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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()
# Priority 5 - Dynamic Hugging Face or CSV Datasets
# If the user defines an external dataset via env var, load that instead.
dataset_source = os.environ.get("DATASET_SOURCE")
target_source = os.environ.get("TARGET_SOURCE")
if dataset_source:
if str(dataset_source).endswith(".csv"):
self.df = pd.read_csv(dataset_source)
elif str(dataset_source).endswith(".parquet"):
self.df = pd.read_parquet(dataset_source)
else:
from datasets import load_dataset
# Fallback to Hugging Face Hub (e.g. "scikit-learn/titanic", "argilla/news-summary")
# We grab the 'train' split by default and convert it to pandas
hf_data = load_dataset(dataset_source, split="train")
self.df = hf_data.to_pandas()
if target_source:
if str(target_source).endswith(".csv"):
self.target_df = pd.read_csv(target_source)
elif str(target_source).endswith(".parquet"):
self.target_df = pd.read_parquet(target_source)
else:
from datasets import load_dataset
hf_target = load_dataset(target_source, split="train")
self.target_df = hf_target.to_pandas()
else:
# If there's no target provided, we force the LLM to simply drop all rows with missing values
# as a baseline goal for Dynamic runs to prevent graded failures on unstructured tests
self.target_df = self.df.dropna()
return
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]
})
elif self.task_level == 3:
# 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]
})
else:
# Level 4: Extremely Hard / Expanded Tools
self.df = pd.DataFrame({
"transaction_info": ["TXN-101 (2020/01/15)", "TXN-102 (2020/01/16)", "TXN-103 (2020/01/16)"],
"customer_category": ["A", "B", "A"],
"amount": ["$500.5", "$250.0", "$300.0"]
})
self.target_df = pd.DataFrame({
"customer_category": ["A", "B"],
"amount": [800.5, 250.0] # Sum'd up
})
def _get_obs(self, feedback: str = "Environment initialized.", done: bool = False, reward: float = 0.0) -> DataWranglerObservation:
stats = {}
for col in self.df.columns:
non_null = self.df[col].dropna()
stats[col] = {
"dtype": str(self.df[col].dtype),
"missing_count": int(self.df[col].isna().sum()),
"sample_values": non_null.astype(str).head(3).tolist(),
}
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 _require_columns(self, *columns: str) -> str | None:
missing = [col for col in columns if not col or col not in self.df.columns]
if missing:
return f"Error: Column(s) not found: {', '.join(missing)}"
return None
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
err = self._require_columns(col)
if not err:
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 = err
elif action.action_type == "rename_column":
col = action.target_column
new_col = action.new_name
err = self._require_columns(col)
if not err:
self.df.rename(columns={col: new_col}, inplace=True)
if new_col in self.target_df.columns:
reward = 0.2
else:
feedback = err
elif action.action_type == "fill_missing":
col = action.target_column
err = self._require_columns(col)
if not err:
self.df[col] = self.df[col].fillna(action.fill_value)
reward = 0.1
else:
feedback = err
elif action.action_type == "cast_type":
col = action.target_column
to_type = (action.cast_to or "").lower()
err = self._require_columns(col)
if not err:
if to_type == "int":
self.df[col] = pd.to_numeric(self.df[col], errors="coerce").astype("Int64")
elif to_type == "float":
self.df[col] = pd.to_numeric(self.df[col], errors="coerce").astype(float)
elif to_type == "datetime":
self.df[col] = pd.to_datetime(self.df[col], errors="coerce")
elif to_type == "string":
self.df[col] = self.df[col].astype(str)
else:
feedback = f"Error: Unsupported cast type '{action.cast_to}'."
return self._get_obs(feedback=feedback, done=done, reward=reward)
reward = 0.2
else:
feedback = err
elif action.action_type == "extract_regex":
col = action.target_column
new_col = action.new_name
pattern = action.regex_pattern
err = self._require_columns(col)
if not err and new_col and pattern:
# extract the first capture group
extracted = self.df[col].astype(str).str.extract(pattern)[0]
self.df[new_col] = extracted
reward = 0.1
else:
feedback = err or "Error: 'new_name' and 'regex_pattern' are required."
elif action.action_type == "datetime_parse":
col = action.target_column
fmt = action.format_string
err = self._require_columns(col)
if not err:
self.df[col] = pd.to_datetime(self.df[col], format=fmt, errors="coerce")
reward = 0.1
else:
feedback = err
elif action.action_type == "group_by_aggregate":
group_col = action.target_column
agg_col = action.agg_column
func = action.agg_func
err = self._require_columns(group_col, agg_col)
if not err and func:
self.df = self.df.groupby(group_col, as_index=False, observed=True).agg({agg_col: func})
reward = 0.2
else:
feedback = err or "Error: 'agg_func' is required."
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
# Priority 2: Partial credit per correct column (name + dtype + values)
correct_columns = 0
target_cols = set(self.target_df.columns)
current_cols = set(self.df.columns)
for col in target_cols:
if col in current_cols:
try:
# Check dtype match
if self.df[col].dtype == self.target_df[col].dtype:
# Check value match
if (self.df[col].equals(self.target_df[col])):
correct_columns += 1
except:
pass
# Max score from matching columns is 0.8 (leaving 0.2 for efficiency)
column_score = (correct_columns / max(len(target_cols), 1)) * 0.8
score += column_score
# Priority 2: Step efficiency bonus
# If solved in few steps, give up to 0.2 bonus
ideal_steps = len(target_cols) # rough estimate
if self._state.step_count <= ideal_steps + 2:
score += 0.2
elif self._state.step_count <= ideal_steps + 5:
score += 0.1
return min(max(score, 0.0), 1.0)
@property
def state(self) -> State:
return self._state
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