def a_valid_RetroHotSpot():
r = testmod.RetroHotSpot()
r.weight = TestWeight()
r.data = open_cp.TimedPoints.from_coords([
datetime.datetime(2017, 3, 1),
datetime.datetime(2017, 3, 2),
datetime.datetime(2017, 3, 3)
], [50, 100, 125], [50, 100, 25])
return r
def test_RetroHotSpot_single_event():
r = testmod.RetroHotSpot()
r.weight = TestWeight()
r.data = open_cp.TimedPoints.from_coords([datetime.datetime(2017, 3, 1)],
[50], [60])
prediction = r.predict()
assert (prediction.risk(50, 60) == 1)
assert (prediction.risk(0, 10) == 1)
assert (prediction.risk(0, 9) == 0)
def runRhsModel(training_data, grid, bandwidth=250, rand_seed=None):
# Set RNG seed if given
if rand_seed != None:
np.random.seed(rand_seed)
grid_cells = getRegionCells(grid)
# Obtain model and prediction on grid cells
rhs_pred = retro.RetroHotSpot()
rhs_pred.data = training_data
rhs_pred.weight = retro.Quartic(bandwidth=bandwidth)
rhs_risk = rhs_pred.predict()
rhs_grid_risk = open_cp.predictors.grid_prediction(rhs_risk, grid)
rhs_grid_risk_matrix = rhs_grid_risk.intensity_matrix
# Sort cellcoords by risk in intensity matrix, highest risk first
return sortCellsByRiskMatrix(grid_cells, rhs_grid_risk_matrix)
def averageRhsModels(training_data, grid, bandwidth=250, num_runs=1, \
rand_seed_list = None):
#
if rand_seed_list == None:
rand_seed_list = range(num_runs)
else:
num_runs = len(rand_seed_list)
grid_cells = getRegionCells(grid)
# Obtain model and prediction on grid cells
rhs_pred = retro.RetroHotSpot()
rhs_pred.data = training_data
rhs_pred.weight = retro.Quartic(bandwidth=bandwidth)
rhs_risk = rhs_pred.predict()
matrix_collection = []
for rand_seed in rand_seed_list:
np.random.seed(rand_seed)
rhs_grid_risk = open_cp.predictors.grid_prediction(rhs_risk, grid)
rhs_grid_risk_matrix = rhs_grid_risk.intensity_matrix
matrix_collection.append(rhs_grid_risk_matrix)
master_matrix = sum(matrix_collection)
for m in matrix_collection:
print(m[12:20, 12:20])
print(master_matrix[12:20, 12:20])
for m in matrix_collection:
print(sortCellsByRiskMatrix(grid_cells, m)[:8])
print(sortCellsByRiskMatrix(grid_cells, master_matrix)[:8])
sys.exit(0)
# Sort cellcoords by risk in intensity matrix, highest risk first
return sortCellsByRiskMatrix(grid_cells, master_matrix)
