def test_stratified_sample_raster():
from pyimpute import load_training_rasters, stratified_sample_raster
# Take a random stratified sample
selected = stratified_sample_raster(response_raster,
target_sample_size=20, min_sample_proportion=0.1)
assert selected.shape == (3849,)
train_xs, train_y = load_training_rasters(response_raster, explanatory_rasters, selected)
assert train_xs.shape == (3849, 7)
assert train_y.shape == (3849,)
def main():
# Define the known data points or "training" data
explanatory_fields = "tmin12c tmax8c p_ph_c pmean_wntrc pmean_sumrc irr_lands gt_demc grwsnc d2u2c".split()
explanatory_rasters = [os.path.join(TRAINING_DIR, r, "hdr.adf") for r in explanatory_fields]
response_raster = os.path.join(TRAINING_DIR, 'iso_zns3-27/hdr.adf')
# Take a random stratified sample
selected = stratified_sample_raster(response_raster,
target_sample_size=20, min_sample_proportion=0.01)
# Load the training rasters using the sampled subset
train_xs, train_y = load_training_rasters(response_raster,
explanatory_rasters, selected)
print(train_xs.shape, train_y.shape)
# Train the classifier
clf = ExtraTreesClassifier(n_estimators=10, n_jobs=1)
clf.fit(train_xs, train_y)
print(clf)
# Cross validate
k = 5
scores = cross_validation.cross_val_score(clf, train_xs, train_y, cv=k)
print("%d-fold Cross Validation Accuracy: %0.2f (+/- %0.2f)" % (k, scores.mean() * 100, scores.std() * 200))
# ... Other model assessment
# Run the model on the current data; i.e. predict itself
print("Imputing response rasters FOR CURRENT DATA")
target_xs, raster_info = load_targets(explanatory_rasters)
impute(target_xs, clf, raster_info, outdir="_aez_output_current",
linechunk=400, class_prob=True, certainty=True)
sys.exit()
years = ['2070s']
for year in years:
print("Loading target explanatory raster data, swapping out for %s climate data" % year)
fdir = os.path.join(TRAINING_DIR, "../RCP85/%s/" % year)
# swap out datasets that are predicted to change over time (i.e the climate data only)
climate_rasters = "grwsnc pmean_sumrc pmean_wntrc tmax8c tmin12c".split()
new_explanatory_rasters = OrderedDict(zip(explanatory_fields, explanatory_rasters))
for cr in climate_rasters:
new_explanatory_rasters[cr] = fdir + cr + "/hdr.adf"
target_xs, raster_info = load_targets(new_explanatory_rasters.values())
print("Imputing response rasters")
impute(target_xs, clf, raster_info, outdir="_aez_output_%s" % year,
linechunk=40, class_prob=True, certainty=True)
def test_stratified_sample_raster():
from pyimpute import load_training_rasters, stratified_sample_raster
# Take a random stratified sample
selected = stratified_sample_raster(response_raster,
target_sample_size=20,
min_sample_proportion=0.1)
assert selected.shape == (3849, )
train_xs, train_y = load_training_rasters(response_raster,
explanatory_rasters, selected)
assert train_xs.shape == (3849, 7)
assert train_y.shape == (3849, )
