《Keras 3 ：AI 使用 Autoencoder 进行时间序列异常检测》

作者：pavithrasv
创建日期：2020/05/31
最后修改时间：2020/05/31
描述：使用自动编码器检测时间序列中的异常。

（i） 此示例使用 Keras 3

 在 Colab 中查看 

 GitHub 源

---

介绍

此脚本演示了如何使用重建卷积 autoencoder 模型来检测时间序列数据中的异常。

---

设置

import numpy as np
import pandas as pd
import keras
from keras import layers
from matplotlib import pyplot as plt

---

加载数据

我们将使用 Numenta Anomaly Benchmark （NAB） 数据集。它提供人工 时间序列数据，其中包含标记的异常行为周期。数据为 有序、带时间戳、单值指标。

我们将使用该文件进行训练，将该文件用于测试。此数据集的简单性 使我们能够有效地演示异常检测。art_daily_small_noise.csvart_daily_jumpsup.csv

master_url_root = "https://raw.githubusercontent.com/numenta/NAB/master/data/"

df_small_noise_url_suffix = "artificialNoAnomaly/art_daily_small_noise.csv"
df_small_noise_url = master_url_root + df_small_noise_url_suffix
df_small_noise = pd.read_csv(
    df_small_noise_url, parse_dates=True, index_col="timestamp"
)

df_daily_jumpsup_url_suffix = "artificialWithAnomaly/art_daily_jumpsup.csv"
df_daily_jumpsup_url = master_url_root + df_daily_jumpsup_url_suffix
df_daily_jumpsup = pd.read_csv(
    df_daily_jumpsup_url, parse_dates=True, index_col="timestamp"
)

---

快速浏览数据

print(df_small_noise.head())

print(df_daily_jumpsup.head())

 value timestamp 2014-04-01 00:00:00 18.324919 2014-04-01 00:05:00 21.970327 2014-04-01 00:10:00 18.624806 2014-04-01 00:15:00 21.953684 2014-04-01 00:20:00 21.909120 value timestamp 2014-04-01 00:00:00 19.761252 2014-04-01 00:05:00 20.500833 2014-04-01 00:10:00 19.961641 2014-04-01 00:15:00 21.490266 2014-04-01 00:20:00 20.187739 

---

可视化数据

无异常的时间序列数据

我们将使用以下数据进行训练。

fig, ax = plt.subplots()
df_small_noise.plot(legend=False, ax=ax)
plt.show()

具有异常的时间序列数据

我们将使用以下数据进行测试，看看 数据被检测为异常。

fig, ax = plt.subplots()
df_daily_jumpsup.plot(legend=False, ax=ax)
plt.show()

---

准备训练数据

从训练时间序列数据文件中获取数据值并规范化数据。我们在 5 天内每 14 分钟一次。valuevalue

• 24 * 60 / 5 = 每天 288 个时间步
• 总共 288 * 14 = 4032 个数据点

# Normalize and save the mean and std we get,
# for normalizing test data.
training_mean = df_small_noise.mean()
training_std = df_small_noise.std()
df_training_value = (df_small_noise - training_mean) / training_std
print("Number of training samples:", len(df_training_value))

Number of training samples: 4032 

创建序列

创建序列，将 训练数据。TIME_STEPS

TIME_STEPS = 288


# Generated training sequences for use in the model.
def create_sequences(values, time_steps=TIME_STEPS):
    output = []
    for i in range(len(values) - time_steps + 1):
        output.append(values[i : (i + time_steps)])
    return np.stack(output)


x_train = create_sequences(df_training_value.values)
print("Training input shape: ", x_train.shape)

Training input shape: (3745, 288, 1) 

---

构建模型

我们将构建一个卷积重建自动编码器模型。模型将 take shape 的 input 并返回 output 的在本例中，为 288 且为 1。(batch_size, sequence_length, num_features)sequence_lengthnum_features

model = keras.Sequential(
    [
        layers.Input(shape=(x_train.shape[1], x_train.shape[2])),
        layers.Conv1D(
            filters=32,
            kernel_size=7,
            padding="same",
            strides=2,
            activation="relu",
        ),
        layers.Dropout(rate=0.2),
        layers.Conv1D(
            filters=16,
            kernel_size=7,
            padding="same",
            strides=2,
            activation="relu",
        ),
        layers.Conv1DTranspose(
            filters=16,
            kernel_size=7,
            padding="same",
            strides=2,
            activation="relu",
        ),
        layers.Dropout(rate=0.2),
        layers.Conv1DTranspose(
            filters=32,
            kernel_size=7,
            padding="same",
            strides=2,
            activation="relu",
        ),
        layers.Conv1DTranspose(filters=1, kernel_size=7, padding="same"),
    ]
)
model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss="mse")
model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape              ┃    Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 144, 32)           │        256 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ dropout (Dropout)               │ (None, 144, 32)           │          0 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ conv1d_1 (Conv1D)               │ (None, 72, 16)            │      3,600 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ conv1d_transpose                │ (None, 144, 16)           │      1,808 │
│ (Conv1DTranspose)               │                           │            │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ dropout_1 (Dropout)             │ (None, 144, 16)           │          0 │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ conv1d_transpose_1              │ (None, 288, 32)           │      3,616 │
│ (Conv1DTranspose)               │                           │            │
├─────────────────────────────────┼───────────────────────────┼────────────┤
│ conv1d_transpose_2              │ (None, 288, 1)            │        225 │
│ (Conv1DTranspose)               │                           │            │
└─────────────────────────────────┴───────────────────────────┴────────────┘

 Total params: 9,505 (37.13 KB)

 Trainable params: 9,505 (37.13 KB)

 Non-trainable params: 0 (0.00 B)

---

训练模型

请注意，我们同时使用 input 和 target 因为这是一个重建模型。x_train

history = model.fit(
    x_train,
    x_train,
    epochs=50,
    batch_size=128,
    validation_split=0.1,
    callbacks=[
        keras.callbacks.EarlyStopping(monitor="val_loss", patience=5, mode="min")
    ],
)

Epoch 1/50 26/27 ━━━━━━━━━━━━━━━━━━━[37m━ 0s 4ms/step - loss: 0.8419 WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1700346169.474466 1961179 device_compiler.h:187] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process. 27/27 ━━━━━━━━━━━━━━━━━━━━ 10s 187ms/step - loss: 0.8262 - val_loss: 0.2280 Epoch 2/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.1485 - val_loss: 0.0513 Epoch 3/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0659 - val_loss: 0.0389 Epoch 4/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0563 - val_loss: 0.0341 Epoch 5/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0489 - val_loss: 0.0298 Epoch 6/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0434 - val_loss: 0.0272 Epoch 7/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0386 - val_loss: 0.0258 Epoch 8/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0349 - val_loss: 0.0241 Epoch 9/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0319 - val_loss: 0.0230 Epoch 10/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0297 - val_loss: 0.0236 Epoch 11/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0279 - val_loss: 0.0233 Epoch 12/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0264 - val_loss: 0.0225 Epoch 13/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0255 - val_loss: 0.0228 Epoch 14/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0245 - val_loss: 0.0223 Epoch 15/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0236 - val_loss: 0.0234 Epoch 16/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0227 - val_loss: 0.0256 Epoch 17/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0219 - val_loss: 0.0240 Epoch 18/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0214 - val_loss: 0.0245 Epoch 19/50 27/27 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.0207 - val_loss: 0.0250 

让我们绘制训练和验证损失图，看看训练进展如何。

plt.plot(history.history["loss"], label="Training Loss")
plt.plot(history.history["val_loss"], label="Validation Loss")
plt.legend()
plt.show()

---

检测异常

我们将通过确定模型重建的程度来检测异常 输入数据。

1. 查找训练样本的 MAE 损失。
2. 查找最大 MAE 损失值。这是我们的模型尝试过的最糟糕的一次 重建样本。我们将 this 设为 for anomaly 检波。threshold
3. 如果样本的重建损失大于此值，那么我们可以推断模型看到了它所没有看到的模式 熟悉。我们将此示例标记为 .thresholdanomaly

# Get train MAE loss.
x_train_pred = model.predict(x_train)
train_mae_loss = np.mean(np.abs(x_train_pred - x_train), axis=1)

plt.hist(train_mae_loss, bins=50)
plt.xlabel("Train MAE loss")
plt.ylabel("No of samples")
plt.show()

# Get reconstruction loss threshold.
threshold = np.max(train_mae_loss)
print("Reconstruction error threshold: ", threshold)

 118/118 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step 

Reconstruction error threshold: 0.1232659916089631 

比较 reconstruction

为了好玩，让我们看看我们的模型是如何重新构造第一个样本的。 这是我们训练数据集第 1 天的 288 个时间步长。

# Checking how the first sequence is learnt
plt.plot(x_train[0])
plt.plot(x_train_pred[0])
plt.show()

准备测试数据

df_test_value = (df_daily_jumpsup - training_mean) / training_std
fig, ax = plt.subplots()
df_test_value.plot(legend=False, ax=ax)
plt.show()

# Create sequences from test values.
x_test = create_sequences(df_test_value.values)
print("Test input shape: ", x_test.shape)

# Get test MAE loss.
x_test_pred = model.predict(x_test)
test_mae_loss = np.mean(np.abs(x_test_pred - x_test), axis=1)
test_mae_loss = test_mae_loss.reshape((-1))

plt.hist(test_mae_loss, bins=50)
plt.xlabel("test MAE loss")
plt.ylabel("No of samples")
plt.show()

# Detect all the samples which are anomalies.
anomalies = test_mae_loss > threshold
print("Number of anomaly samples: ", np.sum(anomalies))
print("Indices of anomaly samples: ", np.where(anomalies))

Test input shape: (3745, 288, 1) 118/118 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step 

Number of anomaly samples: 394 Indices of anomaly samples: (array([1654, 2702, 2703, 2704, 2705, 2706, 2707, 2708, 2709, 2710, 2711, 2712, 2713, 2714, 2715, 2716, 2717, 2718, 2719, 2720, 2721, 2722, 2723, 2724, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804, 2805, 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 2821, 2822, 2823, 2824, 2825, 2826, 2827, 2828, 2829, 2830, 2831, 2832, 2833, 2834, 2835, 2836, 2837, 2838, 2839, 2840, 2841, 2842, 2843, 2844, 2845, 2846, 2847, 2848, 2849, 2850, 2851, 2852, 2853, 2854, 2855, 2856, 2857, 2858, 2859, 2860, 2861, 2862, 2863, 2864, 2865, 2866, 2867, 2868, 2869, 2870, 2871, 2872, 2873, 2874, 2875, 2876, 2877, 2878, 2879, 2880, 2881, 2882, 2883, 2884, 2885, 2886, 2887, 2888, 2889, 2890, 2891, 2892, 2893, 2894, 2895, 2896, 2897, 2898, 2899, 2900, 2901, 2902, 2903, 2904, 2905, 2906, 2907, 2908, 2909, 2910, 2911, 2912, 2913, 2914, 2915, 2916, 2917, 2918, 2919, 2920, 2921, 2922, 2923, 2924, 2925, 2926, 2927, 2928, 2929, 2930, 2931, 2932, 2933, 2934, 2935, 2936, 2937, 2938, 2939, 2940, 2941, 2942, 2943, 2944, 2945, 2946, 2947, 2948, 2949, 2950, 2951, 2952, 2953, 2954, 2955, 2956, 2957, 2958, 2959, 2960, 2961, 2962, 2963, 2964, 2965, 2966, 2967, 2968, 2969, 2970, 2971, 2972, 2973, 2974, 2975, 2976, 2977, 2978, 2979, 2980, 2981, 2982, 2983, 2984, 2985, 2986, 2987, 2988, 2989, 2990, 2991, 2992, 2993, 2994, 2995, 2996, 2997, 2998, 2999, 3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010, 3011, 3012, 3013, 3014, 3015, 3016, 3017, 3018, 3019, 3020, 3021, 3022, 3023, 3024, 3025, 3026, 3027, 3028, 3029, 3030, 3031, 3032, 3033, 3034, 3035, 3036, 3037, 3038, 3039, 3040, 3041, 3042, 3043, 3044, 3045, 3046, 3047, 3048, 3049, 3050, 3051, 3052, 3053, 3054, 3055, 3056, 3057, 3058, 3059, 3060, 3061, 3062, 3063, 3064, 3065, 3066, 3067, 3068, 3069, 3070, 3071, 3072, 3073, 3074, 3075, 3076, 3077, 3078, 3079, 3080, 3081, 3082, 3083, 3084, 3085, 3086, 3087, 3088, 3089, 3090, 3091, 3092, 3093, 3094]),) 

---

情节异常

我们现在知道了数据样本的异常情况。有了这个，我们将 从原始测试数据中找到相应的。我们将 使用以下方法执行此作：timestamps

假设 time_steps = 3，我们有 10 个训练值。我们的意志 如下所示：x_train

• 0, 1, 2
• 1, 2, 3
• 2, 3, 4
• 3, 4, 5
• 4, 5, 6
• 5, 6, 7
• 6, 7, 8
• 7, 8, 9

除初始和最终 time_steps-1 数据值外，所有数据值都将出现在样本数中。因此，如果我们知道样本 [（3， 4， 5）， （4， 5， 6）， （5， 6， 7）] 是异常，我们可以说数据点 5 是异常。time_steps

# data i is an anomaly if samples [(i - timesteps + 1) to (i)] are anomalies
anomalous_data_indices = []
for data_idx in range(TIME_STEPS - 1, len(df_test_value) - TIME_STEPS + 1):
    if np.all(anomalies[data_idx - TIME_STEPS + 1 : data_idx]):
        anomalous_data_indices.append(data_idx)

让我们在原始测试数据图上叠加异常。

df_subset = df_daily_jumpsup.iloc[anomalous_data_indices]
fig, ax = plt.subplots()
df_daily_jumpsup.plot(legend=False, ax=ax)
df_subset.plot(legend=False, ax=ax, color="r")
plt.show()