Style fixes

Author: rushter

mla/datasets/base.py

@@ -1,5 +1,6 @@
 # coding:utf-8
 import os
+
 import numpy as np
 
 

mla/ensemble/gbm.py

@@ -1,6 +1,5 @@
 # coding:utf-8
 import numpy as np
-
 # logistic function
 from scipy.special import expit
 

mla/ensemble/random_forest.py

@@ -4,7 +4,6 @@
 from mla.base import BaseEstimator
 from mla.ensemble.base import information_gain, mse_criterion
 from mla.ensemble.tree import Tree
-from six.moves import range
 
 
 class RandomForest(BaseEstimator):

mla/fm.py

@@ -1,10 +1,11 @@
 # coding:utf-8
 
-from mla.base import BaseEstimator
-from mla.metrics import mean_squared_error, binary_crossentropy
 import autograd.numpy as np
 from autograd import elementwise_grad
 
+from mla.base import BaseEstimator
+from mla.metrics import mean_squared_error, binary_crossentropy
+
 np.random.seed(9999)
 
 """

mla/gaussian_mixture.py

@@ -1,9 +1,11 @@
 # coding:utf-8
 
 import random
+
+import matplotlib.pyplot as plt
 import numpy as np
 from scipy.stats import multivariate_normal
-import matplotlib.pyplot as plt
+
 from mla.base import BaseEstimator
 from mla.kmeans import KMeans
 

mla/kmeans.py

@@ -1,10 +1,10 @@
 # coding:utf-8
 
 import random
-import seaborn as sns
-import matplotlib.pyplot as plt
 
+import matplotlib.pyplot as plt
 import numpy as np
+import seaborn as sns
 
 from mla.base import BaseEstimator
 from mla.metrics.distance import euclidean_distance

mla/metrics/distance.py

@@ -1,7 +1,8 @@
 # coding:utf-8
-import numpy as np
 import math
 
+import numpy as np
+
 
 def euclidean_distance(a, b):
     if isinstance(a, list) and isinstance(b, list):

mla/naive_bayes.py

@@ -1,6 +1,7 @@
 # coding:utf-8
 
 import numpy as np
+
 from mla.base import BaseEstimator
 from mla.neuralnet.activations import softmax
 

mla/neuralnet/layers/normalization.py

@@ -1,7 +1,8 @@
 # coding:utf-8
+import numpy as np
+
 from mla.neuralnet.layers import Layer, PhaseMixin, ParamMixin
 from mla.neuralnet.parameters import Parameters
-import numpy as np
 
 """
 References:

mla/neuralnet/layers/recurrent/__init__.py

@@ -1,3 +1,3 @@
 # coding:utf-8
-from .rnn import *
 from .lstm import *
+from .rnn import *

mla/neuralnet/layers/recurrent/lstm.py

@@ -1,7 +1,6 @@
 # coding:utf-8
 import autograd.numpy as np
 from autograd import elementwise_grad
-from six.moves import range
 
 from mla.neuralnet.activations import sigmoid
 from mla.neuralnet.initializations import get_initializer

mla/neuralnet/layers/recurrent/rnn.py

@@ -1,7 +1,6 @@
 # coding:utf-8
 import autograd.numpy as np
 from autograd import elementwise_grad
-from six.moves import range
 
 from mla.neuralnet.initializations import get_initializer
 from mla.neuralnet.layers import Layer, get_activation, ParamMixin

mla/neuralnet/loss.py

@@ -1,5 +1,4 @@
 from ..metrics import mse, logloss, mae, hinge, binary_crossentropy
-
 categorical_crossentropy = logloss
 
 

mla/neuralnet/optimizers.py

@@ -215,7 +215,7 @@ def update(self, network):
                 self.ms[i][n] = self.beta_1 * self.ms[i][n] + (1.0 - self.beta_1) * grad
                 self.us[i][n] = np.maximum(self.beta_2 * self.us[i][n], np.abs(grad))
 
-                step = self.lr / (1 - self.beta_1 ** self.t) * self.ms[i][n]/(self.us[i][n] + self.epsilon)
+                step = self.lr / (1 - self.beta_1 ** self.t) * self.ms[i][n] / (self.us[i][n] + self.epsilon)
                 layer.parameters.step(n, -step)
         self.t += 1
 

mla/neuralnet/regularizers.py

@@ -1,6 +1,6 @@
 # coding:utf-8
-from autograd import elementwise_grad
 import numpy as np
+from autograd import elementwise_grad
 
 
 class Regularizer(object):

mla/neuralnet/tests/test_activations.py

@@ -1,4 +1,5 @@
 import sys
+
 import numpy as np
 
 from mla.neuralnet.activations import *

mla/neuralnet/tests/test_optimizers.py

@@ -1,6 +1,6 @@
-from sklearn.model_selection import train_test_split
 from sklearn.datasets import make_classification
 from sklearn.metrics import roc_auc_score
+from sklearn.model_selection import train_test_split
 
 from mla.neuralnet import NeuralNet
 from mla.neuralnet.layers import Dense, Activation, Dropout, Parameters
@@ -46,9 +46,11 @@ def test_adadelta():
 def test_adam():
     assert clasifier(Adam()) > 0.9
 
+
 def test_adamax():
     assert clasifier(Adamax()) > 0.9
 
+
 def test_rmsprop():
     assert clasifier(RMSprop()) > 0.9
 

mla/pca.py

@@ -1,8 +1,9 @@
 # coding:utf-8
-from scipy.linalg import svd
-import numpy as np
 import logging
 
+import numpy as np
+from scipy.linalg import svd
+
 from mla.base import BaseEstimator
 
 np.random.seed(1000)

mla/rbm.py

@@ -1,10 +1,10 @@
 # coding:utf-8
 import logging
 
-from mla.base import BaseEstimator
-from scipy.special import expit
 import numpy as np
+from scipy.special import expit
 
+from mla.base import BaseEstimator
 from mla.utils import batch_iterator
 
 np.random.seed(9999)

mla/rl/dqn.py

@@ -5,7 +5,6 @@
 import gym
 import numpy as np
 from gym import wrappers
-from six.moves import range
 
 np.random.seed(9999)
 

mla/svm/svm.py

@@ -1,8 +1,10 @@
 # coding:utf-8
+import logging
+
+import numpy as np
+
 from mla.base import BaseEstimator
 from mla.svm.kernerls import Linear
-import numpy as np
-import logging
 
 np.random.seed(9999)
 

mla/tests/test_reduction.py

@@ -1,8 +1,7 @@
 # coding=utf-8
 import pytest
-
-from sklearn.metrics import roc_auc_score
 from sklearn.datasets import make_classification
+from sklearn.metrics import roc_auc_score
 
 try:
     from sklearn.model_selection import train_test_split

mla/tests/test_regression_accuracy.py

@@ -12,7 +12,6 @@
 from mla.neuralnet.optimizers import Adam
 from mla.neuralnet.parameters import Parameters
 
-
 # Generate a random regression problem
 X, y = make_regression(
     n_samples=1000, n_features=10, n_informative=10, n_targets=1, noise=0.05, random_state=1111, bias=0.5

mla/tsne.py

@@ -2,7 +2,6 @@
 import logging
 
 import numpy as np
-from six.moves import range
 
 from mla.base import BaseEstimator
 from mla.metrics.distance import l2_distance

requirements.txt

@@ -4,6 +4,5 @@ numpy>=1.11.1
 scikit-learn>=0.18
 scipy>=0.18.0
 seaborn>=0.7.1
-six>=1.10.0
 autograd>=1.1.7
 gym