Merge branch 's0tt-master' into dev

Author: cosmic-cortex

.gitignore

@@ -1,4 +1,5 @@
 *pyc
+*.vscode
 .idea
 .ipynb_checkpoints
 *~

.travis.yml

@@ -22,7 +22,7 @@ after_success:
 matrix:
   include:
     install:
-      - pip install numpy==1.13 scikit-learn==0.18 scipy==0.18
+      - pip install numpy==1.20 scikit-learn==0.18 scipy==0.18 torch==1.8.1
       - pip install codecov
       - pip install coverage
       - pip install .

examples/active_regression.py

@@ -2,11 +2,11 @@
 Active regression example with Gaussian processes.
 """
 
-import numpy as np
 import matplotlib.pyplot as plt
-from sklearn.gaussian_process import GaussianProcessRegressor
-from sklearn.gaussian_process.kernels import WhiteKernel, RBF
+import numpy as np
 from modAL.models import ActiveLearner
+from sklearn.gaussian_process import GaussianProcessRegressor
+from sklearn.gaussian_process.kernels import RBF, WhiteKernel
 
 
 # query strategy for regression

examples/bagging.py

@@ -2,11 +2,12 @@
 This example shows how to build models with bagging using the Committee model.
 """
 
-import numpy as np
 from itertools import product
+
+import numpy as np
 from matplotlib import pyplot as plt
-from sklearn.neighbors import KNeighborsClassifier
 from modAL.models import ActiveLearner, Committee
+from sklearn.neighbors import KNeighborsClassifier
 
 # creating the dataset
 im_width = 500
@@ -90,4 +91,4 @@
         plt.subplot(1, n_learners, learner_idx+1)
         plt.imshow(learner.predict(X_pool).reshape(im_height, im_width))
         plt.title('Learner no. %d after refitting' % (learner_idx + 1))
-    plt.show()
+    plt.show()

examples/bayesian_optimization.py

@@ -1,11 +1,12 @@
-import numpy as np
-import matplotlib.pyplot as plt
 from functools import partial
+
+import matplotlib.pyplot as plt
+import numpy as np
+from modAL.acquisition import (max_EI, max_PI, max_UCB, optimizer_EI,
+                               optimizer_PI, optimizer_UCB)
+from modAL.models import BayesianOptimizer
 from sklearn.gaussian_process import GaussianProcessRegressor
 from sklearn.gaussian_process.kernels import Matern
-from modAL.models import BayesianOptimizer
-from modAL.acquisition import optimizer_PI, optimizer_EI, optimizer_UCB, max_PI, max_EI, max_UCB
-
 
 # generating the data
 X = np.linspace(0, 20, 1000).reshape(-1, 1)

examples/bayesian_optimization_multidim.py

@@ -1,9 +1,8 @@
 import numpy as np
+from modAL.acquisition import max_EI
+from modAL.models import BayesianOptimizer
 from sklearn.gaussian_process import GaussianProcessRegressor
 from sklearn.gaussian_process.kernels import Matern
-from modAL.models import BayesianOptimizer
-from modAL.acquisition import max_EI
-
 
 # generating the data
 x1, x2 = np.linspace(0, 10, 11).reshape(-1, 1), np.linspace(0, 10, 11).reshape(-1, 1)

examples/custom_query_strategies.py

@@ -25,18 +25,16 @@ def custom_query_strategy(classifier, X, a_keyword_argument=42):
 and classifier margin.
 """
 
-import numpy as np
 import matplotlib.pyplot as plt
-
+import numpy as np
+from modAL.models import ActiveLearner
+from modAL.uncertainty import classifier_margin, classifier_uncertainty
 from modAL.utils.combination import make_linear_combination, make_product
 from modAL.utils.selection import multi_argmax
-from modAL.uncertainty import classifier_uncertainty, classifier_margin
-from modAL.models import ActiveLearner
 from sklearn.datasets import make_blobs
 from sklearn.gaussian_process import GaussianProcessClassifier
 from sklearn.gaussian_process.kernels import RBF
 
-
 # generating the data
 centers = np.asarray([[-2, 3], [0.5, 5], [1, 1.5]])
 X, y = make_blobs(

examples/deep_bayesian_active_learning.py

@@ -2,12 +2,14 @@
 import numpy as np
 from keras import backend as K
 from keras.datasets import mnist
+from keras.layers import (Activation, Conv2D, Dense, Dropout, Flatten,
+                          MaxPooling2D)
 from keras.models import Sequential
-from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
 from keras.regularizers import l2
 from keras.wrappers.scikit_learn import KerasClassifier
 from modAL.models import ActiveLearner
 
+
 def create_keras_model():
     model = Sequential()
     model.add(Conv2D(32, (4, 4), activation='relu'))

examples/ensemble.py

@@ -1,8 +1,9 @@
-import numpy as np
 from itertools import product
+
+import numpy as np
 from matplotlib import pyplot as plt
-from sklearn.ensemble import RandomForestClassifier
 from modAL.models import ActiveLearner, Committee
+from sklearn.ensemble import RandomForestClassifier
 
 # creating the dataset
 im_width = 500

examples/ensemble_regression.py

@@ -1,9 +1,9 @@
-import numpy as np
 import matplotlib.pyplot as plt
-from sklearn.gaussian_process import GaussianProcessRegressor
-from sklearn.gaussian_process.kernels import WhiteKernel, RBF
-from modAL.models import ActiveLearner, CommitteeRegressor
+import numpy as np
 from modAL.disagreement import max_std_sampling
+from modAL.models import ActiveLearner, CommitteeRegressor
+from sklearn.gaussian_process import GaussianProcessRegressor
+from sklearn.gaussian_process.kernels import RBF, WhiteKernel
 
 # generating the data
 X = np.concatenate((np.random.rand(100)-1, np.random.rand(100)))