Issue yell#8 Issue yell#6 Use absolute import instead relative

boltzmann_machines/__init__.py

@@ -1,12 +1,10 @@
 __author__ = 'Yelysei Bondarenko'
 __email__ = '[email protected]'
 
-from .dbm import *
-from . import dbm
-from . import ebm
-from . import layers
-
-from . import base
-from . import rbm
-from . import utils
+from boltzmann_machines.dbm import *
+from boltzmann_machines.ebm import *
+#from boltzmann_machines.layers import *
 
+#from boltzmann_machines import base
+#from boltzmann_machines import rbm
+#from boltzmann_machines import utils

boltzmann_machines/base/base.py

@@ -1,5 +1,6 @@
 def is_param_name(name):
     return not name.startswith('_') and not name.endswith('_')
 
+
 def is_attribute_name(name):
     return not name.startswith('_') and name.endswith('_')

boltzmann_machines/base/base_model.py

@@ -1,8 +1,8 @@
 import numpy as np
 from copy import deepcopy
-from ..base.base import is_param_name, is_attribute_name
-from .mixin import SeedMixin
-from ..utils.utils import write_during_training
+from boltzmann_machines.base.base import is_param_name, is_attribute_name
+from boltzmann_machines.base.mixin import SeedMixin
+from boltzmann_machines.utils.utils import write_during_training
 
 
 class BaseModel(SeedMixin):

boltzmann_machines/base/mixin.py

@@ -1,7 +1,7 @@
 import numpy as np
 import tensorflow as tf
 
-from ..utils import RNG
+from boltzmann_machines.utils import RNG
 
 
 class BaseMixin(object):

boltzmann_machines/base/tf_model.py

@@ -3,8 +3,7 @@
 import tensorflow as tf
 from functools import wraps
 
-from ..base import (BaseModel, DtypeMixin,
-                                     is_param_name)
+from boltzmann_machines.base import (BaseModel, DtypeMixin, is_param_name)
 
 
 def run_in_tf_session(check_initialized=True, update_seed=False):

boltzmann_machines/dbm.py

@@ -3,10 +3,10 @@
 from tensorflow.core.framework import summary_pb2
 from tensorflow.contrib.distributions import Bernoulli
 
-from .base import run_in_tf_session
-from .ebm import EnergyBasedModel
-from .layers import BernoulliLayer
-from .utils import (make_list_from, write_during_training,
+from boltzmann_machines.base import run_in_tf_session
+from boltzmann_machines.ebm import EnergyBasedModel
+from boltzmann_machines.layers import BernoulliLayer
+from boltzmann_machines.utils import (make_list_from, write_during_training,
                    batch_iter, epoch_iter,
                    log_sum_exp, log_diff_exp, log_mean_exp, log_std_exp)
 

boltzmann_machines/ebm.py

@@ -1,4 +1,4 @@
-from .base import TensorFlowModel
+from boltzmann_machines.base import TensorFlowModel
 
 
 class EnergyBasedModel(TensorFlowModel):

boltzmann_machines/rbm/base_rbm.py

@@ -2,9 +2,9 @@
 import tensorflow as tf
 from tensorflow.core.framework import summary_pb2
 
-from ..ebm import EnergyBasedModel
-from ..base import run_in_tf_session, is_attribute_name
-from ..utils import (make_list_from, batch_iter, epoch_iter,
+from boltzmann_machines.ebm import EnergyBasedModel
+from boltzmann_machines.base import run_in_tf_session, is_attribute_name
+from boltzmann_machines.utils import (make_list_from, batch_iter, epoch_iter,
                                       write_during_training)
 from ..utils.testing import assert_len, assert_shape
 

boltzmann_machines/rbm/rbm.py

@@ -2,9 +2,9 @@
 import tensorflow as tf
 from tensorflow.contrib.distributions import Multinomial
 
-from .env import *
-from .base_rbm import BaseRBM
-from layers import BernoulliLayer, MultinomialLayer, GaussianLayer
+from boltzmann_machines.rbm.env import *
+from boltzmann_machines.rbm.base_rbm import BaseRBM
+from boltzmann_machines.layers import BernoulliLayer, MultinomialLayer, GaussianLayer
 
 
 class BernoulliRBM(BaseRBM):
@@ -125,6 +125,6 @@ def logit_mean(X):
 
 if __name__ == '__main__':
     # run corresponding tests
-    from utils.testing import run_tests
-    from tests import test_rbm as t
+    from boltzmann_machines.utils.testing import run_tests
+    from boltzmann_machines.rbm.tests import test_rbm as t
     run_tests(__file__, t)

boltzmann_machines/rbm/tests/test_rbm.py

@@ -5,8 +5,8 @@
                            assert_almost_equal,
                            assert_raises)
 
-from rbm import BernoulliRBM, MultinomialRBM, GaussianRBM
-from utils import RNG
+from boltzmann_machines.rbm import BernoulliRBM, MultinomialRBM, GaussianRBM
+from boltzmann_machines.utils import RNG
 
 
 class TestRBM(object):

boltzmann_machines/utils/augmentation.py

@@ -11,6 +11,7 @@ def shift(x, offset=(0, 0)):
     y = nd.interpolation.shift(x, shift=offset, mode='nearest')
     return y
 
+
 def horizontal_mirror(x):
-    y = np.fliplr(x[:,:,...])
+    y = np.fliplr(x[:, :, ...])
     return y

boltzmann_machines/utils/dataset.py

@@ -4,7 +4,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-from .rng import RNG
+from boltzmann_machines.utils.rng import RNG
 
 
 def load_mnist(mode='train', path='.'):

boltzmann_machines/utils/testing.py

@@ -14,12 +14,14 @@ def run_tests(script_path, test_module=None):
     params.append('--with-doctest')
     nose.run(argv=params)
 
+
 def assert_shape(obj, name, desired_shape):
     actual_shape = getattr(obj, name).shape
     if actual_shape != desired_shape:
         raise ValueError('`{0}` has invalid shape {1} != {2}'.\
                          format(name, actual_shape, desired_shape))
 
+
 def assert_len(obj, name, desired_len):
     actual_len = len(getattr(obj, name))
     if actual_len != desired_len:

boltzmann_machines/utils/utils.py

@@ -1,6 +1,8 @@
 import numpy as np
 
 from tqdm import tqdm, tqdm_notebook
+
+
 def _is_in_ipython():
     try: __IPYTHON__; return True
     except NameError: return False
@@ -10,6 +12,7 @@ def _is_in_ipython():
 def write_during_training(s):
     tqdm.write(s)
 
+
 def batch_iter(X, batch_size=10, verbose=False, desc='epoch'):
     """Divide input data into batches, with optional
     progress bar.
@@ -41,16 +44,19 @@ def batch_iter(X, batch_size=10, verbose=False, desc='epoch'):
     for i in gen:
         yield X[i*batch_size:(i + 1)*batch_size]
 
+
 def epoch_iter(start_epoch, max_epoch, verbose=False):
     gen = range(start_epoch + 1, max_epoch + 1)
     if verbose:
         gen = progress_bar(gen, leave=True, ncols=84, desc='training')
     for epoch in gen:
         yield epoch
 
+
 def make_list_from(x):
     return list(x) if hasattr(x, '__iter__') else [x]
 
+
 def one_hot(y, n_classes=None):
     """Convert `y` to one-hot encoding.
 
@@ -67,6 +73,7 @@ def one_hot(y, n_classes=None):
     n_classes = n_classes or np.max(y) + 1
     return np.eye(n_classes)[y]
 
+
 def one_hot_decision_function(y):
     """
     Examples
@@ -76,15 +83,16 @@ def one_hot_decision_function(y):
     ...      [0.2, 0.2, 0.6],
     ...      [0.3, 0.4, 0.3]]
     >>> one_hot_decision_function(y)
-    array([[ 0.,  0.,  1.],
-           [ 1.,  0.,  0.],
-           [ 0.,  0.,  1.],
-           [ 0.,  1.,  0.]])
+    array([[0., 0., 1.],
+           [1., 0., 0.],
+           [0., 0., 1.],
+           [0., 1., 0.]])
     """
     z = np.zeros_like(y)
     z[np.arange(len(z)), np.argmax(y, axis=1)] = 1
     return z
 
+
 def unhot(y, n_classes=None):
     """
     Map `y` from one-hot encoding to {0, ..., `n_classes` - 1}.
@@ -105,6 +113,7 @@ def unhot(y, n_classes=None):
         _, n_classes = y.shape
     return y.dot(np.arange(n_classes))
 
+
 def log_sum_exp(x):
     """Compute log(sum(exp(x))) in a numerically stable way.
 
@@ -124,6 +133,7 @@ def log_sum_exp(x):
     a = max(x)
     return a + np.log(sum(np.exp(x - a)))
 
+
 def log_mean_exp(x):
     """Compute log(mean(exp(x))) in a numerically stable way.
 
@@ -135,6 +145,7 @@ def log_mean_exp(x):
     """
     return log_sum_exp(x) - np.log(len(x))
 
+
 def log_diff_exp(x):
     """Compute log(diff(exp(x))) in a numerically stable way.
 
@@ -149,6 +160,7 @@ def log_diff_exp(x):
     a = max(x)
     return a + np.log(np.diff(np.exp(x - a)))
 
+
 def log_std_exp(x, log_mean_exp_x=None):
     """Compute log(std(exp(x))) in a numerically stable way.