def test_append_with_copy(self):
"""Append another grass raster to a RasterStack not modifying
in_place"""
stack = RasterStack(self.predictors[0:5])
other = stack.append(self.predictors[5], in_place=False)
self.assertListEqual(other.names, self.predictors)
self.assertListEqual(stack.names, self.predictors[0:5])
def test_append_identical(self):
"""Append another grass raster containing a single layer with an
identical name and mapset
Expected that this will fail because the name and mapset is identical
and a RasterStack has index keys based on fullnames
"""
stack = RasterStack(self.predictors)
stack.append(self.predictors[5], in_place=True)
def test_append_diff_mapset(self):
"""Append another grass raster containing a single layer that has an
identical name but is in a different mapset
Expected that this will pass because the name is identical but the
mapset is different and a RasterStack has index keys based on
fullnames
"""
stack = RasterStack(self.predictors)
stack.append(self.other, in_place=True)
def setUp(self) -> None:
"""Setup that is run before every test"""
self.stack = RasterStack(self.predictors)
def main():
try:
import sklearn
if sklearn.__version__ < "0.20":
gs.fatal(
"Package python3-scikit-learn 0.20 or newer is not installed")
except ImportError:
gs.fatal("Package python3-scikit-learn 0.20 or newer is not installed")
try:
import pandas as pd
except ImportError:
gs.fatal("Package python3-pandas 0.25 or newer is not installed")
# parser options ----------------------------------------------------------
group = options["group"]
training_map = options["training_map"]
training_points = options["training_points"]
field = options["field"]
model_save = options["save_model"]
model_name = options["model_name"]
hyperparams = {
"penalty": options["penalty"],
"alpha": options["alpha"],
"l1_ratio": options["l1_ratio"],
"C": options["c"],
"epsilon": options["epsilon"],
"min_samples_leaf": options["min_samples_leaf"],
"n_estimators": options["n_estimators"],
"learning_rate": options["learning_rate"],
"subsample": options["subsample"],
"max_depth": options["max_depth"],
"max_features": options["max_features"],
"n_neighbors": options["n_neighbors"],
"weights": options["weights"],
"hidden_layer_sizes": options["hidden_units"],
}
cv = int(options["cv"])
group_raster = options["group_raster"]
importances = flags["f"]
preds_file = options["preds_file"]
classif_file = options["classif_file"]
fimp_file = options["fimp_file"]
param_file = options["param_file"]
norm_data = flags["s"]
random_state = int(options["random_state"])
load_training = options["load_training"]
save_training = options["save_training"]
n_jobs = int(options["n_jobs"])
balance = flags["b"]
category_maps = option_to_list(options["category_maps"])
# define estimator --------------------------------------------------------
hyperparams, param_grid = process_param_grid(hyperparams)
estimator, mode = predefined_estimators(model_name, random_state, n_jobs,
hyperparams)
# remove dict keys that are incompatible for the selected estimator
estimator_params = estimator.get_params()
param_grid = {
key: value
for key, value in param_grid.items() if key in estimator_params
}
scoring, search_scorer = scoring_metrics(mode)
# checks of input options -------------------------------------------------
if (mode == "classification" and balance is True
and model_name not in check_class_weights()):
gs.warning(model_name + " does not support class weights")
balance = False
if mode == "regression" and balance is True:
gs.warning(
"Balancing of class weights is only possible for classification")
balance = False
if classif_file:
if cv <= 1:
gs.fatal("Output of cross-validation global accuracy requires "
"cross-validation cv > 1")
if not os.path.exists(os.path.dirname(classif_file)):
gs.fatal("Directory for output file {} does not exist".format(
classif_file))
# feature importance file selected but no cross-validation scheme used
if importances:
if sklearn.__version__ < "0.22":
gs.fatal("Feature importances calculation requires scikit-learn "
"version >= 0.22")
if fimp_file:
if importances is False:
gs.fatal(
'Output of feature importance requires the "f" flag to be set')
if not os.path.exists(os.path.dirname(fimp_file)):
gs.fatal("Directory for output file {} does not exist".format(
fimp_file))
# predictions file selected but no cross-validation scheme used
if preds_file:
if cv <= 1:
gs.fatal("Output of cross-validation predictions requires "
"cross-validation cv > 1")
if not os.path.exists(os.path.dirname(preds_file)):
gs.fatal("Directory for output file {} does not exist".format(
preds_file))
# define RasterStack ------------------------------------------------------
stack = RasterStack(group=group)
if category_maps is not None:
stack.categorical = category_maps
# extract training data ---------------------------------------------------
if load_training != "":
X, y, cat, class_labels, group_id = load_training_data(load_training)
if class_labels is not None:
a = pd.DataFrame({"response": y, "labels": class_labels})
a = a.drop_duplicates().values
class_labels = {k: v for (k, v) in a}
else:
gs.message("Extracting training data")
if group_raster != "":
stack.append(group_raster)
if training_map != "":
X, y, cat = stack.extract_pixels(training_map)
y = y.flatten()
with RasterRow(training_map) as src:
if mode == "classification":
src_cats = {v: k for (k, v, m) in src.cats}
class_labels = {k: k for k in np.unique(y)}
class_labels.update(src_cats)
else:
class_labels = None
elif training_points != "":
X, y, cat = stack.extract_points(training_points, field)
y = y.flatten()
if y.dtype in (np.object_, np.object):
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(y)
class_labels = {k: v for (k, v) in enumerate(le.classes_)}
else:
class_labels = None
# take group id from last column and remove from predictors
if group_raster != "":
group_id = X[:, -1]
X = np.delete(X, -1, axis=1)
stack.drop(group_raster)
else:
group_id = None
# check for labelled pixels and training data
if y.shape[0] == 0 or X.shape[0] == 0:
gs.fatal("No training pixels or pixels in imagery group ...check "
"computational region")
from sklearn.utils import shuffle
if group_id is None:
X, y, cat = shuffle(X, y, cat, random_state=random_state)
else:
X, y, cat, group_id = shuffle(X,
y,
cat,
group_id,
random_state=random_state)
if save_training != "":
save_training_data(save_training, X, y, cat, class_labels,
group_id, stack.names)
# cross validation settings -----------------------------------------------
# inner resampling method (cv=2)
from sklearn.model_selection import (GridSearchCV, StratifiedKFold,
GroupKFold, KFold)
if any(param_grid) is True:
if group_id is None and mode == "classification":
inner = StratifiedKFold(n_splits=3)
elif group_id is None and mode == "regression":
inner = KFold(n_splits=3)
else:
inner = GroupKFold(n_splits=3)
else:
inner = None
# outer resampling method (cv=cv)
if cv > 1:
if group_id is None and mode == "classification":
outer = StratifiedKFold(n_splits=cv)
elif group_id is None and mode == "regression":
outer = KFold(n_splits=cv)
else:
outer = GroupKFold(n_splits=cv)
# modify estimators that take sample_weights ------------------------------
if balance is True:
from sklearn.utils import compute_class_weight
class_weights = compute_class_weight(class_weight="balanced",
classes=(y),
y=y)
fit_params = {"sample_weight": class_weights}
else:
class_weights = None
fit_params = {}
# preprocessing -----------------------------------------------------------
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
# standardization
if norm_data is True and category_maps is None:
scaler = StandardScaler()
trans = ColumnTransformer(
remainder="passthrough",
transformers=[("scaling", scaler, np.arange(0, stack.count))],
)
# one-hot encoding
elif norm_data is False and category_maps is not None:
enc = OneHotEncoder(handle_unknown="ignore", sparse=False)
trans = ColumnTransformer(remainder="passthrough",
transformers=[("onehot", enc,
stack.categorical)])
# standardization and one-hot encoding
elif norm_data is True and category_maps is not None:
scaler = StandardScaler()
enc = OneHotEncoder(handle_unknown="ignore", sparse=False)
trans = ColumnTransformer(
remainder="passthrough",
transformers=[
("onehot", enc, stack.categorical),
("scaling", scaler,
np.setxor1d(range(stack.count),
stack.categorical).astype('int')),
],
)
# combine transformers
if norm_data is True or category_maps is not None:
estimator = Pipeline([("preprocessing", trans),
("estimator", estimator)])
param_grid = wrap_named_step(param_grid)
fit_params = wrap_named_step(fit_params)
if any(param_grid) is True:
estimator = GridSearchCV(estimator=estimator,
param_grid=param_grid,
scoring=search_scorer,
n_jobs=n_jobs,
cv=inner)
# estimator training ------------------------------------------------------
gs.message(os.linesep)
gs.message(("Fitting model using " + model_name))
if balance is True and group_id is not None:
estimator.fit(X, y, groups=group_id, **fit_params)
elif balance is True and group_id is None:
estimator.fit(X, y, **fit_params)
else:
estimator.fit(X, y)
# message best hyperparameter setup and optionally save using pandas
if any(param_grid) is True:
gs.message(os.linesep)
gs.message("Best parameters:")
optimal_pars = [
(k.replace("estimator__", "").replace("selection__", "") + " = " +
str(v)) for (k, v) in estimator.best_params_.items()
]
for i in optimal_pars:
gs.message(i)
if param_file != "":
param_df = pd.DataFrame(estimator.cv_results_)
param_df.to_csv(param_file)
# cross-validation --------------------------------------------------------
if cv > 1:
from sklearn.metrics import classification_report
from sklearn import metrics
if (mode == "classification"
and cv > np.histogram(y, bins=np.unique(y))[0].min()):
gs.message(os.linesep)
gs.fatal("Number of cv folds is greater than number of samples in "
"some classes ")
gs.message(os.linesep)
gs.message("Cross validation global performance measures......:")
if (mode == "classification" and len(np.unique(y)) == 2
and all([0, 1] == np.unique(y))):
scoring["roc_auc"] = metrics.roc_auc_score
from sklearn.model_selection import cross_val_predict
preds = cross_val_predict(estimator,
X,
y,
group_id,
cv=outer,
n_jobs=n_jobs,
fit_params=fit_params)
test_idx = [test for train, test in outer.split(X, y)]
n_fold = np.zeros((0, ))
for fold in range(outer.get_n_splits()):
n_fold = np.hstack((n_fold, np.repeat(fold,
test_idx[fold].shape[0])))
preds = {"y_pred": preds, "y_true": y, "cat": cat, "fold": n_fold}
preds = pd.DataFrame(data=preds,
columns=["y_pred", "y_true", "cat", "fold"])
gs.message(os.linesep)
gs.message("Global cross validation scores...")
gs.message(os.linesep)
gs.message("Metric \t Mean \t Error")
for name, func in scoring.items():
score_mean = (preds.groupby("fold").apply(
lambda x: func(x["y_true"], x["y_pred"])).mean())
score_std = (preds.groupby("fold").apply(
lambda x: func(x["y_true"], x["y_pred"])).std())
gs.message(name + "\t" + str(score_mean.round(3)) + "\t" +
str(score_std.round(3)))
if mode == "classification":
gs.message(os.linesep)
gs.message("Cross validation class performance measures......:")
report_str = classification_report(
y_true=preds["y_true"],
y_pred=preds["y_pred"],
sample_weight=class_weights,
output_dict=False,
)
report = classification_report(
y_true=preds["y_true"],
y_pred=preds["y_pred"],
sample_weight=class_weights,
output_dict=True,
)
report = pd.DataFrame(report)
gs.message(report_str)
if classif_file != "":
report.to_csv(classif_file, mode="w", index=True)
# write cross-validation predictions to csv file
if preds_file != "":
preds.to_csv(preds_file, mode="w", index=False)
text_file = open(preds_file + "t", "w")
text_file.write('"Real", "Real", "integer", "integer"')
text_file.close()
# feature importances -----------------------------------------------------
if importances is True:
from sklearn.inspection import permutation_importance
fimp = permutation_importance(estimator,
X,
y,
scoring=search_scorer,
n_repeats=5,
n_jobs=n_jobs,
random_state=random_state)
feature_names = deepcopy(stack.names)
feature_names = [i.split("@")[0] for i in feature_names]
fimp = pd.DataFrame({
"feature": feature_names,
"importance": fimp["importances_mean"],
"std": fimp["importances_std"],
})
gs.message(os.linesep)
gs.message("Feature importances")
gs.message("Feature" + "\t" + "Score")
for index, row in fimp.iterrows():
gs.message(row["feature"] + "\t" + str(row["importance"]) + "\t" +
str(row["std"]))
if fimp_file != "":
fimp.to_csv(fimp_file, index=False)
# save the fitted model
import joblib
joblib.dump((estimator, y, class_labels), model_save)
def test_append(self):
"""Append another grass raster to a RasterStack"""
stack = RasterStack(self.predictors[0:5])
stack.append(self.predictors[5])
self.assertListEqual(stack.names, self.predictors)
def main():
try:
import sklearn
import joblib
if sklearn.__version__ < "0.20":
gs.fatal(
"Package python3-scikit-learn 0.20 or newer is not installed")
except ImportError:
gs.fatal("Package python3-scikit-learn 0.20 or newer is not installed")
# parser options
group = options["group"]
output = options["output"]
model_load = options["load_model"]
probability = flags["p"]
prob_only = flags["z"]
chunksize = int(options["chunksize"])
# remove @ from output in case overwriting result
if "@" in output:
output = output.split("@")[0]
# check probabilities=True if prob_only=True
if prob_only is True and probability is False:
gs.fatal("Need to set probabilities=True if prob_only=True")
# reload fitted model and training data
estimator, y, class_labels = joblib.load(model_load)
# define RasterStack
stack = RasterStack(group=group)
# perform raster prediction
region = Region()
row_incr = math.ceil(chunksize / region.cols)
# do not read by increments if increment > n_rows
if row_incr >= region.rows:
row_incr = None
# prediction
if prob_only is False:
gs.message("Predicting classification/regression raster...")
stack.predict(
estimator=estimator,
output=output,
height=row_incr,
overwrite=gs.overwrite(),
)
if probability is True:
gs.message("Predicting class probabilities...")
stack.predict_proba(
estimator=estimator,
output=output,
class_labels=np.unique(y),
overwrite=gs.overwrite(),
height=row_incr,
)
# assign categories for classification map
if class_labels and prob_only is False:
rules = []
for val, lab in class_labels.items():
rules.append(",".join([str(val), str(lab)]))
rules = "\n".join(rules)
rules_file = string_to_rules(rules)
r.category(map=output, rules=rules_file, separator="comma")