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076abd9 | 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 | import os
import pandas as pd
from uuid import uuid4
from openenv.core.env_server.interfaces import Environment
from openenv.core.env_server.types import State
try:
from ..models import DataWranglerAction, DataWranglerObservation
except (ImportError, ValueError, ModuleNotFoundError):
import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from models import DataWranglerAction, DataWranglerObservation
class DataWranglerEnvironment(Environment):
SUPPORTS_CONCURRENT_SESSIONS: bool = True
def __init__(self):
self._state = State(episode_id=str(uuid4()), step_count=0)
self._reset_count = 0
self.df = None
self.target_df = None
self.task_level = int(os.environ.get("TASK_LEVEL", "1"))
self._initialize_task()
def _initialize_task(self):
self.df = pd.DataFrame()
self.target_df = pd.DataFrame()
if self.task_level == 1:
# Easy: Just drop a column and rename one
self.df = pd.DataFrame({
"User Name": ["Alice", "Bob", "Charlie"],
"Unnamed: 0": [0, 1, 2],
"Age": [25, 30, 35]
})
self.target_df = pd.DataFrame({
"user_name": ["Alice", "Bob", "Charlie"],
"age": [25, 30, 35]
})
elif self.task_level == 2:
# Medium: fill missing and cast type
self.df = pd.DataFrame({
"product_ID ": ["101", "102", "103"],
"price": ["10.5", None, "12.0"],
"bad_col": [None, None, None]
})
self.target_df = pd.DataFrame({
"product_id": [101.0, 102.0, 103.0],
"price": [10.5, 0.0, 12.0]
})
else:
# Hard: Multiple issues
self.df = pd.DataFrame({
"date_joined ": ["2020-01-01", "2021-05-15", None],
"Sales_total": ["100", "200", "300"],
"IsActive": [True, False, None],
"DROPME_1": [1,2,3]
})
self.target_df = pd.DataFrame({
"date_joined": [pd.Timestamp("2020-01-01"), pd.Timestamp("2021-05-15"), pd.Timestamp("1970-01-01")],
"sales_total": [100.0, 200.0, 300.0],
"is_active": [True, False, False]
})
def _get_obs(self, feedback: str = "Environment initialized.", done: bool = False, reward: float = 0.0) -> DataWranglerObservation:
stats = {}
for col in self.df.columns:
stats[col] = {
"dtype": str(self.df[col].dtype),
"missing_count": int(self.df[col].isna().sum()),
"sample_values": self.df[col].dropna().astype(str).tolist()[:3]
}
return DataWranglerObservation(
columns=list(self.df.columns),
row_count=len(self.df),
column_stats=stats,
last_action_feedback=feedback,
is_done=done,
reward=reward,
done=done,
metadata={"step": self._state.step_count}
)
def reset(self) -> DataWranglerObservation:
self._state = State(episode_id=str(uuid4()), step_count=0)
self._reset_count += 1
self._initialize_task()
return self._get_obs()
def step(self, action: DataWranglerAction) -> DataWranglerObservation: # type: ignore
self._state.step_count += 1
feedback = "Action executed successfully."
reward = 0.0
done = False
try:
if action.action_type == "drop_column":
col = action.target_column
if col in self.df.columns:
self.df.drop(columns=[col], inplace=True)
if col not in self.target_df.columns:
reward = 0.2
else:
reward = -0.5
feedback = f"Warning: dropped targeting column {col}"
else:
feedback = f"Error: Column '{col}' not found."
elif action.action_type == "rename_column":
col = action.target_column
new_col = action.new_name
if col in self.df.columns:
self.df.rename(columns={col: new_col}, inplace=True)
if new_col in self.target_df.columns:
reward = 0.2
else:
feedback = f"Error: Column '{col}' not found."
elif action.action_type == "fill_missing":
col = action.target_column
if col in self.df.columns:
self.df[col].fillna(action.fill_value, inplace=True)
reward = 0.1
else:
feedback = f"Error: Column '{col}' not found."
elif action.action_type == "cast_type":
col = action.target_column
to_type = action.cast_to
if col in self.df.columns:
if to_type == 'int':
self.df = self.df.astype({col: int})
elif to_type == 'float':
self.df = self.df.astype({col: float})
elif to_type == 'datetime':
self.df[col] = pd.to_datetime(self.df[col])
elif to_type == 'string':
self.df = self.df.astype({col: str})
reward = 0.2
else:
feedback = f"Error: Column '{col}' not found."
elif action.action_type == "submit":
score = self._grade()
reward = score
feedback = f"Submitted. Final Score: {score}"
done = True
else:
feedback = f"Error: Unknown action type {action.action_type}"
except Exception as e:
feedback = f"Exception occurred: {str(e)}"
reward = -0.1
return self._get_obs(feedback=feedback, done=done, reward=reward)
def _grade(self) -> float:
score = 0.0
if list(self.df.columns) == list(self.target_df.columns):
score += 0.5
# Match types and values
value_matches = 0
for col in self.df.columns:
try:
# simple match check
match = (self.df[col] == self.target_df[col]).all()
if match:
value_matches += 1
except:
pass
score += 0.5 * (value_matches / max(len(self.target_df.columns), 1))
return score
@property
def state(self) -> State:
return self._state
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