사이킷런, 아이리스 데이터셋

1. 사이킷런(Scikit-learn)

• 대표적인 파이썬 머신러닝 모듈
• 다양한 머신러닝 알고리즘을 제공
• 다양한 샘플 데이터를 제공
• 머신러닝 결과를 검증하는 기능을 제공
• BSD 라이선스이기 떄문에 무료로 사용 및 배포가 가능
• 사이킷런 공식 홈페이지

2. LinearSVC

• 클래스를 구분으로 하는 분류 문제에서 각 클래스를 잘 구분하는 선을 그려주는 방식을 사용하는 알고리즘
• 지도학습 알고리즘을 사용하는 학습 전용 데이터와 결과 전용 데이터를 모두 가지고 있어야 사용이 가능

from sklearn.svm import LinearSVC
from sklearn.metrics import accuracy_score

# 학습 데이터를 준비
learn_data = [[0,0], [1,0], [0,1], [1,1]] # 독립변수
learn_label = [0,0,0,1] # 종속변수

# 모델 객체 생성
svc = LinearSVC()

# 학습
svc.fit(learn_data, learn_label)

# 검증 데이터 준비
test_data = [[0,0],[1,0],[0,1],[1,1]]

# 에측
test_label = svc.predict(test_data)
test_label

출력: array([0, 0, 0, 1])

# 결과 검증
print(test_data,'의 예측 결과', test_label)
print('정답률: ',accuracy_score([0,0,0,1],test_label))

출력:
[[0, 0], [1, 0], [0, 1], [1, 1]] 의 예측 결과 [0 0 0 1]
정답률:  1.0

3. Iris DataSet

• 데이터셋 : 특정한 작업을 위해 데이터를 관령성 있게 모아놓은 것
• 사이킷런 데이터셋 페이지

from sklearn.datasets import load_iris
iris = load_iris()
iris
출력:
{'data': array([[5.1, 3.5, 1.4, 0.2],
        [4.9, 3. , 1.4, 0.2],
        [4.7, 3.2, 1.3, 0.2],
        [4.6, 3.1, 1.5, 0.2],
        [5. , 3.6, 1.4, 0.2],
        ...
print(iris['DESCR'])
출력:
  .. _iris_dataset:

  Iris plants dataset
  --------------------

  **Data Set Characteristics:**

      :Number of Instances: 150 (50 in each of three classes)
      :Number of Attributes: 4 numeric, predictive attributes and the class
      :Attribute Information:
          - sepal length in cm
          - sepal width in cm
          - petal length in cm
          - petal width in cm
          - class:
                  - Iris-Setosa
                  - Iris-Versicolour
                  - Iris-Virginica

      :Summary Statistics:

      ============== ==== ==== ======= ===== ====================
                      Min  Max   Mean    SD   Class Correlation
      ============== ==== ==== ======= ===== ====================
      sepal length:   4.3  7.9   5.84   0.83    0.7826
      sepal width:    2.0  4.4   3.05   0.43   -0.4194
      petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)
      petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)
      ============== ==== ==== ======= ===== ====================

      :Missing Attribute Values: None
      :Class Distribution: 33.3% for each of 3 classes.
      :Creator: R.A. Fisher
      :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
      :Date: July, 1988

  The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken
  from Fisher's paper. Note that it's the same as in R, but not as in the UCI
  Machine Learning Repository, which has two wrong data points.

  This is perhaps the best known database to be found in the
  pattern recognition literature.  Fisher's paper is a classic in the field and
  is referenced frequently to this day.  (See Duda & Hart, for example.)  The
  data set contains 3 classes of 50 instances each, where each class refers to a
  type of iris plant.  One class is linearly separable from the other 2; the
  latter are NOT linearly separable from each other.

  .. topic:: References

     - Fisher, R.A. "The use of multiple measurements in taxonomic problems"
       Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
       Mathematical Statistics" (John Wiley, NY, 1950).
     - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.
       (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
     - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
       Structure and Classification Rule for Recognition in Partially Exposed
       Environments".  IEEE Transactions on Pattern Analysis and Machine
       Intelligence, Vol. PAMI-2, No. 1, 67-71.
     - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions
       on Information Theory, May 1972, 431-433.
     - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II
       conceptual clustering system finds 3 classes in the data.
     - Many, many more ...

• sepal length in cm : 꽃받침의 길이
• sepal width in cm : 꽃받침의 너비
• petal length in cm : 꽃잎의 길이
• petal width in cm : 꽃잎의 너비

data = iris['data']

feature_names = iris['feature_names']
feature_names

출력:
['sepal length (cm)',
 'sepal width (cm)',
 'petal length (cm)',
 'petal width (cm)']

import pandas as pd

df_iris = pd.DataFrame(data, columns=feature_names)
df_iris

target = iris['target']
target

target.shape

df_iris['target'] = target
df_iris

from sklearn.model_selection import train_test_split

# train_test_split(독립변수, 종속변수, 테스트사이즈, 시드값 ...)
X_train, X_test, y_train, y_test = train_test_split(df_iris.drop('target', axis=1),
                                                    df_iris['target'],
                                                    test_size=0.2,
                                                    random_state=2023)

X_train.shape, X_test.shape

출력: ((120, 4), (30, 4))

y_train.shape, y_test.shape

출력: ((120,), (30,))

X_train

y_train

from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

svc = SVC()

svc.fit(X_train, y_train)

 

y_pred = svc.predict(X_test)
y_pred

출력:
array([2, 1, 1, 2, 1, 2, 1, 1, 0, 1, 0, 1, 0, 2, 0, 2, 0, 1, 0, 0, 1, 0,
       2, 1, 0, 0, 0, 2, 1, 0])

print('정답률', accuracy_score(y_test, y_pred))

출력: 정답률 1.0

# 6.2 2.1 4.1 1.5
y_pred = svc.predict([[6.2, 2.1, 4.1, 1.5]])
y_pred

출력:
/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but SVC was fitted with feature names
  warnings.warn(
array([1])