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The Dataset class allows to abstract a folder holding samples to be analyzed.

>>> from pbox import Dataset


A dataset folder holds the following files/folders:

  +-- files
  |     +-- {executables, renamed to their SHA256 hashes}
  +-- data.csv (contains the labels)        # features for an executable, formatted for ML
  +-- features.json                         # dictionary of feature name/description pairs
  +-- labels.json                           # dictionary of hashes with their full labels
  +-- metadata.json                         # simple statistics about the dataset
  +-- names.json                            # dictionary of hashes and their real filenames

Dataset Class

This class mostly acts as a dictionary for executable entries but also keeps track of some related metadata and, when computed, the related features. When setting a key, it associates the given (real) file and its label and, if required, computes its features available for its executable format.

>>> ds = Dataset()  # this creates a folder named "dataset" if no name is given

>>> ds['/tmp/executable'] = "upx"
<[ this will update ds._data and ds._metadata ]>

The dataset entries can then be manipulated in many ways.

>>> print(ds['/tmp/executable'])
<[ this will display the data row for the given item from ds._data AS A LIST ]>

>>> print(ds['/tmp/executable', True])
<[ this will display the data row for the given item from ds._data AS A DICT using the headers of ds._data ]>

>>> for row in ds:
        # do something with a row

>>> len(ds)
<[ this will tell the length of ds._data ]>

Entries can also be cleaned as with a dict instance.

>>> del ds['/tmp/executable']
<[ this will completely remove the item from the dataset ]>


  • _data: pandas.DataFrame instance holding the data collected from the sample's metadata (hash, creation and modification time, etc), its packing label and, if it is a fileless dataset (that is, for which the features were precomputed), a set of features applicable for the executable formats selected (loaded from and saved to data.csv)
  • _features: dictionary of included features, with short names as keys and their corresponding descriptions as values (loaded from and saved to features.json)
  • _metadata: dictionary of metadata, e.g. holding the list of selected executable formats and counts of included samples (loaded from and saved to metadata.json)
  • formats: list of applicable categories of executable formats
  • logger: logging.Logger instance for producing debug messages
  • packers: list of Packer instances applicable to the dataset, given the selected categories of executable formats
  • path: tinyscript.Path instance holding the path to the dataset folder
  • sources: dictionary containing applicable categories as keys and their corresponding lists of source folders for making the dataset


  • backup (settable): Dataset instance holding the latest backup of the current dataset
  • basename: the name of the dataset
  • files: tinyscript.helpers.path.Path instance pointing on dataset's files subfolder
  • labelling: the labelling rate of the dataset (.0 will mean it is only usable with unsupervised learning algorithms while 1. allows for supervised learning)
  • labels: the series of labels from _data
  • name: dataset's name, composed with the folder's name and, between brackets, the comma-separated list of applicable executable formats
  • overview: string representation of the dataset, for describing it in the terminal


  • exists: for simply checking if the dataset exists
  • fix *: for making dataset's structure and files match
  • is_empty: for checking if this Dataset instance has no sample
  • is_valid: for checking if this Dataset instance has a valid structure
  • list: for listing all the datasets from the given path
  • make *: for making N new samples in the current dataset among the input binary categories, balanced or not according to the number of distinct packers
  • merge: for merging another dataset with the current one ; precedence is set by the update parameter
  • purge: for removing a whole dataset and its backup copies
  • remove(query) *: for removing executables from the dataset based on a Pandas Dataframe filtering query
  • rename: for renaming the current dataset
  • reset: for truncating and recreating a blank dataset
  • revert: for reverting to the latest version of the dataset (if a backup copy exists in /tmp), with a maximum of 3
  • select: for selecting a subset from the current dataset based on multiple criteria
  • show: for showing an overview of the dataset
  • update *: for updating the dataset with a folder of binaries, detecting used packers if detect=True otherwise considering samples as not labelled unless labels are provided (in JSON format with hashes as keys and labels as values)

    * generates a backup copy

Class methods:

  • check(folder): for checking a folder against the required Dataset or FilelessDataset structures ; returns a boolean
  • validate(folder): for checking a folder against the required Dataset or FilelessDataset structures ; raises ValueError if no structure is respected

Static methods:

  • labels_from_file(path): for loading a labels dictionary (from a string path or a Path instance) ; ensures a valid dictionary is returned
  • summarize(path=None, show=False): displays the summary of a dataset (if path=None, the local folder is the used), showing corrupted data too if show=True
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