def test_extract_raster(self):
stack = Raster(self.predictors)
# extract training data from labelled pixels
training_px = rasterio.open(nc.labelled_pixels)
X, y, xy = stack.extract_raster(
response=training_px, value_name='id', return_array=True)
# remove masked values
mask2d = X.mask.any(axis=1)
X = X[~mask2d]
y = y[~mask2d]
xy = xy[~mask2d]
# check shapes of extracted pixels
self.assertTupleEqual(X.shape, (2436, 6))
self.assertTupleEqual(y.shape, (2436, ))
self.assertTupleEqual(xy.shape, (2436, 2))
training_lines['geometry'] = training_lines.geometry.boundary
# Plot some training data
plt.imshow(stack.lsat7_2000_70.read(masked=True),
extent=rasterio.plot.plotting_extent(stack.lsat7_2000_70.ds))
plt.scatter(x=training_pt.bounds.iloc[:, 0],
y=training_pt.bounds.iloc[:, 1],
s=2, color='black')
plt.show()
# Create a training dataset by extracting the raster values at the training point locations:
stack = Raster(predictors)
df_points = stack.extract_vector(response=training_pt, field='id')
df_polygons = stack.extract_vector(response=training_py, field='id')
df_lines = stack.extract_vector(response=training_lines, field='id')
df_raster = stack.extract_raster(response=training_px, value_name='id')
df_points.head()
# Next we can train a logistic regression classifier:
from sklearn.linear_model import LogisticRegressionCV
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.model_selection import cross_validate
# define the classifier with standardization of the input features in a pipeline
lr = Pipeline(
[('scaling', StandardScaler()),
('classifier', LogisticRegressionCV(n_jobs=-1))])
# fit the classifier
X = df_points.drop(columns=['id', 'geometry'])
class TestExtract(TestCase):
def setUp(self) -> None:
self.predictors = [
nc.band1, nc.band2, nc.band3, nc.band4, nc.band5, nc.band7
]
self.extracted_grass = pd.read_table(nc.extracted_pixels,
delimiter=" ")
self.stack = Raster(self.predictors)
def tearDown(self) -> None:
self.stack.close()
def test_extract_points(self):
training_pt = geopandas.read_file(nc.points)
# check that extracted training data as a DataFrame match known values
df = self.stack.extract_vector(gdf=training_pt)
df = df.dropna()
training_pt = training_pt.dropna()
self.assertTrue(
(df["lsat7_2000_10"].values == training_pt["b1"].values).all())
self.assertTrue(
(df["lsat7_2000_20"].values == training_pt["b2"].values).all())
self.assertTrue(
(df["lsat7_2000_30"].values == training_pt["b3"].values).all())
self.assertTrue(
(df["lsat7_2000_40"].values == training_pt["b4"].values).all())
self.assertTrue(
(df["lsat7_2000_50"].values == training_pt["b5"].values).all())
self.assertTrue(
(df["lsat7_2000_70"].values == training_pt["b7"].values).all())
def test_extract_polygons(self):
# extract training data from polygons
training_py = geopandas.read_file(nc.polygons)
df = self.stack.extract_vector(gdf=training_py)
df = df.dropna()
df = df.merge(
right=training_py.loc[:, ("id", "label")],
left_on="geometry_idx",
right_on="index",
right_index=True,
)
# compare to extracted data using GRASS GIS
self.assertEqual(df.shape[0], self.extracted_grass.shape[0])
self.assertAlmostEqual(df["lsat7_2000_10"].mean(),
self.extracted_grass["b1"].mean(),
places=2)
self.assertAlmostEqual(df["lsat7_2000_20"].mean(),
self.extracted_grass["b2"].mean(),
places=2)
self.assertAlmostEqual(df["lsat7_2000_30"].mean(),
self.extracted_grass["b3"].mean(),
places=2)
self.assertAlmostEqual(df["lsat7_2000_40"].mean(),
self.extracted_grass["b4"].mean(),
places=2)
self.assertAlmostEqual(df["lsat7_2000_50"].mean(),
self.extracted_grass["b5"].mean(),
places=2)
self.assertAlmostEqual(df["lsat7_2000_70"].mean(),
self.extracted_grass["b7"].mean(),
places=2)
def test_extract_lines(self):
# extract training data from lines
training_py = geopandas.read_file(nc.polygons)
training_lines = deepcopy(training_py)
training_lines["geometry"] = training_lines.geometry.boundary
df = self.stack.extract_vector(gdf=training_lines).dropna()
# check shapes of extracted pixels
self.assertEqual(df.shape[0], 948)
def test_extract_raster(self):
# extract training data from labelled pixels
with rasterio.open(nc.labelled_pixels) as src:
df = self.stack.extract_raster(src)
df = df.dropna()
self.assertEqual(df.shape[0], self.extracted_grass.shape[0])
self.assertAlmostEqual(df["lsat7_2000_10"].mean(),
self.extracted_grass["b1"].mean(),
places=3)
self.assertAlmostEqual(df["lsat7_2000_20"].mean(),
self.extracted_grass["b2"].mean(),
places=3)
self.assertAlmostEqual(df["lsat7_2000_30"].mean(),
self.extracted_grass["b3"].mean(),
places=3)
self.assertAlmostEqual(df["lsat7_2000_40"].mean(),
self.extracted_grass["b4"].mean(),
places=3)
self.assertAlmostEqual(df["lsat7_2000_50"].mean(),
self.extracted_grass["b5"].mean(),
places=3)
self.assertAlmostEqual(df["lsat7_2000_70"].mean(),
self.extracted_grass["b7"].mean(),
places=3)
