Update requirements.txt

deepcpg/callbacks.py

@@ -12,6 +12,12 @@
 
 
 class PerformanceLogger(Callback):
+    """Logs performance metrics during training.
+
+    Stores and prints performance metrics for each batch, epoch, and output.
+    """
+
+
 
     def __init__(self, metrics=['loss', 'acc'], log_freq=0.1,
                  precision=4, callbacks=[], verbose=1, logger=print):
@@ -250,6 +256,7 @@ def on_batch_end(self, batch, logs={}):
 
 
 class TrainingStopper(Callback):
+    """Stops training after certain time or when file is detected."""
 
     def __init__(self, max_time=None, stop_file=None,
                  verbose=1, logger=print):
@@ -277,114 +284,3 @@ def on_epoch_end(self, batch, logs={}):
             if os.path.isfile(self.stop_file):
                 self.log('Stopping training due to stop file!')
                 self.model.stop_training = True
-
-
-class TensorBoard(Callback):
-    ''' Tensorboard basic visualizations.
-
-    This callback writes a log for TensorBoard, which allows
-    you to visualize dynamic graphs of your training and test
-    metrics, as well as activation histograms for the different
-    layers in your model.
-
-    TensorBoard is a visualization tool provided with TensorFlow.
-
-    If you have installed TensorFlow with pip, you should be able
-    to launch TensorBoard from the command line:
-    ```
-    tensorboard --logdir=/full_path_to_your_logs
-    ```
-    You can find more information about TensorBoard
-    [here](https://www.tensorflow.org/versions/master/how_tos/summaries_and_tensorboard/index.html).
-
-    # Arguments
-        log_dir: the path of the directory where to save the log
-            files to be parsed by Tensorboard
-        histogram_freq: frequency (in epochs) at which to compute activation
-            histograms for the layers of the model. If set to 0,
-            histograms won't be computed.
-        write_graph: whether to visualize the graph in Tensorboard.
-            The log file can become quite large when
-            write_graph is set to True.
-    '''
-
-    def __init__(self, log_dir='./logs', histogram_freq=0, write_graph=True,
-                 write_images=False):
-        super(TensorBoard, self).__init__()
-        if K._BACKEND != 'tensorflow':
-            raise Exception('TensorBoard callback only works '
-                            'with the TensorFlow backend.')
-        self.log_dir = log_dir
-        self.histogram_freq = histogram_freq
-        self.merged = None
-        self.write_graph = write_graph
-        self.write_images = write_images
-
-    def _set_model(self, model):
-        import tensorflow as tf
-        import keras.backend.tensorflow_backend as KTF
-
-        self.model = model
-        self.sess = KTF.get_session()
-        if self.histogram_freq and self.merged is None:
-            for layer in self.model.layers:
-
-                for weight in layer.weights:
-                    tf.histogram_summary(weight.name, weight)
-
-                    if self.write_images:
-                        w_img = tf.squeeze(weight)
-
-                        shape = w_img.get_shape()
-                        if len(shape) > 1 and shape[0] > shape[1]:
-                            w_img = tf.transpose(w_img)
-
-                        if len(shape) == 1:
-                            w_img = tf.expand_dims(w_img, 0)
-
-                        w_img = tf.expand_dims(tf.expand_dims(w_img, 0), -1)
-
-                        tf.image_summary(weight.name, w_img)
-
-                if hasattr(layer, 'output'):
-                    tf.histogram_summary('{}_out'.format(layer.name),
-                                         layer.output)
-        self.merged = tf.merge_all_summaries()
-        if self.write_graph:
-            if parse_version(tf.__version__) >= parse_version('0.8.0'):
-                self.writer = tf.train.SummaryWriter(self.log_dir,
-                                                     self.sess.graph)
-            else:
-                self.writer = tf.train.SummaryWriter(self.log_dir,
-                                                     self.sess.graph_def)
-        else:
-            self.writer = tf.train.SummaryWriter(self.log_dir)
-
-    def on_epoch_end(self, epoch, logs={}):
-        import tensorflow as tf
-
-        if self.model.validation_data and self.histogram_freq:
-            if epoch % self.histogram_freq == 0:
-                # TODO: implement batched calls to sess.run
-                # (current call will likely go OOM on GPU)
-                if self.model.uses_learning_phase:
-                    cut_v_data = len(self.model.inputs)
-                    val_data = self.model.validation_data[:cut_v_data] + [0]
-                    tensors = self.model.inputs + [K.learning_phase()]
-                else:
-                    val_data = self.model.validation_data
-                    tensors = self.model.inputs
-                feed_dict = dict(zip(tensors, val_data))
-                result = self.sess.run([self.merged], feed_dict=feed_dict)
-                summary_str = result[0]
-                self.writer.add_summary(summary_str, epoch)
-
-        for name, value in logs.items():
-            if name in ['batch', 'size']:
-                continue
-            summary = tf.Summary()
-            summary_value = summary.value.add()
-            summary_value.simple_value = value
-            summary_value.tag = name
-            self.writer.add_summary(summary, epoch)
-        self.writer.flush()

deepcpg/evaluation.py

@@ -10,6 +10,7 @@
 
 
 def cor(y, z):
+    """Computes Pearon's correlation."""
     return np.corrcoef(y, z)[0, 1]
 
 

docs/source/conf.py

@@ -68,9 +68,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = '1.0.0'
+version = '1.0.1'
 # The full version, including alpha/beta/rc tags.
-release = '1.0.0'
+release = '1.0.1'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -130,7 +130,8 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'alabaster'
+html_theme = 'nature'
+# nature, haiku
 
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the

docs/source/index.rst

@@ -43,10 +43,13 @@ Interactive examples on how to use DeepCpG can be found `here <https://github.co
 
 API documentation
 -----------------
+DeepCpG modules:
+
 .. toctree::
   :maxdepth: 2
 
-  models/index
+  lib/index
+  lib/models/index
 
 
 Indices and tables

docs/source/lib/index.rst

@@ -0,0 +1,29 @@
+.. _libdoc_deepcpg:
+
+======================================
+:mod:`callbacks` -- Training callbacks
+======================================
+
+.. automodule:: deepcpg.callbacks
+  :members:
+
+===========================================
+:mod:`evaluation` -- Performance evaluation
+===========================================
+
+.. automodule:: deepcpg.evaluation
+  :members:
+
+==============================
+:mod:`motifs` -- Motif related
+==============================
+
+.. automodule:: deepcpg.motifs
+  :members:
+
+=========================
+:mod:`utils` -- Utilities
+=========================
+
+.. automodule:: deepcpg.utils
+  :members:

docs/source/models/index.rst → docs/source/lib/models/index.rst

@@ -1,25 +1,25 @@
 .. _libdoc_model:
 
-=====================================
-:mod:`model` -- model: DeepCpG models
-=====================================
+==============================
+:mod:`model` -- DeepCpG models
+==============================
 
 The models package provides functionalities for building and training models.
 
-Model utils
-===========
+:mod:`model.utils` -- Model utilities
+=====================================
 
 .. automodule:: deepcpg.models.utils
   :members:
 
-CpG models
-==========
+:mod:`model.cpg` -- CpG models
+==============================
 
 .. automodule:: deepcpg.models.cpg
   :members:
 
-DNA models
-==========
+:mod:`model.dna` -- DNA models
+==============================
 
 .. automodule:: deepcpg.models.dna
   :members:

requirements.txt

@@ -6,5 +6,6 @@ numpy
 pandas
 pytest
 keras
+tensorflow
 matplotlib
 seaborn

setup.py

@@ -24,6 +24,7 @@ def read(fname):
                         'pandas',
                         'pytest',
                         'keras',
+                        'tensorflow',
                         'matplotlib',
                         'seaborn'],
       keywords=['Deep learning',