数据分析的N种特征方法实例
2024-05-18 04:03:23
数据分析的N种特征方法实例1
- 1 数据输入
- 2 特征编码
- 2.1 One Hot Encoding 独热编码
- 2.2 Label Encoding 标签编码
- 2.3 Ordinal Encoding 顺序编码
- 2.4 Binary Encoding 二进制编码
- 2.5 Frequency Encoding、Count Encoding
- 2.6 Mean/Target Encoding
- 3 绘图
- 4 Two-Simg实例代码
1 数据输入
import pandas as pd
df = pd.DataFrame({'student_id': [1,2,3,4,5,6,7],'country': ['China', 'USA', 'UK', 'Japan', 'Korea', 'China', 'USA'],'education': ['Master', 'Bachelor', 'Bachelor', 'Master', 'PHD', 'PHD', 'Bachelor'],'target': [1, 0, 1, 0, 1, 0, 1]
})
df.head(10)
| student_id | country | education | target | |
|---|---|---|---|---|
| 0 | 1 | China | Master | 1 |
| 1 | 2 | USA | Bachelor | 0 |
| 2 | 3 | UK | Bachelor | 1 |
| 3 | 4 | Japan | Master | 0 |
| 4 | 5 | Korea | PHD | 1 |
| 5 | 6 | China | PHD | 0 |
| 6 | 7 | USA | Bachelor | 1 |
2 特征编码
2.1 One Hot Encoding 独热编码
pd.get_dummies(df, columns=['education'])
| student_id | country | target | education_Bachelor | education_Master | education_PHD | |
|---|---|---|---|---|---|---|
| 0 | 1 | China | 1 | 0 | 1 | 0 |
| 1 | 2 | USA | 0 | 1 | 0 | 0 |
| 2 | 3 | UK | 1 | 1 | 0 | 0 |
| 3 | 4 | Japan | 0 | 0 | 1 | 0 |
| 4 | 5 | Korea | 1 | 0 | 0 | 1 |
| 5 | 6 | China | 0 | 0 | 0 | 1 |
| 6 | 7 | USA | 1 | 1 | 0 | 0 |
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder()
ohe.fit_transform(df[['country']]).toarray()
array([[1., 0., 0., 0., 0.],[0., 0., 0., 0., 1.],[0., 0., 0., 1., 0.],[0., 1., 0., 0., 0.],[0., 0., 1., 0., 0.],[1., 0., 0., 0., 0.],[0., 0., 0., 0., 1.]])
2.2 Label Encoding 标签编码
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df['country_LabelEncoder'] = le.fit_transform(df['country'])
df.head(10)
| student_id | country | education | target | country_LabelEncoder | |
|---|---|---|---|---|---|
| 0 | 1 | China | Master | 1 | 0 |
| 1 | 2 | USA | Bachelor | 0 | 4 |
| 2 | 3 | UK | Bachelor | 1 | 3 |
| 3 | 4 | Japan | Master | 0 | 1 |
| 4 | 5 | Korea | PHD | 1 | 2 |
| 5 | 6 | China | PHD | 0 | 0 |
| 6 | 7 | USA | Bachelor | 1 | 4 |
df['country_LabelEncoder'] = pd.factorize(df['country'])[0]
df.head(10)
| student_id | country | education | target | country_LabelEncoder | |
|---|---|---|---|---|---|
| 0 | 1 | China | Master | 1 | 0 |
| 1 | 2 | USA | Bachelor | 0 | 1 |
| 2 | 3 | UK | Bachelor | 1 | 2 |
| 3 | 4 | Japan | Master | 0 | 3 |
| 4 | 5 | Korea | PHD | 1 | 4 |
| 5 | 6 | China | PHD | 0 | 0 |
| 6 | 7 | USA | Bachelor | 1 | 1 |
pd.factorize(df['country'])
(array([0, 1, 2, 3, 4, 0, 1]),Index(['China', 'USA', 'UK', 'Japan', 'Korea'], dtype='object'))
2.3 Ordinal Encoding 顺序编码
df['education'] = df['education'].map({'Bachelor': 1, 'Master': 2, 'PHD': 3})
df.head(10)
| student_id | country | education | target | country_LabelEncoder | |
|---|---|---|---|---|---|
| 0 | 1 | China | 2 | 1 | 0 |
| 1 | 2 | USA | 1 | 0 | 1 |
| 2 | 3 | UK | 1 | 1 | 2 |
| 3 | 4 | Japan | 2 | 0 | 3 |
| 4 | 5 | Korea | 3 | 1 | 4 |
| 5 | 6 | China | 3 | 0 | 0 |
| 6 | 7 | USA | 1 | 1 | 1 |
2.4 Binary Encoding 二进制编码
import category_encoders as ce
encoder = ce.BinaryEncoder(cols= ['country'])pd.concat([df, encoder.fit_transform(df['country']).iloc[:, 1:]], axis=1)
| student_id | country | education | target | country_1 | country_2 | country_3 | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | China | Master | 1 | 0 | 0 | 1 |
| 1 | 2 | USA | Bachelor | 0 | 0 | 1 | 0 |
| 2 | 3 | UK | Bachelor | 1 | 0 | 1 | 1 |
| 3 | 4 | Japan | Master | 0 | 1 | 0 | 0 |
| 4 | 5 | Korea | PHD | 1 | 1 | 0 | 1 |
| 5 | 6 | China | PHD | 0 | 0 | 0 | 1 |
| 6 | 7 | USA | Bachelor | 1 | 0 | 1 | 0 |
2.5 Frequency Encoding、Count Encoding
df['country_count'] = df['country'].map(df['country'].value_counts()) / len(df)
df.head(10)
| student_id | country | education | target | country_count | |
|---|---|---|---|---|---|
| 0 | 1 | China | Master | 1 | 0.285714 |
| 1 | 2 | USA | Bachelor | 0 | 0.285714 |
| 2 | 3 | UK | Bachelor | 1 | 0.142857 |
| 3 | 4 | Japan | Master | 0 | 0.142857 |
| 4 | 5 | Korea | PHD | 1 | 0.142857 |
| 5 | 6 | China | PHD | 0 | 0.285714 |
| 6 | 7 | USA | Bachelor | 1 | 0.285714 |
df['country_count'] = df['country'].map(df['country'].value_counts())
df.head(10)
| student_id | country | education | target | country_count | |
|---|---|---|---|---|---|
| 0 | 1 | China | Master | 1 | 2 |
| 1 | 2 | USA | Bachelor | 0 | 2 |
| 2 | 3 | UK | Bachelor | 1 | 1 |
| 3 | 4 | Japan | Master | 0 | 1 |
| 4 | 5 | Korea | PHD | 1 | 1 |
| 5 | 6 | China | PHD | 0 | 2 |
| 6 | 7 | USA | Bachelor | 1 | 2 |
2.6 Mean/Target Encoding
df.groupby(['country'])['target'].mean()
country
China 0.5
Japan 0.0
Korea 1.0
UK 1.0
USA 0.5
Name: target, dtype: float64
df['country_target'] = df['country'].map(df.groupby(['country'])['target'].mean())
df.head(10)
| student_id | country | education | target | country_target | |
|---|---|---|---|---|---|
| 0 | 1 | China | Master | 1 | 0.5 |
| 1 | 2 | USA | Bachelor | 0 | 0.5 |
| 2 | 3 | UK | Bachelor | 1 | 1.0 |
| 3 | 4 | Japan | Master | 0 | 0.0 |
| 4 | 5 | Korea | PHD | 1 | 1.0 |
| 5 | 6 | China | PHD | 0 | 0.5 |
| 6 | 7 | USA | Bachelor | 1 | 0.5 |
df = pd.DataFrame({'student_id': [1,2,3,4,5,6,7],'country': ['China', 'USA', 'UK', 'Japan', 'Korea', 'China', 'USA'],'education': ['Master', 'Bachelor', 'Bachelor', 'Master', 'PHD', 'PHD', 'Bachelor'],'age': [34.5, 28.9, 19.5, 23.6, 19.8, 29.8, 31.7],'target': [1, 0, 1, 0, 1, 0, 1]
})
df.head(10)
| student_id | country | education | age | target | |
|---|---|---|---|---|---|
| 0 | 1 | China | Master | 34.5 | 1 |
| 1 | 2 | USA | Bachelor | 28.9 | 0 |
| 2 | 3 | UK | Bachelor | 19.5 | 1 |
| 3 | 4 | Japan | Master | 23.6 | 0 |
| 4 | 5 | Korea | PHD | 19.8 | 1 |
| 5 | 6 | China | PHD | 29.8 | 0 |
| 6 | 7 | USA | Bachelor | 31.7 | 1 |
df['age_round1'] = df['age'].round()
df['age_round2'] = (df['age'] / 10).astype(int)
df.head(10)
| student_id | country | education | age | target | age_round1 | age_round2 | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | China | Master | 34.5 | 1 | 34.0 | 3 |
| 1 | 2 | USA | Bachelor | 28.9 | 0 | 29.0 | 2 |
| 2 | 3 | UK | Bachelor | 19.5 | 1 | 20.0 | 1 |
| 3 | 4 | Japan | Master | 23.6 | 0 | 24.0 | 2 |
| 4 | 5 | Korea | PHD | 19.8 | 1 | 20.0 | 1 |
| 5 | 6 | China | PHD | 29.8 | 0 | 30.0 | 2 |
| 6 | 7 | USA | Bachelor | 31.7 | 1 | 32.0 | 3 |
df['age_<20'] = (df['age'] <= 20).astype(int)
df['age_20-25'] = ((df['age'] > 20) & (df['age'] <=25)).astype(int)
df['age_20-25'] = ((df['age'] > 25) & (df['age'] <= 30)).astype(int)
df['age_>30'] = (df['age'] > 30).astype(int)
df.head(10)
| student_id | country | education | age | target | age_<20 | age_20-25 | age_>30 | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1 | China | Master | 34.5 | 1 | 0 | 0 | 1 |
| 1 | 2 | USA | Bachelor | 28.9 | 0 | 0 | 1 | 0 |
| 2 | 3 | UK | Bachelor | 19.5 | 1 | 1 | 0 | 0 |
| 3 | 4 | Japan | Master | 23.6 | 0 | 0 | 0 | 0 |
| 4 | 5 | Korea | PHD | 19.8 | 1 | 1 | 0 | 0 |
| 5 | 6 | China | PHD | 29.8 | 0 | 0 | 1 | 0 |
| 6 | 7 | USA | Bachelor | 31.7 | 1 | 0 | 0 | 1 |
df
| student_id | country | education | age | target | age_<20 | age_20-25 | age_>30 | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1 | China | Master | 34.5 | 1 | 0 | 0 | 1 |
| 1 | 2 | USA | Bachelor | 28.9 | 0 | 0 | 1 | 0 |
| 2 | 3 | UK | Bachelor | 19.5 | 1 | 1 | 0 | 0 |
| 3 | 4 | Japan | Master | 23.6 | 0 | 0 | 0 | 0 |
| 4 | 5 | Korea | PHD | 19.8 | 1 | 1 | 0 | 0 |
| 5 | 6 | China | PHD | 29.8 | 0 | 0 | 1 | 0 |
| 6 | 7 | USA | Bachelor | 31.7 | 1 | 0 | 0 | 1 |
3 绘图
用具体的取值衡量特征的重要性
%pylab inline
import numpy as np
from sklearn.ensemble import RandomForestRegressorfrom sklearn.datasets import load_boston
data = load_boston()rf = RandomForestRegressor()
rf.fit(data.data, data.target);
print(rf.feature_importances_)plt.figure(figsize=(12, 6))
plt.bar(range(1, 14), rf.feature_importances_)
_ = plt.xticks(range(1, 14), data.feature_names)
Populating the interactive namespace from numpy and matplotlib
[0.03932896 0.00111874 0.00558259 0.00072371 0.0233215 0.430687690.01233092 0.06505465 0.00364571 0.01419684 0.01678173 0.01082360.37640336]
import numpy as np
from sklearn.ensemble import RandomForestRegressorfrom lightgbm import LGBMRegressor
data = load_boston()clf = LGBMRegressor()
clf.fit(data.data, data.target)plt.figure(figsize=(12, 6))
plt.bar(range(1, 14), clf.feature_importances_)
_ = plt.xticks(range(1, 14), data.feature_names)
import numpy as np
from sklearn.ensemble import RandomForestRegressorfrom xgboost import XGBRegressor
data = load_boston()clf = XGBRegressor()
clf.fit(data.data, data.target)plt.figure(figsize=(12, 6))
plt.bar(range(1, 14), clf.feature_importances_)
_ = plt.xticks(range(1, 14), data.feature_names)
4 Two-Simg实例代码
房价预测，在这整个比赛中的工作。
import os
import sys
import operator
import numpy as np
import pandas as pd
from scipy import sparse
import randomimport xgboost as xgb# 支持sklearn接口
from xgboost import XGBClassifier, XGBRegressor
# 分类器 以 Classifier 结尾
# 回归器 以 Regressor 结尾from sklearn import model_selection, preprocessing, ensemble
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import log_loss
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from collections import defaultdict, Counter
def runXGB(train_X, train_y, val_X, val_y=None, test_X=None, feature_names=None, seed_val=0, num_rounds=1000):param = {}param['objective'] = 'multi:softprob'param['eta'] = 0.03param['max_depth'] = 6param['silent'] = 0param['num_class'] = 3param['eval_metric'] = "mlogloss"param['min_child_weight'] = 1param['subsample'] = 0.7param['colsample_bytree'] = 0.7param['seed'] = seed_valparam['nthread'] = 12 # cpu核数num_rounds = num_roundsplst = list(param.items())xgtrain = xgb.DMatrix(train_X, label=train_y)if val_y is not None:xgval = xgb.DMatrix(val_X, label=val_y)watchlist = [ (xgtrain,'train'), (xgval, 'val') ]# 原生xgboost训练model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=50)else:model = xgb.train(plst, xgtrain, num_rounds)xgtest = xgb.DMatrix(test_X)pred_test_y = model.predict(xgtest)return pred_test_y, model
这里需要下载训练和测试的文件，看这里。
train_df = pd.read_json('../input/train.json.zip', compression='zip')
test_df = pd.read_json('../input/test.json.zip', compression='zip')features_to_use = ["bathrooms", "bedrooms", "latitude", "longitude", "price"]mean_price = int(train_df['price'].mean())
test_df.loc[test_df['price']<200,'price'] = mean_price
train_df.loc[train_df['price']<200,'price'] = mean_price
train_test = pd.concat([train_df, test_df], 0,sort=False)features = train_test[["features"]].apply(lambda _: [list(map(str.strip, map(str.lower, x))) for x in _])n = 5feature_counts = Counter()
for feature in features.features:feature_counts.update(feature)
feature = sorted([k for (k,v) in feature_counts.items() if v > n])
feature[:10]def clean(s):x = s.replace("-", "")x = x.replace(" ", "")x = x.replace("24/7", "24")x = x.replace("24hr", "24")x = x.replace("24-hour", "24")x = x.replace("24hour", "24")x = x.replace("24 hour", "24")x = x.replace("common", "cm")x = x.replace("concierge", "doorman")x = x.replace("bicycle", "bike")x = x.replace("pets:cats", "cats")x = x.replace("allpetsok", "pets")x = x.replace("dogs", "pets")x = x.replace("private", "pv")x = x.replace("deco", "dc")x = x.replace("decorative", "dc")x = x.replace("onsite", "os")x = x.replace("outdoor", "od")x = x.replace("ss appliances", "stainless")return xdef feature_hash(x):cleaned = clean(x, uniq)key = cleaned[:4].strip()return keykey2original = defaultdict(list)
k = 4
for f in feature:cleaned = clean(f)key = cleaned[:k].strip()key2original[key].append(f)def to_tuples():for f in feature:key = clean(f)[:k].strip()yield (f, key2original[key][0])deduped = list(to_tuples())
df = pd.DataFrame(deduped, columns=["original_feature", "unique_feature"])dict_rep_features = pd.Series(df['unique_feature'].values, df['original_feature'].values)
test_df['features'] = test_df['features'].apply(lambda x: list(map(str.strip, map(str.lower, x))))\.apply(lambda x: [dict_rep_features[i] for i in x if i in dict_rep_features.index])\.apply(lambda x: list(set(x)))train_df['features'] = train_df['features'].apply(lambda x: list(map(str.strip, map(str.lower, x))))\.apply(lambda x: [dict_rep_features[i] for i in x if i in dict_rep_features.index])\.apply(lambda x: list(set(x)))import math
def cart2rho(x, y):rho = np.sqrt(x**2 + y**2)return rhodef cart2phi(x, y):phi = np.arctan2(y, x)return phidef rotation_x(row, alpha):x = row['latitude']y = row['longitude']return x*math.cos(alpha) + y*math.sin(alpha)def rotation_y(row, alpha):x = row['latitude']y = row['longitude']return y*math.cos(alpha) - x*math.sin(alpha)def add_rotation(degrees, df):namex = "rot" + str(degrees) + "_X"namey = "rot" + str(degrees) + "_Y"df['num_' + namex] = df.apply(lambda row: rotation_x(row, math.pi/(180/degrees)), axis=1)df['num_' + namey] = df.apply(lambda row: rotation_y(row, math.pi/(180/degrees)), axis=1)return dfdef operate_on_coordinates(tr_df, te_df):for df in [tr_df, te_df]:#polar coordinates systemdf["num_rho"] = df.apply(lambda x: cart2rho(x["latitude"] - 40.78222222, x["longitude"]+73.96527777), axis=1)df["num_phi"] = df.apply(lambda x: cart2phi(x["latitude"] - 40.78222222, x["longitude"]+73.96527777), axis=1)#rotationsfor angle in [15,30,45,60]:df = add_rotation(angle, df)return tr_df, te_dftrain_df, test_df = operate_on_coordinates(train_df, test_df)features_to_use.extend(['num_rho', 'num_phi', 'num_rot15_X', 'num_rot15_Y', 'num_rot30_X','num_rot30_Y', 'num_rot45_X', 'num_rot45_Y', 'num_rot60_X','num_rot60_Y'])
import redef cap_share(x):return sum(1 for c in x if c.isupper())/float(len(x)+1)for df in [train_df, test_df]:# do you think that users might feel annoyed BY A DESCRIPTION THAT IS SHOUTING AT THEM?df['num_cap_share'] = df['description'].apply(cap_share)# how long in lines the desc is?df['num_nr_of_lines'] = df['description'].apply(lambda x: x.count('<br /><br />'))# is the description redacted by the website? df['num_redacted'] = 0df['num_redacted'].loc[df['description'].str.contains('website_redacted')] = 1# can we contact someone via e-mail to ask for the details?df['num_email'] = 0df['num_email'].loc[df['description'].str.contains('@')] = 1#and... can we call them?reg = re.compile(".*?(\(?\d{3}\D{0,3}\d{3}\D{0,3}\d{4}).*?", re.S)def try_and_find_nr(description):if reg.match(description) is None:return 0return 1df['num_phone_nr'] = df['description'].apply(try_and_find_nr)features_to_use.extend(['num_cap_share', 'num_nr_of_lines', 'num_redacted','num_email', 'num_phone_nr'])
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.after removing the cwd from sys.path.
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: DeprecationWarning:
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexingSee the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecatedfrom ipykernel import kernelapp as app
/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:190: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrameSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copyself._setitem_with_indexer(indexer, value)
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:20: DeprecationWarning:
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexingSee the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated
# count of photos #
train_df["num_photos"] = train_df["photos"].apply(len) #photos原始是list类型
test_df["num_photos"] = test_df["photos"].apply(len)# count of "features" #
train_df["num_features"] = train_df["features"].apply(len)
test_df["num_features"] = test_df["features"].apply(len)# count of words present in description column #
train_df["num_description_words"] = train_df["description"].apply(lambda x: len(x.split(" "))) #使用空白符进行分隔，统计有多少个字符类型
test_df["num_description_words"] = test_df["description"].apply(lambda x: len(x.split(" ")))# convert the created column to datetime object so as to extract more features
train_df["created"] = pd.to_datetime(train_df["created"])
test_df["created"] = pd.to_datetime(test_df["created"])# Let us extract some features like year, month, day, hour from date columns #
train_df["created_year"] = train_df["created"].dt.year
test_df["created_year"] = test_df["created"].dt.year
train_df["created_month"] = train_df["created"].dt.month
test_df["created_month"] = test_df["created"].dt.month
train_df["created_day"] = train_df["created"].dt.day
test_df["created_day"] = test_df["created"].dt.day
train_df["created_hour"] = train_df["created"].dt.hour
test_df["created_hour"] = test_df["created"].dt.hour# adding all these new features to use list #
features_to_use.extend(["num_photos", "num_features", "num_description_words","created_year", "created_month", "created_day", "listing_id", "created_hour"])# 类型：数值 类别
train_df["price_t"] =train_df["price"]/train_df["bedrooms"]
test_df["price_t"] = test_df["price"]/test_df["bedrooms"] train_df["price_t"] =train_df["price"]/train_df["bathrooms"]
test_df["price_t"] = test_df["price"]/test_df["bathrooms"] train_df["room_sum"] = train_df["bedrooms"]+train_df["bathrooms"]
test_df["room_sum"] = test_df["bedrooms"]+test_df["bathrooms"] #总共有多少房间train_df["price_t"] =train_df["price"]/train_df["room_sum"]
test_df["price_t"] = test_df["price"]/test_df["room_sum"] #平摊到每个房间的价格features_to_use.extend(["price_t", "room_sum", "num_description_words"])
start_values = [0,0,0]index=list(range(train_df.shape[0]))
random.shuffle(index)
a=[np.nan]*len(train_df)
b=[np.nan]*len(train_df)
c=[np.nan]*len(train_df)# 每次赋值，对未知的样本进行赋值
# manager_id 中介：id 高基数类别特征，target encoding# 训练集部分进行统计，对验证部分进行编码
# 随机把数据集划分5份，4份计算target encoding，对剩余一份进行赋值# 常规的模型：数值输入，sklean，神经网络
# xgboost、lightgbm、catboost：可以输入类别# 分组统计：不同managerid分组下面的标签平均
# 不同managerid分组下面的价格分布for i in range(5):building_level={}for j in train_df['manager_id'].values:building_level[j]= start_values.copy()test_index=index[int((i*train_df.shape[0])/5):int(((i+1)*train_df.shape[0])/5)]train_index=list(set(index).difference(test_index))for j in train_index:temp=train_df.iloc[j]if temp['interest_level']=='low':building_level[temp['manager_id']][0]+=1if temp['interest_level']=='medium':building_level[temp['manager_id']][1]+=1if temp['interest_level']=='high':building_level[temp['manager_id']][2]+=1for j in test_index:temp=train_df.iloc[j]if sum(building_level[temp['manager_id']])!=0:a[j]=building_level[temp['manager_id']][0]*1.0/sum(building_level[temp['manager_id']])b[j]=building_level[temp['manager_id']][1]*1.0/sum(building_level[temp['manager_id']])c[j]=building_level[temp['manager_id']][2]*1.0/sum(building_level[temp['manager_id']])
train_df['manager_level_low']=a
train_df['manager_level_medium']=b
train_df['manager_level_high']=ca=[]
b=[]
c=[]
building_level={}
for j in train_df['manager_id'].values:building_level[j]= start_values.copy()
for j in range(train_df.shape[0]):temp=train_df.iloc[j]if temp['interest_level']=='low':building_level[temp['manager_id']][0]+=1if temp['interest_level']=='medium':building_level[temp['manager_id']][1]+=1if temp['interest_level']=='high':building_level[temp['manager_id']][2]+=1for i in test_df['manager_id'].values:if i not in building_level.keys():a.append(np.nan)b.append(np.nan)c.append(np.nan)else:a.append(building_level[i][0]*1.0/sum(building_level[i]))b.append(building_level[i][1]*1.0/sum(building_level[i]))c.append(building_level[i][2]*1.0/sum(building_level[i]))
test_df['manager_level_low']=a
test_df['manager_level_medium']=b
test_df['manager_level_high']=cfeatures_to_use.append('manager_level_low')
features_to_use.append('manager_level_medium')
features_to_use.append('manager_level_high')
train_df["listing_id1"] = train_df["listing_id"] - 68119576.0
test_df["listing_id1"] = test_df["listing_id"] - 68119576.0train_df["num_price_by_furniture"] = (train_df["price"])/ (train_df["bathrooms"] + train_df["bedrooms"] + 1.0)
test_df["num_price_by_furniture"] = (test_df["price"])/ (test_df["bathrooms"] + test_df["bedrooms"] + 1.0)train_df["price_latitue"] = (train_df["price"])/ (train_df["latitude"]+1.0)
test_df["price_latitue"] = (test_df["price"])/ (test_df["latitude"]+1.0)train_df["price_longtitude"] = (train_df["price"])/ (train_df["longitude"]-1.0)
test_df["price_longtitude"] = (test_df["price"])/ (test_df["longitude"]-1.0) train_df["num_furniture"] = train_df["bathrooms"] + train_df["bedrooms"]
test_df["num_furniture"] = test_df["bathrooms"] + test_df["bedrooms"] train_df["total_days"] = (train_df["created_month"] -4.0)*30 + train_df["created_day"] + train_df["created_hour"] /25.0
test_df["total_days"] =(test_df["created_month"] -4.0)*30 + test_df["created_day"] + test_df["created_hour"] /25.0
train_df["diff_rank"]= train_df["total_days"]/train_df["listing_id1"]
test_df["diff_rank"]= test_df["total_days"]/test_df["listing_id1"]features_to_use.extend([ "total_days","diff_rank",
"num_price_by_furniture","price_latitue","price_longtitude",'num_furniture'])
categorical = ["display_address", "manager_id", "building_id", "street_address"]
for f in categorical:if train_df[f].dtype=='object':#print(f)lbl = preprocessing.LabelEncoder()lbl.fit(list(train_df[f].values) + list(test_df[f].values))train_df[f] = lbl.transform(list(train_df[f].values))test_df[f] = lbl.transform(list(test_df[f].values))features_to_use.append(f)
train_df["price0"] = (train_df["price"]%10==0).astype(int)
test_df["price0"] = (test_df["price"]%10==0).astype(int)train_df["manager_count"] = train_df["manager_id"].replace(train_df["manager_id"].value_counts())
test_df["manager_count"] = test_df["manager_id"].replace(train_df["manager_id"].value_counts())features_to_use.extend(["price0",'manager_count'])train_df['features'] = train_df["features"].apply(lambda x: " ".join(["_".join(i.split(" ")) for i in x]))
test_df['features'] = test_df["features"].apply(lambda x: " ".join(["_".join(i.split(" ")) for i in x]))
print(train_df["features"].head())
tfidf = CountVectorizer(stop_words='english', max_features=70)
te_sparse = tfidf.fit_transform(test_df["features"])
tr_sparse = tfidf.transform(train_df["features"])tfidfdesc=TfidfVectorizer(min_df=20, max_features=50, strip_accents='unicode',lowercase =True,analyzer='word', token_pattern=r'\w{16,}', ngram_range=(1, 2), use_idf=False,smooth_idf=False, sublinear_tf=True, stop_words = 'english') train_df['description'] = train_df['description'].apply(lambda x: str(x).encode('utf-8') if len(x)>2 else "nulldesc")
test_df['description'] = test_df['description'].apply(lambda x: str(x).encode('utf-8') if len(x)>2 else "nulldesc")
te_sparsed = tfidfdesc. fit_transform (test_df["description"])
tr_sparsed = tfidfdesc.transform(train_df["description"])train_X = sparse.hstack([train_df[features_to_use], tr_sparse,tr_sparsed]).tocsr()#
test_X = sparse.hstack([test_df[features_to_use], te_sparse,te_sparsed]).tocsr()#target_num_map = {'high':0, 'medium':1, 'low':2}
train_y = np.array(train_df['interest_level'].apply(lambda x: target_num_map[x]))print(train_X.shape, test_X.shape)
10
10000 elevator concierge fitness_center cats_allowed...
100004 dish_washer hardwood laundry
100007 hardwood no_fee
100013 pre_war
Name: features, dtype: object
(49352, 166) (74659, 166)
preds, model = runXGB(train_X, train_y, val_X=None, val_y=None, test_X=test_X, num_rounds=100)
out_df = pd.DataFrame(preds)
out_df.columns = ["high", "medium", "low"]
out_df["listing_id"] = test_df.listing_id.values
out_df.to_csv("xgb_baseline3.csv", index=False)
cv_scores = []
test_pred = None
kf = model_selection.KFold(n_splits=5, shuffle=True, random_state=2016)
for dev_index, val_index in kf.split(range(train_X.shape[0])):dev_X, val_X = train_X[dev_index,:], train_X[val_index,:]dev_y, val_y = train_y[dev_index], train_y[val_index]preds, model = runXGB(dev_X, dev_y, val_X, val_y, test_X, num_rounds=2000)if test_pred is None:test_pred = predselse:test_pred += preds
test_pred /= 5
out_df = pd.DataFrame(test_pred)
out_df.columns = ["high", "medium", "low"]
out_df["listing_id"] = test_df.listing_id.values
out_df.to_csv("xgb_baseline3.csv", index=False)
[0] train-mlogloss:1.07775 val-mlogloss:1.07827
Multiple eval metrics have been passed: 'val-mlogloss' will be used for early stopping.Will train until val-mlogloss hasn't improved in 50 rounds.
[1] train-mlogloss:1.05804 val-mlogloss:1.05915
[2] train-mlogloss:1.0391 val-mlogloss:1.04076
[3] train-mlogloss:1.02106 val-mlogloss:1.02331
[4] train-mlogloss:1.00379 val-mlogloss:1.00659
[5] train-mlogloss:0.987297 val-mlogloss:0.990692
[6] train-mlogloss:0.971684 val-mlogloss:0.975621
[7] train-mlogloss:0.956643 val-mlogloss:0.961212
[8] train-mlogloss:0.942392 val-mlogloss:0.947541
[9] train-mlogloss:0.928634 val-mlogloss:0.934332
[10] train-mlogloss:0.915499 val-mlogloss:0.921662
[11] train-mlogloss:0.902885 val-mlogloss:0.909531
[12] train-mlogloss:0.890587 val-mlogloss:0.897704
[13] train-mlogloss:0.879018 val-mlogloss:0.886706
[14] train-mlogloss:0.867827 val-mlogloss:0.876054
[15] train-mlogloss:0.857247 val-mlogloss:0.865963
[16] train-mlogloss:0.847002 val-mlogloss:0.856158
[17] train-mlogloss:0.837087 val-mlogloss:0.846743
[18] train-mlogloss:0.827615 val-mlogloss:0.837644
[19] train-mlogloss:0.818583 val-mlogloss:0.82898
[20] train-mlogloss:0.809766 val-mlogloss:0.820555
[21] train-mlogloss:0.801199 val-mlogloss:0.812395
[22] train-mlogloss:0.79294 val-mlogloss:0.804595
[23] train-mlogloss:0.785002 val-mlogloss:0.797013
[24] train-mlogloss:0.777443 val-mlogloss:0.789759
[25] train-mlogloss:0.770085 val-mlogloss:0.782812
[26] train-mlogloss:0.762869 val-mlogloss:0.776079
[27] train-mlogloss:0.756035 val-mlogloss:0.769586
[28] train-mlogloss:0.749341 val-mlogloss:0.763342
[29] train-mlogloss:0.742882 val-mlogloss:0.757373
[30] train-mlogloss:0.736703 val-mlogloss:0.751577
[31] train-mlogloss:0.730713 val-mlogloss:0.74597
[32] train-mlogloss:0.724874 val-mlogloss:0.740554
[33] train-mlogloss:0.719264 val-mlogloss:0.735261
[34] train-mlogloss:0.713798 val-mlogloss:0.730179
[35] train-mlogloss:0.708494 val-mlogloss:0.72532
[36] train-mlogloss:0.703366 val-mlogloss:0.720577
[37] train-mlogloss:0.698472 val-mlogloss:0.716033
[38] train-mlogloss:0.693772 val-mlogloss:0.711691
[39] train-mlogloss:0.689239 val-mlogloss:0.707555
[40] train-mlogloss:0.684838 val-mlogloss:0.703506
[41] train-mlogloss:0.680593 val-mlogloss:0.699603
[42] train-mlogloss:0.676334 val-mlogloss:0.695704
[43] train-mlogloss:0.672309 val-mlogloss:0.692011
[44] train-mlogloss:0.668327 val-mlogloss:0.688428
[45] train-mlogloss:0.664522 val-mlogloss:0.684887
[46] train-mlogloss:0.660933 val-mlogloss:0.681643
[47] train-mlogloss:0.657279 val-mlogloss:0.678362
[48] train-mlogloss:0.653796 val-mlogloss:0.675239
[49] train-mlogloss:0.650425 val-mlogloss:0.672188
[50] train-mlogloss:0.647112 val-mlogloss:0.669102
[51] train-mlogloss:0.643936 val-mlogloss:0.666283
[52] train-mlogloss:0.640879 val-mlogloss:0.663544
[53] train-mlogloss:0.637921 val-mlogloss:0.660922
[54] train-mlogloss:0.634942 val-mlogloss:0.658315
[55] train-mlogloss:0.632088 val-mlogloss:0.655759
[56] train-mlogloss:0.629209 val-mlogloss:0.653304
[57] train-mlogloss:0.626533 val-mlogloss:0.650923
[58] train-mlogloss:0.623935 val-mlogloss:0.648687
[59] train-mlogloss:0.621426 val-mlogloss:0.646462
[60] train-mlogloss:0.618903 val-mlogloss:0.644235
[61] train-mlogloss:0.616492 val-mlogloss:0.642063
[62] train-mlogloss:0.61408 val-mlogloss:0.639956
[63] train-mlogloss:0.611771 val-mlogloss:0.637903
[64] train-mlogloss:0.60947 val-mlogloss:0.635978
[65] train-mlogloss:0.607336 val-mlogloss:0.63416
[66] train-mlogloss:0.605187 val-mlogloss:0.632312
[67] train-mlogloss:0.603116 val-mlogloss:0.630568
[68] train-mlogloss:0.60112 val-mlogloss:0.628791
[69] train-mlogloss:0.599165 val-mlogloss:0.627139
[70] train-mlogloss:0.597214 val-mlogloss:0.62547
[71] train-mlogloss:0.595304 val-mlogloss:0.623908
[72] train-mlogloss:0.593517 val-mlogloss:0.622375
[73] train-mlogloss:0.591808 val-mlogloss:0.620976
[74] train-mlogloss:0.590092 val-mlogloss:0.61953
[75] train-mlogloss:0.588488 val-mlogloss:0.618189
[76] train-mlogloss:0.586666 val-mlogloss:0.616749
[77] train-mlogloss:0.584939 val-mlogloss:0.615338
[78] train-mlogloss:0.58323 val-mlogloss:0.613948
[79] train-mlogloss:0.581509 val-mlogloss:0.612552
[80] train-mlogloss:0.579989 val-mlogloss:0.611326
[81] train-mlogloss:0.578431 val-mlogloss:0.610125
[82] train-mlogloss:0.576922 val-mlogloss:0.60898
[83] train-mlogloss:0.575312 val-mlogloss:0.607687
[84] train-mlogloss:0.573863 val-mlogloss:0.606527
[85] train-mlogloss:0.572426 val-mlogloss:0.605464
[86] train-mlogloss:0.571034 val-mlogloss:0.604398
[87] train-mlogloss:0.569715 val-mlogloss:0.603359
[88] train-mlogloss:0.568427 val-mlogloss:0.602321
[89] train-mlogloss:0.567089 val-mlogloss:0.601322
[90] train-mlogloss:0.565838 val-mlogloss:0.600377
[91] train-mlogloss:0.564603 val-mlogloss:0.599448
[92] train-mlogloss:0.563468 val-mlogloss:0.598632
[93] train-mlogloss:0.562321 val-mlogloss:0.597795
[94] train-mlogloss:0.561166 val-mlogloss:0.596895
[95] train-mlogloss:0.55994 val-mlogloss:0.59598
[96] train-mlogloss:0.558748 val-mlogloss:0.595017
[97] train-mlogloss:0.557595 val-mlogloss:0.594185
[98] train-mlogloss:0.556575 val-mlogloss:0.593453
[99] train-mlogloss:0.555548 val-mlogloss:0.592668
[100] train-mlogloss:0.554452 val-mlogloss:0.591822
[101] train-mlogloss:0.553388 val-mlogloss:0.591059
[102] train-mlogloss:0.552379 val-mlogloss:0.590298
[103] train-mlogloss:0.551296 val-mlogloss:0.589535
[104] train-mlogloss:0.550356 val-mlogloss:0.588745
[105] train-mlogloss:0.549275 val-mlogloss:0.587961
[106] train-mlogloss:0.548295 val-mlogloss:0.587239
[107] train-mlogloss:0.547351 val-mlogloss:0.586562
[108] train-mlogloss:0.546472 val-mlogloss:0.585977
[109] train-mlogloss:0.545532 val-mlogloss:0.585268
[110] train-mlogloss:0.544619 val-mlogloss:0.584629
[111] train-mlogloss:0.543789 val-mlogloss:0.58401
[112] train-mlogloss:0.542922 val-mlogloss:0.583409
[113] train-mlogloss:0.542089 val-mlogloss:0.582749
[114] train-mlogloss:0.541285 val-mlogloss:0.582233
[115] train-mlogloss:0.540366 val-mlogloss:0.58157
[116] train-mlogloss:0.539487 val-mlogloss:0.581026
[117] train-mlogloss:0.538648 val-mlogloss:0.580401
[118] train-mlogloss:0.537875 val-mlogloss:0.579826
[119] train-mlogloss:0.537077 val-mlogloss:0.579299
[120] train-mlogloss:0.536234 val-mlogloss:0.578751
[121] train-mlogloss:0.535424 val-mlogloss:0.578289
[122] train-mlogloss:0.534755 val-mlogloss:0.577885
[123] train-mlogloss:0.533977 val-mlogloss:0.577328
[124] train-mlogloss:0.533203 val-mlogloss:0.57689
[125] train-mlogloss:0.532455 val-mlogloss:0.576445
[126] train-mlogloss:0.531631 val-mlogloss:0.575928
[127] train-mlogloss:0.530873 val-mlogloss:0.575459
[128] train-mlogloss:0.530228 val-mlogloss:0.575039
[129] train-mlogloss:0.529579 val-mlogloss:0.57462
[130] train-mlogloss:0.528915 val-mlogloss:0.574171
[131] train-mlogloss:0.528234 val-mlogloss:0.57379
[132] train-mlogloss:0.527525 val-mlogloss:0.573392
[133] train-mlogloss:0.52684 val-mlogloss:0.57292
[134] train-mlogloss:0.52614 val-mlogloss:0.572507
[135] train-mlogloss:0.525406 val-mlogloss:0.572068
[136] train-mlogloss:0.524808 val-mlogloss:0.571702
[137] train-mlogloss:0.524142 val-mlogloss:0.571292
[138] train-mlogloss:0.523411 val-mlogloss:0.570863
[139] train-mlogloss:0.522725 val-mlogloss:0.570455
[140] train-mlogloss:0.522151 val-mlogloss:0.570082
[141] train-mlogloss:0.521466 val-mlogloss:0.569587
[142] train-mlogloss:0.520799 val-mlogloss:0.569195
[143] train-mlogloss:0.520182 val-mlogloss:0.568809
[144] train-mlogloss:0.519479 val-mlogloss:0.568464
[145] train-mlogloss:0.518863 val-mlogloss:0.568147
[146] train-mlogloss:0.518224 val-mlogloss:0.567792
[147] train-mlogloss:0.51762 val-mlogloss:0.567481
[148] train-mlogloss:0.517022 val-mlogloss:0.567087
[149] train-mlogloss:0.516461 val-mlogloss:0.566718
[150] train-mlogloss:0.515825 val-mlogloss:0.566333
[151] train-mlogloss:0.515273 val-mlogloss:0.565989
[152] train-mlogloss:0.514678 val-mlogloss:0.565621
[153] train-mlogloss:0.514159 val-mlogloss:0.565331
[154] train-mlogloss:0.513648 val-mlogloss:0.565005
[155] train-mlogloss:0.513074 val-mlogloss:0.56471
[156] train-mlogloss:0.512547 val-mlogloss:0.564468
[157] train-mlogloss:0.511923 val-mlogloss:0.564102
[158] train-mlogloss:0.511411 val-mlogloss:0.563827
[159] train-mlogloss:0.510778 val-mlogloss:0.563483
[160] train-mlogloss:0.510313 val-mlogloss:0.563274
[161] train-mlogloss:0.509711 val-mlogloss:0.56301
[162] train-mlogloss:0.50914 val-mlogloss:0.562714
[163] train-mlogloss:0.508492 val-mlogloss:0.562426
[164] train-mlogloss:0.507943 val-mlogloss:0.562215
[165] train-mlogloss:0.507426 val-mlogloss:0.56191
[166] train-mlogloss:0.506847 val-mlogloss:0.561624
[167] train-mlogloss:0.506326 val-mlogloss:0.561331
[168] train-mlogloss:0.505887 val-mlogloss:0.561086
[169] train-mlogloss:0.505338 val-mlogloss:0.560864
[170] train-mlogloss:0.504877 val-mlogloss:0.560647
[171] train-mlogloss:0.504194 val-mlogloss:0.560321
[172] train-mlogloss:0.503824 val-mlogloss:0.560112
[173] train-mlogloss:0.503385 val-mlogloss:0.559882
[174] train-mlogloss:0.502722 val-mlogloss:0.559504
[175] train-mlogloss:0.502224 val-mlogloss:0.559311
[176] train-mlogloss:0.501795 val-mlogloss:0.559084
[177] train-mlogloss:0.50141 val-mlogloss:0.558864
[178] train-mlogloss:0.500948 val-mlogloss:0.558714
[179] train-mlogloss:0.50043 val-mlogloss:0.558479
[180] train-mlogloss:0.499915 val-mlogloss:0.558233
[181] train-mlogloss:0.499412 val-mlogloss:0.557988
[182] train-mlogloss:0.498959 val-mlogloss:0.55773
[183] train-mlogloss:0.498605 val-mlogloss:0.557536
[184] train-mlogloss:0.498041 val-mlogloss:0.557348
[185] train-mlogloss:0.497586 val-mlogloss:0.557168
[186] train-mlogloss:0.497112 val-mlogloss:0.556978
[187] train-mlogloss:0.496679 val-mlogloss:0.556789
[188] train-mlogloss:0.496312 val-mlogloss:0.556634
[189] train-mlogloss:0.495836 val-mlogloss:0.556458
[190] train-mlogloss:0.495502 val-mlogloss:0.55629
[191] train-mlogloss:0.495037 val-mlogloss:0.55608
[192] train-mlogloss:0.494555 val-mlogloss:0.555862
[193] train-mlogloss:0.494057 val-mlogloss:0.555644
[194] train-mlogloss:0.493559 val-mlogloss:0.555467
[195] train-mlogloss:0.493131 val-mlogloss:0.555313
[196] train-mlogloss:0.492775 val-mlogloss:0.555126
[197] train-mlogloss:0.492274 val-mlogloss:0.554935
[198] train-mlogloss:0.491772 val-mlogloss:0.554739
[199] train-mlogloss:0.4913 val-mlogloss:0.554586
[200] train-mlogloss:0.491014 val-mlogloss:0.554454
[201] train-mlogloss:0.490522 val-mlogloss:0.554262
[202] train-mlogloss:0.490031 val-mlogloss:0.554088
[203] train-mlogloss:0.489514 val-mlogloss:0.553818
[204] train-mlogloss:0.488999 val-mlogloss:0.553673
[205] train-mlogloss:0.488622 val-mlogloss:0.553532
[206] train-mlogloss:0.488172 val-mlogloss:0.553354
[207] train-mlogloss:0.487777 val-mlogloss:0.553213
[208] train-mlogloss:0.487338 val-mlogloss:0.553077
[209] train-mlogloss:0.486901 val-mlogloss:0.552915
[210] train-mlogloss:0.486502 val-mlogloss:0.552746
[211] train-mlogloss:0.486131 val-mlogloss:0.552577
[212] train-mlogloss:0.485708 val-mlogloss:0.552356
[213] train-mlogloss:0.485334 val-mlogloss:0.552202
[214] train-mlogloss:0.484887 val-mlogloss:0.552055
[215] train-mlogloss:0.484484 val-mlogloss:0.551843
[216] train-mlogloss:0.484085 val-mlogloss:0.551672
[217] train-mlogloss:0.483713 val-mlogloss:0.551558
[218] train-mlogloss:0.483266 val-mlogloss:0.551436
[219] train-mlogloss:0.482904 val-mlogloss:0.55128
[220] train-mlogloss:0.48248 val-mlogloss:0.551123
[221] train-mlogloss:0.482052 val-mlogloss:0.55102
[222] train-mlogloss:0.481681 val-mlogloss:0.550838
[223] train-mlogloss:0.481307 val-mlogloss:0.550671
[224] train-mlogloss:0.480922 val-mlogloss:0.550494
[225] train-mlogloss:0.480556 val-mlogloss:0.550353
[226] train-mlogloss:0.48024 val-mlogloss:0.550225
[227] train-mlogloss:0.479834 val-mlogloss:0.550061
[228] train-mlogloss:0.479423 val-mlogloss:0.549881
[229] train-mlogloss:0.4791 val-mlogloss:0.549767
[230] train-mlogloss:0.478697 val-mlogloss:0.549616
[231] train-mlogloss:0.478321 val-mlogloss:0.549483
[232] train-mlogloss:0.477951 val-mlogloss:0.549339
[233] train-mlogloss:0.477625 val-mlogloss:0.549255
[234] train-mlogloss:0.477278 val-mlogloss:0.549154
[235] train-mlogloss:0.476827 val-mlogloss:0.548966
[236] train-mlogloss:0.476556 val-mlogloss:0.548858
[237] train-mlogloss:0.4761 val-mlogloss:0.548707
[238] train-mlogloss:0.475678 val-mlogloss:0.548514
[239] train-mlogloss:0.475256 val-mlogloss:0.54832
[240] train-mlogloss:0.474782 val-mlogloss:0.548096
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[866] train-mlogloss:0.336774 val-mlogloss:0.528262
[867] train-mlogloss:0.336627 val-mlogloss:0.528242
[868] train-mlogloss:0.336439 val-mlogloss:0.528233
[869] train-mlogloss:0.33632 val-mlogloss:0.528225
[870] train-mlogloss:0.336174 val-mlogloss:0.528191
[871] train-mlogloss:0.336065 val-mlogloss:0.52818
[872] train-mlogloss:0.335923 val-mlogloss:0.528193
[873] train-mlogloss:0.335758 val-mlogloss:0.528174
[874] train-mlogloss:0.335566 val-mlogloss:0.528174
[875] train-mlogloss:0.335404 val-mlogloss:0.528176
[876] train-mlogloss:0.335273 val-mlogloss:0.528156
[877] train-mlogloss:0.335078 val-mlogloss:0.52816
[878] train-mlogloss:0.334961 val-mlogloss:0.528162
[879] train-mlogloss:0.334827 val-mlogloss:0.528187
[880] train-mlogloss:0.33468 val-mlogloss:0.52816
[881] train-mlogloss:0.334539 val-mlogloss:0.528167
[882] train-mlogloss:0.334436 val-mlogloss:0.528148
[883] train-mlogloss:0.334287 val-mlogloss:0.528145
[884] train-mlogloss:0.334117 val-mlogloss:0.52817
[885] train-mlogloss:0.333965 val-mlogloss:0.528191
[886] train-mlogloss:0.333825 val-mlogloss:0.528194
[887] train-mlogloss:0.333704 val-mlogloss:0.528238
[888] train-mlogloss:0.333523 val-mlogloss:0.528183
[889] train-mlogloss:0.333399 val-mlogloss:0.528182
[890] train-mlogloss:0.333246 val-mlogloss:0.528184
[891] train-mlogloss:0.333128 val-mlogloss:0.528163
[892] train-mlogloss:0.332999 val-mlogloss:0.528165
[893] train-mlogloss:0.332847 val-mlogloss:0.52816
[894] train-mlogloss:0.332722 val-mlogloss:0.528153
[895] train-mlogloss:0.332567 val-mlogloss:0.528141
[896] train-mlogloss:0.332443 val-mlogloss:0.528144
[897] train-mlogloss:0.332266 val-mlogloss:0.528142
[898] train-mlogloss:0.332139 val-mlogloss:0.528152
[899] train-mlogloss:0.331954 val-mlogloss:0.528156
[900] train-mlogloss:0.331802 val-mlogloss:0.528159
[901] train-mlogloss:0.331613 val-mlogloss:0.528143
[902] train-mlogloss:0.331422 val-mlogloss:0.528133
[903] train-mlogloss:0.331245 val-mlogloss:0.528124
[904] train-mlogloss:0.331058 val-mlogloss:0.528107
[905] train-mlogloss:0.330916 val-mlogloss:0.528114
[906] train-mlogloss:0.330829 val-mlogloss:0.52812
[907] train-mlogloss:0.330706 val-mlogloss:0.528104
[908] train-mlogloss:0.330544 val-mlogloss:0.528068
[909] train-mlogloss:0.330381 val-mlogloss:0.528077
[910] train-mlogloss:0.330242 val-mlogloss:0.528057
[911] train-mlogloss:0.330066 val-mlogloss:0.528069
[912] train-mlogloss:0.329893 val-mlogloss:0.528045
[913] train-mlogloss:0.329759 val-mlogloss:0.528064
[914] train-mlogloss:0.329647 val-mlogloss:0.528071
[915] train-mlogloss:0.329545 val-mlogloss:0.528085
[916] train-mlogloss:0.329357 val-mlogloss:0.528074
[917] train-mlogloss:0.329207 val-mlogloss:0.528063
[918] train-mlogloss:0.329094 val-mlogloss:0.528086
[919] train-mlogloss:0.328961 val-mlogloss:0.528106
[920] train-mlogloss:0.328844 val-mlogloss:0.528068
[921] train-mlogloss:0.328632 val-mlogloss:0.528104
[922] train-mlogloss:0.32848 val-mlogloss:0.528151
[923] train-mlogloss:0.328312 val-mlogloss:0.528097
[924] train-mlogloss:0.328147 val-mlogloss:0.528034
[925] train-mlogloss:0.327967 val-mlogloss:0.528044
[926] train-mlogloss:0.327836 val-mlogloss:0.528063
[927] train-mlogloss:0.327692 val-mlogloss:0.52805
[928] train-mlogloss:0.327545 val-mlogloss:0.528045
[929] train-mlogloss:0.327356 val-mlogloss:0.528053
[930] train-mlogloss:0.327215 val-mlogloss:0.528033
[931] train-mlogloss:0.327073 val-mlogloss:0.528029
[932] train-mlogloss:0.326964 val-mlogloss:0.528046
[933] train-mlogloss:0.326814 val-mlogloss:0.52806
[934] train-mlogloss:0.326662 val-mlogloss:0.528055
[935] train-mlogloss:0.326464 val-mlogloss:0.528039
[936] train-mlogloss:0.326306 val-mlogloss:0.528041
[937] train-mlogloss:0.32611 val-mlogloss:0.528025
[938] train-mlogloss:0.325971 val-mlogloss:0.528027
[939] train-mlogloss:0.32585 val-mlogloss:0.528053
[940] train-mlogloss:0.325693 val-mlogloss:0.52808
[941] train-mlogloss:0.325496 val-mlogloss:0.528036
[942] train-mlogloss:0.325352 val-mlogloss:0.528007
[943] train-mlogloss:0.325224 val-mlogloss:0.528002
[944] train-mlogloss:0.325091 val-mlogloss:0.528047
[945] train-mlogloss:0.324915 val-mlogloss:0.528096
[946] train-mlogloss:0.324783 val-mlogloss:0.528074
[947] train-mlogloss:0.324644 val-mlogloss:0.528126
[948] train-mlogloss:0.324525 val-mlogloss:0.528118
[949] train-mlogloss:0.324357 val-mlogloss:0.528098
[950] train-mlogloss:0.324189 val-mlogloss:0.528099
[951] train-mlogloss:0.324061 val-mlogloss:0.528106
[952] train-mlogloss:0.323875 val-mlogloss:0.528109
[953] train-mlogloss:0.323764 val-mlogloss:0.528082
[954] train-mlogloss:0.323699 val-mlogloss:0.528068
[955] train-mlogloss:0.323551 val-mlogloss:0.528051
[956] train-mlogloss:0.32335 val-mlogloss:0.528063
[957] train-mlogloss:0.323188 val-mlogloss:0.528073
[958] train-mlogloss:0.32304 val-mlogloss:0.528051
[959] train-mlogloss:0.322828 val-mlogloss:0.528056
[960] train-mlogloss:0.322638 val-mlogloss:0.528066
[961] train-mlogloss:0.322486 val-mlogloss:0.528053
[962] train-mlogloss:0.322354 val-mlogloss:0.528039
[963] train-mlogloss:0.322163 val-mlogloss:0.52804
[964] train-mlogloss:0.322008 val-mlogloss:0.528058
[965] train-mlogloss:0.32188 val-mlogloss:0.528069
[966] train-mlogloss:0.321747 val-mlogloss:0.528035
[967] train-mlogloss:0.321592 val-mlogloss:0.52801
[968] train-mlogloss:0.32142 val-mlogloss:0.527992
[969] train-mlogloss:0.321268 val-mlogloss:0.527983
[970] train-mlogloss:0.321113 val-mlogloss:0.528005
[971] train-mlogloss:0.320979 val-mlogloss:0.52799
[972] train-mlogloss:0.32084 val-mlogloss:0.52797
[973] train-mlogloss:0.320754 val-mlogloss:0.527985
[974] train-mlogloss:0.320625 val-mlogloss:0.527998
[975] train-mlogloss:0.320502 val-mlogloss:0.527975
[976] train-mlogloss:0.320342 val-mlogloss:0.527982
[977] train-mlogloss:0.32016 val-mlogloss:0.527975
[978] train-mlogloss:0.320044 val-mlogloss:0.527969
[979] train-mlogloss:0.319901 val-mlogloss:0.527954
[980] train-mlogloss:0.319743 val-mlogloss:0.528009
[981] train-mlogloss:0.319617 val-mlogloss:0.528004
[982] train-mlogloss:0.319425 val-mlogloss:0.528063
[983] train-mlogloss:0.319263 val-mlogloss:0.528066
[984] train-mlogloss:0.319153 val-mlogloss:0.528061
[985] train-mlogloss:0.318969 val-mlogloss:0.528075
[986] train-mlogloss:0.318826 val-mlogloss:0.528066
[987] train-mlogloss:0.318721 val-mlogloss:0.528064
[988] train-mlogloss:0.318593 val-mlogloss:0.528105
[989] train-mlogloss:0.318489 val-mlogloss:0.528081
[990] train-mlogloss:0.318339 val-mlogloss:0.528087
[991] train-mlogloss:0.318215 val-mlogloss:0.528056
[992] train-mlogloss:0.318041 val-mlogloss:0.528052
[993] train-mlogloss:0.317847 val-mlogloss:0.52803
[994] train-mlogloss:0.317691 val-mlogloss:0.528032
[995] train-mlogloss:0.317562 val-mlogloss:0.528032
[996] train-mlogloss:0.3174 val-mlogloss:0.528038
[997] train-mlogloss:0.317216 val-mlogloss:0.528041
[998] train-mlogloss:0.317087 val-mlogloss:0.528029
[999] train-mlogloss:0.316924 val-mlogloss:0.528023
[1000] train-mlogloss:0.316755 val-mlogloss:0.528071
[1001] train-mlogloss:0.316637 val-mlogloss:0.528079
[1002] train-mlogloss:0.316485 val-mlogloss:0.528094
[1003] train-mlogloss:0.316314 val-mlogloss:0.528093
[1004] train-mlogloss:0.316211 val-mlogloss:0.528084
[1005] train-mlogloss:0.316092 val-mlogloss:0.528104
[1006] train-mlogloss:0.315957 val-mlogloss:0.528106
[1007] train-mlogloss:0.315782 val-mlogloss:0.52811
[1008] train-mlogloss:0.315625 val-mlogloss:0.528115
[1009] train-mlogloss:0.315469 val-mlogloss:0.528068
[1010] train-mlogloss:0.315374 val-mlogloss:0.528086
[1011] train-mlogloss:0.31525 val-mlogloss:0.5281
[1012] train-mlogloss:0.315127 val-mlogloss:0.52812
[1013] train-mlogloss:0.314985 val-mlogloss:0.528125
[1014] train-mlogloss:0.314815 val-mlogloss:0.528174
[1015] train-mlogloss:0.31463 val-mlogloss:0.528177
[1016] train-mlogloss:0.314486 val-mlogloss:0.528189
[1017] train-mlogloss:0.314336 val-mlogloss:0.528167
[1018] train-mlogloss:0.314189 val-mlogloss:0.528178
[1019] train-mlogloss:0.314041 val-mlogloss:0.528181
[1020] train-mlogloss:0.313979 val-mlogloss:0.528181
[1021] train-mlogloss:0.313818 val-mlogloss:0.528187
[1022] train-mlogloss:0.313686 val-mlogloss:0.528162
[1023] train-mlogloss:0.313532 val-mlogloss:0.528133
[1024] train-mlogloss:0.313365 val-mlogloss:0.528117
[1025] train-mlogloss:0.313227 val-mlogloss:0.52813
[1026] train-mlogloss:0.313062 val-mlogloss:0.528102
[1027] train-mlogloss:0.312919 val-mlogloss:0.528111
[1028] train-mlogloss:0.312773 val-mlogloss:0.528097
[1029] train-mlogloss:0.312653 val-mlogloss:0.528098
Stopping. Best iteration:
[979] train-mlogloss:0.319901 val-mlogloss:0.527954[0] train-mlogloss:1.07767 val-mlogloss:1.07815
Multiple eval metrics have been passed: 'val-mlogloss' will be used for early stopping.Will train until val-mlogloss hasn't improved in 50 rounds.
[1] train-mlogloss:1.05806 val-mlogloss:1.05906
[2] train-mlogloss:1.03919 val-mlogloss:1.04071
[3] train-mlogloss:1.02124 val-mlogloss:1.02323
[4] train-mlogloss:1.00398 val-mlogloss:1.00646
[5] train-mlogloss:0.98748 val-mlogloss:0.990486
[6] train-mlogloss:0.971963 val-mlogloss:0.975414
[7] train-mlogloss:0.956857 val-mlogloss:0.960818
[8] train-mlogloss:0.942506 val-mlogloss:0.946983
[9] train-mlogloss:0.928764 val-mlogloss:0.933616
[10] train-mlogloss:0.915592 val-mlogloss:0.920944
[11] train-mlogloss:0.902997 val-mlogloss:0.908762
[12] train-mlogloss:0.890779 val-mlogloss:0.897061
[13] train-mlogloss:0.879202 val-mlogloss:0.885924
[14] train-mlogloss:0.868047 val-mlogloss:0.875236
[15] train-mlogloss:0.857392 val-mlogloss:0.865048
[16] train-mlogloss:0.847187 val-mlogloss:0.855252
[17] train-mlogloss:0.837292 val-mlogloss:0.845799
[18] train-mlogloss:0.827776 val-mlogloss:0.836699
[19] train-mlogloss:0.818744 val-mlogloss:0.828045
[20] train-mlogloss:0.809986 val-mlogloss:0.819765
[21] train-mlogloss:0.801495 val-mlogloss:0.81174
[22] train-mlogloss:0.793204 val-mlogloss:0.803835
[23] train-mlogloss:0.785279 val-mlogloss:0.796255
[24] train-mlogloss:0.777775 val-mlogloss:0.789106
[25] train-mlogloss:0.770446 val-mlogloss:0.782169
[26] train-mlogloss:0.763309 val-mlogloss:0.775468
[27] train-mlogloss:0.756385 val-mlogloss:0.768883
[28] train-mlogloss:0.749686 val-mlogloss:0.762523
[29] train-mlogloss:0.743241 val-mlogloss:0.756435
[30] train-mlogloss:0.737042 val-mlogloss:0.750627
[31] train-mlogloss:0.731035 val-mlogloss:0.744944
[32] train-mlogloss:0.725258 val-mlogloss:0.739431
[33] train-mlogloss:0.719628 val-mlogloss:0.734157
[34] train-mlogloss:0.714163 val-mlogloss:0.729121
[35] train-mlogloss:0.70897 val-mlogloss:0.724241
[36] train-mlogloss:0.703931 val-mlogloss:0.719499
[37] train-mlogloss:0.699056 val-mlogloss:0.714974
[38] train-mlogloss:0.69434 val-mlogloss:0.710612
[39] train-mlogloss:0.689778 val-mlogloss:0.706456
[40] train-mlogloss:0.685391 val-mlogloss:0.702464
[41] train-mlogloss:0.681112 val-mlogloss:0.698499
[42] train-mlogloss:0.676935 val-mlogloss:0.694608
[43] train-mlogloss:0.672866 val-mlogloss:0.690916
[44] train-mlogloss:0.668868 val-mlogloss:0.687209
[45] train-mlogloss:0.665094 val-mlogloss:0.683743
[46] train-mlogloss:0.661528 val-mlogloss:0.680504
[47] train-mlogloss:0.657986 val-mlogloss:0.677262
[48] train-mlogloss:0.654498 val-mlogloss:0.674131
[49] train-mlogloss:0.651113 val-mlogloss:0.671064
[50] train-mlogloss:0.647764 val-mlogloss:0.668076
[51] train-mlogloss:0.644517 val-mlogloss:0.665133
[52] train-mlogloss:0.641423 val-mlogloss:0.662362
[53] train-mlogloss:0.638415 val-mlogloss:0.659639
[54] train-mlogloss:0.635412 val-mlogloss:0.656943
[55] train-mlogloss:0.632558 val-mlogloss:0.654403
[56] train-mlogloss:0.629715 val-mlogloss:0.651872
[57] train-mlogloss:0.627058 val-mlogloss:0.649485
[58] train-mlogloss:0.624439 val-mlogloss:0.647258
[59] train-mlogloss:0.621903 val-mlogloss:0.644979
[60] train-mlogloss:0.619328 val-mlogloss:0.642668
[61] train-mlogloss:0.616914 val-mlogloss:0.640631
[62] train-mlogloss:0.614496 val-mlogloss:0.63858
[63] train-mlogloss:0.61213 val-mlogloss:0.636518
[64] train-mlogloss:0.609794 val-mlogloss:0.634594
[65] train-mlogloss:0.607601 val-mlogloss:0.632729
[66] train-mlogloss:0.605494 val-mlogloss:0.630907
[67] train-mlogloss:0.60348 val-mlogloss:0.629151
[68] train-mlogloss:0.60144 val-mlogloss:0.627415
[69] train-mlogloss:0.599463 val-mlogloss:0.625695
[70] train-mlogloss:0.597528 val-mlogloss:0.6241
[71] train-mlogloss:0.595494 val-mlogloss:0.62234
[72] train-mlogloss:0.593742 val-mlogloss:0.620837
[73] train-mlogloss:0.592017 val-mlogloss:0.619328
[74] train-mlogloss:0.590297 val-mlogloss:0.617895
[75] train-mlogloss:0.588526 val-mlogloss:0.616409
[76] train-mlogloss:0.586758 val-mlogloss:0.614931
[77] train-mlogloss:0.58496 val-mlogloss:0.613435
[78] train-mlogloss:0.583421 val-mlogloss:0.612149
[79] train-mlogloss:0.581844 val-mlogloss:0.610835
[80] train-mlogloss:0.58026 val-mlogloss:0.609544
[81] train-mlogloss:0.578775 val-mlogloss:0.6083
[82] train-mlogloss:0.577323 val-mlogloss:0.607069
[83] train-mlogloss:0.575818 val-mlogloss:0.605839
[84] train-mlogloss:0.574429 val-mlogloss:0.604657
[85] train-mlogloss:0.572949 val-mlogloss:0.603544
[86] train-mlogloss:0.571471 val-mlogloss:0.602339
[87] train-mlogloss:0.570155 val-mlogloss:0.601325
[88] train-mlogloss:0.568867 val-mlogloss:0.60024
[89] train-mlogloss:0.567562 val-mlogloss:0.599195
[90] train-mlogloss:0.566243 val-mlogloss:0.59823
[91] train-mlogloss:0.564992 val-mlogloss:0.597289
[92] train-mlogloss:0.563815 val-mlogloss:0.596393
[93] train-mlogloss:0.562543 val-mlogloss:0.59543
[94] train-mlogloss:0.561272 val-mlogloss:0.594499
[95] train-mlogloss:0.560079 val-mlogloss:0.593686
[96] train-mlogloss:0.558994 val-mlogloss:0.592837
[97] train-mlogloss:0.557875 val-mlogloss:0.592001
[98] train-mlogloss:0.556803 val-mlogloss:0.591204
[99] train-mlogloss:0.555788 val-mlogloss:0.590443
[100] train-mlogloss:0.554809 val-mlogloss:0.589634
[101] train-mlogloss:0.553729 val-mlogloss:0.588775
[102] train-mlogloss:0.552647 val-mlogloss:0.587944
[103] train-mlogloss:0.551673 val-mlogloss:0.587218
[104] train-mlogloss:0.550573 val-mlogloss:0.586405
[105] train-mlogloss:0.549562 val-mlogloss:0.585682
[106] train-mlogloss:0.548655 val-mlogloss:0.584953
[107] train-mlogloss:0.54765 val-mlogloss:0.584282
[108] train-mlogloss:0.546796 val-mlogloss:0.583678
[109] train-mlogloss:0.545857 val-mlogloss:0.583023
[110] train-mlogloss:0.544953 val-mlogloss:0.582363
[111] train-mlogloss:0.544075 val-mlogloss:0.581778
[112] train-mlogloss:0.543233 val-mlogloss:0.581131
[113] train-mlogloss:0.542374 val-mlogloss:0.580529
[114] train-mlogloss:0.541509 val-mlogloss:0.579951
[115] train-mlogloss:0.540777 val-mlogloss:0.579437
[116] train-mlogloss:0.539953 val-mlogloss:0.57883
[117] train-mlogloss:0.539093 val-mlogloss:0.578232
[118] train-mlogloss:0.538292 val-mlogloss:0.57765
[119] train-mlogloss:0.537557 val-mlogloss:0.577149
[120] train-mlogloss:0.536739 val-mlogloss:0.576564
[121] train-mlogloss:0.535946 val-mlogloss:0.576075
[122] train-mlogloss:0.535196 val-mlogloss:0.57553
[123] train-mlogloss:0.534537 val-mlogloss:0.575127
[124] train-mlogloss:0.533821 val-mlogloss:0.574629
[125] train-mlogloss:0.533063 val-mlogloss:0.574081
[126] train-mlogloss:0.532257 val-mlogloss:0.573573
[127] train-mlogloss:0.531512 val-mlogloss:0.573085
[128] train-mlogloss:0.530777 val-mlogloss:0.572621
[129] train-mlogloss:0.530136 val-mlogloss:0.572178
[130] train-mlogloss:0.529433 val-mlogloss:0.571681
[131] train-mlogloss:0.528791 val-mlogloss:0.571233
[132] train-mlogloss:0.52808 val-mlogloss:0.570762
[133] train-mlogloss:0.527417 val-mlogloss:0.570344
[134] train-mlogloss:0.526735 val-mlogloss:0.569915
[135] train-mlogloss:0.526088 val-mlogloss:0.5695
[136] train-mlogloss:0.525442 val-mlogloss:0.56907
[137] train-mlogloss:0.524697 val-mlogloss:0.568608
[138] train-mlogloss:0.524135 val-mlogloss:0.568235
[139] train-mlogloss:0.523564 val-mlogloss:0.567874
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[767] train-mlogloss:0.353593 val-mlogloss:0.523403
[768] train-mlogloss:0.353433 val-mlogloss:0.523404
[769] train-mlogloss:0.353226 val-mlogloss:0.523345
[770] train-mlogloss:0.353066 val-mlogloss:0.523342
[771] train-mlogloss:0.352944 val-mlogloss:0.523383
[772] train-mlogloss:0.352773 val-mlogloss:0.523393
[773] train-mlogloss:0.352574 val-mlogloss:0.5234
[774] train-mlogloss:0.352416 val-mlogloss:0.523409
[775] train-mlogloss:0.35225 val-mlogloss:0.523392
[776] train-mlogloss:0.3521 val-mlogloss:0.523408
[777] train-mlogloss:0.351972 val-mlogloss:0.523406
[778] train-mlogloss:0.35179 val-mlogloss:0.523377
[779] train-mlogloss:0.351572 val-mlogloss:0.523351
[780] train-mlogloss:0.351361 val-mlogloss:0.52334
[781] train-mlogloss:0.351168 val-mlogloss:0.523309
[782] train-mlogloss:0.350993 val-mlogloss:0.523334
[783] train-mlogloss:0.350819 val-mlogloss:0.523338
[784] train-mlogloss:0.350615 val-mlogloss:0.523344
[785] train-mlogloss:0.3504 val-mlogloss:0.523372
[786] train-mlogloss:0.350234 val-mlogloss:0.523382
[787] train-mlogloss:0.350087 val-mlogloss:0.523383
[788] train-mlogloss:0.349914 val-mlogloss:0.523383
[789] train-mlogloss:0.349769 val-mlogloss:0.523398
[790] train-mlogloss:0.349541 val-mlogloss:0.523437
[791] train-mlogloss:0.349396 val-mlogloss:0.523431
[792] train-mlogloss:0.349209 val-mlogloss:0.523434
[793] train-mlogloss:0.349053 val-mlogloss:0.523419
[794] train-mlogloss:0.348905 val-mlogloss:0.523401
[795] train-mlogloss:0.348702 val-mlogloss:0.523373
[796] train-mlogloss:0.348557 val-mlogloss:0.523318
[797] train-mlogloss:0.348373 val-mlogloss:0.523317
[798] train-mlogloss:0.348187 val-mlogloss:0.523313
[799] train-mlogloss:0.348008 val-mlogloss:0.52329
[800] train-mlogloss:0.347869 val-mlogloss:0.523284
[801] train-mlogloss:0.347708 val-mlogloss:0.52328
[802] train-mlogloss:0.347545 val-mlogloss:0.523307
[803] train-mlogloss:0.347422 val-mlogloss:0.523282
[804] train-mlogloss:0.347309 val-mlogloss:0.52327
[805] train-mlogloss:0.347186 val-mlogloss:0.523286
[806] train-mlogloss:0.34704 val-mlogloss:0.523284
[807] train-mlogloss:0.346885 val-mlogloss:0.523294
[808] train-mlogloss:0.34676 val-mlogloss:0.523258
[809] train-mlogloss:0.346605 val-mlogloss:0.523268
[810] train-mlogloss:0.346444 val-mlogloss:0.523305
[811] train-mlogloss:0.346237 val-mlogloss:0.523309
[812] train-mlogloss:0.346062 val-mlogloss:0.523282
[813] train-mlogloss:0.345885 val-mlogloss:0.523256
[814] train-mlogloss:0.345722 val-mlogloss:0.523252
[815] train-mlogloss:0.345582 val-mlogloss:0.523265
[816] train-mlogloss:0.345406 val-mlogloss:0.523257
[817] train-mlogloss:0.34522 val-mlogloss:0.523215
[818] train-mlogloss:0.345029 val-mlogloss:0.523199
[819] train-mlogloss:0.344815 val-mlogloss:0.523195
[820] train-mlogloss:0.344627 val-mlogloss:0.523149
[821] train-mlogloss:0.344451 val-mlogloss:0.523176
[822] train-mlogloss:0.344286 val-mlogloss:0.523143
[823] train-mlogloss:0.344168 val-mlogloss:0.523117
[824] train-mlogloss:0.34405 val-mlogloss:0.523096
[825] train-mlogloss:0.343914 val-mlogloss:0.523095
[826] train-mlogloss:0.343756 val-mlogloss:0.523072
[827] train-mlogloss:0.343568 val-mlogloss:0.523085
[828] train-mlogloss:0.343444 val-mlogloss:0.523087
[829] train-mlogloss:0.34332 val-mlogloss:0.523075
[830] train-mlogloss:0.343225 val-mlogloss:0.523055
[831] train-mlogloss:0.343039 val-mlogloss:0.52305
[832] train-mlogloss:0.342854 val-mlogloss:0.523085
[833] train-mlogloss:0.342719 val-mlogloss:0.523092
[834] train-mlogloss:0.342519 val-mlogloss:0.52306
[835] train-mlogloss:0.342354 val-mlogloss:0.523065
[836] train-mlogloss:0.342209 val-mlogloss:0.523067
[837] train-mlogloss:0.342027 val-mlogloss:0.523011
[838] train-mlogloss:0.34187 val-mlogloss:0.523017
[839] train-mlogloss:0.34173 val-mlogloss:0.523026
[840] train-mlogloss:0.341499 val-mlogloss:0.523044
[841] train-mlogloss:0.34135 val-mlogloss:0.523059
[842] train-mlogloss:0.341207 val-mlogloss:0.523048
[843] train-mlogloss:0.341056 val-mlogloss:0.523046
[844] train-mlogloss:0.340943 val-mlogloss:0.523037
[845] train-mlogloss:0.34078 val-mlogloss:0.523023
[846] train-mlogloss:0.340651 val-mlogloss:0.523037
[847] train-mlogloss:0.340508 val-mlogloss:0.523051
[848] train-mlogloss:0.340375 val-mlogloss:0.523032
[849] train-mlogloss:0.340164 val-mlogloss:0.522985
[850] train-mlogloss:0.339945 val-mlogloss:0.522923
[851] train-mlogloss:0.339745 val-mlogloss:0.522925
[852] train-mlogloss:0.339524 val-mlogloss:0.522907
[853] train-mlogloss:0.339394 val-mlogloss:0.522922
[854] train-mlogloss:0.339256 val-mlogloss:0.522933
[855] train-mlogloss:0.339093 val-mlogloss:0.522914
[856] train-mlogloss:0.338937 val-mlogloss:0.522909
[857] train-mlogloss:0.338763 val-mlogloss:0.522863
[858] train-mlogloss:0.338585 val-mlogloss:0.522859
[859] train-mlogloss:0.338406 val-mlogloss:0.522886
[860] train-mlogloss:0.338228 val-mlogloss:0.522889
[861] train-mlogloss:0.338095 val-mlogloss:0.522896
[862] train-mlogloss:0.337953 val-mlogloss:0.522874
[863] train-mlogloss:0.337752 val-mlogloss:0.52286
[864] train-mlogloss:0.337593 val-mlogloss:0.522829
[865] train-mlogloss:0.337405 val-mlogloss:0.522804
[866] train-mlogloss:0.337231 val-mlogloss:0.522808
[867] train-mlogloss:0.337117 val-mlogloss:0.522804
[868] train-mlogloss:0.336964 val-mlogloss:0.522855
[869] train-mlogloss:0.336824 val-mlogloss:0.522847
[870] train-mlogloss:0.336689 val-mlogloss:0.522864
[871] train-mlogloss:0.336514 val-mlogloss:0.522887
[872] train-mlogloss:0.336291 val-mlogloss:0.522927
[873] train-mlogloss:0.336134 val-mlogloss:0.522902
[874] train-mlogloss:0.335983 val-mlogloss:0.522891
[875] train-mlogloss:0.335821 val-mlogloss:0.522886
[876] train-mlogloss:0.335676 val-mlogloss:0.522897
[877] train-mlogloss:0.335514 val-mlogloss:0.522909
[878] train-mlogloss:0.335382 val-mlogloss:0.522913
[879] train-mlogloss:0.335245 val-mlogloss:0.522932
[880] train-mlogloss:0.335035 val-mlogloss:0.522914
[881] train-mlogloss:0.334899 val-mlogloss:0.522914
[882] train-mlogloss:0.334784 val-mlogloss:0.522899
[883] train-mlogloss:0.334605 val-mlogloss:0.522893
[884] train-mlogloss:0.334431 val-mlogloss:0.52288
[885] train-mlogloss:0.334255 val-mlogloss:0.522915
[886] train-mlogloss:0.334076 val-mlogloss:0.522914
[887] train-mlogloss:0.333908 val-mlogloss:0.522885
[888] train-mlogloss:0.33372 val-mlogloss:0.52287
[889] train-mlogloss:0.333555 val-mlogloss:0.522884
[890] train-mlogloss:0.333378 val-mlogloss:0.522902
[891] train-mlogloss:0.333235 val-mlogloss:0.522946
[892] train-mlogloss:0.33305 val-mlogloss:0.522927
[893] train-mlogloss:0.332887 val-mlogloss:0.522915
[894] train-mlogloss:0.33272 val-mlogloss:0.522928
[895] train-mlogloss:0.332567 val-mlogloss:0.522931
[896] train-mlogloss:0.332443 val-mlogloss:0.522911
[897] train-mlogloss:0.332324 val-mlogloss:0.522919
[898] train-mlogloss:0.332121 val-mlogloss:0.522884
[899] train-mlogloss:0.331961 val-mlogloss:0.522859
[900] train-mlogloss:0.331842 val-mlogloss:0.522851
[901] train-mlogloss:0.331648 val-mlogloss:0.522883
[902] train-mlogloss:0.331474 val-mlogloss:0.522855
[903] train-mlogloss:0.331273 val-mlogloss:0.522898
[904] train-mlogloss:0.331089 val-mlogloss:0.522878
[905] train-mlogloss:0.330961 val-mlogloss:0.522882
[906] train-mlogloss:0.330827 val-mlogloss:0.522893
[907] train-mlogloss:0.330636 val-mlogloss:0.522899
[908] train-mlogloss:0.330424 val-mlogloss:0.522862
[909] train-mlogloss:0.330288 val-mlogloss:0.522865
[910] train-mlogloss:0.330196 val-mlogloss:0.522886
[911] train-mlogloss:0.330057 val-mlogloss:0.522894
[912] train-mlogloss:0.329876 val-mlogloss:0.522889
[913] train-mlogloss:0.329669 val-mlogloss:0.52291
[914] train-mlogloss:0.329537 val-mlogloss:0.522908
[915] train-mlogloss:0.32941 val-mlogloss:0.522892
Stopping. Best iteration:
[865] train-mlogloss:0.337405 val-mlogloss:0.522804[0] train-mlogloss:1.07785 val-mlogloss:1.07825
Multiple eval metrics have been passed: 'val-mlogloss' will be used for early stopping.Will train until val-mlogloss hasn't improved in 50 rounds.
[1] train-mlogloss:1.0582 val-mlogloss:1.05903
[2] train-mlogloss:1.03946 val-mlogloss:1.04071
[3] train-mlogloss:1.02154 val-mlogloss:1.02319
[4] train-mlogloss:1.00437 val-mlogloss:1.00642
[5] train-mlogloss:0.987855 val-mlogloss:0.990305
[6] train-mlogloss:0.972221 val-mlogloss:0.975104
[7] train-mlogloss:0.957366 val-mlogloss:0.960616
[8] train-mlogloss:0.943297 val-mlogloss:0.946981
[9] train-mlogloss:0.929506 val-mlogloss:0.933642
[10] train-mlogloss:0.916266 val-mlogloss:0.920858
[11] train-mlogloss:0.903524 val-mlogloss:0.908541
[12] train-mlogloss:0.891404 val-mlogloss:0.896886
[13] train-mlogloss:0.879722 val-mlogloss:0.885705
[14] train-mlogloss:0.8689 val-mlogloss:0.875322
[15] train-mlogloss:0.858222 val-mlogloss:0.864991
[16] train-mlogloss:0.847995 val-mlogloss:0.855176
[17] train-mlogloss:0.838038 val-mlogloss:0.84568
[18] train-mlogloss:0.828382 val-mlogloss:0.836429
[19] train-mlogloss:0.819163 val-mlogloss:0.827621
[20] train-mlogloss:0.810383 val-mlogloss:0.819255
[21] train-mlogloss:0.801857 val-mlogloss:0.811102
[22] train-mlogloss:0.793707 val-mlogloss:0.80333
[23] train-mlogloss:0.785836 val-mlogloss:0.795771
[24] train-mlogloss:0.778428 val-mlogloss:0.788722
[25] train-mlogloss:0.771093 val-mlogloss:0.781718
[26] train-mlogloss:0.763979 val-mlogloss:0.774973
[27] train-mlogloss:0.757088 val-mlogloss:0.768487
[28] train-mlogloss:0.750398 val-mlogloss:0.762178
[29] train-mlogloss:0.743901 val-mlogloss:0.75604
[30] train-mlogloss:0.737662 val-mlogloss:0.750153
[31] train-mlogloss:0.731693 val-mlogloss:0.74453
[32] train-mlogloss:0.725852 val-mlogloss:0.738958
[33] train-mlogloss:0.720252 val-mlogloss:0.733735
[34] train-mlogloss:0.714827 val-mlogloss:0.728684
[35] train-mlogloss:0.709578 val-mlogloss:0.723759
[36] train-mlogloss:0.704497 val-mlogloss:0.718999
[37] train-mlogloss:0.699626 val-mlogloss:0.714491
[38] train-mlogloss:0.694773 val-mlogloss:0.710037
[39] train-mlogloss:0.690238 val-mlogloss:0.70579
[40] train-mlogloss:0.685813 val-mlogloss:0.701629
[41] train-mlogloss:0.681533 val-mlogloss:0.697623
[42] train-mlogloss:0.677345 val-mlogloss:0.693759
[43] train-mlogloss:0.673366 val-mlogloss:0.690083
[44] train-mlogloss:0.669395 val-mlogloss:0.686464
[45] train-mlogloss:0.665575 val-mlogloss:0.682988
[46] train-mlogloss:0.661837 val-mlogloss:0.679562
[47] train-mlogloss:0.658217 val-mlogloss:0.676277
[48] train-mlogloss:0.65475 val-mlogloss:0.673134
[49] train-mlogloss:0.651412 val-mlogloss:0.670106
[50] train-mlogloss:0.648128 val-mlogloss:0.667196
[51] train-mlogloss:0.644857 val-mlogloss:0.664234
[52] train-mlogloss:0.641723 val-mlogloss:0.661384
[53] train-mlogloss:0.638677 val-mlogloss:0.658674
[54] train-mlogloss:0.635725 val-mlogloss:0.656072
[55] train-mlogloss:0.632872 val-mlogloss:0.653521
[56] train-mlogloss:0.630086 val-mlogloss:0.650979
[57] train-mlogloss:0.627273 val-mlogloss:0.648463
[58] train-mlogloss:0.624551 val-mlogloss:0.646015
[59] train-mlogloss:0.62197 val-mlogloss:0.643723
[60] train-mlogloss:0.61944 val-mlogloss:0.641483
[61] train-mlogloss:0.616964 val-mlogloss:0.639327
[62] train-mlogloss:0.614619 val-mlogloss:0.637322
[63] train-mlogloss:0.612233 val-mlogloss:0.635285
[64] train-mlogloss:0.609922 val-mlogloss:0.633292
[65] train-mlogloss:0.607697 val-mlogloss:0.631391
[66] train-mlogloss:0.605498 val-mlogloss:0.629606
[67] train-mlogloss:0.603355 val-mlogloss:0.627792
[68] train-mlogloss:0.601346 val-mlogloss:0.626151
[69] train-mlogloss:0.599292 val-mlogloss:0.62445
[70] train-mlogloss:0.597326 val-mlogloss:0.622801
[71] train-mlogloss:0.595447 val-mlogloss:0.621207
[72] train-mlogloss:0.593632 val-mlogloss:0.619683
[73] train-mlogloss:0.591846 val-mlogloss:0.618164
[74] train-mlogloss:0.590101 val-mlogloss:0.616719
[75] train-mlogloss:0.588289 val-mlogloss:0.615183
[76] train-mlogloss:0.586602 val-mlogloss:0.613799
[77] train-mlogloss:0.584893 val-mlogloss:0.61237
[78] train-mlogloss:0.583316 val-mlogloss:0.611039
[79] train-mlogloss:0.581734 val-mlogloss:0.609774
[80] train-mlogloss:0.580268 val-mlogloss:0.608554
[81] train-mlogloss:0.578785 val-mlogloss:0.607345
[82] train-mlogloss:0.577278 val-mlogloss:0.606117
[83] train-mlogloss:0.575804 val-mlogloss:0.604974
[84] train-mlogloss:0.574357 val-mlogloss:0.60382
[85] train-mlogloss:0.572941 val-mlogloss:0.602667
[86] train-mlogloss:0.571605 val-mlogloss:0.60161
[87] train-mlogloss:0.570198 val-mlogloss:0.600547
[88] train-mlogloss:0.568929 val-mlogloss:0.599592
[89] train-mlogloss:0.567643 val-mlogloss:0.598599
[90] train-mlogloss:0.566372 val-mlogloss:0.59768
[91] train-mlogloss:0.565085 val-mlogloss:0.596689
[92] train-mlogloss:0.563884 val-mlogloss:0.59582
[93] train-mlogloss:0.562644 val-mlogloss:0.594855
[94] train-mlogloss:0.561561 val-mlogloss:0.594006
[95] train-mlogloss:0.560391 val-mlogloss:0.593116
[96] train-mlogloss:0.559274 val-mlogloss:0.59229
[97] train-mlogloss:0.558084 val-mlogloss:0.591398
[98] train-mlogloss:0.556943 val-mlogloss:0.590538
[99] train-mlogloss:0.555858 val-mlogloss:0.589803
[100] train-mlogloss:0.554837 val-mlogloss:0.589035
[101] train-mlogloss:0.553805 val-mlogloss:0.58836
[102] train-mlogloss:0.552648 val-mlogloss:0.587532
[103] train-mlogloss:0.551648 val-mlogloss:0.586783
[104] train-mlogloss:0.550663 val-mlogloss:0.586083
[105] train-mlogloss:0.549617 val-mlogloss:0.585348
[106] train-mlogloss:0.54865 val-mlogloss:0.584681
[107] train-mlogloss:0.547785 val-mlogloss:0.584105
[108] train-mlogloss:0.546879 val-mlogloss:0.583442
[109] train-mlogloss:0.545901 val-mlogloss:0.58276
[110] train-mlogloss:0.544986 val-mlogloss:0.582161
[111] train-mlogloss:0.544139 val-mlogloss:0.581622
[112] train-mlogloss:0.543167 val-mlogloss:0.580955
[113] train-mlogloss:0.542304 val-mlogloss:0.58035
[114] train-mlogloss:0.541369 val-mlogloss:0.57978
[115] train-mlogloss:0.540522 val-mlogloss:0.579216
[116] train-mlogloss:0.539658 val-mlogloss:0.578638
[117] train-mlogloss:0.538679 val-mlogloss:0.578041
[118] train-mlogloss:0.537863 val-mlogloss:0.577519
[119] train-mlogloss:0.537107 val-mlogloss:0.576979
[120] train-mlogloss:0.536314 val-mlogloss:0.576421
[121] train-mlogloss:0.535567 val-mlogloss:0.575928
[122] train-mlogloss:0.534803 val-mlogloss:0.575442
[123] train-mlogloss:0.534078 val-mlogloss:0.574993
[124] train-mlogloss:0.533389 val-mlogloss:0.574526
[125] train-mlogloss:0.532703 val-mlogloss:0.574091
[126] train-mlogloss:0.532017 val-mlogloss:0.573663
[127] train-mlogloss:0.531309 val-mlogloss:0.573253
[128] train-mlogloss:0.53063 val-mlogloss:0.572846
[129] train-mlogloss:0.529968 val-mlogloss:0.572415
[130] train-mlogloss:0.529335 val-mlogloss:0.572049
[131] train-mlogloss:0.528685 val-mlogloss:0.571711
[132] train-mlogloss:0.528061 val-mlogloss:0.571366
[133] train-mlogloss:0.527301 val-mlogloss:0.570923
[134] train-mlogloss:0.526539 val-mlogloss:0.570497
[135] train-mlogloss:0.525815 val-mlogloss:0.570027
[136] train-mlogloss:0.525122 val-mlogloss:0.569657
[137] train-mlogloss:0.524489 val-mlogloss:0.569284
[138] train-mlogloss:0.523731 val-mlogloss:0.568811
[139] train-mlogloss:0.522972 val-mlogloss:0.56842
[140] train-mlogloss:0.5223 val-mlogloss:0.568048
[141] train-mlogloss:0.521723 val-mlogloss:0.567638
[142] train-mlogloss:0.521182 val-mlogloss:0.567328
[143] train-mlogloss:0.52058 val-mlogloss:0.566909
[144] train-mlogloss:0.520076 val-mlogloss:0.566641
[145] train-mlogloss:0.519462 val-mlogloss:0.566317
[146] train-mlogloss:0.518859 val-mlogloss:0.565986
[147] train-mlogloss:0.518194 val-mlogloss:0.565587
[148] train-mlogloss:0.51744 val-mlogloss:0.565218
[149] train-mlogloss:0.516725 val-mlogloss:0.564843
[150] train-mlogloss:0.516055 val-mlogloss:0.564456
[151] train-mlogloss:0.515416 val-mlogloss:0.56407
[152] train-mlogloss:0.514901 val-mlogloss:0.563762
[153] train-mlogloss:0.514311 val-mlogloss:0.563434
[154] train-mlogloss:0.513734 val-mlogloss:0.563169
[155] train-mlogloss:0.513167 val-mlogloss:0.562879
[156] train-mlogloss:0.512589 val-mlogloss:0.562547
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[785] train-mlogloss:0.350066 val-mlogloss:0.528931
[786] train-mlogloss:0.349956 val-mlogloss:0.528933
[787] train-mlogloss:0.349845 val-mlogloss:0.528919
[788] train-mlogloss:0.349676 val-mlogloss:0.528895
[789] train-mlogloss:0.349535 val-mlogloss:0.528889
[790] train-mlogloss:0.349377 val-mlogloss:0.528872
[791] train-mlogloss:0.349213 val-mlogloss:0.528879
[792] train-mlogloss:0.349071 val-mlogloss:0.528883
[793] train-mlogloss:0.348935 val-mlogloss:0.528916
[794] train-mlogloss:0.348808 val-mlogloss:0.528907
[795] train-mlogloss:0.348667 val-mlogloss:0.528929
[796] train-mlogloss:0.348498 val-mlogloss:0.528895
[797] train-mlogloss:0.348315 val-mlogloss:0.528914
[798] train-mlogloss:0.348117 val-mlogloss:0.5289
[799] train-mlogloss:0.34794 val-mlogloss:0.528922
[800] train-mlogloss:0.347794 val-mlogloss:0.528917
[801] train-mlogloss:0.347595 val-mlogloss:0.528932
[802] train-mlogloss:0.347467 val-mlogloss:0.528934
[803] train-mlogloss:0.347275 val-mlogloss:0.528948
[804] train-mlogloss:0.347181 val-mlogloss:0.528952
[805] train-mlogloss:0.347061 val-mlogloss:0.528942
[806] train-mlogloss:0.3469 val-mlogloss:0.528919
[807] train-mlogloss:0.346684 val-mlogloss:0.52888
[808] train-mlogloss:0.346548 val-mlogloss:0.528875
[809] train-mlogloss:0.34638 val-mlogloss:0.528848
[810] train-mlogloss:0.346187 val-mlogloss:0.528817
[811] train-mlogloss:0.346078 val-mlogloss:0.528808
[812] train-mlogloss:0.345931 val-mlogloss:0.528806
[813] train-mlogloss:0.345747 val-mlogloss:0.528849
[814] train-mlogloss:0.345546 val-mlogloss:0.528837
[815] train-mlogloss:0.345336 val-mlogloss:0.528836
[816] train-mlogloss:0.345191 val-mlogloss:0.528849
[817] train-mlogloss:0.345036 val-mlogloss:0.528849
[818] train-mlogloss:0.344858 val-mlogloss:0.528857
[819] train-mlogloss:0.344748 val-mlogloss:0.528871
[820] train-mlogloss:0.344569 val-mlogloss:0.528851
[821] train-mlogloss:0.344432 val-mlogloss:0.528881
[822] train-mlogloss:0.344269 val-mlogloss:0.528898
[823] train-mlogloss:0.344124 val-mlogloss:0.528891
[824] train-mlogloss:0.343996 val-mlogloss:0.528878
[825] train-mlogloss:0.343793 val-mlogloss:0.528877
[826] train-mlogloss:0.343683 val-mlogloss:0.528879
[827] train-mlogloss:0.343515 val-mlogloss:0.528899
[828] train-mlogloss:0.343347 val-mlogloss:0.528939
[829] train-mlogloss:0.343194 val-mlogloss:0.528944
[830] train-mlogloss:0.343014 val-mlogloss:0.52897
[831] train-mlogloss:0.342872 val-mlogloss:0.528954
[832] train-mlogloss:0.342719 val-mlogloss:0.528967
[833] train-mlogloss:0.342546 val-mlogloss:0.529004
[834] train-mlogloss:0.342424 val-mlogloss:0.529021
[835] train-mlogloss:0.342271 val-mlogloss:0.529062
[836] train-mlogloss:0.342078 val-mlogloss:0.529077
[837] train-mlogloss:0.341906 val-mlogloss:0.529063
[838] train-mlogloss:0.341761 val-mlogloss:0.529085
[839] train-mlogloss:0.341572 val-mlogloss:0.52909
[840] train-mlogloss:0.341412 val-mlogloss:0.529101
[841] train-mlogloss:0.341252 val-mlogloss:0.529133
[842] train-mlogloss:0.341154 val-mlogloss:0.52911
[843] train-mlogloss:0.341 val-mlogloss:0.529061
[844] train-mlogloss:0.340813 val-mlogloss:0.529042
[845] train-mlogloss:0.340673 val-mlogloss:0.529046
[846] train-mlogloss:0.340509 val-mlogloss:0.529041
[847] train-mlogloss:0.340348 val-mlogloss:0.529069
[848] train-mlogloss:0.340182 val-mlogloss:0.529056
[849] train-mlogloss:0.340047 val-mlogloss:0.529058
[850] train-mlogloss:0.339936 val-mlogloss:0.529047
[851] train-mlogloss:0.339801 val-mlogloss:0.529016
[852] train-mlogloss:0.339628 val-mlogloss:0.52904
[853] train-mlogloss:0.339452 val-mlogloss:0.529004
[854] train-mlogloss:0.339274 val-mlogloss:0.52898
[855] train-mlogloss:0.339145 val-mlogloss:0.528971
[856] train-mlogloss:0.338951 val-mlogloss:0.528997
[857] train-mlogloss:0.338813 val-mlogloss:0.529011
[858] train-mlogloss:0.338664 val-mlogloss:0.529022
[859] train-mlogloss:0.338537 val-mlogloss:0.529038
[860] train-mlogloss:0.338379 val-mlogloss:0.529039
[861] train-mlogloss:0.338255 val-mlogloss:0.52904
[862] train-mlogloss:0.338115 val-mlogloss:0.529058
Stopping. Best iteration:
[812] train-mlogloss:0.345931 val-mlogloss:0.528806[0] train-mlogloss:1.078 val-mlogloss:1.07854
Multiple eval metrics have been passed: 'val-mlogloss' will be used for early stopping.Will train until val-mlogloss hasn't improved in 50 rounds.
[1] train-mlogloss:1.05838 val-mlogloss:1.05926
[2] train-mlogloss:1.0395 val-mlogloss:1.04091
[3] train-mlogloss:1.02156 val-mlogloss:1.02338
[4] train-mlogloss:1.00447 val-mlogloss:1.00668
[5] train-mlogloss:0.988109 val-mlogloss:0.990719
[6] train-mlogloss:0.97252 val-mlogloss:0.975535
[7] train-mlogloss:0.957648 val-mlogloss:0.96101
[8] train-mlogloss:0.943645 val-mlogloss:0.947351
[9] train-mlogloss:0.929912 val-mlogloss:0.934029
[10] train-mlogloss:0.916797 val-mlogloss:0.921278
[11] train-mlogloss:0.904248 val-mlogloss:0.908961
[12] train-mlogloss:0.892116 val-mlogloss:0.897169
[13] train-mlogloss:0.880533 val-mlogloss:0.885966
[14] train-mlogloss:0.869777 val-mlogloss:0.875586
[15] train-mlogloss:0.859157 val-mlogloss:0.865385
[16] train-mlogloss:0.848955 val-mlogloss:0.85548
[17] train-mlogloss:0.839074 val-mlogloss:0.845915
[18] train-mlogloss:0.829461 val-mlogloss:0.836576
[19] train-mlogloss:0.820252 val-mlogloss:0.827593
[20] train-mlogloss:0.811411 val-mlogloss:0.819085
[21] train-mlogloss:0.802868 val-mlogloss:0.810883
[22] train-mlogloss:0.79472 val-mlogloss:0.802979
[23] train-mlogloss:0.78682 val-mlogloss:0.795381
[24] train-mlogloss:0.779531 val-mlogloss:0.788368
[25] train-mlogloss:0.772252 val-mlogloss:0.781388
[26] train-mlogloss:0.765164 val-mlogloss:0.774643
[27] train-mlogloss:0.758381 val-mlogloss:0.768154
[28] train-mlogloss:0.751713 val-mlogloss:0.761825
[29] train-mlogloss:0.745232 val-mlogloss:0.755654
[30] train-mlogloss:0.739061 val-mlogloss:0.749747
[31] train-mlogloss:0.733079 val-mlogloss:0.744019
[32] train-mlogloss:0.727369 val-mlogloss:0.738503
[33] train-mlogloss:0.721756 val-mlogloss:0.733211
[34] train-mlogloss:0.716399 val-mlogloss:0.72808
[35] train-mlogloss:0.711077 val-mlogloss:0.72303
[36] train-mlogloss:0.706048 val-mlogloss:0.7182
[37] train-mlogloss:0.701221 val-mlogloss:0.71359
[38] train-mlogloss:0.696473 val-mlogloss:0.709147
[39] train-mlogloss:0.691877 val-mlogloss:0.704882
[40] train-mlogloss:0.687404 val-mlogloss:0.700675
[41] train-mlogloss:0.683091 val-mlogloss:0.696693
[42] train-mlogloss:0.678879 val-mlogloss:0.692759
[43] train-mlogloss:0.67483 val-mlogloss:0.68896
[44] train-mlogloss:0.670928 val-mlogloss:0.685356
[45] train-mlogloss:0.667148 val-mlogloss:0.68181
[46] train-mlogloss:0.663428 val-mlogloss:0.678469
[47] train-mlogloss:0.659945 val-mlogloss:0.675224
[48] train-mlogloss:0.656485 val-mlogloss:0.672077
[49] train-mlogloss:0.653107 val-mlogloss:0.66905
[50] train-mlogloss:0.649844 val-mlogloss:0.666073
[51] train-mlogloss:0.646588 val-mlogloss:0.66319
[52] train-mlogloss:0.643418 val-mlogloss:0.660272
[53] train-mlogloss:0.64037 val-mlogloss:0.657494
[54] train-mlogloss:0.637434 val-mlogloss:0.654836
[55] train-mlogloss:0.634658 val-mlogloss:0.652281
[56] train-mlogloss:0.631957 val-mlogloss:0.649871
[57] train-mlogloss:0.629209 val-mlogloss:0.647372
[58] train-mlogloss:0.626469 val-mlogloss:0.644922
[59] train-mlogloss:0.62394 val-mlogloss:0.642679
[60] train-mlogloss:0.621431 val-mlogloss:0.640419
[61] train-mlogloss:0.618958 val-mlogloss:0.638179
[62] train-mlogloss:0.616554 val-mlogloss:0.636021
[63] train-mlogloss:0.614231 val-mlogloss:0.633987
[64] train-mlogloss:0.611928 val-mlogloss:0.631921
[65] train-mlogloss:0.609687 val-mlogloss:0.629971
[66] train-mlogloss:0.607554 val-mlogloss:0.628088
[67] train-mlogloss:0.605461 val-mlogloss:0.626329
[68] train-mlogloss:0.603374 val-mlogloss:0.624502
[69] train-mlogloss:0.601335 val-mlogloss:0.622744
[70] train-mlogloss:0.599369 val-mlogloss:0.621052
[71] train-mlogloss:0.597452 val-mlogloss:0.619411
[72] train-mlogloss:0.595599 val-mlogloss:0.617778
[73] train-mlogloss:0.593666 val-mlogloss:0.61616
[74] train-mlogloss:0.591857 val-mlogloss:0.614607
[75] train-mlogloss:0.590084 val-mlogloss:0.613155
[76] train-mlogloss:0.588395 val-mlogloss:0.611659
[77] train-mlogloss:0.586673 val-mlogloss:0.610093
[78] train-mlogloss:0.585048 val-mlogloss:0.608773
[79] train-mlogloss:0.583448 val-mlogloss:0.607423
[80] train-mlogloss:0.581938 val-mlogloss:0.606183
[81] train-mlogloss:0.580435 val-mlogloss:0.604947
[82] train-mlogloss:0.579007 val-mlogloss:0.603798
[83] train-mlogloss:0.577557 val-mlogloss:0.602594
[84] train-mlogloss:0.576182 val-mlogloss:0.601433
[85] train-mlogloss:0.574801 val-mlogloss:0.600222
[86] train-mlogloss:0.573377 val-mlogloss:0.599003
[87] train-mlogloss:0.572076 val-mlogloss:0.597922
[88] train-mlogloss:0.57074 val-mlogloss:0.596781
[89] train-mlogloss:0.569409 val-mlogloss:0.595688
[90] train-mlogloss:0.568155 val-mlogloss:0.594672
[91] train-mlogloss:0.566838 val-mlogloss:0.593646
[92] train-mlogloss:0.565568 val-mlogloss:0.592586
[93] train-mlogloss:0.564356 val-mlogloss:0.591632
[94] train-mlogloss:0.563111 val-mlogloss:0.590669
[95] train-mlogloss:0.562065 val-mlogloss:0.589811
[96] train-mlogloss:0.560951 val-mlogloss:0.588941
[97] train-mlogloss:0.559821 val-mlogloss:0.58811
[98] train-mlogloss:0.558781 val-mlogloss:0.587279
[99] train-mlogloss:0.557637 val-mlogloss:0.586385
[100] train-mlogloss:0.556571 val-mlogloss:0.585549
[101] train-mlogloss:0.555507 val-mlogloss:0.584762
[102] train-mlogloss:0.554522 val-mlogloss:0.583991
[103] train-mlogloss:0.553528 val-mlogloss:0.583171
[104] train-mlogloss:0.552509 val-mlogloss:0.582413
[105] train-mlogloss:0.551488 val-mlogloss:0.581624
[106] train-mlogloss:0.550477 val-mlogloss:0.580861
[107] train-mlogloss:0.549536 val-mlogloss:0.580146
[108] train-mlogloss:0.548559 val-mlogloss:0.579415
[109] train-mlogloss:0.547608 val-mlogloss:0.578645
[110] train-mlogloss:0.546813 val-mlogloss:0.578095
[111] train-mlogloss:0.545947 val-mlogloss:0.577443
[112] train-mlogloss:0.545066 val-mlogloss:0.576827
[113] train-mlogloss:0.544068 val-mlogloss:0.576131
[114] train-mlogloss:0.543099 val-mlogloss:0.575467
[115] train-mlogloss:0.542191 val-mlogloss:0.574828
[116] train-mlogloss:0.541443 val-mlogloss:0.574251
[117] train-mlogloss:0.540685 val-mlogloss:0.573709
[118] train-mlogloss:0.53988 val-mlogloss:0.5731
[119] train-mlogloss:0.539055 val-mlogloss:0.572553
[120] train-mlogloss:0.538308 val-mlogloss:0.572008
[121] train-mlogloss:0.537532 val-mlogloss:0.5715
[122] train-mlogloss:0.536817 val-mlogloss:0.571029
[123] train-mlogloss:0.5361 val-mlogloss:0.570529
[124] train-mlogloss:0.535387 val-mlogloss:0.570097
[125] train-mlogloss:0.534624 val-mlogloss:0.569509
[126] train-mlogloss:0.533941 val-mlogloss:0.569051
[127] train-mlogloss:0.533261 val-mlogloss:0.568553
[128] train-mlogloss:0.532662 val-mlogloss:0.568142
[129] train-mlogloss:0.531976 val-mlogloss:0.567702
[130] train-mlogloss:0.531263 val-mlogloss:0.56726
[131] train-mlogloss:0.530579 val-mlogloss:0.566804
[132] train-mlogloss:0.529914 val-mlogloss:0.566355
[133] train-mlogloss:0.529146 val-mlogloss:0.565838
[134] train-mlogloss:0.528372 val-mlogloss:0.565325
[135] train-mlogloss:0.527586 val-mlogloss:0.56482
[136] train-mlogloss:0.526946 val-mlogloss:0.5644
[137] train-mlogloss:0.526318 val-mlogloss:0.563997
[138] train-mlogloss:0.525646 val-mlogloss:0.56357
[139] train-mlogloss:0.525087 val-mlogloss:0.563192
[140] train-mlogloss:0.524516 val-mlogloss:0.562833
[141] train-mlogloss:0.523932 val-mlogloss:0.562502
[142] train-mlogloss:0.52337 val-mlogloss:0.562144
[143] train-mlogloss:0.522797 val-mlogloss:0.56179
[144] train-mlogloss:0.522291 val-mlogloss:0.561436
[145] train-mlogloss:0.52165 val-mlogloss:0.561031
[146] train-mlogloss:0.520978 val-mlogloss:0.560648
[147] train-mlogloss:0.520288 val-mlogloss:0.560212
[148] train-mlogloss:0.519684 val-mlogloss:0.55979
[149] train-mlogloss:0.518985 val-mlogloss:0.559403
[150] train-mlogloss:0.518446 val-mlogloss:0.559064
[151] train-mlogloss:0.517833 val-mlogloss:0.558647
[152] train-mlogloss:0.517251 val-mlogloss:0.558316
[153] train-mlogloss:0.516719 val-mlogloss:0.558012
[154] train-mlogloss:0.516263 val-mlogloss:0.557748
[155] train-mlogloss:0.515715 val-mlogloss:0.557373
[156] train-mlogloss:0.515111 val-mlogloss:0.556965
[157] train-mlogloss:0.514464 val-mlogloss:0.556587
[158] train-mlogloss:0.513961 val-mlogloss:0.55637
[159] train-mlogloss:0.513409 val-mlogloss:0.555999
[160] train-mlogloss:0.512925 val-mlogloss:0.555658
[161] train-mlogloss:0.512395 val-mlogloss:0.555377
[162] train-mlogloss:0.511972 val-mlogloss:0.555174
[163] train-mlogloss:0.511404 val-mlogloss:0.554854
[164] train-mlogloss:0.510855 val-mlogloss:0.554543
[165] train-mlogloss:0.510246 val-mlogloss:0.554223
[166] train-mlogloss:0.509603 val-mlogloss:0.553921
[167] train-mlogloss:0.509022 val-mlogloss:0.553626
[168] train-mlogloss:0.508429 val-mlogloss:0.553329
[169] train-mlogloss:0.507996 val-mlogloss:0.553115
[170] train-mlogloss:0.507547 val-mlogloss:0.55287
[171] train-mlogloss:0.507051 val-mlogloss:0.552572
[172] train-mlogloss:0.506624 val-mlogloss:0.552307
[173] train-mlogloss:0.506127 val-mlogloss:0.552045
[174] train-mlogloss:0.505663 val-mlogloss:0.551771
[175] train-mlogloss:0.505127 val-mlogloss:0.551501
[176] train-mlogloss:0.504714 val-mlogloss:0.551294
[177] train-mlogloss:0.504072 val-mlogloss:0.550971
[178] train-mlogloss:0.503553 val-mlogloss:0.550729
[179] train-mlogloss:0.503046 val-mlogloss:0.550457
[180] train-mlogloss:0.502547 val-mlogloss:0.550219
[181] train-mlogloss:0.501997 val-mlogloss:0.549913
[182] train-mlogloss:0.501533 val-mlogloss:0.54967
[183] train-mlogloss:0.501068 val-mlogloss:0.549488
[184] train-mlogloss:0.500482 val-mlogloss:0.549217
[185] train-mlogloss:0.500044 val-mlogloss:0.54897
[186] train-mlogloss:0.499613 val-mlogloss:0.548765
[187] train-mlogloss:0.499127 val-mlogloss:0.548537
[188] train-mlogloss:0.498686 val-mlogloss:0.548272
[189] train-mlogloss:0.498142 val-mlogloss:0.547992
[190] train-mlogloss:0.497801 val-mlogloss:0.547782
[191] train-mlogloss:0.497219 val-mlogloss:0.547497
[192] train-mlogloss:0.49669 val-mlogloss:0.547308
[193] train-mlogloss:0.496191 val-mlogloss:0.54703
[194] train-mlogloss:0.495725 val-mlogloss:0.546839
[195] train-mlogloss:0.495359 val-mlogloss:0.546669
[196] train-mlogloss:0.49494 val-mlogloss:0.54651
[197] train-mlogloss:0.494472 val-mlogloss:0.546267
[198] train-mlogloss:0.494085 val-mlogloss:0.546121
[199] train-mlogloss:0.493582 val-mlogloss:0.545925
[200] train-mlogloss:0.493174 val-mlogloss:0.54576
[201] train-mlogloss:0.492775 val-mlogloss:0.545578
[202] train-mlogloss:0.492451 val-mlogloss:0.545403
[203] train-mlogloss:0.492091 val-mlogloss:0.545262
[204] train-mlogloss:0.491646 val-mlogloss:0.545066
[205] train-mlogloss:0.491296 val-mlogloss:0.544885
[206] train-mlogloss:0.490905 val-mlogloss:0.544703
[207] train-mlogloss:0.490515 val-mlogloss:0.544512
[208] train-mlogloss:0.490084 val-mlogloss:0.544313
[209] train-mlogloss:0.489776 val-mlogloss:0.544121
[210] train-mlogloss:0.489361 val-mlogloss:0.54389
[211] train-mlogloss:0.488899 val-mlogloss:0.543696
[212] train-mlogloss:0.488439 val-mlogloss:0.543534
[213] train-mlogloss:0.487968 val-mlogloss:0.543335
[214] train-mlogloss:0.487517 val-mlogloss:0.543197
[215] train-mlogloss:0.487138 val-mlogloss:0.543061
[216] train-mlogloss:0.486747 val-mlogloss:0.542873
[217] train-mlogloss:0.486331 val-mlogloss:0.542708
[218] train-mlogloss:0.485958 val-mlogloss:0.542498
[219] train-mlogloss:0.485549 val-mlogloss:0.54242
[220] train-mlogloss:0.485127 val-mlogloss:0.54226
[221] train-mlogloss:0.484838 val-mlogloss:0.542113
[222] train-mlogloss:0.484514 val-mlogloss:0.541951
[223] train-mlogloss:0.484086 val-mlogloss:0.541822
[224] train-mlogloss:0.483654 val-mlogloss:0.541698
[225] train-mlogloss:0.483205 val-mlogloss:0.541554
[226] train-mlogloss:0.482871 val-mlogloss:0.541421
[227] train-mlogloss:0.482407 val-mlogloss:0.541173
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[853] train-mlogloss:0.34123 val-mlogloss:0.516277
[854] train-mlogloss:0.34109 val-mlogloss:0.516255
[855] train-mlogloss:0.340936 val-mlogloss:0.516271
[856] train-mlogloss:0.340782 val-mlogloss:0.516263
[857] train-mlogloss:0.34062 val-mlogloss:0.51625
[858] train-mlogloss:0.340443 val-mlogloss:0.516268
[859] train-mlogloss:0.340337 val-mlogloss:0.516212
[860] train-mlogloss:0.34017 val-mlogloss:0.516205
[861] train-mlogloss:0.340041 val-mlogloss:0.516201
[862] train-mlogloss:0.339908 val-mlogloss:0.516198
[863] train-mlogloss:0.339725 val-mlogloss:0.516169
[864] train-mlogloss:0.339561 val-mlogloss:0.516156
[865] train-mlogloss:0.339401 val-mlogloss:0.516202
[866] train-mlogloss:0.339242 val-mlogloss:0.516194
[867] train-mlogloss:0.339122 val-mlogloss:0.516188
[868] train-mlogloss:0.338956 val-mlogloss:0.516202
[869] train-mlogloss:0.338764 val-mlogloss:0.516181
[870] train-mlogloss:0.338646 val-mlogloss:0.516149
[871] train-mlogloss:0.33847 val-mlogloss:0.516144
[872] train-mlogloss:0.338337 val-mlogloss:0.516152
[873] train-mlogloss:0.338156 val-mlogloss:0.516149
[874] train-mlogloss:0.33798 val-mlogloss:0.516181
[875] train-mlogloss:0.337821 val-mlogloss:0.516165
[876] train-mlogloss:0.337714 val-mlogloss:0.516149
[877] train-mlogloss:0.337489 val-mlogloss:0.516124
[878] train-mlogloss:0.337341 val-mlogloss:0.516108
[879] train-mlogloss:0.337147 val-mlogloss:0.516059
[880] train-mlogloss:0.337017 val-mlogloss:0.516059
[881] train-mlogloss:0.336847 val-mlogloss:0.516056
[882] train-mlogloss:0.33668 val-mlogloss:0.516076
[883] train-mlogloss:0.336491 val-mlogloss:0.516078
[884] train-mlogloss:0.336316 val-mlogloss:0.516087
[885] train-mlogloss:0.336143 val-mlogloss:0.516118
[886] train-mlogloss:0.335963 val-mlogloss:0.516126
[887] train-mlogloss:0.3358 val-mlogloss:0.516098
[888] train-mlogloss:0.335605 val-mlogloss:0.51607
[889] train-mlogloss:0.335437 val-mlogloss:0.516047
[890] train-mlogloss:0.335283 val-mlogloss:0.516047
[891] train-mlogloss:0.335137 val-mlogloss:0.516056
[892] train-mlogloss:0.334945 val-mlogloss:0.516072
[893] train-mlogloss:0.334805 val-mlogloss:0.516069
[894] train-mlogloss:0.334695 val-mlog
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