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keras33_LSTM3_cancer.py
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keras33_LSTM3_cancer.py
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#사이킷런 데이터셋
#LSTM으로 모델링
#Dense와 성능비교
#이진분류
import numpy as np
from sklearn.datasets import load_breast_cancer
#1. 데이터
datasets = load_breast_cancer()
print(datasets.DESCR)
print(datasets.feature_names) # 데이터셋 컬럼명 확인
x = datasets.data
y = datasets.target
print(x.shape) #(569, 30)
print(y.shape) #(569,)
print(x[:5])
print(y)
x = x.reshape(x.shape[0], x.shape[1], 1) # (569, 30, 1)
#1.1 전처리 / minmax, train_test_split
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, train_size = 0.8, shuffle = True, random_state = 66)
x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, train_size = 0.8, shuffle = True, random_state = 66)
# from sklearn.preprocessing import MinMaxScaler
# scaler = MinMaxScaler()
# scaler.fit(x_train)
# x_train = scaler.transform(x_train)
# x_test = scaler.transform(x_test)
# x_val = scaler.transform(x_val)
#2. 모델링
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Dense, Input, LSTM
input1 = Input(shape=(30,1))
lstm1 = LSTM(50, activation='relu', input_shape = (30,1))(input1)
dense1 = Dense(36, activation='relu')(lstm1)
dense2 = Dense(40, activation='relu')(dense1)
dense3 = Dense(40, activation='relu')(dense2)
dense4 = Dense(40, activation='sigmoid')(dense3)
outputs = Dense(1)(dense4)
model = Model(inputs = input1, outputs = outputs)
model.summary()
#3. 컴파일,
from tensorflow.keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor = 'acc', patience = 30, mode = 'auto')
model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['acc'])
#이진분류 일때는 무조건 binary_crossentropy
#model.compile(loss = 'mse', optimizer = 'adam', metrics = ['acc'])
model.fit(x_train, y_train, epochs = 500, batch_size = 32 ,validation_data = (x_val, y_val), verbose = 1, callbacks = [early_stopping])
#4. 평가
loss, acc = model.evaluate(x_test, y_test)
print('loss : ', loss)
print('acc : ', acc)
y_pred = model.predict(x_test[:10])
y_predict = model.predict(x_test[:10])
y_pred = list(map(int,np.round(y_predict,0)))
y_predict = np.transpose(y_predict)
y_pred = np.transpose(y_pred)
print(y_predict)
print(y_pred)
print(y_test[:10])
#실습1. acc 0.985이상 올릴 것
#실습2. predict 출력해볼것
#y[-5:-1] = ??
# loss : 0.5385931134223938
# acc : 0.9736841917037964
# loss : 0.05537671223282814
# acc : 0.9824561476707458
# loss : 0.05472574755549431
# acc : 0.9912280440330505
# [[0.98721755 0.98863095 0.9889385 0.9889311 0.9870265 0.00612339
# 0.00251615 0.98906994 0.9861438 0.9887173 ]]
# [1 1 1 1 1 0 0 1 1 1]
# [1 1 1 1 1 0 0 1 1 1]
'''
Breast cancer wisconsin (diagnostic) dataset
--------------------------------------------
**Data Set Characteristics:**
:Number of Instances: 569
:Number of Attributes: 30 numeric, predictive attributes and the class
:Attribute Information:
- radius (mean of distances from center to points on the perimeter)
- texture (standard deviation of gray-scale values)
- perimeter
- area
- smoothness (local variation in radius lengths)
- compactness (perimeter^2 / area - 1.0)
- concavity (severity of concave portions of the contour)
- concave points (number of concave portions of the contour)
- symmetry
- fractal dimension ("coastline approximation" - 1)
: 속성 정보 :
-반경 (중심에서 주변 지점까지의 거리 평균)
-텍스처 (회색조 값의 표준 편차)
-둘레
- 지역
-부드러움 (반경 길이의 국부적 변동)
-콤팩트 함 (둘레 ^ 2 / 면적-1.0)
-오목 함 (윤곽의 오목한 부분의 심각도)
-오목한 점 (윤곽의 오목한 부분의 수)
-대칭
-프랙탈 차원 ( "해안선 근사치"-1)
The mean, standard error, and "worst" or largest (mean of the three
worst/largest values) of these features were computed for each image,
resulting in 30 features. For instance, field 0 is Mean Radius, field
10 is Radius SE, field 20 is Worst Radius.
평균, 표준 오차 및 "최악"또는 최대 (3 개 중 평균
이러한 특징 중 최악 / 최대 값)은 각 이미지에 대해 계산되었습니다.
결과적으로 30 개의 기능이 있습니다 예를 들어, 필드 0은 평균 반경, 필드
10은 반경 SE이고 필드 20은 최악의 반경입니다.
- class:
- WDBC-Malignant
- WDBC-Benign
:Summary Statistics:
===================================== ====== ======
Min Max
===================================== ====== ======
radius (mean): 6.981 28.11
texture (mean): 9.71 39.28
perimeter (mean): 43.79 188.5
area (mean): 143.5 2501.0
smoothness (mean): 0.053 0.163
compactness (mean): 0.019 0.345
concavity (mean): 0.0 0.427
concave points (mean): 0.0 0.201
symmetry (mean): 0.106 0.304
fractal dimension (mean): 0.05 0.097
radius (standard error): 0.112 2.873
texture (standard error): 0.36 4.885
perimeter (standard error): 0.757 21.98
area (standard error): 6.802 542.2
smoothness (standard error): 0.002 0.031
compactness (standard error): 0.002 0.135
concavity (standard error): 0.0 0.396
concave points (standard error): 0.0 0.053
symmetry (standard error): 0.008 0.079
fractal dimension (standard error): 0.001 0.03
radius (worst): 7.93 36.04
texture (worst): 12.02 49.54
perimeter (worst): 50.41 251.2
area (worst): 185.2 4254.0
smoothness (worst): 0.071 0.223
compactness (worst): 0.027 1.058
concavity (worst): 0.0 1.252
concave points (worst): 0.0 0.291
symmetry (worst): 0.156 0.664
fractal dimension (worst): 0.055 0.208
===================================== ====== ======
:Missing Attribute Values: None
:Class Distribution: 212 - Malignant, 357 - Benign
:Creator: Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian
:Donor: Nick Street
:Date: November, 1995
This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.
https://goo.gl/U2Uwz2
Features are computed from a digitized image of a fine needle
aspirate (FNA) of a breast mass. They describe
characteristics of the cell nuclei present in the image.
Separating plane described above was obtained using
Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree
Construction Via Linear Programming." Proceedings of the 4th
Midwest Artificial Intelligence and Cognitive Science Society,
pp. 97-101, 1992], a classification method which uses linear
programming to construct a decision tree. Relevant features
were selected using an exhaustive search in the space of 1-4
features and 1-3 separating planes.
The actual linear program used to obtain the separating plane
in the 3-dimensional space is that described in:
[K. P. Bennett and O. L. Mangasarian: "Robust Linear
Programming Discrimination of Two Linearly Inseparable Sets",
Optimization Methods and Software 1, 1992, 23-34].
This database is also available through the UW CS ftp server:
ftp ftp.cs.wisc.edu
cd math-prog/cpo-dataset/machine-learn/WDBC/
이것은 UCI ML 유방암 위스콘신 (진단) 데이터 세트의 사본입니다.
https://goo.gl/U2Uwz2
미세 바늘의 디지털화 된 이미지에서 특징 계산
유방 질량의 흡인 (FNA). 그들은 설명합니다
이미지에 존재하는 세포 핵의 특성.
위에서 설명한 분리 평면은
다중 표면 방법 트리 (MSM-T) [K. P. Bennett, "의사 결정 트리
선형 프로그래밍을 통한 구성. "Proceedings of the 4th
중서부 인공 지능 및인지 과학 학회,
pp. 97-101, 1992], 선형을 사용하는 분류 방법
의사 결정 트리를 구성하는 프로그래밍. 관련 기능
1-4의 공간에서 철저한 검색을 사용하여 선택되었습니다.
기능 및 1-3 개의 분리 평면.
분리 평면을 얻는 데 사용되는 실제 선형 프로그램
3 차원 공간에서는 다음에 설명되어 있습니다.
[케이. P. Bennett 및 O. L. Mangasarian : "강력한 선형
선형 적으로 분리 할 수없는 두 세트의 프로그래밍 차별 ",
최적화 방법 및 소프트웨어 1, 1992, 23-34].
이 데이터베이스는 UW CS ftp 서버를 통해서도 사용할 수 있습니다.
.. topic:: References
- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction
for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on
Electronic Imaging: Science and Technology, volume 1905, pages 861-870,
San Jose, CA, 1993.
- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and
prognosis via linear programming. Operations Research, 43(4), pages 570-577,
July-August 1995.
- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques
to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)
163-171.
-W.N. 스트리트, W.H. Wolberg 및 O.L. Mangasarian. 핵 특징 추출
유방 종양 진단을 위해. IS & T / SPIE 1993 국제 심포지엄
전자 이미징 : 과학 및 기술, 1905 권, 861-870 페이지,
1993 년 캘리포니아 주 산호세.
-O.L. Mangasarian, W.N. Street 및 W.H. Wolberg. 유방암 진단 및
선형 프로그래밍을 통한 예후. 운영 연구, 43 (4), 570-577 페이지,
1995 년 7 월 -8 월.
-W.H. Wolberg, W.N. Street 및 O.L. Mangasarian. 기계 학습 기술
미세 바늘 흡인으로 유방암을 진단합니다. 암 편지 77 (1994)
163-171.
'''