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Per-field tokenizers for domain-specific data.
Each tokenizer converts a single field value into one or more token strings.
These are the building blocks assembled by DomainTokenizerBuilder.
References:
- Nubank nuFormer: sign(2) + amount_bucket(21) + calendar(74) tokenization
- TP-BERTa (arXiv:2403.01841): Relative Magnitude Tokenization for numbers
- Banking TF (arXiv:2410.08243): date + amount + wording composite tokens
- Temporal Tokenization (arXiv:2512.13618): log-based bins for skewed financial data
"""
import json
import math
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
class BaseFieldTokenizer:
"""Base class for all field tokenizers."""
def __init__(self, prefix: str):
self.prefix = prefix
@property
def vocab(self) -> List[str]:
"""All possible token strings this tokenizer can produce."""
raise NotImplementedError
def __call__(self, value: Any) -> Union[str, List[str]]:
"""Tokenize a single value. Returns one token string or a list."""
raise NotImplementedError
@property
def vocab_size(self) -> int:
return len(self.vocab)
def to_dict(self) -> Dict:
"""Serialize tokenizer state for saving."""
return {"type": self.__class__.__name__, "prefix": self.prefix}
@classmethod
def from_dict(cls, d: Dict) -> "BaseFieldTokenizer":
"""Deserialize tokenizer state."""
raise NotImplementedError
class SignTokenizer(BaseFieldTokenizer):
"""Tokenizes the sign of a numerical value.
Nubank uses this for credit/debit distinction (2 tokens).
Can be generalized to inflow/outflow, buy/sell, etc.
Example:
>>> tok = SignTokenizer("AMT_SIGN")
>>> tok(79.99) # -> "[AMT_SIGN_POS]"
>>> tok(-50.0) # -> "[AMT_SIGN_NEG]"
"""
def __init__(self, prefix: str = "SIGN", pos_label: str = "POS", neg_label: str = "NEG"):
super().__init__(prefix)
self.pos_label = pos_label
self.neg_label = neg_label
self._pos_token = f"[{prefix}_{pos_label}]"
self._neg_token = f"[{prefix}_{neg_label}]"
@property
def vocab(self) -> List[str]:
return [self._pos_token, self._neg_token]
def __call__(self, value: float) -> str:
if value is None or (isinstance(value, float) and math.isnan(value)):
return self._pos_token # default to positive for missing
return self._pos_token if value >= 0 else self._neg_token
def to_dict(self) -> Dict:
return {**super().to_dict(), "pos_label": self.pos_label, "neg_label": self.neg_label}
class MagnitudeBucketTokenizer(BaseFieldTokenizer):
"""Quantizes continuous values into bins using quantile-based binning.
Follows Nubank's 21-bin quantization and TP-BERTa's Relative Magnitude
Tokenization principle. Uses absolute values so sign and magnitude are
tokenized independently.
Must be fit on training data before use.
Example:
>>> tok = MagnitudeBucketTokenizer("AMT", n_bins=21)
>>> tok.fit(np.array([1.0, 5.0, 10.0, 50.0, 100.0, 500.0]))
>>> tok(79.99) # -> "[AMT_15]" (some bin in the upper range)
"""
def __init__(self, prefix: str = "AMT", n_bins: int = 21):
super().__init__(prefix)
self.n_bins = n_bins
self.bin_edges: Optional[np.ndarray] = None
self._is_fitted = False
@property
def vocab(self) -> List[str]:
return [f"[{self.prefix}_{i:02d}]" for i in range(self.n_bins)]
def fit(self, values: np.ndarray) -> "MagnitudeBucketTokenizer":
"""Compute bin edges from training data using quantiles on absolute values."""
values = np.asarray(values, dtype=np.float64)
# Filter NaN and take absolute values
valid = values[~np.isnan(values)]
abs_vals = np.abs(valid)
if len(abs_vals) == 0:
raise ValueError("Cannot fit on empty array")
# Compute quantile edges
quantiles = np.linspace(0, 100, self.n_bins + 1)
self.bin_edges = np.unique(np.percentile(abs_vals, quantiles))
# If too few unique edges (degenerate distribution), use linspace
if len(self.bin_edges) < 3:
self.bin_edges = np.linspace(abs_vals.min(), abs_vals.max(), self.n_bins + 1)
self._is_fitted = True
return self
def __call__(self, value: float) -> str:
if not self._is_fitted:
raise RuntimeError(f"MagnitudeBucketTokenizer({self.prefix}) not fitted. Call .fit() first.")
if value is None or (isinstance(value, float) and math.isnan(value)):
return f"[{self.prefix}_00]" # default to lowest bin for missing
abs_val = abs(float(value))
# searchsorted on interior edges (exclude first and last)
bin_idx = int(np.searchsorted(self.bin_edges[1:-1], abs_val))
# Clamp to valid range
bin_idx = min(bin_idx, self.n_bins - 1)
return f"[{self.prefix}_{bin_idx:02d}]"
def to_dict(self) -> Dict:
d = {**super().to_dict(), "n_bins": self.n_bins, "is_fitted": self._is_fitted}
if self._is_fitted:
d["bin_edges"] = self.bin_edges.tolist()
return d
@classmethod
def from_dict(cls, d: Dict) -> "MagnitudeBucketTokenizer":
tok = cls(prefix=d["prefix"], n_bins=d["n_bins"])
if d.get("is_fitted") and "bin_edges" in d:
tok.bin_edges = np.array(d["bin_edges"])
tok._is_fitted = True
return tok
class DiscreteNumericalTokenizer(BaseFieldTokenizer):
"""Tokenizes small discrete numerical values (quantities, counts).
Maps integers 0..max_value to individual tokens, with an overflow token
for values exceeding max_value.
Example:
>>> tok = DiscreteNumericalTokenizer("QTY", max_value=10)
>>> tok(3) # -> "[QTY_03]"
>>> tok(15) # -> "[QTY_OVER]"
"""
def __init__(self, prefix: str = "QTY", max_value: int = 10):
super().__init__(prefix)
self.max_value = max_value
self._overflow_token = f"[{prefix}_OVER]"
@property
def vocab(self) -> List[str]:
tokens = [f"[{self.prefix}_{i:02d}]" for i in range(self.max_value + 1)]
tokens.append(self._overflow_token)
return tokens
def __call__(self, value: Any) -> str:
if value is None:
return f"[{self.prefix}_00]"
v = int(value)
if v < 0:
v = 0
if v > self.max_value:
return self._overflow_token
return f"[{self.prefix}_{v:02d}]"
def to_dict(self) -> Dict:
return {**super().to_dict(), "max_value": self.max_value}
class CalendarTokenizer(BaseFieldTokenizer):
"""Decomposes timestamps into calendar component tokens.
Follows Nubank's approach: month(12) + dow(7) + dom(31) + hour(24) = 74 tokens.
Accepts datetime objects or ISO format strings.
Example:
>>> tok = CalendarTokenizer("TS", fields=["month", "dow", "dom", "hour"])
>>> tok(datetime(2025, 3, 15, 14, 30))
['[TS_MON_03]', '[TS_DOW_5]', '[TS_DOM_15]', '[TS_HOUR_14]']
"""
# Maps field name -> (token format, extraction function, count)
FIELD_REGISTRY = {
"month": (lambda p, i: f"[{p}_MON_{i+1:02d}]", lambda dt: dt.month - 1, 12),
"dow": (lambda p, i: f"[{p}_DOW_{i}]", lambda dt: dt.weekday(), 7),
"dom": (lambda p, i: f"[{p}_DOM_{i+1:02d}]", lambda dt: dt.day - 1, 31),
"hour": (lambda p, i: f"[{p}_HOUR_{i:02d}]", lambda dt: dt.hour, 24),
"quarter": (lambda p, i: f"[{p}_Q{i+1}]", lambda dt: (dt.month-1)//3, 4),
"minute_bin": (lambda p, i: f"[{p}_MINBIN_{i}]", lambda dt: dt.minute // 15, 4),
}
def __init__(self, prefix: str = "TS", fields: Optional[List[str]] = None):
super().__init__(prefix)
self.fields = fields or ["month", "dow", "dom", "hour"]
# Validate
for f in self.fields:
if f not in self.FIELD_REGISTRY:
raise ValueError(f"Unknown calendar field: '{f}'. Available: {list(self.FIELD_REGISTRY.keys())}")
@property
def vocab(self) -> List[str]:
tokens = []
for field_name in self.fields:
fmt_fn, _, count = self.FIELD_REGISTRY[field_name]
tokens.extend(fmt_fn(self.prefix, i) for i in range(count))
return tokens
def _parse_datetime(self, value: Any) -> datetime:
if isinstance(value, datetime):
return value
if isinstance(value, str):
# Try common formats
for fmt in ("%Y-%m-%dT%H:%M:%S", "%Y-%m-%d %H:%M:%S", "%Y-%m-%d"):
try:
return datetime.strptime(value, fmt)
except ValueError:
continue
raise ValueError(f"Cannot parse datetime string: {value}")
raise TypeError(f"Expected datetime or str, got {type(value)}")
def __call__(self, value: Any) -> List[str]:
dt = self._parse_datetime(value)
tokens = []
for field_name in self.fields:
fmt_fn, extract_fn, count = self.FIELD_REGISTRY[field_name]
idx = extract_fn(dt)
idx = max(0, min(idx, count - 1)) # clamp
tokens.append(fmt_fn(self.prefix, idx))
return tokens
def to_dict(self) -> Dict:
return {**super().to_dict(), "fields": self.fields}
class CategoricalTokenizer(BaseFieldTokenizer):
"""Maps categorical string values to fixed vocabulary tokens.
Unknown values map to an [PREFIX_UNK] token.
Example:
>>> tok = CategoricalTokenizer("EVT", ["view", "purchase", "return"])
>>> tok("purchase") # -> "[EVT_001]"
>>> tok("unknown") # -> "[EVT_UNK]"
"""
def __init__(self, prefix: str, categories: List[str]):
super().__init__(prefix)
self.categories = list(categories)
self._token_map = {cat: f"[{prefix}_{i:03d}]" for i, cat in enumerate(categories)}
self._unk_token = f"[{prefix}_UNK]"
# Also build reverse map for decoding
self._reverse_map = {v: k for k, v in self._token_map.items()}
self._reverse_map[self._unk_token] = "<unknown>"
@property
def vocab(self) -> List[str]:
return list(self._token_map.values()) + [self._unk_token]
def __call__(self, value: Any) -> str:
if value is None:
return self._unk_token
return self._token_map.get(str(value), self._unk_token)
def decode_token(self, token: str) -> str:
"""Map a token string back to its category value."""
return self._reverse_map.get(token, "<unknown>")
def to_dict(self) -> Dict:
return {**super().to_dict(), "categories": self.categories}
@classmethod
def from_dict(cls, d: Dict) -> "CategoricalTokenizer":
return cls(prefix=d["prefix"], categories=d["categories"])
# =============================================================================
# Factory function to create field tokenizer from FieldSpec
# =============================================================================
def create_field_tokenizer(spec) -> BaseFieldTokenizer:
"""Create the appropriate field tokenizer from a FieldSpec.
Args:
spec: A FieldSpec instance from schema.py
Returns:
An initialized BaseFieldTokenizer subclass
"""
from ..schema import FieldType # avoid circular import
if spec.field_type == FieldType.SIGN:
return SignTokenizer(prefix=spec.prefix)
elif spec.field_type == FieldType.NUMERICAL_CONTINUOUS:
return MagnitudeBucketTokenizer(prefix=spec.prefix, n_bins=spec.n_bins)
elif spec.field_type == FieldType.NUMERICAL_DISCRETE:
return DiscreteNumericalTokenizer(prefix=spec.prefix, max_value=spec.max_value)
elif spec.field_type == FieldType.CATEGORICAL_FIXED:
return CategoricalTokenizer(prefix=spec.prefix, categories=spec.categories)
elif spec.field_type == FieldType.TEMPORAL:
return CalendarTokenizer(prefix=spec.prefix, fields=spec.calendar_fields)
elif spec.field_type == FieldType.TEXT:
return None # Text is handled by the BPE tokenizer in DomainTokenizerBuilder
else:
raise ValueError(f"Unknown field type: {spec.field_type}")
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