fit_predict#
- geowombat.ml.fit_predict(data, clf, labels=None, col=None, targ_name='targ', targ_dim_name='sample', mask_nodataval=True)#
Fits a classifier given class labels and predicts on a DataArray.
- Parameters:
data (DataArray) – The data to predict on.
clf (object) – The classifier or classification pipeline.
labels (optional[str | Path | GeoDataFrame]) – Class labels as polygon geometry.
col (Optional[str]) – The column in
labels
you want to assign values from. IfNone
, creates a binary raster.targ_name (Optional[str]) – The target name.
targ_dim_name (Optional[str]) – The target coordinate name.
mask_nodataval (Optional[Bool]) – If true, data.attrs[“nodatavals”][0] are replaced with np.nan and the array is returned as type float
- Returns:
Predictions shaped (‘time’ x ‘band’ x ‘y’ x ‘x’)
- Return type:
xarray.DataArray
Example
>>> import geowombat as gw >>> from geowombat.data import l8_224078_20200518, l8_224078_20200518_polygons >>> from geowombat.ml import fit_predict >>> >>> import geopandas as gpd >>> from sklearn_xarray.preprocessing import Featurizer >>> from sklearn.pipeline import Pipeline >>> from sklearn.preprocessing import StandardScaler, LabelEncoder >>> from sklearn.decomposition import PCA >>> from sklearn.naive_bayes import GaussianNB >>> from sklearn.cluster import KMeans >>> >>> le = LabelEncoder() >>> >>> labels = gpd.read_file(l8_224078_20200518_polygons) >>> labels['lc'] = le.fit(labels.name).transform(labels.name) >>> >>> # Use a supervised classification pipeline >>> pl = Pipeline([('scaler', StandardScaler()), >>> ('pca', PCA()), >>> ('clf', GaussianNB()))]) >>> >>> with gw.open(l8_224078_20200518, nodata=0) as src: >>> y = fit_predict(src, pl, labels, col='lc') >>> y.isel(time=0).sel(band='targ').gw.imshow() >>> >>> with gw.open([l8_224078_20200518,l8_224078_20200518], nodata=0) as src: >>> y = fit_predict(src, pl, labels, col='lc') >>> y.isel(time=1).sel(band='targ').gw.imshow() >>> >>> # Use an unsupervised classification pipeline >>> cl = Pipeline([('pca', PCA()), >>> ('cst', KMeans()))]) >>> with gw.open(l8_224078_20200518, nodata=0) as src: >>> y2 = fit_predict(src, cl)