def hyper_pytorch_ml(udf_data: UdfData):
"""Apply a pre-trained pytorch machine learn model on a hypercube
The model must be a pytorch model that has expects the input data in the constructor
The prediction method must accept a torch.autograd.Variable as input.
Args:
udf_data (UdfData): The UDF data object that hypercubes and vector tiles
Returns:
This function will not return anything, the UdfData object "udf_data" must be used to store the resulting
data.
"""
cube = udf_data.get_datacube_list()[0]
# This is the input data of the model.
input = torch.autograd.Variable(torch.Tensor(cube.array.values))
# Get the first model
mlm = udf_data.get_ml_model_list()[0]
m = mlm.get_model()
# Predict the data
pred = m(input)
result = xarray.DataArray(data=pred.detach().numpy(),
dims=cube.array.dims,
coords=cube.array.coords,
name=cube.id + "_pytorch")
# Create the new raster collection tile
result_cube = DataCube(array=result)
# Insert the new hypercube in the input object.
udf_data.set_datacube_list([result_cube])
def rct_sklearn_ml(udf_data: UdfData):
"""Apply a pre-trained sklearn machine learn model on RED and NIR tiles
The model must be a sklearn model that has a prediction method: m.predict(X)
The prediction method must accept a pandas.DataFrame as input.
Tiles with ids "red" and "nir" are required. The machine learn model will be applied to all spatio-temporal pixel
of the two input raster collections.
Args:
udf_data (UdfData): The UDF data object that contains raster and vector tiles
Returns:
This function will not return anything, the UdfData object "udf_data" must be used to store the resulting
data.
"""
red = None
nir = None
# Iterate over each cube
for cube in udf_data.get_datacube_list():
if "red" in cube.id.lower():
red = cube
if "nir" in cube.id.lower():
nir = cube
if red is None:
raise Exception("Red data cube is missing in input")
if nir is None:
raise Exception("Nir data cube is missing in input")
# We need to reshape the data for prediction into one dimensional arrays
three_dim_shape = red.array.shape
one_dim_shape = numpy.prod(three_dim_shape)
red_reshape = red.array.values.reshape((one_dim_shape))
nir_reshape = nir.array.values.reshape((one_dim_shape))
# This is the input data of the model. It must be trained with a DataFrame using the same names.
X = pandas.DataFrame()
X["red"] = red_reshape
X["nir"] = nir_reshape
# Get the first model
mlm = udf_data.get_ml_model_list()[0]
m = mlm.get_model()
# Predict the data
pred = m.predict(X)
# Reshape the one dimensional predicted values to three dimensions based on the input shape
pred_reshape = pred.reshape(three_dim_shape)
result = xarray.DataArray(data=pred_reshape, dims=red.array.dims,
coords=red.array.coords, name=red.id + "_pytorch")
# Create the new raster collection cube
h = DataCube(array=result)
# Insert the new hypercubes in the input object. The new tiles will
# replace the original input tiles.
udf_data.set_datacube_list([h, ])