def __set_trained(self, dictContent): Verbose.print_arguments() modelContent = ModelContent() for kd,d in dictContent.items(): modelContent.add(kd, d) self.trainedModel.new(modelContent, self.wind_dim, self.other_dim, self.humidity_dim, self.nb_altitudes, self.model_type)
def set_trained(self, cells, super_resolution=1, load_weights=True):
Verbose.print_arguments()
model_content = ModelContent()
if self.model_type == ModelType.CELLS:
model_content.set_super_resolution(super_resolution)
for c in cells:
if self.model_type == ModelType.SPOTS:
# All the spots of the cells
model_content.add(c, [s for s in range(len(self.Y_spots[c]))])
else:
# All the cell
model_content.add(c, -1)
# Maybe the cell has no spots, which will crash at network creation
if self.model_type == ModelType.SPOTS and model_content.total_nb_spots(
) == 0:
return False
#===================================================================
# Create model and load weights
#===================================================================
tf.keras.backend.clear_session(
) # https://github.com/keras-team/keras/issues/3579
self.trained_model.new(model_content,
wind_dim=self.wind_dim,
other_dim=self.X_other[0].shape[-1],
humidity_dim=self.X_humidity[0].shape[-1],
nb_altitudes=self.X_wind[0].shape[-1] //
self.wind_dim,
model_type=self.model_type)
# Re-load shared and specific weights if exists
if load_weights:
self.trained_model.load_all_weights()
#===================================================================
# Model intputs/outputs
#===================================================================
self.all_X = self.__get_X(model_content)
self.all_Y = self.__get_Y(model_content)
return True
def __compute_spots_forecasts(self, models_directory, problem_formulation,
lats, lons, meteo_matrix, filename):
Verbose.print_arguments()
predict = Predict(models_directory, ModelType.SPOTS,
problem_formulation)
predict.set_meteo_data(meteo_matrix, GfsData().parameters_vector_all)
#=============================================================
# depend de GribReader.get_values_array(self, params, crops):
forecastCellsLine = {}
line = 0
for crop in self.crops:
for iLat in range(crop[0], crop[1]):
for iLon in range(crop[2], crop[3]):
forecastCellsLine[(iLat, iLon)] = line
line += 1
#=============================================================
# Compute or load cells_and_spots
#=============================================================
filename_cells_and_spots = "Forecast_cellsAndSpots_" + "_".join(
[str(crop[d]) for crop in self.crops for d in range(4)])
if not BinObj.exists(filename_cells_and_spots):
Verbose.print_text(
0,
"Generating precomputation file because of new crop, it may crash on the server... To be computed on my computer"
)
cells_and_spots = {}
# find forecast cell for each spot
# C'est comme le cellsAndSpots de Train sauf que les cellules sont les cells de forecast (32942 cells)
spots_data = SpotsData()
spots = spots_data.getSpots(range(80))
for kc, cell_spots in enumerate(spots):
for ks, spot in enumerate(cell_spots):
iCell = (np.abs(lats - spot.lat).argmin(),
np.abs(lons - spot.lon).argmin())
cellLine = forecastCellsLine[iCell]
kcks_spot = (
(kc, ks), spot.toDict()) # (training cell, ks)
if not cellLine in cells_and_spots:
cells_and_spots[cellLine] = [kcks_spot]
else:
cells_and_spots[cellLine] += [kcks_spot]
BinObj.save(cells_and_spots, filename_cells_and_spots)
else:
cells_and_spots = BinObj.load(filename_cells_and_spots)
#=============================================================
# Create a model with 1 cell of 1 spot
#=============================================================
predict.set_trained_spots()
#=============================================================
# Compute prediction for each spot, one by one
#=============================================================
spots_and_prediction = []
for kcslst, cslst in enumerate(cells_and_spots.items()):
meteoLine, spotsLst = cslst
for cs in spotsLst:
modelContent = ModelContent()
modelContent.add(cs[0][0], cs[0][1])
predict.trainedModel.load_all_weights(
modelContent) # TODO do not reload shared weights
predict.set_prediction_population()
NN_X = predict.get_X([meteoLine])
prediction = predict.trainedModel.model.predict(NN_X)[0][0]
spots_and_prediction += [
Spot((cs[1]['name'], cs[1]['lat'], cs[1]['lon']),
cs[1]['id'], cs[1]['nbFlights'], prediction)
] #[cs[1]]
#=============================================================
# Export all results
#=============================================================
Forecast.__export_spots_forecasts(spots_and_prediction, filename)