Skip to content

Commit 8a61289

Browse files
author
Github Actions
committed
Ravin Kohli: [ADD] Pytest schedule (#234)
1 parent fdbdaf1 commit 8a61289

24 files changed

+401
-329
lines changed

‎development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip‎

0 Bytes
Binary file not shown.

‎development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip‎

0 Bytes
Binary file not shown.

‎development/_images/sphx_glr_example_visualization_001.png‎

3.26 KB
Loading

‎development/_images/sphx_glr_example_visualization_002.png‎

739 Bytes
Loading

‎development/_images/sphx_glr_example_visualization_thumb.png‎

2.96 KB
Loading

‎development/_sources/examples/20_basics/example_image_classification.rst.txt‎

Lines changed: 11 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -87,26 +87,25 @@ Image Classification
8787
Pipeline Random Config:
8888
________________________________________
8989
Configuration:
90-
image_augmenter:GaussianBlur:sigma_min, Value: 2.7815796172433904
91-
image_augmenter:GaussianBlur:sigma_offset, Value: 2.2162192719004334
92-
image_augmenter:GaussianBlur:use_augmenter, Value: True
90+
image_augmenter:GaussianBlur:use_augmenter, Value: False
9391
image_augmenter:GaussianNoise:use_augmenter, Value: False
94-
image_augmenter:RandomAffine:rotate, Value: 211
95-
image_augmenter:RandomAffine:scale_offset, Value: 0.1303552201986558
96-
image_augmenter:RandomAffine:shear, Value: 24
97-
image_augmenter:RandomAffine:translate_percent_offset, Value: 0.1470709970650069
92+
image_augmenter:RandomAffine:rotate, Value: 139
93+
image_augmenter:RandomAffine:scale_offset, Value: 0.23068913599566782
94+
image_augmenter:RandomAffine:shear, Value: 15
95+
image_augmenter:RandomAffine:translate_percent_offset, Value: 0.3649730895991128
9896
image_augmenter:RandomAffine:use_augmenter, Value: True
99-
image_augmenter:RandomCutout:use_augmenter, Value: False
97+
image_augmenter:RandomCutout:p, Value: 0.8026417457168646
98+
image_augmenter:RandomCutout:use_augmenter, Value: True
10099
image_augmenter:Resize:use_augmenter, Value: True
101-
image_augmenter:ZeroPadAndCrop:percent, Value: 0.29994917149110223
102-
normalizer:__choice__, Value: 'ImageNormalizer'
100+
image_augmenter:ZeroPadAndCrop:percent, Value: 0.26059070078518315
101+
normalizer:__choice__, Value: 'NoNormalizer'
103102

104103
Fitting the pipeline...
105104
________________________________________
106105
ImageClassificationPipeline
107106
________________________________________
108107
0-) normalizer:
109-
ImageNormalizer
108+
NoNormalizer
110109

111110
1-) preprocessing:
112111
EarlyPreprocessing
@@ -178,7 +177,7 @@ Image Classification
178177
179178
.. rst-class:: sphx-glr-timing
180179

181-
**Total running time of the script:** ( 0 minutes 8.362 seconds)
180+
**Total running time of the script:** ( 0 minutes 6.439 seconds)
182181

183182

184183
.. _sphx_glr_download_examples_20_basics_example_image_classification.py:

‎development/_sources/examples/20_basics/example_tabular_classification.rst.txt‎

Lines changed: 47 additions & 48 deletions
Original file line numberDiff line numberDiff line change
@@ -133,7 +133,7 @@ Search for an ensemble of machine learning algorithms
133133
.. code-block:: none
134134
135135
136-
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f7f96017d90>
136+
<autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7fe257cbbe50>
137137
138138
139139
@@ -162,7 +162,7 @@ Print the final ensemble performance
162162

163163
.. code-block:: none
164164
165-
<smac.runhistory.runhistory.RunHistory object at 0x7f7f96017130> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
165+
<smac.runhistory.runhistory.RunHistory object at 0x7fe257b3a0d0> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
166166
data_loader:batch_size, Value: 64
167167
encoder:__choice__, Value: 'OneHotEncoder'
168168
feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -194,7 +194,7 @@ Print the final ensemble performance
194194
scaler:__choice__, Value: 'StandardScaler'
195195
trainer:StandardTrainer:weighted_loss, Value: True
196196
trainer:__choice__, Value: 'StandardTrainer'
197-
, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0012085437774658203, budget=0), TrajEntry(train_perf=0.17543859649122806, incumbent_id=1, incumbent=Configuration:
197+
, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0010366439819335938, budget=0), TrajEntry(train_perf=0.17543859649122806, incumbent_id=1, incumbent=Configuration:
198198
data_loader:batch_size, Value: 64
199199
encoder:__choice__, Value: 'OneHotEncoder'
200200
feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -226,63 +226,62 @@ Print the final ensemble performance
226226
scaler:__choice__, Value: 'StandardScaler'
227227
trainer:StandardTrainer:weighted_loss, Value: True
228228
trainer:__choice__, Value: 'StandardTrainer'
229-
, ta_runs=1, ta_time_used=2.6043033599853516, wallclock_time=3.6323750019073486, budget=5.555555555555555), TrajEntry(train_perf=0.1578947368421053, incumbent_id=2, incumbent=Configuration:
230-
data_loader:batch_size, Value: 131
231-
encoder:__choice__, Value: 'NoEncoder'
232-
feature_preprocessor:KernelPCA:coef0, Value: -0.2027355777455664
233-
feature_preprocessor:KernelPCA:degree, Value: 2
234-
feature_preprocessor:KernelPCA:gamma, Value: 0.0029756156161293078
235-
feature_preprocessor:KernelPCA:kernel, Value: 'poly'
236-
feature_preprocessor:KernelPCA:n_components, Value: 4
237-
feature_preprocessor:__choice__, Value: 'KernelPCA'
229+
, ta_runs=1, ta_time_used=1.9255990982055664, wallclock_time=2.953387975692749, budget=5.555555555555555), TrajEntry(train_perf=0.14619883040935677, incumbent_id=2, incumbent=Configuration:
230+
data_loader:batch_size, Value: 54
231+
encoder:__choice__, Value: 'OneHotEncoder'
232+
feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
238233
imputer:categorical_strategy, Value: 'constant_!missing!'
239234
imputer:numerical_strategy, Value: 'mean'
240-
lr_scheduler:CosineAnnealingWarmRestarts:T_0, Value: 20
241-
lr_scheduler:CosineAnnealingWarmRestarts:T_mult, Value: 1.2502829975237466
242-
lr_scheduler:__choice__, Value: 'CosineAnnealingWarmRestarts'
243-
network_backbone:ShapedResNetBackbone:activation, Value: 'sigmoid'
244-
network_backbone:ShapedResNetBackbone:blocks_per_group, Value: 2
245-
network_backbone:ShapedResNetBackbone:max_units, Value: 21
246-
network_backbone:ShapedResNetBackbone:num_groups, Value: 11
247-
network_backbone:ShapedResNetBackbone:output_dim, Value: 128
248-
network_backbone:ShapedResNetBackbone:resnet_shape, Value: 'stairs'
249-
network_backbone:ShapedResNetBackbone:use_dropout, Value: False
250-
network_backbone:ShapedResNetBackbone:use_shake_drop, Value: False
251-
network_backbone:ShapedResNetBackbone:use_shake_shake, Value: False
252-
network_backbone:__choice__, Value: 'ShapedResNetBackbone'
235+
lr_scheduler:CosineAnnealingLR:T_max, Value: 307
236+
lr_scheduler:__choice__, Value: 'CosineAnnealingLR'
237+
network_backbone:ShapedMLPBackbone:activation, Value: 'relu'
238+
network_backbone:ShapedMLPBackbone:max_dropout, Value: 0.543030049110043
239+
network_backbone:ShapedMLPBackbone:max_units, Value: 35
240+
network_backbone:ShapedMLPBackbone:mlp_shape, Value: 'hexagon'
241+
network_backbone:ShapedMLPBackbone:num_groups, Value: 3
242+
network_backbone:ShapedMLPBackbone:output_dim, Value: 18
243+
network_backbone:ShapedMLPBackbone:use_dropout, Value: True
244+
network_backbone:__choice__, Value: 'ShapedMLPBackbone'
253245
network_embedding:__choice__, Value: 'NoEmbedding'
254246
network_head:__choice__, Value: 'fully_connected'
255-
network_head:fully_connected:activation, Value: 'tanh'
256-
network_head:fully_connected:num_layers, Value: 4
257-
network_head:fully_connected:units_layer_1, Value: 415
258-
network_head:fully_connected:units_layer_2, Value: 290
259-
network_head:fully_connected:units_layer_3, Value: 313
260-
network_init:KaimingInit:bias_strategy, Value: 'Normal'
261-
network_init:__choice__, Value: 'KaimingInit'
262-
optimizer:AdamOptimizer:beta1, Value: 0.9981587455677909
263-
optimizer:AdamOptimizer:beta2, Value: 0.9934737249657393
264-
optimizer:AdamOptimizer:lr, Value: 0.0015351906927605823
265-
optimizer:AdamOptimizer:weight_decay, Value: 0.06126849297256112
266-
optimizer:__choice__, Value: 'AdamOptimizer'
247+
network_head:fully_connected:activation, Value: 'relu'
248+
network_head:fully_connected:num_layers, Value: 3
249+
network_head:fully_connected:units_layer_1, Value: 316
250+
network_head:fully_connected:units_layer_2, Value: 503
251+
network_init:SparseInit:bias_strategy, Value: 'Normal'
252+
network_init:__choice__, Value: 'SparseInit'
253+
optimizer:AdamWOptimizer:beta1, Value: 0.9489565046389004
254+
optimizer:AdamWOptimizer:beta2, Value: 0.9647522172509646
255+
optimizer:AdamWOptimizer:lr, Value: 0.0030477242366055836
256+
optimizer:AdamWOptimizer:weight_decay, Value: 0.061913730296919815
257+
optimizer:__choice__, Value: 'AdamWOptimizer'
267258
scaler:__choice__, Value: 'MinMaxScaler'
268-
trainer:StandardTrainer:weighted_loss, Value: False
269-
trainer:__choice__, Value: 'StandardTrainer'
270-
, ta_runs=20, ta_time_used=161.57836866378784, wallclock_time=232.07247638702393, budget=50.0)]
271-
{'accuracy': 0.8728323699421965}
272-
| | Preprocessing | Estimator | Weight |
273-
|---:|:------------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
274-
| 0 | None | CatBoostClassifier | 0.92 |
275-
| 1 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
276-
| 2 | SimpleImputer,NoEncoder,MinMaxScaler,KernelPCA | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
277-
| 3 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
259+
trainer:MixUpTrainer:alpha, Value: 0.8559230573827334
260+
trainer:MixUpTrainer:weighted_loss, Value: True
261+
trainer:__choice__, Value: 'MixUpTrainer'
262+
, ta_runs=18, ta_time_used=170.50614953041077, wallclock_time=220.6971218585968, budget=50.0)]
263+
{'accuracy': 0.861271676300578}
264+
| | Preprocessing | Estimator | Weight |
265+
|---:|:------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
266+
| 0 | None | CatBoostClassifier | 0.28 |
267+
| 1 | SimpleImputer,NoEncoder,Normalizer,Nystroem | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.2 |
268+
| 2 | None | KNNClassifier | 0.1 |
269+
| 3 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.1 |
270+
| 4 | SimpleImputer,OneHotEncoder,StandardScaler,Nystroem | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential | 0.06 |
271+
| 5 | None | LGBMClassifier | 0.06 |
272+
| 6 | None | RFClassifier | 0.06 |
273+
| 7 | SimpleImputer,OneHotEncoder,MinMaxScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
274+
| 8 | SimpleImputer,NoEncoder,NoScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.04 |
275+
| 9 | None | SVC | 0.04 |
276+
| 10 | SimpleImputer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 0.02 |
278277
279278
280279
281280
282281
283282
.. rst-class:: sphx-glr-timing
284283

285-
**Total running time of the script:** ( 5 minutes 28.336 seconds)
284+
**Total running time of the script:** ( 5 minutes 24.537 seconds)
286285

287286

288287
.. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:

‎development/_sources/examples/20_basics/example_tabular_regression.rst.txt‎

Lines changed: 15 additions & 15 deletions
Original file line numberDiff line numberDiff line change
@@ -126,7 +126,7 @@ Search for an ensemble of machine learning algorithms
126126
.. code-block:: none
127127
128128
129-
<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f803aa3a6a0>
129+
<autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7fe2fda056a0>
130130
131131
132132
@@ -158,7 +158,7 @@ Print the final ensemble performance
158158

159159
.. code-block:: none
160160
161-
<smac.runhistory.runhistory.RunHistory object at 0x7f802dfa0040> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
161+
<smac.runhistory.runhistory.RunHistory object at 0x7fe2f0fa5040> [TrajEntry(train_perf=2147483648, incumbent_id=1, incumbent=Configuration:
162162
data_loader:batch_size, Value: 64
163163
encoder:__choice__, Value: 'OneHotEncoder'
164164
feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -189,7 +189,7 @@ Print the final ensemble performance
189189
optimizer:__choice__, Value: 'AdamOptimizer'
190190
scaler:__choice__, Value: 'StandardScaler'
191191
trainer:__choice__, Value: 'StandardTrainer'
192-
, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0011713504791259766, budget=0), TrajEntry(train_perf=5.132313191053143, incumbent_id=1, incumbent=Configuration:
192+
, ta_runs=0, ta_time_used=0.0, wallclock_time=0.0010111331939697266, budget=0), TrajEntry(train_perf=4.001648104799329, incumbent_id=1, incumbent=Configuration:
193193
data_loader:batch_size, Value: 64
194194
encoder:__choice__, Value: 'OneHotEncoder'
195195
feature_preprocessor:__choice__, Value: 'NoFeaturePreprocessor'
@@ -220,7 +220,7 @@ Print the final ensemble performance
220220
optimizer:__choice__, Value: 'AdamOptimizer'
221221
scaler:__choice__, Value: 'StandardScaler'
222222
trainer:__choice__, Value: 'StandardTrainer'
223-
, ta_runs=1, ta_time_used=1.7346484661102295, wallclock_time=2.760028600692749, budget=5.555555555555555), TrajEntry(train_perf=3.390282144755361, incumbent_id=2, incumbent=Configuration:
223+
, ta_runs=1, ta_time_used=1.2097136974334717, wallclock_time=2.233612060546875, budget=5.555555555555555), TrajEntry(train_perf=3.1930668561547075, incumbent_id=2, incumbent=Configuration:
224224
data_loader:batch_size, Value: 67
225225
encoder:__choice__, Value: 'OneHotEncoder'
226226
feature_preprocessor:TruncatedSVD:target_dim, Value: 6
@@ -256,7 +256,7 @@ Print the final ensemble performance
256256
optimizer:__choice__, Value: 'RMSpropOptimizer'
257257
scaler:__choice__, Value: 'NoScaler'
258258
trainer:__choice__, Value: 'StandardTrainer'
259-
, ta_runs=3, ta_time_used=7.148277759552002, wallclock_time=18.647205114364624, budget=5.555555555555555), TrajEntry(train_perf=1.7652469933219221, incumbent_id=3, incumbent=Configuration:
259+
, ta_runs=3, ta_time_used=5.242362022399902, wallclock_time=14.604888677597046, budget=5.555555555555555), TrajEntry(train_perf=1.63612106672499, incumbent_id=3, incumbent=Configuration:
260260
data_loader:batch_size, Value: 150
261261
encoder:__choice__, Value: 'OneHotEncoder'
262262
feature_preprocessor:TruncatedSVD:target_dim, Value: 9
@@ -297,7 +297,7 @@ Print the final ensemble performance
297297
scaler:Normalizer:norm, Value: 'max'
298298
scaler:__choice__, Value: 'Normalizer'
299299
trainer:__choice__, Value: 'StandardTrainer'
300-
, ta_runs=4, ta_time_used=10.926101922988892, wallclock_time=23.508082389831543, budget=5.555555555555555), TrajEntry(train_perf=1.3579328988923458, incumbent_id=4, incumbent=Configuration:
300+
, ta_runs=4, ta_time_used=7.889399766921997, wallclock_time=18.329752445220947, budget=5.555555555555555), TrajEntry(train_perf=1.303718113076385, incumbent_id=4, incumbent=Configuration:
301301
data_loader:batch_size, Value: 82
302302
encoder:__choice__, Value: 'OneHotEncoder'
303303
feature_preprocessor:TruncatedSVD:target_dim, Value: 8
@@ -332,7 +332,7 @@ Print the final ensemble performance
332332
scaler:Normalizer:norm, Value: 'mean_abs'
333333
scaler:__choice__, Value: 'Normalizer'
334334
trainer:__choice__, Value: 'StandardTrainer'
335-
, ta_runs=6, ta_time_used=16.41826319694519, wallclock_time=36.227760314941406, budget=5.555555555555555), TrajEntry(train_perf=1.0, incumbent_id=5, incumbent=Configuration:
335+
, ta_runs=6, ta_time_used=11.728835821151733, wallclock_time=27.961014986038208, budget=5.555555555555555), TrajEntry(train_perf=1.0, incumbent_id=5, incumbent=Configuration:
336336
data_loader:batch_size, Value: 64
337337
encoder:__choice__, Value: 'NoEncoder'
338338
feature_preprocessor:TruncatedSVD:target_dim, Value: 8
@@ -374,7 +374,7 @@ Print the final ensemble performance
374374
scaler:__choice__, Value: 'Normalizer'
375375
trainer:MixUpTrainer:alpha, Value: 0.15674505157760243
376376
trainer:__choice__, Value: 'MixUpTrainer'
377-
, ta_runs=9, ta_time_used=51.96426844596863, wallclock_time=80.42096281051636, budget=5.555555555555555), TrajEntry(train_perf=1.0008126311940073, incumbent_id=6, incumbent=Configuration:
377+
, ta_runs=9, ta_time_used=38.322489976882935, wallclock_time=61.81372833251953, budget=5.555555555555555), TrajEntry(train_perf=1.0090091679942723, incumbent_id=6, incumbent=Configuration:
378378
data_loader:batch_size, Value: 82
379379
encoder:__choice__, Value: 'OneHotEncoder'
380380
feature_preprocessor:TruncatedSVD:target_dim, Value: 8
@@ -409,7 +409,7 @@ Print the final ensemble performance
409409
scaler:Normalizer:norm, Value: 'mean_abs'
410410
scaler:__choice__, Value: 'Normalizer'
411411
trainer:__choice__, Value: 'StandardTrainer'
412-
, ta_runs=10, ta_time_used=54.072187185287476, wallclock_time=83.59581923484802, budget=16.666666666666664), TrajEntry(train_perf=1.0, incumbent_id=7, incumbent=Configuration:
412+
, ta_runs=10, ta_time_used=39.92764472961426, wallclock_time=64.51126146316528, budget=16.666666666666664), TrajEntry(train_perf=1.0, incumbent_id=7, incumbent=Configuration:
413413
data_loader:batch_size, Value: 130
414414
encoder:__choice__, Value: 'OneHotEncoder'
415415
feature_preprocessor:TruncatedSVD:target_dim, Value: 6
@@ -467,19 +467,19 @@ Print the final ensemble performance
467467
optimizer:__choice__, Value: 'AdamOptimizer'
468468
scaler:__choice__, Value: 'StandardScaler'
469469
trainer:__choice__, Value: 'StandardTrainer'
470-
, ta_runs=17, ta_time_used=165.81303334236145, wallclock_time=210.14278411865234, budget=50.0)]
471-
{'r2': -0.002203283150869373}
472-
| | Preprocessing | Estimator | Weight |
473-
|---:|:------------------------------------------------|:-------------------------------------------------------------|---------:|
474-
| 0 | SimpleImputer,OneHotEncoder,Normalizer,TruncSVD | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential | 1 |
470+
, ta_runs=17, ta_time_used=133.23015427589417, wallclock_time=170.4870789051056, budget=50.0)]
471+
{'r2': 0.13995994112861065}
472+
| | Preprocessing | Estimator | Weight |
473+
|---:|:--------------------------------------------------------------|:-------------------------------------------------------------------|---------:|
474+
| 0 | SimpleImputer,OneHotEncoder,StandardScaler,PolynomialFeatures | no embedding,ShapedResNetBackbone,FullyConnectedHead,nn.Sequential | 1 |
475475
476476
477477
478478
479479
480480
.. rst-class:: sphx-glr-timing
481481

482-
**Total running time of the script:** ( 5 minutes 17.148 seconds)
482+
**Total running time of the script:** ( 5 minutes 9.389 seconds)
483483

484484

485485
.. _sphx_glr_download_examples_20_basics_example_tabular_regression.py:

0 commit comments

Comments
 (0)