes*100./(train_sizes + test_sizes)))
logger.debug(" Percent data: %.1f%%" % np.mean((train_sizes + test_sizes)*100./F))
logger.debug("")
logger.debug("Setting up the model:")
model, features = models.load_setup(args.model)
# dwp = data with predictions
dwp = data.copy()
dwp['prediction'] = False
dwp['fold'] = -1
logger.debug("Generating features")
X, y = models.generate_feature_matrix(data, space, features)
# perform cross validation
logger.debug("Performing experiment: %s" % args.experiment)
scores = []
for foldno, (train, test) in enumerate(folds):
logger.debug(" ... fold %2d/%2d" % (foldno, N_FOLDS))
# generate features
train_X, train_y = X[train], y[train]
test_X, test_y = X[test], y[test]
model.fit(train_X, train_y)
preds_y = model.predict(test_X)
dwp.loc[test,'prediction'] = preds_y
dwp.loc[test,'fold'] = foldno
scores.append(metrics.f1_score(test_y, preds_y))
logger.info("F1 across CV: %.3f" % np.mean(scores))
logger.info(" std: %.3f" % np.std(scores))
logger.info(" F1 pooled: %.3f" % metrics.f1_score(dwp['entails'], dwp['prediction']))
dwp.to_csv("%s/exp:%s,data:%s,space:%s,model:%s,seed:%d.csv" % (
args.output, args.experiment, args.data,
os.path.basename(args.space),
args.model, args.seed
), index=False)
if len(dwp[dwp['fold'] == -1]) != 0:
logger.error("Some of the data wasn't predicted!\n" +
dwp[dwp['fold'] == -1])
#f1_results_csv.append({
# 'data': DATA_FOLDER_SHORT,
# 'space': SPACE_FILENAME_SHORT,
# 'model': model_name,
# 'features': features,
# 'dims': space.matrix.shape[1],
# 'mean': mean,
# 'std': stderr,
# 'n_folds': N_FOLDS,
# 'seed': seed,
#})
# make sure we've seen every item
#print "False Positive Issue:"
#recalls = defaultdict(list)
#match_errs = defaultdict(list)
#for fold in folds:
# fake_data = generate_pseudo_data(data, fold[1], 0.5)
# if len(fake_data) == 0:
# continue
# for name, model_name, features in setups:
# Xtr, ytr = generate_feature_matrix(data.ix[fold[0]], space, features, global_vocab)
# Xte, yte = generate_feature_matrix(fake_data, space, features, global_vocab)
# model = model_factory(model_name)
# model.fit(Xtr, ytr)
# preds = model.predict(Xte)
# recalls[(name, model_name, features)].append(metrics.recall_score(yte, preds))
# #match_errs[name].append(metrics.recall_score(~yte, preds))
# match_errs[(name, model_name, features)].append(float(np.sum(~yte & preds)) / np.sum(~yte))
#print "%-10s %-10s r m b recall materr" % (" ", " ")
#for item in recalls.keys():
# name, model_name, features = item
# R = np.mean(recalls[item])
# ME = np.mean(match_errs[item])
# #for r, me in zip(recalls[item], match_errs[item], trues[item]):
# # print "%-10s %.3f %.3f %.3f" % (item, r, me, t)
# m, b, r, p, se = linregress(recalls[item], match_errs[item])
# print "%-10s %-10s %6.3f %6.3f %6.3f %.3f %.3f" % (model_name, features, r, m, b, R, ME)
# for R, ME in zip(recalls[item], match_errs[item]):
# me_results_csv.append({
# 'name': name,
# 'model': model_name,
# 'features': features,
# 'Features + Data': features + " " + DATA_FOLDER_SHORT,
# 'recall': R,
# 'match_error': ME,
# 'space': SPACE_FILENAME_SHORT,
# 'data': DATA_FOLDER_SHORT,
# 'seed': seed,
# })
if __name__ == '__main__':
main()