Organize imports & catch all exceptions in querys

Author: maxkeller321

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)))

examples/information_density.py

@@ -1,5 +1,4 @@
 import matplotlib.pyplot as plt
-
 from modAL.density import information_density
 from sklearn.datasets import make_blobs
 

examples/keras_integration.py

@@ -6,8 +6,8 @@
 import keras
 import numpy as np
 from keras.datasets import mnist
+from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
 from keras.models import Sequential
-from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
 from keras.wrappers.scikit_learn import KerasClassifier
 from modAL.models import ActiveLearner
 

examples/multilabel_svm.py

@@ -1,9 +1,7 @@
-import numpy as np
 import matplotlib.pyplot as plt
-
+import numpy as np
 from modAL.models import ActiveLearner
 from modAL.multilabel import *
-
 from sklearn.multiclass import OneVsRestClassifier
 from sklearn.svm import SVC
 
@@ -33,4 +31,4 @@
 )
 
 query_idx, query_inst = learner.query(X_pool)
-learner.teach(X_pool[query_idx], y_pool[query_idx])
+learner.teach(X_pool[query_idx], y_pool[query_idx])

examples/pool-based_sampling.py

@@ -4,12 +4,12 @@
 For its scikit-learn interface, see http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html
 """
 
-import numpy as np
 import matplotlib.pyplot as plt
-from sklearn.decomposition import PCA
+import numpy as np
+from modAL.models import ActiveLearner
 from sklearn.datasets import load_iris
+from sklearn.decomposition import PCA
 from sklearn.neighbors import KNeighborsClassifier
-from modAL.models import ActiveLearner
 
 # loading the iris dataset
 iris = load_iris()
@@ -65,4 +65,4 @@
     prediction = learner.predict(iris['data'])
     plt.scatter(x=pca[:, 0], y=pca[:, 1], c=prediction, cmap='viridis', s=50)
     plt.title('Classification accuracy after %i queries: %f' % (n_queries, learner.score(iris['data'], iris['target'])))
-    plt.show()
+    plt.show()

examples/pytorch_integration.py

@@ -4,16 +4,14 @@
 For more info, see https://skorch.readthedocs.io/en/stable/
 """
 
-import torch
 import numpy as np
-
+import torch
+from modAL.models import ActiveLearner
+from skorch import NeuralNetClassifier
 from torch import nn
 from torch.utils.data import DataLoader
-from torchvision.transforms import ToTensor
 from torchvision.datasets import MNIST
-from skorch import NeuralNetClassifier
-
-from modAL.models import ActiveLearner
+from torchvision.transforms import ToTensor
 
 
 # build class for the skorch API

examples/pytorch_mc_dropout.py

@@ -2,21 +2,17 @@
 In this file the basic ModAL PyTorch DeepActiveLearner workflow is explained 
 through an example on the MNIST dataset and the MC-Dropout-Bald query strategy.
 """
-import sys
-import os
+import numpy as np
 import torch
-from torch import nn
-from skorch import NeuralNetClassifier
-
-from modAL.models import DeepActiveLearner
-
 # import of query strategies
-from modAL.dropout import mc_dropout_bald, mc_dropout_mean_st
-
-import numpy as np
+from modAL.dropout import mc_dropout_bald
+from modAL.models import DeepActiveLearner
+from skorch import NeuralNetClassifier
+from torch import nn
 from torch.utils.data import DataLoader
-from torchvision.transforms import ToTensor
 from torchvision.datasets import MNIST
+from torchvision.transforms import ToTensor
+
 
 # Standard Pytorch Model (Visit the PyTorch documentation for more details)
 class Torch_Model(nn.Module):

examples/query_by_committee.py

@@ -1,10 +1,11 @@
-import numpy as np
-import matplotlib.pyplot as plt
 from copy import deepcopy
-from sklearn.decomposition import PCA
+
+import matplotlib.pyplot as plt
+import numpy as np
+from modAL.models import ActiveLearner, Committee
 from sklearn.datasets import load_iris
+from sklearn.decomposition import PCA
 from sklearn.ensemble import RandomForestClassifier
-from modAL.models import ActiveLearner, Committee
 
 # loading the iris dataset
 iris = load_iris()

examples/ranked_batch_mode.py

@@ -1,13 +1,13 @@
-import numpy as np
+from functools import partial
+
 import matplotlib as mpl
 import matplotlib.pyplot as plt
+import numpy as np
+from modAL.batch import uncertainty_batch_sampling
+from modAL.models import ActiveLearner
 from sklearn.datasets import load_iris
 from sklearn.decomposition import PCA
 from sklearn.neighbors import KNeighborsClassifier
-from functools import partial
-
-from modAL.batch import uncertainty_batch_sampling
-from modAL.models import ActiveLearner
 
 # Set our RNG for reproducibility.
 RANDOM_STATE_SEED = 123
@@ -161,4 +161,4 @@
     ))
     ax.legend(loc='lower right')
 
-    plt.show()
+    plt.show()

examples/runtime_comparison.py

@@ -1,25 +1,20 @@
-import numpy as np
-
 from time import time
 
-from sklearn.datasets import load_iris
-
+import numpy as np
 from acton.acton import main as acton_main
-
-from alp.active_learning.active_learning import ActiveLearner as ActiveLearnerALP
-
+from alp.active_learning.active_learning import \
+    ActiveLearner as ActiveLearnerALP
 from libact.base.dataset import Dataset
 from libact.labelers import IdealLabeler
-from libact.query_strategies import UncertaintySampling, QueryByCommittee
+from libact.models.logistic_regression import \
+    LogisticRegression as LogisticRegressionLibact
+from libact.query_strategies import QueryByCommittee, UncertaintySampling
 from libact.query_strategies.multiclass.expected_error_reduction import EER
-from libact.models.logistic_regression import LogisticRegression as LogisticRegressionLibact
-
-from modAL.models import ActiveLearner, Committee
 from modAL.expected_error import expected_error_reduction
-
+from modAL.models import ActiveLearner, Committee
+from sklearn.datasets import load_iris
 from sklearn.linear_model import LogisticRegression
 
-
 runtime = {}
 
 

examples/shape_learning.py

@@ -5,11 +5,12 @@
 the scikit-learn implementation of the kNN classifier algorithm.
 """
 
-import numpy as np
-import matplotlib.pyplot as plt
 from copy import deepcopy
-from sklearn.ensemble import RandomForestClassifier
+
+import matplotlib.pyplot as plt
+import numpy as np
 from modAL.models import ActiveLearner
+from sklearn.ensemble import RandomForestClassifier
 
 # creating the image
 im_width = 500

examples/sklearn_workflow.py

@@ -1,9 +1,9 @@
 from modAL.models import ActiveLearner
-from sklearn.ensemble import RandomForestClassifier
 from sklearn.datasets import load_iris
+from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import cross_val_score
 
 X_train, y_train = load_iris().data, load_iris().target
 
 learner = ActiveLearner(estimator=RandomForestClassifier())
-scores = cross_val_score(learner, X_train, y_train, cv=10)
+scores = cross_val_score(learner, X_train, y_train, cv=10)

examples/stream-based_sampling.py

@@ -2,11 +2,11 @@
 In this example the use of ActiveLearner is demonstrated in a stream-based sampling setting.
 """
 
-import numpy as np
 import matplotlib.pyplot as plt
-from sklearn.ensemble import RandomForestClassifier
+import numpy as np
 from modAL.models import ActiveLearner
 from modAL.uncertainty import classifier_uncertainty
+from sklearn.ensemble import RandomForestClassifier
 
 # creating the image
 im_width = 500

modAL/__init__.py

@@ -1,3 +1,3 @@
 from .models import ActiveLearner, Committee, CommitteeRegressor
 
-__all__ = ['ActiveLearner', 'Committee', 'CommitteeRegressor']
+__all__ = ['ActiveLearner', 'Committee', 'CommitteeRegressor']

modAL/acquisition.py

@@ -1,16 +1,15 @@
 """
 Acquisition functions for Bayesian optimization.
 """
-from typing import Tuple
 
 import numpy as np
-from scipy.stats import norm
 from scipy.special import ndtr
+from scipy.stats import norm
 from sklearn.exceptions import NotFittedError
 
-from modAL.utils.selection import multi_argmax
-from modAL.utils.data import modALinput
 from modAL.models.base import BaseLearner
+from modAL.utils.data import modALinput
+from modAL.utils.selection import multi_argmax
 
 
 def PI(mean, std, max_val, tradeoff):

modAL/batch.py

@@ -6,11 +6,12 @@
 
 import numpy as np
 import scipy.sparse as sp
-from sklearn.metrics.pairwise import pairwise_distances, pairwise_distances_argmin_min
+from sklearn.metrics.pairwise import (pairwise_distances,
+                                      pairwise_distances_argmin_min)
 
-from modAL.utils.data import data_vstack, modALinput, data_shape
 from modAL.models.base import BaseCommittee, BaseLearner
 from modAL.uncertainty import classifier_uncertainty
+from modAL.utils.data import data_shape, data_vstack, modALinput
 
 
 def select_cold_start_instance(X: modALinput,