Anon / Aug 29 2019
with Andrea Amantini

HTML Display - Jupyter

%matplotlib inline
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pth = '/.nextjournal/data-named/QmXM1SAUDr39KzBUo4rkFn9VTti8hSGTneAsShAdL6VAcG/'
file = 'Headlines.csv'
D_COL = 'headline'
WEIGHTS = 
Headlines_Glove_Weights.pkl

PADDING = 64
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exec(open(
dweNet.py
).read())
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from fastai import *
from fastai.text import *
from fastai.callbacks import *
import torch.utils.data as data_utils
import numpy
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defaults.device = torch.device('cuda')
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def pad_to(x:Collection[str], pad_til = PADDING) -> Collection[str]:
    res = []
    count = 0
    for t in x:
        res.append(t)
        count += 1
    while count < pad_til:
        res.append(PAD)
        count +=1
    return res
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tokenizer = Tokenizer(SpacyTokenizer, 'en', pre_rules=
                      [fix_html, replace_rep, replace_wrep, spec_add_spaces, 
                       rm_useless_spaces],
                      post_rules=[replace_all_caps, deal_caps, pad_to], n_cpus=1)
processor = [TokenizeProcessor(tokenizer=tokenizer), NumericalizeProcessor()]
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data = (TextList.from_csv(pth, file, cols=D_COL, processor=processor)).split_from_df(col='valid').label_from_df(cols=0).databunch()
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data.show_batch()

# HTML Output works for Jupyter kernel but not for NextJournal
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text target
xxbos pulitzer prize winning play , and a winning director , too nonsarcastic
xxbos video : # xxunk poem on house floor nonsarcastic
xxbos cnn headline news reporter xxunk to face the cold , hard xxunk sarcastic
xxbos remains of minnesota boy missing since 1989 found nonsarcastic
xxbos the perfect little tea cake to kick off fall nonsarcastic 
weights_matrix = pickle.load(open(WEIGHTS, 'rb'))
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net =  DenseNet(weights_matrix) 
net.to(DEVICE);
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learn = Learner(data, net, wd=0.1, loss_func=CrossEntropyFlat(), 
                metrics=[accuracy, FBeta(average='micro',beta=1)])
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learn.fit_one_cycle(1, 1e-03, moms=(0.8,0.7))

# HTML Output works for NextJournal but not Jupyter, it is just rendering a gray rectangle. We can see it is a IPython.core.display.HTML object by changing the 'show result as' to text.
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preds,y,losses = learn.get_preds(with_loss=True)
interp = ClassificationInterpretation(learn, preds, y, losses)
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interp.plot_confusion_matrix()

# Working for Jupyter not NJ
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interp.top_losses()
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torch.return_types.topk( values=tensor([4.1473e+00, 3.8272e+00, 3.8093e+00, ..., 1.4159e-03, 8.2983e-04, 6.8355e-04]), indices=tensor([1690, 839, 4321, ..., 1072, 5098, 710]))
interp.top_losses()[1]
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tensor([1690, 839, 4321, ..., 1072, 5098, 710])
data.valid_ds[1690]
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(Text xxbos do n't nobody wanna hear area man run his mouth xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad xxpad, Category sarcastic)
Shift+Enter to run
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