def fit(self):
'''
模型训练
:return:
'''
fastText.train_supervised(self.train_input,
label="__label__").save_model(self.output)
def test_fasttext():
import os
import codecs
from fastText import train_supervised
data_dir = "/home/davidyu/stock/scripts/davidyu_stock/scripts/analysis/news_report"
train_data = os.path.join(data_dir, "train_text.txt")
valid_data = os.path.join(data_dir, "valid_text.txt")
model = train_supervised(input=train_data,
epoch=50,
lr=0.5,
wordNgrams=2,
dim=100,
verbose=2,
minCount=1,
thread=1)
model = train_supervised(input=train_data,
epoch=50,
lr=0.5,
wordNgrams=2,
dim=100,
verbose=2,
minCount=1,
thread=1,
loss="softmax")
print_results(*model.test(valid_data))
def train_model(fname_prefix: str,
out_fname: str,
label_prefix: str = "__label__",
**kwargs):
# Train the model
import fastText
params = {
"dim": kwargs.get("dim", 300),
"epoch": kwargs.get("epoch", 1000),
"wordNgrams": kwargs.get("wordNgrams", 2),
"verbose": kwargs.get("verbose", 2),
"minCount": kwargs.get("minCount", 15),
"minCountLabel": kwargs.get("minCountLabel", 5),
"lr": kwargs.get("lr", 0.1),
"neg": kwargs.get("neg", 10),
"thread": kwargs.get("thread", 16),
"loss": kwargs.get("loss", "ns"),
"t": kwargs.get("t", 1e-5)
}
logging.info("Training fastText model", extra={"params": params})
model = fastText.train_supervised(input="%s.train" % fname_prefix,
label=label_prefix,
**params)
logging.info("Writing model to disk", extra={"output_file": out_fname})
model.save_model(out_fname)
return model
def train_sup(mode=fasttextConfig.create_data_word):
# train supervised model
print('start train supervised fasttext model')
# init path
if mode == fasttextConfig.create_data_word:
input_path = fileConfig.dir_fasttext + fileConfig.file_fasttext_sup_train_word_data
output_path = fileConfig.dir_fasttext + fileConfig.file_fasttext_sup_word_model
test_path = fileConfig.dir_fasttext + fileConfig.file_fasttext_sup_test_word_data
elif mode == fasttextConfig.create_data_char:
input_path = fileConfig.dir_fasttext + fileConfig.file_fasttext_sup_train_char_data
output_path = fileConfig.dir_fasttext + fileConfig.file_fasttext_sup_char_model
test_path = fileConfig.dir_fasttext + fileConfig.file_fasttext_sup_test_char_data
# init path
model = fastText.train_supervised(input=input_path,
dim=200,
epoch=100,
lr=1.0,
wordNgrams=3,
ws=7,
verbose=2,
minCount=1,
thread=8,
loss='hs')
print_results(*model.test(test_path))
model.save_model(output_path)
print("train sup fasttext finish")
def fit(session, uid, path):
labeled_text = dal.get_text_labeled_text(session, uid)
# not sure if this is the right prereq for fasttext
if len(labeled_text['targets']) < 2:
return
# preprocess training data one line at a time in order to limit pipe buffer issues
preprocessed_data = [preprocess(datum) for datum in labeled_text['data']]
# create a new temporary training data file
fd, train_path = tempfile.mkstemp()
# close the temporary training data file descriptor as we don't need it
os.close(fd)
# fill the temporary training data file
with open(train_path, 'w') as f:
for (target, datum) in zip(labeled_text['targets'], preprocessed_data):
f.write(label_prefix + target + " " + datum + "\n")
# train the fasttext model
model = fastText.train_supervised(input=train_path)
# compress the fasttext model to save space (disabled for now because it requires at least 256 rows)
#model.quantize(input=train_path)
# delete the temporary training data file
os.unlink(train_path)
# serialize the model out to the temporary model file
model.save_model(path)
def fasttext_train(input, output):
import fastText as ft
model = ft.train_supervised(
input=input,
dim=300,
pretrainedVectors='wiki-news-300d-1M-subword.vec')
model.save_model(output)
def fit(self, X, y):
if not isinstance(X, np.ndarray):
X = np.array(X)
if not isinstance(y, np.ndarray):
y = np.array(y)
path = data_to_temp(X, self.label, y)
self._num_classes = len(np.unique(y))
self._model = fastText.train_supervised(path,
lr=self.lr,
dim=self.dim,
ws=self.ws,
epoch=self.epoch,
minCount=self.minCount,
minCountLabel=self.minCountLabel,
minn=self.minn,
maxn=self.maxn,
neg=self.neg,
wordNgrams=self.wordNgrams,
loss=self.loss,
bucket=self.bucket,
thread=self.thread,
lrUpdateRate=self.lrUpdateRate,
t=self.t,
label=self.label,
verbose=self.verbose)
os.remove(path)
fd, path = tempfile.mkstemp()
self._model.save_softmax(path)
self.class_embeddings = pd.read_csv(
path, skiprows=[0], delimiter=' ').dropna(axis=1)
os.remove(path)
return self
def trainFastText(self, sents, labls, argsOut):
if argsOut.load:
# load given model
print("Loading fastText model {0} ..".format(argsOut.load))
return load_model(argsOut.load)
outFile = "ft_runTrain{0}.txt".format(time.time())
labls = self.roundLabelsRegress(labls)
for i in range(0, len(sents)):
sents[i] = "__label__{0} {1}".format(labls[i], sents[i])
with open(outFile, mode="w", encoding="utf-8") as file:
file.writelines(sents)
# Get model
model = train_supervised(input=outFile,
epoch=argsOut.epochs,
dim=argsOut.dim,
bucket=10000000,
lr=argsOut.lr,
wordNgrams=argsOut.wordNgrams,
verbose=2,
minCount=1)
os.remove(outFile)
return model
def train():
if FLAGS.word_level:
train_file = 'data/train_w.fast'
dev_file = 'data/dev_w.fast'
model_file = 'temp/ml/fast_model_w.bin'
else:
train_file = 'data/train_c.fast'
dev_file = 'data/dev_c.fast'
model_file = 'temp/ml/fast_model_c.bin'
model = fastText.train_supervised(input=train_file,
dim=100,
epoch=15,
thread=40,
minCount=10,
loss='softmax',
wordNgrams=2)
model.save_model(model_file)
vocab_size, p, r = model.test(train_file)
f1 = (p * r * 2) * 1.0 / (p + r)
print 'Train:vocab_size:%d p:%.5f r:%.5f f1:%.5f' % (vocab_size, p, r,
f1)
vocab_size, p, r = model.test(dev_file)
f1 = (p * r * 2) * 1.0 / (p + r)
print 'Dev:vocab_size:%d p:%.5f r:%.5f f1:%.5f' % (vocab_size, p, r,
f1)
def fit(self, X, y):
'''
Method that trains the fasttext clasifier on the provided dataset. If the file with the serialized model
of the classifier trained on the exact same dataset exists, then just load the model, otherwise
train the classifier and save model.
:param X: the set of instances in the dataset
:param y: the set of labels in the dataset
:return: None
'''
file_identifier = self._get_identifier_for_model_file(X, y)
serialized_file = os.path.join(self.folder, file_identifier)
if os.path.isfile(serialized_file):
print("Loading the model from file " + str(serialized_file))
# file already exists, fit means just loading the model
self.model = load_model(serialized_file)
else:
print("Training the classifier " + str(self.name))
# means that file does not exist, we have to train the model
with open(SWAP_FILE, 'w', encoding='utf8') as f:
for tweet, label in zip(X, y):
f.write(LABEL_IDENTIFIER + str(label) + SEPARATOR +
tweet.strip() + '\n')
self.model = train_supervised(SWAP_FILE, **self.params)
os.remove(SWAP_FILE) # DELETE SWAP FILE AFTER USAGE
# lastly, save model. Firstly, create folder for classifier, if it doesn't exist
os.makedirs(self.folder, exist_ok=True)
self.model.save_model(serialized_file)
def fit(self, X, y):
# Fit model
if not isinstance(X, np.ndarray):
X = np.array(X)
if not isinstance(y, np.ndarray):
y = np.array(y)
path = data_to_temp(X, self.label, y)
self._num_classes = len(np.unique(y))
self._model = fastText.train_supervised(path,
lr=self.lr,
dim=self.dim,
ws=self.ws,
epoch=self.epoch,
minCount=self.minCount,
minCountLabel=self.minCountLabel,
minn=self.minn,
maxn=self.maxn,
neg=self.neg,
wordNgrams=self.wordNgrams,
loss=self.loss,
bucket=self.bucket,
thread=self.thread,
lrUpdateRate=self.lrUpdateRate,
t=self.t,
label=self.label,
verbose=self.verbose)
os.remove(path)
return self
def _train_tagging_model(self, output, quantization=False):
"""Train a tagging model using an annotated file and save it to output.
Pre-processing of the input file must have been done prior to training.
Training file should be utf-8.
The model can be quantized to reduce memory usage
(but quantization is quite expensive).
"""
log.info('Start training model.')
new_model = fastText.train_supervised(input=self.tempfilename,
epoch=self.epoch,
lr=self.lr,
wordNgrams=self.wordNgrams,
minCount=self.minCount,
dim=self.dim,
loss=self.loss,
thread=self.thread,
neg=self.neg)
log.info('Training model done.')
if quantization:
log.info('Start quantization.')
new_model.quantize(thread=self.thread, retrain=False)
log.info('Quantization done.')
model_file = '{}/{}.bin'.format(resources_path, output)
new_model.save_model(model_file)
log.info('Model saved at {}.'.format(model_file))
return model_file
def get_predict(train_path, test_path):
"""
:param train_path: 处理好的可以用来训练的数据
:param test_path: 处理好的用来预测的无类标数据
"""
import fastText
classifier = fastText.train_supervised(
train_path,
lr=0.3,
dim=200,
epoch=8,
minn=1,
maxn=4,
wordNgrams=2,
loss='hs',
)
test_data = np.load(test_path)
li = []
j = 0
for i in test_data:
j += 1
pre = classifier.predict(i[:-1], k=10)
result = handle_ft_predict_result(
pre) # 每一个句子对应一个二维数组，形如[['价格,' '0' '0.65184933']...]
li.append(result)
loc_predict_probability = rename(test_path,
'probability_fastT_subOnly.npy')
np.save(loc_predict_probability, li)
def process(self, data_file):
json_object = self.load_json(data_file)
json_object = self.replace_entities(json_object)
self.load_answers(json_object)
self.create_labeled_questions_file(json_object, "fasttext_data.txt")
self.fast_text_model = fastText.train_supervised("fasttext_data.txt",
epoch=50)
def train(self, data, labels, dim=100, ng=2, epoch=10):
if len(data) != len(labels):
raise ValueError("Length of data (" + str(len(data)) +
") does not match length of labels (" +
str(len(labels)) + ")")
# Convert training data into strings for fastText
mapped_report_strs = []
for i in range(len(data)):
report_string = data[i].replace("\n", " ")
label = " __label__" + str(labels[i])
mapped_report_strs.append(report_string + label)
shuffle(mapped_report_strs)
# Write strings to a temp file
train_path = "./MODEL_TRAIN_TEMP.bin"
with open(train_path, 'w') as outfile:
for mrs in mapped_report_strs:
outfile.write(mrs)
outfile.write("\n")
# Train fastText model
self.model = fastText.train_supervised(train_path,
dim=dim,
epoch=epoch,
thread=4,
wordNgrams=ng)
# Delete temp file
os.remove(train_path)
def fasttext_classify(data, extra_params={}):
class_to_predict = 'type' # product importance
data[class_to_predict] = data[class_to_predict].map(lambda s : s.replace(" ", ""))
data_for_fasttext = data['text'] + ' __label__' + data[class_to_predict]
data_for_fasttext = shuffle(data_for_fasttext, random_state=77)
num_records = len(data_for_fasttext)
data_train = data_for_fasttext[:int(0.85 * num_records)]
data_test = data_for_fasttext[int(0.85 * num_records):]
data_train.to_csv(TRAIN_PATH, sep='\t', header=0, index=False)
data_test.to_csv(TEST_PATH, sep='\t', header=0, index=False)
model = fastText.train_supervised(TRAIN_PATH, **extra_params)
#model.save_model(MODEL_PATH)
print('Training accuracy:')
train_accuracy = model.test(TRAIN_PATH)
print(train_accuracy[-1])
print('Test accuracy:')
test_accuracy = model.test(TEST_PATH)
print(test_accuracy[-1])
y_pred = []
y_true = []
for test_item in data_test:
test_text, test_label = test_item.split('__label__')
y_pred.append(model.predict(test_text)[0])
y_true.append('__label__' + test_label)
print('F1 score: ' + str(f1_score(y_true, y_pred, average='weighted')))
return test_accuracy[-1] # accuracy is a tuple
def train_model(self, traverse_list):
"""
This is used for LR and FT model as each step of the model will output a probability.
:param traverse_list:
:return: None
"""
for i, node in enumerate(traverse_list):
print("Node key: " + node.key)
print("Child keys:")
print(node.child.keys())
print("Trained {0} models".format(i))
if len(node.child.keys()) <= 2:
continue
if node.key == "*":
continue
if not node.child:
continue
train_data = self.get_train_data(node)
if self.tree_type == "FT":
node.classifier = train_supervised(input=train_data)
elif self.tree_type == "LR":
node.classifier = LogisticRegression(multi_class='multinomial', solver='newton-cg')
node.classifier.fit(train_data[0], train_data[1])
elif self.tree_type == "SVM":
node.classifier = LinearSVC()
node.classifier.fit(train_data[0], train_data[1])
def train(self):
master = master_file.MasterFile()
data = master.get_training_data()
traning = traning_file.TraningFile()
traning.reset()
hasText = False
for uuid in data:
for name in data[uuid]:
traning.append('__label__' + uuid + ' , ' +
app.get_wakati(data[uuid][name]['title']))
hasText = True
if hasText is False:
return
self._model = train_supervised(input=app.get_output_train_text(),
epoch=200,
lr=0.7,
wordNgrams=2,
loss="hs",
dim=100)
with self._lock:
self.__print_results(
*self._model.test(app.get_output_train_text()))
self._model.save_model(app.get_output_fasttext_model())
def _train_tagging_model(self, output, quantization=False):
"""Train a tagging model using an annotated file and save it to output.
Pre-processing of the input file must have been done prior to training.
Training file should be utf-8.
The model can be quantized to reduce memory usage
(but quantization is quite expensive).
"""
log.info('Start training model.')
new_model = fastText.train_supervised(input=self.tempfilename,
epoch=self.epoch,
lr=self.lr,
wordNgrams=self.wordNgrams,
minCount=self.minCount,
dim=self.dim,
loss=self.loss,
thread=self.thread,
neg=self.neg)
log.info('Training model done.')
if quantization:
log.info('Start quantization.')
new_model.quantize(thread=self.thread, retrain=False)
log.info('Quantization done.')
model_file = '{}/{}.bin'.format(resources_path, output)
new_model.save_model(model_file)
log.info('Model saved at {}.'.format(model_file))
return model_file
def trainModel(request):
if request.method == 'POST':
epoch = int(request.POST['epochValue'])
lr = float(request.POST['learningRateValue'])
user = request.session['userEmail']
user = user.split('@')
"""print('epoch:')
print(epoch)
print('lr')
print(lr)"""
#Train the model
model = fastText.train_supervised(
settings.BASE_DIR + '/media/temp/' + user[0] + 'trainData.txt', lr,
epoch)
#Finished test
result = model.test(settings.BASE_DIR + '/media/temp/' + user[0] +
'testData.txt')
precision = float(result[1])
recall = float(result[2])
#Compute Accuracy
Fmeasure = 2 * ((precision * recall) / (precision + recall))
Fmeasure = "{:.2%}".format(Fmeasure)
#print('accuracy%')
#print(Fmeasure)
#Save model
model.save_model(settings.BASE_DIR + '/media/temp/' + user[0] +
'trainedModel.bin')
data = {'status': Fmeasure}
return JsonResponse(data)
def run(ps, i):
lr = random.uniform(0, 1)
epoch = round(random.uniform(5, 50))
wordNgrams = round(random.uniform(1, 5))
minCount = round(random.uniform(1, 10))
model = fastText.train_supervised(input=ps[0],
lr=lr,
epoch=epoch,
wordNgrams=wordNgrams,
minCount=minCount)
# had to do it like this because I want to get the prediction and not just a metric from the model
preds = Path(ps[1]).read_text().split('\n')
truth = []
output = []
for p in preds:
label = p[:10]
text = p[11:]
truth.append(label)
output.append(model.predict(text)[0][0])
rpt = sklearn.metrics.classification_report(truth,
output,
output_dict=True)
rpt['lr'] = lr
rpt['epoch'] = epoch
rpt['wordNgrams'] = wordNgrams
rpt['minCount'] = minCount
rpt['kappa'] = sklearn.metrics.cohen_kappa_score(truth, output)
Path(ps[0].replace('train.csv',
f"{i}_results.json")).write_text(json.dumps(rpt))
def get_predict(train_path, test_path):
"""
:param train_path: 处理好的可以用来训练的数据
:param test_path: 处理好的用来预测的无类标数据
"""
import fastText
classifier = fastText.train_supervised(
train_path,
lr=0.3,
dim=10,
epoch=10,
minn=1,
maxn=4,
wordNgrams=1,
loss='hs',
)
test_data = np.load(test_path)
li = []
j = 0
for i in test_data:
j += 1
pre = classifier.predict(i[:-1], k=10)
# print(pre)
result = handle_ft_predict_result(
pre) # 每一个句子对应一个二维数组，形如[['价格,' '0' '0.65184933']...]
# if each_c == 10:
# break
li.append(result)
# print(li)
# li_np = np.array(li)
loc_predict_probability = rename(test_path,
'/probability_fastT_multiLabel.npy')
np.save(loc_predict_probability, li)
def fasttext_train_valid(train_X, train_y, model_dir, model_name, **kwargs):
"""
:param train_X: 训练数据[type:txt]
:param train_y: 验证数据[type:txt]
:param model_dir: 模型保存的文件夹[type:string]
:param model_name: 模型的命名
:parma **kwargs: 模型训练的参数
:return clf, precision, recall: 返回分类器，precision表现和recall表现
"""
clf = train_supervised(input=train_X, **kwargs)
result = clf.test(train_y)
precision = result[1]
recall = result[2]
if not os.path.exists(model_dir):
os.makedirs(model_dir)
__model = model_dir + model_name
clf.save_model('%s.bin' % __model)
__record = {
'train_X': train_X,
'train_y': train_y,
'model_path': __model,
'training_parameter': kwargs,
'precision': precision,
'recall': recall
}
pprint.pprint(__record, width=1)
return clf, precision, recall
def model():
# model = fastText.train_supervised(path + 'train_set.txt', label='__myprefix__',bucket=400000
# ,wordNgrams=2,minCount=3,lr=1,lrUpdateRate=0)
model = fastText.train_supervised(path + 't_train_set.txt', label='__myprefix__', bucket=39759
, wordNgrams=3, minCount=3, lr=1, lrUpdateRate=200
,dim=128)
result = model.test(path + 't_test_set.txt')
print(result)
# model.save_model(model_path + 'model')
true_labels = []
all_words = []
f = open(path + 't_test_set.txt', 'r')
for line in f:
words, labels = model.get_line(line.strip())
if len(labels) == 0:
continue
all_words.append(" ".join(words))
true_labels += [labels]
predictions, _ = model.predict(all_words)
n = 0
for i in range(len(true_labels)):
if (predictions[i]==true_labels[i]):
n+=1
print(n/len(true_labels))
def train():
# (先准备好训练语料)
ftrain = 'reviews_fasttext_train.txt'
ftest = 'reviews_fasttext_test.txt'
# 训练模型
classifier = fastText.train_supervised(ftrain, label="__label__")
classifier.save_model("reviews_fasttext.bin")
def retrain(self):
if len(self.data
) < self.config['models']['fast_text']['min_train_data']:
return
self.log.info('Retraining bilinear model, explore_rate = %.2f' %
self.explore_rate)
train_file = tempfile.NamedTemporaryFile(mode='w')
best_tactic = {}
for scored_candidate, status in self.data:
smt_file = scored_candidate.benchmarks[0].file
tactics = get_tactics(scored_candidate.t)
if status != ScoredCandidateStatus.SOLVED:
continue
for i, tactic in enumerate(tactics):
fast_text_line = self.encode(tactics[:i])
key = (smt_file, fast_text_line)
if key not in best_tactic or best_tactic[key][
1] > scored_candidate.rlimit:
best_tactic[key] = (tactic.s, scored_candidate.rlimit)
entry = '__label__%s %s' % (tactic.s, fast_text_line)
train_file.write(entry + '\n')
for scored_candidate, status in self.data:
smt_file = scored_candidate.benchmarks[0].file
tactics = get_tactics(scored_candidate.t)
if status != ScoredCandidateStatus.SOLVED:
continue
for i, tactic in enumerate(tactics):
params = self.strategy_enum.extract_params([tactic])[0]
disc_idx = self.map_to_discretized(tactic.s, params)
fast_text_line = self.encode(tactics[:i])
key = (smt_file, fast_text_line)
if best_tactic[key][1] == scored_candidate.rlimit:
entry = '__label__%s_%d %s' % (tactic.s, disc_idx,
fast_text_line)
train_file.write(entry + '\n')
train_file.flush()
os.fsync(train_file.fileno())
self.log.info('Created dataset of %d entries' % len(best_tactic))
self.bilinear_model = fastText.train_supervised(
input=train_file.name,
epoch=self.config['models']['fast_text']['epoch'],
lr=self.config['models']['fast_text']['lr'],
wordNgrams=self.config['models']['fast_text']['ngrams'],
verbose=self.config['models']['fast_text']['verbose'],
minCount=self.config['models']['fast_text']['min_count'],
dim=self.config['models']['fast_text']['dim'],
)
def train():
model = fastText.train_supervised(path + "/materials/fastTexttrain.input",
dim=108,
epoch=100,
lr=0.001,
pretrainedVectors=path +
"/materials/umls.embeddings")
model.save_model(path + "/materials/fastTexttrain.model")
print_results(*model.test(path + "/materials/fastTextval.input"))
def fasttext(input, epoch=25, wordNgrams=2):
model = train_supervised(input=input,
epoch=epoch,
lr=0.9,
wordNgrams=wordNgrams,
verbose=2,
minCount=1)
return model
def _create_model(self):
self.info('creating fastText model')
trainpath = os.path.join(self.datadir, self.TRAIN_FILE)
modelpath = os.path.join(self.datadir, self.MODEL_FILE)
params = {param: self.FASTTEXT_PARAMS[param](val)
for param, val in self.params.items()
if param in self.FASTTEXT_PARAMS}
self._model = fastText.train_supervised(trainpath, **params)
self._model.save_model(modelpath)
def build_supervised_model(data, kwargs):
kwargs = default_kwargs(kwargs)
with tempfile.NamedTemporaryFile(delete=False) as tmpf:
for line in data:
line = "__label__" + line.strip() + "\n"
tmpf.write(line.encode("UTF-8"))
tmpf.flush()
model = train_supervised(input=tmpf.name, **kwargs)
return model
def main(argv):
input_file = argv[0]
output_file = argv[1]
model = ft.train_supervised(input=input_file,
dim=200,
epoch=30,
lr=0.1,
label='__label__',
thread=8)
model.save_model(output_file)
def sup_test(self):
def check(
output_local, test_local, n_local, p1_local, r1_local, size_local,
lessthan
):
test_args = self.default_test_args(output_local, test_local)
test_output = self.get_test_output(test_args)
self.assertEqual(
str(test_output[0]),
str(n_local),
"N: Want: " + str(n_local) + " Is: " + str(test_output[0])
)
self.assertTrue(
float(test_output[1]) >= float(p1_local),
"p1: Want: " + str(p1_local) + " Is: " + str(test_output[1])
)
self.assertTrue(
float(test_output[2]) >= float(r1_local),
"r1: Want: " + str(r1_local) + " Is: " + str(test_output[2])
)
path_size = self.get_path_size(output_local)
if lessthan:
self.assertTrue(
path_size <= size_local, "Size: Want at most: " +
str(size_local) + " Is: " + str(path_size)
)
else:
self.assertTrue(
path_size == size_local,
"Size: Want: " + str(size_local) + " Is: " + str(path_size)
)
train, test, output = self.build_paths(
dataset + ".train", dataset + ".test", dataset
)
model = train_supervised(
input=train,
dim=10,
lr=lr,
wordNgrams=2,
minCount=1,
bucket=10000000,
epoch=5,
thread=self.num_thread()
)
model.save_model(output)
check(output, test, n, p1, r1, size, False)
# Exercising
model.predict("hello world")
model.quantize(input=train, retrain=True, cutoff=100000, qnorm=True)
model.save_model(output + ".ftz")
# Exercising
model.predict("hello world")
check(output + ".ftz", test, n, p1_q, r1_q, quant_size, True)
def sup_test(self):
def get_path_size(path):
path_size = subprocess.check_output(["stat", "-c", "%s",
path]).decode('utf-8')
path_size = int(path_size)
return path_size
def check(model, model_filename, test, lessthan, msg_prefix=""):
lines, labels = read_labels(test["data"])
predictions = []
for line in lines:
pred_label, _ = model.predict(line)
predictions.append(pred_label)
p1_local_out, r1_local_out = util.test(predictions, labels)
self.assertEqual(
len(predictions), test["n"], msg_prefix + "N: Want: " +
str(test["n"]) + " Is: " + str(len(predictions))
)
self.assertTrue(
p1_local_out >= test["p1"], msg_prefix + "p1: Want: " +
str(test["p1"]) + " Is: " + str(p1_local_out)
)
self.assertTrue(
r1_local_out >= test["r1"], msg_prefix + "r1: Want: " +
str(test["r1"]) + " Is: " + str(r1_local_out)
)
path_size = get_path_size(model_filename)
size_msg = str(test["size"]) + " Is: " + str(path_size)
if lessthan:
self.assertTrue(
path_size <= test["size"],
msg_prefix + "Size: Want at most: " + size_msg
)
else:
self.assertTrue(
path_size == test["size"],
msg_prefix + "Size: Want: " + size_msg
)
output = os.path.join(tempfile.mkdtemp(), configuration["dataset"])
model = train_supervised(**configuration["train_args"])
model.save_model(output + ".bin")
check(model, output + ".bin", configuration["test"], False)
model.quantize(**configuration["quant_args"])
model.save_model(output + ".ftz")
check(
model, output + ".ftz", configuration["quant_test"], True, "Quant: "
)
def do_fasttext(text,stars):
#删除通用词
text_cleaned=[]
list_stopWords = list(set(stopwords.words('english')))
english_punctuations = [',', '.', ':', ';', '?', '(', ')', '[', ']', '&', '!', '*', '@', '#', '$', '%']
d = enchant.Dict("en_US")
for line in text:
# 分词
list_words = word_tokenize(line.lower())
# 去掉标点符号
list_words = [word for word in list_words if word not in english_punctuations]
# 实用wordnet删除非常见英文单词
#list_words = [word for word in list_words if wordnet.synsets(word) ]
list_words = [word for word in list_words if d.check(word)]
# 过滤停止词
filtered_words = [w for w in list_words if not w in list_stopWords]
text_cleaned.append( " ".join(filtered_words) )
# 分割训练集和测试集 测试集占20%
#x_train, x_test, y_train, y_test = train_test_split(text, stars, test_size=0.2)
x_train, x_test, y_train, y_test = train_test_split(text_cleaned, stars, test_size=0.2)
# 按照fasttest的要求生成训练数据和测试数据
dump_file(x_train, y_train, "yelp_train.txt")
dump_file(x_test, y_test, "yelp_test.txt")
model = train_supervised(
input="yelp_train.txt", epoch=20, lr=0.6, wordNgrams=2, verbose=2, minCount=1
)
def print_results(N, p, r):
print("N\t" + str(N))
print("P@{}\t{:.3f}".format(1, p))
print("R@{}\t{:.3f}".format(1, r))
print_results(*model.test("yelp_test.txt"))
def check(data):
third = int(len(data) / 3)
train_data = data[:2 * third]
valid_data = data[third:]
with tempfile.NamedTemporaryFile(
delete=False
) as tmpf, tempfile.NamedTemporaryFile(delete=False) as tmpf2:
for line in train_data:
tmpf.write(
("__label__" + line.strip() + "\n").encode("UTF-8")
)
tmpf.flush()
for line in valid_data:
tmpf2.write(
("__label__" + line.strip() + "\n").encode("UTF-8")
)
tmpf2.flush()
model = train_supervised(input=tmpf.name, **kwargs)
true_labels = []
all_words = []
with open(tmpf2.name, 'r') as fid:
for line in fid:
if sys.version_info < (3, 0):
line = line.decode("UTF-8")
if len(line.strip()) == 0:
continue
words, labels = model.get_line(line.strip())
if len(labels) == 0:
continue
all_words.append(" ".join(words))
true_labels += [labels]
predictions, _ = model.predict(all_words)
p, r = util.test(predictions, true_labels)
N = len(predictions)
Nt, pt, rt = model.test(tmpf2.name)
self.assertEqual(N, Nt)
self.assertEqual(p, pt)
self.assertEqual(r, rt)
def fit(self, X, y):
"""Fits the classifier
Parameters
----------
X : array-like, shape = [n_samples, n_features]
The training input samples.
y : array-like, shape = [n_samples]
The target values. An array of int.
Returns
-------
self : object
Returns self.
"""
# Check that X and y have correct shape
self._validate_x(X)
y = self._validate_y(y)
# Store the classes seen during fit
self.classes_ = unique_labels(y)
self.num_classes_ = len(self.classes_)
self.class_labels_ = [
'__label__{}'.format(lbl) for lbl in self.classes_]
# Dump training set to a fasttext-compatible file
temp_trainset_fpath = temp_dataset_fpath()
input_col = self._input_col(X)
dump_xy_to_fasttext_format(input_col, y, temp_trainset_fpath)
# train
self.model = train_supervised(
input=temp_trainset_fpath, **self.kwargs)
# Return the classifier
try:
os.remove(temp_trainset_fpath)
except FileNotFoundError: # pragma: no cover
pass
return self
def sup_test(self):
def get_path_size(path):
path_size = subprocess.check_output(["stat", "-c", "%s",
path]).decode('utf-8')
path_size = int(path_size)
return path_size
def check(model, model_filename, test, lessthan, msg_prefix=""):
N_local_out, p1_local_out, r1_local_out = model.test(test["data"])
self.assertEqual(
N_local_out, test["n"], msg_prefix + "N: Want: " +
str(test["n"]) + " Is: " + str(N_local_out)
)
self.assertTrue(
p1_local_out >= test["p1"], msg_prefix + "p1: Want: " +
str(test["p1"]) + " Is: " + str(p1_local_out)
)
self.assertTrue(
r1_local_out >= test["r1"], msg_prefix + "r1: Want: " +
str(test["r1"]) + " Is: " + str(r1_local_out)
)
path_size = get_path_size(model_filename)
size_msg = str(test["size"]) + " Is: " + str(path_size)
if lessthan:
self.assertTrue(
path_size <= test["size"],
msg_prefix + "Size: Want at most: " + size_msg
)
else:
self.assertTrue(
path_size == test["size"],
msg_prefix + "Size: Want: " + size_msg
)
configuration["args"]["input"] = os.path.join(
data_dir, configuration["args"]["input"]
)
configuration["quant_args"]["input"] = configuration["args"]["input"]
configuration["test"]["data"] = os.path.join(
data_dir, configuration["test"]["data"]
)
configuration["quant_test"]["data"] = configuration["test"]["data"]
output = os.path.join(tempfile.mkdtemp(), configuration["dataset"])
print()
model = train_supervised(**configuration["args"])
model.save_model(output + ".bin")
check(
model,
output + ".bin",
configuration["test"],
False,
msg_prefix="Supervised: "
)
print()
model.quantize(**configuration["quant_args"])
model.save_model(output + ".ftz")
check(
model,
output + ".ftz",
configuration["quant_test"],
True,
msg_prefix="Quantized: "
)
import os
from fastText import train_supervised
def print_results(N, p, r):
print("N\t" + str(N))
print("P@{}\t{:.3f}".format(1, p))
print("R@{}\t{:.3f}".format(1, r))
if __name__ == "__main__":
train_data = os.path.join(os.getenv("DATADIR", ''), 'cooking.train')
valid_data = os.path.join(os.getenv("DATADIR", ''), 'cooking.valid')
# train_supervised uses the same arguments and defaults as the fastText cli
model = train_supervised(
input=train_data, epoch=25, lr=1.0, wordNgrams=2, verbose=2, minCount=1
)
print_results(*model.test(valid_data))
model = train_supervised(
input=train_data, epoch=25, lr=1.0, wordNgrams=2, verbose=2, minCount=1,
loss="hs"
)
print_results(*model.test(valid_data))
model.save_model("cooking.bin")
model.quantize(input=train_data, qnorm=True, retrain=True, cutoff=100000)
print_results(*model.test(valid_data))
model.save_model("cooking.ftz")
print("f1: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
scores = cross_val_score(clf, x, y, cv = 5,scoring='accuracy')
#print scores
print("accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
if __name__ == '__main__':
SogouTCE_kv=load_SogouTCE()
#labels=load_url(SogouTCE_kv)
x,y=load_selecteddata(SogouTCE_kv)
stopwords=load_stopwords()
#切割token
x=[ [word for word in line.split() if word not in stopwords] for line in x]
# 分割训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
#按照fasttest的要求生成训练数据和测试数据
dump_file(x_train,y_train,"../data/sougou_train.txt")
dump_file(x_test, y_test, "../data/sougou_test.txt")
# train_supervised uses the same arguments and defaults as the fastText cli
model = train_supervised(
input="../data/sougou_train.txt",
epoch=25, lr=0.9, wordNgrams=2, verbose=2, minCount=1
)
print_results(*model.test("../data/sougou_test.txt"))
