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Documentation Index

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Data Preparation Configuration

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DataPreparationConfig

The DataPreparationConfig class defines how raw data should be processed and tokenized for training.
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input_path
str or readable object
required
Source location for raw data files or a readable object from which data can be accessed.
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output_path
str or writable object
required
Destination location for processed data files or a writable object where processed data will be stored.
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tokenization
str, TimeSeriesTokenizerConfig, or callable
required
Tokenization strategy based on data modality:
  • Text/string/code: String denoting a Hugging Face tokenizer name or path to a local tokenizer
  • Time series: An instance of TimeSeriesTokenizerConfig
  • Custom tokenization: A callable function that can map string → tensor of floats/integers (2D for patch-based)
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max_sequence_length
int
default:"4096"
Maximum sequence length for tokenized data.

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TimeSeriesTokenizerConfig

Configuration for time series-specific tokenization parameters.
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type
str
required
Tokenization approach - "patch_based" (Chronos Bolt, TSFM, TiRex style) or "bin_quant_based" (Chronos style).
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input_patch_size
int or None
default:"None"
Size of input patches. Required for patch-based tokenization.
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output_patch_size
int or None
default:"None"
Size of output patches. Required for patch-based tokenization. Can differ from input patch size (e.g., TimesFM uses longer output patches than input).
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patch_masking
bool or None
default:"False"
Enables patch masking strategy. Only applicable for patch-based tokenization. Helps models learn to predict well for context lengths that are multiples of input patch length (see TimesFM paper).
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number_of_bins
int or None
default:"4096"
Number of quantization bins. Required for bin quantization-based tokenization.
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normalization_method
str or callable
default:"z-norm"
Normalization strategy. Accepts custom function that maps a list of numbers to a list of numbers.
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normalization_mean
float or None
default:"None"
Mean value for normalization. Should remain None for custom normalization methods. For z-norm, computes mean over first patch (patch-based tokenization, following TimesFM) or entire series (bin quantization, following Chronos).
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normalization_std
float or None
default:"None"
Standard deviation for normalization. Computes series-wise standard deviation based on first patch (patch-based) or entire series (bin quantization).
The system automatically handles padding, missing values, and end-of-sequence tokens.

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Supported Data Formats and Modalities

  • Text/Code: JSONL files containing a list of dictionaries, each with a text key
  • Time Series: AutoGluonTS compatible formats (format specification coming soon)

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Example Usage

from pynolano import DataPreparationConfig

def build() -> DataPreparationConfig:
    return DataPreparationConfig(
        input_path="./text_data.jsonl",
        output_path="./prepared_text",
        tokenization="Qwen/Qwen3-4B",
        max_sequence_length=2048
    )