def main(argv):
if len(argv) < 3:
usage()
model_dir = sys.argv[1]
data_file = sys.argv[2]
# Load tokenizer
tokenizer = WordTokenizer()
tokenizer.load(os.path.join(model_dir, 'tokenizer'))
# Load classifier
classifier = ProductClassifier()
classifier.load(os.path.join(model_dir, 'classifier'))
# Load named entity recognizer
ner = ProductNER()
ner.load(os.path.join(model_dir, 'ner'))
with open(data_file, 'rb') as f:
reader = csv.DictReader(f)
outfile = open(
'.'.join(data_file.split('.')[:-1] + ['processed', 'csv']), 'wb')
writer = csv.DictWriter(outfile,
fieldnames=reader.fieldnames +
['category', 'brand'])
writer.writeheader()
count = 0
for row in reader:
count += 1
processed_row = process(row, tokenizer, classifier, ner)
print(processed_row)
writer.writerow(processed_row)
def load_models(model_dir):
# Load tokenizer
tokenizer = WordTokenizer()
tokenizer.load(os.path.join(model_dir, 'tokenizer'))
# Load classifier
classifier = ProductClassifier()
classifier.load(os.path.join(model_dir, 'classifier'))
# Load named entity recognizer
ner = ProductNER()
ner.load(os.path.join(model_dir, 'ner'))
return tokenizer, classifier, ner
def main(argv):
if len(argv) < 2:
usage()
# Fetch data
texts, categories = [], []
with open(sys.argv[1], 'rb') as f:
reader = csv.DictReader(
f, fieldnames=["title", "brand", "description", "categories"])
count = 0
for row in reader:
count += 1
text, category = row['title'] + ' ' + row['description'], row[
'categories'].split(' / ')[0]
texts.append(text)
categories.append(category)
if count >= MAX_TEXTS:
break
print('Processed %s texts.' % len(texts))
# Tokenize texts
tokenizer = WordTokenizer()
tokenizer.load()
data = tokenizer.tokenize(texts)
# Get labels from classifier
classifier = ProductClassifier()
labels = classifier.get_labels(categories)
# Compile classifier network and train
classifier.compile(tokenizer)
classifier.train(data, labels)
def main(argv):
if len(argv) < 2:
usage()
# Fetch data
texts, categories = [], []
with open(sys.argv[1], 'rb') as f:
reader = csv.DictReader(
f, fieldnames=["title", "brand", "description", "categories"])
count = 0
for row in reader:
count += 1
# TODO change here what we train on, and what categories are used
text, category = row['title'], row['categories'].split(' / ')[0]
texts.append(text)
categories.append(category)
if count >= MAX_TEXTS:
break
print(('Processed %s texts.' % len(texts)))
tmpx, tmpy = [], []
c = Counter(categories)
for x, y in zip(texts, categories):
if c[y] > 200:
tmpx.append(x)
tmpy.append(y)
texts = tmpx
categories = tmpy
print(Counter(tmpy))
# Tokenize texts
tokenizer = WordTokenizer()
tokenizer.load()
data = tokenizer.tokenize(texts)
# Get labels from classifier
classifier = ProductClassifier()
labels = classifier.get_labels(categories)
# Compile classifier network and train
classifier.compile(tokenizer)
classifier.train(data, labels)
import sys, os, csv import numpy as np from operator import itemgetter from tokenizer import WordTokenizer from classifier import ProductClassifier model_dir = './models' tokenizer = WordTokenizer() tokenizer.load(os.path.join(model_dir, 'tokenizer')) # Load classifier classifier = ProductClassifier() classifier.load(os.path.join(model_dir, 'classifier')) data = tokenizer.tokenize(["Cambridge wall calender"]) classScores = classifier.classify(data)[0] print(classScores) bestValIdx = np.argmax(classScores.values()) bestVal = classScores.values()[bestValIdx] bestClass = list(classScores)[bestValIdx] print(bestVal, bestClass)