Learner

fastai Learner extensions.

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Learner.show_batch

 Learner.show_batch (**kwargs)

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Learner.remove_all_cbs

 Learner.remove_all_cbs (max_iters=10)

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Learner.one_batch

 Learner.one_batch (i, b)

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Learner.inverse_transform

 Learner.inverse_transform (df:pandas.core.frame.DataFrame)

Applies sklearn-type pipeline inverse transforms

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Learner.transform

 Learner.transform (df:pandas.core.frame.DataFrame)

Applies sklearn-type pipeline transforms

⚠️ Important: save_all and load_all methods are designed for small datasets only. If you are using a larger dataset, you should use the standard save and load_learner methods.

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load_all

 load_all (path='export', dls_fname='dls', model_fname='model',
           learner_fname='learner', device=None, pickle_module=<module
           'pickle' from '/opt/hostedtoolcache/Python/3.10.18/x64/lib/pyth
           on3.10/pickle.py'>, verbose=False)

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Learner.save_all

 Learner.save_all (path='export', dls_fname='dls', model_fname='model',
                   learner_fname='learner', verbose=False)

from tsai.data.core import get_ts_dls
from tsai.utils import remove_dir

X = np.random.rand(100, 2, 10)
dls = get_ts_dls(X)
learn = Learner(dls, InceptionTimePlus(2, 1), loss_func=MSELossFlat())
learn.save_all(Path.home()/'tmp', verbose=True)
learn2 = load_all(Path.home()/'tmp', verbose=True)
remove_dir(Path.home()/'tmp')

Learner saved:
path          = '/Users/nacho/tmp'
dls_fname     = '['dls_0.pth', 'dls_1.pth']'
model_fname   = 'model.pth'
learner_fname = 'learner.pkl'
Learner loaded:
path          = '/Users/nacho/tmp'
dls_fname     = '['dls_0.pth', 'dls_1.pth']'
model_fname   = 'model.pth'
learner_fname = 'learner.pkl'
/Users/nacho/tmp directory removed.

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Learner.plot_metrics

 Learner.plot_metrics (nrows:int=1, ncols:int=1, figsize:tuple=None,
                       imsize:int=3, suptitle:str=None, sharex:"bool|Liter
                       al['none','all','row','col']"=False, sharey:"bool|L
                       iteral['none','all','row','col']"=False,
                       squeeze:bool=True,
                       width_ratios:Sequence[float]|None=None,
                       height_ratios:Sequence[float]|None=None,
                       subplot_kw:dict[str,Any]|None=None,
                       gridspec_kw:dict[str,Any]|None=None)

	Type	Default	Details
nrows	int	1	Number of rows in returned axes grid
ncols	int	1	Number of columns in returned axes grid
figsize	tuple	None	Width, height in inches of the returned figure
imsize	int	3	Size (in inches) of images that will be displayed in the returned figure
suptitle	str	None	Title to be set to returned figure
sharex	bool | Literal[‘none’, ‘all’, ‘row’, ‘col’]	False
sharey	bool | Literal[‘none’, ‘all’, ‘row’, ‘col’]	False
squeeze	bool	True
width_ratios	Sequence[float] | None	None
height_ratios	Sequence[float] | None	None
subplot_kw	dict[str, Any] | None	None
gridspec_kw	dict[str, Any] | None	None
Returns	(plt.Figure, plt.Axes)		Returns both fig and ax as a tuple

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Recorder.plot_metrics

 Recorder.plot_metrics (nrows=None, ncols=None, figsize=None,
                        final_losses=True, perc=0.5, imsize:int=3,
                        suptitle:str=None, sharex:"bool|Literal['none','al
                        l','row','col']"=False, sharey:"bool|Literal['none
                        ','all','row','col']"=False, squeeze:bool=True,
                        width_ratios:Sequence[float]|None=None,
                        height_ratios:Sequence[float]|None=None,
                        subplot_kw:dict[str,Any]|None=None,
                        gridspec_kw:dict[str,Any]|None=None)

	Type	Default	Details
nrows	int	1	Number of rows in returned axes grid
ncols	int	1	Number of columns in returned axes grid
figsize	tuple	None	Width, height in inches of the returned figure
final_losses	bool	True
perc	float	0.5
imsize	int	3	Size (in inches) of images that will be displayed in the returned figure
suptitle	str	None	Title to be set to returned figure
sharex	bool | Literal[‘none’, ‘all’, ‘row’, ‘col’]	False
sharey	bool | Literal[‘none’, ‘all’, ‘row’, ‘col’]	False
squeeze	bool	True
width_ratios	Sequence[float] | None	None
height_ratios	Sequence[float] | None	None
subplot_kw	dict[str, Any] | None	None
gridspec_kw	dict[str, Any] | None	None
Returns	(plt.Figure, plt.Axes)		Returns both fig and ax as a tuple

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get_arch

 get_arch (arch_name)

for arch_name in all_arch_names:
    get_arch(arch_name)

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ts_learner

 ts_learner (dls, arch=None, c_in=None, c_out=None, seq_len=None, d=None,
             s_cat_idxs=None, s_cat_embeddings=None,
             s_cat_embedding_dims=None, s_cont_idxs=None, o_cat_idxs=None,
             o_cat_embeddings=None, o_cat_embedding_dims=None,
             o_cont_idxs=None, splitter=<function trainable_params>,
             loss_func=None, opt_func=<function Adam>, lr=0.001, cbs=None,
             metrics=None, path=None, model_dir='models', wd=None,
             wd_bn_bias=False, train_bn=True, moms=(0.95, 0.85, 0.95),
             train_metrics=False, valid_metrics=True, seed=None,
             device=None, verbose=False, patch_len=None,
             patch_stride=None, fusion_layers=128, fusion_act='relu',
             fusion_dropout=0.0, fusion_use_bn=True, pretrained=False,
             weights_path=None, exclude_head=True, cut=-1, init=None,
             arch_config={})

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tsimage_learner

 tsimage_learner (dls, arch=None, pretrained=False, loss_func=None,
                  opt_func=<function Adam>, lr=0.001, cbs=None,
                  metrics=None, path=None, model_dir='models', wd=None,
                  wd_bn_bias=False, train_bn=True, moms=(0.95, 0.85,
                  0.95), c_in=None, c_out=None, device=None,
                  verbose=False, init=None, arch_config={})

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Learner.decoder

 Learner.decoder (o)

from tsai.data.core import *
from tsai.data.external import get_UCR_data
from tsai.models.FCNPlus import FCNPlus

X, y, splits = get_UCR_data('OliveOil', verbose=True, split_data=False)
tfms  = [None, [TSCategorize()]]
dls = get_ts_dls(X, y, splits=splits, tfms=tfms)
learn = ts_learner(dls, FCNPlus)
for p in learn.model.parameters():
    p.requires_grad=False
test_eq(count_parameters(learn.model), 0)
learn.freeze()
test_eq(count_parameters(learn.model), 1540)
learn.unfreeze()
test_eq(count_parameters(learn.model), 264580)

learn = ts_learner(dls, 'FCNPlus')
for p in learn.model.parameters():
    p.requires_grad=False
test_eq(count_parameters(learn.model), 0)
learn.freeze()
test_eq(count_parameters(learn.model), 1540)
learn.unfreeze()
test_eq(count_parameters(learn.model), 264580)

Dataset: OliveOil
X      : (60, 1, 570)
y      : (60,)
splits : (#30) [0,1,2,3,4,5,6,7,8,9...] (#30) [30,31,32,33,34,35,36,37,38,39...] 

learn.show_batch();

from fastai.metrics import accuracy
from tsai.data.preprocessing import TSRobustScale

X, y, splits = get_UCR_data('OliveOil', split_data=False)
tfms  = [None, TSClassification()]
batch_tfms = TSRobustScale()
dls = get_ts_dls(X, y, tfms=tfms, splits=splits, batch_tfms=batch_tfms)
learn = ts_learner(dls, FCNPlus, metrics=accuracy, train_metrics=True)
learn.fit_one_cycle(2)
learn.plot_metrics()

epoch	train_loss	train_accuracy	valid_loss	valid_accuracy	time
0	1.480875	0.266667	1.390461	0.300000	00:02
1	1.476655	0.266667	1.387370	0.300000	00:01

if not os.path.exists("./models"): os.mkdir("./models")
if not os.path.exists("./data"): os.mkdir("./data")
np.save("data/X_test.npy", X[splits[1]])
np.save("data/y_test.npy", y[splits[1]])
learn.export("./models/test.pth")