Spaces:
Running
Running
File size: 7,972 Bytes
fede53c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 | """Feature-Engineering task implementation.
Supported actions: ``create_interaction``, ``bin_column``,
``one_hot_encode``, ``scale_column``, ``log_transform``, ``submit``.
Reward logic:
reward = (new_accuracy - old_accuracy) * 15 - 0.01 (step penalty)
Termination:
* The agent calls ``SubmitAction``, **or**
* The maximum step count is reached.
"""
from __future__ import annotations
from typing import Any, Dict, Set
import numpy as np
import pandas as pd
from ..models import (
Action,
BinColumnAction,
CreateInteractionAction,
Difficulty,
LogTransformAction,
OneHotEncodeAction,
ScaleColumnAction,
TaskType,
)
from .base_task import BaseTask
class FeatureEngineeringTask(BaseTask):
"""Engineer new features to improve model accuracy."""
TASK_TYPE = TaskType.FEATURE_ENGINEERING
SUPPORTED_ACTIONS: Set[str] = {
"create_interaction",
"bin_column",
"one_hot_encode",
"scale_column",
"log_transform",
}
# -------------------------------------------------------------------------
# Action application
# -------------------------------------------------------------------------
def apply_action(self, action: Action) -> None: # noqa: D401
df = self.data_state.df.copy()
if isinstance(action, CreateInteractionAction):
df = self._create_interaction(df, action)
label = f"interaction({action.column_a}*{action.column_b})"
elif isinstance(action, BinColumnAction):
df = self._bin_column(df, action)
label = f"bin({action.column}, n={action.n_bins})"
elif isinstance(action, OneHotEncodeAction):
df = self._one_hot_encode(df, action)
label = f"ohe({action.column})"
elif isinstance(action, ScaleColumnAction):
df = self._scale_column(df, action)
label = f"scale({action.column}, {action.method})"
elif isinstance(action, LogTransformAction):
df = self._log_transform(df, action)
label = f"log1p({action.column})"
else:
return
# --- keep the feature list in sync -------------------------
new_cols = set(df.columns) - set(self.data_state.df.columns)
for c in new_cols:
if (
c != self.dataset_config.target_column
and c not in self.dataset_config.exclude_columns
and c not in self.dataset_config.feature_columns
):
self.dataset_config.feature_columns.append(c)
# Remove columns that were dropped (e.g. OHE drop_original)
self.dataset_config.feature_columns = [
c for c in self.dataset_config.feature_columns if c in df.columns
]
self.data_state.apply_update(df, label)
# -------------------------------------------------------------------------
# Reward
# -------------------------------------------------------------------------
def calculate_reward(
self,
old_accuracy: float,
new_accuracy: float,
action: Action,
) -> float:
accuracy_gain = new_accuracy - old_accuracy
return accuracy_gain * 10.0 - 0.01
# -------------------------------------------------------------------------
# Termination
# -------------------------------------------------------------------------
def is_done(self) -> bool:
# FE tasks only auto-terminate on max steps (otherwise via submit)
return self.step_count >= self.max_steps
# -------------------------------------------------------------------------
# Grading
# -------------------------------------------------------------------------
def grade(self) -> Dict[str, Any]:
accuracy = self.calculate_accuracy()
improvement = accuracy - self._initial_accuracy
details: Dict[str, Any] = {
"initial_accuracy": round(self._initial_accuracy, 4),
"final_accuracy": round(accuracy, 4),
"improvement": round(improvement, 4),
"features_created": len(self.data_state.history),
"steps_taken": self.step_count,
"action_history": list(self.data_state.history),
}
# Score bands based on relative improvement
if improvement >= 0.10:
score = 1.0
elif improvement >= 0.05:
score = 0.75
elif improvement >= 0.02:
score = 0.5
elif improvement > 0:
score = 0.25
else:
score = 0.0
details["score"] = score
return details
# -------------------------------------------------------------------------
# Goal
# -------------------------------------------------------------------------
def get_goal_description(self) -> str:
return (
"FEATURE ENGINEERING: Create new features to improve model accuracy. "
"Use create_interaction, bin_column, one_hot_encode, scale_column, "
"or log_transform actions. Submit when finished."
)
# -------------------------------------------------------------------------
# Private helpers
# -------------------------------------------------------------------------
@staticmethod
def _create_interaction(
df: pd.DataFrame,
action: CreateInteractionAction,
) -> pd.DataFrame:
if action.column_a not in df.columns or action.column_b not in df.columns:
return df
a, b = df[action.column_a], df[action.column_b]
if pd.api.types.is_numeric_dtype(a) and pd.api.types.is_numeric_dtype(b):
df = df.copy()
df[action.new_column] = a * b
return df
@staticmethod
def _bin_column(df: pd.DataFrame, action: BinColumnAction) -> pd.DataFrame:
if action.column not in df.columns:
return df
col = df[action.column]
if not pd.api.types.is_numeric_dtype(col):
return df
df = df.copy()
new_col = f"{action.column}_binned"
try:
if action.strategy == "quantile":
df[new_col] = pd.qcut(
col, q=action.n_bins, labels=False, duplicates="drop",
)
else:
df[new_col] = pd.cut(col, bins=action.n_bins, labels=False)
except Exception:
pass # graceful no-op on degenerate data
return df
@staticmethod
def _one_hot_encode(
df: pd.DataFrame,
action: OneHotEncodeAction,
) -> pd.DataFrame:
if action.column not in df.columns:
return df
dummies = pd.get_dummies(df[action.column], prefix=action.column)
df = pd.concat([df, dummies], axis=1)
if action.drop_original:
df = df.drop(columns=[action.column])
return df
@staticmethod
def _scale_column(
df: pd.DataFrame,
action: ScaleColumnAction,
) -> pd.DataFrame:
if action.column not in df.columns:
return df
col = df[action.column]
if not pd.api.types.is_numeric_dtype(col):
return df
df = df.copy()
if action.method == "standard":
std = col.std()
if std > 0:
df[action.column] = (col - col.mean()) / std
elif action.method == "minmax":
cmin, cmax = col.min(), col.max()
if cmax > cmin:
df[action.column] = (col - cmin) / (cmax - cmin)
return df
@staticmethod
def _log_transform(
df: pd.DataFrame,
action: LogTransformAction,
) -> pd.DataFrame:
if action.column not in df.columns:
return df
col = df[action.column]
if not pd.api.types.is_numeric_dtype(col):
return df
df = df.copy()
df[action.column] = np.log1p(np.abs(col))
return df |