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preprocessing.py
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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
import argparse
import time
from collections import defaultdict
from datetime import datetime
import os
import numpy as np
import pandas as pd
import utm
from scipy.spatial import distance
from sklearn import manifold
from sklearn.metrics.pairwise import haversine_distances
from tqdm import tqdm
import joblib
"""
preprocess routes data for training the TSP model
"""
def _dist2coord(dist_matrix):
seed = np.random.RandomState(seed=3)
mds = manifold.MDS(
n_components=2,
max_iter=300,
eps=1e-3,
random_state=seed,
dissimilarity="precomputed",
n_jobs=1,
verbose=0,
n_init=4,
)
fit_re = mds.fit(dist_matrix)
pos, stress = fit_re.embedding_, fit_re.stress_
return pos[:, 0], pos[:, 1]
def _scale_coordinates(df, space=True, exclude_station=True):
col_nm_suffix = "_norm"
if exclude_station:
col_nm_suffix += "_wo_st" # without station
else:
col_nm_suffix += "_w_st"
if space:
x_col = "x_sp"
y_col = "y_sp"
else:
x_col = "x_tm"
y_col = "y_tm"
df_len = df.shape[0]
new_x_col = f"{x_col}{col_nm_suffix}"
new_y_col = f"{y_col}{col_nm_suffix}"
df[new_x_col] = [-1.0] * df_len
df[new_y_col] = [-1.0] * df_len
for i, col in enumerate(df.columns):
if col == new_x_col:
x_col_ind = i
elif col == new_y_col:
y_col_ind = i
if exclude_station:
ind = df.index[df.is_station == 0].tolist()
X = df[df.is_station == 0][x_col]
Y = df[df.is_station == 0][y_col]
else:
ind = list(range(df_len))
X = df[x_col]
Y = df[y_col]
max_value = max(np.max(X - np.min(X)), np.max(Y - np.min(Y)))
# scale by the same magnitude to keep the correlation
X1 = (X - np.min(X)) / max_value
Y1 = (Y - np.min(Y)) / max_value
df.iloc[ind, x_col_ind] = X1
df.iloc[ind, y_col_ind] = Y1
def normalise_route(df_r, route_id, df_p, row=None, project_2d=True):
"""
if df_t (travel time table) is not None, use travel_time to "recover" temporal node coordinates
"project_2d" is checked only of df_t == None
"""
lats, lons = [], []
X_sp = []
Y_sp = []
is_station = []
stops = []
for idx, (k, v) in enumerate(df_r.loc[route_id].stops.items()):
stops.append(k)
lat = v["lat"]
lon = v["lng"]
lats.append(lat)
lons.append(lon)
if project_2d:
x, y, _, _ = utm.from_latlon(v["lat"], v["lng"]) # 3D project to 2D
else:
x, y = lon + 360 if lon < 0 else lon, v["lat"]
X_sp.append(x)
Y_sp.append(y)
if v["type"] == "Station":
is_station.append(1)
else:
is_station.append(0)
df = pd.DataFrame(
{
"stop": stops,
"latitude": lats,
"longitude": lons,
"is_station": is_station,
"x_sp": X_sp,
"y_sp": Y_sp,
}
)
df = df.sort_values(["is_station"], ascending=False)
df.reset_index(drop=True, inplace=True)
_scale_coordinates(df, space=True, exclude_station=True)
_scale_coordinates(df, space=True, exclude_station=False)
if row is not None:
# TODO need to check "exclude_station" first
dist_matrix = _get_raw_time_matrix(row, df.stop.values)
X_tm, Y_tm = _dist2coord(dist_matrix)
df["x_tm"] = X_tm
df["y_tm"] = Y_tm
_scale_coordinates(df, space=False, exclude_station=False)
_scale_coordinates(df, space=False, exclude_station=True)
return df
def normalise_time_window(df_p, route_id, df_r, df, exclude_station=True):
if exclude_station:
norm_coords = df[df.is_station == 0].loc[:, ["x_tm_norm_wo_st", "y_tm_norm_wo_st"]].values
origin_coords = df[df.is_station == 0].loc[:, ["x_tm", "y_tm"]].values
else:
norm_coords = df.loc[:, ["x_tm_norm_w_st", "y_tm_norm_w_st"]].values
origin_coords = df.loc[:, ["x_tm", "y_tm"]].values
station_node = df[df.is_station == 1].stop[0]
norm_dist_matrix = distance.cdist(norm_coords, norm_coords, "euclidean")
origin_dist_matrix = distance.cdist(origin_coords, origin_coords, "euclidean")
# this produces a matrix where all elements are the same (i.e. scaling factor)
# except the diagonal, so we skip divisioning on the entire matrix
# scf = np.divide(origin_dist_matrix, norm_dist_matrix)
scale_factor = origin_dist_matrix[2, 4] / norm_dist_matrix[2, 4]
r_route = df_r.loc[route_id]
depart_dtstr = r_route.date_YYYY_MM_DD + " " + r_route.departure_time_utc
depart_dto = datetime.strptime(depart_dtstr, "%Y-%m-%d %H:%M:%S")
# assuming the Courier works up to 12 hours a day
scaled_one_day_in_seconds = 12 * 3600 / scale_factor
p_route = df_p.loc[route_id]
st_times = defaultdict(list)
ed_times = defaultdict(list)
st_times_orig = defaultdict(list)
ed_times_orig = defaultdict(list)
svc_times = defaultdict(list)
for col in df_p.columns:
it = p_route[col]
if type(it) is not dict: # TODO - better ways to check NaN value?
continue
for pkg_id, pkg_info in it.items():
svc_time = pkg_info["planned_service_time_seconds"]
if svc_time is not None:
svc_times[col].append(svc_time)
tw = pkg_info["time_window"]
stu = tw["start_time_utc"]
etu = tw["end_time_utc"]
if stu is None:
st_times[col].append(0)
else:
sto = datetime.strptime(stu, "%Y-%m-%d %H:%M:%S")
ttt = (sto - depart_dto).total_seconds() / scale_factor
st_times[col].append(max(ttt, 0))
st_times_orig[col].append(sto)
if etu is None:
ed_times[col].append(scaled_one_day_in_seconds)
else:
edo = datetime.strptime(etu, "%Y-%m-%d %H:%M:%S")
ed_times[col].append((edo - depart_dto).total_seconds() / scale_factor)
ed_times_orig[col].append(edo)
node_to_index_map = {stop: i for i, stop in enumerate(df["stop"])}
# merge window using a strict rule (get the smallest window)
df_len = df.shape[0]
st_times_rt = [0] * df_len
ed_times_rt = [0] * df_len
none_dto = datetime.strptime("1970-01-01 00:00:00", "%Y-%m-%d %H:%M:%S")
st_times_orig_rt = [none_dto] * df_len
ed_times_orig_rt = [none_dto] * df_len
svc_time_list = [0] * df_len
for k, v in svc_times.items():
svc_time_list[node_to_index_map[k]] = np.sum(v) / scale_factor
for k, v in st_times.items():
st_times_rt[node_to_index_map[k]] = np.max(v)
for k, v in ed_times.items():
ed_times_rt[node_to_index_map[k]] = np.min(v)
for k, v in st_times_orig.items():
if len(v) > 0:
st_times_orig_rt[node_to_index_map[k]] = np.max(v)
# datetime.strftime(np.max(v), '%Y-%m-%d %H:%M:%S')
for k, v in ed_times_orig.items():
if len(v) > 0:
ed_times_orig_rt[node_to_index_map[k]] = np.min(v)
# st_times_rt[node_to_index_map[station_node]] = 0
st_times_orig_rt[node_to_index_map[station_node]] = depart_dto
ed_times_rt[node_to_index_map[station_node]] = scaled_one_day_in_seconds
if exclude_station:
df["stt_wo_st"] = st_times_rt
df["edt_wo_st"] = ed_times_rt
df["svc_tm_wo_st"] = svc_time_list
df["tm_sc_wo_st"] = scale_factor
else:
df["stt_w_st"] = st_times_rt
df["edt_w_st"] = ed_times_rt
df["stt_orig"] = st_times_orig_rt
df["edt_orig"] = ed_times_orig_rt
df["svc_tm_w_st"] = svc_time_list
df["tm_sc_w_st"] = scale_factor
# return st_times_rt, ed_times_rt, scale_factor
def _get_raw_time_matrix(row, stops, symmetric=True):
dim_mat = len(stops)
raw_time_matrix = np.zeros((dim_mat, dim_mat))
for i, col1 in enumerate(stops):
for j, col2 in enumerate(stops):
a = row[col1][0][col2] # + svc_time_dict.get(col2, 0)
b = row[col2][0][col1] # + svc_time_dict.get(col1, 0)
if symmetric:
c = np.mean([a, b])
raw_time_matrix[i][j] = c
raw_time_matrix[j][i] = c
else:
raw_time_matrix[i][j] = a
raw_time_matrix[j][i] = b
return raw_time_matrix
def gen_all_routes(df_r, df_p, df_t, output_root_dir):
df_list = []
tt_len = df_r.shape[0]
for ind, route_id in tqdm(enumerate(list(df_r.index)), position=0, leave=True):
row = (df_t.loc[[route_id]]).dropna(axis=1)
df = normalise_route(df_r, route_id, df_p, row)
normalise_time_window(df_p, route_id, df_r, df)
normalise_time_window(df_p, route_id, df_r, df, exclude_station=False)
df.drop(columns=["x_sp", "y_sp", "x_tm", "y_tm"], inplace=True)
df["route_id"] = route_id
new_col_list = [x.replace("_norm", "") for x in list(df.columns)]
df.columns = new_col_list
df_list.append(df)
# if (ind > 10):
# break
print("Concating now")
dfpq = pd.concat(df_list)
tm = int(time.time())
try:
dfpq.to_parquet(os.path.join(output_root_dir, f"lmc_route_full_{tm}.parquet"), index=False)
except:
dfpq.to_csv(os.path.join(output_root_dir, f"lmc_route_full_{tm}.csv"), index=False)
def gen_distance_matrix(
df_r, df_t, out_dir, include_station=False, add_softmax=True
):
"""
Attention distance matrix
exclude the station
"""
for route_id in tqdm(list(df_r.index)):
stops = [k for k, v in df_r.loc[route_id].stops.items() if (v["type"] != "Station")]
if include_station:
station_list = [
k for k, v in df_r.loc[route_id].stops.items() if (v["type"] == "Station")
]
stops = station_list + stops
row = (df_t.loc[[route_id]]).dropna(axis=1)
dist_matrix = _get_raw_time_matrix(row, stops, symmetric=False)
if include_station:
np.save(f"{out_dir}/{route_id}_raw_w_st.npy", dist_matrix)
else:
np.save(f"{out_dir}/{route_id}_raw.npy", dist_matrix)
def get_actual_route_by_route_id(df_act_seq, df_val, route_id, x_col, y_col):
stop_seq_dict = df_act_seq.loc[route_id].values[0]
ranks, idxs = [], []
# df_route = df_val[(df_val.route_id == route_id) & (df_val.is_station == 0)]
df_route = df_val[(df_val.route_id == route_id)].sort_values(
["is_station", "stop"], ascending=(False, True)
)
for idx, k in enumerate(df_route.stop):
ranks.append(stop_seq_dict[k])
idxs.append(idx)
df_route["rank"] = ranks
df_route["ind"] = idxs
X = df_route[[x_col, y_col, "stt_wo_st", "edt_wo_st"]].values
X = X.reshape([1, X.shape[0], X.shape[1]])
return X, df_route
def sort_df_into_sequence(df_route):
df_sequence = df_route.sort_values(["rank"])
target_lat = list(df_sequence.latitude.values[1:])
target_lon = list(df_sequence.longitude.values[1:])
target_lat.append(df_sequence.latitude.values[0])
target_lon.append(df_sequence.longitude.values[0])
df_sequence["tlat"] = target_lat
df_sequence["tlon"] = target_lon
return df_sequence
def get_actual_zone(df_val, df_act_seq, df_r, data_dir, mode):
he_set = set()
# only pick sequences with Hith or Medium quality
for idx, item in df_r.iterrows():
if (item.route_score != 'Low'):
he_set.add(idx)
lines = []
fline = "route_id,zone_penalty,zone_seq,full_zone_seq"
lines.append(fline)
for route_id in df_val.route_id.unique():
if route_id not in he_set:
continue
_, df_route = get_actual_route_by_route_id(
df_act_seq, df_val, route_id, "x_sp_wo_st", "y_sp_wo_st"
)
df_sequence = sort_df_into_sequence(df_route)
so_list = df_sequence.ind.values
zfn = f"{data_dir}/zone_list/{route_id}_zone_w_st.joblib"
zone_list = joblib.load(zfn)
actual_zone = [zone_list[x] for x in so_list]
zone_seq = []
full_zone_seq = []
last_zone = None
for zone in actual_zone:
if (zone != last_zone):
zone_seq.append(zone)
last_zone = zone
full_zone_seq.append(zone)
zone_seq_str = '|'.join(zone_seq)
full_zone_seq_str = '|'.join(full_zone_seq)
fline = ','.join([route_id, zone_seq_str, full_zone_seq_str])
lines.append(fline)
lines_str = os.linesep.join(lines)
output_fn = os.path.join(data_dir, 'zone_list', f'actual_zone-{mode}.csv')
with open(output_fn, "w") as fout:
fout.write(lines_str)
print(f'File saved to {output_fn}')
def gen_zone_list(df_r, out_dir="data/zone_list"):
os.makedirs(out_dir, exist_ok=True)
for route_id in list(df_r.index):
stops = []
coords = []
zones = []
none_zones = []
# In Python 3 - df_r.loc[route_id].stops.items() will keep stop sorted alphabetically
# which happens to be its insertion order, this sorting is inline with `get_route_with_station()`
# in the file ro/demo/first_call.py, this is quite important (and rather brittle)
for idx, (stop, v) in enumerate(df_r.loc[route_id].stops.items()):
coord = [v['lat'], v['lng']]
zone = v['zone_id']
if v["type"] == "Station":
stops.insert(0, stop)
coords.insert(0, coord)
zones.insert(0, 'stz')
else:
stops.append(stop)
coords.append(coord)
zones.append(zone)
for idx, z in enumerate(zones):
if z is None:
none_zones.append(idx)
for idx in none_zones:
x = np.array(coords[idx]).reshape([1, 2])
y = np.array(coords)
result = haversine_distances(np.radians(x), np.radians(y))[0]
sort_idx = np.argsort(result)
for nn_idx in sort_idx: # nn -> nearest neighbour
nn_zone = zones[nn_idx]
if nn_zone is None or nn_zone == 'stz':
continue
zones[idx] = nn_zone
break
joblib.dump(zones, f"{out_dir}/{route_id}_zone_w_st.joblib")
#break # debug break
if __name__ == "__main__":
"""
The structure of the data folder and how each action is related to some sub-folders
└── {train_or_eval_data_dir} # e.g. almrrc2021-data-training OR almrrc2021-data-evaluation
├── distance_matrix/ # a directory with all distance matrix files
│ ├── {route_id_0}_raw_w_st.npy # distance matrix file produced by action `gen_dist_mat`
│ ├── ...
│ └── {route_id_N}_raw_w_st.npy
├── model_apply_inputs/
├── model_apply_outputs/
├── model_build_inputs/
├── model_score_inputs/
├── model_score_outputs/
├── processed/ # output processed parquet file produced by action `gen_route`
└── zone_list # A directory with all zone files
├── {route_id_0}_zone_w_st.joblib # zone file produced produced by action `gen_zone_list`
├── ...
├── {route_id_N}_zone_w_st.joblib
└── actual_zone-{mode}.csv # ground-truth zone sequence file produced by action `gen_actual_zone`
"""
parser = argparse.ArgumentParser()
parser.add_argument("--act", default="gen_route", help="actions i.e. `gen_route`, `gen_dist_mat`, `gen_zone_list`, `gen_actual_zone`")
parser.add_argument("--mode", default="train", help="train or eval")
parser.add_argument("--pkfn", default=None, help="Parquet file name generated by the `gen_route` action")
parser.add_argument("--data_dir", default="data/almrrc2021-data-training",
help="`almrrc2021-data-training` is for training, and `almrrc2021-data-evaluation` is for evaluation")
args = parser.parse_args()
DATA_DIR = args.data_dir
if not os.path.exists(DATA_DIR):
raise Exception(f'{DATA_DIR} not found')
if args.mode.lower() == 'train':
LMC_ACTUAL_FN = f"{DATA_DIR}/model_build_inputs/actual_sequences.json"
LMC_PACKAGE_FN = f"{DATA_DIR}/model_build_inputs/package_data.json"
LMC_ROUTE_FN = f"{DATA_DIR}/model_build_inputs/route_data.json"
LMC_TRAVEL_TIME_FN = f"{DATA_DIR}/model_build_inputs/travel_times.json"
else:
LMC_ACTUAL_FN = f"{DATA_DIR}/model_score_inputs/eval_actual_sequences.json"
LMC_PACKAGE_FN = f"{DATA_DIR}/model_apply_inputs/eval_package_data.json"
LMC_ROUTE_FN = f"{DATA_DIR}/model_apply_inputs/eval_route_data.json"
LMC_TRAVEL_TIME_FN = f"{DATA_DIR}/model_apply_inputs/eval_travel_times.json"
print(LMC_ROUTE_FN)
print("Reading Route file...", end='', flush=True)
df_r = pd.read_json(LMC_ROUTE_FN).T
print('done')
if "gen_route" == args.act:
print("Reading travel time file...", end='', flush=True)
df_t = pd.read_json(LMC_TRAVEL_TIME_FN).T
print('done')
print("Reading package file...", end='', flush=True)
df_p = pd.read_json(LMC_PACKAGE_FN).T
print('done')
gen_all_routes(df_r, df_p, df_t, DATA_DIR)
elif "gen_dist_mat" == args.act:
print("Reading travel time file...", end='', flush=True)
df_t = pd.read_json(LMC_TRAVEL_TIME_FN).T
print("done")
dist_mat_dir = os.path.join(DATA_DIR, "distance_matrix")
if (not os.path.exists(dist_mat_dir)):
os.mkdir(dist_mat_dir)
gen_distance_matrix(
df_r, df_t, out_dir=dist_mat_dir, include_station=True, add_softmax=False
)
elif "gen_zone_list" == args.act:
zone_dir = os.path.join(DATA_DIR, "zone_list")
if (not os.path.exists(zone_dir)):
os.mkdir(zone_dir)
gen_zone_list(df_r, out_dir=zone_dir)
elif "gen_actual_zone" == args.act:
if args.pkfn is None:
raise Exception("Please use argument '--pkfn' to specify the name of the parquet file (e.g. lmc_route_full_1637316909.parquet) generated by the gen_route action")
zdir = os.path.join(DATA_DIR, "zone_list")
if not os.path.exists(zdir):
raise Exception('Missing zone directory, please run the gen_zone_list action first')
df_val = pd.read_parquet(f'{DATA_DIR}/{args.pkfn}')
df_act_seq = pd.read_json(LMC_ACTUAL_FN).T
get_actual_zone(df_val, df_act_seq, df_r, DATA_DIR, args.mode)