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투빅스 7&8기 1주차 과제 알고리즘 - 8기 김강열
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | from scipy import io from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score from sklearn.metrics import confusion_matrix from sklearn.linear_model import LogisticRegression as lr from sklearn.decomposition import PCA import pandas as pd import numpy as np import matplotlib import matplotlib.pyplot as plt import seaborn as sns import time mnist=io.loadmat('mnist-original.mat') md=mnist['data'] #784*70000 ml=mnist['label'] #1*70000 #train_test_split은 col은 그대로고 row만 랜덤추출함. #때문에 행과 열을 바꿔준다. 전치행렬 #애초에 mat파일 자체가 행,열이 반대인듯 하다. md=md.T ml=ml.T #6:1로 train,test 분할 md_train,md_test,ml_train,ml_test = train_test_split( md,ml,test_size=1/7,random_state=1) #이미지 플롯 def plot_digits(instances, images_per_row=5, **options): size = 28 images_per_row = min(len(instances), images_per_row) images = [instance.reshape(size,size) for instance in instances] n_rows = (len(instances) - 1) // images_per_row + 1 row_images = [] n_empty = n_rows * images_per_row - len(instances) images.append(np.zeros((size, size * n_empty))) for row in range(n_rows): rimages = images[row * images_per_row : (row + 1) * images_per_row] row_images.append(np.concatenate(rimages, axis=1)) image = np.concatenate(row_images, axis=0) plt.imshow(image, cmap = matplotlib.cm.binary, **options) plt.axis("off") #Softmax def lrsoftmax(matrix): smdf = sm.decision_function(matrix) smdfe = np.exp(smdf) prob = smdfe/smdfe.sum(axis=1).reshape(len(smdfe.sum(axis=1)),1) return(prob) def pcalrsoftmax(matrix): smdf = smpca.decision_function(matrix) smdfe = np.exp(smdf) prob = smdfe/smdfe.sum(axis=1).reshape(len(smdfe.sum(axis=1)),1) return(prob) #기존 데이터 확인 plot_digits(md_train[::2100]) plt.show() #RF 분류기 print('Random Forest Classifying Start') rf_time_start = time.time() rf = RandomForestClassifier(n_estimators=100, oob_score=True, random_state=1) rf.fit(md_train,ml_train.ravel()) rfpredict = rf.predict(md_test) rfaccuracy = accuracy_score(ml_test,rfpredict) rf_time = time.time()-rf_time_start print(f'Test Label : {ml_test.ravel()}') print(f'Rf predict : {rfpredict}') print('Accuracy : %.4f Out of bag : %.4f\nTime : %.2f sec\n' %(rfaccuracy, rf.oob_score_, rf_time)) #Accuracy : 0.9697 Time : 46.58 sec #예측 플롯 ax1 = plt.subplot(111) ax1.set_title('Random Forest') ax1.set_ylabel('Predict') pd.DataFrame(rfpredict[0:50],columns=['Predict']).plot(ax=ax1) plt.show() #heatmap 플롯 rfcm = pd.DataFrame(confusion_matrix(ml_test,rfpredict)) sns.heatmap(rfcm, annot=True) plt.show() #Softmax 분류기 max_iter를 10정도만 하면 아주 빠름. print('Softmax Classifying Start') sm_time_start = time.time() sm = lr(max_iter=100).fit(md_train,ml_train.ravel()) probtrain = lrsoftmax(md_train) probtest = lrsoftmax(md_test) smpredict = sm.predict(md_test) smaccuracy = accuracy_score(ml_test,smpredict) sm_time = time.time()-sm_time_start print(f'Test Label : {ml_test.ravel()}') print(f'Sm predict : {smpredict}') print('Accuracy : %.4f\nTime : %.2f sec\n'%(smaccuracy,sm_time)) #Accuracy : 0.9143 Time : 2810.69 sec #예측 플롯 ax1 = plt.subplot(211) ax1.set_title('SoftMAX') ax1.set_ylabel('Probability') pd.DataFrame(probtest[0:50]).plot(ax=ax1) ax2 = plt.subplot(212) ax2.set_ylabel('Predict') pd.DataFrame(smpredict[0:50],columns=['Predict']).plot(ax=ax2) plt.show() #heatmap 플롯 smcm = pd.DataFrame(confusion_matrix(ml_test,smpredict)) sns.heatmap(smcm, annot=True) plt.show() #PCA print('PCA start') pca = PCA(n_components=0.96,random_state=1) pca.fit(md_train) mdn = pca.n_components_ #mdn = 179 print(f'Number of Components : {mdn}') pca = PCA(n_components=mdn,random_state=1) print('PCA fitting\n') pca.fit(md_train) md_train_reduced = pca.transform(md_train) md_test_reduced = pca.transform(md_test) md_recovered = pca.inverse_transform(md_train_reduced) #원본과 복원 비교 plt.subplot(121) plt.title('Original') plot_digits(md_train[::2100]) plt.subplot(122) plt.title('Recovered') plot_digits(md_recovered[::2100]) plt.show() #PCA - RF 분류기 print('PCA - Random Forest Classifying Start') rfpca_time_start = time.time() rfpca = RandomForestClassifier(n_estimators=100, oob_score=True, random_state=1) rfpca.fit(md_train_reduced,ml_train.ravel()) rfpcapredict = rfpca.predict(md_test_reduced) rfpcaaccuracy = accuracy_score(ml_test,rfpcapredict) rfpca_time = time.time()-rfpca_time_start print(f'Test Label : {ml_test.ravel()}') print(f'Rf predict : {rfpcapredict}') print('Accuracy : %.4f Out of bag : %.4f\nTime : %.2f sec\n' %(rfpcaaccuracy, rfpca.oob_score_, rfpca_time)) #Accuracy : 0.9466 Time : 108.29 sec #예측 플롯 ax1 = plt.subplot(111) ax1.set_title('PCA - Random Forest') ax1.set_ylabel('Predict') pd.DataFrame(rfpcapredict[0:50],columns=['Predict']).plot(ax=ax1) plt.show() #heatmap 플롯 rfpcacm = pd.DataFrame(confusion_matrix(ml_test,rfpcapredict)) sns.heatmap(rfpcacm, annot=True) plt.show() #PCA - Softmax 분류기 max_iter를 10정도만 하면 아주 빠름. print('PCA - Softmax Classifying Start') smpca_time_start = time.time() smpca = lr(max_iter=100).fit(md_train_reduced,ml_train.ravel()) probtrain_reduced = pcalrsoftmax(md_train_reduced) probtest_reduced = pcalrsoftmax(md_test_reduced) smpcapredict = smpca.predict(md_test_reduced) smpcaaccuracy = accuracy_score(ml_test,smpcapredict) smpca_time = time.time()-smpca_time_start print(f'Test Label : {ml_test.ravel()}') print(f'Sm predict : {smpcapredict}') print('Accuracy : %.4f\nTime : %.2f sec\n'%(smpcaaccuracy,smpca_time)) #Accuracy : 0.9149 Time : 925.86 sec #예측 플롯 ax1 = plt.subplot(211) ax1.set_title('PCA - SoftMAX') ax1.set_ylabel('Probability') pd.DataFrame(probtest_reduced[0:50]).plot(ax=ax1) ax2 = plt.subplot(212) ax2.set_ylabel('Predict') pd.DataFrame(smpcapredict[0:50],columns=['Predict']).plot(ax=ax2) plt.show() #heatmap 플롯 smpcacm = pd.DataFrame(confusion_matrix(ml_test,smpcapredict)) sns.heatmap(smpcacm, annot=True) plt.show() #TimeTable time_data={'Softmax':[sm_time,smpca_time], 'RandomForest':[rf_time,rfpca_time]} timetable = pd.DataFrame(time_data, columns=['RandomForest','Softmax'], index=['Time','PCA Time']) ''' RandomForest Softmax Time 46.581252 2810.692777 PCA Time 108.294196 925.859520 ''' | cs |
RandomForest Test set 예측 결과 플롯 (정확도 0.9697)
RandomForest Heatmap
Logistic Decision Function[10000개중 0~50번] - Softmax 정규화 전
[0~50번] Softmax 결과, 예측 결과 (정확도 0.9143)
random_state는 모두 1로 주었으며, PCA 결과 784개 열이 179개로 줄어들었다.
RF는 소요시간이 2배 증가했고, SM은 소요시간이 1/3로 크게 감소했다.
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