SMOTE RF MLP demo use cross_val_score to find best argument 处理不平衡数据的demo代码 先做smote处理 再用交叉验证找到最好的模型参数 实践表明MLP更好
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 | # _*_coding:UTF-8_*_from sklearn.externals.six import StringIO from sklearn import treeimport pydot import sklearnimport numpy as npimport sysimport pickleimport osfrom sklearn.cross_validation import train_test_splitimport sklearn.ensemblefrom sklearn.model_selection import cross_val_score# from sklearn.ensemble import ExtraTreesClassifierfrom sklearn import preprocessingimport pdbfrom sklearn.neural_network import MLPClassifierfrom sklearn.metrics import classification_reportfrom sklearn.model_selection import StratifiedShuffleSplitimport osimport collectionsimport imblearndef iterbrowse(path): for home, dirs, files in os.walk(path): for filename in files: yield os.path.join(home, filename)def get_data(filename): white_verify = [] with open(filename) as f: lines = f.readlines() data = {} for line in lines: a = line.split("\t") if len(a) != 78: print(line) raise Exception("fuck") white_verify.append([float(n) for n in a[3:]]) return white_verify# 显示测试结果def show_cm(cm, labels): # Compute percentanges percent = (cm * 100.0) / np.array(np.matrix(cm.sum(axis=1)).T) print 'Confusion Matrix Stats' for i, label_i in enumerate(labels): for j, label_j in enumerate(labels): print "%s/%s: %.2f%% (%d/%d)" % (label_i, label_j, (percent[i][j]), cm[i][j], cm[i].sum())def save_model_to_disk(name, model, model_dir='.'): serialized_model = pickle.dumps(model, protocol=pickle.HIGHEST_PROTOCOL) model_path = os.path.join(model_dir, name + '.model') print 'Storing Serialized Model to Disk (%s:%.2fMeg)' % (name, len(serialized_model) / 1024.0 / 1024.0) open(model_path, 'wb').write(serialized_model)wanted_feature = {15, #正向头部直方图中位数,-----H12, # 正向头部直方图最小,-----H14, #正向头部直方图平均数,-----H13, # 正向头部直方图最大,-----H16, #正向头部直方图标准差, -----H52, #反向头部直方图不同长度类型数, -----M51, #反向头部直方图平均数, --------------H47, #反向头部直方图最小,--------------H48, #反向头部直方图最大,--------------H49, #反向头部直方图平均数,--------------H50, #反向头部直方图平均数,--------------H23, #正向载荷直方图最大, --------------H24, #正向载荷直方图平均值,--------------H25, #正向载荷直方图中位数,--------------H26, #正向载荷直方图标准差,--------------H17, #正向头部直方图不同长度类型数,---H46, #反向包文的时间间隔（时间/包数）, ----H28, #正向载荷直方图小于128字节数个数,----H29, #正向载荷直方图≥128、＜512字节数个数,----H30, #正向载荷直方图≥512、＜1024字节数个数,----H31, #正向载荷直方图＞1024字节数个数,----H57, #x反向载荷直方图最小,--------------H60, #反向载荷直方图中位数,--------------H59, #反向载荷直方图平均值, --------------H61, #反向载荷直方图标准差,--------------H58, #反向载荷直方图最大,--------------H42, #反向当前流的数据包数量,21, #正向头部直方图大于等于40字节数个数, -----------------------H56, #反向头部直方图大于等于40字节数个数,------------------------H65, #反向载荷直方图＞1024字节数个数,------------------------H63, #反向载荷直方图小于128字节数个数,------------------------H64, #反向载荷直方图≥128、＜512字节数个数, ------------------------H66, #反向载荷直方图≥512、＜1024字节数个数,------------------------H}unwanted_features = {6, 7, 8, 41,42,43,67,68,69,70,71,72,73,74,75}def get_wanted_data(x): """ return x """ ans = [] for item in x: #row = [data for i, data in enumerate(item) if i+6 in wanted_feature] row = [data for i, data in enumerate(item) if i+6 not in unwanted_features] ans.append(row) #assert len(row) == len(wanted_feature) assert len(row) == len(x[0])-len(unwanted_features) return ansif __name__ == '__main__': # pdb.set_trace() neg_file = "cc_data/black/black_all.txt" pos_file = "cc_data/white/white_all.txt" X = [] y = [] if os.path.isfile(pos_file): if pos_file.endswith('.txt'): pos_set = np.genfromtxt(pos_file) elif pos_file.endswith('.npy'): pos_set = np.load(pos_file) X.extend(pos_set) y += [0] * len(pos_set) print("len of white X:", len(X)) l = len(X) if os.path.isfile(neg_file): if neg_file.endswith('.txt'): neg_set = np.genfromtxt(neg_file) elif neg_file.endswith('.npy'): neg_set = np.load(neg_file) #X.extend(list(neg_set)*5) #y += [1] * (5*len(neg_set)) X.extend(neg_set) y += [1] * len(neg_set) print("len of black X:", len(X)-l) print("len of X:", len(X)) print("X sample:", X[:3]) print("len of y:", len(y)) print("y sample:", y[:3]) X = [x[3:] for x in X] X = get_wanted_data(X) print("filtered X sample:", X[:1]) black_verify = [] for f in iterbrowse("todo/top"): print(f) black_verify += get_data(f) #ValueError: operands could not be broadcast together with shapes (1,74) (75,) (1,74) black_verify = get_wanted_data(black_verify) print(black_verify) black_verify_labels = [1]*len(black_verify) white_verify = get_data("todo/white_verify.txt") white_verify = get_wanted_data(white_verify) print(white_verify) white_verify_labels = [0]*len(white_verify) unknown_verify = get_data("todo/pek_feature74.txt") unknown_verify = get_wanted_data(unknown_verify) print(unknown_verify) black_verify2 = get_data("todo/x_rat.txt") black_verify2 = get_wanted_data(black_verify2) print(black_verify2) black_verify_labels2 = [1]*len(black_verify2) """ # Smote use KNN, so use standard scaler """ from sklearn import preprocessing scaler = preprocessing.StandardScaler().fit(X) #scaler = preprocessing.MinMaxScaler().fit(X) X = scaler.transform(X) print("standard X sample:", X[:3]) black_verify = scaler.transform(black_verify) print(black_verify) white_verify = scaler.transform(white_verify) print(white_verify) unknown_verify = scaler.transform(unknown_verify) print(unknown_verify) black_verify2 = scaler.transform(black_verify2) print(black_verify2) # ValueError: operands could not be broadcast together with shapes (756140,75) (42,75) (756140,75) for i in range(200): # add weight 加大必须检出数据的权重，因为只有10+个样本所以x200增多 X = np.concatenate((X, black_verify)) y += black_verify_labels y = np.array(y) labels = ['white', 'CC'] #if True: for depth in (128, 64, 32): print "***"*20 print "hidden_layer_sizes=>", depth sss = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=42) for train_index, test_index in sss.split(X, y): X_train, X_test = X[train_index], X[test_index] y_train, y_test = y[train_index], y[test_index] #ratio_of_train = 0.8 #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=(1 - ratie_of_train)) print "smote before:" print(sorted(collections.Counter(y_train).items())) print(sorted(collections.Counter(y_test).items())) from imblearn.over_sampling import SMOTE X_train, y_train = SMOTE().fit_sample(X_train, y_train) print "smote after:" print(sorted(collections.Counter(y_train).items())) X_test2, y_test2 = SMOTE().fit_sample(X_test, y_test) # X_train=preprocessing.normalize(X_train) # X_test=preprocessing.normalize(X_test) """ from sklearn.linear_model import LogisticRegression clf = LogisticRegression(C=0.1, penalty='l2', tol=0.01) import xgboost as xgb clf = xgb.XGBClassifier(learning_rate=0.1,n_estimators=50,max_depth=6, objective= 'binary:logistic',nthread=40,scale_pos_weight=0.02,seed=666) clf = sklearn.ensemble.RandomForestClassifier(n_estimators=100, n_jobs=10, max_depth=3, random_state=666, oob_score=True) """ clf = MLPClassifier(batch_size=128, learning_rate='adaptive', max_iter=1024, hidden_layer_sizes=(depth,), random_state=666) clf.fit(X_train, y_train) print "test confusion_matrix:" # print clf.feature_importances_ y_pred = clf.predict(X_test) print(sklearn.metrics.confusion_matrix(y_test, y_pred)) print(classification_report(y_test, y_pred)) print "test confusion_matrix (SMOTE):" y_pred2 = clf.predict(X_test2) print(sklearn.metrics.confusion_matrix(y_test2, y_pred2)) print(classification_report(y_test2, y_pred2)) print "all confusion_matrix:" y_pred = clf.predict(X) print(sklearn.metrics.confusion_matrix(y, y_pred)) print(classification_report(y, y_pred)) print "black verify confusion_matrix:" black_verify_pred = clf.predict(black_verify) print(black_verify_pred) print(classification_report(black_verify_labels, black_verify_pred)) print "black verify2 confusion_matrix:" black_verify_pred2 = clf.predict(black_verify2) print(black_verify_pred2) print(classification_report(black_verify_labels2, black_verify_pred2)) print "white verify confusion_matrix:" white_verify_pred = clf.predict(white_verify) print(white_verify_pred) print(classification_report(white_verify_labels, white_verify_pred)) print("unknown_verify:") print(clf.predict(unknown_verify)) print "hidden_layer_sizes=>", depth print "***"*20 else: #clf = pickle.loads(open("mpl-acc97-recall98.pkl", 'rb').read()) clf = pickle.loads(open("mlp-add-topx10.model", 'rb').read()) y_pred = clf.predict(X) print(sklearn.metrics.confusion_matrix(y, y_pred)) print(classification_report(y, y_pred)) import sys sys.exit(0) """ dot_data = StringIO() tree.export_graphviz(clf, out_file=dot_data) graph = pydot.graph_from_dot_data(dot_data.getvalue()) graph.write_pdf("iris.pdf") """ model_name = "rf_smote" save_model_to_disk(model_name, clf) # print clf.oob_score_ scores = cross_val_score(clf, X, y, cv=5) print "scores:" print scores |
实验结果：
MLP 隐藏层神经元个数 128
test confusion_matrix (SMOTE): 测试数据的混淆矩阵
[[131946 120]
[ 299 131767]]
precision recall f1-score support
0 1.00 1.00 1.00 132066
1 1.00 1.00 1.00 132066
avg / total 1.00 1.00 1.00 264132
all confusion_matrix: 整体数据混淆矩阵
[[659846 483]
[ 52 32474]]
precision recall f1-score support
0 1.00 1.00 1.00 660329
1 0.99 1.00 0.99 32526
avg / total 1.00 1.00 1.00 692855
black verify confusion_matrix: #需要必须检测出来的样本 OK 都检出了
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1]
precision recall f1-score support
1 1.00 1.00 1.00 42
avg / total 1.00 1.00 1.00 42
black verify2 confusion_matrix: # 现网是黑的数据，很难区分的
[0 0 0 0 0 0 0 1 1 1 1]
precision recall f1-score support
0 0.00 0.00 0.00 0
1 1.00 0.36 0.53 11
avg / total 1.00 0.36 0.53 11
white verify confusion_matrix: # 现网是白的数据 很难区分的
[1 1 1 1 0 0 0]
precision recall f1-score support
0 1.00 0.43 0.60 7
1 0.00 0.00 0.00 0
avg / total 1.00 0.43 0.60 7
unknown_verify: # 现网采集的 有好些是黑的数据 希望检出率高 但是不能过高
[1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 0 1
0 1 1 1 1 0 0 1 0 1 0 0 0 1 1 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1] 现网验证检出率还不错
隐藏层为64
************************************************************
hidden_layer_sizes=> 64
smote before:
[(0, 528263), (1, 26021)]
[(0, 132066), (1, 6505)]
smote after:
[(0, 528263), (1, 528263)]
test confusion_matrix:
[[131912 154]
[ 24 6481]]
precision recall f1-score support
0 1.00 1.00 1.00 132066
1 0.98 1.00 0.99 6505
avg / total 1.00 1.00 1.00 138571
test confusion_matrix (SMOTE):
[[131912 154]
[ 193 131873]]
precision recall f1-score support
0 1.00 1.00 1.00 132066
1 1.00 1.00 1.00 132066
avg / total 1.00 1.00 1.00 264132
all confusion_matrix:
[[659566 763]
[ 34 32492]]
precision recall f1-score support
0 1.00 1.00 1.00 660329
1 0.98 1.00 0.99 32526
avg / total 1.00 1.00 1.00 692855
black verify confusion_matrix:
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1]
precision recall f1-score support
1 1.00 1.00 1.00 42
avg / total 1.00 1.00 1.00 42
black verify2 confusion_matrix:
[0 0 0 0 0 0 0 1 1 1 1]
precision recall f1-score support
0 0.00 0.00 0.00 0
1 1.00 0.36 0.53 11
avg / total 1.00 0.36 0.53 11
white verify confusion_matrix:
[1 1 0 1 0 0 0]
precision recall f1-score support
0 1.00 0.57 0.73 7
1 0.00 0.00 0.00 0
avg / total 1.00 0.57 0.73 7
unknown_verify:
[1 0 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 1 0 0 1 0 1 1 0 1
0 0 1 1 1 0 0 1 1 1 1 0 0 1 1 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1]
看起来也还不错！
看看随机森林的表现：depth=15,100棵树
test confusion_matrix:
[[132045 21]
[ 16 4818]]
precision recall f1-score support
0 1.00 1.00 1.00 132066
1 1.00 1.00 1.00 4834
avg / total 1.00 1.00 1.00 136900
test confusion_matrix (SMOTE):
[[132045 21]
[ 246 131820]]
precision recall f1-score support
0 1.00 1.00 1.00 132066
1 1.00 1.00 1.00 132066
avg / total 1.00 1.00 1.00 264132
all confusion_matrix:
[[660227 102]
[ 29 24139]]
precision recall f1-score support
0 1.00 1.00 1.00 660329
1 1.00 1.00 1.00 24168
avg / total 1.00 1.00 1.00 684497
black verify confusion_matrix:
[0 1 0 0 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 0 0 1 0 0 1 1 1 0 0 0 0 1 1 1 1 1 1
1 1 1 1 1] 这个是必须要全部检出的
precision recall f1-score support
0 0.00 0.00 0.00 0
1 1.00 0.67 0.80 42
avg / total 1.00 0.67 0.80 42
white verify confusion_matrix:
[0 0 0 0 0 0 1]
precision recall f1-score support
0 1.00 0.86 0.92 7
1 0.00 0.00 0.00 0
avg / total 1.00 0.86 0.92 7
unknown_verify: 现网的检出太低了！过拟合比较严重。。。。
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]
depth=14的一个
test confusion_matrix:
[[132038 28]
[ 16 4818]]
precision recall f1-score support
0 1.00 1.00 1.00 132066
1 0.99 1.00 1.00 4834
avg / total 1.00 1.00 1.00 136900
test confusion_matrix (SMOTE):
[[132038 28]
[ 257 131809]]
precision recall f1-score support
0 1.00 1.00 1.00 132066
1 1.00 1.00 1.00 132066
avg / total 1.00 1.00 1.00 264132
all confusion_matrix:
[[660220 109]
[ 34 24134]]
precision recall f1-score support
0 1.00 1.00 1.00 660329
1 1.00 1.00 1.00 24168
avg / total 1.00 1.00 1.00 684497
black verify confusion_matrix:
[1 1 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 0 1 0 0 0 1 1 1 0 0 0 0 1 1 1 1 1 1
1 1 1 1 1]
precision recall f1-score support
0 0.00 0.00 0.00 0
1 1.00 0.64 0.78 42
avg / total 1.00 0.64 0.78 42
white verify confusion_matrix:
[0 0 0 0 0 1 1]
precision recall f1-score support
0 1.00 0.71 0.83 7
1 0.00 0.00 0.00 0
avg / total 1.00 0.71 0.83 7
unknown_verify:
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1]
稍微好点，
depth=13的
test confusion_matrix (SMOTE):
[[132037 29]
[ 301 131765]]
precision recall f1-score support
0 1.00 1.00 1.00 132066
1 1.00 1.00 1.00 132066
avg / total 1.00 1.00 1.00 264132
all confusion_matrix:
[[660217 112]
[ 36 24132]]
precision recall f1-score support
0 1.00 1.00 1.00 660329
1 1.00 1.00 1.00 24168
avg / total 1.00 1.00 1.00 684497
black verify confusion_matrix:
[0 1 0 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 0 0 0 0 1 0 1 1 1 1
0 1 1 1 1]
precision recall f1-score support
0 0.00 0.00 0.00 0
1 1.00 0.55 0.71 42
avg / total 1.00 0.55 0.71 42
white verify confusion_matrix:
[0 0 0 0 0 1 1]
precision recall f1-score support
0 1.00 0.71 0.83 7
1 0.00 0.00 0.00 0
avg / total 1.00 0.71 0.83 7
unknown_verify:
[0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
也差不多，再调整depth也差不多。
整体表现，没有MLP好！
看看逻辑回归的：
test confusion_matrix (SMOTE):
[[114699 17367]
[ 11921 120145]]
precision recall f1-score support
0 0.91 0.87 0.89 132066
1 0.87 0.91 0.89 132066
avg / total 0.89 0.89 0.89 264132
all confusion_matrix:
[[573083 87246]
[ 2877 29649]]
precision recall f1-score support
0 1.00 0.87 0.93 660329
1 0.25 0.91 0.40 32526
avg / total 0.96 0.87 0.90 692855
black verify confusion_matrix:
[1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0
1 1 0 1 1]
precision recall f1-score support
0 0.00 0.00 0.00 0
1 1.00 0.88 0.94 42
avg / total 1.00 0.88 0.94 42
black verify2 confusion_matrix:
[1 1 0 0 0 0 0 1 1 1 1]
precision recall f1-score support
0 0.00 0.00 0.00 0
1 1.00 0.55 0.71 11
avg / total 1.00 0.55 0.71 11
white verify confusion_matrix:
[1 1 1 1 1 1 1]
precision recall f1-score support
0 0.00 0.00 0.00 7
1 0.00 0.00 0.00 0
avg / total 0.00 0.00 0.00 7
unknown_verify:
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
整体精度不够。才0.25.。。。
看看xgboost的：
[[132018 48]
[ 11 6494]]
precision recall f1-score support
0 1.00 1.00 1.00 132066
1 0.99 1.00 1.00 6505
avg / total 1.00 1.00 1.00 138571
test confusion_matrix (SMOTE):
[[132018 48]
[ 82 131984]]
precision recall f1-score support
0 1.00 1.00 1.00 132066
1 1.00 1.00 1.00 132066
avg / total 1.00 1.00 1.00 264132
all confusion_matrix:
[[660134 195]
[ 29 32497]]
precision recall f1-score support
0 1.00 1.00 1.00 660329
1 0.99 1.00 1.00 32526
avg / total 1.00 1.00 1.00 692855
black verify confusion_matrix:
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1]
precision recall f1-score support
1 1.00 1.00 1.00 42
avg / total 1.00 1.00 1.00 42
black verify2 confusion_matrix:
[0 0 0 0 0 0 0 1 0 1 1]
precision recall f1-score support
0 0.00 0.00 0.00 0
1 1.00 0.27 0.43 11
avg / total 1.00 0.27 0.43 11
white verify confusion_matrix:
[0 0 1 0 1 0 1]
precision recall f1-score support
0 1.00 0.57 0.73 7
1 0.00 0.00 0.00 0
avg / total 1.00 0.57 0.73 7
unknown_verify:
[0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0
0 1 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0]
整体看来比随机森林好！
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