-
-
Notifications
You must be signed in to change notification settings - Fork 9
/
evaluate.py
203 lines (183 loc) · 7.05 KB
/
evaluate.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
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
import pathlib
import json
import numpy as np
import scipy # type: ignore
import math
import argparse
def sigdig(value, CI):
def num_lead_zeros(x):
return math.inf if x == 0 else -math.floor(math.log10(abs(x))) - 1
n_lead_zeros_CI = num_lead_zeros(CI)
CI_sigdigs = 2
decimals = n_lead_zeros_CI + CI_sigdigs
rounded_CI = round(CI, decimals)
rounded_value = round(value, decimals - 1)
if n_lead_zeros_CI > num_lead_zeros(rounded_CI):
return str(f"{round(value, decimals - 2):.{decimals - 2}f}"), str(
f"{round(CI, decimals - 1):.{decimals - 1}f}"
)
else:
return str(f"{rounded_value:.{decimals - 1}f}"), str(
f"{rounded_CI:.{decimals}f}"
)
# tests to ensure that sigdigs is working as intended
value = 0.084111111
CI = 0.0010011111
assert sigdig(value, CI) == ("0.084", "0.0010")
value = 0.084111111
CI = 0.000999999999
assert sigdig(value, CI) == ("0.084", "0.0010")
def confidence_interval(values, sizes):
identifiers = [i for i in range(len(values))]
dict_x_w = {
identifier: (value, weight)
for identifier, (value, weight) in enumerate(zip(values, sizes))
}
def weighted_mean(z, axis):
# creating an array of weights, by mapping z to dict_x_w
data = np.vectorize(dict_x_w.get)(z)
return np.average(data[0], weights=data[1], axis=axis)
CI_99_bootstrap = scipy.stats.bootstrap(
(identifiers,),
statistic=weighted_mean,
confidence_level=0.99,
axis=0,
method="BCa",
)
low = list(CI_99_bootstrap.confidence_interval)[0]
high = list(CI_99_bootstrap.confidence_interval)[1]
return (high - low) / 2
def weighted_avg_and_std(values, weights):
"""
Return the weighted average and standard deviation.
They weights are in effect first normalized so that they
sum to 1 (and so they must not all be 0).
values, weights -- NumPy ndarrays with the same shape.
"""
average = np.average(values, weights=weights)
# Fast and numerically precise:
variance = np.average((values - average) ** 2, weights=weights)
return (average, math.sqrt(variance))
if __name__ == "__main__":
dev_mode_name = "FSRS-5-dev"
dev_file = pathlib.Path(f"./result/{dev_mode_name}.jsonl")
if dev_file.exists():
with open(dev_file, "r") as f:
common_set = set([json.loads(x)["user"] for x in f.readlines()])
else:
common_set = set()
parser = argparse.ArgumentParser()
parser.add_argument("--fast", action="store_true")
parser.add_argument("--secs", action="store_true")
args = parser.parse_args()
models = (
[
(dev_mode_name, None),
("GRU-P-short", 297),
("GRU-P", 297),
("FSRS-5", 19),
("FSRS-rs", 19),
("FSRS-4.5", 17),
("FSRS-5-binary", 15),
("FSRSv4", 17),
("DASH", 9),
("DASH-short", 9),
("DASH[MCM]", 9),
("DASH[ACT-R]", 5),
("FSRSv3", 13),
("FSRS-5-pretrain", 4),
("NN-17", 39),
("GRU", 39),
("FSRS-5-dry-run", 0),
("ACT-R", 5),
("AVG", 0),
("HLR", 3),
("SM2-short", 0),
("Ebisu-v2", 0),
("SM2", 0),
("Transformer", 127),
]
if not args.secs
else [
(dev_mode_name, None),
("GRU-P-secs", 297),
("DASH-secs", 9),
("DASH[MCM]-secs", 9),
("FSRS-4.5-secs", 17),
("DASH[ACT-R]-secs", 5),
("ACT-R-secs", 5),
("AVG-secs", 0),
("GRU-secs", 39),
]
)
if args.fast:
for model, _ in models:
print(f"Model: {model}")
m = []
parameters = []
sizes = []
result_file = pathlib.Path(f"./result/{model}.jsonl")
if not result_file.exists():
continue
with open(result_file, "r") as f:
data = [json.loads(x) for x in f.readlines()]
for result in data:
if common_set and result["user"] not in common_set:
continue
m.append(result["metrics"])
sizes.append(result["size"])
if "parameters" in result:
parameters.append(result["parameters"])
if len(sizes) == 0:
continue
print(f"Total number of users: {len(sizes)}")
print(f"Total number of reviews: {sum(sizes)}")
for scale, size in (
("reviews", np.array(sizes)),
("log(reviews)", np.log(sizes)),
("users", np.ones_like(sizes)),
):
print(f"Weighted average by {scale}:")
for metric in ("LogLoss", "RMSE(bins)", "AUC"):
metrics = np.array([item[metric] for item in m])
size = size[~np.isnan(metrics.astype(float))]
metrics = metrics[~np.isnan(metrics.astype(float))]
wmean, wstd = weighted_avg_and_std(metrics, size)
print(f"{model} {metric} (mean±std): {wmean:.4f}±{wstd:.4f}")
print()
if len(parameters) > 0:
print(f"parameters: {np.median(parameters, axis=0).round(6).tolist()}")
else:
for scale in ("reviews", "users"):
print(f"Weighted by number of {scale}\n")
print("| Model | #Params | LogLoss | RMSE(bins) | AUC |")
print("| --- | --- | --- | --- | --- |")
for model, n_param in models:
m = []
parameters = []
sizes = []
result_file = pathlib.Path(f"./result/{model}.jsonl")
if not result_file.exists():
continue
with open(result_file, "r") as f:
data = [json.loads(x) for x in f.readlines()]
for result in data:
if common_set and result["user"] not in common_set:
continue
m.append(result["metrics"])
sizes.append(result["size"])
if "parameters" in result:
parameters.append(result["parameters"])
if len(sizes) == 0:
continue
size = np.array(sizes) if scale == "reviews" else np.ones_like(sizes)
result = f"| {model} | {n_param} |"
for metric in ("LogLoss", "RMSE(bins)", "AUC"):
metrics = np.array([item[metric] for item in m])
size = size[~np.isnan(metrics.astype(float))]
metrics = metrics[~np.isnan(metrics.astype(float))]
wmean, wstd = weighted_avg_and_std(metrics, size)
CI = confidence_interval(metrics, size)
rounded_mean, rounded_CI = sigdig(wmean, CI)
result += f" {rounded_mean}±{rounded_CI} |"
print(result)