import numpy as np
import pandas as pd
from sklearn import datasets
# 참고: 분류용 가상 데이터 만들기
from sklearn.datasets import make_classification

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn import svm
from sklearn import tree
from sklearn.ensemble import RandomForestClassifier

import matplotlib.pyplot as plt

import numpy as np                                       ## 기초 수학 연산 및 행렬계산
import pandas as pd                                      ## 데이터프레임 사용
from sklearn import datasets                             ## iris와 같은 내장 데이터 사용
from sklearn.model_selection import train_test_split     ## train, test 데이터 분할

from sklearn.linear_model import LinearRegression        ## 선형 회귀분석
from sklearn.linear_model import LogisticRegression      ## 로지스틱 회귀분석
from sklearn.naive_bayes import GaussianNB               ## 나이브 베이즈
from sklearn import svm                                  ## 서포트 벡터 머신
from sklearn import tree                                 ## 의사결정나무
from sklearn.ensemble import RandomForestClassifier      ## 랜덤포레스트

import matplotlib.pyplot as plt                          ## plot 그릴때 사용

# 분류용 가상 데이터 만들기
from sklearn.datasets import make_classification

X, Y = make_classification(n_samples=1000, n_features=4,
                        n_informative=2, n_redundant=0,
                        random_state=0, suffle=False)

raw = datasets.load_breast_cancer()         ## sklearn에 내장된 원본 데이터 불러오기
print(raw.feature_names)                    ## 열(column) 이름 확인
['mean radius' 'mean texture' 'mean perimeter' 'mean area'
 'mean smoothness' 'mean compactness' 'mean concavity'
 'mean concave points' 'mean symmetry' 'mean fractal dimension'
 'radius error' 'texture error' 'perimeter error' 'area error'
 'smoothness error' 'compactness error' 'concavity error'
 'concave points error' 'symmetry error' 'fractal dimension error'
 'worst radius' 'worst texture' 'worst perimeter' 'worst area'
 'worst smoothness' 'worst compactness' 'worst concavity'
 'worst concave points' 'worst symmetry' 'worst fractal dimension']

data = pd.DataFrame(raw.data)               ## 독립변수 데이터 모음  
target = pd.DataFrame(raw.target)           ## 종속변수 데이터 모음
rawData = pd.concat([data,target], axis=1)  ## 독립변수 + 종속변수 열 결합

## 열(column)이름 설정
rawData.columns=['mean radius', 'mean texture', 'mean perimeter', 'mean area',
 'mean smoothness', 'mean compactness', 'mean concavity',
 'mean concave points', 'mean symmetry', 'mean fractal dimension',
 'radius error', 'texture error', 'perimeter error', 'area error',
 'smoothness error', 'compactness error', 'concavity error',
 'concave points error', 'symmetry error', 'fractal dimension error',
 'worst radius', 'worst texture', 'worst perimeter', 'worst area',
 'worst smoothness', 'worst compactness', 'worst concavity',
 'worst concave points', 'worst symmetry', 'worst fractal dimension'
 , 'cancer']

rawData.head(10)                                ## 데이터 확인 

x = rawData[['mean radius', 'mean texture']]
y = rawData['cancer']

x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.25, random_state=0)

fit(x_train, y_train)     ## 모수 추정(estimat)
get_params()              ## 추정된 모수 확인
predict(x_test)           ## x_test로부터 라벨 예측
predict_log_proba(x_test) ## 로그 취한 확률 예측
predict_proba(x_test)     ## 각 라벨로 예측될 확률
score(x_test, y_test)     ## 모델 정확도 평가를 위한 mean accuracy

clf = LinearRegression()
clf.fit(x_train,y_train)  # 모수 추정
clf.coef_                 # 추정 된 모수 확인(상수항 제외)
clf.intercept_            # 추정 된 상수항 확인
clf.predic(x_test)        # 예측
clf.score(x_test, y_test) # 모형 성능 평가

clf = LogisticRegression(solver='lbfgs').fit(x_train,y_train)
clf.predict(x_test)
clf.predict_proba(x_test)
clf.score(x_test,y_test)

gnb = GaussianNB()
gnb.fit(x_train, y_train)
gnb.predict(x_test)
gnb.score(x_test, y_test)

clf = tree.DecisionTreeClassifier()
clf.fit(x_train, y_train)
clf.predict(x_test)
clf.predict_proba(x_test)
clf.score(x_test, y_test)

clf = svm.SVC(kernel='linear')
clf.fit(x_train, y_train)
clf.predict(x_test)
clf.score(x_test, y_test)

clf = RandomForestClassifier(max_depth=2, random_state=0)
clf.fit(x_train, y_train)
clf.feature_importances_
clf.predict(x_test)