Fixed more imports for the documentation and upcoming 0.2 self-contai…

…ned build.

docs/conf.py

@@ -10,32 +10,9 @@
 copyright = u'2015, scikit-neuralnetwork developers (BSD License)' 
 
 
-# -- Overrides for modules ----------------------------------------------------
-
-from mock import Mock as MagicMock
-
-class Mock(MagicMock):
-    @classmethod
-    def __getattr__(cls, name):
-        if name in ('BaseEstimator', 'RegressorMixin', 'ClassifierMixin'):
-            return object
-        return Mock()
-
-MOCK_MODULES = ['numpy', 'theano', 'sknn.pywrap2',
-                'sklearn', 'sklearn.base', 'sklearn.pipeline',
-                'sklearn.cross_validation', 'sklearn.preprocessing']
-
-for fullname in MOCK_MODULES:
-    segments = []
-    for s in fullname.split('.'):
-        segments.append(s)
-        mod_name = ".".join(segments)
-        sys.modules[mod_name] = Mock()
-
-
 # -- Configuration of documentation -------------------------------------------
 
-sys.path.append(os.path.dirname(os.path.dirname(__file__)).encode('utf-8'))
+# sys.path.append(os.path.dirname(os.path.dirname(__file__)).encode('utf-8'))
 
 import sknn
 version = sknn.__version__
@@ -54,6 +31,31 @@ def __getattr__(cls, name):
 todo_include_todos = False
 
 
+# -- Overrides for modules ----------------------------------------------------
+
+from mock import Mock as MagicMock
+
+class Mock(MagicMock):
+    @classmethod
+    def __getattr__(cls, name):
+        if name in ('BaseEstimator', 'TransformerMixin',
+                    'RegressorMixin', 'ClassifierMixin'):
+            return object
+        return Mock()
+
+MOCK_MODULES = ['numpy', 'theano', 'sknn.pywrap2', 'sknn.dataset',
+                'sklearn', 'sklearn.base', 'sklearn.pipeline',
+                'sklearn.cross_validation', 'sklearn.preprocessing']
+
+for fullname in MOCK_MODULES:
+    segments = []
+    for s in fullname.split('.'):
+        segments.append(s)
+        mod_name = ".".join(segments)
+        if mod_name not in sys.modules:
+            sys.modules[mod_name] = Mock()
+
+
 # -- Options for HTML output --------------------------------------------------
 
 html_title = 'scikit-neuralnetwork documentation'

docs/guide_advanced.rst

@@ -21,6 +21,7 @@ To setup the library to use your GPU or CPU explicitly in 32-bit or 64-bit mode,
 WARNING: This will only work if your program has not yet imported the ``theano`` module, due to the way the library is designed.  If ``THEANO_FLAGS`` are set on the command-line, they are not overwridden.
 
 
+.. _example-pipeline:
 
 Pipeline
 --------
@@ -47,7 +48,7 @@ You can then use the pipeline as you would the neural network, or any other stan
 Grid Search
 -----------
 
-In scikit-learn, you can use a ``GridSearchCV`` to optimize your neural network's hyper-parameters automatically, both the top-level parameters and the parameters within the layers.  For example, assuming you have your MLP constructed as in the :ref:`Regression` example in the local variable called ``nn``, the layers are named automatically so you can refer to them as follows:
+In scikit-learn, you can use a ``GridSearchCV`` to optimize your neural network's hyper-parameters automatically, both the top-level parameters and the parameters within the layers.  For example, assuming you have your MLP constructed as in the :ref:`example-regression` example in the local variable called ``nn``, the layers are named automatically so you can refer to them as follows:
 
     * ``hidden0``
     * ``hidden1``

docs/guide_beginners.rst

@@ -1,10 +1,12 @@
 Simple Examples
 ===============
 
+.. _example-regression:
+
 Regression
 ----------
 
-Assuming your data is in the form of ``numpy.ndarray`` stored in the variables ``X_train`` and ``y_train`` you can train a :class:`sknn.mlp.Regressor` neural network.  The input and output arrays are continuous values in this case, but it's best if you normalize or standardize your inputs to the ``[0..1]`` or ``[-1..1]`` range. (See the :ref:`Pipeline` example below.)
+Assuming your data is in the form of ``numpy.ndarray`` stored in the variables ``X_train`` and ``y_train`` you can train a :class:`sknn.mlp.Regressor` neural network.  The input and output arrays are continuous values in this case, but it's best if you normalize or standardize your inputs to the ``[0..1]`` or ``[-1..1]`` range. (See the :ref:`example-pipeline` example below.)
 
 .. code:: python
 
@@ -29,6 +31,8 @@ Then you can use the trained NN as follows:
 This will return a new ``numpy.ndarray`` with the results of the feed-forward simulation of the network and the estimates given the input features.
 
 
+.. _example-classification:
+
 Classification
 --------------
 
@@ -46,7 +50,7 @@ If your data in ``numpy.ndarray`` contains integer labels as outputs and you wan
         n_iter=25)
     nn.fit(X_train, y_train)
 
-It's also a good idea to normalize or standardize your data in this case too, for example using a :ref:`Pipeline` below.  The code here will train for 25 iterations.  Note that a ``Softmax`` output layer activation type is used here, and it's recommended as a default for classification problems.
+It's also a good idea to normalize or standardize your data in this case too, for example using a :ref:`example-pipeline` below.  The code here will train for 25 iterations.  Note that a ``Softmax`` output layer activation type is used here, and it's recommended as a default for classification problems.
 
 .. code:: python
 

docs/index.rst

@@ -17,7 +17,7 @@ Module Reference
     :maxdepth: 2
 
     module_mlp
-    madule_ae
+    module_ae
 
 
 User Guide

docs/pypi.rst

@@ -1,3 +1,5 @@
+:orphan:
+
 scikit-neuralnetwork
 ====================
 

sknn/dataset.py

@@ -1,23 +1,22 @@
 # -*- coding: utf-8 -*-
 from __future__ import (absolute_import, unicode_literals, print_function)
 
-from pylearn2.space import Space, CompositeSpace, VectorSpace
-from pylearn2.utils import safe_zip
-from pylearn2.datasets.dataset import Dataset
-from pylearn2.utils.iteration import (FiniteDatasetIterator, resolve_iterator_class)
+__all__ = ['FastVectorSpace', 'SparseDesignMatrix']
 
 import functools
 import theano
 
+from .pywrap2 import (utils, dataset, space, iteration)
 
-class FastVectorSpace(VectorSpace):
+
+class FastVectorSpace(space.VectorSpace):
     """
     More efficient version of the VectorSpace input that doesn't do any validation.
     This is used to speed up training times by default; when your data needs debugging,
     specify the ``debug=True`` flag in your MLP.
     """
 
-    @functools.wraps(VectorSpace._validate)
+    @functools.wraps(space.VectorSpace._validate)
     def _validate(self, is_numeric, batch):
         """
         Short-circuit the entire validation if the user has specified it's not necessary.
@@ -28,7 +27,7 @@ def __eq__(self, other):
         """
         Equality should work between Fast and slow VectorSpace instances.
         """
-        return (type(other) in (FastVectorSpace, VectorSpace)
+        return (type(other) in (FastVectorSpace, space.VectorSpace)
             and self.dim == other.dim
             and self.sparse == other.sparse
             and self.dtype == other.dtype)
@@ -37,10 +36,10 @@ def __hash__(self):
         """
         Override necessary for Python 3.x.
         """
-        return hash((type(VectorSpace), self.dim, self.sparse, self.dtype))
+        return hash((type(space.VectorSpace), self.dim, self.sparse, self.dtype))
 
 
-class SparseDesignMatrix(Dataset):
+class SparseDesignMatrix(dataset.Dataset):
     """
     SparseDesignMatrix is a type of Dataset used in training by PyLearn2 that takes
     a numpy/scipy sparse matrix and calls ``.todense()`` as the batches are passed
@@ -58,17 +57,17 @@ def __init__(self, X, y):
         self.num_examples = self.data_n_rows
         self.fancy = False
         self.stochastic = False
-        X_space = VectorSpace(dim=self.X.shape[1])
+        X_space = space.VectorSpace(dim=self.X.shape[1])
         X_source = 'features'
 
         dim = self.y.shape[-1] if self.y.ndim > 1 else 1
-        y_space = VectorSpace(dim=dim)
+        y_space = space.VectorSpace(dim=dim)
         y_source = 'targets'
 
-        space = CompositeSpace((X_space, y_space))
+        composite = space.CompositeSpace((X_space, y_space))
         source = (X_source, y_source)
 
-        self.data_specs = (space, source)
+        self.data_specs = (composite, source)
         self.X_space = X_space
 
     def get_num_examples(self):
@@ -93,7 +92,7 @@ def get_data(self):
         """
         return (self.X, self.y)
 
-    @functools.wraps(Dataset.iterator)
+    @functools.wraps(dataset.Dataset.iterator)
     def iterator(self, mode=None, batch_size=None, num_batches=None,
                  rng=None, data_specs=None, return_tuple=False):
         """
@@ -105,24 +104,24 @@ def iterator(self, mode=None, batch_size=None, num_batches=None,
 
         # TODO: If there is a view_converter, we have to use it to convert
         # the stored data for "features" into one that the iterator can return.
-        space, source = data_specs or (self.X_space, 'features')
-        assert isinstance(space, CompositeSpace),\
+        composite, source = data_specs or (self.X_space, 'features')
+        assert isinstance(composite, space.CompositeSpace),\
             "Unexpected input space for the data."
-        sub_spaces = space.components
+        sub_spaces = composite.components
         sub_sources = source
 
         conv_fn = lambda x: x.todense().astype(theano.config.floatX)
         convert = []
-        for sp, src in safe_zip(sub_spaces, sub_sources):
+        for sp, src in utils.safe_zip(sub_spaces, sub_sources):
             convert.append(conv_fn if src in ('features', 'targets') else None)
 
         assert mode is not None,\
                 "Iteration mode not provided for %s" % str(self)
-        mode = resolve_iterator_class(mode)
+        mode = iteration.resolve_iterator_class(mode)
         subset_iterator = mode(self.X.shape[0], batch_size, num_batches, rng)
 
-        return FiniteDatasetIterator(self,
-                                     subset_iterator,
-                                     data_specs=data_specs,
-                                     return_tuple=return_tuple,
-                                     convert=convert)
+        return iteration.FiniteDatasetIterator(self,
+                                               subset_iterator,
+                                               data_specs=data_specs,
+                                               return_tuple=return_tuple,
+                                               convert=convert)

sknn/pywrap2.py

@@ -19,10 +19,10 @@
 
 
 # This wrapper module can only contain 'leaf' modules.
-from pylearn2 import (space, datasets, blocks, corruption)
-from pylearn2.datasets import transformer_dataset
-from pylearn2 import termination_criteria
+from pylearn2 import (space, datasets, blocks, corruption, utils, termination_criteria)
+from pylearn2.datasets import (transformer_dataset, dataset)
+from pylearn2.utils import (iteration)
 from pylearn2.models import (mlp, maxout, autoencoder)
 from pylearn2.training_algorithms import (sgd, learning_rule)
 from pylearn2.costs import (mlp as mlp_cost, autoencoder as ae_costs, cost)
-from pylearn2.costs.mlp import dropout
+from pylearn2.costs.mlp import (dropout)