keras.preprocessing.timeseries_dataset_from_array 较小数据集下的充分使用

keras.preprocessing.timeseries_dataset_from_array(
    data,
    targets,
    sequence_length,  # 窗口大小
    sequence_stride=1, #连续输出序列之间的周期。对于步幅s，输出采样将开始索引data[i]，data[i + s]，data[i + 2 * s]，等。
    sampling_rate=1, # 序列中连续的各个时间步之间的时间间隔。对于rate r，时间步 用于创建样本序列。 data[i], data[i + r], ... data[i + sequence_length]
    batch_size=128,  # 每批中时间序列样本的数量
    shuffle=False,
    seed=None,
    start_index=None,
    end_index=None,
)

Example 1:
  Consider indices `[0, 1, ... 99]`.
  With `sequence_length=10,  sampling_rate=2, sequence_stride=3`,
  `shuffle=False`, the dataset will yield batches of sequences
  composed of the following indices:

```
First sequence:  [0  2  4  6  8 10 12 14 16 18]
Second sequence: [3  5  7  9 11 13 15 17 19 21]
Third sequence:  [6  8 10 12 14 16 18 20 22 24]
...
Last sequence:   [78 80 82 84 86 88 90 92 94 96]
```

In this case the last 3 data points are discarded since no full sequence
can be generated to include them (the next sequence would have started
at index 81, and thus its last step would have gone over 99).

d = {'a': [i for i in range(29)], 'b': [i for i in range(29)], 'c': [i for i in range(29)]}
df = pd.DataFrame(d)
df.head(5)

step = 1 # 数据的选取步频
train_split = 20
past = 3 # 使用前3个数据时间进行预测，时间窗口
future = 0 # 预测0个数据时点后的数据，就是下一个时点


features = pd.DataFrame(df.values)
features = features.astype('float64')

val_data = features.loc[train_split:]  # 测试数据

# 构造验证数据
label_start =past + future # 因为用前past个时点进行去测，所以要从第4个开始（即3）

# 测试值已经再尾部了，所以要腾挪出(future+1)个来
x_val = val_data[:-(future+1)][[i for i in range(3)]].values
# 由于输入的序列长度需要一样，所以需要补充y_val序列长度，补充传长度为past-(future+1)
y_val = val_data.iloc[label_start:][[2]] # 为了补空
for i in range(past-(future+1)):
    y_val.loc['n'+str(i)] = 0 

dataset_val = keras.preprocessing.timeseries_dataset_from_array(
    x_val,
    y_val,
    sequence_length=sequence_length,
    sampling_rate=step,
    batch_size=batch_size,
)

# 输出数据
for batch in dataset_val.take(1):
    inputs, targets = batch

print("val Input shape:", inputs.numpy().shape)
print("val Target shape:", targets.numpy().shape)