/
dataset.py
188 lines (173 loc) · 6.99 KB
/
dataset.py
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
import numpy as np
from torch.utils.data import Dataset
from joblib import Memory
from models import TRAIN_PERIODS
memory = Memory(cachedir="cache/", verbose=1)
MEMMAP_SHAPES = {
"train": [(2459797, 71, 6), (2459797, 71, 16), (2459797, 16)],
"val": [(154764, 71, 6), (154764, 71, 16), (154764, 16)],
"test": [(154571, 71, 6), (154571, 71, 16)]
}
def read_dataset():
x_train = "cache/xtrain_seq.npy"
x_i_train = "cache/xtrain_i_seq.npy"
y_train = "cache/ytrain_seq.npy"
x_val = "cache/xval_seq.npy"
x_i_val = "cache/xval_i_seq.npy"
y_val = "cache/yval_seq.npy"
x_test = "cache/xtest_seq.npy"
x_i_test = "cache/xtest_i_seq.npy"
train_dataset = FavoritaDataset(
MEMMAP_SHAPES["train"], x_train, x_i_train, y_train, train_periods=TRAIN_PERIODS)
val_dataset = FavoritaDataset(
MEMMAP_SHAPES["val"], x_val, x_i_val, y_val, train_periods=TRAIN_PERIODS,
reference_dataset=train_dataset)
test_dataset = FavoritaDataset(
MEMMAP_SHAPES["test"], x_test, x_i_test, train_periods=TRAIN_PERIODS,
reference_dataset=train_dataset)
return train_dataset, val_dataset, test_dataset
@memory.cache
def fit_residual_series_stats(x, shape, train_periods, cols=[0, 1, 2, 3, 4, 5], get_stds=False):
series = np.memmap(
x, mode="r", order="C", dtype="float64", shape=shape)
means = np.zeros((series.shape[0], len(cols)))
stds = np.zeros(len(cols))
for i, col in enumerate(cols):
data = series[:, :, col]
means[:, i] = np.mean(data[:, :train_periods], axis=1)
if get_stds:
data = data - np.expand_dims(means[:, i], 1)
stds[i] = np.std(
data[:, :train_periods].reshape(-1))
mean_of_means = np.mean(means, axis=0)
std_of_means = np.std(means, axis=0)
return means, stds, mean_of_means, std_of_means
class FavoritaDataset(Dataset):
def __init__(self, shapes, x, x_i, y=None, reference_dataset=None, train_periods=TRAIN_PERIODS, clip_low=-3, clip_high=3):
"""
args
----
x:
y: pass False for test dataset
split_point: where to split data for encoder/decoder
"""
self.reference_dataset = reference_dataset
self.clip_low = clip_low
self.clip_high = clip_high
self.series = np.memmap(
x, mode="r", order="C", dtype="float64", shape=shapes[0])
self.series_i = np.memmap(
x_i, mode="r", order="C", dtype="int16", shape=shapes[1])
self.train_periods = train_periods
if y is None:
self.is_train = False
else:
self.is_train = True
self.y = np.memmap(y, mode="r", order="C",
dtype="float64", shape=shapes[2])
print("Fitting residual stats for {}...".format(x))
self.means, self.stds, self.mean_of_means, self.std_of_means = fit_residual_series_stats(
x, shapes[0], train_periods, get_stds=(reference_dataset is None)
)
if reference_dataset is not None:
self.stds = reference_dataset.stds
def __len__(self):
return len(self.series)
def normalize_series(self, idx):
raw_data = self.series[idx, :, :6]
residual_data = (
raw_data - self.means[idx, np.newaxis, :]) / self.stds[np.newaxis, :]
return np.clip(np.nan_to_num(
residual_data), self.clip_low, self.clip_high), raw_data
def trim_series(self, s1, s2):
"""Not doing anything useful here. Just for backward compatibility."""
assert len(s1) == len(s2)
return s1, s2
def derive_features(self, idx):
feat = np.zeros(6)
cnt = 0
series = np.trim_zeros(self.series[idx, :self.train_periods, 0])
# Yearly correlation
y2, y1 = self.trim_series(
# year 2
self.series[idx, 1:self.train_periods, 0],
# year 1
self.series[idx, :(self.train_periods - 1), 1]
)
if len(y2) > 5 and np.std(y1) > 0.01 and np.std(y2) > 0.01:
feat[cnt] = np.corrcoef(y2, y1)[0, 1]
cnt += 1
# Cluster sales Yearly correlation
y2, y1 = self.trim_series(
# year 2 item sales
self.series[idx, 1:self.train_periods, 0],
# year 1 cluster item sales
self.series[idx, :(self.train_periods - 1), 3]
)
if len(y2) > 5 and np.std(y1) > 0.01 and np.std(y2) > 0.01:
feat[cnt] = np.corrcoef(y2, y1)[0, 1]
cnt += 1
# Item class yearly correlation
# y2, y1 = self.trim_series(
# # year 2 item sales
# numeric_series[1:self.train_periods, 0],
# # year 1 item class sales
# numeric_series[:(self.train_periods - 1), 5]
# )
# if len(y2) > 5 and np.std(y1) > 0.01 and np.std(y2) > 0.01:
# feat[cnt] = np.corrcoef(y2, y1)[0, 1]
# cnt += 1
# Year 2 cluster sales mean
feat[cnt] = (self.means[idx, 2] - self.mean_of_means[2]) / \
self.std_of_means[2]
cnt += 1
# Year 1 cluster sales mean
feat[cnt] = (self.means[idx, 3] - self.mean_of_means[3]) / \
self.std_of_means[3]
cnt += 1
# # Year 2 item class mean
# feat[cnt] = (self.means[idx, 4] - self.mean_of_means[4]) / \
# self.std_of_means[4]
# cnt += 1
# # Year 1 item class mean
# feat[cnt] = (self.means[idx, 5] - self.mean_of_means[5]) / \
# self.std_of_means[5]
# cnt += 1
# year 2 mean
feat[cnt] = (self.means[idx, 0] - self.mean_of_means[0]) / \
self.std_of_means[0]
cnt += 1
# year 1 mean
feat[cnt] = (self.means[idx, 1] - self.mean_of_means[1]) / \
self.std_of_means[1]
cnt += 1
assert cnt == feat.shape[0]
return np.nan_to_num(feat)
def __getitem__(self, idx):
numeric_series, raw_series = self.normalize_series(idx)
integer_series = self.series_i[idx, :, :].__array__().astype("int32")
if self.is_train:
return (
numeric_series.astype("float32"),
self.derive_features(idx).astype("float32"),
np.concatenate([
integer_series[:, :-1],
# year 2 zero sale
(raw_series[:, :1] == 0).astype("int32"),
integer_series[:, -1:]
], axis=1),
self.means[idx].astype("float32"),
self.y[idx, :].__array__().astype("float32")
)
else:
return (
numeric_series.astype("float32"),
self.derive_features(idx).astype("float32"),
np.concatenate([
integer_series[:, :-1],
# year 2 zero sale
(raw_series[:, :1] == 0).astype("int32"),
integer_series[:, -1:],
], axis=1),
self.means[idx].astype("float32")
)