A crucial element of systems for data-analysis is laying out all the hyperparameters of that system so they can be easily examined and modified. We add a few useful extensions to a popular human-readable data-serialization language known as YAML (YAML Ain't Markup Language). This provides support for a rather expansive idea of what constitutes a hyperparameter, and cleans up python files for data analysis to just the bare algorithm.
YAML is a data-serialization language, similar to JSON, and it supports three basic types of nodes: scalar, sequential, and mapping. PyYAML naturally converts sequential nodes to python lists and mapping nodes to python dicts.
Scalar nodes can take one of the following forms:
string: abcd # No quotes needed
integer: 1
float: 1.3
bool: True
none: nullNote that we've used a simple mapping to demonstrate the scalar nodes. A mapping
is a set of key: value pairs, defined so that the key can be used to easily
retrieve the corresponding value. In addition to the format above, mappings
can also be specified in a similar manner to JSON:
{foo: 1, bar: 2.5, baz: "abc"}Sequences, or lists of items, can also be specified in two ways:
- foo
- bar
- bazor
[foo, bar, baz]Note that when not using the inline version, YAML uses whitespace to denote nested items:
foo:
a: 1
b: 2
bar:
- c
- dYAML has a few more advanced features (such as aliases and merge keys) that you may want to explore on your own. We will briefly discuss one here since it is relevant for our extensions: YAML tags.
Tags are added with a ! prefix, and they specify the type of the node. This
allows types beyond the simple types listed above to be used. PyYAML supports a
few additional types, such as:
!!set # set
!!timestamp # datetime.datetime
!!python/tuple # tuple
!!python/complex # complex
!!python/name:module.name # A class or function
!!python/module:package.module # A module
!!python/object/new:module.cls # An instance of a classThese can all be quite useful, however we found that this system was a bit cumbersome, especially with the frequency with which we were using them. So we decided to implement some shortcuts for these features, which we are calling "HyperPyYAML".
We make several extensions to yaml including easier object creation, nicer aliases, and tuples.
Our first extension is to simplify the structure for specifying an instance, module, class, or function. As an example:
model: !new:collections.CounterThis tag, prefixed with !new:, constructs an instance of the specified class.
If the node is a mapping node, all the items are passed as keyword arguments
to the class when the instance is created. A list can similarly be used to
pass positional arguments. See the following examples:
foo: !new:collections.Counter
- abracadabra
bar: !new: collections.Counter
a: 2
b: 1
c: 5We also simplify the interface for specifying a function or class or other static Python entity:
add: !name:operator.addThis code stores the add function. It can later be used in the usual way:
>>> loaded_yaml = load_hyperpyyaml("add: !name:operator.add")
>>> loaded_yaml["add"](2, 4)
6Another extension is a nicer alias system that supports things like
string interpolation. We've added a tag written !ref that
takes keys in angle brackets, and searches for them inside the yaml
file itself. As an example:
folder1: abc/def
folder2: ghi/jkl
folder3: !ref <folder1>/<folder2>
foo: 1024
bar: 512
baz: !ref <foo> // <bar> + 1This allows us to change some values and automatically change the dependent values accordingly. You can also refer to other references, and to sub-nodes using brackets.
block_index: 1
cnn1:
out_channels: !ref <block_index> * 64
kernel_size: (3, 3)
cnn2:
out_channels: !ref <cnn1[out_channels]>
kernel_size: (3, 3)Finally, you can make references to nodes that are objects, not just scalars.
yaml_string = """
foo: !new:collections.Counter
a: 4
bar: !ref <foo>
baz: !copy <foo>
"""
loaded_yaml = load_hyperpyyaml(yaml_string)
loaded_yaml["foo"].update({"b": 10})
print(loaded_yaml["bar"])
print(loaded_yaml["baz"])This provides the output:
Counter({'b': 10, 'a': 4})
Counter({'a': 4})
Note that !ref makes only a shallow copy, so updating foo
also updates bar. If you want a deep copy, use the !copy tag.
There are some issues (#7 #11) mentioning that !ref cannot refer to the return value of !apply function.
Thus we provide another !applyref tag to work with !ref, which can be used in four ways:
# 1. Pass the positional and keyword arguments at the same time. Like `!!python/object/apply:module.function` in pyyaml
c: !applyref:sorted
_args:
- [3, 4, 1, 2]
_kwargs:
reverse: False
d: !ref <c>-<c>
# 2. Only pass the keyword arguments
e: !applyref:random.randint
a: 1
b: 3
f: !ref <e><e>
# 3. Only pass the positional arguments
g: !applyref:random.randint
- 1
- 3
h: !ref <g><g>
# 4. No arguments
i: !applyref:random.random
j: !ref <i><i>Note that !applyref cannot return an object, otherwise the RepresenterError will be raised.
One last minor extension to the yaml syntax we've made is to implicitly
resolve any string starting with ( and ending with ) to a tuple.
This makes the use of YAML more intuitive for Python users.
All of the listed extensions are available by loading yaml using the
load_hyperpyyaml function. This function returns an object in a similar
manner to pyyaml and other yaml libraries.
Also, load_hyperpyyaml takes an optional argument, overrides
which allows changes to any of the parameters listed in the YAML.
The following example demonstrates changing the out_channels
of the CNN layer:
>>> yaml_string = """
... block_index: 1
... cnn1:
... out_channels: !ref <block_index> * 64
... kernel_size: (3, 3)
... cnn2:
... out_channels: !ref <cnn1[out_channels]>
... kernel_size: (3, 3)
... """
>>> overrides = {"block_index": 2}
>>> with open("hyperparameters.yaml") as f:
... hyperparameters = load_hyperpyyaml(f, overrides)
>>> hyperparameters["block_index"]
2
>>> hyperparameters["cnn2"]["out_channels"]
128We've defined a number of extensions to the YAML syntax, designed to make it easier to use for hyperparameter specification. Feedback is welcome!