def main():
# Define the known data points or "training" data
explanatory_fields = "d100 dd0 dd5 fday ffp gsdd5 gsp map mat_tenths mmax_tenths mmindd0 mmin_tenths mtcm_tenths mtwm_tenths sday".split()
explanatory_rasters = [os.path.join(TRAINING_DIR, "current_" + r + ".img") for r in explanatory_fields]
response_shapes = os.path.join(TRAINING_DIR, "DF.shp")
# Load the training rasters using the sampled subset
try:
cached = json.load(open("_cached_training.json"))
train_xs = np.array(cached['train_xs'])
train_y = np.array(cached['train_y'])
except IOError:
train_xs, train_y = load_training_vector(response_shapes,
explanatory_rasters, response_field='GRIDCODE')
cache = {'train_xs': train_xs.tolist(), 'train_y': train_y.tolist()}
with open("_cached_training.json", 'w') as fh:
fh.write(json.dumps(cache))
print(train_xs.shape, train_y.shape)
# Train the classifier
clf = ExtraTreesClassifier(n_estimators=120, n_jobs=3)
clf.fit(train_xs, train_y)
print(clf)
evaluate_clf(clf, train_xs, train_y, feature_names=explanatory_fields)
def main():
# Define the known data points or "training" data
explanatory_fields = "d100 dd0 dd5 fday ffp gsdd5 gsp map mat_tenths mmax_tenths mmindd0 mmin_tenths mtcm_tenths mtwm_tenths sday".split(
)
explanatory_rasters = [
os.path.join(TRAINING_DIR, "current_" + r + ".img")
for r in explanatory_fields
]
response_shapes = os.path.join(TRAINING_DIR, "DF.shp")
# Load the training rasters using the sampled subset
try:
cached = json.load(open("_cached_training.json"))
train_xs = np.array(cached['train_xs'])
train_y = np.array(cached['train_y'])
except IOError:
train_xs, train_y = load_training_vector(response_shapes,
explanatory_rasters,
response_field='GRIDCODE')
cache = {'train_xs': train_xs.tolist(), 'train_y': train_y.tolist()}
with open("_cached_training.json", 'w') as fh:
fh.write(json.dumps(cache))
print(train_xs.shape, train_y.shape)
# Train the classifier
clf = ExtraTreesClassifier(n_estimators=120, n_jobs=3)
clf.fit(train_xs, train_y)
print(clf)
evaluate_clf(clf, train_xs, train_y, feature_names=explanatory_fields)
def test_load_training_vector():
from pyimpute import load_training_vector
response_shapes = os.path.join(DATA, "points.geojson")
train_xs, train_y = load_training_vector(response_shapes,
explanatory_rasters, response_field='ZONE')
assert train_xs.shape == (245, 7)
assert train_y.shape == (245, )
def test_load_training_vector():
from pyimpute import load_training_vector
response_shapes = os.path.join(DATA, "points.geojson")
train_xs, train_y = load_training_vector(response_shapes,
explanatory_rasters,
response_field='ZONE')
assert train_xs.shape == (250, 7)
assert train_y.shape == (250, )
def main():
# Define the known data points or "training" data
explanatory_fields = "d100 dd0 dd5 fday ffp gsdd5 gsp map mat_tenths mmax_tenths mmindd0 mmin_tenths mtcm_tenths mtwm_tenths sday".split()
explanatory_rasters = [os.path.join(TRAINING_DIR, "current_" + r + ".img") for r in explanatory_fields]
response_shapes = os.path.join(TRAINING_DIR, "DF.shp")
# Load the training rasters using the sampled subset
try:
cached = json.load(open("_cached_training.json"))
train_xs = np.array(cached['train_xs'])
train_y = np.array(cached['train_y'])
except IOError:
train_xs, train_y = load_training_vector(response_shapes,
explanatory_rasters, response_field='GRIDCODE')
cache = {'train_xs': train_xs.tolist(), 'train_y': train_y.tolist()}
with open("_cached_training.json", 'w') as fh:
fh.write(json.dumps(cache))
print(train_xs.shape, train_y.shape)
# Train the classifier
clf = ExtraTreesClassifier(n_estimators=120, n_jobs=3)
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))
# Run the model on the current data; i.e. predict current conditions
print("Imputing response rasters FOR CURRENT DATA")
target_xs, raster_info = load_targets(explanatory_rasters)
impute(target_xs, clf, raster_info, outdir="_usfs_output_current",
linechunk=400, class_prob=True, certainty=True)
years = ['2060']
for year in years:
print("Loading target explanatory raster data, swapping out for %s climate data" % year)
# Swap out for future climate rasters
new_explanatory_rasters = [os.path.join(TRAINING_DIR, "Ensemble_rcp60_y%s_%s.img" % (year, r))
for r in explanatory_fields]
target_xs, raster_info = load_targets(new_explanatory_rasters)
print("Imputing response rasters")
impute(target_xs, clf, raster_info, outdir="_usfs_output_%s" % year,
linechunk=400, class_prob=True, certainty=True)
def main():
# Define the known data points or "training" data
explanatory_fields = "d100 dd0 dd5 fday ffp gsdd5 gsp map mat_tenths mmax_tenths mmindd0 mmin_tenths mtcm_tenths mtwm_tenths sday".split(
)
explanatory_rasters = [
os.path.join(TRAINING_DIR, "current_" + r + ".img")
for r in explanatory_fields
]
response_shapes = os.path.join(TRAINING_DIR, "DF.shp")
# Load the training rasters using the sampled subset
try:
cached = json.load(open("_cached_training.json"))
train_xs = np.array(cached['train_xs'])
train_y = np.array(cached['train_y'])
except IOError:
train_xs, train_y = load_training_vector(response_shapes,
explanatory_rasters,
response_field='GRIDCODE')
cache = {'train_xs': train_xs.tolist(), 'train_y': train_y.tolist()}
with open("_cached_training.json", 'w') as fh:
fh.write(json.dumps(cache))
print(train_xs.shape, train_y.shape)
# Train the classifier
clf = ExtraTreesClassifier(n_estimators=120, n_jobs=3)
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))
# Run the model on the current data; i.e. predict current conditions
print("Imputing response rasters FOR CURRENT DATA")
target_xs, raster_info = load_targets(explanatory_rasters)
impute(target_xs,
clf,
raster_info,
outdir="_usfs_output_current",
linechunk=400,
class_prob=True,
certainty=True)
years = ['2060']
for year in years:
print(
"Loading target explanatory raster data, swapping out for %s climate data"
% year)
# Swap out for future climate rasters
new_explanatory_rasters = [
os.path.join(TRAINING_DIR, "Ensemble_rcp60_y%s_%s.img" % (year, r))
for r in explanatory_fields
]
target_xs, raster_info = load_targets(new_explanatory_rasters)
print("Imputing response rasters")
impute(target_xs,
clf,
raster_info,
outdir="_usfs_output_%s" % year,
linechunk=400,
class_prob=True,
certainty=True)
