One hot encoding categorical chromatography features

one-hot-encoding-categorical-chromatography-features

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Summary

Converts categorical HPLC column metadata (company, USP designation, solvent identities) into binary feature vectors suitable for machine learning. This preprocessing step is essential for method-independent retention time prediction, enabling graph transformer models to ingest heterogeneous categorical and continuous column parameters as a unified feature array.

When to use

Apply this skill when you have raw HPLC column metadata containing categorical fields (e.g., column manufacturer 'Waters', USP type 'L1', solvent identities 'H2O'/'MeOH'/'ACN') that must be converted into numerical representations before featurization for a machine learning pipeline. Specifically use it in the first stage of the featurize_column function when building HSM parameter blocks that will be concatenated with Tanaka physicochemical vectors.

When NOT to use

• Input columns are already pre-encoded as numerical indices or one-hot vectors — skip re-encoding to avoid data corruption.
• Categorical values are not from a predefined, finite set — one-hot encoding assumes exhaustive enumeration; for open-ended or unseen categories, use hashing or embedding approaches instead.
• Feature vector is being used in a model that natively handles categorical data (e.g., tree-based ensemble) — one-hot encoding may waste dimensionality and reduce interpretability.

Inputs

• column_params array (raw HPLC metadata including company name string, USP code string, solvent A identity string, solvent B identity string, additive A flag, additive B flag)
• predefined category lists: companies list, USP codes list, solvents list (e.g., ['h2o', 'meoh', 'acn', 'Other'])

Outputs

• integer_encodings: concatenated one-hot and binary vectors as NumPy array (dtype int or float)
• feature vector component (to be concatenated with normalized numericals and Tanaka parameters into final HSM feature vector)

How to apply

For each categorical column metadata field (company name, USP designation, solvent A, solvent B, additive presence flags), create a binary vector of length equal to the number of unique categories in a predefined category list. Set the index corresponding to the observed category value to 1 and all others to 0. For example, if company list is ['Waters', 'Agilent', 'Phenomenex'] and the input is 'Agilent', produce [0, 1, 0]. Use np.where to convert additive flags (non-zero/zero) to binary (1/0). Concatenate all resulting one-hot vectors in a fixed order (company, then USP, then solvent A, then solvent B, then additives A and B) to form the integer encoding block (indices 0–91 of the final feature array). This ordering ensures reproducible feature alignment across all samples.

Related tools

• NumPy (Implements one-hot encoding logic and np.where for binary conversion of additive flags; used for concatenating encoded vectors into final feature array)
• pandas (Optional: loading and organizing column metadata tables for batch encoding of multiple column records)
• Graphormer-RT (Downstream graph transformer model that accepts the encoded feature vectors as part of the column featurization input) — https://github.com/HopkinsLaboratory/Graphormer-RT

Examples

companies = ['Waters', 'Agilent']; one_hot = [0] * len(companies); one_hot[companies.index('Waters')] = 1; add_A = np.where(0.5 != 0, 1, 0)

Evaluation signals

• Each one-hot vector sums to exactly 1 (i.e., sum(encoded_category) == 1) — verifies exactly one category is selected.
• Total length of concatenated integer encodings equals sum of lengths of all category lists (e.g., 10 companies + 5 USP codes + 4 solvents × 2 + 1 additive A + 1 additive B = 26 dimensions for integer block) — ensures no fields are missing or duplicated.
• Additive flags are binary (only 0 or 1 values present) after np.where conversion — verifies binary conversion succeeded.
• Feature vector indices 0–91 contain only integer values (0 or 1); indices 92+ contain float Tanaka parameters — confirms proper demarcation between integer and float encoding blocks.
• Repeated encoding of the same input produces identical output vector — verifies deterministic, reproducible encoding.

Limitations

• One-hot encoding is inflexible to unseen categories at inference time; if a new company or solvent identity appears in test data, it will not match any predefined category and must be manually handled (e.g., mapped to 'Other' or excluded).
• Predefined category lists must be complete and consistent across training and inference; any inconsistency in spelling or ordering breaks reproducibility.
• One-hot encoding increases dimensionality linearly with the number of categories; with many categorical fields (e.g., >100 unique companies), the resulting feature space may become sparse and high-dimensional, potentially hurting model generalization.
• Additive presence flags are assumed to be non-zero/zero indicators; if additives are encoded as continuous concentrations or multiple presence states, np.where binary conversion will lose information.

Evidence

• [other] Apply one-hot encoding to company name using predefined company list, producing binary vector.: "Apply one-hot encoding to company name using predefined company list, producing binary vector."
• [results] def one_hot_company(company): one_hot = [0] * len(companies) one_hot[companies.index(company)] = 1: "def one_hot_company(company): one_hot = [0] * len(companies) one_hot[companies.index(company)] = 1"
• [other] Conversion of additive values to binary (0 or 1) by checking if non-zero: "Conversion of additive values to binary (0 or 1) by checking if non-zero"
• [results] add_A_vals = np.where(add_A_vals != 0, 1, add_A_vals) add_B_vals = np.where(add_B_vals != 0, 1, add_B_vals): "add_A_vals = np.where(add_A_vals != 0, 1, add_A_vals) add_B_vals = np.where(add_B_vals != 0, 1, add_B_vals)"
• [other] Concatenate all encoded features (company, USP, solvents, additives, normalized numericals) into HSM parameter block (indices 0–91): "Concatenate all encoded features (company, USP, solvents, additives, normalized numericals) into HSM parameter block (indices 0–91)"
• [results] int_encodings = np.concatenate([[-2],company, USP, solv_A, solv_B, add_A_vals,: "int_encodings = np.concatenate([[-2],company, USP, solv_A, solv_B, add_A_vals,"