├── requirements.txt
├── actleto
    ├── __init__.py
    ├── annotator
    │   ├── visualizers
    │   │   ├── __init__.py
    │   │   ├── textarea.py
    │   │   └── image.py
    │   ├── __init__.py
    │   ├── active_learner.py
    │   ├── annotator_widget.py
    │   └── ui_widget.py
    ├── strategies
    │   ├── __init__.py
    │   ├── libact_adaptor.py
    │   ├── mperr.py
    │   ├── positive_corrector.py
    │   ├── utils.py
    │   └── adwes.py
    └── models
    │   ├── utils_data.py
    │   └── model_wrappers.py
├── docs
    └── al.png
├── LICENSE
├── setup.py
├── README.md
└── examples
    ├── 20newsgroups.ipynb
    └── MNIST_annotation.ipynb


/requirements.txt:
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1 | .


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/actleto/__init__.py:
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1 | from .annotator import *
2 | from .strategies import *
3 | 


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/docs/al.png:
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https://raw.githubusercontent.com/IINemo/active_learning_toolbox/HEAD/docs/al.png


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/actleto/annotator/visualizers/__init__.py:
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1 | from .image import ImageVisualizer
2 | from .textarea import TextAreaVisualizer
3 | 


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/actleto/annotator/__init__.py:
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1 | from .active_learner import ActiveLearner
2 | from .annotator_widget import AnnotatorWidget
3 | from .ui_widget import ActiveLearnerUiWidget
4 | from .visualizers import ImageVisualizer, TextAreaVisualizer


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/actleto/strategies/__init__.py:
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1 | from .adwes import ADWeS
2 | from .libact_adaptor import AdaptorLibAct, make_libact_strategy_ctor
3 | from .mperr import MPErr
4 | from .positive_corrector import PositiveCorrector
5 | from .utils import MultipleQueryStrategy, SklearnProbaAdapterWithUnlabeled, SklearnRealAdapter
6 | 


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/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2020 Artem Shelmanov and Roman Suvorov
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/actleto/strategies/libact_adaptor.py:
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 1 | from .utils import MultipleQueryStrategy
 2 | from libact.base.dataset import Dataset
 3 | import numpy as np
 4 | 
 5 | 
 6 | import logging
 7 | logger = logging.getLogger('actleto')
 8 | 
 9 | 
10 | class AdaptorLibAct:
11 |     """Adaptor for libact query strategies."""
12 |     
13 |     def __init__(self, 
14 |                  X_full_dataset,
15 |                  y_full_dataset,
16 |                  libact_query_alg_ctor,
17 |                  max_samples_number=40):
18 |         self._train_dataset = Dataset(X_full_dataset, y_full_dataset)
19 |         self._libact_query_alg = MultipleQueryStrategy(impl=libact_query_alg_ctor(self._train_dataset), 
20 |                                                        query_n=max_samples_number)
21 | 
22 |     def make_iteration(self, indexes, y):
23 |         for i in range(indexes.shape[0]):
24 |             self._train_dataset.update(indexes[i], y[i])
25 | 
26 |     def choose_samples_for_annotation(self):
27 |         res = np.array(list(self._libact_query_alg.make_query()))
28 |         return res
29 | 
30 | 
31 | def make_libact_strategy_ctor(stg_ctor):
32 |     """Creates functor with adaptor for active learning strategies for libact."""
33 |     def _ctor(X, y):
34 |         return AdaptorLibAct(X, y, libact_query_alg_ctor = stg_ctor)
35 |     return _ctor
36 | 


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/actleto/annotator/visualizers/textarea.py:
--------------------------------------------------------------------------------
 1 | from ipywidgets import Layout, Label, Textarea
 2 | 
 3 | 
 4 | class TextAreaVisualizer(object):
 5 |     """Visualizer for texts via text areas."""
 6 | 
 7 |     def __init__(self, text_columns, width = '90%', height = '150px'):
 8 |         """
 9 |         Args:
10 |             text_columns (list): List of labels (index elements) of columns that should be
11 |                 visualized via textarea.
12 |             width (str): width of the text area.
13 |             height (str): height of the text are.
14 |         """
15 |         super().__init__()
16 |         self._text_columns = text_columns
17 |         self._text_layout = Layout(width = width, height = height)
18 | 
19 |     def __call__(self, dataframe, index):
20 |         """Invokes the visuzlizer.
21 | 
22 |         Args:
23 |             dataframe (pandas.DataFrame): the dataframe that contains the data for visualization.
24 |             index (int): the positional (iloc) index of the row to visualize.
25 | 
26 |         Returns:
27 |             list: widgets that visualize the row
28 |         """
29 |         result = []
30 |         row = dataframe.iloc[index]
31 |         for label in self._text_columns:
32 |             result.append(Label('{}:'.format(label)))
33 |             result.append(Textarea(value = str(row[label]),
34 |                                    layout = self._text_layout,
35 |                                    disabled = True))
36 |             
37 |         return tuple(result)
38 | 


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/setup.py:
--------------------------------------------------------------------------------
 1 | #!/usr/bin/env python
 2 | # -*- encoding: utf-8 -*-
 3 | #from __future__ import absolute_import, print_function
 4 | 
 5 | import io
 6 | from glob import glob
 7 | from os.path import basename
 8 | from os.path import dirname
 9 | from os.path import join
10 | from os.path import splitext
11 | 
12 | from setuptools import find_packages
13 | from setuptools import setup
14 | 
15 | 
16 | def read(*names, **kwargs):
17 |     return io.open(
18 |         join(dirname(__file__), *names),
19 |         encoding=kwargs.get('encoding', 'utf8')
20 |     ).read()
21 | 
22 | # print(find_packages())
23 | # 1 / 0
24 | 
25 | setup(
26 |     name='actleto',
27 |     version='0.1.0',
28 |     description='Toolbox for rapid dataset creation and classifier training with active machine learning',
29 |     author='ISA RAS',
30 |     author_email='',
31 |     license='MIT',
32 |     python_requires='>=3.5',
33 |     packages=find_packages(),
34 | #     package_dir={'': 'actleto'},
35 |     include_package_data=True,
36 |     keywords='development active machine learning annotation corpus',
37 |     zip_safe=False,
38 |     package_dir={'examples': 'examples'},
39 |     package_data={'examples': ['*.ipynb']},
40 |     install_requires=['cython', 
41 |                       'numpy>=1.12.1',
42 |                       'pandas>=0.20.1',
43 |                       'scikit-learn>=0.18',
44 |                       'scipy>=0.19.0',
45 |                       'Pillow>=4.2.1',
46 |                       'ipywidgets>=4',
47 |                       'annoy'],
48 |     dependency_links=['git+https://github.com/windj007/libact/#egg=libact']
49 | )
50 | 


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/actleto/strategies/mperr.py:
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 1 | import numpy
 2 | from libact.base.interfaces import ProbabilisticModel, ContinuousModel
 3 | from libact.base.dataset import Dataset, ensure_sklearn_compat
 4 | 
 5 | import logging
 6 | logger = logging.getLogger('actleto')
 7 | 
 8 | 
 9 | class MPErr:
10 |     """Most probable error query strategy.
11 |     
12 |     Finds the negative samples that are likely to be positive ones 
13 |     from the model point of view. On each iteration, it uses the whole
14 |     dataset for training the model: it treats unannotated samples as
15 |     negative samples.
16 |     
17 |     Note: supports only binary classification so far.
18 |     """
19 |     
20 |     def __init__(self, dataset, model):
21 |         """
22 |         Args:
23 |             dataset: libact dataset with features.
24 |             model: the model for active learning.
25 |             
26 |         """
27 |         self.dataset = dataset
28 |         self.model = model
29 |         assert isinstance(self.model, (ProbabilisticModel, ContinuousModel))
30 | 
31 |     def make_query(self, return_score=False):
32 |         X, y = list(zip(*self.dataset.data))
33 |         self.model.train(Dataset(X,
34 |                                  numpy.array([label if not label is None else False
35 |                                  for label in y])))
36 | 
37 |         unlabeled_entry_ids, X_pool = list(zip(*self.dataset.get_unlabeled_entries()))
38 |         unlabeled_entry_ids = numpy.asarray(unlabeled_entry_ids)
39 |         X_pool = ensure_sklearn_compat(X_pool)
40 | 
41 |         if isinstance(self.model, ProbabilisticModel):
42 |             score = self.model.predict_proba(X_pool)[:, 1]
43 |         elif isinstance(self.model, ContinuousModel):
44 |             score = self.model.predict_real(X_pool)[:, 1]
45 |         
46 |         best_id = unlabeled_entry_ids[numpy.argmax(score)]
47 |         if return_score:
48 |             return best_id, \
49 |                    list(zip(unlabeled_entry_ids, score))
50 |         else:
51 |             return best_id
52 |         


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/actleto/annotator/visualizers/image.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | from ipywidgets import Image as WidgImage
 3 | from PIL import Image
 4 | import io
 5 | 
 6 | 
 7 | class ImageVisualizer(object):
 8 |     """Visualizer for images.
 9 |     
10 |     This visualizer can display images (e.g., MNIST), which are stored as rows 
11 |     in a dataframe.
12 |     """
13 |     
14 |     def __init__(self, columns_range, img_shape, img_mode, preview_shape):
15 |         """ImageVisualizer constructor. 
16 |         
17 |         Args:
18 |             rng (tuple): tuple (start, end) - range of columns in pandas.DataFrame, which contain image data
19 |             img_shape (tuple): original image shape width x height.
20 |             img_format (str): image format: "L" - black&white (MNIST); "RGB"; "CMYK"; "1".
21 |             preview_shape (tuple): output image size.
22 |             
23 |         """
24 |         super().__init__()
25 |         self._columns_range = columns_range
26 |         self._img_shape = img_shape
27 |         self._img_mode = img_mode
28 |         self._preview_shape = preview_shape
29 |     
30 |     def __call__(self, dataframe, index):
31 |         """Invokes the visualizer.
32 |         
33 |         Args:
34 |             dataframe (pandas.DataFrame): the dataframe that contains the data for visualization.
35 |             index (int): the positional (iloc) index of the row to visualize.
36 |             
37 |         Returns:
38 |             tuple: The list of widgets that visualize the row with number index.
39 |             
40 |         """
41 |         img_array = dataframe.iloc[index][self._columns_range[0] : self._columns_range[1]].as_matrix()
42 | 
43 |         if img_array.shape[0] > np.product(self._img_shape):
44 |             cur_img_shape = self._img_shape + (-1,)
45 |         else:
46 |             cur_img_shape = self._img_shape
47 | 
48 |         img = Image.fromarray(img_array.reshape(cur_img_shape), self._img_mode)
49 |         
50 |         buffer = io.BytesIO()
51 |         img.convert('RGB').save(buffer, format = 'PNG')
52 |         
53 |         return (WidgImage(value = buffer.getvalue(), 
54 |                           format = 'PNG', 
55 |                           width = self._preview_shape[0], 
56 |                           height = self._preview_shape[1]),)
57 | 


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/actleto/strategies/positive_corrector.py:
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 1 | import pandas, numpy as np
 2 | from libact.base.dataset import Dataset, ensure_sklearn_compat
 3 | from libact.base.interfaces import ProbabilisticModel, ContinuousModel
 4 | 
 5 | 
 6 | class PositiveCorrector(object):
 7 |     """The strategy that fixes the most unprobable positive labels (from the model point of view).
 8 |     
 9 |     Note: Supports only binary classification so far.
10 |     
11 |     """
12 |     
13 |     def __init__(self, dataset, fully_annotated_y, model, strategy = 'uncertainty'):
14 |         """
15 |         Args:
16 |             dataset: the libact dataset without bad annotated positive 
17 |                 labels and fully_annotated_y with positive labels.
18 |             fully_annotated_y: the array with y labels for positives 
19 |                 and negative samples.
20 |             model: the model for active learning.
21 |             strategy (str): the string identifier of strategy type from 
22 |                 the list ['uncertainty', 'least_prob', 'most_prob'].
23 |         
24 |         """
25 |         self.dataset = dataset
26 |         self.dataset.on_update(self._register_update)
27 |         
28 |         assert isinstance(model, (ProbabilisticModel, ContinuousModel))
29 |         self.model = model
30 |         
31 |         assert (strategy in ['uncertainty', 'least_prob', 'most_prob']), 'Wrong strategy identifier'
32 |         self._strategy = strategy
33 |         
34 |         self._internal_dataset = pandas.DataFrame(fully_annotated_y, index = range(len(self.dataset)))
35 |         
36 |     def make_query(self, return_score=False):
37 |         X, y = list(zip(*self.dataset.data))
38 |         self.model.train(Dataset(X, self._internal_dataset.values.reshape(-1)))
39 |         
40 |         unlabeled_entry_ids, X_pool = list(zip(*self.dataset.get_unlabeled_entries()))
41 |         unlabeled_entry_ids = np.asarray(unlabeled_entry_ids)
42 |         X_pool = ensure_sklearn_compat(X_pool)
43 | 
44 |         if isinstance(self.model, ProbabilisticModel):
45 |             score = self.model.predict_proba(X_pool)[:, 1]
46 |         elif isinstance(self.model, ContinuousModel):
47 |             score = self.model.predict_real(X_pool)[:, 1]
48 |         
49 |         if self._strategy == 'uncertainty':
50 |             score = -np.abs(score - 0.5)
51 |             best_id = unlabeled_entry_ids[np.argmax(score)]
52 |         elif self._strategy == 'least_prob':
53 |             score = -score
54 |             best_id = unlabeled_entry_ids[np.argmax(score)]
55 |         elif self._strategy == 'most_prob':
56 |             best_id = unlabeled_entry_ids[np.argmax(score)]
57 |         
58 |         if return_score:
59 |             return best_id, \
60 |                    list(zip(unlabeled_entry_ids, score))
61 |         else:
62 |             return best_id
63 |         
64 |     def _register_update(self, index, answer):
65 |         self._internal_dataset.iloc[index, 0] = answer
66 | 


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/actleto/strategies/utils.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | from libact.base.dataset import ensure_sklearn_compat
 3 | from libact.base.interfaces import ProbabilisticModel, ContinuousModel
 4 | 
 5 | 
 6 | class MultipleQueryStrategy:    
 7 |     """The helper class for quering multiple instances from the unlabeled dataset
 8 |     
 9 |     Decorator for libact strategies that queries multiple instances from unlabeled
10 |     dataset.
11 |     
12 |     """
13 |     
14 |     def __init__(self, impl, query_n=10):
15 |         """
16 |         Args:
17 |             impl: the implementation of query strategy (libact compatible).
18 |             query_n (int): number of unannotated examples to query.
19 |             
20 |         """
21 |         self.impl = impl
22 |         self.query_n = query_n
23 | 
24 |     def make_query(self):
25 |         try:
26 |             id_score_list = self.impl.make_query(return_score=True)[1]
27 |             id_score_list.sort(key = lambda p: -p[1])
28 |             return { sample for sample, _ in id_score_list[:self.query_n] }
29 |         except TypeError:
30 |             return { self.impl.make_query() for _ in range(self.query_n) }
31 | 
32 | 
33 | class SklearnProbaAdapterWithUnlabeled(ProbabilisticModel):
34 |     """The adaptor of sklearn models for libact strategies."""
35 |     
36 |     def __init__(self, clf):
37 |         self._model = clf
38 | 
39 |     def train(self, dataset, *args, **kwargs):
40 |         X, y = list(zip(*dataset.data))
41 |         X = ensure_sklearn_compat(X)
42 |         y = np.asarray(y)
43 |         self._model.fit(X, y, *args, **kwargs)
44 | 
45 |     def predict(self, feature, *args, **kwargs):
46 |         return self._model.predict(feature, *args, **kwargs)
47 | 
48 |     def score(self, testing_dataset, *args, **kwargs):
49 |         X, y = list(zip(*testing_dataset.data))
50 |         X = ensure_sklearn_compat(X)
51 |         y = np.asarray(y)
52 |         return self._model.score(X, y, *args, **kwargs)
53 | 
54 |     def predict_real(self, feature, *args, **kwargs):
55 |         return self._model.predict_proba(feature, *args, **kwargs) * 2 - 1
56 | 
57 |     def predict_proba(self, feature, *args, **kwargs):
58 |         return self._model.predict_proba(feature, *args, **kwargs)
59 | 
60 | 
61 | class SklearnRealAdapter(ContinuousModel):
62 |     """The adaptor of sklearn models for libact strategies."""
63 |     
64 |     def __init__(self, clf):
65 |         self._model = clf
66 | 
67 |     def train(self, dataset, *args, **kwargs):
68 |         self._model.fit(*(dataset.format_sklearn() + args), **kwargs)
69 | 
70 |     def predict(self, feature, *args, **kwargs):
71 |         return self._model.predict(feature, *args, **kwargs)
72 | 
73 |     def score(self, testing_dataset, *args, **kwargs):
74 |         return self._model.score(*(testing_dataset.format_sklearn() + args),
75 |                                 **kwargs)
76 | 
77 |     def predict_real(self, feature, *args, **kwargs):
78 |         return self._model.decision_function(feature, *args, **kwargs)
79 | 


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/actleto/annotator/active_learner.py:
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 1 | import numpy as np
 2 | import pandas as pd
 3 | import logging
 4 | 
 5 | # TODO: partial fit.
 6 | # TODO: separate thread for evaluation.
 7 | # TODO: separate thread for training al model.
 8 | 
 9 | logger = logging.getLogger('actleto')
10 | 
11 | 
12 | class ActiveLearner:
13 |     """The class that implements active learning logic."""
14 |     
15 |     def __init__(self, 
16 |                  active_learn_alg_ctor,
17 |                  X_full_dataset, 
18 |                  y_dtype,
19 |                  y_full_dataset = None, 
20 |                  model_evaluate = None,
21 |                  X_test_dataset = None,
22 |                  y_test_dataset = None,
23 |                  eval_metrics = None,
24 |                  rnd_start_steps = 0): 
25 |         """ActiveLearner constructor.
26 |         
27 |         Args:
28 |             active_learn_alg_ctor (functor): functor object that returns active learning strategy.
29 |             X_full_dataset (np.array or sparse matrix): feature matrix.
30 |             y_dtype: type of y labels.
31 |             y_full_dataset (np.array): known answers (e.g., None -- unknown, True -- positive class, False -- negative class) 
32 |             model_evaluate: the model that will be evaluated on the holdout.
33 |             X_test_dataset: feature matrix for testing via holdout.
34 |             y_test_dataset: y labels for testing via holdout.
35 |             eval_metrics (list): list of sklearn evaluation metrics.
36 |             rnd_start_steps: AL will can make several seed steps by choosing random samples (without model suggestions).
37 |             logger (logging.Logger): the object for logging.
38 |             
39 |         """
40 |         super().__init__()
41 |         
42 |         self._y_dtype = y_dtype
43 |         self._model_evaluate = model_evaluate
44 |         self._eval_metrics = eval_metrics
45 |         
46 |         self._X_full_dataset = X_full_dataset
47 |         if y_full_dataset is not None:
48 |             self._y_full_dataset = y_full_dataset # TODO: validate dimentions
49 |         else:
50 |             self._y_full_dataset = np.array([None] * self._X_full_dataset.shape[0])
51 |             
52 |         self._active_learn_algorithm = active_learn_alg_ctor(self._X_full_dataset, 
53 |                                                              self._y_full_dataset)
54 |         
55 |         self._X_test_dataset = X_test_dataset
56 |         self._y_test_dataset = y_test_dataset
57 |         
58 |         self._iteration_num = 0
59 |         self._rnd_start_steps = rnd_start_steps
60 | 
61 |     def _select_unannotated(self, labels):
62 |         return np.where(labels.map(lambda x: x is None))[0]
63 | 
64 |     def choose_random_sample_for_annotation(self, number = 40):
65 |         return np.random.choice(self._select_unannotated(self._y_full_dataset), 
66 |                                 size = number, 
67 |                                 replace = False)
68 |         
69 |     def choose_samples_for_annotation(self):
70 |         if self._iteration_num < self._rnd_start_steps:
71 |             return self.choose_random_sample_for_annotation()
72 |         else:
73 |             return self._active_learn_algorithm.choose_samples_for_annotation()
74 |     
75 |     def evaluate(self):
76 |         if self._model_evaluate is None:
77 |             return None
78 |             
79 |         y_fit = pd.Series(self._y_full_dataset)
80 |         y_fit = y_fit[y_fit.notnull()].astype(self._y_dtype)
81 |         logger.info('Number of training samples: {}'.format(y_fit.shape[0]))
82 |         
83 |         self._model_evaluate.fit(self._X_full_dataset[y_fit.index], y_fit)
84 |         
85 |         preds = self._model_evaluate.predict(self._X_test_dataset)
86 |         return {metric.__name__ : metric(preds, self._y_test_dataset) 
87 |                 for metric in self._eval_metrics}
88 |     
89 |     def get_annotation(self):
90 |         return self._y_full_dataset
91 |     
92 |     def make_iteration(self, indexes, answers):
93 |         self._iteration_num += 1
94 |         answers = pd.Series(answers, index = indexes)
95 |         answers = answers[answers.notnull()] 
96 |         res = self._active_learn_algorithm.make_iteration(answers.index, answers.values)
97 |         self._y_full_dataset[list(answers.index)] = answers.values
98 |         return res
99 | 


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/README.md:
--------------------------------------------------------------------------------
  1 | Active learning toolbox
  2 | =======================
  3 | 
  4 | This repo contains some query strategies and utils for active learning, as well
  5 | as the widget for dataset annotation in Jupyter IDE. The repo has tight
  6 | integration with [libact](https://github.com/ntucllab/libact) Python library.
  7 | 
  8 |  
  9 | 
 10 | Example of active learning annotation of MNIST dataset with the Jupyter widget.
 11 | 
 12 | ![](https://github.com/IINemo/jupyter_al_annotator/blob/master/docs/al.png?raw=true)
 13 | 
 14 | Active learning
 15 | ===============
 16 | 
 17 | Active learning (AL) is an interactive approach to simultaneously building a
 18 | labeled dataset and training a machine learning model. AL algorithm:
 19 | 
 20 | 1.  A relatively large unlabeled dataset is gathered.
 21 | 
 22 | 2.  A domain expert labels a few positive examples in the dataset.
 23 | 
 24 | 3.  A classifier is trained on labeled samples.
 25 | 
 26 | 4.  The classifier is applied to the rest of the corpus.
 27 | 
 28 | 5.  Few most “useful” examples are selected (e.g., that increase classification
 29 |     performance).
 30 | 
 31 | 6.  The examples labeled by the expert are added to the training set.
 32 | 
 33 | 7.  Goto 3.
 34 | 
 35 | The procedure repeats until the performance of the classifier stops improving or
 36 | the expert is bored.
 37 | 
 38 | Requirements
 39 | ============
 40 | 
 41 | 1.  Python 3.6 (the package has not been tested with earlier versions)
 42 | 
 43 | 2.  numpy (1.12.1)
 44 | 
 45 | 3.  pandas (0.20.1)
 46 | 
 47 | 4.  sklearn (0.18.1)
 48 | 
 49 | 5.  scipy (0.19.0)
 50 | 
 51 | 6.  Pillow (4.2.1)
 52 | 
 53 | 7.  Jupyter (4.3.0)
 54 | 
 55 | 8.  LibAct from the [fork](https://github.com/windj007/libact) (`pip install
 56 |     git+https://github.com/windj007/libact`)
 57 | 
 58 | Installation
 59 | ============
 60 | 
 61 | Enabling widgets in Jupyter IDE
 62 | -------------------------------
 63 | 
 64 | The Jupyter widgets are not enabled by default. To install and activate them do
 65 | the following.
 66 | 
 67 | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 68 | pip install ipywidgets
 69 | jupyter nbextension enable --py --sys-prefix widgetsnbextension
 70 | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 71 | 
 72 | For further details, please, refer to [jupyter-widgets
 73 | repo](https://github.com/jupyter-widgets/ipywidgets).
 74 | 
 75 | Installing the library and the widget
 76 | -------------------------------------
 77 | 
 78 | To install the library and the widget execute in command line with root
 79 | priviledges:
 80 | 
 81 | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 82 | pip install git+https://github.com/IINemo/active_learning_toolbox
 83 | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 84 | 
 85 | Usage
 86 | =====
 87 | 
 88 | See an example for [MNIST dataset
 89 | annotation](https://github.com/IINemo/active_learning_toolbox/blob/master/examples/MNIST_annotation.ipynb)
 90 | and an example for [20 newsgroups
 91 | annotation](https://github.com/IINemo/active_learning_toolbox/blob/master/examples/20newsgroups.ipynb).
 92 | 
 93 | If you have Docker installed, you can test the examples with [windj007/jupyter-keras-tool](https://hub.docker.com/r/windj007/jupyter-keras-tools/):
 94 | 
 95 | ```
 96 | cd `<package dir>`/examples
 97 | docker run -ti --rm -v `pwd`:/notebook -p 8888:8888 windj007/jupyter-keras-tools
 98 | ```
 99 | Then open [http://localhost:8888](http://localhost:8888) in a browser (will launch Jupyter IDE) and open an example notebook.
100 | 
101 | 
102 | Cite
103 | ====
104 | 
105 | If you use active learning toolbox in academic works, please cite (to be
106 | published):
107 | 
108 |  
109 | 
110 | *BibTex:*
111 | 
112 | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
113 | @inproceedings{suvorovshelmanov2017ainl,
114 |     title={Active Learning with Adaptive Density Weighted Sampling for Information Extraction from Scientific Papers},
115 |     author={Roman Suvorov and Artem Shelmanov and Ivan Smirnov},
116 |     booktitle={Proceedings of AINL: Artificial Intelligence and Natural Language Conference},
117 |     publisher = {Springer, Communications in Computer and Information Science},
118 |     year={2017}
119 | }
120 | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
121 |  
122 | 
123 | *Russian GOST:*
124 | 
125 | Suvorov R., Shelmanov A., Smirnov I. Active learning with adaptive density
126 | weighted sampling for information extraction from scientific papers //
127 | Proceedings of AINL: Artificial Intelligence and Natural Language Conference. —
128 | Springer, Communications in Computer and Information Science, 2017.
129 | 


--------------------------------------------------------------------------------
/actleto/annotator/annotator_widget.py:
--------------------------------------------------------------------------------
  1 | from traitlets import Int, Instance
  2 | from ipywidgets import Button, VBox, HBox, HTML, Box, Layout, Label, Text, ToggleButtons
  3 | import pandas as pd
  4 | import numpy as np
  5 | from .visualizers import TextAreaVisualizer
  6 | 
  7 | 
  8 | class AnnotatorWidget(Box):
  9 |     """The widget for Jupyter that implements example annotator.
 10 |     
 11 |     The widget can be used without active learning.
 12 |     
 13 |     """
 14 |     
 15 |     _max_examples = Int(5, allow_none = False).tag(sync=True)
 16 |     _current_position = Int(0, allow_none = False).tag(sync = True)
 17 |     _dataframe = Instance(klass = pd.DataFrame)
 18 |     
 19 |     _example_layout = Instance(klass=Layout)
 20 |     _example_layout = Layout(width = '100%', 
 21 |                              border = 'solid 2px', 
 22 |                              margin = '3px', 
 23 |                              align_items = 'stretch',
 24 |                              padding = '2px')
 25 |     
 26 |     ANNOT_DONT_KNOW = "Don't know"
 27 | 
 28 |     def __init__(self, 
 29 |                  dataframe, 
 30 |                  answers = None,
 31 |                  max_examples = 5,
 32 |                  current_position = 0, 
 33 |                  visualize_columns = [], 
 34 |                  drop_labels = [],
 35 |                  y_labels = {'True' : True,
 36 |                              'False' : False},
 37 |                  visualizer = None,
 38 |                  display_feature_table = True,
 39 |                  *args, **kwargs):
 40 |         """Annotator constructor.
 41 |         
 42 |         Args:
 43 |             dataframe (pandas.DataFrame):  the dataframe that contains data for examples visualizatin.
 44 |             answers (numpy.array): the array that contains known answers 
 45 |                 (that will not be marked as Don't Know check button).
 46 |             max_examples (int): maximum number of examples per page.
 47 |             current_position (int): index of position, from which iteration should start.
 48 |             textual_labels (list): list of string labels that will be visualized with VisualizerTextArea.
 49 |             drop_labels (list): list of string labels that will be dropped from visualization via table.
 50 |             y_labels (dict): dict {<y_textual_label> : <y_value>}.
 51 |             visualizer (object): visualizer for X_helper representation. The default is None. If None the widget
 52 |                 will invoke VisualizerTextArea by deafult.
 53 |             
 54 |         """
 55 |         super(Box, self).__init__(*args, **kwargs)
 56 |         
 57 |         self._y_labels = y_labels
 58 |         self._y_labels[self.ANNOT_DONT_KNOW] = None
 59 |         self._y_labels_reversed = {v : k for k, v in self._y_labels.items()}
 60 |         self._display_feature_table = display_feature_table
 61 |         
 62 |         self._dataframe = dataframe
 63 |         self._current_position = min(current_position, self._dataframe.shape[0] - 1)
 64 |         self._max_examples = max_examples
 65 |         self._answers = (answers if answers is not None 
 66 |                          else np.array([None] * self._dataframe.shape[0]))
 67 |         assert self._answers.shape[0] == self._dataframe.shape[0], \
 68 |                'The length of dataframe should match the length of numpy.array with answers.'
 69 |         
 70 |         self._drop_labels = drop_labels + visualize_columns
 71 |         self._visualizer = visualizer
 72 |         if self._visualizer is None:
 73 |             self._visualizer = TextAreaVisualizer(visualize_columns)
 74 | 
 75 |         self._draw()
 76 |         self.observe(self._draw, names='_current_position')
 77 |         
 78 |     def get_answers(self):
 79 |         """Returns numpy.array with answers."""
 80 |         return self._answers
 81 | 
 82 |     def get_dataframe(self):
 83 |         """Returns pandas.DataFrame with feature values."""
 84 |         return self._dataframe
 85 | 
 86 |     def _click_prev(self, button):
 87 |         self._current_position = max(self._current_position - self._max_examples, 0)
 88 | 
 89 |     def _click_next(self, button):
 90 |         self._current_position = min(self._current_position + self._max_examples, self._dataframe.shape[0])
 91 | 
 92 |     def _int_text_value_changed(self, wdg):
 93 |         try:
 94 |             new_value = int(wdg.value)
 95 |         except ValueError:
 96 |             return
 97 |         
 98 |         if new_value < 0:
 99 |             new_value = self._dataframe.shape[0] + new_value
100 |             if new_value < 0:
101 |                 return
102 |             
103 |         if new_value >= self._dataframe.shape[0]:
104 |             return
105 |             
106 |         self._current_position = new_value
107 |             
108 |     def _make_controls(self):
109 |         controls = HBox(children = [Button(description='Prev'), 
110 |                                     Button(description='Next'),
111 |                                     Text(value = str(self._current_position), 
112 |                                          layout = Layout(width = '80px')),
113 |                                     Label(value = 'out of', 
114 |                                           layout = Layout(width = '35px')),
115 |                                     Text(value = str(self._dataframe.shape[0]), 
116 |                                          disabled = True,
117 |                                          layout = Layout(width = '80px'))])
118 |         controls.children[0].on_click(self._click_prev)
119 |         controls.children[1].on_click(self._click_next)
120 |         controls.children[2].on_submit(self._int_text_value_changed)
121 | 
122 |         return controls
123 |     
124 |     def _annotate(self, num, change):
125 |         ch = change['new']
126 |         if ch == self.ANNOT_DONT_KNOW:
127 |             self._answers[num] = None
128 |         else:
129 |             self._answers[num] = self._y_labels[ch]
130 |     
131 |     def _answer_to_label(self, answer):
132 |         return self._y_labels_reversed[answer]
133 |     
134 |     def _draw(self, change = None):
135 |         self._table = VBox(layout = Layout(width = '100%'))
136 |         self._table.children += (self._make_controls(),)
137 | 
138 |         last_element = min(self._current_position + self._max_examples, self._dataframe.shape[0])
139 | 
140 |         for i in range(self._current_position, last_element):
141 |             data_row = VBox(layout = self._example_layout)
142 | 
143 |             if self._display_feature_table:
144 |                 elem = self._dataframe.iloc[i].drop(self._drop_labels)
145 |                 data_row.children += (HTML(value = pd.DataFrame([elem.values], 
146 |                                                                 columns = elem.index, 
147 |                                                                 index = [self._dataframe.index[i]])
148 |                                            .to_html(classes=['table', 'table-striped'])),)
149 | 
150 |             data_row.children += self._visualizer(self._dataframe, i)
151 |             
152 |             data_row.children += (ToggleButtons(options=([self.ANNOT_DONT_KNOW] + 
153 |                                                 [k for k in self._y_labels.keys() if k != self.ANNOT_DONT_KNOW]),
154 |                                                 value = self._answer_to_label(self._answers[i]),
155 |                                                 description='Your annotation:',
156 |                                                 disabled=False),)
157 |             data_row.children[-1].observe(lambda tgl_bt, num = i: self._annotate(num, tgl_bt), 
158 |                                           names='value')
159 | 
160 |             self._table.children += (data_row,)
161 |         
162 |         self._table.children += (self._make_controls(),)
163 |         self.children = (self._table,)
164 |     


--------------------------------------------------------------------------------
/actleto/annotator/ui_widget.py:
--------------------------------------------------------------------------------
  1 | from .annotator_widget import AnnotatorWidget
  2 | 
  3 | from ipywidgets import Button, VBox, HBox, Label
  4 | import pandas as pd
  5 | import numpy as np
  6 | import logging
  7 | from threading import Timer
  8 | import os
  9 | 
 10 | 
 11 | logger = logging.getLogger('actleto')
 12 | 
 13 | 
 14 | def prep_log(obj):
 15 |     return '\n' + str(obj)
 16 | 
 17 | 
 18 | class EvaluationCallbackLogging:
 19 |     def __init__(self, logger):
 20 |         self._logger = logger
 21 |         
 22 |     def __call__(self, eval_res):
 23 |         self._logger.info('Evaluation: {}'.format(prep_log(pd.DataFrame([eval_res])
 24 |                                                            .to_string(index=False))))
 25 | 
 26 |     
 27 | class ActiveLearnerUiWidget(VBox):
 28 |     """The main ui widget for active learning annotation.
 29 |     
 30 |     Create widget in Jupyter, configure it with ActiveLearner object and invoke.
 31 |     
 32 |     """
 33 |     
 34 |     _reset_check_time = 4
 35 |     
 36 |     def __init__(self, 
 37 |                  active_learner, 
 38 |                  X_helper,
 39 |                  visualize_columns = [],
 40 |                  drop_labels = [],
 41 |                  display_feature_table = True,
 42 |                  y_labels = {"True" : True, 
 43 |                              "False" : False},
 44 |                  visualizer = None,
 45 |                  save_path = 'annotation',
 46 |                  evaluation_callback = None,
 47 |                  save_time = 0,
 48 |                  *args, **kwargs):
 49 |         """Widget constructor.
 50 |         
 51 |         Args:
 52 |             active_learner (ActiveLearner): the ActiveLearner object configured with query strategy.
 53 |             X_helper (pandas.DataFrame): the dataframe with data for visualization.
 54 |             textual_labels (list): list of string labels that will be visualized with VisualizerTextArea.
 55 |             drop_labels (list): list of string labels that will be dropped from visualization via table.
 56 |             y_labels (dict): dict {<y_textual_label> : <y_value>}.
 57 |             y_visualizer (object): visualizer for X_helper representation. The default is None. If None the widget
 58 |                 will invoke VisualizerTextArea by deafult.
 59 |             save_path (str): the path to save the results.
 60 |             evaluation_callback (functor): the callback for evaluation. The default is logging callback.
 61 |             save_time (int): Autosave time. If 0 then autosave is disabled. If u use auto save u have to 
 62 |                 call stop() method to disabel autosave in the current widget.
 63 |             
 64 |         """
 65 |         super(VBox, self).__init__(*args, **kwargs)
 66 | 
 67 |         self._X_helper = X_helper
 68 |         self._active_learner = active_learner
 69 |         self._save_path = save_path
 70 |         self._evaluation_callback = evaluation_callback or EvaluationCallbackLogging(logger)
 71 | 
 72 |         self._y_labels = y_labels
 73 |         self._visualizer = visualizer
 74 |         self._drop_labels = drop_labels
 75 |         self._visualize_columns = visualize_columns
 76 |         self._display_feature_table = display_feature_table
 77 |         
 78 |         controls = HBox()
 79 |         
 80 |         self._button_next_iter = Button(description = 'Next iteration')
 81 |         self._button_next_iter.on_click(self._click_next_iteration)
 82 |         controls.children += (self._button_next_iter,)
 83 |         
 84 |         self._iteration_num = 0
 85 |         controls.children += (Label(self._iteration_label()),)
 86 |         
 87 |         self._button_save = Button(description = 'Save')
 88 |         self._button_save.on_click(self._click_save)
 89 |         controls.children += (self._button_save,)
 90 | 
 91 |         self.children = (controls, self._make_annotator_widget())
 92 |         
 93 |         self._save_time = save_time
 94 |         self._timer = None
 95 |         if self._save_time > 0:
 96 |             self._start_save_timer()
 97 |             
 98 |         self._timer_check_save_reset = None
 99 |         self._timer_check_next_iteration_reset = None
100 |     
101 |     def __del__(self):
102 |         self.stop()
103 |         
104 |     def get_active_learner(self):
105 |         """Returns the active learner object that was delivered to the constructor."""
106 |         return self._active_learner
107 |     
108 |     def stop(self):
109 |         if self._timer is not None:
110 |             self._timer.cancel()
111 |     
112 |     def _save_on_timer(self):
113 |         logger.info('Autosave.')
114 |         self._save_answers(os.path.splitext(self._save_path)[0] + '_autosave')
115 |         self._start_save_timer()
116 |         
117 |     def _start_save_timer(self):
118 |         self._timer = Timer(self._save_time, self._save_on_timer)
119 |         self._timer.start()
120 |         
121 |     def _get_annotator_widget(self):
122 |         return self.children[1]
123 |     
124 |     def _iteration_label(self):
125 |         return 'Iteration #{}'.format(self._iteration_num)
126 |     
127 |     def _increment_iteration_num(self):
128 |         self._iteration_num += 1
129 |         self.children[0].children[1].value = self._iteration_label()
130 |         
131 |     def _make_annotator_widget(self):
132 |         samples_to_annotate = self._active_learner.choose_samples_for_annotation()
133 |         return AnnotatorWidget(dataframe = self._X_helper.iloc[samples_to_annotate],
134 |                                visualize_columns = self._visualize_columns,
135 |                                drop_labels = self._drop_labels,
136 |                                visualizer = self._visualizer,
137 |                                display_feature_table = self._display_feature_table,
138 |                                y_labels = self._y_labels)
139 |     
140 |     def _click_next_iteration(self, button):
141 |         if self._timer_check_next_iteration_reset:
142 |             self._timer_check_next_iteration_reset.cancel()
143 |         
144 |         self._button_next_iter.disabled = True
145 |         self._button_next_iter.icon = 'clock-o'
146 |         annotated_indexes = [self._X_helper.index.get_loc(e) 
147 |                              for e in self._get_annotator_widget().get_dataframe().index]
148 |         
149 |         self._active_learner.make_iteration(annotated_indexes, 
150 |                                             self._get_annotator_widget().get_answers())
151 |         
152 |         logger.info(self._iteration_label())
153 |         eval_res = self._active_learner.evaluate()
154 |         if eval_res is not None:
155 |             self._evaluation_callback(eval_res)
156 |         
157 |         self._increment_iteration_num()
158 |         self.children = (self.children[0], self._make_annotator_widget())
159 |         self._button_next_iter.icon = 'check'
160 |         self._button_next_iter.disabled = False
161 |         
162 |         self._timer_check_next_iteration_reset = Timer(self._reset_check_time, 
163 |                                                        self._check_next_iteration_reset)
164 |         self._timer_check_next_iteration_reset.start()
165 |         
166 |     def _check_next_iteration_reset(self):
167 |         self._button_next_iter.icon = ''
168 |                              
169 |     def _click_save(self, button):
170 |         if self._timer_check_save_reset:
171 |             self._timer_check_save_reset.cancel()
172 |         
173 |         self._button_save.disabled = True
174 |         self._button_save.icon = 'clock-o'
175 |         self._save_answers(self._save_path)
176 |         self._button_save.icon = 'check'
177 |         self._button_save.disabled = False
178 |         
179 |         self._timer_check_save_reset = Timer(self._reset_check_time, self._check_save_reset)
180 |         self._timer_check_save_reset.start()
181 |                                              
182 |     def _check_save_reset(self):
183 |         self._button_save.icon = ''
184 |                                              
185 |     def _save_answers(self, path):
186 |         np.save(path, self._active_learner.get_annotation())
187 |         logger.info('Saved. File path: {}'.format(os.path.splitext(path)[0] + '.npy'))
188 |         


--------------------------------------------------------------------------------
/actleto/strategies/adwes.py:
--------------------------------------------------------------------------------
  1 | import logging, numpy as np, scipy.stats
  2 | from annoy import AnnoyIndex
  3 | from sklearn.decomposition import TruncatedSVD
  4 | from libact.base.dataset import ensure_sklearn_compat
  5 | 
  6 | 
  7 | logger = logging.getLogger('actleto')
  8 | 
  9 | 
 10 | class ADWeS(object):
 11 |     def __init__(self,
 12 |                  dataset,
 13 |                  basic_strategy,
 14 |                  svd_components=300,
 15 |                  index_trees=10,
 16 |                  get_nearest_n=10,
 17 |                  get_most_uncertain_n=0,
 18 |                  exp_rel_power=0.8,
 19 |                  exp_rel_rate=1.0,
 20 |                  uncertainty_factor=0.5,
 21 |                  us_method='lc',
 22 |                  plot_each=20):
 23 |         self.dataset = dataset
 24 |         self.basic_strategy = basic_strategy
 25 | 
 26 |         self.get_nearest_n = get_nearest_n
 27 |         self.get_most_uncertain_n = get_most_uncertain_n
 28 |         self.exp_rel_power = exp_rel_power
 29 |         self.exp_rel_rate = exp_rel_rate
 30 |         self.uncertainty_factor = uncertainty_factor
 31 |         self.us_method = us_method
 32 |         self.plot_each = plot_each
 33 | 
 34 |         self.index = AnnoyIndex(svd_components)
 35 |         all_features = ensure_sklearn_compat(zip(*dataset.data)[0])
 36 |         self.data = TruncatedSVD(n_components=svd_components).fit_transform(all_features)
 37 |         for i, item in enumerate(self.data):
 38 |             self.index.add_item(i, item)
 39 |         self.index.build(index_trees)
 40 | 
 41 |         self.labeled_ids = set() # will be updated in make_query before all job
 42 |         
 43 |         # calculate mean and maximum distances
 44 |         self.explore_relevance = []
 45 |         self.explore_relevance_max = 0
 46 |         for i in range(self.data.shape[0]):
 47 |             cur_dist = self.index.get_nns_by_item(i,
 48 |                                                   self.get_nearest_n,
 49 |                                                   include_distances=True)[1]
 50 |             if len(cur_dist) > 0:
 51 |                 cur_mean = np.mean(cur_dist)
 52 |                 cur_max_dist = np.max(cur_dist)
 53 |                 if cur_max_dist > self.explore_relevance_max:
 54 |                     self.explore_relevance_max = cur_max_dist
 55 |             else:
 56 |                 cur_mean = np.nan
 57 |             self.explore_relevance.append(cur_mean)
 58 |         self.explore_relevance = np.array(self.explore_relevance)
 59 | 
 60 |         # fill na
 61 |         samples_without_neighbors = np.isnan(self.explore_relevance)
 62 |         self.explore_relevance[samples_without_neighbors] = self.explore_relevance_max
 63 | 
 64 |         # normalize
 65 |         logger.debug('init dist %s' % str(scipy.stats.describe(self.explore_relevance)))
 66 |         self.explore_relevance = ((self.explore_relevance - self.explore_relevance.min()) /
 67 |                                   (self.explore_relevance.max() - self.explore_relevance.min()))
 68 |         self.explore_relevance = (1 - self.explore_relevance) ** self.exp_rel_power
 69 | 
 70 |         self.iter_i = 0
 71 | 
 72 |     def make_query(self, return_score=False):
 73 |         self._update_exp_rel()
 74 | 
 75 |         self.model.train(self.dataset)
 76 | 
 77 |         unlabeled_entry_ids, X_pool = list(zip(*self.dataset.get_unlabeled_entries()))
 78 |         unlabeled_entry_ids = np.asarray(unlabeled_entry_ids)
 79 |         X_pool = ensure_sklearn_compat(X_pool)
 80 | 
 81 |         _, ids_with_scores = self.base_strategy.make_query(return_score=True)
 82 |         unlabeled_entry_ids, base_score = zip(*ids_with_scores)
 83 | 
 84 |         # normalize: we dont care about absolute values, only relative to rank samples
 85 |         #base_score = base_score - base_score.mean()
 86 |         #base_score /= base_score.std()
 87 |         base_score = base_score - base_score.min()
 88 |         base_score /= base_score.max()
 89 | 
 90 |         if self.get_most_uncertain_n > 0:
 91 |             most_base_relevant_indices = np.argpartition(-base_score, self.get_most_uncertain_n)[:self.get_most_uncertain_n]
 92 |         else:
 93 |             most_base_relevant_indices = list(range(len(base_score)))
 94 |         most_base_relevant_ids = unlabeled_entry_ids[most_base_relevant_indices]
 95 |         most_base_relevant_score = base_score[most_base_relevant_indices]
 96 |         logger.debug('most base relevant score %s' % str(scipy.stats.describe(most_base_relevant_score)))
 97 |         most_base_relevant_exp_rel = self.explore_relevance[most_base_relevant_ids]
 98 |         
 99 |         # normalize: we dont care about absolute values, only relative to rank samples
100 |         #most_uncertain_exp_rel = most_uncertain_exp_rel - most_uncertain_exp_rel.mean()
101 |         #most_uncertain_exp_rel /= most_uncertain_exp_rel.std()
102 |         most_base_relevant_exp_rel = most_base_relevant_exp_rel - most_base_relevant_exp_rel.min()
103 |         most_base_relevant_exp_rel /= most_base_relevant_exp_rel.max()
104 |         logger.debug('most exp rel %s' % str(scipy.stats.describe(most_base_relevant_exp_rel)))
105 | 
106 |         # f-beta
107 |         result_score = ((1 + self.uncertainty_factor ** 2) * most_base_relevant_score * most_base_relevant_exp_rel /
108 |                         ((self.uncertainty_factor ** 2) * most_base_relevant_score + most_base_relevant_exp_rel))
109 |         #result_score = (self.uncertainty_factor * most_uncertain_uncert_score
110 |         #                + (1 - self.uncertainty_factor) * most_uncertain_exp_rel)
111 |         result_score[np.isnan(result_score)] = 0.0
112 |         logger.debug('most res %s' % str(scipy.stats.describe(result_score)))
113 | 
114 | #         if self.iter_i % self.plot_each == 0:
115 | #             import matplotlib.pyplot as plt
116 | #             fig, ax = plt.subplots()
117 | #             fig.set_size_inches((9, 6))
118 | #             ax.hist(most_base_relevant_score, label='uncert')
119 | #             ax.hist(most_base_relevant_exp_rel, label='exp_rel')
120 | #             ax.hist(result_score, label='res')
121 | #             fig.savefig('./debug/%05d_hist.png' % self.iter_i)
122 | #             plt.close(fig)
123 | # 
124 | #             _, ax = plot_samples(np.array([most_base_relevant_score,
125 | #                                            most_base_relevant_exp_rel]).T,
126 | #                                  result_score,
127 | #                                  with_kde=False,
128 | #                                  filename='./debug/%05d_scores.png' % self.iter_i,
129 | #                                  do_not_display=True)
130 | #             ax.set_xlabel('uncert')
131 | #             ax.set_ylabel('exp_rel')
132 | 
133 |         best_i = np.argmax(result_score)
134 |         best_id = most_base_relevant_ids[best_i]
135 |         logger.debug('best %r %r %r %r' % (best_i,
136 |                                            result_score[best_i],
137 |                                            most_base_relevant_score[best_i],
138 |                                            most_base_relevant_exp_rel[best_i]))
139 |         if return_score:
140 |             return best_id, \
141 |                    list(zip(most_base_relevant_ids, result_score))
142 |         else:
143 |             return best_id
144 | 
145 |     def _update_exp_rel(self):
146 |         data = self.dataset.data
147 |         newly_labeled_ids = { i for i in range(len(data))
148 |                              if not data[i][1] is None
149 |                              and not i in self.labeled_ids }
150 |         self.labeled_ids.update(newly_labeled_ids)
151 |         for ex_id in newly_labeled_ids:
152 |             neighbor_ids, neighbor_dist = self.index.get_nns_by_item(ex_id,
153 |                                                                      self.get_nearest_n,
154 |                                                                      include_distances=True)
155 |             neighbor_dist = np.asarray(neighbor_dist, dtype='float')
156 |             neighbor_discount_factor = (1 - neighbor_dist / self.explore_relevance_max) ** self.exp_rel_power
157 |             neighbor_discount_factor= 1 - self.exp_rel_rate * neighbor_discount_factor
158 |             #logger.debug('dist: %s' % neighbor_dist)
159 |             #logger.debug('factor: %s' % neighbor_discount_factor)
160 |             assert np.count_nonzero(np.isnan(neighbor_discount_factor)) == 0
161 |             self.explore_relevance[neighbor_ids] *= neighbor_discount_factor
162 | 
163 |         self.iter_i += 1
164 | #         if self.iter_i % self.plot_each == 0:
165 | #             plot_samples(self.data,
166 | #                          self.explore_relevance,
167 | #                          kind='svd',
168 | #                          with_kde=False,
169 | #                          filename='./debug/%05d.png' % self.iter_i,
170 | #                          do_not_display=True)
171 | #             


--------------------------------------------------------------------------------
/examples/20newsgroups.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# 20newsgroups example"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": null,
 13 |    "metadata": {
 14 |     "ExecuteTime": {
 15 |      "end_time": "2017-09-19T07:56:12.283842Z",
 16 |      "start_time": "2017-09-19T07:56:12.255566Z"
 17 |     }
 18 |    },
 19 |    "outputs": [],
 20 |    "source": [
 21 |     "%load_ext autoreload\n",
 22 |     "%autoreload 2\n",
 23 |     "\n",
 24 |     "import sys\n",
 25 |     "sys.path.append('../')\n",
 26 |     "\n",
 27 |     "import os\n",
 28 |     "os.environ['CUDA_VISIBLE_DEVICES'] = ''"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "markdown",
 33 |    "metadata": {},
 34 |    "source": [
 35 |     "# Initialization"
 36 |    ]
 37 |   },
 38 |   {
 39 |    "cell_type": "code",
 40 |    "execution_count": null,
 41 |    "metadata": {
 42 |     "ExecuteTime": {
 43 |      "end_time": "2017-09-19T07:56:13.148431Z",
 44 |      "start_time": "2017-09-19T07:56:12.285935Z"
 45 |     },
 46 |     "collapsed": true
 47 |    },
 48 |    "outputs": [],
 49 |    "source": [
 50 |     "import collections, pandas as pd, numpy as np\n",
 51 |     "\n",
 52 |     "from sklearn.linear_model import LogisticRegression\n",
 53 |     "from sklearn.datasets import fetch_20newsgroups\n",
 54 |     "from sklearn.feature_extraction.text import TfidfVectorizer\n",
 55 |     "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n",
 56 |     "\n",
 57 |     "from libact.models import LogisticRegression as LibActLogReg\n",
 58 |     "from libact.query_strategies import UncertaintySampling\n",
 59 |     "\n",
 60 |     "from actleto import ActiveLearner, ActiveLearnerUiWidget, make_libact_strategy_ctor"
 61 |    ]
 62 |   },
 63 |   {
 64 |    "cell_type": "markdown",
 65 |    "metadata": {},
 66 |    "source": [
 67 |     "# Load and prepare data"
 68 |    ]
 69 |   },
 70 |   {
 71 |    "cell_type": "code",
 72 |    "execution_count": null,
 73 |    "metadata": {
 74 |     "ExecuteTime": {
 75 |      "end_time": "2017-09-19T07:56:13.820225Z",
 76 |      "start_time": "2017-09-19T07:56:13.151600Z"
 77 |     },
 78 |     "collapsed": true
 79 |    },
 80 |    "outputs": [],
 81 |    "source": [
 82 |     "# Loading the 20newsgroups dataset\n",
 83 |     "train_dataset = fetch_20newsgroups(subset='train')\n",
 84 |     "test_dataset = fetch_20newsgroups(subset='test')"
 85 |    ]
 86 |   },
 87 |   {
 88 |    "cell_type": "code",
 89 |    "execution_count": null,
 90 |    "metadata": {
 91 |     "ExecuteTime": {
 92 |      "end_time": "2017-09-19T07:56:18.834411Z",
 93 |      "start_time": "2017-09-19T07:56:13.823592Z"
 94 |     },
 95 |     "collapsed": true
 96 |    },
 97 |    "outputs": [],
 98 |    "source": [
 99 |     "# Preparing features\n",
100 |     "vectorizer = TfidfVectorizer(min_df=3, max_df=0.5, sublinear_tf=True)\n",
101 |     "\n",
102 |     "X_train = vectorizer.fit_transform(train_dataset.data)\n",
103 |     "Y_train = train_dataset.target\n",
104 |     "\n",
105 |     "X_test = vectorizer.transform(test_dataset.data)\n",
106 |     "Y_test = test_dataset.target"
107 |    ]
108 |   },
109 |   {
110 |    "cell_type": "markdown",
111 |    "metadata": {},
112 |    "source": [
113 |     "# Train-test without active learning"
114 |    ]
115 |   },
116 |   {
117 |    "cell_type": "code",
118 |    "execution_count": null,
119 |    "metadata": {
120 |     "ExecuteTime": {
121 |      "end_time": "2017-09-19T07:56:24.748942Z",
122 |      "start_time": "2017-09-19T07:56:18.836135Z"
123 |     }
124 |    },
125 |    "outputs": [],
126 |    "source": [
127 |     "clf = LogisticRegression()\n",
128 |     "clf.fit(X_train, Y_train)\n",
129 |     "Y_pred = clf.predict(X_test)\n",
130 |     "Y_pred_proba = clf.predict_proba(X_test)\n",
131 |     "print('Accuracy', accuracy_score(Y_test, Y_pred))\n",
132 |     "print('F1 macro', f1_score(Y_test, Y_pred, average='macro'))"
133 |    ]
134 |   },
135 |   {
136 |    "cell_type": "markdown",
137 |    "metadata": {},
138 |    "source": [
139 |     "# Train-test with active learning and human-in-the-loop"
140 |    ]
141 |   },
142 |   {
143 |    "cell_type": "code",
144 |    "execution_count": null,
145 |    "metadata": {
146 |     "ExecuteTime": {
147 |      "end_time": "2017-09-19T07:56:24.779279Z",
148 |      "start_time": "2017-09-19T07:56:24.754386Z"
149 |     },
150 |     "collapsed": true
151 |    },
152 |    "outputs": [],
153 |    "source": [
154 |     "# We choose 20 random examples as seed set for active learning.\n",
155 |     "Y_seed = Y_train.copy().astype('O')\n",
156 |     "unknown_indexes = np.random.randint(Y_seed.shape[0], size = Y_seed.shape[0] - 20)\n",
157 |     "Y_seed[unknown_indexes] = None"
158 |    ]
159 |   },
160 |   {
161 |    "cell_type": "code",
162 |    "execution_count": null,
163 |    "metadata": {
164 |     "ExecuteTime": {
165 |      "end_time": "2017-09-19T07:56:40.927054Z",
166 |      "start_time": "2017-09-19T07:56:38.006929Z"
167 |     },
168 |     "collapsed": true
169 |    },
170 |    "outputs": [],
171 |    "source": [
172 |     "# Creating query strategy. The strategy selects examples for annotations from unlabeled dataset.\n",
173 |     "# We use standard uncertainty sampling strategy from libact and wrap it into adaptor.\n",
174 |     "active_learn_alg_ctor = make_libact_strategy_ctor(lambda trn_ds:\n",
175 |     "                                                  UncertaintySampling(trn_ds,\n",
176 |     "                                                                      model = LibActLogReg()))\n",
177 |     "\n",
178 |     "# Creating ActiveLearning object that implements AL logic.\n",
179 |     "active_learner = ActiveLearner(active_learn_alg_ctor = active_learn_alg_ctor,\n",
180 |     "                               y_dtype = 'int',\n",
181 |     "                               X_full_dataset = X_train, \n",
182 |     "                               y_full_dataset = Y_seed,\n",
183 |     "                               X_test_dataset = X_test,\n",
184 |     "                               y_test_dataset = Y_test,\n",
185 |     "                               model_evaluate = LibActLogReg(), # We use logreg from libact for choosing samples.\n",
186 |     "                               eval_metrics = [accuracy_score, f1_score],\n",
187 |     "                               rnd_start_steps = 0)\n",
188 |     "\n",
189 |     "# Creaing X_helper object for visualization dataset for humans.\n",
190 |     "X_helper = pd.DataFrame(data={ 'text' : train_dataset.data })"
191 |    ]
192 |   },
193 |   {
194 |    "cell_type": "code",
195 |    "execution_count": null,
196 |    "metadata": {
197 |     "ExecuteTime": {
198 |      "end_time": "2017-09-19T07:56:47.558000Z",
199 |      "start_time": "2017-09-19T07:56:45.335239Z"
200 |     }
201 |    },
202 |    "outputs": [],
203 |    "source": [
204 |     "# This try-catch block is needed to stop autosave thread in case we invoke the cell multiple times.\n",
205 |     "try:\n",
206 |     "    if active_learn_ui:\n",
207 |     "        active_learn_ui.stop()\n",
208 |     "except NameError:\n",
209 |     "    pass\n",
210 |     "\n",
211 |     "# Creaing the active learner widget itself and configure it with active_learner, X_helper.\n",
212 |     "# The default visualizer will preview the visualize_columns with VisualizerTextArea.\n",
213 |     "active_learn_ui = ActiveLearnerUiWidget(active_learner = active_learner, \n",
214 |     "                                        X_helper = X_helper,\n",
215 |     "                                        display_feature_table = False,\n",
216 |     "                                        drop_labels = [],\n",
217 |     "                                        visualize_columns = ['text'],\n",
218 |     "                                        y_labels = { label : i for i, label in enumerate(train_dataset.target_names) },\n",
219 |     "                                        save_path = './20ng_active_dump.npy',\n",
220 |     "                                        save_time = 120)\n",
221 |     "\n",
222 |     "active_learn_ui"
223 |    ]
224 |   }
225 |  ],
226 |  "metadata": {
227 |   "kernelspec": {
228 |    "display_name": "Python 3",
229 |    "language": "python",
230 |    "name": "python3"
231 |   },
232 |   "language_info": {
233 |    "codemirror_mode": {
234 |     "name": "ipython",
235 |     "version": 3
236 |    },
237 |    "file_extension": ".py",
238 |    "mimetype": "text/x-python",
239 |    "name": "python",
240 |    "nbconvert_exporter": "python",
241 |    "pygments_lexer": "ipython3",
242 |    "version": "3.6.0"
243 |   },
244 |   "toc": {
245 |    "colors": {
246 |     "hover_highlight": "#DAA520",
247 |     "running_highlight": "#FF0000",
248 |     "selected_highlight": "#FFD700"
249 |    },
250 |    "moveMenuLeft": true,
251 |    "nav_menu": {
252 |     "height": "105px",
253 |     "width": "252px"
254 |    },
255 |    "navigate_menu": true,
256 |    "number_sections": true,
257 |    "sideBar": true,
258 |    "threshold": 4,
259 |    "toc_cell": false,
260 |    "toc_section_display": "block",
261 |    "toc_window_display": false
262 |   }
263 |  },
264 |  "nbformat": 4,
265 |  "nbformat_minor": 2
266 | }
267 | 


--------------------------------------------------------------------------------
/examples/MNIST_annotation.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# MNIST annotation example"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": null,
 13 |    "metadata": {
 14 |     "ExecuteTime": {
 15 |      "end_time": "2017-09-19T07:06:59.047801Z",
 16 |      "start_time": "2017-09-19T07:06:58.786121Z"
 17 |     }
 18 |    },
 19 |    "outputs": [],
 20 |    "source": [
 21 |     "%load_ext autoreload\n",
 22 |     "%autoreload 2\n",
 23 |     "\n",
 24 |     "import os, sys\n",
 25 |     "os.environ['CUDA_VISIBLE_DEVICES'] = ''\n",
 26 |     "\n",
 27 |     "sys.path.append('../')\n",
 28 |     "\n",
 29 |     "import matplotlib.pyplot as plt, collections, logging\n",
 30 |     "%pylab inline"
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "markdown",
 35 |    "metadata": {},
 36 |    "source": [
 37 |     "# Initialization"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "code",
 42 |    "execution_count": null,
 43 |    "metadata": {
 44 |     "ExecuteTime": {
 45 |      "end_time": "2017-09-19T07:07:00.044946Z",
 46 |      "start_time": "2017-09-19T07:06:59.933855Z"
 47 |     }
 48 |    },
 49 |    "outputs": [],
 50 |    "source": [
 51 |     "import pandas\n",
 52 |     "import numpy\n",
 53 |     "\n",
 54 |     "from keras.datasets import mnist\n",
 55 |     "from keras.models import Sequential\n",
 56 |     "from keras.layers import Dense\n",
 57 |     "from keras.layers import Dropout\n",
 58 |     "from keras.utils import np_utils\n",
 59 |     "from keras.wrappers.scikit_learn import KerasClassifier\n",
 60 |     "\n",
 61 |     "from sklearn.metrics import f1_score, accuracy_score\n",
 62 |     "\n",
 63 |     "from libact.query_strategies import UncertaintySampling\n",
 64 |     "from libact.models import LogisticRegression as LibActLogisticRegression, SklearnProbaAdapter\n",
 65 |     "\n",
 66 |     "from actleto import ActiveLearner, make_libact_strategy_ctor, MPErr, ActiveLearnerUiWidget, ImageVisualizer"
 67 |    ]
 68 |   },
 69 |   {
 70 |    "cell_type": "code",
 71 |    "execution_count": null,
 72 |    "metadata": {
 73 |     "ExecuteTime": {
 74 |      "end_time": "2017-09-19T07:07:02.574046Z",
 75 |      "start_time": "2017-09-19T07:07:02.485003Z"
 76 |     }
 77 |    },
 78 |    "outputs": [],
 79 |    "source": [
 80 |     "import logging\n",
 81 |     "\n",
 82 |     "# Logger is needed for the default evaluation output and some notifications\n",
 83 |     "logger = logging.getLogger('actleto')\n",
 84 |     "formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')    \n",
 85 |     "strm_hdl = logging.StreamHandler(sys.stdout)\n",
 86 |     "strm_hdl.setFormatter(formatter)\n",
 87 |     "logger.addHandler(strm_hdl)\n",
 88 |     "logger.setLevel(logging.INFO)"
 89 |    ]
 90 |   },
 91 |   {
 92 |    "cell_type": "markdown",
 93 |    "metadata": {},
 94 |    "source": [
 95 |     "# Dataset preparation"
 96 |    ]
 97 |   },
 98 |   {
 99 |    "cell_type": "code",
100 |    "execution_count": null,
101 |    "metadata": {
102 |     "ExecuteTime": {
103 |      "end_time": "2017-09-19T07:07:04.315465Z",
104 |      "start_time": "2017-09-19T07:07:03.634325Z"
105 |     }
106 |    },
107 |    "outputs": [],
108 |    "source": [
109 |     "# Loading MNIST dataset\n",
110 |     "(X_train_orig, y_train), (X_test_orig, y_test) = mnist.load_data()\n",
111 |     "\n",
112 |     "num_classes = numpy.unique(y_train).shape[0]\n",
113 |     "\n",
114 |     "# flatten 28*28 images to a 784 vector for each image\n",
115 |     "num_pixels = X_train_orig.shape[1] * X_train_orig.shape[2]\n",
116 |     "X_train = X_train_orig.reshape(X_train_orig.shape[0], num_pixels).astype('float32')\n",
117 |     "X_test = X_test_orig.reshape(X_test_orig.shape[0], num_pixels).astype('float32')\n",
118 |     "\n",
119 |     "# normalize inputs from 0-255 to 0-1\n",
120 |     "X_train = X_train / 255\n",
121 |     "X_test = X_test / 255"
122 |    ]
123 |   },
124 |   {
125 |    "cell_type": "markdown",
126 |    "metadata": {},
127 |    "source": [
128 |     "# Model construction"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": null,
134 |    "metadata": {
135 |     "ExecuteTime": {
136 |      "end_time": "2017-09-19T07:17:10.995936Z",
137 |      "start_time": "2017-09-19T07:17:10.904563Z"
138 |     }
139 |    },
140 |    "outputs": [],
141 |    "source": [
142 |     "# Simple 2-layer perceptron\n",
143 |     "def baseline_model():\n",
144 |     "    model = Sequential()\n",
145 |     "    model.add(Dense(num_pixels, input_dim=num_pixels, kernel_initializer='normal', activation='relu'))\n",
146 |     "    model.add(Dense(num_classes, kernel_initializer='normal', activation='softmax'))\n",
147 |     "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
148 |     "    return model"
149 |    ]
150 |   },
151 |   {
152 |    "cell_type": "markdown",
153 |    "metadata": {},
154 |    "source": [
155 |     "# Active learning prepartions"
156 |    ]
157 |   },
158 |   {
159 |    "cell_type": "code",
160 |    "execution_count": null,
161 |    "metadata": {
162 |     "ExecuteTime": {
163 |      "end_time": "2017-09-19T07:18:22.421123Z",
164 |      "start_time": "2017-09-19T07:18:22.327649Z"
165 |     }
166 |    },
167 |    "outputs": [],
168 |    "source": [
169 |     "# For demonstration we sample 50 images from MNIST as seed examples for AL. \n",
170 |     "# The rest of the images are considered \"unlabeled\"\n",
171 |     "y_seed = pandas.Series([None] * y_train.shape[0], index = range(y_train.shape[0]))\n",
172 |     "known_indexes = np.random.randint(y_train.shape[0], size = 50)\n",
173 |     "y_seed.iloc[known_indexes] = y_train[known_indexes]"
174 |    ]
175 |   },
176 |   {
177 |    "cell_type": "code",
178 |    "execution_count": null,
179 |    "metadata": {
180 |     "ExecuteTime": {
181 |      "end_time": "2017-09-19T07:10:29.091372Z",
182 |      "start_time": "2017-09-19T07:10:28.398883Z"
183 |     },
184 |     "scrolled": true
185 |    },
186 |    "outputs": [],
187 |    "source": [
188 |     "# Creating model for selecting examples from the unlabeled dataset.\n",
189 |     "# We use special wrapper to make sklearn model compatible with libact library.\n",
190 |     "query_model = SklearnProbaAdapter(KerasClassifier(build_fn = baseline_model, verbose = 0))\n",
191 |     "\n",
192 |     "# We use uncertainty sampling strategy from libact and wrap it with adaptor.\n",
193 |     "active_learn_alg_ctor = make_libact_strategy_ctor(lambda trn_ds:\n",
194 |     "                                                  UncertaintySampling(trn_ds,\n",
195 |     "                                                                      model = query_model))\n",
196 |     "\n",
197 |     "# Now we create model for evaluation. In this example it is the same model as the one\n",
198 |     "# for selecting examples.\n",
199 |     "evaluation_model = KerasClassifier(build_fn = baseline_model, verbose = 0)\n",
200 |     "\n",
201 |     "\n",
202 |     "def f1_macro(y_t, y_p):\n",
203 |     "    return f1_score(y_t, y_p, average = 'macro')\n",
204 |     "\n",
205 |     "# We create ActiveLearner object that incupsulates the logic of active learning.\n",
206 |     "active_learner = ActiveLearner(active_learn_alg_ctor = active_learn_alg_ctor,\n",
207 |     "                               y_dtype = 'int',\n",
208 |     "                               X_full_dataset = X_train, \n",
209 |     "                               y_full_dataset = y_seed.values,\n",
210 |     "                               X_test_dataset = X_test,\n",
211 |     "                               y_test_dataset = y_test,\n",
212 |     "                               model_evaluate = evaluation_model,\n",
213 |     "                               eval_metrics = [accuracy_score, f1_macro],\n",
214 |     "                               rnd_start_steps = 0)\n",
215 |     "\n",
216 |     "# X_helper object is needed for data visualization for human annotators. \n",
217 |     "X_helper = pandas.DataFrame(X_train_orig.reshape(-1, 28*28), index = range(X_train_orig.shape[0]))"
218 |    ]
219 |   },
220 |   {
221 |    "cell_type": "markdown",
222 |    "metadata": {},
223 |    "source": [
224 |     "# Invoking annotation widget"
225 |    ]
226 |   },
227 |   {
228 |    "cell_type": "code",
229 |    "execution_count": null,
230 |    "metadata": {
231 |     "ExecuteTime": {
232 |      "end_time": "2017-09-19T07:33:44.367082Z",
233 |      "start_time": "2017-09-19T07:33:40.577203Z"
234 |     }
235 |    },
236 |    "outputs": [],
237 |    "source": [
238 |     "# We need this try catch if we use autosave feature. It stops autosave thread for the older annotator widgets if we\n",
239 |     "# invoke this cell for the second and next times.\n",
240 |     "try:\n",
241 |     "    if active_learn_ui:\n",
242 |     "        active_learn_ui.stop()\n",
243 |     "except NameError:\n",
244 |     "    pass\n",
245 |     "    \n",
246 |     "# Now we create the active learner widget itself and configure it with created active learning object,\n",
247 |     "# data for visualization (X_helper), and visualizer for images.\n",
248 |     "active_learn_ui = ActiveLearnerUiWidget(active_learner = active_learner, \n",
249 |     "                                        X_helper = X_helper,\n",
250 |     "                                        display_feature_table = False,\n",
251 |     "                                        drop_labels = list(range(0, X_helper.shape[1])),\n",
252 |     "                                        visualizer = ImageVisualizer(columns_range = (0, X_helper.shape[1]), \n",
253 |     "                                                                     img_shape = (28, 28),\n",
254 |     "                                                                     img_mode = 'L',\n",
255 |     "                                                                     preview_shape = (100, 100)),\n",
256 |     "                                        y_labels = {str(elem) : elem for elem in range(10)},\n",
257 |     "                                        save_path = 'mnist.npy',\n",
258 |     "                                        save_time = 120)\n",
259 |     "\n",
260 |     "active_learn_ui"
261 |    ]
262 |   }
263 |  ],
264 |  "metadata": {
265 |   "kernelspec": {
266 |    "display_name": "Python 3",
267 |    "language": "python",
268 |    "name": "python3"
269 |   },
270 |   "language_info": {
271 |    "codemirror_mode": {
272 |     "name": "ipython",
273 |     "version": 3
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376 | skoltech_cardiology
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425 | <li class="fly-out-top-item"><a href="/INemo/skoltech_cardiology/-/issues"><strong class="fly-out-top-item-name">
426 | Issues
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433 | List
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436 | Boards
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450 | Merge Requests
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468 | CI / CD
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476 | Pipelines
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479 | Jobs
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482 | Schedules
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498 | Metrics
499 | </span>
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501 | Alerts
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503 | </a></li>
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505 | Environments
506 | </span>
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508 | Error Tracking
509 | </span>
510 | </a></li><li class=""><a title="Serverless" href="/INemo/skoltech_cardiology/-/serverless/functions"><span>
511 | Serverless
512 | </span>
513 | </a></li><li class=""><a title="Logs" href="/INemo/skoltech_cardiology/-/logs"><span>
514 | Logs
515 | </span>
516 | </a></li><li class=""><a title="Kubernetes" class="shortcuts-kubernetes" href="/INemo/skoltech_cardiology/-/clusters"><span>
517 | Kubernetes
518 | </span>
519 | <div class="feature-highlight js-feature-highlight" data-container="body" data-dismiss-endpoint="/-/user_callouts" data-highlight="gke_cluster_integration" data-placement="right" data-toggle="popover" data-trigger="manual" disabled></div>
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521 | <img class="feature-highlight-illustration" alt="Kubernetes popover" src="https://assets.gitlab-static.net/assets/illustrations/cluster_popover-889b8203a86c86d1fd3f0b181e588071d00cd9c77590fd87dba4d19035e1c0df.svg" />
522 | <div class="feature-highlight-popover-sub-content">
523 | <p>Allows you to add and manage Kubernetes clusters.</p>
524 | <p>
525 | Protip:
526 | <a href="/help/topics/autodevops/index.md">Auto DevOps</a>
527 | <span>uses Kubernetes clusters to deploy your code!</span>
528 | </p>
529 | <hr>
530 | <button class="btn btn-success btn-sm dismiss-feature-highlight" type="button">
531 | <span>Got it!</span>
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534 | </div>
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541 | <span class="nav-item-name">
542 | Packages &amp; Registries
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545 | <li class="fly-out-top-item"><a href="/INemo/skoltech_cardiology/container_registry"><strong class="fly-out-top-item-name">
546 | Packages &amp; Registries
547 | </strong>
548 | </a></li><li class="divider fly-out-top-item"></li>
549 | <li class=""><a class="shortcuts-container-registry" title="Container Registry" href="/INemo/skoltech_cardiology/container_registry"><span>Container Registry</span>
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556 | Analytics
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559 | <li class="fly-out-top-item"><a href="/INemo/skoltech_cardiology/-/value_stream_analytics"><strong class="fly-out-top-item-name">
560 | Analytics
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563 | <li class=""><a title="CI / CD" href="/INemo/skoltech_cardiology/-/pipelines/charts"><span>CI / CD</span>
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565 | </a></li><li class=""><a class="shortcuts-project-cycle-analytics" title="Value Stream" href="/INemo/skoltech_cardiology/-/value_stream_analytics"><span>Value Stream</span>
566 | </a></li></ul>
567 | </li>
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571 | <span class="nav-item-name">
572 | Wiki
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576 | Wiki
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583 | Snippets
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373 | <div class="avatar s40 avatar-tile identicon bg2">S</div>
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376 | skoltech_cardiology
377 | </div>
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425 | <li class="fly-out-top-item"><a href="/INemo/skoltech_cardiology/-/issues"><strong class="fly-out-top-item-name">
426 | Issues
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429 | 13
430 | </span>
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433 | List
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436 | Boards
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450 | Merge Requests
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468 | CI / CD
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471 | <li class="fly-out-top-item"><a href="/INemo/skoltech_cardiology/-/pipelines"><strong class="fly-out-top-item-name">
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476 | Pipelines
477 | </span>
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479 | Jobs
480 | </span>
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482 | Schedules
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498 | Metrics
499 | </span>
500 | </a></li><li class=""><a title="Alerts" class="shortcuts-tracking qa-operations-tracking-link" href="/INemo/skoltech_cardiology/-/alert_management"><span>
501 | Alerts
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503 | </a></li>
504 | <li class=""><a title="Environments" class="shortcuts-environments qa-operations-environments-link" href="/INemo/skoltech_cardiology/-/environments"><span>
505 | Environments
506 | </span>
507 | </a></li><li class=""><a title="Error Tracking" class="shortcuts-tracking qa-operations-tracking-link" href="/INemo/skoltech_cardiology/-/error_tracking"><span>
508 | Error Tracking
509 | </span>
510 | </a></li><li class=""><a title="Serverless" href="/INemo/skoltech_cardiology/-/serverless/functions"><span>
511 | Serverless
512 | </span>
513 | </a></li><li class=""><a title="Logs" href="/INemo/skoltech_cardiology/-/logs"><span>
514 | Logs
515 | </span>
516 | </a></li><li class=""><a title="Kubernetes" class="shortcuts-kubernetes" href="/INemo/skoltech_cardiology/-/clusters"><span>
517 | Kubernetes
518 | </span>
519 | <div class="feature-highlight js-feature-highlight" data-container="body" data-dismiss-endpoint="/-/user_callouts" data-highlight="gke_cluster_integration" data-placement="right" data-toggle="popover" data-trigger="manual" disabled></div>
520 | </a><div class="feature-highlight-popover-content">
521 | <img class="feature-highlight-illustration" alt="Kubernetes popover" src="https://assets.gitlab-static.net/assets/illustrations/cluster_popover-889b8203a86c86d1fd3f0b181e588071d00cd9c77590fd87dba4d19035e1c0df.svg" />
522 | <div class="feature-highlight-popover-sub-content">
523 | <p>Allows you to add and manage Kubernetes clusters.</p>
524 | <p>
525 | Protip:
526 | <a href="/help/topics/autodevops/index.md">Auto DevOps</a>
527 | <span>uses Kubernetes clusters to deploy your code!</span>
528 | </p>
529 | <hr>
530 | <button class="btn btn-success btn-sm dismiss-feature-highlight" type="button">
531 | <span>Got it!</span>
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534 | </div>
535 | </div>
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541 | <span class="nav-item-name">
542 | Packages &amp; Registries
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545 | <li class="fly-out-top-item"><a href="/INemo/skoltech_cardiology/container_registry"><strong class="fly-out-top-item-name">
546 | Packages &amp; Registries
547 | </strong>
548 | </a></li><li class="divider fly-out-top-item"></li>
549 | <li class=""><a class="shortcuts-container-registry" title="Container Registry" href="/INemo/skoltech_cardiology/container_registry"><span>Container Registry</span>
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556 | Analytics
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559 | <li class="fly-out-top-item"><a href="/INemo/skoltech_cardiology/-/value_stream_analytics"><strong class="fly-out-top-item-name">
560 | Analytics
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563 | <li class=""><a title="CI / CD" href="/INemo/skoltech_cardiology/-/pipelines/charts"><span>CI / CD</span>
564 | </a></li><li class=""><a class="shortcuts-repository-charts" title="Repository" href="/INemo/skoltech_cardiology/-/graphs/master/charts"><span>Repository</span>
565 | </a></li><li class=""><a class="shortcuts-project-cycle-analytics" title="Value Stream" href="/INemo/skoltech_cardiology/-/value_stream_analytics"><span>Value Stream</span>
566 | </a></li></ul>
567 | </li>
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571 | <span class="nav-item-name">
572 | Wiki
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576 | Wiki
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911 | </div>
912 | 
913 | <div class="form-group row branch">
914 | <label class="col-form-label col-sm-2" for="branch_name">Target Branch</label>
915 | <div class="col-sm-10">
916 | <input type="text" name="branch_name" id="branch_name" value="master" required="required" class="form-control js-branch-name ref-name" />
917 | <div class="js-create-merge-request-container">
918 | <div class="form-check gl-mt-3">
919 | <input type="checkbox" name="create_merge_request" id="create_merge_request-05048de30add53ed7ff2d00dc762f71a" value="1" class="js-create-merge-request form-check-input" checked="checked" />
920 | <label class="form-check-label" for="create_merge_request-05048de30add53ed7ff2d00dc762f71a">Start a <strong>new merge request</strong> with these changes
921 | </label></div>
922 | 
923 | </div>
924 | </div>
925 | </div>
926 | <input type="hidden" name="original_branch" id="original_branch" value="master" class="js-original-branch" />
927 | 
928 | <div class="form-actions">
929 | <button name="button" type="button" class="btn btn-success btn-upload-file" id="submit-all"><i aria-hidden="true" data-hidden="true" class="fa fa-spin fa-spinner js-loading-icon hidden"></i>
930 | Replace file
931 | </button><a class="btn btn-cancel" data-dismiss="modal" href="#">Cancel</a>
932 | 
933 | </div>
934 | </form></div>
935 | </div>
936 | </div>
937 | </div>
938 | 
939 | </div>
940 | 
941 | 
942 | </div>
943 | </div>
944 | </div>
945 | </div>
946 | 
947 | 
948 | 
949 | 
950 | <script data-cfasync="false" src="/cdn-cgi/scripts/5c5dd728/cloudflare-static/email-decode.min.js"></script></body>
951 | </html>
952 | 
953 | 


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