def run(path, verbose=1):
print(path)
trajs = read_geolife(path)
preprocessed = list()
for traj in trajs:
traj_new = time_duplication_filter(traj)
traj_new = speed_filter_abs(traj_new, 300, in_kmh=True)
preprocessed.append(traj_new)
if verbose > 1:
print('Trajectories preprocessed')
df = make_df(preprocessed)
if verbose > 1:
print('Trajectories split at staypoints')
length = len(df)
df = cluster_into_spots(df, init_eps=300, levels=1, threshold=0.2)
if verbose > 1:
print('Trajectories clustered in sps')
ts = df['end_date'].iloc[-1] - df['start_date'].iloc[0]
train_weeks = int((ts.days / 7) * 4/5)
duration = timedelta(days=7 * train_weeks)
train_start = df['start_date'].iloc[0]
train_end = train_start + duration
train = df[train_start:train_end]
test = df[train_end:]
dates = list()
for i in test.index:
if len(dates) > 1:
if dates[-1] == (i.month, i.day):
continue
dates.append((i.month, i.day))
ts = TimeSensitiveMostFrequentRoute()
ts.fit(train)
preds = ts.predict_proba(test)
ranks = list()
for i in range(len(preds)):
correct = False
for j, pred in enumerate(preds[i].index):
if pred == test['end_cluster'].iloc[i]:
ranks.append((j + 1, len(preds[i])))
correct = True
break
if not correct:
ranks.append((None, len(preds[i])))
if verbose > 0:
print('Average rank/pred', np.mean(ranks, axis=0))
print('Total predictions:', len(test))
return ranks, len(test)
def prepare(path, verbose=1):
print(path)
trajs = read_geolife(path)
preprocessed = list()
for traj in trajs:
traj_new = time_duplication_filter(traj)
traj_new = speed_filter_abs(traj_new, 300, in_kmh=True)
preprocessed.append(traj_new)
if verbose > 1:
print('Trajectories preprocessed')
df = make_df(preprocessed)
if verbose > 1:
print('Trajectories split at staypoints')
df = kmeans_cluster_into_spots(df, 20, 100)
if verbose > 1:
print('Trajectories clustered in sps')
def run(path, test_traj_len, predict_after_frac=100, verbose=1):
print(path)
trajs = read_geolife(path)
preprocessed = list()
for traj in trajs:
traj_new = time_duplication_filter(traj)
traj_new = speed_filter_abs(traj_new, 300, in_kmh=True)
preprocessed.append(traj_new)
if verbose > 1:
print('Trajectories preprocessed')
df = make_df(preprocessed)
if verbose > 1:
print('Trajectories split at staypoints')
length = len(df)
df = cluster_into_spots(df, init_eps=300, levels=0, threshold=0.2)
if verbose > 1:
print('Trajectories clustered in sps')
ts = df['end_date'].iloc[-1] - df['start_date'].iloc[0]
train_weeks = int((ts.days / 7) * 4/5)
duration = timedelta(days=7 * train_weeks)
train_start = df['start_date'].iloc[0]
train_end = train_start + duration
train = df[train_start:train_end]
test = df[train_end:]
test_trajs = get_test_trajs(test, preprocessed)
kse = KnownStartEstimator()
kse = kse.fit(train)
filtered_test_trajs = list()
for traj in test_trajs:
if len(traj) >= test_traj_len:
filtered_test_trajs.append(traj)
if verbose > 1:
print(len(filtered_test_trajs), "of", len(test_trajs), "have the minimal length of", test_traj_len)
res = dict()
for traj in filtered_test_trajs:
res[traj[-1]] = kse.predict_proba(traj[:predict_after_frac])
ranks = list()
for p in res:
predictions = res[p]
truth = resolve_endcluster(train, p)
if predictions is None or len(predictions) < 1:
ranks.append((None, None))
else:
ranked = False
for i in range(len(predictions)):
if predictions.index[i] == truth:
ranks.append((i+1, len(predictions)))
ranked = True
break
if not ranked:
ranks.append((None, len(predictions)))
return ranks
def run(path, verbose=1):
print(path)
trajs = read_geolife(path)
if len(trajs) < 30:
return None
preprocessed = list()
for traj in trajs:
traj_new = time_duplication_filter(traj)
traj_new = speed_filter_abs(traj_new, 300, in_kmh=True)
preprocessed.append(traj_new)
if verbose > 1:
print('Trajectories preprocessed')
df = make_df(preprocessed)
if verbose > 1:
print('Trajectories split at staypoints')
df = agglomerative_cluster_into_spots(df, 20, 100)
if verbose > 1:
print('Trajectories clustered in sps')
ts = df['end_date'].iloc[-1] - df['start_date'].iloc[0]
train_weeks = int((ts.days / 7) * 4 / 5)
duration = timedelta(days=7 * train_weeks)
train_start = df['start_date'].iloc[0]
train_end = train_start + duration
train = df[train_start:train_end]
test = df[train_end:]
dates = list()
for i in test.index:
if len(dates) > 1:
if dates[-1] == (i.month, i.day):
continue
dates.append((i.month, i.day))
bwe = FrequentistEstimator()
bwe = bwe.fit(train)
counter_no_prob = 0
ranks = list()
for date in dates:
month, day = date
x = pd.DataFrame(
data=[[49.475752, 8.482531]],
index=pd.DatetimeIndex(
[pd.Timestamp("2008-{}-{} 19:45:21".format(month, day))]),
columns=['lat', 'lon'])
if verbose > 2:
print(x)
pred = bwe.predict_proba(x)
sorted_pred = sorted(pred.items(),
key=operator.itemgetter(1),
reverse=True)
for i, row in df.loc[(df.index.month == month)
& (df.index.day == day)].iterrows():
if verbose > 2:
print((row['start_cluster'], row['end_cluster']))
if (row['start_cluster'], row['end_cluster']) in pred.keys():
if verbose > 2:
print(pred[(row['start_cluster'], row['end_cluster'])])
for j, s_pred in enumerate(sorted_pred):
if s_pred[0] == (row['start_cluster'], row['end_cluster']):
if verbose > 2:
print('Ranked:', j + 1, 'of total', len(pred),
'predictions')
ranks.append((j + 1, len(pred)))
else:
if verbose > 2:
print("no prob")
counter_no_prob += 1
if verbose > 0:
print('Average rank/pred', np.mean(ranks, axis=0))
print('Number of no prob:', counter_no_prob)
print('Total predictions:', len(test))
return ranks, len(test), counter_no_prob
def run(path, verbose=1):
print(path)
trajs = read_geolife(path)
preprocessed = list()
for traj in trajs:
traj_new = time_duplication_filter(traj)
traj_new = speed_filter_abs(traj_new, 300, in_kmh=True)
preprocessed.append(traj_new)
if verbose > 1:
print('Trajectories preprocessed')
df = make_df(preprocessed)
if verbose > 1:
print('Trajectories split at staypoints')
length = len(df)
df = cluster_into_spots(df, init_eps=300, levels=1, threshold=0.2)
if verbose > 1:
print('Trajectories clustered in sps')
ts = df['end_date'].iloc[-1] - df['start_date'].iloc[0]
train_weeks = int((ts.days / 7) * 4 / 5)
duration = timedelta(days=7 * train_weeks)
train_start = df['start_date'].iloc[0]
train_end = train_start + duration
train = df[train_start:train_end]
test = df[train_end:]
ddf = DenseDepartureTimes(0.03)
train_ddt = ddf.fit_transform(train.copy())
bdte = BayesDepartureTimeEstimator()
bdte = bdte.fit(train_ddt)
counter_prob = 0
ranks = list()
for i, row in test.iterrows():
x = pd.DataFrame(data=[[row['start_lat'], row['start_lon']]],
index=[i],
columns=['lat', 'lon'])
pred = bdte.predict_proba(x)
sorted_pred = sorted(pred.items(),
key=operator.itemgetter(1),
reverse=True)
for key in pred.keys():
time_wa, time_wb = bdte.resolve_start_time_cluster(key)
dummydate = date(1970, 1, 1)
# time_wa = (datetime.combine(dummydate,time_wa)-timedelta(hours=1)).time()
# time_wb = (datetime.combine(dummydate,time_wb)+timedelta(hours=1)).time()
if len(x.between_time(time_wa, time_wb)) == 1:
for j, s_pred in enumerate(sorted_pred):
if s_pred[0] == key:
if verbose > 1:
print('Ranked:', j + 1, 'of total', len(pred),
'predictions')
counter_prob += 1
if verbose > 0:
print('Average rank/pred', np.mean(ranks, axis=0))
print('Number of no prob:', counter_prob)
print('Total predictions:', len(test))
return ranks, len(test), counter_prob