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KerasCodeExamples.txt

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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))
png
Test input shape: (3745, 288, 1)
png
Number of anomaly samples: 399
Indices of anomaly samples: (array([ 789, 1653, 1654, 1941, 2697, 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, 3095]),)
Plot anomalies
We now know the samples of the data which are anomalies. With this, we will find the corresponding timestamps from the original test data. We will be using the following method to do that:
Let's say time_steps = 3 and we have 10 training values. Our x_train will look like this:
0, 1, 2
1, 2, 3
2, 3, 4
3, 4, 5
4, 5, 6
5, 6, 7
6, 7, 8
7, 8, 9
All except the initial and the final time_steps-1 data values, will appear in time_steps number of samples. So, if we know that the samples [(3, 4, 5), (4, 5, 6), (5, 6, 7)] are anomalies, we can say that the data point 5 is an anomaly.
# 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)
Let's overlay the anomalies on the original test data plot.
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()
png
Training a timeseries classifier from scratch on the FordA dataset from the UCR/UEA archive.
Introduction
This example shows how to do timeseries classification from scratch, starting from raw CSV timeseries files on disk. We demonstrate the workflow on the FordA dataset from the UCR/UEA archive.
Setup
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
Load the data: the FordA dataset
Dataset description
The dataset we are using here is called FordA. The data comes from the UCR archive. The dataset contains 3601 training instances and another 1320 testing instances. Each timeseries corresponds to a measurement of engine noise captured by a motor sensor. For this task, the goal is to automatically detect the presence of a specific issue with the engine. The problem is a balanced binary classification task. The full description of this dataset can be found here.
Read the TSV data
We will use the FordA_TRAIN file for training and the FordA_TEST file for testing. The simplicity of this dataset allows us to demonstrate effectively how to use ConvNets for timeseries classification. In this file, the first column corresponds to the label.
def readucr(filename):
data = np.loadtxt(filename, delimiter=\"\t\")
y = data[:, 0]