TabNet for fastai

Related tags

Deep Learning fast_tabnet
Overview

TabNet for fastai

This is an adaptation of TabNet (Attention-based network for tabular data) for fastai (>=2.0) library. The original paper https://arxiv.org/pdf/1908.07442.pdf.

Install

pip install fast_tabnet

How to use

model = TabNetModel(emb_szs, n_cont, out_sz, embed_p=0., y_range=None, n_d=8, n_a=8, n_steps=3, gamma=1.5, n_independent=2, n_shared=2, epsilon=1e-15, virtual_batch_size=128, momentum=0.02)

Parameters emb_szs, n_cont, out_sz, embed_p, y_range are the same as for fastai TabularModel.

  • n_d : int Dimension of the prediction layer (usually between 4 and 64)
  • n_a : int Dimension of the attention layer (usually between 4 and 64)
  • n_steps: int Number of sucessive steps in the newtork (usually betwenn 3 and 10)
  • gamma : float Float above 1, scaling factor for attention updates (usually betwenn 1.0 to 2.0)
  • momentum : float Float value between 0 and 1 which will be used for momentum in all batch norm
  • n_independent : int Number of independent GLU layer in each GLU block (default 2)
  • n_shared : int Number of independent GLU layer in each GLU block (default 2)
  • epsilon: float Avoid log(0), this should be kept very low

Example

Below is an example from fastai library, but the model in use is TabNet

from fastai.basics import *
from fastai.tabular.all import *
from fast_tabnet.core import *
path = untar_data(URLs.ADULT_SAMPLE)
df = pd.read_csv(path/'adult.csv')
df_main,df_test = df.iloc[:-1000].copy(),df.iloc[-1000:].copy()
df_main.head()
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age workclass fnlwgt education education-num marital-status occupation relationship race sex capital-gain capital-loss hours-per-week native-country salary
0 49 Private 101320 Assoc-acdm 12.0 Married-civ-spouse NaN Wife White Female 0 1902 40 United-States >=50k
1 44 Private 236746 Masters 14.0 Divorced Exec-managerial Not-in-family White Male 10520 0 45 United-States >=50k
2 38 Private 96185 HS-grad NaN Divorced NaN Unmarried Black Female 0 0 32 United-States <50k
3 38 Self-emp-inc 112847 Prof-school 15.0 Married-civ-spouse Prof-specialty Husband Asian-Pac-Islander Male 0 0 40 United-States >=50k
4 42 Self-emp-not-inc 82297 7th-8th NaN Married-civ-spouse Other-service Wife Black Female 0 0 50 United-States <50k 
cat_names = ['workclass', 'education', 'marital-status', 'occupation', 
             'relationship', 'race', 'native-country', 'sex']
cont_names = ['age', 'fnlwgt', 'education-num']
procs = [Categorify, FillMissing, Normalize]
splits = RandomSplitter()(range_of(df_main))
to = TabularPandas(df_main, procs, cat_names, cont_names, y_names="salary", 
                   y_block = CategoryBlock(), splits=splits)
dls = to.dataloaders(bs=32)
dls.valid.show_batch()
workclass education marital-status occupation relationship race native-country sex education-num_na age fnlwgt education-num salary
0 Private HS-grad Married-civ-spouse Other-service Wife White United-States Female False 39.000000 196673.000115 9.0 <50k
1 Private HS-grad Married-civ-spouse Craft-repair Husband White United-States Male False 32.000000 198067.999771 9.0 <50k
2 State-gov HS-grad Never-married Adm-clerical Own-child White United-States Female False 18.999999 176633.999931 9.0 <50k
3 Private Some-college Married-civ-spouse Prof-specialty Husband White United-States Male False 67.999999 107626.998490 10.0 <50k
4 Private Masters Never-married Exec-managerial Not-in-family Black United-States Male False 29.000000 214925.000260 14.0 <50k
5 Private HS-grad Married-civ-spouse Priv-house-serv Wife White United-States Female False 22.000000 200109.000126 9.0 <50k
6 Private Some-college Never-married Sales Own-child White United-States Female False 18.000000 60980.998429 10.0 <50k
7 Private Some-college Separated Adm-clerical Not-in-family White United-States Female False 28.000000 334367.998199 10.0 <50k
8 Private 11th Married-civ-spouse Transport-moving Husband White United-States Male False 49.000000 123584.001097 7.0 <50k
9 Private Masters Never-married Prof-specialty Not-in-family White United-States Female False 26.000000 397316.999922 14.0 <50k 
to_tst = to.new(df_test)
to_tst.process()
to_tst.all_cols.head()
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workclass education marital-status occupation relationship race native-country sex education-num_na age fnlwgt education-num salary
31561 5 2 5 9 3 3 40 2 1 -1.505833 -0.559418 -1.202170 0
31562 2 12 5 2 5 3 40 1 1 -1.432653 0.421241 -0.418032 0
31563 5 7 3 4 1 5 40 2 1 -0.115406 0.132868 -1.986307 0
31564 8 12 3 9 1 5 40 2 1 1.494561 0.749805 -0.418032 0
31565 1 12 1 1 5 3 40 2 1 -0.481308 7.529798 -0.418032 0 
emb_szs = get_emb_sz(to)

That's the use of the model

model = TabNetModel(emb_szs, len(to.cont_names), dls.c, n_d=8, n_a=8, n_steps=5, mask_type='entmax');
learn = Learner(dls, model, CrossEntropyLossFlat(), opt_func=Adam, lr=3e-2, metrics=[accuracy])
learn.lr_find()
SuggestedLRs(lr_min=0.2754228591918945, lr_steep=1.9054607491852948e-06)

png

learn.fit_one_cycle(5)
epoch train_loss valid_loss accuracy time
0 0.446274 0.414451 0.817015 00:30
1 0.380002 0.393030 0.818916 00:30
2 0.371149 0.359802 0.832066 00:30
3 0.349027 0.352255 0.835868 00:30
4 0.355339 0.349360 0.836819 00:30

Tabnet interpretability

# feature importance for 2k rows
dl = learn.dls.test_dl(df.iloc[:2000], bs=256)
feature_importances = tabnet_feature_importances(learn.model, dl)
# per sample interpretation
dl = learn.dls.test_dl(df.iloc[:20], bs=20)
res_explain, res_masks = tabnet_explain(learn.model, dl)
plt.xticks(rotation='vertical')
plt.bar(dl.x_names, feature_importances, color='g')
plt.show()

png

def plot_explain(masks, lbls, figsize=(12,12)):
    "Plots masks with `lbls` (`dls.x_names`)"
    fig = plt.figure(figsize=figsize)
    ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
    plt.yticks(np.arange(0, len(masks), 1.0))
    plt.xticks(np.arange(0, len(masks[0]), 1.0))
    ax.set_xticklabels(lbls, rotation=90)
    plt.ylabel('Sample Number')
    plt.xlabel('Variable')
    plt.imshow(masks)
plot_explain(res_explain, dl.x_names)

png

Hyperparameter search with Bayesian Optimization

If your dataset isn't huge you can tune hyperparameters for tabular models with Bayesian Optimization. You can optimize directly your metric using this approach if the metric is sensitive enough (in our example it is not and we use validation loss instead). Also, you should create the second validation set, because you will use the first as a training set for Bayesian Optimization.

You may need to install the optimizer pip install bayesian-optimization

from functools import lru_cache
# The function we'll optimize
@lru_cache(1000)
def get_accuracy(n_d:Int, n_a:Int, n_steps:Int):
    model = TabNetModel(emb_szs, len(to.cont_names), dls.c, n_d=n_d, n_a=n_a, n_steps=n_steps, gamma=1.5)
    learn = Learner(dls, model, CrossEntropyLossFlat(), opt_func=opt_func, lr=3e-2, metrics=[accuracy])
    learn.fit_one_cycle(5)
    return float(learn.validate(dl=learn.dls.valid)[1])

This implementation of Bayesian Optimization doesn't work naturally with descreet values. That's why we use wrapper with lru_cache.

def fit_accuracy(pow_n_d, pow_n_a, pow_n_steps):
    n_d, n_a, n_steps = map(lambda x: 2**int(x), (pow_n_d, pow_n_a, pow_n_steps))
    return get_accuracy(n_d, n_a, n_steps)
from bayes_opt import BayesianOptimization

# Bounded region of parameter space
pbounds = {'pow_n_d': (0, 8), 'pow_n_a': (0, 8), 'pow_n_steps': (0, 4)}

optimizer = BayesianOptimization(
    f=fit_accuracy,
    pbounds=pbounds,
)
optimizer.maximize(
    init_points=15,
    n_iter=100,
)
|   iter    |  target   |  pow_n_a  |  pow_n_d  | pow_n_... |
-------------------------------------------------------------
epoch train_loss valid_loss accuracy time
0 0.404888 0.432834 0.793885 00:10
1 0.367979 0.384840 0.818600 00:09
2 0.366444 0.372005 0.819708 00:09
3 0.362771 0.366949 0.823511 00:10
4 0.353682 0.367132 0.823511 00:10 
| �[0m 1       �[0m | �[0m 0.8235  �[0m | �[0m 0.9408  �[0m | �[0m 1.898   �[0m | �[0m 1.652   �[0m |
epoch train_loss valid_loss accuracy time
0 0.393301 0.449742 0.810836 00:08
1 0.379140 0.413773 0.815589 00:07
2 0.355790 0.388907 0.822560 00:07
3 0.349984 0.362671 0.828739 00:07
4 0.348000 0.360150 0.827313 00:07 
| �[95m 2       �[0m | �[95m 0.8273  �[0m | �[95m 4.262   �[0m | �[95m 5.604   �[0m | �[95m 0.2437  �[0m |
epoch train_loss valid_loss accuracy time
0 0.451572 0.434189 0.781210 00:12
1 0.423763 0.413420 0.805450 00:12
2 0.398922 0.408688 0.814164 00:12
3 0.390981 0.392398 0.808935 00:12
4 0.376418 0.382250 0.817174 00:12 
| �[0m 3       �[0m | �[0m 0.8172  �[0m | �[0m 7.233   �[0m | �[0m 6.471   �[0m | �[0m 2.508   �[0m |
epoch train_loss valid_loss accuracy time
0 0.403187 0.413986 0.798162 00:07
1 0.398544 0.390102 0.820184 00:07
2 0.390569 0.389703 0.825253 00:07
3 0.375426 0.385706 0.826996 00:07
4 0.370446 0.383366 0.831115 00:06 
| �[95m 4       �[0m | �[95m 0.8311  �[0m | �[95m 5.935   �[0m | �[95m 1.241   �[0m | �[95m 0.3809  �[0m |
epoch train_loss valid_loss accuracy time
0 0.464145 0.458641 0.751267 00:18
1 0.424691 0.436968 0.788023 00:18
2 0.431576 0.436581 0.775824 00:18
3 0.432143 0.437062 0.759506 00:18
4 0.429915 0.438332 0.758555 00:18 
| �[0m 5       �[0m | �[0m 0.7586  �[0m | �[0m 2.554   �[0m | �[0m 0.4992  �[0m | �[0m 3.111   �[0m |
epoch train_loss valid_loss accuracy time
0 0.470359 0.475826 0.748891 00:12
1 0.411564 0.409433 0.797053 00:12
2 0.392718 0.397363 0.809727 00:12
3 0.387564 0.380033 0.814322 00:12
4 0.374153 0.378258 0.818916 00:12 
| �[0m 6       �[0m | �[0m 0.8189  �[0m | �[0m 4.592   �[0m | �[0m 2.138   �[0m | �[0m 2.824   �[0m |
epoch train_loss valid_loss accuracy time
0 0.547042 0.588752 0.754119 00:18
1 0.491731 0.469795 0.771863 00:18
2 0.454340 0.433961 0.775190 00:18
3 0.424386 0.432385 0.782953 00:18
4 0.397645 0.406420 0.805767 00:19 
| �[0m 7       �[0m | �[0m 0.8058  �[0m | �[0m 6.186   �[0m | �[0m 7.016   �[0m | �[0m 3.316   �[0m |
epoch train_loss valid_loss accuracy time
0 0.485245 0.487635 0.751109 00:18
1 0.450832 0.446423 0.750317 00:18
2 0.448203 0.449419 0.755228 00:18
3 0.430258 0.443562 0.744297 00:18
4 0.429821 0.437173 0.761565 00:18 
| �[0m 8       �[0m | �[0m 0.7616  �[0m | �[0m 2.018   �[0m | �[0m 1.316   �[0m | �[0m 3.675   �[0m |
epoch train_loss valid_loss accuracy time
0 0.458425 0.455733 0.751584 00:12
1 0.439781 0.467807 0.751109 00:12
2 0.420331 0.432216 0.775190 00:12
3 0.421012 0.421412 0.782319 00:12
4 0.401828 0.413434 0.801014 00:12 
| �[0m 9       �[0m | �[0m 0.801   �[0m | �[0m 2.051   �[0m | �[0m 1.958   �[0m | �[0m 2.332   �[0m |
epoch train_loss valid_loss accuracy time
0 0.546997 0.506728 0.761407 00:18
1 0.489712 0.439324 0.799588 00:18
2 0.448558 0.448419 0.786122 00:18
3 0.436869 0.435375 0.801648 00:18
4 0.417128 0.421093 0.798321 00:18 
| �[0m 10      �[0m | �[0m 0.7983  �[0m | �[0m 5.203   �[0m | �[0m 7.719   �[0m | �[0m 3.407   �[0m |
epoch train_loss valid_loss accuracy time
0 0.380781 0.463409 0.786439 00:07
1 0.359212 0.461147 0.798321 00:07
2 0.351414 0.368950 0.822719 00:07
3 0.347257 0.367056 0.829373 00:07
4 0.337212 0.362375 0.830799 00:07 
| �[0m 11      �[0m | �[0m 0.8308  �[0m | �[0m 6.048   �[0m | �[0m 4.376   �[0m | �[0m 0.08141 �[0m |
epoch train_loss valid_loss accuracy time
0 0.430772 0.430897 0.767744 00:12
1 0.402611 0.432137 0.764259 00:12
2 0.407579 0.409651 0.812104 00:12
3 0.374988 0.391822 0.816698 00:12
4 0.378011 0.389278 0.816065 00:12 
| �[0m 12      �[0m | �[0m 0.8161  �[0m | �[0m 7.083   �[0m | �[0m 1.385   �[0m | �[0m 2.806   �[0m |
epoch train_loss valid_loss accuracy time
0 0.402018 0.412051 0.812262 00:09
1 0.372804 0.464937 0.811629 00:09
2 0.368274 0.384675 0.820184 00:09
3 0.364502 0.371920 0.820659 00:09
4 0.348998 0.369445 0.823828 00:09 
| �[0m 13      �[0m | �[0m 0.8238  �[0m | �[0m 4.812   �[0m | �[0m 3.785   �[0m | �[0m 1.396   �[0m |
| �[0m 14      �[0m | �[0m 0.8172  �[0m | �[0m 7.672   �[0m | �[0m 6.719   �[0m | �[0m 2.72    �[0m |
epoch train_loss valid_loss accuracy time
0 0.476033 0.442598 0.803549 00:12
1 0.405236 0.414015 0.788973 00:11
2 0.406291 0.451269 0.789449 00:11
3 0.391013 0.393243 0.816065 00:12
4 0.374160 0.377635 0.821451 00:12 
| �[0m 15      �[0m | �[0m 0.8215  �[0m | �[0m 6.464   �[0m | �[0m 7.954   �[0m | �[0m 2.647   �[0m |
epoch train_loss valid_loss accuracy time
0 0.390142 0.390678 0.810995 00:06
1 0.381717 0.382202 0.813055 00:06
2 0.368564 0.378705 0.823828 00:06
3 0.358858 0.368329 0.823511 00:07
4 0.353392 0.363913 0.825887 00:06 
| �[0m 16      �[0m | �[0m 0.8259  �[0m | �[0m 0.1229  �[0m | �[0m 7.83    �[0m | �[0m 0.3708  �[0m |
epoch train_loss valid_loss accuracy time
0 0.381215 0.422651 0.800697 00:06
1 0.377345 0.380863 0.815906 00:06
2 0.366631 0.370579 0.822877 00:06
3 0.362745 0.366619 0.823352 00:07
4 0.356861 0.364835 0.825887 00:07 
| �[0m 17      �[0m | �[0m 0.8259  �[0m | �[0m 0.03098 �[0m | �[0m 3.326   �[0m | �[0m 0.007025�[0m |
epoch train_loss valid_loss accuracy time
0 0.404604 0.443035 0.824461 00:07
1 0.361872 0.388880 0.823669 00:06
2 0.375164 0.369968 0.825095 00:06
3 0.352091 0.363823 0.827947 00:06
4 0.335458 0.362544 0.829373 00:07 
| �[0m 18      �[0m | �[0m 0.8294  �[0m | �[0m 7.81    �[0m | �[0m 7.976   �[0m | �[0m 0.0194  �[0m |
epoch train_loss valid_loss accuracy time
0 0.679292 0.677299 0.248891 00:05
1 0.675403 0.678406 0.248891 00:05
2 0.673259 0.673374 0.248891 00:06
3 0.674996 0.673514 0.248891 00:07
4 0.668813 0.673671 0.248891 00:07 
| �[0m 19      �[0m | �[0m 0.2489  �[0m | �[0m 0.4499  �[0m | �[0m 0.138   �[0m | �[0m 0.001101�[0m |
epoch train_loss valid_loss accuracy time
0 0.524201 0.528132 0.729880 00:30
1 0.419377 0.403198 0.812104 00:31
2 0.399398 0.418890 0.812421 00:31
3 0.381651 0.391744 0.819075 00:31
4 0.368742 0.377904 0.822085 00:31 
| �[0m 20      �[0m | �[0m 0.8221  �[0m | �[0m 0.0     �[0m | �[0m 6.575   �[0m | �[0m 4.0     �[0m |
epoch train_loss valid_loss accuracy time
0 0.681083 0.682397 0.248891 00:05
1 0.672935 0.679371 0.248891 00:06
2 0.675200 0.673466 0.248891 00:06
3 0.674251 0.673356 0.248891 00:06
4 0.668861 0.673186 0.248891 00:06 
| �[0m 21      �[0m | �[0m 0.2489  �[0m | �[0m 8.0     �[0m | �[0m 0.0     �[0m | �[0m 0.0     �[0m |
epoch train_loss valid_loss accuracy time
0 0.407246 0.432203 0.801331 00:10
1 0.385086 0.399513 0.811312 00:11
2 0.377365 0.384121 0.816065 00:12
3 0.366855 0.371010 0.823194 00:12
4 0.361931 0.368933 0.825095 00:12 
| �[0m 22      �[0m | �[0m 0.8251  �[0m | �[0m 0.0     �[0m | �[0m 4.502   �[0m | �[0m 2.193   �[0m |
epoch train_loss valid_loss accuracy time
0 0.493623 0.476921 0.766001 00:30
1 0.441126 0.443774 0.776774 00:31
2 0.424523 0.437125 0.783904 00:31
3 0.402457 0.408628 0.795944 00:31
4 0.439420 0.431756 0.788973 00:32 
| �[0m 23      �[0m | �[0m 0.789   �[0m | �[0m 8.0     �[0m | �[0m 3.702   �[0m | �[0m 4.0     �[0m |
epoch train_loss valid_loss accuracy time
0 0.515615 0.513919 0.751109 00:31
1 0.462674 0.495322 0.751584 00:31
2 0.465430 0.483685 0.751267 00:31
3 0.481308 0.495375 0.755070 00:31
4 0.481324 0.491275 0.754911 00:32 
| �[0m 24      �[0m | �[0m 0.7549  �[0m | �[0m 6.009   �[0m | �[0m 0.0     �[0m | �[0m 4.0     �[0m |
epoch train_loss valid_loss accuracy time
0 0.422837 0.403953 0.819392 00:06
1 0.380753 0.367345 0.826838 00:06
2 0.353045 0.365174 0.830006 00:07
3 0.348628 0.364282 0.826362 00:07
4 0.343561 0.361509 0.829214 00:07 
| �[0m 25      �[0m | �[0m 0.8292  �[0m | �[0m 3.522   �[0m | �[0m 8.0     �[0m | �[0m 0.0     �[0m |
epoch train_loss valid_loss accuracy time
0 0.807766 1.307279 0.481622 00:31
1 0.513308 0.499470 0.783587 00:32
2 0.445906 0.492620 0.798004 00:31
3 0.385094 0.399986 0.807509 00:32
4 0.387228 0.384739 0.817015 00:31 
| �[0m 26      �[0m | �[0m 0.817   �[0m | �[0m 0.0     �[0m | �[0m 8.0     �[0m | �[0m 4.0     �[0m |
epoch train_loss valid_loss accuracy time
0 0.442076 0.491338 0.755387 00:31
1 0.441078 0.443674 0.760773 00:31
2 0.417575 0.418758 0.792142 00:31
3 0.410825 0.417581 0.788498 00:34
4 0.403407 0.410941 0.798321 00:46 
| �[0m 27      �[0m | �[0m 0.7983  �[0m | �[0m 0.0     �[0m | �[0m 0.0     �[0m | �[0m 4.0     �[0m |
epoch train_loss valid_loss accuracy time
0 0.407006 0.419679 0.792142 00:08
1 0.390913 0.392631 0.810520 00:08
2 0.365560 0.394330 0.817491 00:08
3 0.378459 0.387244 0.820659 00:08
4 0.375275 0.385417 0.828897 00:08 
| �[0m 28      �[0m | �[0m 0.8289  �[0m | �[0m 3.379   �[0m | �[0m 2.848   �[0m | �[0m 0.0     �[0m |
epoch train_loss valid_loss accuracy time
0 0.430604 0.469592 0.781210 00:45
1 0.423074 0.429704 0.797529 00:45
2 0.400120 0.393398 0.810995 00:45
3 0.382361 0.390651 0.816065 00:46
4 0.389520 0.401878 0.807193 00:46 
| �[0m 29      �[0m | �[0m 0.8072  �[0m | �[0m 0.0     �[0m | �[0m 2.588   �[0m | �[0m 4.0     �[0m |
epoch train_loss valid_loss accuracy time
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epoch train_loss valid_loss accuracy time
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epoch train_loss valid_loss accuracy time
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4 0.346463 0.366307 0.825412 00:09 
| �[0m 74      �[0m | �[0m 0.8254  �[0m | �[0m 6.837   �[0m | �[0m 7.988   �[0m | �[0m 1.055   �[0m |
| �[0m 75      �[0m | �[0m 0.8259  �[0m | �[0m 0.6629  �[0m | �[0m 7.012   �[0m | �[0m 0.03222 �[0m |
| �[0m 76      �[0m | �[0m 0.8311  �[0m | �[0m 5.177   �[0m | �[0m 1.457   �[0m | �[0m 0.5857  �[0m |
epoch train_loss valid_loss accuracy time
0 0.455741 0.530008 0.794994 00:11
1 0.421961 0.423317 0.805292 00:11
2 0.405799 0.405729 0.807351 00:12
3 0.383895 0.395092 0.816857 00:12
4 0.378882 0.386044 0.818758 00:12 
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epoch train_loss valid_loss accuracy time
0 0.412261 0.410950 0.798954 00:08
1 0.382314 0.408471 0.803390 00:09
2 0.347488 0.387500 0.815273 00:09
3 0.343408 0.372050 0.821451 00:09
4 0.344963 0.366158 0.822719 00:09 
| �[0m 78      �[0m | �[0m 0.8227  �[0m | �[0m 0.4036  �[0m | �[0m 7.997   �[0m | �[0m 1.439   �[0m |
epoch train_loss valid_loss accuracy time
0 0.412856 0.410016 0.799113 00:08
1 0.410852 0.416405 0.788498 00:08
2 0.373897 0.384385 0.824303 00:09
3 0.353164 0.366129 0.822719 00:09
4 0.353253 0.362269 0.826362 00:09 
| �[0m 79      �[0m | �[0m 0.8264  �[0m | �[0m 3.438   �[0m | �[0m 7.982   �[0m | �[0m 1.829   �[0m |
epoch train_loss valid_loss accuracy time
0 0.419316 0.408936 0.798162 00:08
1 0.393826 0.390526 0.820184 00:08
2 0.372879 0.374823 0.822719 00:08
3 0.358019 0.370913 0.820342 00:08
4 0.346020 0.362252 0.829690 00:08 
| �[0m 80      �[0m | �[0m 0.8297  �[0m | �[0m 6.88    �[0m | �[0m 2.404   �[0m | �[0m 1.666   �[0m |
epoch train_loss valid_loss accuracy time
0 0.433481 0.437320 0.790082 00:18
1 0.415280 0.402946 0.814164 00:18
2 0.365575 0.376285 0.822877 00:18
3 0.363206 0.371865 0.820501 00:18
4 0.356401 0.370252 0.823828 00:18 
| �[0m 81      �[0m | �[0m 0.8238  �[0m | �[0m 0.03221 �[0m | �[0m 1.306   �[0m | �[0m 3.909   �[0m |
epoch train_loss valid_loss accuracy time
0 0.393150 0.420964 0.783745 00:07
1 0.375065 0.371380 0.823986 00:07
2 0.362952 0.387037 0.813688 00:07
3 0.347245 0.370225 0.824937 00:07
4 0.348406 0.361420 0.830640 00:07 
| �[0m 82      �[0m | �[0m 0.8306  �[0m | �[0m 1.575   �[0m | �[0m 2.689   �[0m | �[0m 0.8684  �[0m |
epoch train_loss valid_loss accuracy time
0 0.395530 0.397430 0.818600 00:06
1 0.358679 0.396773 0.818283 00:07
2 0.349305 0.372877 0.823828 00:07
3 0.347346 0.363006 0.828422 00:07
4 0.335652 0.362567 0.830957 00:07 
| �[0m 83      �[0m | �[0m 0.831   �[0m | �[0m 2.765   �[0m | �[0m 5.439   �[0m | �[0m 0.04047 �[0m |
epoch train_loss valid_loss accuracy time
0 0.380831 0.386072 0.814322 00:05
1 0.369778 0.392521 0.810520 00:07
2 0.368286 0.383131 0.816857 00:07
3 0.356585 0.367839 0.821768 00:07
4 0.344722 0.366639 0.825253 00:07 
| �[0m 84      �[0m | �[0m 0.8253  �[0m | �[0m 0.1961  �[0m | �[0m 4.123   �[0m | �[0m 0.02039 �[0m |
epoch train_loss valid_loss accuracy time
0 0.502605 0.463703 0.772180 00:11
1 0.407231 0.404386 0.804499 00:13
2 0.406849 0.411254 0.817491 00:12
3 0.379910 0.389118 0.817174 00:12
4 0.370964 0.379835 0.821293 00:12 
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epoch train_loss valid_loss accuracy time
0 0.402574 0.431237 0.786755 00:11
1 0.380494 0.392253 0.817966 00:11
2 0.363284 0.393907 0.815748 00:11
3 0.355680 0.368488 0.822560 00:12
4 0.362978 0.367755 0.823511 00:12 
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epoch train_loss valid_loss accuracy time
0 0.448572 0.451255 0.789766 00:10
1 0.417093 0.411838 0.808143 00:11
2 0.400799 0.400185 0.816223 00:12
3 0.378641 0.385082 0.820342 00:12
4 0.365278 0.380320 0.818441 00:11 
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epoch train_loss valid_loss accuracy time
0 0.399453 0.429288 0.783112 00:07
1 0.368987 0.376985 0.825729 00:07
2 0.358226 0.372103 0.830165 00:07
3 0.348940 0.362069 0.831115 00:07
4 0.341437 0.361470 0.830482 00:07 
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| �[0m 89      �[0m | �[0m 0.8294  �[0m | �[0m 7.995   �[0m | �[0m 7.186   �[0m | �[0m 0.1199  �[0m |
epoch train_loss valid_loss accuracy time
0 0.390914 0.403780 0.799905 00:07
1 0.375241 0.400406 0.821610 00:07
2 0.359710 0.373233 0.826046 00:07
3 0.351108 0.367255 0.823986 00:07
4 0.348230 0.362827 0.830482 00:07 
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| �[0m 91      �[0m | �[0m 0.8294  �[0m | �[0m 0.03285 �[0m | �[0m 5.742   �[0m | �[0m 0.9747  �[0m |
epoch train_loss valid_loss accuracy time
0 0.384485 0.413711 0.816857 00:06
1 0.358383 0.382125 0.813055 00:06
2 0.349632 0.380644 0.825570 00:07
3 0.350005 0.363857 0.833492 00:07
4 0.341342 0.362533 0.829848 00:07 
| �[0m 92      �[0m | �[0m 0.8298  �[0m | �[0m 4.112e-0�[0m | �[0m 8.0     �[0m | �[0m 0.0     �[0m |
epoch train_loss valid_loss accuracy time
0 0.461806 0.454314 0.752218 00:11
1 0.429429 0.452721 0.768061 00:13
2 0.408792 0.422315 0.785013 00:12
3 0.400290 0.411134 0.805292 00:12
4 0.396974 0.409337 0.804658 00:12 
| �[0m 93      �[0m | �[0m 0.8047  �[0m | �[0m 4.563   �[0m | �[0m 0.6868  �[0m | �[0m 2.461   �[0m |
epoch train_loss valid_loss accuracy time
0 0.464920 0.440214 0.772655 00:11
1 0.401611 0.400240 0.805767 00:11
2 0.391905 0.414512 0.798638 00:13
3 0.391561 0.407271 0.805608 00:13
4 0.387596 0.397586 0.814639 00:13 
| �[0m 94      �[0m | �[0m 0.8146  �[0m | �[0m 4.697   �[0m | �[0m 3.412   �[0m | �[0m 2.514   �[0m |
epoch train_loss valid_loss accuracy time
0 0.417917 0.423588 0.755070 00:05
1 0.390158 0.396916 0.817649 00:07
2 0.374562 0.389764 0.828422 00:07
3 0.368317 0.385268 0.822719 00:07
4 0.373367 0.385092 0.827471 00:07 
| �[0m 95      �[0m | �[0m 0.8275  �[0m | �[0m 5.04    �[0m | �[0m 0.4492  �[0m | �[0m 0.5899  �[0m |
epoch train_loss valid_loss accuracy time
0 0.368991 0.388251 0.822402 00:07
1 0.363111 0.399670 0.814797 00:08
2 0.370390 0.377726 0.818441 00:10
3 0.353493 0.368603 0.823828 00:10
4 0.355493 0.368281 0.823669 00:09 
| �[0m 96      �[0m | �[0m 0.8237  �[0m | �[0m 0.6025  �[0m | �[0m 2.712   �[0m | �[0m 1.166   �[0m |
epoch train_loss valid_loss accuracy time
0 0.426161 0.417567 0.793726 00:07
1 0.397633 0.392852 0.818124 00:08
2 0.371073 0.371678 0.821768 00:09
3 0.351716 0.361607 0.831749 00:10
4 0.347291 0.359881 0.832066 00:09 
| �[95m 97      �[0m | �[95m 0.8321  �[0m | �[95m 6.389   �[0m | �[95m 3.648   �[0m | �[95m 1.016   �[0m |
epoch train_loss valid_loss accuracy time
0 0.408937 0.440755 0.785805 00:12
1 0.369800 0.379622 0.819867 00:12
2 0.356048 0.370119 0.827630 00:11
3 0.354271 0.366091 0.826362 00:11
4 0.359324 0.366020 0.827313 00:12 
| �[0m 98      �[0m | �[0m 0.8273  �[0m | �[0m 0.5927  �[0m | �[0m 1.715   �[0m | �[0m 2.847   �[0m |
epoch train_loss valid_loss accuracy time
0 0.443831 0.469775 0.753802 00:11
1 0.411636 0.423362 0.787864 00:11
2 0.411669 0.421874 0.797529 00:13
3 0.392242 0.395377 0.816382 00:12
4 0.383947 0.390270 0.818441 00:12 
| �[0m 99      �[0m | �[0m 0.8184  �[0m | �[0m 6.602   �[0m | �[0m 2.266   �[0m | �[0m 2.535   �[0m |
epoch train_loss valid_loss accuracy time
0 0.413688 0.469193 0.811153 00:08
1 0.383908 0.401520 0.815589 00:08
2 0.363908 0.375178 0.824937 00:08
3 0.360694 0.366536 0.828739 00:08
4 0.341851 0.362886 0.830165 00:08 
| �[0m 100     �[0m | �[0m 0.8302  �[0m | �[0m 3.099   �[0m | �[0m 6.058   �[0m | �[0m 1.058   �[0m |
epoch train_loss valid_loss accuracy time
0 0.404324 0.402310 0.807668 00:09
1 0.383628 0.428126 0.780577 00:09
2 0.360917 0.375198 0.826996 00:09
3 0.349922 0.367114 0.828264 00:09
4 0.338715 0.363762 0.830165 00:09 
| �[0m 101     �[0m | �[0m 0.8302  �[0m | �[0m 4.268   �[0m | �[0m 5.28    �[0m | �[0m 1.397   �[0m |
epoch train_loss valid_loss accuracy time
0 0.381317 0.410546 0.815431 00:06
1 0.355632 0.424577 0.802440 00:06
2 0.361193 0.377675 0.818124 00:07
3 0.355751 0.363205 0.827313 00:07
4 0.338802 0.362403 0.827471 00:07 
| �[0m 102     �[0m | �[0m 0.8275  �[0m | �[0m 4.735   �[0m | �[0m 3.398   �[0m | �[0m 0.01904 �[0m |
epoch train_loss valid_loss accuracy time
0 0.381150 0.398499 0.813213 00:07
1 0.370997 0.413040 0.798162 00:07
2 0.364154 0.369066 0.823352 00:07
3 0.354434 0.362925 0.825095 00:07
4 0.348736 0.362125 0.824461 00:07 
| �[0m 103     �[0m | �[0m 0.8245  �[0m | �[0m 0.0     �[0m | �[0m 6.371   �[0m | �[0m 9.721e-0�[0m |
epoch train_loss valid_loss accuracy time
0 0.388378 0.412839 0.810361 00:06
1 0.377533 0.380432 0.822560 00:06
2 0.356242 0.372250 0.824937 00:07
3 0.346274 0.364236 0.830323 00:07
4 0.349273 0.362597 0.830482 00:07 
| �[0m 104     �[0m | �[0m 0.8305  �[0m | �[0m 1.683   �[0m | �[0m 6.381   �[0m | �[0m 0.8564  �[0m |
epoch train_loss valid_loss accuracy time
0 0.410286 0.413506 0.801648 00:07
1 0.386787 0.397328 0.812262 00:07
2 0.365268 0.390419 0.825570 00:07
3 0.368566 0.386955 0.828264 00:07
4 0.362751 0.383124 0.830482 00:07 
| �[0m 105     �[0m | �[0m 0.8305  �[0m | �[0m 6.387   �[0m | �[0m 2.333   �[0m | �[0m 0.4877  �[0m |
epoch train_loss valid_loss accuracy time
0 0.432347 0.450028 0.780894 00:17
1 0.414766 0.402565 0.809569 00:17
2 0.382495 0.382281 0.812421 00:18
3 0.366852 0.373158 0.822085 00:18
4 0.353692 0.368471 0.824461 00:18 
| �[0m 106     �[0m | �[0m 0.8245  �[0m | �[0m 0.9452  �[0m | �[0m 6.902   �[0m | �[0m 3.991   �[0m |
epoch train_loss valid_loss accuracy time
0 0.397604 0.407966 0.814797 00:06
1 0.386755 0.382698 0.800063 00:07
2 0.361672 0.373610 0.823194 00:07
3 0.345655 0.363278 0.829848 00:07
4 0.349931 0.362171 0.830957 00:07 
| �[0m 107     �[0m | �[0m 0.831   �[0m | �[0m 5.475   �[0m | �[0m 5.721   �[0m | �[0m 0.02659 �[0m |
| �[0m 108     �[0m | �[0m 0.8308  �[0m | �[0m 4.597   �[0m | �[0m 7.994   �[0m | �[0m 0.1318  �[0m |
epoch train_loss valid_loss accuracy time
0 0.391720 0.395161 0.819550 00:06
1 0.372859 0.387859 0.810203 00:07
2 0.367385 0.376952 0.812262 00:07
3 0.346810 0.365312 0.827155 00:07
4 0.341221 0.363528 0.829056 00:07 
| �[0m 109     �[0m | �[0m 0.8291  �[0m | �[0m 2.391   �[0m | �[0m 4.915   �[0m | �[0m 0.8695  �[0m |
epoch train_loss valid_loss accuracy time
0 0.391635 0.402138 0.804658 00:08
1 0.402425 0.468013 0.803866 00:09
2 0.368443 0.372927 0.823669 00:09
3 0.361579 0.364913 0.826679 00:09
4 0.347989 0.363416 0.828580 00:09 
| �[0m 110     �[0m | �[0m 0.8286  �[0m | �[0m 5.711   �[0m | �[0m 7.031   �[0m | �[0m 1.157   �[0m |
epoch train_loss valid_loss accuracy time
0 0.393225 0.414461 0.812738 00:07
1 0.377641 0.381910 0.820659 00:07
2 0.363979 0.375042 0.826838 00:07
3 0.349867 0.363547 0.830165 00:07
4 0.350035 0.364254 0.830006 00:07 
| �[0m 111     �[0m | �[0m 0.83    �[0m | �[0m 3.867   �[0m | �[0m 7.351   �[0m | �[0m 0.7373  �[0m |
| �[0m 112     �[0m | �[0m 0.8275  �[0m | �[0m 5.568   �[0m | �[0m 0.8565  �[0m | �[0m 0.9522  �[0m |
| �[0m 113     �[0m | �[0m 0.8311  �[0m | �[0m 5.553   �[0m | �[0m 1.45    �[0m | �[0m 0.0     �[0m |
epoch train_loss valid_loss accuracy time
0 0.404215 0.421293 0.817807 00:10
1 0.380753 0.388533 0.803074 00:09
2 0.362475 0.381199 0.820342 00:09
3 0.353416 0.368431 0.824620 00:09
4 0.335854 0.365448 0.825570 00:09 
| �[0m 114     �[0m | �[0m 0.8256  �[0m | �[0m 3.949   �[0m | �[0m 1.125   �[0m | �[0m 1.109   �[0m |
epoch train_loss valid_loss accuracy time
0 0.397351 0.482881 0.766160 00:08
1 0.373190 0.372999 0.819075 00:08
2 0.354075 0.368455 0.826046 00:08
3 0.350617 0.362793 0.830323 00:08
4 0.347381 0.361980 0.831274 00:08 
| �[0m 115     �[0m | �[0m 0.8313  �[0m | �[0m 7.258   �[0m | �[0m 4.724   �[0m | �[0m 1.639   �[0m |
=============================================================

optimizer.max['target']
0.8428390622138977

{key: 2**int(value)
  for key, value in optimizer.max['params'].items()}
{'pow_n_a': 2, 'pow_n_d': 64, 'pow_n_steps': 1}

Out of memory dataset

If your dataset is so big it doesn't fit in memory, you can load a chunk of it each epoch.

df = pd.read_csv(path/'adult.csv')
df_main,df_valid = df.iloc[:-1000].copy(),df.iloc[-1000:].copy()
# choose size that fit in memory
dataset_size = 1000
# load chunk with your own code
def load_chunk():
    return df_main.sample(dataset_size).copy()
df_small = load_chunk()
cat_names = ['workclass', 'education', 'marital-status', 'occupation', 
             'relationship', 'race', 'native-country', 'sex']
cont_names = ['age', 'fnlwgt', 'education-num']
procs = [Categorify, FillMissing, Normalize]
splits = RandomSplitter()(range_of(df_small))
to = TabularPandas(df_small, procs, cat_names, cont_names, y_names="salary", y_block = CategoryBlock(), 
                   splits=None, do_setup=True)
# save the validation set
to_valid = to.new(df_valid)
to_valid.process()
val_dl = TabDataLoader(to_valid.train)
len(to.train)
1000

class ReloadCallback(Callback):
    def begin_epoch(self): 
        df_small = load_chunk()
        to_new = to.new(df_small)
        to_new.process()
        trn_dl = TabDataLoader(to_new.train)
        self.learn.dls = DataLoaders(trn_dl, val_dl).cuda()
dls = to.dataloaders()
emb_szs = get_emb_sz(to)
model = TabNetModel(emb_szs, len(to.cont_names), dls.c, n_d=8, n_a=32, n_steps=1); 
opt_func = partial(Adam, wd=0.01, eps=1e-5)
learn = Learner(dls, model, CrossEntropyLossFlat(), opt_func=opt_func, lr=3e-2, metrics=[accuracy])
learn.add_cb(ReloadCallback());
learn.fit_one_cycle(10)
epoch train_loss valid_loss accuracy time
0 0.587740 0.550544 0.756000 00:01
1 0.545411 0.515772 0.782000 00:01
2 0.484289 0.468586 0.813000 00:01
3 0.447111 0.435774 0.817000 00:01
4 0.449050 0.394715 0.819000 00:01
5 0.428863 0.382005 0.835000 00:01
6 0.382100 0.404258 0.826000 00:01
7 0.383915 0.376179 0.833000 00:01
8 0.389460 0.367857 0.834000 00:01
9 0.376486 0.367577 0.834000 00:01
Comments
  • ERROR while running example code

    ERROR while running example code

    When I am running example script provided here: on step, learn.lr_find() , I am getting bellow error: `--------------------------------------------------------------------------- ValueError Traceback (most recent call last) in ----> 1 learn.lr_find()

    /opt/conda/lib/python3.7/site-packages/fastai2/callback/schedule.py in lr_find(self, start_lr, end_lr, num_it, stop_div, show_plot, suggestions) 226 n_epoch = num_it//len(self.dls.train) + 1 227 cb=LRFinder(start_lr=start_lr, end_lr=end_lr, num_it=num_it, stop_div=stop_div) --> 228 with self.no_logging(): self.fit(n_epoch, cbs=cb) 229 if show_plot: self.recorder.plot_lr_find() 230 if suggestions:

    /opt/conda/lib/python3.7/site-packages/fastcore/utils.py in _f(*args, **kwargs) 429 init_args.update(log) 430 setattr(inst, 'init_args', init_args) --> 431 return inst if to_return else f(*args, **kwargs) 432 return _f 433

    /opt/conda/lib/python3.7/site-packages/fastai2/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt) 198 try: 199 self.epoch=epoch; self('begin_epoch') --> 200 self._do_epoch_train() 201 self._do_epoch_validate() 202 except CancelEpochException: self('after_cancel_epoch')

    /opt/conda/lib/python3.7/site-packages/fastai2/learner.py in _do_epoch_train(self) 173 try: 174 self.dl = self.dls.train; self('begin_train') --> 175 self.all_batches() 176 except CancelTrainException: self('after_cancel_train') 177 finally: self('after_train')

    /opt/conda/lib/python3.7/site-packages/fastai2/learner.py in all_batches(self) 151 def all_batches(self): 152 self.n_iter = len(self.dl) --> 153 for o in enumerate(self.dl): self.one_batch(*o) 154 155 def one_batch(self, i, b):

    /opt/conda/lib/python3.7/site-packages/fastai2/learner.py in one_batch(self, i, b) 157 try: 158 self._split(b); self('begin_batch') --> 159 self.pred = self.model(*self.xb); self('after_pred') 160 if len(self.yb) == 0: return 161 self.loss = self.loss_func(self.pred, *self.yb); self('after_loss')

    /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs) 548 result = self._slow_forward(*input, **kwargs) 549 else: --> 550 result = self.forward(*input, **kwargs) 551 for hook in self._forward_hooks.values(): 552 hook_result = hook(self, input, result)

    /opt/conda/lib/python3.7/site-packages/fast_tabnet/core.py in forward(self, x_cat, x_cont) 32 x_cont = self.bn_cont(x_cont) 33 x = torch.cat([x, x_cont], 1) if self.n_emb != 0 else x_cont ---> 34 x, _, _, _ = self.tab_net(x) 35 if self.y_range is not None: 36 x = (self.y_range[1]-self.y_range[0]) * torch.sigmoid(x) + self.y_range[0]

    ValueError: not enough values to unpack (expected 4, got 2)`

    opened by saurabh502 10
  • LR finder graph : isn't is suppose to go up?

    LR finder graph : isn't is suppose to go up?

    Hello,

    Happy to create the first issue! :)

    I just have a question : what do you think about the learning rate finder graph? From what I understand we are supposed to see the loss going up if the LR is too high, but here it does not seem to be the case? Any idea of why?

    Also it seems that batch norm is 10? (or maybe I'm wrong) I did not perform an extensive benchmark but the paper's authors are using very large batch, so maybe the LR finder on such small batches is not a great choice.

    Thanks!

    wontfix 
    opened by Optimox 4
  • How to use fast_tabnet for a multi-label classification task?

    How to use fast_tabnet for a multi-label classification task?

    Thank you very much for creating this library.

    I couldn't figure out how exactly to solve a multi-label classification problem using it. Any tip will be very helpful.

    Thank you very much.

    opened by pankaj-kvhld 3
  • Cannot apply

    Cannot apply "EarlyStoppingCallback" and "SaveModelCallback" in fast_tabnet

    Hi,

    I tried to apply "EarlyStoppingCallback" and "SaveModelCallback" in fast_tabnet regressor as the following,

    cycle_num = 10
model = TabNetModel(emb_szs, len(to.cont_names), dls.c, n_d=64, n_a=64, n_steps=5, virtual_batch_size=256)
opt_func = partial(Adam, wd=0.01, eps=1e-5)
learn = Learner(dls, model, MSELossFlat(), opt_func=opt_func, lr=1e-2, metrics = rmse,
                path=Path('../model/fast_tabnet/'),
                callback_fns=[
                    partial(SaveModelCallback, monitor ='val_loss', every='epoch', mode ='min', name = f'emb_szs_{cycle_num}'),
                    partial(EarlyStoppingCallback, monitor='val_loss', patience=2) 
                ]
               )
learn.lr_find()
_SuggestedLRs(lr_min=0.06309573650360108, lr_steep=1.9054607491852948e-06)_ (this is the output of learn.lr_find())
learn.fit_one_cycle(4, 0.06309573650360108)

    However, after learn.fit_one_cycle(), in the path I specified, I cannot find any model saved, and it does not early stop even though there are more than two iterations that validation error increases consecutively. May I have your suggestions? Besides, I select the lr_min as the lr used in learn.fit_one_cycle(), is that correct? Thank you very much.

    opened by zjgbz 3
  • Regressor function for fast_tabnet

    Regressor function for fast_tabnet

    Hi,

    Thank you very much for building this library, and since my task is actually a regression problem, I am wondering if this fast_tabnet has any regressor function? And if so, could you provide some simple examples? Thank you very much.

    Best regards, Wu-Sheng

    opened by zjgbz 2
  • Bump nokogiri from 1.12.5 to 1.13.2 in /docs

    Bump nokogiri from 1.12.5 to 1.13.2 in /docs

    Bumps nokogiri from 1.12.5 to 1.13.2.

    Release notes

    Sourced from nokogiri's releases.

    1.13.2 / 2022-02-21

    Security

    • [CRuby] Vendored libxml2 is updated from 2.9.12 to 2.9.13. This update addresses CVE-2022-23308.
    • [CRuby] Vendored libxslt is updated from 1.1.34 to 1.1.35. This update addresses CVE-2021-30560.

    Please see GHSA-fq42-c5rg-92c2 for more information about these CVEs.

    Dependencies

    SHA256 checksums:

    63469a9bb56a21c62fbaea58d15f54f8f167ff6fde51c5c2262072f939926fdd  nokogiri-1.13.2-aarch64-linux.gem
2986617f982f645c06f22515b721e6d2613dd69493e5c41ddd03c4830c3b3065  nokogiri-1.13.2-arm64-darwin.gem
aca1d66206740b29d0d586b1d049116adcb31e6cdd7c4dd3a96eb77da215a0c4  nokogiri-1.13.2-java.gem
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01937a6551d997aca32468da08ced0878ba4e1dfd0b51d953617185eefc57ffa  nokogiri-1.13.2-x86_64-darwin.gem
70112ae29939d4b5e1c8ba13e1f0d82ff43cd5564ce138f622fe6ebddc503654  nokogiri-1.13.2-x86_64-linux.gem
def6b330c53dcabe8b7d545cf9db4dfb45e9d39040b531eb81aee0c840fd66c2  nokogiri-1.13.2.gem

    1.13.1 / 2022-01-13

    Fixed

    • Fix Nokogiri::XSLT.quote_params regression in v1.13.0 that raised an exception when non-string stylesheet parameters were passed. Non-string parameters (e.g., integers and symbols) are now explicitly supported and both keys and values will be stringified with #to_s. [#2418]
    • Fix HTML5 CSS selector query regression in v1.13.0 that raised an Nokogiri::XML::XPath::SyntaxError when parsing XPath attributes mixed into the CSS query. Although this mash-up of XPath and CSS syntax previously worked unintentionally, it is now an officially supported feature and is documented as such. [#2419]

    SHA265 checksums

    9206569b36f0066f943f174a832b50e69551c2a81333b7a62d4073e97ea4c3c6  nokogiri-1.13.1-aarch64-linux.gem
39d73197506acd3748c84600e000bb44ccd930695a9fc8b489b1b4df37dd14f0  nokogiri-1.13.1-arm64-darwin.gem
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690958426e3151ba0c22e8d88637dba5e0c636107f3def2ffc10e334d451e61f  nokogiri-1.13.1-x86-linux.gem
</tr></table>

    ... (truncated)

    Changelog

    Sourced from nokogiri's changelog.

    1.13.2 / 2022-02-21

    Security

    • [CRuby] Vendored libxml2 is updated from 2.9.12 to 2.9.13. This update addresses CVE-2022-23308.
    • [CRuby] Vendored libxslt is updated from 1.1.34 to 1.1.35. This update addresses CVE-2021-30560.

    Please see GHSA-fq42-c5rg-92c2 for more information about these CVEs.

    Dependencies

    1.13.1 / 2022-01-13

    Fixed

    • Fix Nokogiri::XSLT.quote_params regression in v1.13.0 that raised an exception when non-string stylesheet parameters were passed. Non-string parameters (e.g., integers and symbols) are now explicitly supported and both keys and values will be stringified with #to_s. [#2418]
    • Fix CSS selector query regression in v1.13.0 that raised an Nokogiri::XML::XPath::SyntaxError when parsing XPath attributes mixed into the CSS query. Although this mash-up of XPath and CSS syntax previously worked unintentionally, it is now an officially supported feature and is documented as such. [#2419]

    1.13.0 / 2022-01-06

    Notes

    Ruby

    This release introduces native gem support for Ruby 3.1. Please note that Windows users should use the x64-mingw-ucrt platform gem for Ruby 3.1, and x64-mingw32 for Ruby 2.6–3.0 (see RubyInstaller 3.1.0 release notes).

    This release ends support for:

    Faster, more reliable installation: Native Gem for ARM64 Linux

    This version of Nokogiri ships experimental native gem support for the aarch64-linux platform, which should support AWS Graviton and other ARM Linux platforms. We don't yet have CI running for this platform, and so we're interested in hearing back from y'all whether this is working, and what problems you're seeing. Please send us feedback here: Feedback: Have you used the aarch64-linux native gem?

    Publishing

    This version of Nokogiri opts-in to the "MFA required to publish" setting on Rubygems.org. This and all future Nokogiri gem files must be published to Rubygems by an account with multi-factor authentication enabled. This should provide some additional protection against supply-chain attacks.

    A related discussion about Trust exists at #2357 in which I invite you to participate if you have feelings or opinions on this topic.

    ... (truncated)

    Commits
    • 49b8663 version bump to v1.13.2
    • 4729133 Merge pull request #2457 from sparklemotion/flavorjones-libxml-2.9.13-v1.13.x
    • 379f757 dev(package): work around gnome mirrors with expired certs
    • 95cf66c dep: upgrade libxml2 2.9.12 → 2.9.13
    • d37dd02 dep: upgrade libxslt 1.1.34 → 1.1.35
    • 59a9398 dep: upgrade mini_portile 2.7 to 2.8
    • e885463 dev(package): handle either .tar.gz or .tar.xz archive names
    • 7957c7b style: rubocop
    • 93d37f7 ci: Change Ruby setup to standard
    • 15d32f6 ci: skip html5lib-tests on v1.13.x branch
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Hooking on tabnet

    Hooking on tabnet

    First, I would like to apologize is my question is rather naive because I am not quite familiar with tabnet.

    I am trying to register_forward_hook on tabnet in order to merge the last "meaning" weights into some other classifier.

    Concretely, I am trying to do somethin similar with this notebook that uses fastai tabular_learner and performs the hook in the last liner layer (model.layers[2][0]). It would make sense to do the same in tabnet, meaning hooking in model.tab_net.final_mapping? I ask because it ends with in_features=4 and I don't know if this could be the proper place for hooking.

    wontfix 
    opened by Tato14 1
  • Bump nokogiri from 1.13.9 to 1.13.10 in /docs

    Bump nokogiri from 1.13.9 to 1.13.10 in /docs

    Bumps nokogiri from 1.13.9 to 1.13.10.

    Release notes

    Sourced from nokogiri's releases.

    1.13.10 / 2022-12-07

    Security

    • [CRuby] Address CVE-2022-23476, unchecked return value from xmlTextReaderExpand. See GHSA-qv4q-mr5r-qprj for more information.

    Improvements

    • [CRuby] XML::Reader#attribute_hash now returns nil on parse errors. This restores the behavior of #attributes from v1.13.7 and earlier. [#2715]

    sha256 checksums:

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0f85a1ad8c2b02c166a6637237133505b71a05f1bb41b91447005449769bced0  nokogiri-1.13.10-x64-mingw32.gem
91fa3a8724a1ce20fccbd718dafd9acbde099258183ac486992a61b00bb17020  nokogiri-1.13.10-x86-linux.gem
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81755fc4b8130ef9678c76a2e5af3db7a0a6664b3cba7d9fe8ef75e7d979e91b  nokogiri-1.13.10-x86_64-darwin.gem
51d5246705dedad0a09b374d09cc193e7383a5dd32136a690a3cd56e95adf0a3  nokogiri-1.13.10-x86_64-linux.gem
d3ee00f26c151763da1691c7fc6871ddd03e532f74f85101f5acedc2d099e958  nokogiri-1.13.10.gem
    Changelog

    Sourced from nokogiri's changelog.

    1.13.10 / 2022-12-07

    Security

    • [CRuby] Address CVE-2022-23476, unchecked return value from xmlTextReaderExpand. See GHSA-qv4q-mr5r-qprj for more information.

    Improvements

    • [CRuby] XML::Reader#attribute_hash now returns nil on parse errors. This restores the behavior of #attributes from v1.13.7 and earlier. [#2715]
    Commits
    • 4c80121 version bump to v1.13.10
    • 85410e3 Merge pull request #2715 from sparklemotion/flavorjones-fix-reader-error-hand...
    • 9fe0761 fix(cruby): XML::Reader#attribute_hash returns nil on error
    • 3b9c736 Merge pull request #2717 from sparklemotion/flavorjones-lock-psych-to-fix-bui...
    • 2efa87b test: skip large cdata test on system libxml2
    • 3187d67 dep(dev): pin psych to v4 until v5 builds in CI
    • a16b4bf style(rubocop): disable Minitest/EmptyLineBeforeAssertionMethods
    • See full diff in compare view

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    dependencies 
    opened by dependabot[bot] 0
  • Bump nokogiri from 1.13.6 to 1.13.9 in /docs

    Bump nokogiri from 1.13.6 to 1.13.9 in /docs

    Bumps nokogiri from 1.13.6 to 1.13.9.

    Release notes

    Sourced from nokogiri's releases.

    1.13.9 / 2022-10-18

    Security

    Dependencies

    • [CRuby] Vendored libxml2 is updated to v2.10.3 from v2.9.14.
    • [CRuby] Vendored libxslt is updated to v1.1.37 from v1.1.35.
    • [CRuby] Vendored zlib is updated from 1.2.12 to 1.2.13. (See LICENSE-DEPENDENCIES.md for details on which packages redistribute this library.)

    Fixed

    • [CRuby] Nokogiri::XML::Namespace objects, when compacted, update their internal struct's reference to the Ruby object wrapper. Previously, with GC compaction enabled, a segmentation fault was possible after compaction was triggered. [#2658] (Thanks, @​eightbitraptor and @​peterzhu2118!)
    • [CRuby] Document#remove_namespaces! now defers freeing the underlying xmlNs struct until the Document is GCed. Previously, maintaining a reference to a Namespace object that was removed in this way could lead to a segfault. [#2658]

    sha256 checksums:

    9b69829561d30c4461ea803baeaf3460e8b145cff7a26ce397119577a4083a02  nokogiri-1.13.9-aarch64-linux.gem
e76ebb4b7b2e02c72b2d1541289f8b0679fb5984867cf199d89b8ef485764956  nokogiri-1.13.9-arm64-darwin.gem
15bae7d08bddeaa898d8e3f558723300137c26a2dc2632a1f89c8574c4467165  nokogiri-1.13.9-java.gem
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ebaf82aa9a11b8fafb67873d19ee48efb565040f04c898cdce8ca0cd53ff1a12  nokogiri-1.13.9-x86-linux.gem
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    1.13.8 / 2022-07-23

    Deprecated

    • XML::Reader#attribute_nodes is deprecated due to incompatibility between libxml2's xmlReader memory semantics and Ruby's garbage collector. Although this method continues to exist for backwards compatibility, it is unsafe to call and may segfault. This method will be removed in a future version of Nokogiri, and callers should use #attribute_hash instead. [#2598]

    Improvements

    • XML::Reader#attribute_hash is a new method to safely retrieve the attributes of a node from XML::Reader. [#2598, #2599]

    Fixed

    ... (truncated)

    Changelog

    Sourced from nokogiri's changelog.

    1.13.9 / 2022-10-18

    Security

    Dependencies

    • [CRuby] Vendored libxml2 is updated to v2.10.3 from v2.9.14.
    • [CRuby] Vendored libxslt is updated to v1.1.37 from v1.1.35.
    • [CRuby] Vendored zlib is updated from 1.2.12 to 1.2.13. (See LICENSE-DEPENDENCIES.md for details on which packages redistribute this library.)

    Fixed

    • [CRuby] Nokogiri::XML::Namespace objects, when compacted, update their internal struct's reference to the Ruby object wrapper. Previously, with GC compaction enabled, a segmentation fault was possible after compaction was triggered. [#2658] (Thanks, @​eightbitraptor and @​peterzhu2118!)
    • [CRuby] Document#remove_namespaces! now defers freeing the underlying xmlNs struct until the Document is GCed. Previously, maintaining a reference to a Namespace object that was removed in this way could lead to a segfault. [#2658]

    1.13.8 / 2022-07-23

    Deprecated

    • XML::Reader#attribute_nodes is deprecated due to incompatibility between libxml2's xmlReader memory semantics and Ruby's garbage collector. Although this method continues to exist for backwards compatibility, it is unsafe to call and may segfault. This method will be removed in a future version of Nokogiri, and callers should use #attribute_hash instead. [#2598]

    Improvements

    • XML::Reader#attribute_hash is a new method to safely retrieve the attributes of a node from XML::Reader. [#2598, #2599]

    Fixed

    • [CRuby] Calling XML::Reader#attributes is now safe to call. In Nokogiri <= 1.13.7 this method may segfault. [#2598, #2599]

    1.13.7 / 2022-07-12

    Fixed

    XML::Node objects, when compacted, update their internal struct's reference to the Ruby object wrapper. Previously, with GC compaction enabled, a segmentation fault was possible after compaction was triggered. [#2578] (Thanks, @​eightbitraptor!)

    Commits
    • 897759c version bump to v1.13.9
    • aeb1ac3 doc: update CHANGELOG
    • c663e49 Merge pull request #2671 from sparklemotion/flavorjones-update-zlib-1.2.13_v1...
    • 212e07d ext: hack to cross-compile zlib v1.2.13 on darwin
    • 76dbc8c dep: update zlib to v1.2.13
    • 24e3a9c doc: update CHANGELOG
    • 4db3b4d Merge pull request #2668 from sparklemotion/flavorjones-namespace-scopes-comp...
    • 73d73d6 fix: Document#remove_namespaces! use-after-free bug
    • 5f58b34 fix: namespace nodes behave properly when compacted
    • b08a858 test: repro namespace_scopes compaction issue
    • Additional commits viewable in compare view

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  • Bump tzinfo from 1.2.7 to 1.2.10 in /docs

    Bump tzinfo from 1.2.7 to 1.2.10 in /docs

    Bumps tzinfo from 1.2.7 to 1.2.10.

    Release notes

    Sourced from tzinfo's releases.

    v1.2.10

    TZInfo v1.2.10 on RubyGems.org

    v1.2.9

    • Fixed an incorrect InvalidTimezoneIdentifier exception raised when loading a zoneinfo file that includes rules specifying an additional transition to the final defined offset (for example, Africa/Casablanca in version 2018e of the Time Zone Database). #123.

    TZInfo v1.2.9 on RubyGems.org

    v1.2.8

    • Added support for handling "slim" format zoneinfo files that are produced by default by zic version 2020b and later. The POSIX-style TZ string is now used calculate DST transition times after the final defined transition in the file. The 64-bit section is now always used regardless of whether Time has support for 64-bit times. #120.
    • Rubinius is no longer supported.

    TZInfo v1.2.8 on RubyGems.org

    Changelog

    Sourced from tzinfo's changelog.

    Version 1.2.10 - 19-Jul-2022

    Version 1.2.9 - 16-Dec-2020

    • Fixed an incorrect InvalidTimezoneIdentifier exception raised when loading a zoneinfo file that includes rules specifying an additional transition to the final defined offset (for example, Africa/Casablanca in version 2018e of the Time Zone Database). #123.

    Version 1.2.8 - 8-Nov-2020

    • Added support for handling "slim" format zoneinfo files that are produced by default by zic version 2020b and later. The POSIX-style TZ string is now used calculate DST transition times after the final defined transition in the file. The 64-bit section is now always used regardless of whether Time has support for 64-bit times. #120.
    • Rubinius is no longer supported.
    Commits
    • 0814dcd Fix the release date.
    • fd05e2a Preparing v1.2.10.
    • b98c32e Merge branch 'fix-directory-traversal-1.2' into 1.2
    • ac3ee68 Remove unnecessary escaping of + within regex character classes.
    • 9d49bf9 Fix relative path loading tests.
    • 394c381 Remove private_constant for consistency and compatibility.
    • 5e9f990 Exclude Arch Linux's SECURITY file from the time zone index.
    • 17fc9e1 Workaround for 'Permission denied - NUL' errors with JRuby on Windows.
    • 6bd7a51 Update copyright years.
    • 9905ca9 Fix directory traversal in Timezone.get when using Ruby data source
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  • Bump nokogiri from 1.13.5 to 1.13.6 in /docs

    Bump nokogiri from 1.13.5 to 1.13.6 in /docs

    Bumps nokogiri from 1.13.5 to 1.13.6.

    Release notes

    Sourced from nokogiri's releases.

    1.13.6 / 2022-05-08

    Security

    • [CRuby] Address CVE-2022-29181, improper handling of unexpected data types, related to untrusted inputs to the SAX parsers. See GHSA-xh29-r2w5-wx8m for more information.

    Improvements

    • {HTML4,XML}::SAX::{Parser,ParserContext} constructor methods now raise TypeError instead of segfaulting when an incorrect type is passed.

    sha256:

    58417c7c10f78cd1c0e1984f81538300d4ea98962cfd3f46f725efee48f9757a  nokogiri-1.13.6-aarch64-linux.gem
a2b04ec3b1b73ecc6fac619b41e9fdc70808b7a653b96ec97d04b7a23f158dbc  nokogiri-1.13.6-arm64-darwin.gem
4437f2d03bc7da8854f4aaae89e24a98cf5c8b0212ae2bc003af7e65c7ee8e27  nokogiri-1.13.6-java.gem
99d3e212bbd5e80aa602a1f52d583e4f6e917ec594e6aa580f6aacc253eff984  nokogiri-1.13.6-x64-mingw-ucrt.gem
a04f6154a75b6ed4fe2d0d0ff3ac02f094b54e150b50330448f834fa5726fbba  nokogiri-1.13.6-x64-mingw32.gem
a13f30c2863ef9e5e11240dd6d69ef114229d471018b44f2ff60bab28327de4d  nokogiri-1.13.6-x86-linux.gem
63a2ca2f7a4f6bd9126e1695037f66c8eb72ed1e1740ef162b4480c57cc17dc6  nokogiri-1.13.6-x86-mingw32.gem
2b266e0eb18030763277b30dc3d64337f440191e2bd157027441ac56a59d9dfe  nokogiri-1.13.6-x86_64-darwin.gem
3fa37b0c3b5744af45f9da3e4ae9cbd89480b35e12ae36b5e87a0452e0b38335  nokogiri-1.13.6-x86_64-linux.gem
b1512fdc0aba446e1ee30de3e0671518eb363e75fab53486e99e8891d44b8587  nokogiri-1.13.6.gem
    Changelog

    Sourced from nokogiri's changelog.

    1.13.6 / 2022-05-08

    Security

    • [CRuby] Address CVE-2022-29181, improper handling of unexpected data types, related to untrusted inputs to the SAX parsers. See GHSA-xh29-r2w5-wx8m for more information.

    Improvements

    • {HTML4,XML}::SAX::{Parser,ParserContext} constructor methods now raise TypeError instead of segfaulting when an incorrect type is passed.
    Commits
    • b7817b6 version bump to v1.13.6
    • 61b1a39 Merge pull request #2530 from sparklemotion/flavorjones-check-parse-memory-ty...
    • 83cc451 fix: {HTML4,XML}::SAX::{Parser,ParserContext} check arg types
    • See full diff in compare view

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  • Bump kramdown from 2.3.0 to 2.3.1 in /docs

    Bump kramdown from 2.3.0 to 2.3.1 in /docs

    Bumps kramdown from 2.3.0 to 2.3.1.

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Mikhail Grankin
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