Using YAML Syntax in pySPACE

This chapter will give an overview on YAML (“YAML Ain’t Markup Language”) and it’s usage in the framework. The focus will be on a general description and the use in node chain specification files, but there will also be hints to configuration and other specification files. A standard example of a YAML specification file for a node chain can be found at the end of this tutorial.

General Information on YAML

A complete description on YAML in general can be found at:, where you should have a look at the specification website. There you can find the general description: YAML™ (rhymes with “camel”) is a human-friendly, cross language, Unicode based data serialization language designed around the common native data types of agile programming languages. It is broadly useful for programming needs ranging from configuration files to Internet messaging to object persistence to data auditing.

The usage of comments in YAML is the same as in Python with a hash (“#”) at the beginning of each comment line. For the entries in your YAML file you have always to keep in mind, that if you do not define the type (called untagged in YAML framework), the YAML loading program tries to detect it automatically and if it can not detect it, the entry will be tagged as a string. Strings can also be defined by using double or normal quotation marks. YAML can detect for example:

  • integers (e.g. 42),
  • floats (e.g. 42.42),
  • null (None in Python, Syntax: use Null, NULL or nothing),
  • string (e.g. "fourtytwo" or 'fourtytwo'),
  • lists (e.g. node chain specification file),
  • dictionaries (e.g. operation specification file),
  • booleans (True and False),
  • and much more, which is yet not relevant for this software framework.

For the framework it is mainly important to know, how to define something as a string. If you are further interested in explicit tagging and new data types, just check the the specification website.


YAML does not support all float formats. So check beforehand, if you try special formats. Python also has problems with special formats

Application in the Framework

YAML is mainly used in this framework, because it

  • delivers a unified way of defining configuration and specification files,
  • is easy to read,
  • can be easily loaded into Python, and
  • the loading already detects standard data types even if they are used in nested structures, like dictionaries of lists.

In pySPACE configuration files, in operation specification files and for dataset descriptions the main structure is a dictionary (mapping), which is simply defined by key: value pairs at each line. The empty space after the colon is important. Additional empty spaces between key and colon can be used for cosmetic reasons. If the value is another substructure you may use a new line instead of the empty space, but then you have to indent everything belonging to the definition of the value, to show, that it belongs to this key. For the definition of dictionaries you can also use braces ({key1: value1, key2: value2}). Some of the values may be lists. They can be defined in two ways:

  • use a dash followed by a space (“- ”) or new line with indentation or
  • use squared brackets ([item1, item2]) as in Python.

Single-line versions are mainly used if you have lists of lists or dictionaries of dictionaries, but they do not have to be used. These data types are called collections in YAML.

Eval(uate) syntax for Python code to ease specification file writing

Finally, it is important to mention that we made two small syntax modifications.

When loading YAML specification files with specification of nodes you may want to use Python commands to define your parameter values. Therefore, a string of the form eval(command) results in a new value, which is the result of the command, and which is then send to the initialization of the node. Normally, this is only applicable to the values of the keys in the parameters dictionary of the node specification. This syntax can be even used to specify the node name.

The same syntax possibility is included in the framework, when using the key parameter_ranges in operations. Here you can use for example eval(range(10)) instead of [0,1,3,4,5,6,7,8,9,10].


In contrast to the the normal Python syntax, the command must not be marked as string.

Usage of YAML in node chain specification files

Since a signal processing node chain is mainly a linear concatenation of elemental processing steps (called nodes), the corresponding specification file is a list, specifying the elemental processing steps in the form of nodes.

A node itself is always a dictionary of the keys node, containing the node name, and parameters (optional), which value is another dictionary, defining the variables for the node. Normally these variables are directly given to the initialization function of the node via a method called node_from_yaml of the general BaseNode. Since variables may be lists or dictionaries, you may get a lot of indentations, but this is not the normal use-case.

For constructing your node chain you first have to choose your nodes. To find out the possibilities of a node just check its documentation. There you find an example and (normally) an exhaustive description of all mandatory, recommended and optional parameters. Finally the specifications of your chosen nodes are concatenated as a list in your file and you are finished.

Example Node Chain File

    node: Time_Series_Source
    node : CV_Splitter
    parameters : 
        splits : 10
    node : Devariancing
    parameters : 
        devariance_method : "eval(__import__('numpy').std)"
    node : PCA
    parameters : 
        retained_channels : 32
    node : FFT_Band_Pass_Filter
    parameters : 
        pass_band : [${__LOWER_CUTOFF__}, ${__UPPER_CUTOFF__}]
    node : Subsampling
    parameters : 
        target_frequency : 20.0
    node : BBCI_Features
    parameters : 
        datapoints : [-4,-3,-2,-1]
    node: Labeled_Feature_Vector_CV_Sink
    parameters :
        classes_names : ["LRP", "NoLRP"]


This file is responsible for defining dataset properties. It can be always found in the data folder, associated with the dataset. It is a dictionary written in YAML syntax. If you get a dataset as a result of processing with pySPACE, this file is stored automatically and some information about previous processing is added. For further processing, the resulting dataset can be immediately used.

From the programming perspective, the dictionary is loaded and forwarded as dataset_md to the respective datasets. It is even possible to access this data with the get_metadata() method in a node.

If you want to define your own metadata.yaml, to enable pySPACE to read your data, there are three categories of parameters you can define:

mandatory parameters:

This is always the type of the data and the storage_format and potentially additional required information for loading the data. For the additional loading information, check the documentation of the respective dataset which corresponds to your chosen type.

There is a direct mapping between the type variable, the respective dataset class <pySPACE.resources.dataset_defs>, and the used data type. The type is written in lower case with underscores. The data type is the same but with camel case and for respective dataset, Dataset is added to the class name. The respective module names are the same as the type.

Currently implemented types are:

The storage_format might consist of two components: [general_format, value_format]. Currently, for the value_format only real is supported but it is used as a placeholder, to also for example support symbolic or text data in future. The general_format is the important part. For the stream type, only this parameter is used and for the other types it is possible, to for example use storage_format: csv in the metadata.yaml. By default, the framework stores data in the Python specific pickle format. the most commonly used other format is csv.

An example for additional loading information is the data_pattern. It contains the placeholders _run for the run number, _sp for the split number, and _tt for the distinction between training and test data. These parameters are later on replaced in the pattern to get the needed file names. Hence, these keywords should not occur in the name of the dataset as for example in data/my_special_running_tt_train_data.

optional parameter:

Optional parameters are either determined automatically or set with a sufficient default.

The really important ones are:


This parameter defines the number of total processing repetitions, which were applied to the original data.

If you define a new dataset, the correct value is 1, which is also the default.

The respective dataset will then contain a separate file for each repetition as specified in the aforementioned data_pattern: _run is replaced by an underscore and the respective run_number. The repetitions are sometimes needed, to account for randomness effects. For each repetition, the random seed is fixed, using the number of the current repetition (run_number). This is needed to get reproducible results and for example to get the same splitting into training and test data when processing the same dataset with different parametrization.

(optional, default: 1)


This parameter defines the total number of splits, which were created in previous processing of a dataset using a cross-validation scheme. This parameter is handled in the same way as the run number with the data_pattern: _sp is replaced by an underscore and the respective index of the splitting.

(optional, default: 1)


Defines if the data is already split into training and testing data. This holds true for the MNIST dataset of handwritten digits but not for the data in our example_summary, provided in the default pySPACE_center.

By default this parameter is set to false and all data is assumed to be testing data. This can be for example changed with nodes for the splitter module.

If the parameter is set to true, the loading procedure needs to know, which part is used for training and which one for testing. This is usually done by using the aforementioned data_pattern with the place holder _tt for the strings _train and _test to define training and testing data.

For example the dataset pattern data/MINST_1_vs_2_tt.csv

(optional, default: false)

For special dataset dependent parameters and loading parameters refer to the documentation of the respective dataset (e.g, electrode positions from an EEG recording). Furthermore, parameter_setting of previous processing might be specified. This can be later on used for comparisons in an evaluation.

additional information:

There can be a lot of additional information specified for the dataset which are not used by the software but which can provide useful information about the data and previous processing steps. This can be the class names, a dataset description, the source url, the data of creation, the author, or the detailed the specifications of previous processing which was applied to the data.

Example of a FeatureVectorDataset metadata.yaml

type: feature_vector
author: Max Mustermann
date: '2009_4_5'
node_chain_file_name: example_flow.yaml
input_collection_name: input_collection_example
classes_names: [Standard, Target]
feature_names: [feature1, feature2, feature3]
num_features: 3
parameter_setting: {__LOWER_CUTOFF__: 0.1, __UPPER_CUTOFF__: 4.0}
runs: 10
splits: 5
storage_format: [arff, real]
data_pattern: data_run/features_sp_tt.arff