Python

【Signate練習問題】機械学習の基本手法を使用した国勢調査からの収入予測

決定木を使った例

import pandas as pd
import numpy as np
import seaborn as sns
import sklearn.preprocessing as sp
import pydotplus
from IPython.display import Image
from sklearn.model_selection import train_test_split 
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn import tree
# Scikit-learn(評価算出)
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import StratifiedKFold, cross_val_score
from sklearn import metrics
# graphvizのdotファイルを生成する
from sklearn import datasets
from sklearn.cluster import MiniBatchKMeans
from sklearn import tree

train=pd.read_table('./train.tsv')
test=pd.read_table('./test.tsv')
sub=pd.read_csv('./sample_submit.csv')

drop_col = ['id','native-country','education','workclass','sex','race','fnlwgt','occupation','marital-status','capital-loss','capital-gain','hours-per-week']
train=train.drop(drop_col, axis=1)
test=test.drop(drop_col, axis=1)    

l = ['relationship']
le = sp.LabelEncoder()
    
for name in l:
    le.fit(train[name].unique())
    le.fit(test[name].unique())
    train[name] = le.fit_transform(train[name])
    test[name] = le.fit_transform(test[name])
    

train["Y"] =train["Y"].replace(['<=50K', '>50K'], [0, 1])

#############################
# sns.heatmap(X_train.corr(),annot=True)
# sns.pairplot(X_train,hue='Y')
#############################

Y=train['Y']
X=train.drop('Y', axis=1)


X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=1) 
 

clf=tree.DecisionTreeRegressor(max_depth=20, random_state=1)
clf.fit(X_train, y_train)
y_pred = clf.predict(test)
 
print ('Score: ',clf.score(X_test, y_test))#正解率の表示

#K分割交差検証
stratifiedkfold = StratifiedKFold(n_splits=10)  #K=10分割
scores = cross_val_score(clf, X_train, y_train, cv=stratifiedkfold)
print('Cross-Validation scores: {}'.format(scores))   # 各分割におけるスコア
print('Average score: {}'.format(np.mean(scores)))  # スコアの平均値


kmeans = MiniBatchKMeans(n_clusters=7, max_iter=300)
kmeans_result = kmeans.fit_predict(X_train)
features = X_train.columns

with open("tree.dot", 'w') as f:
    tree.export_graphviz(
        clf,
        out_file=f,
        feature_names=features,
        filled=True,
        rounded=True,  
        special_characters=True,
        impurity=False,
        proportion=False,
        class_names=list(map(str, range(0, np.max(kmeans_result)+1)))
    )
Score:  0.27173435546622315
Cross-Validation scores: [0.16014322 0.1993833  0.22275287 0.23680694 0.23153493 0.19696691
 0.21440115 0.25998513 0.24984117 0.22169596]
Average score: 0.21935115860744903

実際の正答率:81.4%

決定木の中身を見る

print(y_pred)

for i in range(16281):
    if y_pred[i]>=0.5:
        y_pred[i]=1
    else:
        y_pred[i]=0
        
print(y_pred)        

# sub['id'] = test['id']
sub['Y'] = list(map(int, y_pred))
sub['Y']=sub['Y'].replace([1, 0], [">50K", "<=50K"])
sub.to_csv('submission.csv', index=False)
import pydotplus
from IPython.display import Image
 
graph = pydotplus.graphviz.graph_from_dot_file('tree.dot')
graph.write_png('tree.png')
Image(graph.create_png())

max_depth=20の場合

max_depth=3の場合

ランダムフォレスト

clf = RandomForestClassifier(n_estimators=100, max_depth=20, random_state=1) 
clf.fit(X_train, y_train)
y_pred = clf.predict(test)
 
print ('Score: ',clf.score(X_test, y_test))#正解率の表示

#K分割交差検証
stratifiedkfold = StratifiedKFold(n_splits=10)  #K=10分割
scores = cross_val_score(clf, X_train, y_train, cv=stratifiedkfold)
print('Cross-Validation scores: {}'.format(scores))   # 各分割におけるスコア
print('Average score: {}'.format(np.mean(scores)))  # スコアの平均値
Score:  0.8108108108108109
Cross-Validation scores: [0.78741366 0.79969302 0.79892556 0.80583269 0.8172043  0.80798771
 0.80875576 0.81490015 0.8156682  0.79185868]
Average score: 0.804823973507904

実際の正答率:80.9%

ロジスティック回帰

clf = LogisticRegression(penalty='l2', solver='sag', random_state=1)
clf.fit(X_train, y_train)
y_pred = clf.predict(test)
 
print ('Score: ',clf.score(X_test, y_test))#正解率の表示

#K分割交差検証
stratifiedkfold = StratifiedKFold(n_splits=10)  #K=10分割
scores = cross_val_score(clf, X_train, y_train, cv=stratifiedkfold)
print('Cross-Validation scores: {}'.format(scores))   # 各分割におけるスコア
print('Average score: {}'.format(np.mean(scores)))  # スコアの平均値
Score:  0.7926904176904177
Cross-Validation scores: [0.78434382 0.7981581  0.80122794 0.81427475 0.80721966 0.78725038
 0.79800307 0.79262673 0.79953917 0.78417819]
Average score: 0.7966821809059326

実際の正答率:80.2%

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Mickey@コーヒー好きエンジニア
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