update docs

.readthedocs.yml

@@ -19,5 +19,5 @@ formats: all
 # Optionally set the version of Python and requirements required to build your docs
 python:
   version: 3.6
-  install:
-    - requirements: site/requirements-docs.txt
+#  install:
+#    - requirements: site/requirements-docs.txt

Makefile

@@ -54,6 +54,7 @@ pydoc: clean-doc ## generate pydoc HTML documentation based on docstrings
 	cat requirements.txt | xargs -L 1 .venv/bin/pip install; \
 	.venv/bin/python -m pydoc -w subaligner; mv subaligner.html docs/index.html
 	.venv/bin/python -m pydoc -w subaligner.embedder; mv subaligner.embedder.html docs
+	.venv/bin/python -m pydoc -w subaligner.hparam_tuner; mv subaligner.hparam_tuner.html docs
 	.venv/bin/python -m pydoc -w subaligner.hyperparameters; mv subaligner.hyperparameters.html docs
 	.venv/bin/python -m pydoc -w subaligner.media_helper; mv subaligner.media_helper.html docs
 	.venv/bin/python -m pydoc -w subaligner.network; mv subaligner.network.html docs

site/requirements-docs.txt

@@ -1 +1,2 @@
-psutil==5.6.6
+hyperopt==0.2.4
+psutil==5.6.6

site/source/conf.py

@@ -70,9 +70,11 @@
     "absl-py",
     "aeneas",
     "h5py",
+    "hyperopt",
     "librosa",
     "matplotlib",
     "numpy",
+    "psutil",
     "pycaption",
     "pysrt",
     "sklearn",

subaligner/hparam_tuner.py

@@ -8,6 +8,7 @@
 
 
 class HyperParameterTuner(object):
+    """Hyper parameter tuning using the Bayesian Optimizer"""
 
     SEARCH_SPACE = {
         "learning_rate": hp.loguniform("learning_rate", np.log(0.00001), np.log(0.1)),
@@ -25,6 +26,17 @@ def __init__(self,
                  training_dump_dir,
                  num_of_trials=5,
                  tuning_epochs=5):
+        """Hyper parameter tuner initialiser
+
+        Arguments:
+            av_file_paths {list} -- A list of paths to the input audio/video files.
+            subtitle_file_paths {list} -- A list of paths to the subtitle files.
+            training_dump_dir {string} --  The directory of the training data dump file.
+
+        Keyword Arguments:
+            num_of_trials {int} -- The number of trials for tuning (default: {5}).
+            tuning_epochs {int} -- The number of training epochs for each trial (default: {5}).
+        """
         self.__trainer = Trainer(FeatureEmbedder())
         self.__av_file_paths = av_file_paths
         self.__subtitle_file_paths = subtitle_file_paths
@@ -40,6 +52,8 @@ def hyperparameters(self):
         return self.__hyperparameters.clone()
 
     def tune_hyperparameters(self):
+        """Tune the hyper parameters"""
+
         trials = hyperopt.Trials()
         minimised = hyperopt.fmin(fn=self.__get_val_loss,
                                   space=self.SEARCH_SPACE,

subaligner/hyperparameters.py

@@ -7,6 +7,8 @@ class Hyperparameters(object):
     """
 
     def __init__(self):
+        """Hyper parameters initialiser setting default values"""
+
         self.__learning_rate = 0.001
         self.__hidden_size = {
             "front_layers": [64],
@@ -26,6 +28,8 @@ def __init__(self):
         self.__network_type = "lstm"
 
     def __eq__(self, other):
+        """Comparator for Hyperparameters objects"""
+
         if isinstance(other, Hyperparameters):
             return all([
                 self.__learning_rate == other.learning_rate,
@@ -172,22 +176,53 @@ def network_type(self, value):
         self.__network_type = value
 
     def to_json(self):
+        """Serialise hyper parameters into JSON string
+
+        Returns:
+            string -- The serialised hyper parameters in JSON
+        """
         return json.dumps(self, default=lambda o: o.__dict__, sort_keys=True, indent=4)
 
     def to_file(self, file_path):
+        """Serialise hyper parameters into JSON and save the content to a file
+
+        Arguments:
+            file_path {string} -- The path to the file containing saved hyper parameters.
+        """
         with open(file_path, "w", encoding="utf8") as file:
             file.write(self.to_json())
 
     def clone(self):
+        """Make a cloned hyper parameters object
+
+        Returns:
+            Hyperparameters -- The cloned Hyperparameters object.
+        """
         return self.from_json(self.to_json())
 
     @classmethod
     def from_json(cls, json_str):
+        """Deserialise JSON string into a Hyperparameters object
+
+        Arguments:
+            json_str {string} -- Hyper parameters in JSON.
+
+        Returns:
+            Hyperparameters -- The deserialised Hyperparameters object.
+        """
         hp = cls()
         hp.__dict__ = json.loads(json_str)
         return hp
 
     @classmethod
     def from_file(cls, file_path):
+        """Deserialise a file content into a Hyperparameters object
+
+        Arguments:
+            file_path {string} -- The path to the file containing hyper parameters.
+
+        Returns:
+            Hyperparameters -- The deserialised Hyperparameters object.
+        """
         with open(file_path, "r", encoding="utf8") as file:
             return cls.from_json(file.read())