당뇨병예측,시각화(with 캐글)

당뇨병 예측¶

이 데이터 세트는 1988년부터 시작되었으며 4개의 데이터베이스(Cleveland, 헝가리, 스위스, Long Beach V)로 구성되어 있습니다. 여기에는 예측된 속성을 포함하여 76개의 속성이 포함되어 있지만 게시된 모든 실험은 그 중 14개의 하위 집합을 사용하는 것을 참조합니다. "표적" 필드는 환자의 심장 질환의 존재를 나타냅니다. 정수 값 0 = 질병 없음 및 1 = 질병입니다.

• reference
  • https://philosopher-chan.tistory.com/853

컬럼¶

age :나이

sex: 성별

cp: 흉통 유형(4개 값)

trestbps: 안정시 혈압

chol: mg/dl의 혈청 콜레스테롤

fbs: 공복 혈당 > 120mg/dl

restecg: 안정시 심전도 결과(값 0,1,2)

thalach: 최대 심박수 달성

exang:운동 유발 협심증

oldpeak = 휴식에 비해 운동으로 유발된 ST 우울증

slope: 피크 운동 ST 세그먼트의 기울기

ca: 형광등으로 채색된 주요 혈관(0-3)의 수

thal: 0 = 정상; 1 = 고정된 결함; 2 = 가역적 결함

환자의 이름과 사회 보장 번호는 최근에 데이터베이스에서 제거되어 더미 값으로 대체

데이터 살펴보기¶

In [1]:

import pandas as pd

In [2]:

df = pd.read_csv('./data/heart.csv')

In [3]:

df

Out[3]:

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal	target
0	52	1	0	125	212	0	1	168	0	1.0	2	2	3	0
1	53	1	0	140	203	1	0	155	1	3.1	0	0	3	0
2	70	1	0	145	174	0	1	125	1	2.6	0	0	3	0
3	61	1	0	148	203	0	1	161	0	0.0	2	1	3	0
4	62	0	0	138	294	1	1	106	0	1.9	1	3	2	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1020	59	1	1	140	221	0	1	164	1	0.0	2	0	2	1
1021	60	1	0	125	258	0	0	141	1	2.8	1	1	3	0
1022	47	1	0	110	275	0	0	118	1	1.0	1	1	2	0
1023	50	0	0	110	254	0	0	159	0	0.0	2	0	2	1
1024	54	1	0	120	188	0	1	113	0	1.4	1	1	3	0

1025 rows × 14 columns

In [4]:

df.shape

Out[4]:

(1025, 14)

In [6]:

df.isnull().sum()

Out[6]:

age         0
sex         0
cp          0
trestbps    0
chol        0
fbs         0
restecg     0
thalach     0
exang       0
oldpeak     0
slope       0
ca          0
thal        0
target      0
dtype: int64

In [11]:

df.dtypes

Out[11]:

age           int64
sex           int64
cp            int64
trestbps      int64
chol          int64
fbs           int64
restecg       int64
thalach       int64
exang         int64
oldpeak     float64
slope         int64
ca            int64
thal          int64
target        int64
dtype: object

In [12]:

df['age'] == 0

Out[12]:

0       False
1       False
2       False
3       False
4       False
        ...  
1020    False
1021    False
1022    False
1023    False
1024    False
Name: age, Length: 1025, dtype: bool

In [18]:

(df['age'] == 50).sum()

Out[18]:

21

데이터 시각화¶

In [20]:

import matplotlib.pyplot as plt
import seaborn as sns

In [23]:

corrmat = df.corr()
fig = plt.figure(figsize=(16,16))

sns.heatmap(corrmat, annot = True)
plt.show()

In [24]:

sns.countplot(x='sex', data = df)

Out[24]:

<AxesSubplot:xlabel='sex', ylabel='count'>

In [25]:

df["sex"].value_counts()

Out[25]:

1    713
0    312
Name: sex, dtype: int64

In [26]:

sns.countplot(x='sex', data = df, palette = 'coolwarm')

Out[26]:

<AxesSubplot:xlabel='sex', ylabel='count'>

In [27]:

df['target']

Out[27]:

0       0
1       0
2       0
3       0
4       0
       ..
1020    1
1021    0
1022    0
1023    1
1024    0
Name: target, Length: 1025, dtype: int64

In [28]:

df['target'].value_counts()

Out[28]:

1    526
0    499
Name: target, dtype: int64

In [29]:

df["age"].value_counts()

Out[29]:

58    68
57    57
54    53
59    46
52    43
56    39
51    39
62    37
60    37
44    36
64    34
41    32
63    32
61    31
67    31
55    30
65    27
53    26
43    26
42    26
66    25
45    25
48    23
46    23
50    21
47    18
49    17
35    15
39    14
70    14
38    12
68    12
40    11
71    11
69     9
37     6
34     6
29     4
74     3
76     3
77     3
Name: age, dtype: int64

In [31]:

plt.figure(figsize=(16,16))
sns.countplot(x='age', data = df)

Out[31]:

<AxesSubplot:xlabel='age', ylabel='count'>

In [32]:

df.hist(figsize=(14,14))
plt.show()

In [33]:

sns.pairplot(df, hue='target')

Out[33]:

<seaborn.axisgrid.PairGrid at 0x7f19fe041a00>

In [35]:

plt.figure(figsize = (16,16))
sns.swarmplot (x=df["age"])

Out[35]:

<AxesSubplot:xlabel='age'>

In [44]:

pd.crosstab(df.age, df.target).plot(kind='bar', figsize = (15,15))

Out[44]:

<AxesSubplot:xlabel='age'>

In [45]:

pd.crosstab(df.ca, df.target).plot(kind='bar', figsize = (15,15))

Out[45]:

<AxesSubplot:xlabel='ca'>

In [52]:

plt.figure(figsize=(15,5))
sns.scatterplot(x="age", y="trestbps", hue="target", data=df )

Out[52]:

<AxesSubplot:xlabel='age', ylabel='trestbps'>

In [53]:

plt.figure(figsize=(15,5))
sns.pointplot(x="age", y="trestbps", hue="target", data = df)

Out[53]:

<AxesSubplot:xlabel='age', ylabel='trestbps'>

In [56]:

plt.figure(figsize=(15,5))
sns.lineplot(x="age", y="trestbps", hue="target", data = df)

Out[56]:

<AxesSubplot:xlabel='age', ylabel='trestbps'>

데이터 분석¶

gridSearchCV 실습

ridSearchCV 는?

사이킷런에서는 분류 알고리즘이나 회귀 알고리즘에 사용되는 하이퍼파라미터를 순차적으로 입력해 학습을 하고 측정을 하면서 가장 좋은 파라미터를 알려준다. GridSearchCV가 없다면 max_depth 가 2일때 가장 최적의 스코어를 뽑아내는지 1일때 가장 최적인 스코어를 뽑아내는지 일일이 학습을 해야 한다. 하지만 grid 파라미터 안에서 집합을 만들고 적용하면 최적화된 파라미터를 뽑아낼 수 있다.

In [58]:

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score

In [62]:

dataX = df.drop('target',axis=1)
dataX

Out[62]:

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal
0	52	1	0	125	212	0	1	168	0	1.0	2	2	3
1	53	1	0	140	203	1	0	155	1	3.1	0	0	3
2	70	1	0	145	174	0	1	125	1	2.6	0	0	3
3	61	1	0	148	203	0	1	161	0	0.0	2	1	3
4	62	0	0	138	294	1	1	106	0	1.9	1	3	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...
1020	59	1	1	140	221	0	1	164	1	0.0	2	0	2
1021	60	1	0	125	258	0	0	141	1	2.8	1	1	3
1022	47	1	0	110	275	0	0	118	1	1.0	1	1	2
1023	50	0	0	110	254	0	0	159	0	0.0	2	0	2
1024	54	1	0	120	188	0	1	113	0	1.4	1	1	3

1025 rows × 13 columns

In [63]:

dataY = df['target']

In [64]:

dataY

Out[64]:

0       0
1       0
2       0
3       0
4       0
       ..
1020    1
1021    0
1022    0
1023    1
1024    0
Name: target, Length: 1025, dtype: int64

In [65]:

x_train, x_test, y_train, y_test = train_test_split(dataX, dataY, test_size = 0.2, random_state = 0)

In [66]:

x_train

Out[66]:

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal
315	42	1	3	148	244	0	0	178	0	0.8	2	2	2
204	66	0	2	146	278	0	0	152	0	0.0	1	1	2
363	53	1	2	130	246	1	0	173	0	0.0	2	3	2
5	58	0	0	100	248	0	0	122	0	1.0	1	0	2
1017	53	1	0	123	282	0	1	95	1	2.0	1	2	3
...	...	...	...	...	...	...	...	...	...	...	...	...	...
835	49	1	2	118	149	0	0	126	0	0.8	2	3	2
192	67	0	2	115	564	0	0	160	0	1.6	1	0	3
629	65	1	3	138	282	1	0	174	0	1.4	1	1	2
559	67	1	0	120	237	0	1	71	0	1.0	1	0	2
684	60	1	2	140	185	0	0	155	0	3.0	1	0	2

820 rows × 13 columns

In [67]:

y_test

Out[67]:

807    1
27     0
77     0
406    1
886    0
      ..
877    1
320    1
362    1
452    0
500    1
Name: target, Length: 205, dtype: int64

In [82]:

import numpy as np
penalty = ['l1', 'l2']
C = np.logspace(0,4,10)

In [83]:

hyperparametes = dict(C=C, penalty = penalty)

In [84]:

hyperparametes

Out[84]:

{'C': array([1.00000000e+00, 2.78255940e+00, 7.74263683e+00, 2.15443469e+01,
        5.99484250e+01, 1.66810054e+02, 4.64158883e+02, 1.29154967e+03,
        3.59381366e+03, 1.00000000e+04]),
 'penalty': ['l1', 'l2']}

In [103]:

logistic = LogisticRegression()

In [104]:

logistic

Out[104]:

LogisticRegression()

In [105]:

from sklearn.model_selection import GridSearchCV

In [106]:

clf = GridSearchCV(logistic, hyperparametes)

In [107]:

clf.fit(x_train, y_train)

/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py", line 443, in _check_solver
    raise ValueError("Solver %s supports only 'l2' or 'none' penalties, "
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn("Estimator fit failed. The score on this train-test"
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_search.py:918: UserWarning: One or more of the test scores are non-finite: [       nan 0.85121951        nan 0.84878049        nan 0.84756098
        nan 0.84756098        nan 0.85121951        nan 0.85121951
        nan 0.85              nan 0.85              nan 0.85121951
        nan 0.85      ]
  warnings.warn(
/home/goldang/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

Out[107]:

GridSearchCV(estimator=LogisticRegression(),
             param_grid={'C': array([1.00000000e+00, 2.78255940e+00, 7.74263683e+00, 2.15443469e+01,
       5.99484250e+01, 1.66810054e+02, 4.64158883e+02, 1.29154967e+03,
       3.59381366e+03, 1.00000000e+04]),
                         'penalty': ['l1', 'l2']})

In [108]:

predict = clf.predict(x_test)

In [109]:

accuracy_score(y_test, predict)

Out[109]:

0.8634146341463415

In [110]:

clf.best_params_

Out[110]:

{'C': 1.0, 'penalty': 'l2'}

KNN 알고리즘 (최근접이웃알고리즘)¶

In [134]:

from sklearn.neighbors import KNeighborsClassifier

In [143]:

classifier = KNeighborsClassifier(n_neighbors=3)

In [144]:

classifier.fit(x_train, y_train)

Out[144]:

KNeighborsClassifier(n_neighbors=3)

In [145]:

predict = classifier.predict(x_test)
accuracy_score(y_test, predict)

Out[145]:

0.9121951219512195

In [163]:

params = {'n_neighbors' : list(range(1,20)),
          'p' : [1,2,3,4,5,6,7,8,9,10],
          'leaf_size' : list(range(1,20)),
          'weights' : ['uniform', 'distance']
          }

In [164]:

knn = KNeighborsClassifier()

In [165]:

KNNModel = GridSearchCV(knn, params, cv=3, n_jobs=-1)

In [166]:

KNNModel.fit(x_train, y_train)

Out[166]:

GridSearchCV(cv=3, estimator=KNeighborsClassifier(), n_jobs=-1,
             param_grid={'leaf_size': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
                                       13, 14, 15, 16, 17, 18, 19],
                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
                                         13, 14, 15, 16, 17, 18, 19],
                         'p': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                         'weights': ['uniform', 'distance']})

In [167]:

KNNModel.best_params_

Out[167]:

{'leaf_size': 1, 'n_neighbors': 18, 'p': 1, 'weights': 'distance'}

In [168]:

predict = KNNModel.predict(x_test)
accuracy_score(y_test, predict)

Out[168]:

1.0

너무 높은 정확도가 나와서 문제이다.

데이터 가공¶

• SVM(support vectors machine)

In [158]:

from sklearn.svm import SVC

In [160]:

SVC_classifier = SVC(kernel = 'linear', gamma=10, C=1.0)

In [161]:

SVC_classifier.fit(x_train, y_train)

Out[161]:

SVC(gamma=10, kernel='linear')

In [162]:

predict = SVC_classifier.predict(x_test)
accuracy_score(y_test, predict)

Out[162]:

0.8390243902439024