“Klasifikasi pemungutan suara dengan fitur yang berbeda” Kode Jawaban

Klasifikasi pemungutan suara dengan fitur yang berbeda

from sklearn.base import TransformerMixin, BaseEstimator
import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import VotingClassifier

######################
# custom transformer for sklearn pipeline
class ColumnExtractor(TransformerMixin, BaseEstimator):
    def __init__(self, cols):
        self.cols = cols

    def transform(self, X):
        col_list = []
        for c in self.cols:
            col_list.append(X[:, c:c+1])
        return np.concatenate(col_list, axis=1)

    def fit(self, X, y=None):
        return self

######################
# processing data
data = load_iris()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y)

######################
# fit clf1 with df1
pipe1 = Pipeline([
    ('col_extract', ColumnExtractor( cols=range(0,2) )), # selecting features 0 and 1 (df1) to be used with LR (clf1)
    ('clf', LogisticRegression())
    ])

pipe1.fit(X_train, y_train) # sanity check
pipe1.score(X_test,y_test) # sanity check
# output: 0.6842105263157895

######################
# fit clf2 with df2
pipe2 = Pipeline([
    ('col_extract', ColumnExtractor( cols=range(2,4) )), # selecting features 2 and 3 (df2) to be used with SVC (clf2)
    ('clf', SVC(probability=True))
    ])

pipe2.fit(X_train, y_train) # sanity check
pipe2.score(X_test,y_test) # sanity check
# output: 0.9736842105263158

######################
# ensemble/voting classifier where clf1 fitted with df1 and clf2 fitted with df2
eclf = VotingClassifier(estimators=[('df1-clf1', pipe1), ('df2-clf2', pipe2)], voting='soft', weights= [1, 0.5])
eclf.fit(X_train, y_train)
eclf.score(X_test,y_test)
# output: 0.9473684210526315