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import pandas
import numpy
import pickle
from typing import Optional, Sequence
from pathlib import Path
import pathlib
import os
from sklearn.dummy import DummyRegressor
import pandas as pd
import numpy
import numpy as np
import pandas
from pydantic_settings import BaseSettings, SettingsConfigDict
import structlog
from batteryswap_public.interfaces import Planner, RULModel
from batteryswap_public.utils import load_dataset, iterate_scenarios
from batteryswap_public.evaluate import evaluate_plan, check_plan_valid
log = structlog.get_logger()
class OrderedPlanner(Planner):
def __init__(self, rul_estimator):
self.rul_estimator = rul_estimator
def plan(self, battery_data, locations, travel_costs, settings):
# Remaining Useful Life estimation
# FIXME: consider balance of over/under-estimation
percentile = 'p50'
rul = self.rul_estimator.predict(battery_data)
rul_days = rul[percentile]
# convert to a date
start_time = battery_data.reset_index()['end_time'].max().normalize()
predict_eol = start_time + pandas.to_timedelta(rul_days, unit='D')
loc = locations.copy().set_index('battery')
loc['eol_time'] = predict_eol
order = loc.sort_values('eol_time', ascending=True)
# Planner
# Stupid heuristic: Do one swap per day
# FIXME: take travel distances into account
# FIXME: take co-location into account
# FIXME: take daily and weekly limits into account
days = start_time + pandas.to_timedelta(numpy.arange(len(order)), unit='D')
plan = pandas.DataFrame({
'day': days,
'battery': order.index,
})
check_plan_valid(plan, locations, start_time=start_time)
return plan
class DummyRULModel(RULModel):
# RUL model that predicts (no information rate)
# FIXME: make a model that actually uses the data to improve predictions
def __init__(
self,
time_col: str = 'end_time',
group_col: str = 'device_id',
value_cols: Sequence[str] = ('voltage', 'temperature'),
quantiles: Sequence[float] = (0.5, ),
):
self.time_col = time_col
self.group_col = group_col
self.value_cols = list(value_cols)
self.quantiles = sorted(quantiles)
self.quantile_cols = [f"p{round(q * 100):02d}" for q in self.quantiles]
self.model = None
self.use_total_elapsed_days = True
def _compute_features(self, unit_df: pd.DataFrame) -> np.ndarray:
unit_df = unit_df.sort_values(self.time_col)
all_stats = {}
# FIXME: actually compute some features
feature_names = sorted(all_stats.keys())
self._feature_names_ = feature_names # stored for inspection/debugging
return np.array([all_stats[k] for k in feature_names], dtype=float)
def _build_feature_matrix(self, timeseries: pd.DataFrame) -> tuple[np.ndarray, list]:
rows, ids = [], []
for unit_id, unit_df in timeseries.groupby(self.group_col):
if len(unit_df) < 2:
continue
rows.append(self._compute_features(unit_df))
ids.append(unit_id)
return np.vstack(rows), ids
def fit(self, timeseries: pd.DataFrame, rul: pd.Series):
X, ids = self._build_feature_matrix(timeseries)
y = np.array([rul[unit_id] for unit_id in ids])
# FIXME: actually use an estimator that learns
self.model = DummyRegressor(strategy='median')
self.model.fit(X, y)
return self
def predict(self, timeseries: pd.DataFrame) -> pd.DataFrame:
rows, ids = [], []
for unit_id, unit_df in timeseries.groupby(self.group_col):
rows.append(self._compute_features(unit_df))
ids.append(unit_id)
X = np.vstack(rows)
# every quantile column just gets the single point prediction.
point_pred = self.model.predict(X)
preds = {col: point_pred for col in self.quantile_cols}
out = pd.DataFrame(preds, index=pd.Index(ids, name=self.group_col))
return out[self.quantile_cols]
def train_rul_model(locations, timeseries, eol_times, scenarios, limit_scenarios=None):
# Collect training data
# FIXME: train/validate/test split to estimate generalized predictive performance
gen = iterate_scenarios(locations, timeseries, eol_times, scenarios)
cut_dfs = []
cut_ruls = []
scenarios_loaded = 0
for scenario, locs, cut, eol in gen:
print('load-scenario')
cut = cut.reset_index()
cut['device_id'] = cut['device_id'].astype(str) + scenario['name']
cut = cut.set_index(['device_id', 'end_time'])
cut_dfs.append(cut)
plan_start = pandas.Timestamp(scenario['start_time'])
unobserved_rul = 120
# Convert EOL datetimes to RUL in days relative to planning time
rul_days = (eol - plan_start) / pandas.Timedelta(days=1)
rul_days.index = pandas.Series(rul_days.index) + scenario['name']
rul_days = rul_days.fillna(unobserved_rul)
cut_ruls.append(rul_days)
assert set(rul_days.index) == set(cut.index.get_level_values('device_id'))
if limit_scenarios is not None:
if scenarios_loaded > limit_scenarios:
break
scenarios_loaded += 1
# Train a RUL prediction model
rul_model = DummyRULModel()
X = pandas.concat(cut_dfs)
Y = pandas.concat(cut_ruls)
print(X.head())
print(Y.head())
rul_model.fit(X, Y)
# FIXME: do model selection
return rul_model
class Config(BaseSettings):
"""
Automatically provides command-line argument support for specified fields
"""
model_config = SettingsConfigDict(
env_prefix="",
cli_parse_args=True,
cli_ignore_unknown_args=True,
)
dataset_path: Optional[Path] = None
split : str = 'train'
def main():
cfg = Config()
if cfg.dataset_path is None:
dataset_path = os.environ.get('BATTERYSWAP_DATASET_PATH', None)
assert dataset_path
dataset_path = Path(dataset_path)
else:
dataset_path = cfg.dataset_path
split_path = dataset_path / cfg.split
locations, timeseries, eol_times, scenarios = load_dataset(split_path)
log.info('evaluate-load-data', path=dataset_path)
# Prediction model training
#rul_model = DummyRULModel()
rul_model = train_rul_model(locations, timeseries, eol_times, scenarios, limit_scenarios=1)
log.info('train-done')
log.info('evaluate')
# Evaluate on the planning scenarios
gen = iterate_scenarios(locations, timeseries, eol_times, scenarios)
for scenario, locs, cut, eol in gen:
scenario_name = scenario['name']
travel_costs = scenario['travel_costs']
settings = scenario['settings']
planner = OrderedPlanner(rul_model)
plan = planner.plan(cut, locs, travel_costs, settings)
start_time = pandas.Timestamp(scenario['start_time'])
transitions, daily, overall = evaluate_plan(plan, locs, travel_costs, settings, eol_times=eol, start_time=start_time)
print('scores', scenario_name, overall)
# Save best planner
planner = OrderedPlanner(rul_model)
planner_path = 'batteryswap_example/planners/best.pickle'
with open(planner_path, "wb") as f:
pickle.dump(planner, f)
print('planner-save', planner_path)
if __name__ == '__main__':
main()
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