SKLEARN Archives - icodebroker

[PYTHON/SKLEARN] Precision-Recall Graph 그리기

■ Precision-Recall Graph를 그리는 방법을 보여준다. ▶ 예제 코드 (PY)


import matplotlib.pyplot as pp
import numpy as np
import sklearn.metrics as metrics

modelAClassValueNDArray       = np.array([0   , 0   , 0   , 0   , 0   , 0   , 1   , 0   , 1   , 1   , 0   , 0   , 0   , 1   , 1   ])
modelAClassProbabilityNDArray = np.array([0.05, 0.05, 0.15, 0.15, 0.25, 0.25, 0.35, 0.35, 0.45, 0.45, 0.55, 0.55, 0.65, 0.85, 0.95])

modelBClassValueNDArray       = np.array([0   , 0   , 0   , 1   , 1   , 0   , 0   , 1   , 0   , 0   , 1   , 0   , 0   , 0   , 1   ])
modelBClassProbabilityNDArray = np.array([0.05, 0.05, 0.15, 0.15, 0.25, 0.25, 0.25, 0.35, 0.35, 0.45, 0.55, 0.55, 0.65, 0.75, 0.95])

modelAPrecisionNDArray, modelARecallRateNDArray, _ = metrics.precision_recall_curve(modelAClassValueNDArray, modelAClassProbabilityNDArray)
modelBPrecisionNDArray, modelBRecallRateNDArray, _ = metrics.precision_recall_curve(modelBClassValueNDArray, modelBClassProbabilityNDArray)

modelAAP = metrics.average_precision_score(modelAClassValueNDArray, modelAClassProbabilityNDArray)
modelBAP = metrics.average_precision_score(modelBClassValueNDArray, modelBClassProbabilityNDArray)

pp.title("Precision-Recall Graph")

pp.xlabel("Recall"   )
pp.ylabel("Precision")

pp.plot(modelARecallRateNDArray, modelAPrecisionNDArray, "b", label = "Model A (AP = %0.2F)" % modelAAP)
pp.plot(modelBRecallRateNDArray, modelBPrecisionNDArray, "g", label = "Model B (AP = %0.2F)" % modelBAP)

pp.legend(loc = "upper right")

pp.show()

import matplotlib.pyplot as pp

import numpy as np

import sklearn.metrics as metrics

modelAClassValueNDArray = np.array([0 , 0 , 0 , 0 , 0 , 0 , 1 , 0 , 1 , 1 , 0 , 0 , 0 , 1 , 1 ])

modelAClassProbabilityNDArray = np.array([0.05, 0.05, 0.15, 0.15, 0.25, 0.25, 0.35, 0.35, 0.45, 0.45, 0.55, 0.55, 0.65, 0.85, 0.95])

modelBClassValueNDArray = np.array([0 , 0 , 0 , 1 , 1 , 0 , 0 , 1 , 0 , 0 , 1 , 0 , 0 , 0 , 1 ])

modelBClassProbabilityNDArray = np.array([0.05, 0.05, 0.15, 0.15, 0.25, 0.25, 0.25, 0.35, 0.35, 0.45, 0.55, 0.55, 0.65, 0.75, 0.95])

modelAPrecisionNDArray, modelARecallRateNDArray, _ = metrics.precision_recall_curve(modelAClassValueNDArray, modelAClassProbabilityNDArray)

modelBPrecisionNDArray, modelBRecallRateNDArray, _ = metrics.precision_recall_curve(modelBClassValueNDArray, modelBClassProbabilityNDArray)

modelAAP = metrics.average_precision_score(modelAClassValueNDArray, modelAClassProbabilityNDArray)

modelBAP = metrics.average_precision_score(modelBClassValueNDArray, modelBClassProbabilityNDArray)

pp.title("Precision-Recall Graph")

pp.xlabel("Recall" )

pp.ylabel("Precision")

pp.plot(modelARecallRateNDArray, modelAPrecisionNDArray, "b", label = "Model A (AP = %0.2F)" % modelAAP)

pp.plot(modelBRecallRateNDArray, modelBPrecisionNDArray, "g", label = "Model B (AP = %0.2F)" % modelBAP)

pp.legend(loc = "upper right")

pp.show()

[PYTHON/SKLEARN] ROC(Receiver Operating Characteristic) Curve 그리기

■ ROC(Receiver Operating Characteristic) Curve를 그리는 방법을 보여준다. ▶ 예제 코드 (PY)


import matplotlib.pyplot as pp
import numpy as np
import sklearn.metrics as metrics

modelAClassValueNDArray       = np.array([0   , 0   , 0   , 0   , 1   , 1   , 0   , 0   , 1   , 1   ])
modelAClassProbabilityNDArray = np.array([0.05, 0.15, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.95, 0.95])

modelBClassValueNDArray       = np.array([0   , 0   , 1   , 0   , 1   , 0   , 0   , 1   , 0   , 1   ])
modelBClassProbabilityNDArray = np.array([0.05, 0.05, 0.15, 0.25, 0.35, 0.45, 0.65, 0.75, 0.85, 0.95])

modelAFalsePositiveRate, modelATruePositiveRate, modelAThreshold = metrics.roc_curve(modelAClassValueNDArray, modelAClassProbabilityNDArray)
modelBFalsePositiveRate, modelBTruePositiveRate, modelBThreshold = metrics.roc_curve(modelBClassValueNDArray, modelBClassProbabilityNDArray)

modelAROCAUC = metrics.auc(modelAFalsePositiveRate, modelATruePositiveRate)
modelBROCAUC = metrics.auc(modelBFalsePositiveRate, modelBTruePositiveRate)

pp.title("Receiver Operating Characteristic")

pp.xlabel("False Positive Rate(1 - Specificity)")
pp.ylabel("True Positive Rate(Sensitivity)")

pp.plot(modelAFalsePositiveRate, modelATruePositiveRate, "b", label = "Model A (AUC = %0.2f)" % modelAROCAUC)
pp.plot(modelBFalsePositiveRate, modelBTruePositiveRate, "g", label = "Model B (AUC = %0.2f)" % modelBROCAUC)

pp.plot([0, 1], [1, 1], "y--")
pp.plot([0, 1], [0, 1], "r--")

pp.legend(loc = "lower right")

pp.show()

import matplotlib.pyplot as pp

import numpy as np

import sklearn.metrics as metrics

modelAClassValueNDArray = np.array([0 , 0 , 0 , 0 , 1 , 1 , 0 , 0 , 1 , 1 ])

modelAClassProbabilityNDArray = np.array([0.05, 0.15, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.95, 0.95])

modelBClassValueNDArray = np.array([0 , 0 , 1 , 0 , 1 , 0 , 0 , 1 , 0 , 1 ])

modelBClassProbabilityNDArray = np.array([0.05, 0.05, 0.15, 0.25, 0.35, 0.45, 0.65, 0.75, 0.85, 0.95])

modelAFalsePositiveRate, modelATruePositiveRate, modelAThreshold = metrics.roc_curve(modelAClassValueNDArray, modelAClassProbabilityNDArray)

modelBFalsePositiveRate, modelBTruePositiveRate, modelBThreshold = metrics.roc_curve(modelBClassValueNDArray, modelBClassProbabilityNDArray)

modelAROCAUC = metrics.auc(modelAFalsePositiveRate, modelATruePositiveRate)

modelBROCAUC = metrics.auc(modelBFalsePositiveRate, modelBTruePositiveRate)

pp.title("Receiver Operating Characteristic")

pp.xlabel("False Positive Rate(1 - Specificity)")

pp.ylabel("True Positive Rate(Sensitivity)")

pp.plot(modelAFalsePositiveRate, modelATruePositiveRate, "b", label = "Model A (AUC = %0.2f)" % modelAROCAUC)

pp.plot(modelBFalsePositiveRate, modelBTruePositiveRate, "g", label = "Model B (AUC = %0.2f)" % modelBROCAUC)

pp.plot([0, 1], [1, 1], "y--")

pp.plot([0, 1], [0, 1], "r--")

pp.legend(loc = "lower right")

pp.show()

[PYTHON/SKLEARN] MLPClassifier 클래스 : 다층 퍼셉트론 신경망 만들기 (MNIST)

■ MLPClassifier 클래스를 사용해 다층 퍼셉트론 신경망을 만드는 방법을 보여준다. ▶ main.py


import sklearn.datasets as datasets
import sklearn.model_selection as model_selection
import sklearn.neural_network as neural_network
import sklearn.preprocessing as preprocessing

print("MNIST 데이터를 구합니다...")

mnistBunch = datasets.fetch_openml("mnist_784")

print("MNIST 데이터를 다운로드 했습니다.")

imageNDArray = mnistBunch.data
labelNDArray = mnistBunch.target

imageNDArray = preprocessing.normalize(imageNDArray, norm = "l2") # "l1" 정규화도 사용 가능하다.

trainImageNDArray, testImageNDArray, trainLabelNDArray, testLabelNDArray = model_selection.train_test_split(imageNDArray, labelNDArray, test_size = 0.25, random_state = 17)

mlpClassifier = neural_network.MLPClassifier(hidden_layer_sizes = (300, 300, 300), max_iter = 50, solver = "sgd", learning_rate_init = 0.01, verbose = True)

print("신경망 학습을 시작합니다...")

mlpClassifier.fit(trainImageNDArray, trainLabelNDArray)

print("신경망 학습을 완료했습니다.")

print("네트워크 성능 : %f" % mlpClassifier.score(testImageNDArray, testLabelNDArray))

"""
MNIST 데이터를 구합니다...
MNIST 데이터를 다운로드 했습니다.
신경망 학습을 시작합니다...
Iteration 1, loss = 2.01495220
Iteration 2, loss = 0.69619084
Iteration 3, loss = 0.42475069
Iteration 4, loss = 0.35455444
Iteration 5, loss = 0.31833714
Iteration 6, loss = 0.29176438
Iteration 7, loss = 0.26984975
Iteration 8, loss = 0.24962781
Iteration 9, loss = 0.23111968
Iteration 10, loss = 0.21396440
Iteration 11, loss = 0.19797201
Iteration 12, loss = 0.18409881
Iteration 13, loss = 0.17178327
Iteration 14, loss = 0.16095801
Iteration 15, loss = 0.15054954
Iteration 16, loss = 0.14160059
Iteration 17, loss = 0.13336125
Iteration 18, loss = 0.12576072
Iteration 19, loss = 0.11900718
Iteration 20, loss = 0.11208487
Iteration 21, loss = 0.10663341
Iteration 22, loss = 0.10152752
Iteration 23, loss = 0.09642898
Iteration 24, loss = 0.09181761
Iteration 25, loss = 0.08624251
Iteration 26, loss = 0.08296494
Iteration 27, loss = 0.07905917
Iteration 28, loss = 0.07541192
Iteration 29, loss = 0.07185268
Iteration 30, loss = 0.06843048
Iteration 31, loss = 0.06545506
Iteration 32, loss = 0.06233578
Iteration 33, loss = 0.05947011
Iteration 34, loss = 0.05644594
Iteration 35, loss = 0.05451590
Iteration 36, loss = 0.05169441
Iteration 37, loss = 0.04988134
Iteration 38, loss = 0.04732568
Iteration 39, loss = 0.04542914
Iteration 40, loss = 0.04318924
Iteration 41, loss = 0.04146527
Iteration 42, loss = 0.03971327
Iteration 43, loss = 0.03743316
Iteration 44, loss = 0.03632515
Iteration 45, loss = 0.03451298
Iteration 46, loss = 0.03323524
Iteration 47, loss = 0.03185525
Iteration 48, loss = 0.03023780
Iteration 49, loss = 0.02904490
Iteration 50, loss = 0.02757689
D:\TestProject\TestProject\env\lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.
  warnings.warn(
신경망 학습을 완료했습니다.
네트워크 성능 : 0.976457
"""

import sklearn.datasets as datasets

import sklearn.model_selection as model_selection

import sklearn.neural_network as neural_network

import sklearn.preprocessing as preprocessing

print("MNIST 데이터를 구합니다...")

mnistBunch = datasets.fetch_openml("mnist_784")

print("MNIST 데이터를 다운로드 했습니다.")

imageNDArray = mnistBunch.data

labelNDArray = mnistBunch.target

imageNDArray = preprocessing.normalize(imageNDArray, norm = "l2") # "l1" 정규화도 사용 가능하다.

trainImageNDArray, testImageNDArray, trainLabelNDArray, testLabelNDArray = model_selection.train_test_split(imageNDArray, labelNDArray, test_size = 0.25, random_state = 17)

mlpClassifier = neural_network.MLPClassifier(hidden_layer_sizes = (300, 300, 300), max_iter = 50, solver = "sgd", learning_rate_init = 0.01, verbose = True)

print("신경망 학습을 시작합니다...")

mlpClassifier.fit(trainImageNDArray, trainLabelNDArray)

print("신경망 학습을 완료했습니다.")

print("네트워크 성능 : %f" % mlpClassifier.score(testImageNDArray, testLabelNDArray))

"""

MNIST 데이터를 구합니다...

MNIST 데이터를 다운로드 했습니다.

신경망 학습을 시작합니다...

Iteration 1, loss = 2.01495220

Iteration 2, loss = 0.69619084

Iteration 3, loss = 0.42475069

Iteration 4, loss = 0.35455444

Iteration 5, loss = 0.31833714

Iteration 6, loss = 0.29176438

Iteration 7, loss = 0.26984975

Iteration 8, loss = 0.24962781

Iteration 9, loss = 0.23111968

Iteration 10, loss = 0.21396440

Iteration 11, loss = 0.19797201

Iteration 12, loss = 0.18409881

Iteration 13, loss = 0.17178327

Iteration 14, loss = 0.16095801

Iteration 15, loss = 0.15054954

Iteration 16, loss = 0.14160059

Iteration 17, loss = 0.13336125

Iteration 18, loss = 0.12576072

Iteration 19, loss = 0.11900718

Iteration 20, loss = 0.11208487

Iteration 21, loss = 0.10663341

Iteration 22, loss = 0.10152752

Iteration 23, loss = 0.09642898

Iteration 24, loss = 0.09181761

Iteration 25, loss = 0.08624251

Iteration 26, loss = 0.08296494

Iteration 27, loss = 0.07905917

Iteration 28, loss = 0.07541192

Iteration 29, loss = 0.07185268

Iteration 30, loss = 0.06843048

Iteration 31, loss = 0.06545506

Iteration 32, loss = 0.06233578

Iteration 33, loss = 0.05947011

Iteration 34, loss = 0.05644594

Iteration 35, loss = 0.05451590

Iteration 36, loss = 0.05169441

Iteration 37, loss = 0.04988134

Iteration 38, loss = 0.04732568

Iteration 39, loss = 0.04542914

Iteration 40, loss = 0.04318924

Iteration 41, loss = 0.04146527

Iteration 42, loss = 0.03971327

Iteration 43, loss = 0.03743316

Iteration 44, loss = 0.03632515

Iteration 45, loss = 0.03451298

Iteration 46, loss = 0.03323524

Iteration 47, loss = 0.03185525

Iteration 48, loss = 0.03023780

Iteration 49, loss = 0.02904490

Iteration 50, loss = 0.02757689

D:\TestProject\TestProject\env\lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.

warnings.warn(

신경망 학습을 완료했습니다.

네트워크 성능 : 0.976457

"""

▶ requirements.txt


cycler==0.11.0
fonttools==4.34.4
joblib==1.1.0
kiwisolver==1.4.4
matplotlib==3.5.3
numpy==1.23.1
packaging==21.3
pandas==1.4.3
Pillow==9.2.0
pip==22.0.4
pyparsing==3.0.9
python-dateutil==2.8.2
pytz==2022.2
scikit-learn==1.1.2
scipy==1.9.0
setuptools==58.1.0
six==1.16.0
sklearn==0.0
threadpoolctl==3.1.0

cycler==0.11.0

fonttools==4.34.4

joblib==1.1.0

kiwisolver==1.4.4

matplotlib==3.5.3

numpy==1.23.1

packaging==21.3

pandas==1.4.3

Pillow==9.2.0

pip==22.0.4

pyparsing==3.0.9

python-dateutil==2.8.2

pytz==2022.2

scikit-learn==1.1.2

scipy==1.9.0

setuptools==58.1.0

six==1.16.0

sklearn==0.0

threadpoolctl==3.1.0

[PYTHON/SKLEARN] k-평균 클러스터링(k-means clustering) 사용하기

■ k-평균 클러스터링(k-means clustering)을 사용하는 방법을 보여준다. ▶ 예제 코드 (PY)


import sklearn.cluster as cluster
import sklearn.datasets as datasets
import sklearn.metrics as metrics

mnistBunch   = datasets.load_digits()
imageNDArray = mnistBunch.images
imageCount   = len(imageNDArray)
imageNDArray = imageNDArray.reshape(len(imageNDArray), -1)
labelNDArray = mnistBunch.target

trainCount = int((imageCount / 4) * 3)
testCount  = int((imageCount / 4))

kmeans = cluster.KMeans(n_clusters = 10, init = "k-means++", n_init = 10)

kmeans.fit(imageNDArray[:trainCount])

print(kmeans.labels_)
print(type(kmeans.labels_))
print(kmeans.labels_.shape)
print()

testLabelList = labelNDArray[testCount:]

predictionLabelList = kmeans.predict(imageNDArray[testCount:])

print("성능 리포트 : \n %s \n" % (metrics.classification_report(testLabelList, predictionLabelList)))

"""
[6 4 4 ... 3 1 3]
<class 'numpy.ndarray'>
(1347,)

성능 리포트 :
               precision    recall  f1-score   support

           0       0.00      0.00      0.00       131
           1       0.01      0.01      0.01       137
           2       0.00      0.00      0.00       131
           3       0.85      0.82      0.83       136
           4       0.00      0.00      0.00       139
           5       0.91      0.72      0.80       136
           6       0.01      0.01      0.01       138
           7       0.85      0.98      0.91       134
           8       0.02      0.02      0.02       130
           9       0.00      0.00      0.00       136

    accuracy                           0.26      1348
   macro avg       0.27      0.26      0.26      1348
weighted avg       0.27      0.26      0.26      1348
"""

import sklearn.cluster as cluster

import sklearn.datasets as datasets

import sklearn.metrics as metrics

mnistBunch = datasets.load_digits()

imageNDArray = mnistBunch.images

imageCount = len(imageNDArray)

imageNDArray = imageNDArray.reshape(len(imageNDArray), -1)

labelNDArray = mnistBunch.target

trainCount = int((imageCount / 4) * 3)

testCount = int((imageCount / 4))

kmeans = cluster.KMeans(n_clusters = 10, init = "k-means++", n_init = 10)

kmeans.fit(imageNDArray[:trainCount])

print(kmeans.labels_)

print(type(kmeans.labels_))

print(kmeans.labels_.shape)

print()

testLabelList = labelNDArray[testCount:]

predictionLabelList = kmeans.predict(imageNDArray[testCount:])

print("성능 리포트 : \n %s \n" % (metrics.classification_report(testLabelList, predictionLabelList)))

"""

[6 4 4 ... 3 1 3]

(1347,)

성능 리포트 :

precision recall f1-score support

0 0.00 0.00 0.00 131

1 0.01 0.01 0.01 137

2 0.00 0.00 0.00 131

3 0.85 0.82 0.83 136

4 0.00 0.00 0.00 139

5 0.91 0.72 0.80 136

6 0.01 0.01 0.01 138

7 0.85 0.98 0.91 134

8 0.02 0.02 0.02 130

9 0.00 0.00 0.00 136

accuracy 0.26 1348

macro avg 0.27 0.26 0.26 1348

weighted avg 0.27 0.26 0.26 1348

"""

[PYTHON/SKLEARN] t-SNE 시각화 알고리즘 사용하기

■ t-SNE 시각화 알고리즘을 사용하는 방법을 보여준다. ▶ 예제 코드 (PY)


import matplotlib.pyplot as pp
import numpy as np
import sklearn.datasets as datasets
import sklearn.manifold as manifold

mnistBunch   = datasets.load_digits(n_class = 6)
imageNDArray = mnistBunch.data
labelNDArray = mnistBunch.target

tsneNDArray = manifold.TSNE(n_components = 2, init = "pca", random_state = 0).fit_transform(imageNDArray)

figure, axesSubplot = pp.subplots()

axesSubplot.scatter(tsneNDArray[:, 0], tsneNDArray[:, 1], c = labelNDArray)
axesSubplot.set_xticks(())
axesSubplot.set_yticks(())

pp.show()

import matplotlib.pyplot as pp

import numpy as np

import sklearn.datasets as datasets

import sklearn.manifold as manifold

mnistBunch = datasets.load_digits(n_class = 6)

imageNDArray = mnistBunch.data

labelNDArray = mnistBunch.target

tsneNDArray = manifold.TSNE(n_components = 2, init = "pca", random_state = 0).fit_transform(imageNDArray)

figure, axesSubplot = pp.subplots()

axesSubplot.scatter(tsneNDArray[:, 0], tsneNDArray[:, 1], c = labelNDArray)

axesSubplot.set_xticks(())

axesSubplot.set_yticks(())

pp.show()

[PYTHON/SKLEARN] 주성분 분석(PCA, Principle Component Analysis) 시각화 알고리즘 사용하기

■ 주성분 분석(PCA, Principle Component Analysis) 시각화 알고리즘을 사용하는 방법을 보여준다. ▶ 예제 코드 (PY)


import matplotlib.pyplot as pp
import numpy as np
import sklearn.datasets as datasets
import sklearn.decomposition as decomposition

mnistBunch   = datasets.load_digits(n_class = 6)
imageNDArray = mnistBunch.data
labelNDArray = mnistBunch.target
pcaNDArray   = decomposition.TruncatedSVD(n_components = 2).fit_transform(imageNDArray)

figure, axesSubplot = pp.subplots()

axesSubplot.scatter(pcaNDArray[:, 0], pcaNDArray[:, 1], c = labelNDArray)
axesSubplot.set_xticks(())
axesSubplot.set_yticks(())

pp.show()

import matplotlib.pyplot as pp

import numpy as np

import sklearn.datasets as datasets

import sklearn.decomposition as decomposition

mnistBunch = datasets.load_digits(n_class = 6)

imageNDArray = mnistBunch.data

labelNDArray = mnistBunch.target

pcaNDArray = decomposition.TruncatedSVD(n_components = 2).fit_transform(imageNDArray)

figure, axesSubplot = pp.subplots()

axesSubplot.scatter(pcaNDArray[:, 0], pcaNDArray[:, 1], c = labelNDArray)

axesSubplot.set_xticks(())

axesSubplot.set_yticks(())

pp.show()

[PYTHON/SKLEARN] SVM(Support Vector Machine) 사용하기

■ SVM(Support Vector Machine)을 사용하는 방법을 보여준다. ▶ 예제 코드 (PY)


import sklearn.datasets as datasets
import sklearn.linear_model as linear_model
import sklearn.metrics as metrics
import sklearn.svm as svm

mnistBunch   = datasets.load_digits()
imageNDArray = mnistBunch.images
imageCount   = len(imageNDArray)
imageNDArray = imageNDArray.reshape(len(imageNDArray), -1)
labelNDArray = mnistBunch.target
classifier   = svm.SVC(gamma = 0.001)

trainCount = int((imageCount / 4) * 3)

trainImageNDArray = imageNDArray[:trainCount]
trainLabelNDArray = labelNDArray[:trainCount]

classifier.fit(trainImageNDArray, trainLabelNDArray)

testCount = int((imageCount / 4))

testImageNDArray = imageNDArray[testCount:]
testLabelList    = labelNDArray[testCount:]

predictionLabelList = classifier.predict(testImageNDArray)

print("성능 리포트 :\n%s\n" %(metrics.classification_report(testLabelList, predictionLabelList)))

"""
성능 리포트 :
              precision    recall  f1-score   support

           0       1.00      0.99      1.00       131
           1       0.99      1.00      1.00       137
           2       1.00      1.00      1.00       131
           3       0.99      0.95      0.97       136
           4       0.99      0.98      0.99       139
           5       0.98      0.99      0.99       136
           6       0.99      1.00      1.00       138
           7       0.99      1.00      1.00       134
           8       0.96      0.99      0.98       130
           9       0.99      0.99      0.99       136

    accuracy                           0.99      1348
   macro avg       0.99      0.99      0.99      1348
weighted avg       0.99      0.99      0.99      1348
"""

import sklearn.datasets as datasets

import sklearn.linear_model as linear_model

import sklearn.metrics as metrics

import sklearn.svm as svm

mnistBunch = datasets.load_digits()

imageNDArray = mnistBunch.images

imageCount = len(imageNDArray)

imageNDArray = imageNDArray.reshape(len(imageNDArray), -1)

labelNDArray = mnistBunch.target

classifier = svm.SVC(gamma = 0.001)

trainCount = int((imageCount / 4) * 3)

trainImageNDArray = imageNDArray[:trainCount]

trainLabelNDArray = labelNDArray[:trainCount]

classifier.fit(trainImageNDArray, trainLabelNDArray)

testCount = int((imageCount / 4))

testImageNDArray = imageNDArray[testCount:]

testLabelList = labelNDArray[testCount:]

predictionLabelList = classifier.predict(testImageNDArray)

print("성능 리포트 :\n%s\n" %(metrics.classification_report(testLabelList, predictionLabelList)))

"""

성능 리포트 :

precision recall f1-score support

0 1.00 0.99 1.00 131

1 0.99 1.00 1.00 137

2 1.00 1.00 1.00 131

3 0.99 0.95 0.97 136

4 0.99 0.98 0.99 139

5 0.98 0.99 0.99 136

6 0.99 1.00 1.00 138

7 0.99 1.00 1.00 134

8 0.96 0.99 0.98 130

9 0.99 0.99 0.99 136

accuracy 0.99 1348

macro avg 0.99 0.99 0.99 1348

weighted avg 0.99 0.99 0.99 1348

"""

[PYTHON/SKLEARN] 로직스틱 회귀 모델 사용하기

■ 로직스틱 회귀 모델을 사용하는 방법을 보여준다. ▶ main.py


import sklearn.datasets as datasets
import sklearn.linear_model as linear_model
import sklearn.metrics as metrics

mnistBunch   = datasets.load_digits()
imageNDArray = mnistBunch.images
imageCount   = len(imageNDArray)
imageNDArray = imageNDArray.reshape(len(imageNDArray), -1)
labelNDArray = mnistBunch.target
classifier   = linear_model.LogisticRegression(C = 0.01, penalty = 'l2', tol = 0.01)

trainCount = int((imageCount / 4) * 3)

trainImageNDArray = imageNDArray[:trainCount]
trainLabelNDArray = labelNDArray[:trainCount]

classifier.fit(trainImageNDArray, trainLabelNDArray)

testCount = int((imageCount / 4))

testImageNDArray = imageNDArray[testCount:]
testLabelList    = labelNDArray[testCount:]

predictionLabelList = classifier.predict(testImageNDArray)

print("성능 리포트 :\n%s\n" %(metrics.classification_report(testLabelList, predictionLabelList)))

"""
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
성능 리포트 :
              precision    recall  f1-score   support

           0       1.00      0.98      0.99       131
           1       0.97      0.96      0.96       137
           2       1.00      1.00      1.00       131
           3       0.98      0.92      0.95       136
           4       0.99      0.97      0.98       139
           5       0.96      0.99      0.98       136
           6       0.99      0.99      0.99       138
           7       0.97      0.99      0.98       134
           8       0.95      0.97      0.96       130
           9       0.94      0.98      0.96       136

    accuracy                           0.97      1348
   macro avg       0.98      0.97      0.97      1348
weighted avg       0.98      0.97      0.97      1348
"""

import sklearn.datasets as datasets

import sklearn.linear_model as linear_model

import sklearn.metrics as metrics

mnistBunch = datasets.load_digits()

imageNDArray = mnistBunch.images

imageCount = len(imageNDArray)

imageNDArray = imageNDArray.reshape(len(imageNDArray), -1)

labelNDArray = mnistBunch.target

classifier = linear_model.LogisticRegression(C = 0.01, penalty = 'l2', tol = 0.01)

trainCount = int((imageCount / 4) * 3)

trainImageNDArray = imageNDArray[:trainCount]

trainLabelNDArray = labelNDArray[:trainCount]

classifier.fit(trainImageNDArray, trainLabelNDArray)

testCount = int((imageCount / 4))

testImageNDArray = imageNDArray[testCount:]

testLabelList = labelNDArray[testCount:]

predictionLabelList = classifier.predict(testImageNDArray)

print("성능 리포트 :\n%s\n" %(metrics.classification_report(testLabelList, predictionLabelList)))

"""

STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:

https://scikit-learn.org/stable/modules/preprocessing.html

Please also refer to the documentation for alternative solver options:

https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

n_iter_i = _check_optimize_result(

성능 리포트 :

precision recall f1-score support

0 1.00 0.98 0.99 131

1 0.97 0.96 0.96 137

2 1.00 1.00 1.00 131

3 0.98 0.92 0.95 136

4 0.99 0.97 0.98 139

5 0.96 0.99 0.98 136

6 0.99 0.99 0.99 138

7 0.97 0.99 0.98 134

8 0.95 0.97 0.96 130

9 0.94 0.98 0.96 136

accuracy 0.97 1348

macro avg 0.98 0.97 0.97 1348

weighted avg 0.98 0.97 0.97 1348

"""

▶ requirements.txt


cycler==0.11.0
fonttools==4.34.4
joblib==1.1.0
kiwisolver==1.4.4
matplotlib==3.5.3
numpy==1.23.1
packaging==21.3
Pillow==9.2.0
pip==22.0.4
pyparsing==3.0.9
python-dateutil==2.8.2
scikit-learn==1.1.2
scipy==1.9.0
setuptools==58.1.0
six==1.16.0
sklearn==0.0
threadpoolctl==3.1.0

cycler==0.11.0

fonttools==4.34.4

joblib==1.1.0

kiwisolver==1.4.4

matplotlib==3.5.3

numpy==1.23.1

packaging==21.3

Pillow==9.2.0

pip==22.0.4

pyparsing==3.0.9

python-dateutil==2.8.2

scikit-learn==1.1.2

scipy==1.9.0

setuptools==58.1.0

six==1.16.0

sklearn==0.0

threadpoolctl==3.1.0