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Draco Data Format

Input

The minimum input expected by the Draco system consists of the following two elements, which need to be passed as pandas.DataFrame objects:

Target Times

A table containing the specification of the problem that we are solving, which has three columns:

  • turbine_id: Unique identifier of the turbine which this label corresponds to.
  • cutoff_time: Time associated with this target
  • target: The value that we want to predict. This can either be a numerical value or a categorical label. This column can also be skipped when preparing data that will be used only to make predictions and not to fit any pipeline.
turbine_id cutoff_time target
0 T1 2001-01-02 00:00:00 0
1 T1 2001-01-03 00:00:00 1
2 T2 2001-01-04 00:00:00 0

Readings

A table containing the signal data from the different sensors, with the following columns:

  • turbine_id: Unique identifier of the turbine which this reading comes from.
  • signal_id: Unique identifier of the signal which this reading comes from.
  • timestamp (datetime): Time where the reading took place, as a datetime.
  • value (float): Numeric value of this reading.
turbine_id signal_id timestamp value
0 T1 S1 2001-01-01 00:00:00 1
1 T1 S1 2001-01-01 12:00:00 2
2 T1 S1 2001-01-02 00:00:00 3
3 T1 S1 2001-01-02 12:00:00 4
4 T1 S1 2001-01-03 00:00:00 5
5 T1 S1 2001-01-03 12:00:00 6
6 T1 S2 2001-01-01 00:00:00 7
7 T1 S2 2001-01-01 12:00:00 8
8 T1 S2 2001-01-02 00:00:00 9
9 T1 S2 2001-01-02 12:00:00 10
10 T1 S2 2001-01-03 00:00:00 11
11 T1 S2 2001-01-03 12:00:00 12

Turbines

Optionally, a third table can be added containing metadata about the turbines. The only requirement for this table is to have a turbine_id field, and it can have an arbitraty number of additional fields.

turbine_id manufacturer ... ... ...
0 T1 Siemens ... ... ...
1 T2 Siemens ... ... ...

CSV Format

As explained in a previous section, the input expected by the Draco system consists of two tables which need to be passed as pandas.DataFrame objects:

  • The target_times table, which containing the specification of the problem that we are solving in the form of training examples with a turbine_id, a cutoff_time and a target value.
  • The readings table, which contains the signal readings from the different sensors, with turbine_id, signal_id, timestamp and value fields.

However, in most scenarios the size of the available will far exceed the memory limitations of the system on which Draco is being run, so loading all the data in a single pandas.DataFrame will not be possible.

In order to solve this situation, Draco provides a CSVLoader class which can be used to load data from what we call the Raw Data Format.

Raw Data Format

The Raw Data Format consists on a collection of CSV files stored in a single folder with the following structure:

Folder Structure

  • All the data from all the turbines is inside a single folder, which here we will call readings.
  • Inside the readings folder, one folder exists for each turbine, named exactly like the turbine:
    • readings/T001
    • readings/T002
    • ...
  • Inside each turbine folder one CSV file exists for each month, named %Y-%m.csv.
    • readings/T001/2010-01.csv
    • readings/T001/2010-02.csv
    • readings/T001/2010-03.csv
    • ...

CSV Contents

  • Each CSV file contains three columns:
    • signal_id: name or id of the signal.
    • timestamp: timestamp of the reading formatted as %m/%d/%y %H:%M:%S.
    • value: value of the reading.

This is an example of what a CSV contents look like:

signal_id timestamp value
0 S1 01/01/01 00:00:00 1
1 S1 01/01/01 12:00:00 2
2 S1 01/02/01 00:00:00 3
3 S1 01/02/01 12:00:00 4
4 S1 01/03/01 00:00:00 5
5 S1 01/03/01 12:00:00 6
6 S2 01/01/01 00:00:00 7
7 S2 01/01/01 12:00:00 8
8 S2 01/02/01 00:00:00 9
9 S2 01/02/01 12:00:00 10
10 S2 01/03/01 00:00:00 11
11 S2 01/03/01 12:00:00 12