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KPIAnmalyDetect.zip
大小:1.72MB
价格:44积分
下载量:0
评分:
5.0
上传者:weixin_44245188
更新日期:2025-09-22

时间序列异常检测相关代码

资源文件列表(大概)

文件名
大小
KPIAnmalyDetect/
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KPIAnmalyDetect/.git/
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KPIAnmalyDetect/.git/COMMIT_EDITMSG
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KPIAnmalyDetect/.git/config
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KPIAnmalyDetect/.git/description
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KPIAnmalyDetect/.git/HEAD
21B
KPIAnmalyDetect/.git/hooks/
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KPIAnmalyDetect/.git/hooks/applypatch-msg.sample
478B
KPIAnmalyDetect/.git/hooks/commit-msg.sample
896B
KPIAnmalyDetect/.git/hooks/fsmonitor-watchman.sample
4.62KB
KPIAnmalyDetect/.git/hooks/post-update.sample
189B
KPIAnmalyDetect/.git/hooks/pre-applypatch.sample
424B
KPIAnmalyDetect/.git/hooks/pre-commit.sample
1.6KB
KPIAnmalyDetect/.git/hooks/pre-merge-commit.sample
416B
KPIAnmalyDetect/.git/hooks/pre-push.sample
1.34KB
KPIAnmalyDetect/.git/hooks/pre-rebase.sample
4.78KB
KPIAnmalyDetect/.git/hooks/pre-receive.sample
544B
KPIAnmalyDetect/.git/hooks/prepare-commit-msg.sample
1.46KB
KPIAnmalyDetect/.git/hooks/push-to-checkout.sample
2.72KB
KPIAnmalyDetect/.git/hooks/sendemail-validate.sample
2.25KB
KPIAnmalyDetect/.git/hooks/update.sample
3.56KB
KPIAnmalyDetect/.git/index
1.4KB
KPIAnmalyDetect/.git/info/
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KPIAnmalyDetect/.git/info/exclude
240B
KPIAnmalyDetect/.git/logs/
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KPIAnmalyDetect/.git/logs/HEAD
1.22KB
KPIAnmalyDetect/.git/logs/refs/
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KPIAnmalyDetect/.git/logs/refs/heads/
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KPIAnmalyDetect/.git/logs/refs/heads/main
704B
KPIAnmalyDetect/.git/objects/
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KPIAnmalyDetect/.git/objects/17/
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KPIAnmalyDetect/.git/objects/17/b5e7d565beafa42a2cd0953dbc1a78522fc162
1.7KB
KPIAnmalyDetect/.git/objects/1b/
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KPIAnmalyDetect/.git/objects/1b/c18208157ff47e142215efc1c8453a3a3ee420
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KPIAnmalyDetect/.git/objects/1d/
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KPIAnmalyDetect/.git/objects/1d/f235d02f5c89fde19bea84560e50d2c1aba2ed
568B
KPIAnmalyDetect/.git/objects/29/
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KPIAnmalyDetect/.git/objects/29/84a226111e9062d53a20e6efd9fee7085ad056
125.86KB
KPIAnmalyDetect/.git/objects/2c/
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KPIAnmalyDetect/.git/objects/2c/82a03e6eb026710532ce3d00a32183fe72fb61
160.67KB
KPIAnmalyDetect/.git/objects/3d/
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KPIAnmalyDetect/.git/objects/3d/ea055931304295907b3f1f4936d3f6700c23b2
45KB
KPIAnmalyDetect/.git/objects/3e/
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KPIAnmalyDetect/.git/objects/3e/6cbccba817f8567d1be18a227dcd15d6afce35
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KPIAnmalyDetect/.git/objects/47/
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KPIAnmalyDetect/.git/objects/47/b626174f346b79c5e7e7e22fe9c61249f7d800
123B
KPIAnmalyDetect/.git/objects/78/
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KPIAnmalyDetect/.git/objects/78/29311f0dc4c9b57d380a8f353196f6b7f43f96
1.21KB
KPIAnmalyDetect/.git/objects/84/
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KPIAnmalyDetect/.git/objects/84/cbbefd43a1aed9c28c95fe9e6e88deb7864230
125.94KB
KPIAnmalyDetect/.git/objects/88/
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KPIAnmalyDetect/.git/objects/88/ed2cfddd29cc16227680426828e106942e3ed1
1.82KB
KPIAnmalyDetect/.git/objects/ad/
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KPIAnmalyDetect/.git/objects/ad/3f8692d0f4dd79acc3148aff0f0420405063f8
242.75KB
KPIAnmalyDetect/.git/objects/bc/
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KPIAnmalyDetect/.git/objects/bc/e24835b88393abd6512775d9ccc9acf790b0e9
909B
KPIAnmalyDetect/.git/objects/c7/
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KPIAnmalyDetect/.git/objects/c7/45fcee35a90c0de27e1db55859d9d89fc32d8e
620B
KPIAnmalyDetect/.git/objects/d8/
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KPIAnmalyDetect/.git/objects/d8/141f7fd4aabbde9f48c957fcb5fbe2a28b289f
503B
KPIAnmalyDetect/.git/objects/db/
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KPIAnmalyDetect/.git/objects/db/730e30b58ddcda997728b68a48efd528018ee0
742B
KPIAnmalyDetect/.git/objects/e0/
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KPIAnmalyDetect/.git/objects/e0/119ddd1d1903a584450a2e0ddfb2d89987a8c3
118.69KB
KPIAnmalyDetect/.git/objects/e3/
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KPIAnmalyDetect/.git/objects/e3/c126db3fd114f76b700645fbc6267e8b826347
1.17KB
KPIAnmalyDetect/.git/objects/e4/
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KPIAnmalyDetect/.git/objects/e4/0ca1fb03d838e55163c783864ae9058d424087
116.76KB
KPIAnmalyDetect/.git/objects/info/
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KPIAnmalyDetect/.git/objects/pack/
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KPIAnmalyDetect/.git/refs/
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KPIAnmalyDetect/.git/refs/heads/
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KPIAnmalyDetect/.git/refs/heads/main
41B
KPIAnmalyDetect/.git/refs/tags/
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KPIAnmalyDetect/box.py
2.64KB
KPIAnmalyDetect/data.csv
425.83KB
KPIAnmalyDetect/data_balance.csv
569.81KB
KPIAnmalyDetect/data_box.csv
543.96KB
KPIAnmalyDetect/data_explore.ipynb
236.52KB
KPIAnmalyDetect/data_k_means.csv
462.26KB
KPIAnmalyDetect/data_normal.csv
543.96KB
KPIAnmalyDetect/demo1_svm.py
1.15KB
KPIAnmalyDetect/demo2_lstm.py
1.83KB
KPIAnmalyDetect/demo2_lstm_.py
1.91KB
KPIAnmalyDetect/demo3_k_means_.py
3.56KB
KPIAnmalyDetect/demo4_iForest.py
751B
KPIAnmalyDetect/demo5_anencoder.py
5.26KB
KPIAnmalyDetect/demo6_one-class-SVM.py
1.42KB
KPIAnmalyDetect/k_means_result.csv
336.78KB
KPIAnmalyDetect/make_balance.py
737B
KPIAnmalyDetect/normal.py
2.76KB

资源内容介绍

时间序列异常检测代码
import osimport pandas as pdimport numpy as npimport tensorflow as tffrom sklearn import preprocessingimport seaborn as snssns.set(color_codes=True)import matplotlib.pyplot as pltdef data_load(): # Load data set # 读取数据集 merged_data = pd.read_csv("data.csv", index_col='_time') print(merged_data.head()) #merged_data.plot() # Data pre-processing # Split the training and test sets merged_data=merged_data.sort_index(ascending=True) dataset_train,dataset_test=merged_data.iloc[:int(len(merged_data)*0.7)],merged_data.iloc[int(len(merged_data)*0.7):] #dataset_train.plot(figsize = (12,6)) """ Normalize data """ scaler = preprocessing.MinMaxScaler() # 归一化 X_train = pd.DataFrame(scaler.fit_transform(dataset_train), # Find the mean and standard deviation of X_train and apply them to X_train columns=dataset_train.columns, index=dataset_train.index) # Random shuffle training data X_train.sample(frac=1) X_test = pd.DataFrame(scaler.transform(dataset_test), columns=dataset_test.columns, index=dataset_test.index) return X_train,X_test# Build AutoEncoding modeldef AutoEncoder_build( X_train, act_func): tf.random.set_seed(10) # act_func = 'elu' # Input layer: model = tf.keras.Sequential() # Sequential() is a container that describes the network structure of the neural network, sequentially processing the model # First hidden layer, connected to input vector X. model.add(tf.keras.layers.Dense(10, activation=act_func, # activation function kernel_initializer='glorot_uniform', # Weight initialization kernel_regularizer=tf.keras.regularizers.l2(0.0), # Regularization to prevent overfitting input_shape=(X_train.shape[1],) ) ) model.add(tf.keras.layers.Dense(2, activation=act_func, kernel_initializer='glorot_uniform')) model.add(tf.keras.layers.Dense(10, activation=act_func, kernel_initializer='glorot_uniform')) model.add(tf.keras.layers.Dense(X_train.shape[1], kernel_initializer='glorot_uniform')) model.compile(loss='mse', optimizer='adam') # 设置编译器 print(model.summary()) tf.keras.utils.plot_model(model, show_shapes=True) return modeldef AutoEncoder_main(model, Epochs, BATCH_SIZE, validation_split): # Train model for 100 epochs, batch size of 10: # Epochs=100 # BATCH_SIZE=10 factor = 0.5 X_train,X_test=data_load() X_train_noise = X_train + factor * np.random.normal(0,1,size=X_train.shape) # 设置噪声 history = model.fit(np.array(X_train), np.array(X_train), batch_size=BATCH_SIZE, epochs=Epochs, validation_split=validation_split, # Training set ratio # shuffle=True, verbose=1) return historydef plot_AE_history(history): plt.plot(history.history['loss'], 'b', label='Training loss') plt.plot(history.history['val_loss'], 'r', label='Validation loss') plt.legend(loc='upper right') plt.xlabel('Epochs') plt.ylabel('Loss, [mse]') plt.ylim([0,.1]) plt.show() plt.close()X_train,X_test=data_load() model=AutoEncoder_build( X_train, act_func='relu')history=AutoEncoder_main(model=model, Epochs=100, BATCH_SIZE=32, validation_split=0.5)plot_AE_history(history)X_pred = model.predict(np.array(X_train))X_pred = pd.DataFrame(X_pred, columns=X_train.columns)X_pred.index = X_train.indexscored = pd.DataFrame(index=X_train.index)scored['Loss_mae'] = np.mean(np.abs(X_pred-X_train), axis = 1)plt.figure()sns.distplot(scored['Loss_mae'], bins = 10, kde= True, color = 'blue')plt.xlim([0.0,.5])plt.show()plt.close()X_pred = model.predict(np.array(X_test))X_pred = pd.DataFrame(X_pred, columns=X_test.columns)X_pred.index = X_test.indexthreshod = 0.3scored = pd.DataFrame(index=X_test.index)scored['Loss_mae'] = np.mean(np.abs(X_pred-X_test), axis = 1)scored['Threshold'] = threshodscored['Anomaly'] = scored['Loss_mae'] > scored['Threshold']scored.head()X_pred_train = model.predict(np.array(X_train))X_pred_train = pd.DataFrame(X_pred_train, columns=X_train.columns)X_pred_train.index = X_train.indexscored_train = pd.DataFrame(index=X_train.index)scored_train['Loss_mae'] = np.mean(np.abs(X_pred_train-X_train), axis = 1)scored_train['Threshold'] = threshodscored_train['Anomaly'] = scored_train['Loss_mae'] > scored_train['Threshold']scored = pd.concat([scored_train, scored])scored.plot(logy=True, figsize = (10,6), ylim = [1e-2,1e2], color = ['blue','red'])plt.show()plt.close()

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