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Mitch predict_xr #1270

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merged 6 commits into from
Oct 9, 2024
Merged

Mitch predict_xr #1270

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72bb55f
add probability array output to predict_xr
mitchest Sep 30, 2024
507f53b
predict_xr at proba_max args
mitchest Sep 30, 2024
9a00722
predict_xr match arg names
mitchest Sep 30, 2024
8d11aa6
xr_predict deal with multiband prob outout
mitchest Sep 30, 2024
c50ff73
xr_predict merge output probs
mitchest Sep 30, 2024
a6e937f
clean up comments and spacing
mitchest Sep 30, 2024
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47 changes: 35 additions & 12 deletions Tools/dea_tools/classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -226,6 +226,7 @@ def predict_xr(
chunk_size=None,
persist=False,
proba=False,
max_proba=True,
clean=False,
return_input=False,
):
Expand Down Expand Up @@ -255,6 +256,11 @@ def predict_xr(
distributed RAM.
proba : bool
If True, predict probabilities
max_proba : bool
If True, the probabilities array will be flattened to contain
only the probabiltiy for the "Predictions" class. If False,
the "Probabilities" object will be an array of prediction
probaiblities for each classes
clean : bool
If True, remove Infs and NaNs from input and output arrays
return_input : bool
Expand Down Expand Up @@ -282,7 +288,7 @@ def predict_xr(
input_xr.chunks["y"][0]
)

def _predict_func(model, input_xr, persist, proba, clean, return_input):
def _predict_func(model, input_xr, persist, proba, max_proba, clean, return_input):
x, y, crs = input_xr.x, input_xr.y, input_xr.geobox.crs

input_data = []
Expand Down Expand Up @@ -330,18 +336,35 @@ def _predict_func(model, input_xr, persist, proba, clean, return_input):
print(" probabilities...")
out_proba = model.predict_proba(input_data_flattened)

# convert to %
out_proba = da.max(out_proba, axis=1) * 100.0
# return either one band with the max probability, or the whole probability array
if max_proba == True:
print(" returning single probability band.")
out_proba = da.max(out_proba, axis=1) * 100.0
out_proba = out_proba.reshape(len(y), len(x))
out_proba = xr.DataArray(
out_proba, coords={"x": x, "y": y}, dims=["y", "x"]
)
output_xr["Probabilities"] = out_proba
else:
print(" returning class probability array.")
out_proba = out_proba * 100.0
class_names = model.classes_ # Get the unique class names from the fitted classifier

# Loop through each class (band)
probabilities_dataset = xr.Dataset()
for i, class_name in enumerate(class_names):
reshaped_band = out_proba[:, i].reshape(len(y), len(x))
reshaped_da = xr.DataArray(
reshaped_band, coords={"x": x, "y": y}, dims=["y", "x"]
)
probabilities_dataset[f"prob_{class_name}"] = reshaped_da

# merge in the probabilities
output_xr = xr.merge([output_xr, probabilities_dataset])

if clean == True:
out_proba = da.where(da.isfinite(out_proba), out_proba, 0)

out_proba = out_proba.reshape(len(y), len(x))

out_proba = xr.DataArray(
out_proba, coords={"x": x, "y": y}, dims=["y", "x"]
)
output_xr["Probabilities"] = out_proba


if return_input == True:
print(" input features...")
Expand Down Expand Up @@ -391,12 +414,12 @@ def _predict_func(model, input_xr, persist, proba, clean, return_input):
model = ParallelPostFit(model)
with joblib.parallel_backend("dask"):
output_xr = _predict_func(
model, input_xr, persist, proba, clean, return_input
model, input_xr, persist, proba, max_proba, clean, return_input
)

else:
output_xr = _predict_func(
model, input_xr, persist, proba, clean, return_input
model, input_xr, persist, proba, max_proba, clean, return_input
).compute()

return output_xr
Expand Down
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