결정트리 데모¶

라이브러리 import 및 설정¶

%reload_ext autoreload
%autoreload 2
%matplotlib inline

import graphviz
from matplotlib import pyplot as plt
from matplotlib import rcParams
import numpy as np
from pathlib import Path
import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.metrics import accuracy_score
from sklearn.model_selection import KFold
import warnings

rcParams['figure.figsize'] = (16, 8)
plt.style.use('fivethirtyeight')
pd.set_option('max_columns', 100)
pd.set_option("display.precision", 4)
warnings.simplefilter('ignore')

데이터 로드¶

data_dir = Path('../data/dacon-dku')
sub_dir = Path('../build/sub')

trn_file = data_dir / 'train.csv'
tst_file = data_dir / 'test.csv'
sample_file = data_dir / 'sample_submission.csv'

target_col = 'class'
seed = 42

algo_name = 'dt'
feature_name = 'j1'
model_name = f'{algo_name}_{feature_name}'

sub_file = sub_dir / f'{model_name}.csv'

trn = pd.read_csv(trn_file, index_col=0)
tst = pd.read_csv(tst_file, index_col=0)
y = trn[target_col]
trn.drop(target_col, axis=1, inplace=True)
print(y.shape, trn.shape, tst.shape)
trn.head()

(320000,) (320000, 18) (80000, 18)

	u	g	r	i	z	redshift	dered_u	dered_g	dered_r	dered_i	dered_z	nObserve	nDetect	airmass_u	airmass_g	airmass_r	airmass_i	airmass_z
id
0	23.2640	20.3368	19.0095	17.6724	16.9396	-8.1086e-05	23.1243	20.2578	18.9551	17.6321	16.9089	18	18	1.1898	1.1907	1.1890	1.1894	1.1902
1	15.0521	14.0620	13.4524	13.2684	13.1689	4.5061e-03	14.9664	14.0045	13.4114	13.2363	13.1347	1	1	1.2533	1.2578	1.2488	1.2510	1.2555
2	16.7864	15.8254	15.5363	15.3935	15.3500	4.7198e-04	16.6076	15.6866	15.4400	15.3217	15.2961	2	2	1.0225	1.0241	1.0210	1.0217	1.0233
3	25.6606	21.1887	20.2212	19.8949	19.6346	5.8143e-06	25.3536	20.9947	20.0873	19.7947	19.5552	4	3	1.2054	1.2061	1.2049	1.2051	1.2057
4	24.4534	20.6992	19.0424	18.3242	17.9826	-3.3247e-05	23.7714	20.4338	18.8630	18.1903	17.8759	13	12	1.1939	1.1943	1.1937	1.1938	1.1941

결정트리 학습¶

clf = DecisionTreeClassifier(max_depth=5, min_samples_leaf=10, random_state=42)
clf.fit(trn, y)

DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
                       max_depth=5, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=10, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, presort='deprecated',
                       random_state=42, splitter='best')

print(f'{accuracy_score(y, clf.predict(trn)) * 100:.4f}%')

88.8669%

결정트리 시각화¶

dot_data = export_graphviz(clf, out_file=None,
                           feature_names=trn.columns,
                           filled=True,
                           rounded=True,
                           special_characters=True)  
graph = graphviz.Source(dot_data)  
graph 

../_images/06-decision-trees_13_0.svg

시험데이터 예측¶

sub = pd.read_csv(sample_file, index_col=0)
print(sub.shape)
sub.head()

(80000, 1)

	class
id
320000	0
320001	0
320002	0
320003	0
320004	0

sub[target_col] = clf.predict(tst)
sub.head()

	class
id
320000	2
320001	0
320002	2
320003	0
320004	2

sub[target_col].value_counts()

  45299
  29882
   4819
Name: class, dtype: int64

제출파일 저장¶

sub.to_csv(sub_file)

6. Scikit-learn (sklearn) 7. Random Forest, GBM