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A ridiculously simple search engine

Project description

grub

A ridiculously simple search engine

Example: Search code

from grub import SearchStore
import sklearn  # instead of talking any file, let's search the files of sklearn itself!

path_format = os.path.dirname(sklearn.__file__) + '{}.py'
search = SearchStore(path_format)
search('ANN')  # see that it guessed, pretty early, that I was talking about neural networks
array(['sklearn/tree/_export.py', 'sklearn/linear_model/_least_angle.py',
       'sklearn/feature_selection/_base.py',
       'sklearn/feature_selection/tests/test_variance_threshold.py',
       'sklearn/neural_network/tests/test_stochastic_optimizers.py',
       'sklearn/neural_network/__init__.py',
       'sklearn/neural_network/_stochastic_optimizers.py',
       'sklearn/neural_network/_multilayer_perceptron.py',
       'sklearn/neural_network/rbm.py',
       'sklearn/neural_network/tests/test_rbm.py'], dtype='<U75')
search('how to calibrate the estimates of my classifier')  # and yep... good keyword promisses: robust, calibration, feature selection and validation...
array(['sklearn/covariance/_robust_covariance.py',
       'sklearn/svm/_classes.py',
       'sklearn/covariance/_elliptic_envelope.py',
       'sklearn/neighbors/_lof.py', 'sklearn/ensemble/_iforest.py',
       'sklearn/feature_selection/_rfe.py', 'sklearn/calibration.py',
       'sklearn/model_selection/_validation.py',
       'sklearn/ensemble/_forest.py', 'sklearn/ensemble/_gb.py'],
      dtype='<U75')

Project details


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Files for grub, version 0.0.3
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Filename, size grub-0.0.3-py3-none-any.whl (3.2 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size grub-0.0.3.tar.gz (3.4 kB) File type Source Python version None Upload date Hashes View

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