Convert API code snippets from JS to Py (misc 1)

PiperOrigin-RevId: 571353628

samples/python/apidocs/ee_classifier_explain.py

@@ -0,0 +1,99 @@
+# Copyright 2023 The Google Earth Engine Community Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# [START earthengine__apidocs__ee_classifier_explain]
+# A Sentinel-2 surface reflectance image, reflectance bands selected,
+# serves as the source for training and prediction in this contrived example.
+img = ee.Image(
+    'COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG'
+).select('B.*')
+
+# ESA WorldCover land cover map, used as label source in classifier training.
+lc = ee.Image('ESA/WorldCover/v100/2020')
+
+# Remap the land cover class values to a 0-based sequential series.
+class_values = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100]
+remap_values = ee.List.sequence(0, 10)
+label = 'lc'
+lc = lc.remap(class_values, remap_values).rename(label).toByte()
+
+# Add land cover as a band of the reflectance image and sample 100 pixels at
+# 10 m scale from each land cover class within a region of interest.
+roi = ee.Geometry.Rectangle(-122.347, 37.743, -122.024, 37.838)
+sample = img.addBands(lc).stratifiedSample(
+    numPoints=100, classBand=label, region=roi, scale=10, geometries=True
+)
+
+# Add a random value field to the sample and use it to approximately split 80%
+# of the features into a training set and 20% into a validation set.
+sample = sample.randomColumn()
+training_sample = sample.filter('random <= 0.8')
+validation_sample = sample.filter('random > 0.8')
+
+# Train a 10-tree random forest classifier from the training sample.
+trained_classifier = ee.Classifier.smileRandomForest(10).train(
+    features=training_sample,
+    classProperty=label,
+    inputProperties=img.bandNames(),
+)
+
+# Get information about the trained classifier.
+display('Results of trained classifier', trained_classifier.explain())
+
+# Get a confusion matrix and overall accuracy for the training sample.
+train_accuracy = trained_classifier.confusionMatrix()
+display('Training error matrix', train_accuracy)
+display('Training overall accuracy', train_accuracy.accuracy())
+
+# Get a confusion matrix and overall accuracy for the validation sample.
+validation_sample = validation_sample.classify(trained_classifier)
+validation_accuracy = validation_sample.errorMatrix(label, 'classification')
+display('Validation error matrix', validation_accuracy)
+display('Validation accuracy', validation_accuracy.accuracy())
+
+# Classify the reflectance image from the trained classifier.
+img_classified = img.classify(trained_classifier)
+
+# Add the layers to the map.
+class_vis = {
+    'min': 0,
+    'max': 10,
+    'palette': [
+        '006400',
+        'ffbb22',
+        'ffff4c',
+        'f096ff',
+        'fa0000',
+        'b4b4b4',
+        'f0f0f0',
+        '0064c8',
+        '0096a0',
+        '00cf75',
+        'fae6a0',
+    ],
+}
+m = geemap.Map()
+m.set_center(-122.184, 37.796, 12)
+m.add_ee_layer(
+    img, {'bands': ['B11', 'B8', 'B3'], 'min': 100, 'max': 3500}, 'img'
+)
+m.add_ee_layer(lc, class_vis, 'lc')
+m.add_ee_layer(img_classified, class_vis, 'Classified')
+m.add_ee_layer(roi, {'color': 'white'}, 'ROI', False, 0.5)
+m.add_ee_layer(training_sample, {'color': 'black'}, 'Training sample', False)
+m.add_ee_layer(
+    validation_sample, {'color': 'white'}, 'Validation sample', False
+)
+m
+# [END earthengine__apidocs__ee_classifier_explain]

samples/python/apidocs/ee_classifier_libsvm.py

@@ -0,0 +1,100 @@
+# Copyright 2023 The Google Earth Engine Community Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# [START earthengine__apidocs__ee_classifier_libsvm]
+# A Sentinel-2 surface reflectance image, reflectance bands selected,
+# serves as the source for training and prediction in this contrived example.
+img = ee.Image(
+    'COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG'
+).select('B.*')
+
+# ESA WorldCover land cover map, used as label source in classifier training.
+lc = ee.Image('ESA/WorldCover/v100/2020')
+
+# Remap the land cover class values to a 0-based sequential series.
+class_values = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100]
+remap_values = ee.List.sequence(0, 10)
+label = 'lc'
+lc = lc.remap(class_values, remap_values).rename(label).toByte()
+
+# Add land cover as a band of the reflectance image and sample 100 pixels at
+# 10 m scale from each land cover class within a region of interest.
+roi = ee.Geometry.Rectangle(-122.347, 37.743, -122.024, 37.838)
+sample = img.addBands(lc).stratifiedSample(
+    numPoints=100, classBand=label, region=roi, scale=10, geometries=True
+)
+
+# Add a random value field to the sample and use it to approximately split 80%
+# of the features into a training set and 20% into a validation set.
+sample = sample.randomColumn()
+training_sample = sample.filter('random <= 0.8')
+validation_sample = sample.filter('random > 0.8')
+
+# Train an SVM classifier (C-SVM classification, voting decision procedure,
+# linear kernel) from the training sample.
+trained_classifier = ee.Classifier.libsvm().train(
+    features=training_sample,
+    classProperty=label,
+    inputProperties=img.bandNames(),
+)
+
+# Get information about the trained classifier.
+display('Results of trained classifier', trained_classifier.explain())
+
+# Get a confusion matrix and overall accuracy for the training sample.
+train_accuracy = trained_classifier.confusionMatrix()
+display('Training error matrix', train_accuracy)
+display('Training overall accuracy', train_accuracy.accuracy())
+
+# Get a confusion matrix and overall accuracy for the validation sample.
+validation_sample = validation_sample.classify(trained_classifier)
+validation_accuracy = validation_sample.errorMatrix(label, 'classification')
+display('Validation error matrix', validation_accuracy)
+display('Validation accuracy', validation_accuracy.accuracy())
+
+# Classify the reflectance image from the trained classifier.
+img_classified = img.classify(trained_classifier)
+
+# Add the layers to the map.
+class_vis = {
+    'min': 0,
+    'max': 10,
+    'palette': [
+        '006400',
+        'ffbb22',
+        'ffff4c',
+        'f096ff',
+        'fa0000',
+        'b4b4b4',
+        'f0f0f0',
+        '0064c8',
+        '0096a0',
+        '00cf75',
+        'fae6a0',
+    ],
+}
+m = geemap.Map()
+m.set_center(-122.184, 37.796, 12)
+m.add_ee_layer(
+    img, {'bands': ['B11', 'B8', 'B3'], 'min': 100, 'max': 3500}, 'img'
+)
+m.add_ee_layer(lc, class_vis, 'lc')
+m.add_ee_layer(img_classified, class_vis, 'Classified')
+m.add_ee_layer(roi, {'color': 'white'}, 'ROI', False, 0.5)
+m.add_ee_layer(training_sample, {'color': 'black'}, 'Training sample', False)
+m.add_ee_layer(
+    validation_sample, {'color': 'white'}, 'Validation sample', False
+)
+m
+# [END earthengine__apidocs__ee_classifier_libsvm]

samples/python/apidocs/ee_classifier_smilecart.py

@@ -0,0 +1,100 @@
+# Copyright 2023 The Google Earth Engine Community Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# [START earthengine__apidocs__ee_classifier_smilecart]
+# A Sentinel-2 surface reflectance image, reflectance bands selected,
+# serves as the source for training and prediction in this contrived example.
+img = ee.Image(
+    'COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG'
+).select('B.*')
+
+# ESA WorldCover land cover map, used as label source in classifier training.
+lc = ee.Image('ESA/WorldCover/v100/2020')
+
+# Remap the land cover class values to a 0-based sequential series.
+class_values = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100]
+remap_values = ee.List.sequence(0, 10)
+label = 'lc'
+lc = lc.remap(class_values, remap_values).rename(label).toByte()
+
+# Add land cover as a band of the reflectance image and sample 100 pixels at
+# 10 m scale from each land cover class within a region of interest.
+roi = ee.Geometry.Rectangle(-122.347, 37.743, -122.024, 37.838)
+sample = img.addBands(lc).stratifiedSample(
+    numPoints=100, classBand=label, region=roi, scale=10, geometries=True
+)
+
+# Add a random value field to the sample and use it to approximately split 80%
+# of the features into a training set and 20% into a validation set.
+sample = sample.randomColumn()
+training_sample = sample.filter('random <= 0.8')
+validation_sample = sample.filter('random > 0.8')
+
+# Train a CART classifier (up to 10 leaf nodes in each tree) from the
+# training sample.
+trained_classifier = ee.Classifier.smileCart(10).train(
+    features=training_sample,
+    classProperty=label,
+    inputProperties=img.bandNames(),
+)
+
+# Get information about the trained classifier.
+display('Results of trained classifier', trained_classifier.explain())
+
+# Get a confusion matrix and overall accuracy for the training sample.
+train_accuracy = trained_classifier.confusionMatrix()
+display('Training error matrix', train_accuracy)
+display('Training overall accuracy', train_accuracy.accuracy())
+
+# Get a confusion matrix and overall accuracy for the validation sample.
+validation_sample = validation_sample.classify(trained_classifier)
+validation_accuracy = validation_sample.errorMatrix(label, 'classification')
+display('Validation error matrix', validation_accuracy)
+display('Validation accuracy', validation_accuracy.accuracy())
+
+# Classify the reflectance image from the trained classifier.
+img_classified = img.classify(trained_classifier)
+
+# Add the layers to the map.
+class_vis = {
+    'min': 0,
+    'max': 10,
+    'palette': [
+        '006400',
+        'ffbb22',
+        'ffff4c',
+        'f096ff',
+        'fa0000',
+        'b4b4b4',
+        'f0f0f0',
+        '0064c8',
+        '0096a0',
+        '00cf75',
+        'fae6a0',
+    ],
+}
+m = geemap.Map()
+m.set_center(-122.184, 37.796, 12)
+m.add_ee_layer(
+    img, {'bands': ['B11', 'B8', 'B3'], 'min': 100, 'max': 3500}, 'img'
+)
+m.add_ee_layer(lc, class_vis, 'lc')
+m.add_ee_layer(img_classified, class_vis, 'Classified')
+m.add_ee_layer(roi, {'color': 'white'}, 'ROI', False, 0.5)
+m.add_ee_layer(training_sample, {'color': 'black'}, 'Training sample', False)
+m.add_ee_layer(
+    validation_sample, {'color': 'white'}, 'Validation sample', False
+)
+m
+# [END earthengine__apidocs__ee_classifier_smilecart]