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v3 — hierarchical categories, causal dataset properties, failure modes.
"""
from __future__ import annotations
from enum import Enum
from typing import Any
from pydantic import BaseModel, Field, field_validator
# =========================================================================
# Hierarchical category mappings
# technique → category (the single source of truth)
# =========================================================================
CATEGORY_MAP: dict[str, dict[str, str]] = {
"set_cv": {
"kfold": "standard",
"repeated_kfold": "standard",
"group_kfold": "group",
"stratified_group_kfold": "group",
"time_split": "temporal",
"combined_group_time": "temporal",
},
"feature_engineering": {
"log_transform": "distribution",
"normalize": "distribution",
"quantile_features": "distribution",
"interaction_terms": "interaction",
"domain_ratios": "interaction",
"sin_cos_encoding": "encoding",
"target_encoding": "encoding",
"spatial_encoding": "encoding",
"tfidf_features": "encoding",
"relative_coordinates": "spatial",
"distance_features": "spatial",
"frequency_features": "signal",
"multi_layer_features": "signal",
"fourier_resampling": "signal",
},
"detect_shift": {
"adversarial_validation": "detection",
"feature_importance_shift": "detection",
"remove_identifiers": "mitigation",
"domain_invariant_features": "mitigation",
},
"train_model": {
"xgboost": "tree",
"lightgbm": "tree",
"catboost": "tree",
"random_forest": "tree",
"linear": "linear",
"neural_network": "neural",
"pretrained_backbone": "neural",
"temporal_cnn": "neural",
"transformer_encoder": "neural",
},
"handle_imbalance": {
"scale_pos_weight": "weighting",
"class_weighted_loss": "weighting",
"calibrate_probabilities": "calibration",
"optimize_threshold": "calibration",
"hierarchical_labels": "hierarchy",
"lower_thresholds_recall": "hierarchy",
},
"clean_data": {
"remove_corrupted": "removal",
"remove_outliers": "removal",
"remove_leaky_features": "removal",
"analytical_reconstruction": "reconstruction",
"nan_native_model": "reconstruction",
"domain_augmentation": "reconstruction",
"clean_subset_training": "reconstruction",
},
"augmentation": {
"geometric": "geometric",
"rotation_invariant": "geometric",
"image_rectification": "geometric",
"color_transform": "color",
"clahe": "color",
"gaussian_noise": "noise",
"robustness_augmentation": "noise",
"camera_simulation": "domain",
"temporal_augmentation": "domain",
"symmetry_augmentation": "domain",
"multi_view_processing": "domain",
},
"ensemble": {
"weighted_average": "averaging",
"multi_seed_averaging": "averaging",
"swa": "averaging",
"stacking": "stacking",
"diverse_features": "diversity",
"heterogeneous": "diversity",
},
"postprocess": {
"bias_correction": "calibration",
"prediction_shrinkage": "calibration",
"per_group_calibration": "calibration",
"domain_rules": "domain",
"physics_constraints": "domain",
"tta": "inference",
},
"tune_loss": {
"asymmetric_loss": "asymmetric",
"epsilon_insensitive": "asymmetric",
"gaussian_nll": "uncertainty",
"multi_task": "multi_objective",
"interval_regression": "multi_objective",
"quantile_regression": "multi_objective",
"sample_weighted": "weighting",
"auxiliary_physics_loss": "weighting",
},
"regularize": {
"strong_regularization": "weight",
"ema": "weight",
"dropout": "weight",
"freeze_backbone": "transfer",
},
}
def get_categories_for_action(action_type: str) -> dict[str, list[str]]:
"""Return {category: [techniques]} for an action type."""
mapping = CATEGORY_MAP.get(action_type, {})
result: dict[str, list[str]] = {}
for technique, cat in mapping.items():
result.setdefault(cat, []).append(technique)
return result
def validate_category(action_type: str, category: str | None, technique: str) -> str | None:
"""Validate and return the correct category. Returns error message on failure."""
mapping = CATEGORY_MAP.get(action_type)
if mapping is None:
return None
expected_cat = mapping.get(technique)
if expected_cat is None:
return f"Unknown technique '{technique}' for {action_type}"
if category is not None and category != expected_cat:
return (
f"Wrong category '{category}' for {action_type}:{technique}. "
f"Expected '{expected_cat}'."
)
return None
def infer_category(action_type: str, technique: str) -> str | None:
"""Infer category from action_type + technique."""
mapping = CATEGORY_MAP.get(action_type, {})
return mapping.get(technique)
# =========================================================================
# Action types
# =========================================================================
class ActionType(str, Enum):
SET_CV = "set_cv"
FEATURE_ENGINEERING = "feature_engineering"
DETECT_SHIFT = "detect_shift"
TRAIN_MODEL = "train_model"
HANDLE_IMBALANCE = "handle_imbalance"
CLEAN_DATA = "clean_data"
AUGMENTATION = "augmentation"
ENSEMBLE = "ensemble"
PSEUDO_LABEL = "pseudo_label"
POSTPROCESS = "postprocess"
TUNE_LOSS = "tune_loss"
REGULARIZE = "regularize"
INSPECT_TOP_SOLUTION = "inspect_top_solution"
SUBMIT = "submit"
_PARAM_KEY_MAP: dict[str, str] = {
"set_cv": "strategy",
"feature_engineering": "technique",
"detect_shift": "method",
"train_model": "algorithm",
"handle_imbalance": "method",
"clean_data": "method",
"augmentation": "method",
"ensemble": "method",
"postprocess": "method",
"tune_loss": "method",
"regularize": "method",
}
def get_param_key(action_type: str) -> str | None:
return _PARAM_KEY_MAP.get(action_type)
def get_allowed_values(action_type: str) -> list[str]:
mapping = CATEGORY_MAP.get(action_type, {})
return list(mapping.keys())
# =========================================================================
# Action
# =========================================================================
class Action(BaseModel):
"""A single structured action with hierarchical category."""
action_type: ActionType
parameters: dict[str, Any] = Field(default_factory=dict)
@field_validator("parameters", mode="before")
@classmethod
def _coerce_parameters(cls, v: Any) -> dict[str, Any]:
if v is None:
return {}
return v
def technique_value(self) -> str | None:
"""Return the technique/strategy/method/algorithm value."""
key = get_param_key(self.action_type.value)
if key is None:
return None
return self.parameters.get(key)
def category_value(self) -> str | None:
"""Return the declared category, or infer it."""
declared = self.parameters.get("category")
if declared:
return declared
tv = self.technique_value()
if tv:
return infer_category(self.action_type.value, tv)
return None
def tag(self) -> str:
"""Concise string for history tracking (without category)."""
at = self.action_type
if at == ActionType.PSEUDO_LABEL:
return f"pseudo_label:{self.parameters.get('iterations', 1)}"
if at in (ActionType.INSPECT_TOP_SOLUTION, ActionType.SUBMIT):
return at.value
tv = self.technique_value()
if tv:
return f"{at.value}:{tv}"
return at.value
def full_tag(self) -> str:
"""Verbose string including category."""
cat = self.category_value()
base = self.tag()
if cat:
parts = base.split(":", 1)
if len(parts) == 2:
return f"{parts[0]}:{cat}:{parts[1]}"
return base
# =========================================================================
# Causal dataset properties
# =========================================================================
class DatasetProperties(BaseModel):
"""Ground-truth properties that govern causal reward logic."""
has_shift: bool = False
has_leakage: bool = False
has_noise_features: bool = False
has_missing_data: bool = False
has_imbalance: bool = False
imbalance_ratio: float = 0.5
has_heavy_tails: bool = False
has_time_column: bool = False
has_group_column: bool = False
has_images: bool = False
has_spatial_data: bool = False
has_text: bool = False
is_safety_critical: bool = False
needs_physics: bool = False
# =========================================================================
# Failure mode definition
# =========================================================================
class FailureMode(BaseModel):
"""A trap the agent can fall into."""
name: str
trigger_tag: str
condition_field: str
condition_value: bool
cv_effect: float
test_effect: float
message: str
# =========================================================================
# Dataset metadata (observation-facing)
# =========================================================================
class ColumnInfo(BaseModel):
name: str
dtype: str
missing_pct: float = Field(ge=0.0, le=100.0)
unique_count: int = Field(ge=0)
class DatasetMetadata(BaseModel):
num_rows: int
num_features: int
columns: list[ColumnInfo]
target_column: str
task_type: str = "classification"
has_time_column: bool = False
has_group_column: bool = False
has_image_data: bool = False
has_text_data: bool = False
has_spatial_data: bool = False
class_balance: dict[str, float] = Field(default_factory=dict)
target_distribution: str = "normal"
# =========================================================================
# Observation
# =========================================================================
class Observation(BaseModel):
dataset_metadata: DatasetMetadata
applied_strategies: list[str]
current_cv_score: float
leaderboard_rank: int
step_count: int
max_steps: int
done: bool
message: str = ""
# =========================================================================
# Reward
# =========================================================================
class RewardBreakdown(BaseModel):
cv_improvement: float = 0.0
strategy_bonus: float = 0.0
context_bonus: float = 0.0
combo_bonus: float = 0.0
redundancy_penalty: float = 0.0
irrelevant_penalty: float = 0.0
trap_penalty: float = 0.0
overfitting_penalty: float = 0.0
submission_bonus: float = 0.0
class Reward(BaseModel):
total: float
breakdown: RewardBreakdown
# =========================================================================
# Full environment state
# =========================================================================
class EnvState(BaseModel):
task_id: str
step_count: int
max_steps: int
done: bool
cv_score: float
test_score: float
applied_strategies: list[str]
strategy_history: list[str]
leaderboard_rank: int
leaderboard: list[dict[str, Any]]
submitted: bool
hint_count: int
active_combos: list[str] = Field(default_factory=list)
traps_triggered: list[str] = Field(default_factory=list)
# =========================================================================
# Step response
# =========================================================================
class StepResponse(BaseModel):
observation: Observation
reward: Reward
done: bool
info: dict[str, Any] = Field(default_factory=dict)
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