-
Notifications
You must be signed in to change notification settings - Fork 27
/
tabzilla_alg_handler.py
227 lines (136 loc) · 4.03 KB
/
tabzilla_alg_handler.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
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
# this script defines two objects for accessing ML models/algorithms:
# - dictionary ALL_MODELS: each key is a model/alg name, and each value is a function that imports and returns the model class
# - function get_model(model_name), which returns the model class by evaluating the model-getter
#
# to add a new model/algorithm, simply add a new model-getter function that imports and returns the model class,
# and add the decorator @register_model to this function.
# dictionary of all model names
ALL_MODELS = {}
def register_model(func):
"""add model to the list of all models"""
ALL_MODELS[func.__name__] = func
return func
##############################################################
# sklearn-based models
@register_model
def LinearModel():
from models.baseline_models import LinearModel as model
return model
@register_model
def KNN():
from models.baseline_models import KNN as model
return model
@register_model
def SVM():
from models.baseline_models import SVM as model
return model
@register_model
def DecisionTree():
from models.baseline_models import DecisionTree as model
return model
@register_model
def RandomForest():
from models.baseline_models import RandomForest as model
return model
##############################################################
# gbdt models
@register_model
def XGBoost():
from models.tree_models import XGBoost as model
return model
@register_model
def CatBoost():
from models.tree_models import CatBoost as model
return model
@register_model
def LightGBM():
from models.tree_models import LightGBM as model
return model
# Not tested
# @register_model
# def ModelTree():
# from models.modeltree import ModelTree as model
# return model
##############################################################
# torch-based models
@register_model
def MLP():
from models.mlp import MLP as model
return model
@register_model
def TabNet():
from models.tabnet import TabNet as model
return model
@register_model
def VIME():
from models.vime import VIME as model
return model
@register_model
def TabTransformer():
from models.tabtransformer import TabTransformer as model
return model
@register_model
def NODE():
from models.node import NODE as model
return model
@register_model
def DeepGBM():
from models.deepgbm import DeepGBM as model
return model
@register_model
def STG():
from models.stochastic_gates import STG as model
return model
@register_model
def NAM():
from models.neural_additive_models import NAM as model
return model
@register_model
def DeepFM():
from models.deepfm import DeepFM as model
return model
@register_model
def SAINT():
from models.saint import SAINT as model
return model
@register_model
def DANet():
from models.danet import DANet as model
return model
# not implemented yet.
# @register_model
# def Hopular_model():
# from models.hopular_model import Hopular_model as model
# return model
@register_model
def TabPFNModel():
from models.tabpfn import TabPFNModel as model
return model
##############################################################
# rtdl models (also using torch)
# code: https://yura52.github.io
# paper: https://arxiv.org/abs/2106.11959
@register_model
def rtdl_MLP():
from models.rtdl import rtdl_MLP as model
return model
@register_model
def rtdl_ResNet():
from models.rtdl import rtdl_ResNet as model
return model
@register_model
def rtdl_FTTransformer():
from models.rtdl import rtdl_FTTransformer as model
return model
def get_model(model_name):
if model_name in ALL_MODELS:
# get the model-getter
model_getter = ALL_MODELS[model_name]
# evaluate the model-getting function to return the model class
return model_getter()
else:
raise NotImplementedError(f"Model {model_name} not implemented")
if __name__ == "__main__":
print("all algorithms:")
for n in ALL_MODELS.keys():
print(n)