def __init__(self, pickle_model="", datadir=None):
self.wb = wordbatch.WordBatch(
normalize_text,
extractor=(Hstack, [
(WordVec, {
"wordvec_file":
"../../../data/word2vec/glove.twitter.27B.100d.txt.gz",
"normalize_text": normalize_text
}),
(WordVec, {
"wordvec_file":
"../../../data/word2vec/glove.6B.50d.txt.gz",
"normalize_text": normalize_text
})
]))
self.wb.dictionary_freeze = True
self.clf = FTRL(alpha=1.0,
beta=1.0,
L1=0.00001,
L2=1.0,
D=2**25,
iters=1,
inv_link="identity")
if datadir == None:
(self.wb, self.clf) = pkl.load(gzip.open(pickle_model, 'rb'))
else:
self.train(datadir, pickle_model)
class WordvecRegressor(object):
def __init__(self, pickle_model="", datadir=None):
self.wb= wordbatch.WordBatch(normalize_text,
extractor=(Hstack,
[(WordVec, {"wordvec_file": "../../../data/word2vec/glove.twitter.27B.100d.txt.gz",
"normalize_text": normalize_text}),
(WordVec, {"wordvec_file": "../../../data/word2vec/glove.6B.50d.txt.gz",
"normalize_text": normalize_text})]))
self.wb.dictionary_freeze= True
self.clf= FTRL(alpha=1.0, beta=1.0, L1=0.00001, L2=1.0, D=2 ** 25, iters=1, inv_link= "identity")
if datadir==None: (self.wb, self.clf)= pkl.load(gzip.open(pickle_model, 'rb'))
else: self.train(datadir, pickle_model)
def fit_batch(self, texts, labels, rcount):
texts, labels = self.wb.shuffle_batch(texts, labels, rcount)
print("Transforming", rcount)
texts= self.wb.fit_transform(texts, reset= False)
print("Training", rcount)
self.clf.fit(texts, labels, reset= False)
def train(self, datadir, pickle_model=""):
texts= []
labels= []
training_data= os.listdir(datadir)
rcount= 0
batchsize= 100000
p= None
for jsonfile in training_data:
with open(datadir + "/" + jsonfile, 'r') as inputfile:
for line in inputfile:
#if rcount > 1000000: break
try: line= json.loads(line.strip())
except: continue
for review in line["Reviews"]:
rcount+= 1
if rcount % 100000 == 0: print(rcount)
if rcount % 6 != 0: continue
if "Overall" not in review["Ratings"]: continue
texts.append(review["Content"])
labels.append((float(review["Ratings"]["Overall"]) - 3) *0.5)
if len(texts) % batchsize == 0:
if p != None: p.join()
p= threading.Thread(target=self.fit_batch, args=(texts, labels, rcount))
p.start()
texts= []
labels= []
if p != None: p.join()
self.fit_batch(texts, labels, rcount)
if pickle_model!="":
with gzip.open(pickle_model, 'wb') as model_file:
pkl.dump((self.wb, self.clf), model_file, protocol=2)
def predict(self, texts):
vecs= self.wb.transform(texts)
return self.clf.predict(vecs)
def __init__(self, pickle_model="", datadir=None):
self.wb = wordbatch.WordBatch(normalize_text,
extractor=(WordBag, {
"hash_ngrams":
3,
"hash_ngrams_weights":
[-1.0, -1.0, 1.0],
"hash_size":
2**23,
"norm":
'l2',
"tf":
'binary',
"idf":
50.0
}))
self.clf = FTRL(alpha=1.0,
beta=1.0,
L1=0.00001,
L2=1.0,
D=2**23,
iters=1,
inv_link="identity")
if datadir == None:
(self.wb, self.clf) = pkl.load(gzip.open(pickle_model, 'rb'))
else:
self.train(datadir, pickle_model)
class WordbagRegressor(object):
def __init__(self, pickle_model="", datadir=None):
self.wordbatch = wordbatch.WordBatch(normalize_text, extractors=[(wordbatch.WordBag, {"hash_ngrams":3,
"hash_ngrams_weights":[-1.0, -1.0, 1.0],"hash_size":2**23, "norm":'l2', "tf":'binary', "idf":50.0})])
self.clf= FTRL(alpha=1.0, beta=1.0, L1=0.00001, L2=1.0, D=2 ** 25, iters=1, inv_link="identity")
if datadir==None: (self.wordbatch, self.clf)= pkl.load(gzip.open(pickle_model, u'rb'))
else: self.train(datadir, pickle_model)
def fit_batch(self, texts, labels, rcount):
texts, labels= self.wordbatch.shuffle_batch(texts, labels, rcount)
print "Transforming", rcount
texts= self.wordbatch.transform(texts)
print "Training", rcount
self.clf.fit(texts, labels)
def train(self, datadir, pickle_model=""):
texts= []
labels= []
training_data= os.listdir(datadir)
rcount= 0
batchsize= 100000
p = None
for jsonfile in training_data:
with open(datadir + "/" + jsonfile, u'r') as inputfile:
for line in inputfile:
#if rcount > 1000000: break
try: line = json.loads(line.strip())
except: continue
for review in line["Reviews"]:
rcount+= 1
if rcount % 100000 == 0: print rcount
if rcount % 7 != 0: continue
if "Overall" not in review["Ratings"]: continue
texts.append(review["Content"])
labels.append((float(review["Ratings"]["Overall"]) - 3) *0.5)
if len(texts) % batchsize == 0:
if p != None: p.join()
p= threading.Thread(target=self.fit_batch, args=(texts, labels, rcount))
p.start()
texts= []
labels= []
if p != None: p.join()
self.fit_batch(texts, labels, rcount)
self.wordbatch.dictionary_freeze= True
if pickle_model!="":
with gzip.open(pickle_model, u'wb') as model_file:
pkl.dump((self.wordbatch, self.clf), model_file, protocol=2)
def predict(self, texts):
counts= self.wordbatch.transform(texts)
return self.clf.predict(counts)
def __init__(self, param_dict, feature_dim):
alpha = param_dict['alpha']
beta = param_dict['beta']
L1 = param_dict['L1']
L2 = param_dict['L2']
iters = param_dict['iters']
self.model = FTRL(alpha=alpha,
beta=beta,
L1=L1,
L2=L2,
D=feature_dim,
iters=iters,
inv_link="identity",
threads=6)
def __init__(self, spar_type, spar_penalty):
# We create a separate model for each action in the environment's
# action space. Alternatively we could somehow encode the action
# into the features, but this way it's easier to code up.
self.models = []
for _ in range(env.action_space.n):
#model=Lasso(alpha=0.01)
model = SGDRegressor(learning_rate='constant',
penalty=spar_type,
l1_ratio=spar_penalty,
max_iter=1000)
model1 = PassiveAggressiveRegressor()
model2 = Lasso(alpha=0.1, normalize=True, warm_start=True)
model3 = FTRL(alpha=1.0,
beta=1.0,
L1=0.00001,
L2=1.0,
D=2**25,
iters=1)
#l2,l1,none,elasticnet
#,penalty='l1',l1_ratio=0)
#learning_rate="constant"
# We need to call partial_fit once to initialize the model
# or we get a NotFittedError when trying to make a prediction
# This is quite hacky.
#model2.fit([self.featurize_state(env.reset())], [0])
#X = np.array([self.featurize_state(env.reset())])
#Y = np.array([0])
#print X.shape, Y.shape
#model.partial_fit(X,Y)
model.partial_fit([self.featurize_state(env.reset())], [0])
self.models.append(model)
def __init__(self, pickle_model="", datadir=None):
from pyspark import SparkContext
self.sc= SparkContext()
self.wordbatch = wordbatch.WordBatch(normalize_text, extractor=(WordBag, {"hash_ngrams":3,
"hash_ngrams_weights":[-1.0, -1.0, 1.0],"hash_size":2**23, "norm":'l2', "tf":'binary', "idf":50.0}))
self.clf= FTRL(alpha=1.0, beta=1.0, L1=0.00001, L2=1.0, D=2 ** 25, iters=1, inv_link="identity")
self.wordbatch.use_sc= True
if datadir==None: (self.wordbatch, self.clf)= pkl.load(gzip.open(pickle_model, 'rb'))
else: self.train(datadir, pickle_model)
class vanila_FTRL_Regressor:
def __init__(self, param_dict, feature_dim):
alpha = param_dict['alpha']
beta = param_dict['beta']
L1 = param_dict['L1']
L2 = param_dict['L2']
iters = param_dict['iters']
self.model = FTRL(alpha=alpha,
beta=beta,
L1=L1,
L2=L2,
D=feature_dim,
iters=iters,
inv_link="identity",
threads=6)
def fit(self, X_train, y_train):
self.model.fit(X_train, y_train)
def predict(self, X_valid):
return self.model.predict(X_valid)
def __init__(self, pickle_model="", datadir=None, batcher=None):
self.wb = WordBatch(normalize_text=normalize_text,
extractor=WordBag(
hash_ngrams=3,
hash_ngrams_weights=[-1.0, -1.0, 1.0],
hash_size=2**23,
norm='l2',
tf='binary',
idf=50.0),
batcher=batcher)
self.clf = FTRL(alpha=1.0,
beta=1.0,
L1=0.00001,
L2=1.0,
D=2**23,
iters=1,
inv_link="identity")
if datadir == None:
(self.wb, self.clf) = pkl.load(gzip.open(pickle_model, 'rb'))
else:
self.train(datadir, pickle_model)
def __init__(self, pickle_model="", datadir=None):
self.wb= wordbatch.WordBatch(normalize_text,
extractor=(Hstack,
[(WordVec, {"wordvec_file": "../../../data/word2vec/glove.twitter.27B.100d.txt.gz",
"normalize_text": normalize_text}),
(WordVec, {"wordvec_file": "../../../data/word2vec/glove.6B.50d.txt.gz",
"normalize_text": normalize_text})]))
self.wb.dictionary_freeze= True
self.clf= FTRL(alpha=1.0, beta=1.0, L1=0.00001, L2=1.0, D=2 ** 25, iters=1, inv_link= "identity")
if datadir==None: (self.wb, self.clf)= pkl.load(gzip.open(pickle_model, 'rb'))
else: self.train(datadir, pickle_model)
def __init__(self, pickle_model="", datadir=None, batcher=None):
self.wb = WordBatch(
normalize_text,
extractor=Hstack([
WordVec(wordvec_file=
"../../../data/word2vec/glove.twitter.27B.100d.txt.gz",
normalize_text=normalize_text,
encoding="utf8"),
WordVec(
wordvec_file="../../../data/word2vec/glove.6B.50d.txt.gz",
normalize_text=normalize_text,
encoding="utf8")
]))
# from wordbatch.pipelines import FeatureUnion
# from wordbatch.transformers import Dictionary, TextNormalizer
# from sklearn.pipeline import Pipeline
# tn= TextNormalizer(normalize_text=normalize_text)
# dct= Dictionary()
# vec1= WordVec(wordvec_file="../../../data/word2vec/glove.twitter.27B.100d.txt.gz",
# normalize_text=normalize_text, encoding="utf8", dictionary= dct)
# vec2= WordVec(wordvec_file="../../../data/word2vec/glove.6B.50d.txt.gz",
# normalize_text=normalize_text, encoding="utf8", dictionary= dct)
# self.wb = Pipeline(steps= [("tn", tn), ("dct", dct), ("vecs", FeatureUnion([("vec1", vec1), ("vec2", vec2)]))])
self.batcher = batcher
self.clf = FTRL(alpha=1.0,
beta=1.0,
L1=0.00001,
L2=1.0,
D=100 + 50,
iters=1,
inv_link="identity")
if datadir == None:
(self.wb, self.clf) = pkl.load(gzip.open(pickle_model, 'rb'))
else:
self.train(datadir, pickle_model)
def FTRL_train(train_X, train_y, isQuickRun):
if isQuickRun:
model = FTRL(alpha=0.01,
beta=0.1,
L1=0.00001,
L2=1.0,
D=train_X.shape[1],
iters=9,
inv_link="identity",
threads=4)
else:
model = FTRL(alpha=0.01,
beta=0.1,
L1=0.00001,
L2=1.0,
D=train_X.shape[1],
iters=47,
inv_link="identity",
threads=4)
model.fit(train_X, train_y)
return model
gc.collect()
# Take the log of the target
y = np.log1p(target)
if enable_validation:
truth_sr = np.log1p(truth)
del train, target
gc.collect()
FTRL_model = FTRL(alpha=0.01,
beta=0.1,
L1=0.00001,
L2=1.0,
D=X.shape[1],
iters=50,
inv_link="identity",
threads=1)
FTRL_model.fit(X, y)
print("[{}] Train FTRL completed".format(time.time() - start_time))
FM_FTRL_model = FM_FTRL(alpha=0.01,
beta=0.01,
L1=0.00001,
L2=0.1,
D=X.shape[1],
alpha_fm=0.01,
L2_fm=0.0,
init_fm=0.01,
D_fm=200,
aver_rmse = 0.0
for train_index, val_index in kf.split(y):
fold_id += 1
print("Fold {} start...".format(fold_id))
train_X, valid_X = X[train_index], X[val_index]
train_y, valid_y = y[train_index], y[val_index]
# train_X, valid_X, train_y, valid_y = train_test_split(X_train, y, test_size=0.2, random_state=42)
# del X_train, y
d_shape = train_X.shape[1]
print('d_shape', d_shape)
model = FTRL(alpha=0.01,
beta=0.1,
L1=0.1,
L2=10,
D=d_shape,
iters=5,
inv_link="identity",
threads=8)
model.fit(train_X, train_y)
def rmse(predictions, targets):
print("calculating RMSE ...")
return np.sqrt(((predictions - targets)**2).mean())
preds_valid_ftrl = model.predict(X=valid_X)
# print(" FTRL dev RMSLE:", rmsle(np.expm1(valid_y), np.expm1(preds_valid_ftrl)))
print(" FTRL dev RMSLE:", rmse(valid_y, preds_valid_ftrl))
# # model = FM_FTRL(alpha=0.01, beta=0.01, L1=0.00001, L2=0.1, D=X_train.shape[1], alpha_fm=0.01, L2_fm=0.0,
# # init_fm=0.01,
# # D_fm=200, e_noise=0.0001, iters=15, inv_link="identity", threads=4)
#from sklearn.feature_extraction.text import HashingVectorizer
#from sklearn.linear_model import *
#vct= HashingVectorizer()
#clf= SGDRegressor()
import wordbatch
from wordbatch.models import FTRL
from wordbatch.extractors import WordBag
wb= wordbatch.WordBatch(extractor=(WordBag, {"hash_ngrams":2, "hash_ngrams_weights":[0.5, -1.0], "hash_size":2**23, "norm":'l2', "tf":'log', "idf":50.0}))
clf= FTRL(alpha=1.0, beta=1.0, L1=0.00001, L2=1.0, D=2 ** 25, iters=1)
train_texts= ["Cut down a tree with a herring? It can't be done.", "Don't say that word.", "How can we not say the word if you don't tell us what it is?"]
train_labels= [1, 0, 1]
test_texts= ["Wait! I said it! I said it! Ooh! I said it again!"]
values = wb.transform(train_texts)
clf.fit(values, train_labels)
preds= clf.predict(wb.transform(test_texts))
print("values={}".format(values))
print("values={}".format(len(values)))
print("texts={}".format(test_texts))
print("transformed={}".format(wb.transform(test_texts)))
print(preds)
def get_pred_ftrl(submission):
start_time = time.time()
from time import gmtime, strftime
print(strftime("%Y-%m-%d %H:%M:%S", gmtime()))
# if 1 == 1:
train = pd.read_table(
'../input/mercari-price-suggestion-challenge/train.tsv', engine='c')
test = pd.read_table(
'../input/mercari-price-suggestion-challenge/test.tsv', engine='c')
#train = pd.read_table('../input/train.tsv', engine='c')
#test = pd.read_table('../input/test.tsv', engine='c')
print('[{}] Finished to load data'.format(time.time() - start_time))
print('Train shape: ', train.shape)
print('Test shape: ', test.shape)
nrow_test = train.shape[0] # -dftt.shape[0]
train = train[train["price"] != 0]
#Xtrain,Xvalid = train_test_split(train, test_size=0.01,random_state=1)
nrow_train = train.shape[0]
#nrow_valid = Xvalid.shape[0]
# print(nrow_train, nrow_test)
y = np.log1p(train["price"])
merge: pd.DataFrame = pd.concat([train, test])
#submission: pd.DataFrame = test[['test_id']]
del train
del test
gc.collect()
merge['general_cat'], merge['subcat_1'], merge['subcat_2'] = \
zip(*merge['category_name'].apply(lambda x: split_cat(x)))
merge.drop('category_name', axis=1, inplace=True)
print('[{}] Split categories completed.'.format(time.time() - start_time))
handle_missing_inplace(merge)
print('[{}] Handle missing completed.'.format(time.time() - start_time))
cutting(merge)
print('[{}] Cut completed.'.format(time.time() - start_time))
to_categorical(merge)
print('[{}] Convert categorical completed'.format(time.time() -
start_time))
wb = wordbatch.WordBatch(normalize_text,
extractor=(WordBag, {
"hash_ngrams": 2,
"hash_ngrams_weights": [1.5, 1.0],
"hash_size": 2**29,
"norm": None,
"tf": 'binary',
"idf": None,
}),
procs=8)
wb.dictionary_freeze = True
X_name = wb.fit_transform(merge['name'])
del (wb)
X_name = X_name[:,
np.
array(np.clip(X_name.getnnz(axis=0) -
1, 0, 1), dtype=bool)]
print('[{}] Vectorize `name` completed.'.format(time.time() - start_time))
wb = CountVectorizer()
X_category1 = wb.fit_transform(merge['general_cat'])
X_category2 = wb.fit_transform(merge['subcat_1'])
X_category3 = wb.fit_transform(merge['subcat_2'])
print('[{}] Count vectorize `categories` completed.'.format(time.time() -
start_time))
# wb= wordbatch.WordBatch(normalize_text, extractor=(WordBag, {"hash_ngrams": 3, "hash_ngrams_weights": [1.0, 1.0, 0.5],
wb = wordbatch.WordBatch(normalize_text,
extractor=(WordBag, {
"hash_ngrams": 2,
"hash_ngrams_weights": [1.0, 1.0],
"hash_size": 2**28,
"norm": "l2",
"tf": 1.0,
"idf": None
}),
procs=8)
wb.dictionary_freeze = True
X_description = wb.fit_transform(merge['item_description'])
del (wb)
X_description = X_description[:,
np.array(np.clip(
X_description.getnnz(axis=0) - 1, 0, 1),
dtype=bool)]
print('[{}] Vectorize `item_description` completed.'.format(time.time() -
start_time))
lb = LabelBinarizer(sparse_output=True)
X_brand = lb.fit_transform(merge['brand_name'])
print('[{}] Label binarize `brand_name` completed.'.format(time.time() -
start_time))
X_dummies = csr_matrix(
pd.get_dummies(merge[['item_condition_id', 'shipping']],
sparse=True).values)
print('[{}] Get dummies on `item_condition_id` and `shipping` completed.'.
format(time.time() - start_time))
print(X_dummies.shape, X_description.shape, X_brand.shape,
X_category1.shape, X_category2.shape, X_category3.shape,
X_name.shape)
sparse_merge = hstack((X_dummies, X_description, X_brand, X_category1,
X_category2, X_category3, X_name)).tocsr()
print('[{}] Create sparse merge completed'.format(time.time() -
start_time))
# pd.to_pickle((sparse_merge, y), "xy.pkl")
# else:
# nrow_train, nrow_test= 1481661, 1482535
# sparse_merge, y = pd.read_pickle("xy.pkl")
# Remove features with document frequency <=1
print(sparse_merge.shape)
mask = np.array(np.clip(sparse_merge.getnnz(axis=0) - 1, 0, 1), dtype=bool)
sparse_merge = sparse_merge[:, mask]
X = sparse_merge[:nrow_train]
X_test = sparse_merge[nrow_train:]
print(sparse_merge.shape)
gc.collect()
train_X, train_y = X, y
#'''
if develop:
train_X, valid_X, train_y, valid_y = train_test_split(X,
y,
test_size=0.05,
random_state=100)
model = FTRL(alpha=0.01,
beta=0.1,
L1=0.00001,
L2=1.0,
D=sparse_merge.shape[1],
iters=50,
inv_link="identity",
threads=1)
model.fit(train_X, train_y)
print('[{}] Train FTRL completed'.format(time.time() - start_time))
if develop:
preds = model.predict(X=valid_X)
print("FTRL dev RMSLE:", rmsle(np.expm1(valid_y), np.expm1(preds)))
predsF = model.predict(X_test)
submission['price_FTRL'] = predsF
#print(rmsle(np.expm1(predsF),y_valid))
#'''
print('[{}] Predict FTRL completed'.format(time.time() - start_time))
model = FM_FTRL(alpha=0.01,
beta=0.01,
L1=0.00001,
L2=0.1,
D=sparse_merge.shape[1],
alpha_fm=0.01,
L2_fm=0.0,
init_fm=0.01,
D_fm=200,
e_noise=0.0001,
iters=17,
inv_link="identity",
threads=4)
model.fit(train_X, train_y)
print('[{}] Train ridge v2 completed'.format(time.time() - start_time))
if develop:
preds = model.predict(X=valid_X)
print("FM_FTRL dev RMSLE:", rmsle(np.expm1(valid_y), np.expm1(preds)))
predsFM = model.predict(X_test)
print('[{}] Predict FM_FTRL completed'.format(time.time() - start_time))
submission['price_FM_FTRL'] = predsFM
class WordvecRegressor(object):
def __init__(self, pickle_model="", datadir=None):
self.wb = wordbatch.WordBatch(
normalize_text,
extractor=(Hstack, [
(WordVec, {
"wordvec_file":
"../../../data/word2vec/glove.twitter.27B.100d.txt.gz",
"normalize_text": normalize_text
}),
(WordVec, {
"wordvec_file":
"../../../data/word2vec/glove.6B.50d.txt.gz",
"normalize_text": normalize_text
})
]))
self.wb.dictionary_freeze = True
self.clf = FTRL(alpha=1.0,
beta=1.0,
L1=0.00001,
L2=1.0,
D=2**25,
iters=1,
inv_link="identity")
if datadir == None:
(self.wb, self.clf) = pkl.load(gzip.open(pickle_model, 'rb'))
else:
self.train(datadir, pickle_model)
def fit_batch(self, texts, labels, rcount):
texts, labels = self.wb.shuffle_batch(texts, labels, rcount)
print("Transforming", rcount)
texts = self.wb.transform(texts)
print("Training", rcount)
self.clf.fit(texts, labels)
def train(self, datadir, pickle_model=""):
texts = []
labels = []
training_data = os.listdir(datadir)
rcount = 0
batchsize = 100000
p = None
for jsonfile in training_data:
with open(datadir + "/" + jsonfile, 'r') as inputfile:
for line in inputfile:
#if rcount > 1000000: break
try:
line = json.loads(line.strip())
except:
continue
for review in line["Reviews"]:
rcount += 1
if rcount % 100000 == 0: print(rcount)
if rcount % 6 != 0: continue
if "Overall" not in review["Ratings"]: continue
texts.append(review["Content"])
labels.append(
(float(review["Ratings"]["Overall"]) - 3) * 0.5)
if len(texts) % batchsize == 0:
if p != None: p.join()
p = threading.Thread(target=self.fit_batch,
args=(texts, labels, rcount))
p.start()
texts = []
labels = []
if p != None: p.join()
self.fit_batch(texts, labels, rcount)
if pickle_model != "":
with gzip.open(pickle_model, 'wb') as model_file:
pkl.dump((self.wb, self.clf), model_file, protocol=2)
def predict(self, texts):
vecs = self.wb.transform(texts)
return self.clf.predict(vecs)
def main():
start_time = time.time()
from time import gmtime, strftime
print(strftime("%Y-%m-%d %H:%M:%S", gmtime()))
# if 1 == 1:
###train = pd.read_table('../input/mercari-price-suggestion-challenge/train.tsv', engine='c')
###test = pd.read_table('../input/mercari-price-suggestion-challenge/test.tsv', engine='c')
train = pd.read_table('../input/train.tsv', engine='c')
test = pd.read_table('../input/test.tsv', engine='c')
print('[{}] Finished to load data'.format(time.time() - start_time))
print('Train shape: ', train.shape)
print('Test shape: ', test.shape)
nrow_test = train.shape[0] # -dftt.shape[0]
dftt = train[(train.price < 1.0)]
train = train.drop(train[(train.price < 1.0)].index)
del dftt['price']
nrow_train = train.shape[0]
# print(nrow_train, nrow_test)
y = np.log1p(train["price"])
merge = pd.concat([train, dftt, test])
submission = test[['test_id']]
del train
del test
gc.collect()
merge['general_cat'], merge['subcat_1'], merge['subcat_2'] = \
zip(*merge['category_name'].apply(lambda x: split_cat(x)))
merge.drop('category_name', axis=1, inplace=True)
print('[{}] Split categories completed.'.format(time.time() - start_time))
handle_missing_inplace(merge)
print('[{}] Handle missing completed.'.format(time.time() - start_time))
cutting(merge)
print('[{}] Cut completed.'.format(time.time() - start_time))
to_categorical(merge)
print('[{}] Convert categorical completed'.format(time.time() -
start_time))
wb = wordbatch.WordBatch(normalize_text,
extractor=(WordBag, {
"hash_ngrams": 2,
"hash_ngrams_weights": [1.5, 1.0],
"hash_size": 2**29,
"norm": None,
"tf": 'binary',
"idf": None,
}),
procs=8)
wb.dictionary_freeze = True
X_name = wb.fit_transform(merge['name'])
del (wb)
X_name = X_name[:,
np.
array(np.clip(X_name.getnnz(axis=0) -
1, 0, 1), dtype=bool)]
print('[{}] Vectorize `name` completed.'.format(time.time() - start_time))
wb = CountVectorizer()
X_category1 = wb.fit_transform(merge['general_cat'])
X_category2 = wb.fit_transform(merge['subcat_1'])
X_category3 = wb.fit_transform(merge['subcat_2'])
print('[{}] Count vectorize `categories` completed.'.format(time.time() -
start_time))
# wb= wordbatch.WordBatch(normalize_text, extractor=(WordBag, {"hash_ngrams": 3, "hash_ngrams_weights": [1.0, 1.0, 0.5],
wb = wordbatch.WordBatch(normalize_text,
extractor=(WordBag, {
"hash_ngrams": 2,
"hash_ngrams_weights": [1.0, 1.0],
"hash_size": 2**28,
"norm": "l2",
"tf": 1.0,
"idf": None
}),
procs=8)
wb.dictionary_freeze = True
X_description = wb.fit_transform(merge['item_description'])
del (wb)
X_description = X_description[:,
np.array(np.clip(
X_description.getnnz(axis=0) - 1, 0, 1),
dtype=bool)]
print('[{}] Vectorize `item_description` completed.'.format(time.time() -
start_time))
lb = LabelBinarizer(sparse_output=True)
X_brand = lb.fit_transform(merge['brand_name'])
print('[{}] Label binarize `brand_name` completed.'.format(time.time() -
start_time))
X_dummies = csr_matrix(
pd.get_dummies(merge[['item_condition_id', 'shipping']],
sparse=True).values)
print('[{}] Get dummies on `item_condition_id` and `shipping` completed.'.
format(time.time() - start_time))
print(X_dummies.shape, X_description.shape, X_brand.shape,
X_category1.shape, X_category2.shape, X_category3.shape,
X_name.shape)
sparse_merge = hstack((X_dummies, X_description, X_brand, X_category1,
X_category2, X_category3, X_name)).tocsr()
print('[{}] Create sparse merge completed'.format(time.time() -
start_time))
# pd.to_pickle((sparse_merge, y), "xy.pkl")
# else:
# nrow_train, nrow_test= 1481661, 1482535
# sparse_merge, y = pd.read_pickle("xy.pkl")
# Remove features with document frequency <=1
print(sparse_merge.shape)
mask = np.array(np.clip(sparse_merge.getnnz(axis=0) - 1, 0, 1), dtype=bool)
sparse_merge = sparse_merge[:, mask]
X = sparse_merge[:nrow_train]
X_test = sparse_merge[nrow_test:]
print(sparse_merge.shape)
gc.collect()
train_X, train_y = X, y
if develop:
train_X, valid_X, train_y, valid_y = train_test_split(X,
y,
test_size=0.05,
random_state=100)
model = FTRL(alpha=0.01,
beta=0.1,
L1=0.00001,
L2=1.0,
D=sparse_merge.shape[1],
iters=50,
inv_link="identity",
threads=1)
model.fit(train_X, train_y)
print('[{}] Train FTRL completed'.format(time.time() - start_time))
if develop:
preds = model.predict(X=valid_X)
print("FTRL dev RMSLE:", rmsle(np.expm1(valid_y), np.expm1(preds)))
predsF = model.predict(X_test)
print('[{}] Predict FTRL completed'.format(time.time() - start_time))
model = FM_FTRL(alpha=0.01,
beta=0.01,
L1=0.00001,
L2=0.1,
D=sparse_merge.shape[1],
alpha_fm=0.01,
L2_fm=0.0,
init_fm=0.01,
D_fm=200,
e_noise=0.0001,
iters=15,
inv_link="identity",
threads=4)
model.fit(train_X, train_y)
print('[{}] Train ridge v2 completed'.format(time.time() - start_time))
if develop:
preds = model.predict(X=valid_X)
print("FM_FTRL dev RMSLE:", rmsle(np.expm1(valid_y), np.expm1(preds)))
predsFM = model.predict(X_test)
print('[{}] Predict FM_FTRL completed'.format(time.time() - start_time))
params = {
'learning_rate': 0.6,
'application': 'regression',
'max_depth': 4,
'num_leaves': 31,
'verbosity': -1,
'metric': 'RMSE',
'data_random_seed': 1,
'bagging_fraction': 0.6,
'bagging_freq': 5,
'feature_fraction': 0.6,
'nthread': 4,
'min_data_in_leaf': 100,
'max_bin': 31
}
# Remove features with document frequency <=100
print(sparse_merge.shape)
mask = np.array(np.clip(sparse_merge.getnnz(axis=0) - 100, 0, 1),
dtype=bool)
sparse_merge = sparse_merge[:, mask]
X = sparse_merge[:nrow_train]
X_test = sparse_merge[nrow_test:]
print(sparse_merge.shape)
train_X, train_y = X, y
if develop:
train_X, valid_X, train_y, valid_y = train_test_split(X,
y,
test_size=0.05,
random_state=100)
d_train = lgb.Dataset(train_X, label=train_y)
watchlist = [d_train]
if develop:
d_valid = lgb.Dataset(valid_X, label=valid_y)
watchlist = [d_train, d_valid]
model = lgb.train(params, train_set=d_train, num_boost_round=6000, valid_sets=watchlist, \
early_stopping_rounds=1000, verbose_eval=1000)
if develop:
preds = model.predict(valid_X)
print("LGB dev RMSLE:", rmsle(np.expm1(valid_y), np.expm1(preds)))
predsL = model.predict(X_test)
print('[{}] Predict LGB completed.'.format(time.time() - start_time))
preds = (predsF * 0.2 + predsL * 0.3 + predsFM * 0.5)
submission['price'] = np.expm1(preds)
submission.to_csv("submission_wordbatch_ftrl_fm_lgb.csv", index=False)
def main():
start_time = time.time()
from time import gmtime, strftime
print(strftime("%Y-%m-%d %H:%M:%S", gmtime()))
# if 1 == 1:
train = pd.read_table('../input/mercari-price-suggestion-challenge/train.tsv', engine='c')
test = pd.read_table('../input/mercari-price-suggestion-challenge/test.tsv', engine='c')
#train = pd.read_table('../input/train.tsv', engine='c')
#test = pd.read_table('../input/test.tsv', engine='c')
print('[{}] Finished to load data'.format(time.time() - start_time))
print('Train shape: ', train.shape)
print('Test shape: ', test.shape)
nrow_test = train.shape[0] # -dftt.shape[0]
dftt = train[(train.price < 1.0)]
train = train.drop(train[(train.price < 1.0)].index)
del dftt['price']
nrow_train = train.shape[0]
# print(nrow_train, nrow_test)
y = np.log1p(train["price"])
merge: pd.DataFrame = pd.concat([train, dftt, test])
submission: pd.DataFrame = test[['test_id']]
del train
del test
gc.collect()
merge['general_cat'], merge['subcat_1'], merge['subcat_2'] = \
zip(*merge['category_name'].apply(lambda x: split_cat(x)))
merge.drop('category_name', axis=1, inplace=True)
print('[{}] Split categories completed.'.format(time.time() - start_time))
handle_missing_inplace(merge)
print('[{}] Handle missing completed.'.format(time.time() - start_time))
cutting(merge)
print('[{}] Cut completed.'.format(time.time() - start_time))
to_categorical(merge)
print('[{}] Convert categorical completed'.format(time.time() - start_time))
wb = wordbatch.WordBatch(normalize_text, extractor=(WordBag, {"hash_ngrams": 2, "hash_ngrams_weights": [1.5, 1.0],
"hash_size": 2 ** 29, "norm": None, "tf": 'binary',
"idf": None,
}), procs=8)
wb.dictionary_freeze= True
X_name = wb.fit_transform(merge['name'])
del(wb)
X_name = X_name[:, np.array(np.clip(X_name.getnnz(axis=0) - 1, 0, 1), dtype=bool)]
print('[{}] Vectorize `name` completed.'.format(time.time() - start_time))
wb = CountVectorizer()
X_category1 = wb.fit_transform(merge['general_cat'])
X_category2 = wb.fit_transform(merge['subcat_1'])
X_category3 = wb.fit_transform(merge['subcat_2'])
print('[{}] Count vectorize `categories` completed.'.format(time.time() - start_time))
# wb= wordbatch.WordBatch(normalize_text, extractor=(WordBag, {"hash_ngrams": 3, "hash_ngrams_weights": [1.0, 1.0, 0.5],
wb = wordbatch.WordBatch(normalize_text, extractor=(WordBag, {"hash_ngrams": 2, "hash_ngrams_weights": [1.0, 1.0],
"hash_size": 2 ** 28, "norm": "l2", "tf": 1.0,
"idf": None})
, procs=8)
wb.dictionary_freeze= True
X_description = wb.fit_transform(merge['item_description'])
del(wb)
X_description = X_description[:, np.array(np.clip(X_description.getnnz(axis=0) - 1, 0, 1), dtype=bool)]
print('[{}] Vectorize `item_description` completed.'.format(time.time() - start_time))
lb = LabelBinarizer(sparse_output=True)
X_brand = lb.fit_transform(merge['brand_name'])
print('[{}] Label binarize `brand_name` completed.'.format(time.time() - start_time))
X_dummies = csr_matrix(pd.get_dummies(merge[['item_condition_id', 'shipping']],
sparse=True).values)
print('[{}] Get dummies on `item_condition_id` and `shipping` completed.'.format(time.time() - start_time))
print(X_dummies.shape, X_description.shape, X_brand.shape, X_category1.shape, X_category2.shape, X_category3.shape,
X_name.shape)
sparse_merge = hstack((X_dummies, X_description, X_brand, X_category1, X_category2, X_category3, X_name)).tocsr()
print('[{}] Create sparse merge completed'.format(time.time() - start_time))
# pd.to_pickle((sparse_merge, y), "xy.pkl")
# else:
# nrow_train, nrow_test= 1481661, 1482535
# sparse_merge, y = pd.read_pickle("xy.pkl")
# Remove features with document frequency <=1
print(sparse_merge.shape)
mask = np.array(np.clip(sparse_merge.getnnz(axis=0) - 1, 0, 1), dtype=bool)
sparse_merge = sparse_merge[:, mask]
X = sparse_merge[:nrow_train]
X_test = sparse_merge[nrow_test:]
print(sparse_merge.shape)
gc.collect()
train_X, train_y = X, y
if develop:
train_X, valid_X, train_y, valid_y = train_test_split(X, y, test_size=0.05, random_state=100)
model = FTRL(alpha=0.01, beta=0.1, L1=0.00001, L2=1.0, D=sparse_merge.shape[1], iters=50, inv_link="identity", threads=1)
model.fit(train_X, train_y)
print('[{}] Train FTRL completed'.format(time.time() - start_time))
if develop:
preds = model.predict(X=valid_X)
print("FTRL dev RMSLE:", rmsle(np.expm1(valid_y), np.expm1(preds)))
predsF = model.predict(X_test)
print('[{}] Predict FTRL completed'.format(time.time() - start_time))
model = FM_FTRL(alpha=0.01, beta=0.01, L1=0.00001, L2=0.1, D=sparse_merge.shape[1], alpha_fm=0.01, L2_fm=0.0, init_fm=0.01,
D_fm=200, e_noise=0.0001, iters=15, inv_link="identity", threads=4)
model.fit(train_X, train_y)
print('[{}] Train ridge v2 completed'.format(time.time() - start_time))
if develop:
preds = model.predict(X=valid_X)
print("FM_FTRL dev RMSLE:", rmsle(np.expm1(valid_y), np.expm1(preds)))
predsFM = model.predict(X_test)
print('[{}] Predict FM_FTRL completed'.format(time.time() - start_time))
params = {
'learning_rate': 0.6,
'application': 'regression',
'max_depth': 4,
'num_leaves': 31,
'verbosity': -1,
'metric': 'RMSE',
'data_random_seed': 1,
'bagging_fraction': 0.6,
'bagging_freq': 5,
'feature_fraction': 0.6,
'nthread': 4,
'min_data_in_leaf': 100,
'max_bin': 31
}
# Remove features with document frequency <=100
print(sparse_merge.shape)
mask = np.array(np.clip(sparse_merge.getnnz(axis=0) - 100, 0, 1), dtype=bool)
sparse_merge = sparse_merge[:, mask]
X = sparse_merge[:nrow_train]
X_test = sparse_merge[nrow_test:]
print(sparse_merge.shape)
train_X, train_y = X, y
if develop:
train_X, valid_X, train_y, valid_y = train_test_split(X, y, test_size=0.05, random_state=100)
d_train = lgb.Dataset(train_X, label=train_y)
watchlist = [d_train]
if develop:
d_valid = lgb.Dataset(valid_X, label=valid_y)
watchlist = [d_train, d_valid]
model = lgb.train(params, train_set=d_train, num_boost_round=6000, valid_sets=watchlist, \
early_stopping_rounds=1000, verbose_eval=1000)
if develop:
preds = model.predict(valid_X)
print("LGB dev RMSLE:", rmsle(np.expm1(valid_y), np.expm1(preds)))
predsL = model.predict(X_test)
print('[{}] Predict LGB completed.'.format(time.time() - start_time))
preds = (predsF * 0.2 + predsL * 0.3 + predsFM * 0.5)
submission['price'] = np.expm1(preds)
submission.to_csv("submission_wordbatch_ftrl_fm_lgb.csv", index=False)
class WordbagRegressor(object):
def __init__(self, pickle_model="", datadir=None, batcher=None):
self.wb = WordBatch(normalize_text=normalize_text,
extractor=WordBag(
hash_ngrams=3,
hash_ngrams_weights=[-1.0, -1.0, 1.0],
hash_size=2**23,
norm='l2',
tf='binary',
idf=50.0),
batcher=batcher)
self.clf = FTRL(alpha=1.0,
beta=1.0,
L1=0.00001,
L2=1.0,
D=2**23,
iters=1,
inv_link="identity")
if datadir == None:
(self.wb, self.clf) = pkl.load(gzip.open(pickle_model, 'rb'))
else:
self.train(datadir, pickle_model)
def fit_batch(self, texts, labels, rcount):
texts, labels = self.wb.batcher.shuffle_batch(texts, labels, rcount)
print("Transforming", rcount)
texts = self.wb.fit_transform(texts, reset=False)
print("Training", rcount)
self.clf.fit(texts, labels, reset=False)
def train(self, datadir, pickle_model=""):
texts = []
labels = []
training_data = os.listdir(datadir)
rcount = 0
batchsize = 100000
p = None
for jsonfile in training_data:
with open(datadir + "/" + jsonfile, 'r') as inputfile:
for line in inputfile:
#if rcount > 1000000: break
try:
line = json.loads(line.strip())
except:
continue
for review in line["Reviews"]:
rcount += 1
if rcount % 100000 == 0: print(rcount)
if rcount % 7 != 0: continue
if "Overall" not in review["Ratings"]: continue
texts.append(review["Content"])
labels.append(
(float(review["Ratings"]["Overall"]) - 3) * 0.5)
if len(texts) % batchsize == 0:
if p != None: p.join()
p = threading.Thread(target=self.fit_batch,
args=(texts, labels, rcount))
p.start()
texts = []
labels = []
if p != None: p.join()
self.fit_batch(texts, labels, rcount)
self.wb.dictionary_freeze = True
if pickle_model != "":
with gzip.open(pickle_model, 'wb') as model_file:
backend = self.wb.batcher.backend
backend_handle = self.wb.batcher.backend_handle
self.wb.batcher.backend = "serial"
self.wb.batcher.backend_handle = None
pkl.dump((self.wb, self.clf), model_file, protocol=2)
self.wb.batcher.backend = backend
self.wb.batcher.backend_handle = backend_handle
def predict(self, texts):
counts = self.wb.transform(texts)
return self.clf.predict(counts)
class WordbagRegressor(object):
def __init__(self, pickle_model="", datadir=None):
from pyspark import SparkContext
self.sc= SparkContext()
self.wordbatch = wordbatch.WordBatch(normalize_text, backend="spark", backend_handle=self.sc,
extractor=(WordBag, {"hash_ngrams":3,
"hash_ngrams_weights":[-1.0, -1.0, 1.0],
"hash_size":2**23, "norm":'l2',
"tf":'binary', "idf":50.0}))
self.clf= FTRL(alpha=1.0, beta=1.0, L1=0.00001, L2=1.0, D=2 ** 23, iters=1, inv_link="identity")
if datadir==None: (self.wordbatch, self.clf)= pkl.load(gzip.open(pickle_model, 'rb'))
else: self.train(datadir, pickle_model)
def fit_batch(self, texts, labels, rcount):
print("Transforming", rcount)
# if self.sc != None:
# data_rdd= self.wordbatch.lists2rddbatches([texts, labels], self.sc)
# data_rdd= self.wordbatch.transform(data_rdd)
# [texts, labels]= self.wordbatch.rddbatches2lists(data_rdd)
# else:
# print(texts[:2])
# print(pd.Series(labels).value_counts())
texts= self.wordbatch.partial_fit_transform(texts)
print("Training", rcount)
self.clf.partial_fit(texts, labels)
def train(self, datadir, pickle_model=""):
texts= []
labels= []
training_data= os.listdir(datadir)
rcount= 0
batchsize= 20000
p = None
for jsonfile in training_data:
with open(datadir + "/" + jsonfile, 'r') as inputfile:
for line in inputfile:
#if rcount > 1000000: break
try: line = json.loads(line.strip())
except: continue
for review in line["Reviews"]:
rcount+= 1
if rcount % 100000 == 0: print(rcount)
if rcount % 7 != 0: continue
if "Overall" not in review["Ratings"]: continue
texts.append(review["Content"])
labels.append((float(review["Ratings"]["Overall"]) - 3) *0.5)
if len(texts) % batchsize == 0:
if p != None: p.join()
p= threading.Thread(target=self.fit_batch, args=(texts, labels, rcount))
p.start()
texts= []
labels= []
if p != None: p.join()
self.fit_batch(texts, labels, rcount)
self.wordbatch.dictionary_freeze= True
if pickle_model!="":
with gzip.open(pickle_model, 'wb') as model_file:
pkl.dump((self.wordbatch, self.clf), model_file, protocol=2)
def predict(self, texts):
# if self.sc != None:
# data_rdd= self.wordbatch.lists2rddbatches([texts, []], self.sc)
# data_rdd= self.wordbatch.transform(data_rdd)
# [counts, labels]= self.wordbatch.rddbatches2lists(data_rdd)
# else:
counts= self.wordbatch.transform(texts)
return self.clf.predict(counts)
def predict_parallel(self, texts):
# if self.sc != None:
# data_rdd= self.wordbatch.lists2rddbatches([texts, []] , self.sc)
# counts_rdd= self.wordbatch.transform(data_rdd)
# return self.wordbatch.rddbatches2lists(self.wordbatch.predict_parallel(counts_rdd, self.clf))[0]
counts= self.wordbatch.transform(texts)
return self.wordbatch.predict_parallel(counts, self.clf)
def main(test, logger):
logger.info('Start . . .')
train = pd.read_table('../input/train.tsv', engine='c')
logger.info('Load train')
logger.info('train shape {}'.format(train.shape))
logger.info('test shape {}'.format(test.shape))
nrow_train = train.shape[0]
y = np.log1p(train['price'])
train_low_price = train.loc[train['price'] < 1.]
train = train.drop(train[train['price'] < 1.].index)
del train_low_price['price']
logger.info('train_low_price shape {}'.format(train_low_price.shape))
df_full = pd.concat([train, train_low_price, test])
logger.info('df_full shape {}'.format(df_full.shape))
sub = test[['test_id']]
logger.info('sub shape {}'.format(sub.shape))
del train, test
gc.collect()
df_full['general_cat'], df_full['subcat_1'], df_full['subcat_2'] = zip(
*df_full['category_name'].apply(lambda x: split_category(x)))
df_full.drop(['category_name'], axis=1, inplace=True)
logger.info('Split category_name')
gc.collect()
df_full = impute_missing_value(df_full)
logger.info('Impute missing value')
gc.collect()
df_full = cut_df(df_full)
logger.info('Cut categories')
gc.collect()
df_full = to_categorical(df_full)
logger.info('Convert to categorical features')
gc.collect()
wb = wordbatch.WordBatch(normalize_text,
extractor=(WordBag, {
"hash_ngrams": 2,
"hash_ngrams_weights": [1.5, 1.0],
"hash_size": 2**29,
"norm": None,
"tf": 'binary',
"idf": None,
}),
procs=8)
wb.dictionary_freeze = True
X_name = wb.fit_transform(df_full['name'])
del wb
gc.collect()
X_name = X_name[:,
np.
array(np.clip(X_name.getnnz(axis=0) -
1, 0, 1), dtype=bool)]
logger.info('Vectorize name')
gc.collect()
cnt_vec = CountVectorizer()
X_cat_1 = cnt_vec.fit_transform(df_full['general_cat'])
X_cat_2 = cnt_vec.fit_transform(df_full['subcat_1'])
X_cat_3 = cnt_vec.fit_transform(df_full['subcat_2'])
df_full.drop(['general_cat', 'subcat_1', 'subcat_2'], axis=1, inplace=True)
del cnt_vec
gc.collect()
logger.info('Vectorize category (general_cat, subcat_1, subcat_2)')
wb = wordbatch.WordBatch(normalize_text,
extractor=(WordBag, {
"hash_ngrams": 2,
"hash_ngrams_weights": [1.0, 1.0],
"hash_size": 2**29,
"norm": "l2",
"tf": 1.0,
"idf": None
}),
procs=2)
wb.dictionary_freeze = True
X_description = wb.fit_transform(df_full['item_description'])
del wb
gc.collect()
X_description = X_description[:,
np.array(np.clip(
X_description.getnnz(axis=0) - 1, 0, 1),
dtype=bool)]
logger.info('Vectorize item_description')
gc.collect()
lb = LabelBinarizer(sparse_output=True)
X_brand = lb.fit_transform(df_full['brand_name'])
df_full.drop(['brand_name'], axis=1, inplace=True)
del lb
gc.collect()
logger.info('Label binarize brand_name')
X_dummies = csr_matrix(
pd.get_dummies(df_full[['item_condition_id', 'shipping']],
sparse=True).values)
df_full.drop(['item_condition_id', 'shipping'], axis=1, inplace=True)
logger.info('Get dummies on item_condition_id and shipping')
gc.collect()
sparse_merge = hstack((X_dummies, X_description, X_brand, X_cat_1, X_cat_2,
X_cat_3, X_name)).tocsr()
logger.info('Create sparse features')
logger.info('sparse_merge shape {}'.format(sparse_merge.shape))
del X_dummies, X_description, X_brand, X_cat_1, X_cat_2, X_cat_3, X_name
gc.collect()
mask = np.array(np.clip(sparse_merge.getnnz(axis=0) - 1, 0, 1), dtype=bool)
sparse_merge = sparse_merge[:, mask]
logger.info('Remove features with doc frequency <= 1')
logger.info('sparse_merge shape {}'.format(sparse_merge.shape))
X = sparse_merge[:nrow_train]
X_test = sparse_merge[nrow_train:]
sparse_merge_shape = sparse_merge.shape
del sparse_merge
gc.collect()
model = FTRL(alpha=0.01,
beta=0.1,
L1=0.00001,
L2=1.0,
D=sparse_merge_shape[1],
iters=30,
inv_link="identity",
threads=1)
model.fit(X, y)
logger.info('Fit FTRL')
preds_FTRL = model.predict(X_test)
logger.info('Predict FTRL')
model = FM_FTRL(alpha=0.01,
beta=0.1,
L1=0.00001,
L2=0.1,
D=sparse_merge_shape[1],
alpha_fm=0.01,
L2_fm=0.0,
init_fm=0.01,
D_fm=200,
e_noise=0.0001,
iters=20,
inv_link="identity",
threads=4)
model.fit(X, y)
logger.info('Fit FM_FTRL')
preds_FM_FTRL = model.predict(X_test)
logger.info('Predict FM_FTRL')
preds = (np.expm1(preds_FTRL) * 0.15 + np.expm1(preds_FM_FTRL) * 0.85)
logger.info('Final predictions generated')
return preds
def main():
start_time = time.time()
from time import gmtime, strftime
print(strftime("%Y-%m-%d %H:%M:%S", gmtime()))
# if 1 == 1:
train = pd.read_table(
'../input/mercari-price-suggestion-challenge/train.tsv', engine='c')
test = pd.read_table(
'../input/mercari-price-suggestion-challenge/test.tsv', engine='c')
#train = pd.read_table('../input/train.tsv', engine='c')
#test = pd.read_table('../input/test.tsv', engine='c')
print('[{}] Finished to load data'.format(time.time() - start_time))
print('Train shape: ', train.shape)
print('Test shape: ', test.shape)
nrow_test = train.shape[0] # -dftt.shape[0]
dftt = train[(train.price < 1.0)]
train = train.drop(train[(train.price < 1.0)].index)
del dftt['price']
nrow_train = train.shape[0]
# print(nrow_train, nrow_test)
y = np.log1p(train["price"])
merge: pd.DataFrame = pd.concat([train, dftt, test])
submission: pd.DataFrame = test[['test_id']]
del train
del test
gc.collect()
merge['general_cat'], merge['subcat_1'], merge['subcat_2'] = \
zip(*merge['category_name'].apply(lambda x: split_cat(x)))
merge.drop('category_name', axis=1, inplace=True)
print('[{}] Split categories completed.'.format(time.time() - start_time))
handle_missing_inplace(merge)
print('[{}] Handle missing completed.'.format(time.time() - start_time))
cutting(merge)
print('[{}] Cut completed.'.format(time.time() - start_time))
to_categorical(merge)
print('[{}] Convert categorical completed'.format(time.time() -
start_time))
wb = wordbatch.WordBatch(normalize_text,
extractor=(WordBag, {
"hash_ngrams": 2,
"hash_ngrams_weights": [1.5, 1.0],
"hash_size": 2**29,
"norm": None,
"tf": 'binary',
"idf": None,
}),
procs=8)
wb.dictionary_freeze = True
X_name = wb.fit_transform(merge['name'])
del (wb)
X_name = X_name[:,
np.
array(np.clip(X_name.getnnz(axis=0) -
1, 0, 1), dtype=bool)]
print('[{}] Vectorize `name` completed.'.format(time.time() - start_time))
wb = CountVectorizer()
X_category1 = wb.fit_transform(merge['general_cat'])
X_category2 = wb.fit_transform(merge['subcat_1'])
X_category3 = wb.fit_transform(merge['subcat_2'])
print('[{}] Count vectorize `categories` completed.'.format(time.time() -
start_time))
# wb= wordbatch.WordBatch(normalize_text, extractor=(WordBag, {"hash_ngrams": 3, "hash_ngrams_weights": [1.0, 1.0, 0.5],
wb = wordbatch.WordBatch(normalize_text,
extractor=(WordBag, {
"hash_ngrams": 2,
"hash_ngrams_weights": [1.0, 1.0],
"hash_size": 2**28,
"norm": "l2",
"tf": 1.0,
"idf": None
}),
procs=8)
wb.dictionary_freeze = True
X_description = wb.fit_transform(merge['item_description'])
del (wb)
X_description = X_description[:,
np.array(np.clip(
X_description.getnnz(axis=0) - 1, 0, 1),
dtype=bool)]
print('[{}] Vectorize `item_description` completed.'.format(time.time() -
start_time))
lb = LabelBinarizer(sparse_output=True)
X_brand = lb.fit_transform(merge['brand_name'])
print('[{}] Label binarize `brand_name` completed.'.format(time.time() -
start_time))
X_dummies = csr_matrix(
pd.get_dummies(merge[['item_condition_id', 'shipping']],
sparse=True).values)
print('[{}] Get dummies on `item_condition_id` and `shipping` completed.'.
format(time.time() - start_time))
print(X_dummies.shape, X_description.shape, X_brand.shape,
X_category1.shape, X_category2.shape, X_category3.shape,
X_name.shape)
sparse_merge = hstack((X_dummies, X_description, X_brand, X_category1,
X_category2, X_category3, X_name)).tocsr()
print('[{}] Create sparse merge completed'.format(time.time() -
start_time))
del X_dummies, merge, X_description, lb, X_brand, X_category1, X_category2, X_category3, X_name
gc.collect()
# pd.to_pickle((sparse_merge, y), "xy.pkl")
# else:
# nrow_train, nrow_test= 1481661, 1482535
# sparse_merge, y = pd.read_pickle("xy.pkl")
# Remove features with document frequency <=1
print(sparse_merge.shape)
mask = np.array(np.clip(sparse_merge.getnnz(axis=0) - 1, 0, 1), dtype=bool)
sparse_merge = sparse_merge[:, mask]
X = sparse_merge[:nrow_train]
X_test = sparse_merge[nrow_test:]
print(sparse_merge.shape)
train_X, train_y = X, y
if develop:
train_X, valid_X, train_y, valid_y = train_test_split(X,
y,
test_size=0.05,
random_state=100)
model = FTRL(alpha=0.01,
beta=0.1,
L1=0.00001,
L2=1.0,
D=sparse_merge.shape[1],
iters=30,
inv_link="identity",
threads=1)
del X
gc.collect()
model.fit(train_X, train_y)
print('[{}] Train FTRL completed'.format(time.time() - start_time))
if develop:
preds = model.predict(X=valid_X)
print("FTRL dev RMSLE:", rmsle(np.expm1(valid_y), np.expm1(preds)))
predsF = model.predict(X_test)
print('[{}] Predict FTRL completed'.format(time.time() - start_time))
model = FM_FTRL(alpha=0.012,
beta=0.01,
L1=0.00001,
L2=0.1,
D=sparse_merge.shape[1],
alpha_fm=0.01,
L2_fm=0.0,
init_fm=0.01,
D_fm=200,
e_noise=0.0001,
iters=17,
inv_link="identity",
threads=4)
model.fit(train_X, train_y)
del train_X, train_y
gc.collect()
print('[{}] Train ridge v2 completed'.format(time.time() - start_time))
if develop:
preds = model.predict(X=valid_X)
print("FM_FTRL dev RMSLE:", rmsle(np.expm1(valid_y), np.expm1(preds)))
predsFM = model.predict(X_test)
print('[{}] Predict FM_FTRL completed'.format(time.time() - start_time))
del X_test
gc.collect()
params = {
'learning_rate': 0.65,
'application': 'regression',
'max_depth': 4,
'num_leaves': 42,
'verbosity': -1,
'metric': 'RMSE',
'data_random_seed': 1,
'bagging_fraction': 0.71,
'bagging_freq': 5,
'feature_fraction': 0.67,
'nthread': 4,
'min_data_in_leaf': 120,
'max_bin': 40
}
# Remove features with document frequency <=100
print(sparse_merge.shape)
mask = np.array(np.clip(sparse_merge.getnnz(axis=0) - 100, 0, 1),
dtype=bool)
sparse_merge = sparse_merge[:, mask]
X = sparse_merge[:nrow_train]
X_test = sparse_merge[nrow_test:]
print(sparse_merge.shape)
del sparse_merge
gc.collect()
train_X, train_y = X, y
if develop:
train_X, valid_X, train_y, valid_y = train_test_split(X,
y,
test_size=0.05,
random_state=100)
del X, y
gc.collect()
d_train = lgb.Dataset(train_X, label=train_y)
# del train_X, train_y; gc.collect()
watchlist = [d_train]
if develop:
d_valid = lgb.Dataset(valid_X, label=valid_y)
del valid_y
gc.collect()
watchlist = [d_train, d_valid]
#model = lgb.train(params, train_set=d_train, num_boost_round=7500, valid_sets=watchlist, \
# early_stopping_rounds=1000, verbose_eval=1000)
model = lgb.train(params, train_set=d_train, num_boost_round=3000, valid_sets=watchlist, \
early_stopping_rounds=1000, verbose_eval=1000)
del d_train
gc.collect()
if develop:
preds = model.predict(valid_X)
del valid_X
gc.collect()
print("LGB dev RMSLE:", rmsle(np.expm1(valid_y), np.expm1(preds)))
predsL = model.predict(X_test)
# del X_test; gc.collect()
print('[{}] Predict LGB completed.'.format(time.time() - start_time))
#--- BEGIN Huber
# Details: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.HuberRegressor.html
# class sklearn.linear_model.HuberRegressor(epsilon=1.35,
# max_iter=100, alpha=0.0001, warm_start=False, fit_intercept=True,
# tol=1e-05)[source]
setup_Huber = 2
if (setup_Huber == 1):
model = HuberRegressor(fit_intercept=True,
alpha=0.01,
max_iter=80,
epsilon=363)
if (setup_Huber == 2):
model = HuberRegressor(fit_intercept=True,
alpha=0.05,
max_iter=200,
epsilon=1.2)
model.fit(train_X, train_y)
print('[{}] Predict Huber completed.'.format(time.time() - start_time))
predsH = model.predict(X=X_test)
#--- END Huber
# original
# preds = (predsF * 0.2 + predsL * 0.3 + predsFM * 0.5)
# modified setup (IT NEEDS MORE TUNING TESTS)
w = (0.09, 0.11, 0.23, 0.57)
preds = predsH * w[0] + predsF * w[1] + predsL * w[2] + predsFM * w[3]
submission['price'] = np.expm1(preds)
submission.to_csv("sub ftrl_fm_lgb_huber v3.csv", index=False)
nm = (time.time() - start_time) / 60
print("Total processing time %s min" % nm)
# sparse_merge, y = pd.read_pickle("xy.pkl")
# Remove features with document frequency <=1
print(sparse_merge.shape)
mask = np.array(np.clip(sparse_merge.getnnz(axis=0) - 1, 0, 1), dtype=bool)
sparse_merge = sparse_merge[:, mask]
X = sparse_merge[:nrow_train]
X_test = sparse_merge[nrow_test:]
print(sparse_merge.shape)
d_shape = sparse_merge.shape[1]
gc.collect()
train_X, train_y = X, y
if develop:
train_X, valid_X, train_y, valid_y = train_test_split(X, y, test_size=0.05, random_state=100)
model = FTRL(alpha=0.1680, beta=9.7895, L1=0.0011, L2=8.9635, D=d_shape, iters=int(11), inv_link="identity", threads=4)
###
del lb
del mask
del X_name
del X_category1
del X_category2
del X_category3
del X
del y
del merge
del X_dummies
del X_brand
del dftt
del X_description
class WordvecRegressor(object):
def __init__(self, pickle_model="", datadir=None, batcher=None):
self.wb = WordBatch(
normalize_text,
extractor=Hstack([
WordVec(wordvec_file=
"../../../data/word2vec/glove.twitter.27B.100d.txt.gz",
normalize_text=normalize_text,
encoding="utf8"),
WordVec(
wordvec_file="../../../data/word2vec/glove.6B.50d.txt.gz",
normalize_text=normalize_text,
encoding="utf8")
]))
# from wordbatch.pipelines import FeatureUnion
# from wordbatch.transformers import Dictionary, TextNormalizer
# from sklearn.pipeline import Pipeline
# tn= TextNormalizer(normalize_text=normalize_text)
# dct= Dictionary()
# vec1= WordVec(wordvec_file="../../../data/word2vec/glove.twitter.27B.100d.txt.gz",
# normalize_text=normalize_text, encoding="utf8", dictionary= dct)
# vec2= WordVec(wordvec_file="../../../data/word2vec/glove.6B.50d.txt.gz",
# normalize_text=normalize_text, encoding="utf8", dictionary= dct)
# self.wb = Pipeline(steps= [("tn", tn), ("dct", dct), ("vecs", FeatureUnion([("vec1", vec1), ("vec2", vec2)]))])
self.batcher = batcher
self.clf = FTRL(alpha=1.0,
beta=1.0,
L1=0.00001,
L2=1.0,
D=100 + 50,
iters=1,
inv_link="identity")
if datadir == None:
(self.wb, self.clf) = pkl.load(gzip.open(pickle_model, 'rb'))
else:
self.train(datadir, pickle_model)
def fit_batch(self, texts, labels, rcount):
texts, labels = shuffle(texts, labels)
print("Transforming", rcount)
#texts= self.wb.fit_transform(texts, tn__batcher=self.batcher, dct__reset= False, dct__batcher= self.batcher)
texts = self.wb.fit_transform(texts)
print("Training", rcount)
self.clf.fit(texts, labels, reset=False)
def train(self, datadir, pickle_model=""):
texts = []
labels = []
training_data = os.listdir(datadir)
rcount = 0
batchsize = 80000
p = None
for jsonfile in training_data:
with open(datadir + "/" + jsonfile, 'r') as inputfile:
for line in inputfile:
#if rcount > 1000000: break
try:
line = json.loads(line.strip())
except:
continue
for review in line["Reviews"]:
rcount += 1
if rcount % 100000 == 0: print(rcount)
if rcount % 6 != 0: continue
if "Overall" not in review["Ratings"]: continue
texts.append(review["Content"])
labels.append(
(float(review["Ratings"]["Overall"]) - 3) * 0.5)
if len(texts) % batchsize == 0:
if p != None: p.join()
p = threading.Thread(target=self.fit_batch,
args=(texts, labels, rcount))
p.start()
texts = []
labels = []
if p != None: p.join()
self.fit_batch(texts, labels, rcount)
# if pickle_model!="":
# with gzip.open(pickle_model, 'wb') as model_file:
# backend = self.wb.batcher.backend
# backend_handle = self.wb.batcher.backend_handle
# self.wb.batcher.backend = "serial"
# self.wb.batcher.backend_handle = None
# pkl.dump((self.wb, self.clf), model_file, protocol=2)
# self.wb.batcher.backend = backend
# self.wb.batcher.backend_handle = backend_handle
def predict(self, texts):
vecs = self.wb.transform(texts)
return self.clf.predict(vecs)
def wordbatch_algo(test):
import time
print(strftime("%Y-%m-%d %H:%M:%S", gmtime()))
# if 1 == 1:
# train = pd.read_table('../input/mercari-price-suggestion-challenge/train.tsv', engine='c')
# test = pd.read_table('../input/mercari-price-suggestion-challenge/test.tsv', engine='c')
train = pd.read_table('../input/train.tsv', engine='c')
# test = pd.read_table('../input/test.tsv', engine='c')
print('[{}] Finished to load data'.format(time.time() - start_time))
print('Train shape: ', train.shape)
print('Test shape: ', test.shape)
nrow_test = train.shape[0] # -dftt.shape[0]
dftt = train[(train.price < 1.0)]
train = train.drop(train[(train.price < 1.0)].index)
del dftt['price']
nrow_train = train.shape[0]
# print(nrow_train, nrow_test)
y = np.log1p(train["price"])
merge: pd.DataFrame = pd.concat([train, dftt, test])
# submission: pd.DataFrame = test[['test_id']]
'''
# Mean of each group # https://stackoverflow.com/questions/30244952/python-pandas-create-new-column-with-groupby-sum
cat_mean = train['price'].groupby(train['category_name']).mean()
cat_mean = pd.DataFrame({'category_name':cat_mean.index, 'cat_mean':cat_mean.values})
merge = merge.merge(cat_mean, on=['category_name'], how='left')
# print(merge.head())
X_cat_mean = merge['cat_mean'].as_matrix().reshape(-1, 1)
# X_cat_mean = normalize(np.nan_to_num(X_cat_mean).reshape(-1, 1), norm='max')
cond_mean = train['price'].groupby(train['item_condition_id']).mean()
cond_mean = pd.DataFrame({'item_condition_id':cond_mean.index, 'cond_mean':cond_mean.values})
merge = merge.merge(cond_mean, on=['item_condition_id'], how='left')
X_cond_mean = merge['cond_mean'].as_matrix().reshape(-1, 1)
brand_mean = train['price'].groupby(train['brand_name']).mean()
brand_mean = pd.DataFrame({'brand_name':brand_mean.index, 'brand_mean':brand_mean.values})
merge = merge.merge(brand_mean, on=['brand_name'], how='left')
X_brand_mean = merge['brand_mean'].as_matrix().reshape(-1, 1)
ship_mean = train['price'].groupby(train['shipping']).mean()
ship_mean = pd.DataFrame({'shipping':ship_mean.index, 'ship_mean':ship_mean.values})
merge = merge.merge(ship_mean, on=['shipping'], how='left')
X_ship_mean = merge['ship_mean'].as_matrix().reshape(-1, 1)
'''
del train
del test
gc.collect()
merge['general_cat'], merge['subcat_1'], merge['subcat_2'] = \
zip(*merge['category_name'].apply(lambda x: split_cat(x)))
merge.drop('category_name', axis=1, inplace=True)
print('[{}] Split categories completed.'.format(time.time() - start_time))
handle_missing_inplace(merge)
print('[{}] Handle missing completed.'.format(time.time() - start_time))
cutting(merge)
print('[{}] Cut completed.'.format(time.time() - start_time))
to_categorical(merge)
print('[{}] Convert categorical completed'.format(time.time() -
start_time))
# Add some new features:
X_len_desc = merge['item_description'].apply(
lambda x: len(x)).as_matrix().reshape(-1, 1)
X_len_name = merge['name'].apply(lambda x: len(x)).as_matrix().reshape(
-1, 1)
# X_len_description = normalize(np.nan_to_num(X_len_description).reshape(-1, 1), norm='max')
# X_len_name = normalize(np.nan_to_num(X_len_name).reshape(-1, 1), norm='max')
print('[{}] Length `item_description` completed.'.format(time.time() -
start_time))
print('[{}] Length `name` completed.'.format(time.time() - start_time))
wb = wordbatch.WordBatch(normalize_text,
extractor=(WordBag, {
"hash_ngrams": 2,
"hash_ngrams_weights": [1.5, 1.0],
"hash_size": 2**29,
"norm": None,
"tf": 'binary',
"idf": None,
}),
procs=8)
wb.dictionary_freeze = True
X_name = wb.fit_transform(merge['name'])
del (wb)
X_name = X_name[:,
np.
array(np.clip(X_name.getnnz(axis=0) -
1, 0, 1), dtype=bool)]
print('[{}] Vectorize `name` completed.'.format(time.time() - start_time))
wb = CountVectorizer()
X_category1 = wb.fit_transform(merge['general_cat'])
X_category2 = wb.fit_transform(merge['subcat_1'])
X_category3 = wb.fit_transform(merge['subcat_2'])
print('[{}] Count vectorize `categories` completed.'.format(time.time() -
start_time))
# wb= wordbatch.WordBatch(normalize_text, extractor=(WordBag, {"hash_ngrams": 3, "hash_ngrams_weights": [1.0, 1.0, 0.5],
wb = wordbatch.WordBatch(normalize_text,
extractor=(WordBag, {
"hash_ngrams": 2,
"hash_ngrams_weights": [1.0, 1.0],
"hash_size": 2**28,
"norm": "l2",
"tf": 1.0,
"idf": None
}),
procs=8)
wb.dictionary_freeze = True
X_description = wb.fit_transform(merge['item_description'])
del (wb)
X_description = X_description[:,
np.array(np.clip(
X_description.getnnz(axis=0) - 1, 0, 1),
dtype=bool)]
print('[{}] Vectorize `item_description` completed.'.format(time.time() -
start_time))
lb = LabelBinarizer(sparse_output=True)
X_brand = lb.fit_transform(merge['brand_name'])
print('[{}] Label binarize `brand_name` completed.'.format(time.time() -
start_time))
X_dummies = csr_matrix(
pd.get_dummies(merge[['item_condition_id', 'shipping']],
sparse=True).values)
print('[{}] Get dummies on `item_condition_id` and `shipping` completed.'.
format(time.time() - start_time))
print(
X_dummies.shape, X_description.shape, X_brand.shape, X_category1.shape,
X_category2.shape, X_category3.shape, X_name.shape
) #, X_glove.shape, X_len_description.shape, X_len_name.shape, X_cat_mean.shape)
# sparse_merge = hstack((X_dummies, X_description, X_brand, X_category1, X_category2, X_category3, X_name)).tocsr()
sparse_merge = hstack((X_dummies, X_description, X_brand, X_category1,
X_category2, X_category3, X_name)).tocsr()
print('[{}] Create sparse merge completed'.format(time.time() -
start_time))
del X_dummies, merge, X_description, lb, X_brand, X_category1, X_category2, X_category3, X_name
gc.collect()
# pd.to_pickle((sparse_merge, y), "xy.pkl")
# else:
# nrow_train, nrow_test= 1481661, 1482535
# sparse_merge, y = pd.read_pickle("xy.pkl")
# Remove features with document frequency <=1
print(sparse_merge.shape)
mask = np.array(np.clip(sparse_merge.getnnz(axis=0) - 1, 0, 1), dtype=bool)
sparse_merge = sparse_merge[:, mask]
X = sparse_merge[:nrow_train]
X_test = sparse_merge[nrow_test:]
print(sparse_merge.shape)
train_X, train_y = X, y
if develop:
train_X, valid_X, train_y, valid_y = train_test_split(
X, y, test_size=TEST_SIZE, random_state=SPLIT_SEED)
model = FM_FTRL(alpha=0.01,
beta=0.01,
L1=0.00001,
L2=0.1,
D=sparse_merge.shape[1],
alpha_fm=0.01,
L2_fm=0.0,
init_fm=0.01,
D_fm=200,
e_noise=0.0001,
iters=FM_iter,
inv_link="identity",
threads=4)
model.fit(train_X, train_y)
gc.collect()
print('[{}] Train FM_FTRL completed'.format(time.time() - start_time))
if develop:
preds = model.predict(X=valid_X)
print("FM_FTRL dev RMSLE:", rmsle(np.expm1(valid_y), np.expm1(preds)))
predsFM = model.predict(X_test)
print('[{}] Predict FM_FTRL completed'.format(time.time() - start_time))
gc.collect()
print(predsFM)
#model = FTRL(alpha=0.01, beta=0.1, L1=0.00001, L2=1.0, D=sparse_merge.shape[1], iters=50, inv_link="identity", threads=1)
model = FTRL(alpha=0.01,
beta=0.1,
L1=0.00001,
L2=1.0,
D=sparse_merge.shape[1],
iters=FTRL_iter,
inv_link="identity",
threads=1)
del X
gc.collect()
model.fit(train_X, train_y)
print('[{}] Train FTRL completed'.format(time.time() - start_time))
if develop:
preds = model.predict(X=valid_X)
print("FTRL dev RMSLE:", rmsle(np.expm1(valid_y), np.expm1(preds)))
predsF = model.predict(X_test)
print('[{}] Predict FTRL completed'.format(time.time() - start_time))
print(predsF)
del train_X, train_y
del X_test
return predsFM, predsF
del(wb)
X_description = X_description[:, np.where(X_description.getnnz(axis=0) > 1)[0]]
lb = LabelBinarizer(sparse_output=True)
X_brand = lb.fit_transform(merge['Source'])
X_dummies = csr_matrix(pd.get_dummies(merge[['IDLink', 'Facebook']],
sparse=True).values)
sparse_merge = hstack((X_dummies, X_description, X_brand, X_category1, X_name)).tocsr()
sparse_merge = sparse_merge[:, np.where(sparse_merge.getnnz(axis=0) > 100)[0]]
X = sparse_merge[:nrow_train]
X_test = sparse_merge[nrow_test:]
print(sparse_merge.shape)
gc.collect()
train_X, train_y = X, y
if develop:
train_X, valid_X, train_y, valid_y = train_test_split(X, y, test_size=0.05, random_state=100)
model = FTRL(alpha=0.01, beta=0.1, L1=0.00001, L2=1.0, D=sparse_merge.shape[1], iters=50, inv_link="identity", threads=1)
model.fit(train_X, train_y)
print('[{}] Train FTRL completed'.format(time.time() - start_time))
if develop:
preds = model.predict(X=valid_X)
print("FTRL dev RMSLE:", rmsle(np.expm1(valid_y), np.expm1(preds)))
predsF = model.predict(X_test)
print('[{}] Predict FTRL completed'.format(time.time() - start_time))
model = FM_FTRL(alpha=0.01, beta=0.01, L1=0.00001, L2=0.1, D=sparse_merge.shape[1], alpha_fm=0.01, L2_fm=0.0, init_fm=0.01,
D_fm=200, e_noise=0.0001, iters=17, inv_link="identity", threads=4)
model.fit(train_X, train_y)
print('[{}] Train ridge v2 completed'.format(time.time() - start_time))
if develop:
preds = model.predict(X=valid_X)
print("FM_FTRL dev RMSLE:", rmsle(np.expm1(valid_y), np.expm1(preds)))
predsFM = model.predict(X_test)
use_avx=1,
verbose=0)
# iters changed to 2 from 116_7
# threads=4, use_avx=1, verbose=0)
elif wordbatch_model == 'NN_ReLU_H1':
clf = NN_ReLU_H1(alpha=0.05,
D=D,
verbose=9,
e_noise=0.0,
threads=4,
inv_link="sigmoid")
elif wordbatch_model == 'FTRL':
clf = FTRL(alpha=0.05,
beta=0.1,
L1=0.0,
L2=0.0,
D=D,
iters=3,
threads=4,
verbose=9)
dtypes = {
'ip': 'uint32',
'app': 'uint16',
'device': 'uint16',
'os': 'uint16',
'channel': 'uint16',
'is_attributed': 'uint8',
}
p = None
rcount = 0
class WordbagRegressor(object):
def __init__(self, pickle_model="", datadir=None):
from pyspark import SparkContext
self.sc= SparkContext()
self.wordbatch = wordbatch.WordBatch(normalize_text, extractor=(WordBag, {"hash_ngrams":3,
"hash_ngrams_weights":[-1.0, -1.0, 1.0],"hash_size":2**23, "norm":'l2', "tf":'binary', "idf":50.0}))
self.clf= FTRL(alpha=1.0, beta=1.0, L1=0.00001, L2=1.0, D=2 ** 25, iters=1, inv_link="identity")
self.wordbatch.use_sc= True
if datadir==None: (self.wordbatch, self.clf)= pkl.load(gzip.open(pickle_model, 'rb'))
else: self.train(datadir, pickle_model)
def fit_batch(self, texts, labels, rcount):
texts, labels= self.wordbatch.shuffle_batch(texts, labels, rcount)
print("Transforming", rcount)
if self.sc != None:
data_rdd= self.wordbatch.lists2rddbatches([texts, labels], self.sc)
data_rdd= self.wordbatch.transform(data_rdd)
[texts, labels]= self.wordbatch.rddbatches2lists(data_rdd)
else:
texts= self.wordbatch.transform(texts)
print("Training", rcount)
self.clf.fit(texts, labels)
def train(self, datadir, pickle_model=""):
texts= []
labels= []
training_data= os.listdir(datadir)
rcount= 0
batchsize= 20000
p = None
for jsonfile in training_data:
with open(datadir + "/" + jsonfile, 'r') as inputfile:
for line in inputfile:
#if rcount > 1000000: break
try: line = json.loads(line.strip())
except: continue
for review in line["Reviews"]:
rcount+= 1
if rcount % 100000 == 0: print(rcount)
if rcount % 7 != 0: continue
if "Overall" not in review["Ratings"]: continue
texts.append(review["Content"])
labels.append((float(review["Ratings"]["Overall"]) - 3) *0.5)
if len(texts) % batchsize == 0:
if p != None: p.join()
p= threading.Thread(target=self.fit_batch, args=(texts, labels, rcount))
p.start()
texts= []
labels= []
if p != None: p.join()
self.fit_batch(texts, labels, rcount)
self.wordbatch.dictionary_freeze= True
if pickle_model!="":
with gzip.open(pickle_model, 'wb') as model_file:
pkl.dump((self.wordbatch, self.clf), model_file, protocol=2)
def predict(self, texts):
if self.sc != None:
data_rdd= self.wordbatch.lists2rddbatches([texts, []], self.sc)
data_rdd= self.wordbatch.transform(data_rdd)
[counts, labels]= self.wordbatch.rddbatches2lists(data_rdd)
else: counts= self.wordbatch.transform(texts)
return self.clf.predict(counts)
def predict_parallel(self, texts):
if self.sc != None:
data_rdd= self.wordbatch.lists2rddbatches([texts, []], self.sc)
counts_rdd= self.wordbatch.transform(data_rdd)
return self.wordbatch.rddbatches2lists(self.wordbatch.predict_parallel(counts_rdd, self.clf))[0]
counts= self.wordbatch.transform(texts)
return self.wordbatch.predict_parallel(counts, self.clf)
def wordbatch_algo():
import time
# print(strftime("%Y-%m-%d %H:%M:%S", gmtime()))
train = pd.read_table('../input/train.tsv', engine='c')
# Drop rows where price = 0
train = train[train.price != 0].reset_index(drop=True)
print('[{}] Finished to load data'.format(time.time() - start_time))
print('Train shape: ', train.shape)
y = np.log1p(train["price"])
nrow_train = train.shape[0]
# Training
train['general_cat'], train['subcat_1'], train['subcat_2'] = \
zip(*train['category_name'].apply(lambda x: split_cat(x)))
train.drop('category_name', axis=1, inplace=True)
print('[{}] Split categories completed.'.format(time.time() - start_time))
handle_missing_inplace(train)
print('[{}] Handle missing completed.'.format(time.time() - start_time))
cutting(train)
print('[{}] Cut completed.'.format(time.time() - start_time))
to_categorical(train)
print('[{}] Convert categorical completed'.format(time.time() -
start_time))
# Add some new features:
X_len_desc = train['item_description'].apply(
lambda x: len(x)).as_matrix().reshape(-1, 1)
X_len_name = train['name'].apply(lambda x: len(x)).as_matrix().reshape(
-1, 1)
print('[{}] Length of text completed.'.format(time.time() - start_time))
# Name
wb_name = wordbatch.WordBatch(normalize_text,
extractor=(WordBag, {
"hash_ngrams": 2,
"hash_ngrams_weights": [1.5, 1.0],
"hash_size": 2**29,
"norm": None,
"tf": 'binary',
"idf": None,
}),
procs=8)
wb_name.dictionary_freeze = True
wb_name.fit(train['name'])
X_name = wb_name.transform(train['name'])
# X_name = X_name[:, np.array(np.clip(X_name.getnnz(axis=0) - 1, 0, 1), dtype=bool)]
print('[{}] Vectorize `name` completed.'.format(time.time() - start_time))
wb_cat1 = CountVectorizer()
wb_cat2 = CountVectorizer()
wb_cat3 = CountVectorizer()
wb_cat1.fit(train['general_cat'])
wb_cat2.fit(train['subcat_1'])
wb_cat3.fit(train['subcat_2'])
X_category1 = wb_cat1.transform(train['general_cat'])
X_category2 = wb_cat2.transform(train['subcat_1'])
X_category3 = wb_cat3.transform(train['subcat_2'])
print('[{}] Count vectorize `categories` completed.'.format(time.time() -
start_time))
# wb= wordbatch.WordBatch(normalize_text, extractor=(WordBag, {"hash_ngrams": 3, "hash_ngrams_weights": [1.0, 1.0, 0.5],
wb_desc = wordbatch.WordBatch(normalize_text,
extractor=(WordBag, {
"hash_ngrams": 2,
"hash_ngrams_weights": [1.0, 1.0],
"hash_size": 2**28,
"norm": "l2",
"tf": 1.0,
"idf": None
}),
procs=8)
wb_desc.dictionary_freeze = True
wb_desc.fit(train['item_description'])
X_description = wb_desc.transform(train['item_description'])
# X_description = X_description[:, np.array(np.clip(X_description.getnnz(axis=0) - 1, 0, 1), dtype=bool)]
print('[{}] Vectorize `item_description` completed.'.format(time.time() -
start_time))
lb = LabelBinarizer(sparse_output=True)
lb.fit(train['brand_name'])
X_brand = lb.transform(train['brand_name'])
print('[{}] Label binarize `brand_name` completed.'.format(time.time() -
start_time))
X_cond, d_cond = fit_dummy(train['item_condition_id'].tolist())
X_ship, d_ship = fit_dummy(train['shipping'].tolist())
print('[{}] Get dummies on `item_condition_id` and `shipping` completed.'.
format(time.time() - start_time))
del train
gc.collect()
print(X_cond.shape, X_ship.shape, X_description.shape, X_brand.shape,
X_category1.shape, X_category2.shape, X_category3.shape,
X_name.shape)
sparse_merge = hstack((X_cond, X_ship, X_description, X_brand, X_category1,
X_category2, X_category3, X_name)).tocsr()
print('[{}] Create sparse merge completed'.format(time.time() -
start_time))
del X_description, X_brand, X_category1, X_category2, X_category3, X_name
gc.collect()
# Remove features with document frequency <=1
print(sparse_merge.shape)
mask = np.array(np.clip(sparse_merge.getnnz(axis=0) - 1, 0, 1), dtype=bool)
sparse_merge = sparse_merge[:, mask]
print(sparse_merge.shape)
X = sparse_merge
# ---------------------------------------
# FM model fit
train_X, train_y = X, y
if develop:
train_X, valid_X, train_y, valid_y = train_test_split(
X, y, test_size=TEST_SIZE, random_state=SPLIT_SEED)
model = FM_FTRL(alpha=0.01,
beta=0.01,
L1=0.00001,
L2=0.1,
D=train_X.shape[1],
alpha_fm=0.01,
L2_fm=0.0,
init_fm=0.01,
D_fm=200,
e_noise=0.0001,
iters=FM_iter,
inv_link="identity",
threads=4)
model.fit(train_X, train_y)
print('[{}] Train FM_FTRL completed'.format(time.time() - start_time))
print('-' * 20)
if develop:
preds = model.predict(X=valid_X)
print("->>>> FM_FTRL dev RMSLE:",
rmsle(np.expm1(valid_y), np.expm1(preds)))
# ---------------------------------------
# FTRL model fit
model2 = FTRL(alpha=0.01,
beta=0.01,
L1=0.00001,
L2=1.0,
D=train_X.shape[1],
iters=FTRL_iter,
inv_link="identity",
threads=1)
# del X; gc.collect()
model2.fit(train_X, train_y)
print('[{}] Train FTRL completed'.format(time.time() - start_time))
if develop:
preds = model2.predict(X=valid_X)
print("->>>> FTRL dev RMSLE:",
rmsle(np.expm1(valid_y), np.expm1(preds)))
# Clear variables:
del X, train_X, train_y, sparse_merge
gc.collect()
# ---------------------------------------
# Testing by chunk
print(' FM/FTRL: ...reading the test data...')
predsFM = []
predsF = []
for test in load_test():
test['general_cat'], test['subcat_1'], test['subcat_2'] = \
zip(*test['category_name'].apply(lambda x: split_cat(x)))
test.drop('category_name', axis=1, inplace=True)
handle_missing_inplace(test)
#print('[{}] Handle missing completed.'.format(time.time() - start_time))
cutting(test)
# print('[{}] Cut completed.'.format(time.time() - start_time))
to_categorical(test)
# print('[{}] Convert categorical completed'.format(time.time() - start_time))
# Add some new features:
X_len_desc_test = test['item_description'].apply(
lambda x: len(x)).as_matrix().reshape(-1, 1)
X_len_name_test = test['name'].apply(
lambda x: len(x)).as_matrix().reshape(-1, 1)
X_name_test = wb_name.transform(test['name'])
# X_name = X_name[:, np.array(np.clip(X_name.getnnz(axis=0) - 1, 0, 1), dtype=bool)]
X_category1_test = wb_cat1.transform(test['general_cat'])
X_category2_test = wb_cat2.transform(test['subcat_1'])
X_category3_test = wb_cat3.transform(test['subcat_2'])
X_description_test = wb_desc.transform(test['item_description'])
# X_description_test = X_description[:, np.array(np.clip(X_description.getnnz(axis=0) - 1, 0, 1), dtype=bool)]
X_brand_test = lb.transform(test['brand_name'])
X_cond_test = transform_dummy(test['item_condition_id'].tolist(),
d_cond)
X_ship_test = transform_dummy(test['shipping'].tolist(), d_ship)
X_test = hstack((X_cond_test, X_ship_test, X_description_test, X_brand_test, X_category1_test, \
X_category2_test, X_category3_test, X_name_test)).tocsr()
X_test = X_test[:, mask]
# Clear variables:
del X_cond_test, X_ship_test, X_description_test, X_brand_test, X_category1_test, X_category2_test, X_category3_test, X_name_test
del test
gc.collect()
predsFM_batch = model.predict(X_test)
predsFM += np.array(predsFM_batch).flatten().tolist()
predsF_batch = model2.predict(X_test)
predsF += np.array(predsF_batch).flatten().tolist()
print(np.array(predsFM))
print('-' * 20)
print(np.array(predsF))
print('-' * 20)
return np.array(predsFM), np.array(predsF)
def main():
train = pd.read_table('../input/train.tsv', engine='c')
test = pd.read_table('../input/test.tsv', engine='c')
print('Finished to load data')
nrow_test = train.shape[0]
dftt = train[(train.price < 1.0)]
train = train.drop(train[(train.price < 1.0)].index)
del dftt['price']
nrow_train = train.shape[0]
y = np.log1p(train["price"])
merge: pd.DataFrame = pd.concat([train, dftt, test])
submission: pd.DataFrame = test[['test_id']]
del train, test
gc.collect()
merge['general_cat'], merge['subcat_1'], merge['subcat_2'] = \
zip(*merge['category_name'].apply(lambda x: split_cat(x)))
merge.drop('category_name', axis=1, inplace=True)
print('Split categories completed.')
handle_missing_inplace(merge)
print('Handle missing completed.')
cutting(merge)
print('Cut completed.')
to_categorical(merge)
print('Convert categorical completed')
cv = CountVectorizer(min_df=NAME_MIN_DF)
X_name_cv = cv.fit_transform(merge['name'])
cv = CountVectorizer()
X_category1_cv = cv.fit_transform(merge['general_cat'])
X_category2_cv = cv.fit_transform(merge['subcat_1'])
X_category3_cv = cv.fit_transform(merge['subcat_2'])
wb = wordbatch.WordBatch(normalize_text,
extractor=(WordBag, {
'hash_ngrams': 2,
'hash_ngrams_weights': [1.5, 1.0],
'hash_size': 2**29,
'norm': None,
'tf': 'binary',
'idf': None,
}),
procs=8)
wb.dictionary_freeze = True
X_name = wb.fit_transform(merge['name'])
del wb
X_name = X_name[:,
np.
array(np.clip(X_name.getnnz(axis=0) -
1, 0, 1), dtype=bool)]
print('Vectorize `name` completed.')
wb = CountVectorizer()
X_category1 = wb.fit_transform(merge['general_cat'])
X_category2 = wb.fit_transform(merge['subcat_1'])
X_category3 = wb.fit_transform(merge['subcat_2'])
print('Count vectorize `categories` completed.')
wb = wordbatch.WordBatch(normalize_text,
extractor=(WordBag, {
'hash_ngrams': 2,
'hash_ngrams_weights': [1.0, 1.0],
'hash_size': 2**28,
'norm': 'l2',
'tf': 1.0,
'idf': None
}),
procs=8)
wb.dictionary_freeze = True
X_description = wb.fit_transform(merge['item_description'])
del wb
X_description = X_description[:,
np.array(np.clip(
X_description.getnnz(axis=0) - 1, 0, 1),
dtype=bool)]
print('Vectorize `item_description` completed.')
lb = LabelBinarizer(sparse_output=True)
X_brand = lb.fit_transform(merge['brand_name'])
print('Label binarize `brand_name` completed.')
X_dummies = csr_matrix(
pd.get_dummies(merge[['item_condition_id', 'shipping']],
sparse=True).values)
print('Get dummies on `item_condition_id` and `shipping` completed.')
num_chars = merge['item_description'].apply(lambda x: len(x)).values
num_words = merge['item_description'].apply(
lambda x: len(x.split(' '))).values
num_upper = merge['item_description'].apply(
lambda x: len(re.findall('[A-Z]+', x))).values
num_chars = num_chars / max(num_chars)
num_words = num_words / max(num_words)
num_upper = num_upper / max(num_upper)
X_feature = np.vstack([num_chars, num_words, num_upper]).T
print('musicmilif features completed.')
sparse_merge = hstack(
(X_dummies, X_description, X_brand, X_category1, X_category2,
X_category3, X_name, X_category1_cv, X_category2_cv, X_category3_cv,
X_name_cv, X_feature)).tocsr()
print('Create sparse merge completed')
del X_dummies, X_description, X_brand, X_category1, X_category2, X_category3
del X_name, X_category1_cv, X_category2_cv, X_category3_cv, X_name_cv, X_feature
del num_chars, num_words, num_upper
gc.collect()
# Remove features with document frequency <=1
mask = np.array(np.clip(sparse_merge.getnnz(axis=0) - 1, 0, 1), dtype=bool)
sparse_merge = sparse_merge[:, mask]
X = sparse_merge[:nrow_train]
X_test = sparse_merge[nrow_test:]
gc.collect()
train_X, train_y = X, y
model = Ridge(solver='auto',
fit_intercept=True,
alpha=5.0,
max_iter=100,
normalize=False,
tol=0.05)
model.fit(train_X, train_y)
print('Train Ridge completed')
predsR = model.predict(X_test)
print('Predict Ridge completed')
model = FTRL(alpha=0.01,
beta=0.1,
L1=0.00001,
L2=1.0,
D=sparse_merge.shape[1],
iters=50,
inv_link="identity",
threads=1)
model.fit(train_X, train_y)
print('Train FTRL completed')
predsF = model.predict(X_test)
print('Predict FTRL completed')
model = FM_FTRL(alpha=0.01,
beta=0.01,
L1=0.00001,
L2=0.1,
D=sparse_merge.shape[1],
alpha_fm=0.01,
L2_fm=0.0,
init_fm=0.01,
D_fm=200,
e_noise=0.0001,
iters=17,
inv_link="identity",
threads=4)
model.fit(train_X, train_y)
print('Train FM_FTRL completed')
predsFM = model.predict(X_test)
print('Predict FM_FTRL completed')
params = {
'learning_rate': 0.6,
'application': 'regression',
'max_depth': 9,
'num_leaves': 24,
'verbosity': -1,
'metric': 'RMSE',
'data_random_seed': 1,
'bagging_fraction': 0.9,
'bagging_freq': 6,
'feature_fraction': 0.8,
'nthread': 4,
'min_data_in_leaf': 51,
'max_bin': 64
}
# Remove features with document frequency <=200
mask = np.array(np.clip(sparse_merge.getnnz(axis=0) - 200, 0, 1),
dtype=bool)
sparse_merge = sparse_merge[:, mask]
X = sparse_merge[:nrow_train]
X_test = sparse_merge[nrow_test:]
train_X, train_y = X, y
d_train = lgb.Dataset(train_X, label=train_y)
watchlist = [d_train]
model = lgb.train(params,
train_set=d_train,
num_boost_round=1800,
valid_sets=watchlist,
early_stopping_rounds=500,
verbose_eval=400)
predsL = model.predict(X_test)
print('Predict LGBM completed')
preds = (predsR * 1 + predsF * 1 + predsFM * 16 + predsL * 6) / (1 + 1 +
16 + 6)
submission['price'] = np.expm1(preds)
submission.to_csv("submission.csv", index=False)
