"TRIP_ID", "START_POINT", "GRID_POLYLINE",
"TRUNC_GRID_POLYLINE"
],
converters={
"START_POINT": lambda x: eval(x),
"GRID_POLYLINE": lambda x: eval(x),
"TRUNC_GRID_POLYLINE": lambda x: eval(x)
})
# Loop through every trip to make a prediction
for nr, partial_trip in validation.iterrows():
# Select the last segment of the partial trip
end = partial_trip.TRUNC_GRID_POLYLINE[-k:]
# Create a DestinationGrid object
posterior = DestinationGrid(N, M)
# Match this segment with trips in the training set
for i, chunk in enumerate(train):
# Only look at trips that start from the same position
trips = chunk[chunk.START_CELL == partial_trip.GRID_POLYLINE[0]]
for idx, row in trips.iterrows():
if match(end, row.GRID_POLYLINE, k):
destination = row.GRID_POLYLINE[-1]
posterior._table[destination] += 1
posterior.normalizeProbs()
# Export the probabilities
dests = [id_to_nr(dest) for dest in posterior]
{ #"TIMESTAMP" : lambda x: datetime.datetime.fromtimestamp(x),
"POLYLINE": lambda x: json.loads(x),
"START_POINT": lambda x: eval(x),
"GRID_POLYLINE": lambda x: eval(x),
"TRUNC_POLYLINE": lambda x: eval(x),
"TRUNC_GRID_POLYLINE": lambda x: eval(x)
})
# Select a partial trip
partial_trip = test.loc[0]
# Select the last segment of the partial trip
end = partial_trip.TRUNC_GRID_POLYLINE[-k:]
# Create a DestinationGrid object
grid = DestinationGrid(N, M)
# Match this segment with trips in the training set
for i, chunk in enumerate(trips):
for idx, row in chunk.iterrows():
if match(end, row.GRID_POLYLINE, k):
dest = row.GRID_POLYLINE[-1]
grid._table[dest] += 1
print "Processed chunk %d" % i
grid.normalizeProbs()
# Plot the new distribution
plt.subplot(1, 2, 1)
plt.imshow(np.log(grid.as_array() +
