Chapter 8 Decision Trees

8.1 Decision Trees for Regression

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import scipy
from numpy.random import randn # Gaussian Random Number

def sq_loss(y):
    if len(y)==0:
        return(0)
    else:
        y_bar=np.mean(y)
        return(np.linalg.norm(y-y_bar)**2)

def branch(x, y, S, rf=0):
    if rf==0:
        m=x.shape[1]
    if x.shape[0]==0:
        return([0,0,0,0,0,0,0])
    best_score=np.inf
    for j in range(x.shape[1]):
        for i in S:
            left=[]; right=[]
            for k in S:
                if x[k,j]<x[i,j]:
                    left.append(k)
                else:
                    right.append(k)
            left_score=f(y[left]); right_score=f(y[right])
            score=left_score+right_score
            if score < best_score:
                best_score=score
                i_1=i; j_1=j
                left_1=left; right_1=right 
                left_score_1=left_score; right_score_1=right_score
    return [i_1, j_1, left_1, right_1, best_score, left_score_1, right_score_1]

class Stack:
    def __init__(self, parent, set, score):
        self.parent = parent
        self.set = set
        self.score = score

class Node:
    def __init__(self, parent, j, th, set):
        self.parent = parent
        self.j = j
        self.th=th
        self.set=set

def dt(x, y, alpha=0, n_min=1, rf=0):
    if rf==0:
        m=x.shape[1]
    # Construct a stack consisting of itself, and initialize the tree.
    stack=[Stack(0, list(range(x.shape[0])),f(y))]  # Function f is global
    node=[]
    k=-1
    # Pop the last element from stack, and update the decision tree.
    while len(stack)>0:
        popped=stack.pop()
        k=k+1
        i, j, left, right, score, left_score, right_score=branch(x, y, popped.set,rf)
        if popped.score-score<alpha or len(popped.set)<n_min or len(left)==0 or len(right)==0:
            node.append(Node(popped.parent, -1, 0, popped.set))
        else:
            node.append(Node(popped.parent, j, x[i,j], popped.set))
            stack.append(Stack(k, right, right_score))  
            stack.append(Stack(k, left, left_score))
    # Set the values of node.left, node.right below:
    for h in range(k,-1,-1):
        node[h].left=0; node[h].right=0; 
    for h in range(k,0,-1):
        pa=node[h].parent
        if node[pa].right==0:
            node[pa].right=h
        else:
            node[pa].left=h
    # Set the values of node.center below:
    if f==sq_loss:
        g=np.mean
    else:
        g=mode_max
    for h in range(k+1):
        if node[h].j==-1:
            node[h].center=g(y[node[h].set])
        else:
            node[h].center=0
    return(node)

from sklearn.datasets import load_boston
boston = load_boston()
X=boston.data
y=boston.target
f=sq_loss
node=dt(X,y,n_min=50)
len(node)

37

from igraph import *

r=len(node)
edge=[]
for h in range(1,r):
    edge.append([node[h].parent,h])

TAB=[];
for h in range(r):
    if not node[h].j==0:
        TAB.append([h, node[h].j, node[h].th])
TAB

[[0, 5, 6.943],
 [1, 12, 14.43],
 [2, 7, 1.413],
 [3, -1, 0],
 [4, 5, 6.546],
 [5, 12, 7.6],
 [6, -1, 0],
 [7, 9, 223.0],
 [8, -1, 0],
 [9, 5, 6.083],
 [10, 6, 69.5],
 [11, 7, 4.4986],
 [12, -1, 0],
 [13, -1, 0],
 [14, -1, 0],
 [15, 12, 10.15],
 [16, -1, 0],
 [17, -1, 0],
 [18, 9, 273.0],
 [19, -1, 0],
 [20, -1, 0],
 [22, 4, 0.538],
 [23, -1, 0],
 [24, 12, 19.01],
 [25, 6, 85.7],
 [26, -1, 0],
 [27, -1, 0],
 [28, -1, 0],
 [29, 4, 0.614],
 [30, -1, 0],
 [31, 12, 19.77],
 [32, -1, 0],
 [33, -1, 0],
 [34, 5, 7.454],
 [35, -1, 0],
 [36, -1, 0]]

def draw_graph(node):
    r=len(node)
    col=[]
    for h in range(r):
        col.append(node[h].j)
    colorlist = ['#ffffff', '#fff8ff', '#fcf9ce', '#d6fada', '#d7ffff', '#d9f2f8', '#fac8be', '#ffebff','#ffffe0','#fdf5e6','#fac8be', '#f8ecd5', '#ee82ee']
    color=[colorlist[col[i]] for i in range(r)]
    edge=[]
    for h in range(1,r):
        edge.append([node[h].parent,h])
        g = Graph(edges=edge,directed=True)
        layout=g.layout_reingold_tilford(root=[0])
    out=plot(g,vertex_size=15,layout=layout,bbox=(300,300),vertex_label=list(range(r)), vertex_color=color)
    return(out)

draw_graph(node)

def value(u, node):
    r=0
    while node[r].j !=-1:
        if u[node[r].j] < node[r].th:
            r=node[r].left
        else:
            r=node[r].right
    return(node[r].center)

from sklearn.datasets import load_boston
boston = load_boston()
n=100
X=boston.data[range(n),:]
y=boston.target[range(n)]
f=sq_loss
alpha_seq=np.arange(0, 1.5, 0.1)
s=np.int(n/10)
out=[]
for alpha in alpha_seq:
    SS=0
    for h in range(10):
        test=list(range(h*s,h*s+s))
        train=list(set(range(n))-set(test))
        node=dt(X[train,:],y[train], alpha=alpha)
        for t in test:
            SS=SS+(y[t]-value(X[t,:],node))**2
    print(SS/n)
    out.append(SS/n)
plt.plot(alpha_seq,out)
plt.xlabel('alpha')
plt.ylabel('square error')
plt.title("optimum alpha (N=100)")

11.2123
11.169741666666662
11.138088888888886
11.073173611111109
10.98831805555555
10.918474305555554
10.923048228458047
10.894865589569157
10.970726006235825
11.051589873456788
11.008887095679011
10.790952453955654
10.788552453955653
10.975752453955657
10.972330231733434

Text(0.5, 1.0, 'optimum alpha (N=100)')

from sklearn.datasets import load_boston
boston = load_boston()
n=100
X=boston.data[range(n),:]
y=boston.target[range(n)]
n_min_seq=np.arange(1, 13, 1)
s=np.int(n/10)
out=[]
for n_min in n_min_seq:
    SS=0
    for h in range(10):
        test=list(range(h*s,h*s+s))
        train=list(set(range(n))-set(test))
        node=dt(X[train,:],y[train], n_min=n_min)
        for t in test:
            SS=SS+(y[t]-value(X[t,:],node))**2
    print(SS/n)
    out.append(SS/n)
plt.plot(n_min_seq,out)
plt.xlabel('n_min')
plt.ylabel('square error')
plt.title("optimum n_min (N=100)")

11.2123
11.2123
11.160350000000001
10.779608333333336
10.974831944444444
10.776290444444447
10.778481666666671
10.686344513038552
10.585849200538545
11.914784839238472
12.298008067255603
12.478392363347837

Text(0.5, 1.0, 'optimum n_min (N=100)')

8.2 Decision Trees for Classification

def freq(y):
    y=list(y)
    return([y.count(i) for i in set(y)])

# mode
def mode(y):
    n=len(y)
    if n==0:
        return(0)
    return(max(freq(y)))

# Error Rate
def mis_match(y):
    return(len(y)-mode(y))

# Gini
def gini(y):
    n=len(y)
    if n==0:
        return(0)
    fr=freq(y)
    return(sum([fr[i]*(n-fr[i])/n for i in range(len(fr))]))

# Entropy
def entropy(y):
    n=len(y)
    if n==0:
        return(0)
    freq=[y.count(i) for i in set(y)]
    return(np.sum([-freq[i]*np.log (freq[i]/n) for i in range(len(freq))]))

def table_count(m,u,v):    # This appeared before
    n=u.shape[0]
    count=np.zeros([m,m])
    for i in range(n):
        count[int(u[i]),int(v[i])]+=1
    return(count)

def mode_max(y):
    if len(y)==0:
        return(-np.inf)
    count = np.bincount(y)
    return(np.argmax(count))

from sklearn.datasets import load_iris
iris = load_iris()
iris.target_names
f=mis_match
n=iris.data.shape[0]
x=iris.data
y=iris.target
n=len(x)
node=dt(x,y,n_min=4)
m=len(node)
u=[]; v=[]
for h in range(m):
    if node[h].j==-1:
        w=y[node[h].set]
        u.extend([node[h].center]*len(w))
        v.extend(w)
table_count(3,np.array(u),np.array(v))
#sum([u[i]==v[i] for i in range(150)])

array([[50.,  0.,  0.],
       [ 0., 48.,  0.],
       [ 0.,  2., 50.]])

draw_graph(node)

from sklearn.datasets import load_iris
iris = load_iris()
iris.target_names
f=mis_match
index=np.random.choice(n, n, replace=False) # Change the order
X=iris.data[index,:]
y=iris.target[index]
n_min_seq=np.arange(5,51,5)
s=15
for n_min in n_min_seq:
    SS=0
    for h in range(10):
        test=list(range(h*s,h*s+s))
        train=list(set(range(n))-set(test))
        node=dt(X[train,:],y[train], n_min=n_min)
        for t in test:
            SS=SS+np.sum(y[t]!=value(X[t,:],node))
    print(SS/n)

0.08
0.08
0.08666666666666667
0.08666666666666667
0.08666666666666667
0.08666666666666667
0.08666666666666667
0.08666666666666667
0.08666666666666667
0.1

8.3 Bagging

n=200
p=5
X=np.random.randn(n,p)
beta=randn(p)
Y=np.array(np.abs(np.dot(X,beta)+randn(n)),dtype=np.int64)
f=mis_match

node_seq=[]
for h in range(8):
    index=np.random.choice(n, n, replace=True) # Change the order
    x=X[index,:]
    y=Y[index]
    node_seq.append(dt(x,y,n_min=6))

draw_graph(node_seq[0])

draw_graph(node_seq[1])

draw_graph(node_seq[2])

def branch(x, y, S, rf=0):                                  ##
    if rf==0:                                               ##
        T=np.arange(x.shape[1])                                        ##
    else:                                                   ##
        T=np.random.choice(x.shape[1], rf, replace=False)   ##
    if x.shape[0]==0:
        return([0,0,0,0,0,0,0])
    best_score=np.inf
    for j in T:                                             ##
        for i in S:
            left=[]; right=[]
            for k in S:
                if x[k,j]<x[i,j]:
                    left.append(k)
                else:
                    right.append(k)
            left_score=f(y[left]); right_score=f(y[right])
            score=left_score+right_score
            if score < best_score:
                best_score=score
                i_1=i; j_1=j
                left_1=left; right_1=right 
                left_score_1=left_score; right_score_1=right_score
    return [i_1, j_1, left_1, right_1, best_score, left_score_1, right_score_1]

8.4 Random Forest

def rf(z):
    z=np.array(z,dtype=np.int64)
    zz=[]
    for b in range(B):
        u=sum([mode_max(z[range(b+1),i])==y[i+100] for i in range(50)])
        zz.append(u)
    return(zz)

iris = load_iris()
iris.target_names
f=mis_match
n=iris.data.shape[0]
order=np.random.choice(n, n, replace=False) # Change the order
X=iris.data[order,:]
y=iris.target[order]
train=list(range(100))
test=list(range(100,150))
B=100
plt.ylim([35, 55])
m_seq=[1,2,3,4]
c_seq=["r","b","g","y"]
label_seq=['m=1','m=2','m=3','m=4']
plt.xlabel('Repetition b')
plt.ylabel('The number of correct answers for fifty test data')
plt.title('Random Forest')
for m in m_seq:
    z=np.zeros((B,50))
    for b in range(B):
        index=np.random.choice(train, 100, replace=True)
        node=dt(X[index,:],y[index],n_min=2,rf=m)
        for i in test:
            z[b,i-100]=value(X[i,],node)
    plt.plot(list(range(B)),np.array(rf(z))-0.2*(m-2), label=label_seq[m-1], linewidth=0.8, c=c_seq[m-1])
plt.legend(loc='lower right')
plt.axhline(y=50,c="b",linewidth=0.5,linestyle = "dashed")

<matplotlib.lines.Line2D at 0x19b6411f040>

8.5 Boosting

def b_dt(x, y, d):
    n=x.shape[0]
    node=[]
    first=Node(0, -1, 0, np.arange(n))
    first.score=f(y[first.set])
    node.append(first)
    while len(node)<=2*d-1:
        r=len(node)
        gain_max=-np.inf
        for h in range(r):
            if node[h].j==-1:
                i, j, left, right, score, left_score, right_score=branch(x, y, node[h].set)
                gain=node[h].score-score
                if gain >gain_max:
                    gain_max=gain
                    h_max=h
                    i_0=i; j_0=j 
                    left_0=left; right_0=right
                    left_score_0=left_score; right_score_0=right_score
        node[h_max].th=x[i_0,j_0]; node[h_max].j=j_0
        next=Node(h_max, -1, 0, left_0)
        next.score=f(y[next.set]); node.append(next)
        next=Node(h_max, -1, 0, right_0)
        next.score=f(y[next.set]); node.append(next)
    r=2*d+1
    for h in range(r):
        node[h].left=0; node[h].right=0
    for h in range(r-1,1,-1):
        pa=node[h].parent
        if node[pa].right==0:
            node[pa].right=h
        else:
            node[pa].left=h
        if node[h].right==0 and node[h].left==0:
            node[h].j=-1
    if f==sq_loss:
        g=np.mean
    else:
        g=mode_max
    for h in range(r):
        if node[h].j==-1:
            node[h].center=g(node[h].set)
# Set the values of node.left,node.right below:
    for h in range(r-1,-1,-1):
        node[h].left=0; node[h].right=0; 
    for h in range(r-1,0,-1):
        pa=node[h].parent
        if node[pa].right==0:
            node[pa].right=h
        else:
            node[pa].left=h
# Compute the values of node.center below:
    if f==sq_loss:
        g=np.mean
    else:
        g=mode_max
    for h in range(r):
        if node[h].j==-1:
            node[h].center=g(y[node[h].set])
        else:
            node[h].center=0
    return(node)

from sklearn.datasets import load_boston
boston = load_boston()
B=200
lam=0.1
X=boston.data
y=boston.target
f=sq_loss
train=list(range(200))
test=list(range(200,300))
# Generate B boosting trees
# it takes 15 mins (5 mins for each d)
trees_set=[]
for d in range(1,4):
    trees=[]
    r=y[train]
    for b in range(B):
        trees.append(b_dt(X[train,:],r,d))
        for i in train:
            r[i]=r[i]-lam*value(X[i,:],trees[b])
        print(b)
    trees_set.append(trees)

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# Evaluate via test data
out_set=[]
for d in range(1,4):
    trees=trees_set[d-1]
    z=np.zeros((B,600))
    for i in test:
        z[0,i]=lam*value(X[i,],trees[0])
        for b in range(1,B):
            for i in test:
                z[b,i]=z[b-1,i]+lam*value(X[i,:],trees[b])
    out=[]
    for b in range(B):
        out.append(sum((y[test]-z[b,test])**2)/len(test))
    out_set.append(out)

# Display the graph
plt.ylim([0, 40])
c_seq=["r","b","g"]
label_seq=['d=1','d=2','d=3']
plt.xlabel('The number of generated trees')
plt.ylabel('Square Error for test data')
plt.title('The program in this book (lambda=0.1)')
for d in range(1,4):
    out=out_set[d-1]
    u=range(20,100)
    v=out[20:100]; 
    plt.plot(u,v,label=label_seq[d-1], linewidth=0.8, c=c_seq[d-1])
plt.legend(loc='upper right')

<matplotlib.legend.Legend at 0x19b65ca5370>

conda install -c conda-forge lightgbm

Collecting package metadata (current_repodata.json): ...working... done
Solving environment: ...working... done

## Package Plan ##

  environment location: C:\Users\prof-\anaconda3

  added / updated specs:
    - lightgbm


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    conda-4.10.3               |   py38haa244fe_0         3.1 MB  conda-forge
    lightgbm-3.2.1             |   py38h885f38d_0         889 KB  conda-forge
    ------------------------------------------------------------
                                           Total:         4.0 MB

The following NEW packages will be INSTALLED:

  lightgbm           conda-forge/win-64::lightgbm-3.2.1-py38h885f38d_0

The following packages will be UPDATED:

  conda                                4.9.0-py38h9bdc248_1 --> 4.10.3-py38haa244fe_0



Downloading and Extracting Packages

conda-4.10.3         | 3.1 MB    |            |   0% 
conda-4.10.3         | 3.1 MB    | ###4       |  35% 
conda-4.10.3         | 3.1 MB    | ########## | 100% 
conda-4.10.3         | 3.1 MB    | ########## | 100% 

lightgbm-3.2.1       | 889 KB    |            |   0% 
lightgbm-3.2.1       | 889 KB    | 1          |   2% 
lightgbm-3.2.1       | 889 KB    | ######6    |  67% 
lightgbm-3.2.1       | 889 KB    | ########## | 100% 
lightgbm-3.2.1       | 889 KB    | ########## | 100% 
Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working... done

Note: you may need to restart the kernel to use updated packages.

import lightgbm as lgb
from sklearn.datasets import load_boston
boston = load_boston()
X=boston.data
y=boston.target
train=list(range(200))
test=list(range(200,300))
B=200
lgb_train = lgb.Dataset(X[train,:], y[train])
lgb_eval = lgb.Dataset(X[test,:], y[test], reference=lgb_train)

B=5000
nn_seq=list(range(1,10,1))+list(range(10,91,10))+list(range(100,B,50))
out_set=[]
for d in range(1,4):
    lgbm_params = {
        'objective': 'regression',
        'metric': 'rmse',
        'num_leaves': d+1,
        'learning_rate': 0.001
    }
    out=[]
    for nn in nn_seq:
        model = lgb.train(lgbm_params,lgb_train, valid_sets=lgb_eval,verbose_eval=False, num_boost_round=nn)
        z= model.predict(X[test,:], num_iteration=model.best_iteration)
        out.append(sum((z-y[test])**2)/100)
    out_set.append(out)

[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000134 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000066 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000083 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000073 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000077 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000078 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000079 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000059 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000084 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000075 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000129 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000081 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000084 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000121 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000085 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000090 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000106 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000090 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000086 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000084 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000108 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000079 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000099 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000083 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000097 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000113 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000084 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000096 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000070 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000086 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000082 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000072 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000091 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000111 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000093 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000113 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000093 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000090 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000084 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000092 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000111 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000074 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000074 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000084 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000070 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000081 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000077 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000105 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000145 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000149 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000139 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000112 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000133 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000111 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000102 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000129 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000104 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000157 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000146 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000114 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000154 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000120 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000095 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000098 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000114 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000111 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000151 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000099 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000107 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000097 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000100 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000101 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000096 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000142 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000105 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000105 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000115 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000099 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000107 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000110 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000160 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000152 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000110 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000114 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000106 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000095 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000127 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000114 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000119 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000111 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000135 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000114 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000120 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000123 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000129 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000090 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000091 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000093 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000091 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000092 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000091 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000096 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000094 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000092 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000090 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000163 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000090 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000092 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000091 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000092 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000091 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000089 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000089 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000091 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000115 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000124 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000095 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000099 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000091 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000143 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000095 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000130 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000097 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000097 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000101 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000086 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000096 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000122 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000093 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000092 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000091 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000114 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000104 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000097 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000102 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000098 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000095 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000097 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000094 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000092 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000101 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000121 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000066 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000100 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000101 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000155 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000106 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000121 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000108 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000153 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000116 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000104 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000101 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000137 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000105 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000098 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000141 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000102 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000099 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000135 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000095 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000132 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000102 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000131 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000099 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000097 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000099 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000101 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000145 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000107 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000105 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000107 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000142 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000147 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000140 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000108 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000134 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000097 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000129 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000108 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000141 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000096 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000105 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000128 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000144 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000125 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000140 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000133 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000101 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000138 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000130 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000114 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000100 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000104 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000108 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000163 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000125 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000102 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000141 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000107 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000126 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000163 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000100 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000131 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000094 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000090 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000091 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000092 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000094 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000093 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000128 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000121 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000095 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000096 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000090 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000097 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000094 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000091 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000095 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000094 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000108 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000146 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000098 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000102 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000101 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000150 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000101 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000130 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000130 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000104 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000105 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000128 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000133 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000107 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000102 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000122 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000097 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000105 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000135 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000102 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000099 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000130 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000098 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000097 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000130 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000096 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000104 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000102 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000116 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000146 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000098 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000112 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000170 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000107 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000125 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000138 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000104 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000139 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000127 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000107 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000116 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000102 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000096 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000109 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000104 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000105 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000119 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000095 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000108 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000150 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000111 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000141 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000136 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000105 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000109 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000111 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000105 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000102 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000110 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000165 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000111 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000115 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000122 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000142 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000160 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000107 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000145 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000159 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000112 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000119 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000115 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000117 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000118 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000118 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000156 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000153 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000157 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000160 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 497
[LightGBM] [Info] Number of data points in the train set: 200, number of used features: 12
[LightGBM] [Info] Start training from score 23.261000

# Display the graph
plt.ylim([0, 80])
c_seq=["r","b","g"]
label_seq=['d=1','d=2','d=3']
plt.xlabel('The number of generated trees')
plt.ylabel('Square Error for test data')
plt.title('lightgbm Package (lambda=0.001)')
for d in range(1,4):
    plt.plot(nn_seq,out_set[d-1],label=label_seq[d-1], linewidth=0.8, c=c_seq[d-1])
plt.legend(loc='upper right')

<matplotlib.legend.Legend at 0x19b65b58970>

f=sq_loss
n=100; p=5
x=randn(n,p)
y=randn(n)
S=np.random.choice(n, 10, replace=False)
branch(x,y,S)

[64,
 1,
 [96, 77, 70, 3, 40],
 [18, 34, 64, 36, 62],
 6.59648504172793,
 1.3064137672898777,
 5.290071274438052]