def test_nNN(model, data_descriptions, data_codes, nNNmin=2, nNNmax=10):
for nNN in xrange(nNNmin, nNNmax + 1):
classifier = KNNClassifier(n_neighbors=nNN)
t1 = time()
classifier.fit(model.official_embeddings,
get_section_codes(model.official_codes))
# classifier.fit(model.official_embeddings, coarsen_codes(model.official_codes))
pred_section_codes = classifier.predict_with_edit_dist(
model.data_embeddings, data_descriptions,
model.official_descriptions)
# true_coarse_codes = coarsen_codes(data_codes) # .reshape((-1,1))
# errors = pred_codes - true_coarse_codes
true_section_codes = get_section_codes(data_codes) # .reshape((-1,1))
errors = pred_section_codes - true_section_codes
print '------------------------------'
print 'nNN:', nNN
print 'Correctly predicted', 1.0 * np.sum(
errors ==
0) / errors.shape[0], 'percent of top level codes w/ edit dist kNN'
print 'Took', time() - t1, 'seconds'
t1 = time()
# pred_codes = classifier.predict(model.data_embeddings)
# errors = pred_codes - true_coarse_codes
pred_section_codes = classifier.predict(model.data_embeddings)
errors = pred_section_codes - true_section_codes
print 'Correctly predicted', 1.0 * np.sum(
errors ==
0) / errors.shape[0], 'percent of top level code w/ euclidean kNN'
print 'Took', time() - t1, 'seconds'
print '------------------------------'
def match_codes():
"""Matching full dataset in parallel with customizable embedding method"""
# 0.398657046863 hard, 0.275397642306 soft
working_directory = './data/'
data_codes, data_descriptions = get_data_to_match('slim')
official_codes, official_descriptions = get_official_data()
level = 1
model = HashingEmbedder(
level=level, analyzer='char_wb', ngram_range=(4, 5), norm='l2'
) # word2vecEmbedder() # word2vecEmbedder() # HashingEmbedder() # [HashingEmbedder(level=level, analyzer='char', ngram_range=(3,5), norm='l2')] #[HashingEmbedder(level=level, analyzer='char', ngram_range=(2,3))]
model.embed_data(data_descriptions)
print 'loaded and embedded data'
# test_nNN(model, data_descriptions, data_codes)
official_code_labels = None
true_data_codes = None
use_section = False
if use_section:
official_code_labels = get_section_codes(model.official_codes)
true_data_codes = get_section_codes(data_codes)
else:
official_code_labels = coarsen_codes(model.official_codes)
true_data_codes = coarsen_codes(data_codes)
nNN = 4
classifier = KNNClassifier(n_neighbors=nNN)
classifier.fit(model.official_embeddings, official_code_labels)
pred_codes = classifier.predict(
model.data_embeddings, pbar=True
) # classifier.predict_with_edit_dist(model.data_embeddings, data_descriptions, model.official_descriptions)
errors = pred_codes - true_data_codes
print 'Correctly predicted', 1.0 * np.sum(
errors == 0) / errors.shape[0], 'percent of top level codes'
# plot_confusion_matrix(true_data_codes, pred_codes)
model = word2vecEmbedder()
model.embed_data(data_descriptions)
classifier = KNNClassifier(n_neighbors=nNN)
classifier.fit(model.official_embeddings, official_code_labels)
pred_codes = classifier.predict(
model.data_embeddings
) # classifier.predict_with_edit_dist(model.data_embeddings, data_descriptions, model.official_descriptions)