fit#
- geowombat.ml.fit(data, clf, labels=None, col=None, targ_name='targ', targ_dim_name='sample')#
Fits a classifier given class labels.
- 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.
- Returns:
Original DataArray augmented to accept prediction dimension Xna if unsupervised classifier: tuple(xarray.DataArray, sklearn_xarray.Target): X:Reshaped feature data without NAs removed, y:None Xna if supervised classifier: tuple(xarray.DataArray, sklearn_xarray.Target): X:Reshaped feature data with NAs removed, y:Array holding target data clf, (sklearn pipeline): Fitted pipeline object
- Return type:
X (xarray.DataArray)
Example
>>> import geowombat as gw >>> from geowombat.data import l8_224078_20200518, l8_224078_20200518_polygons >>> from geowombat.ml import fit >>> >>> 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 >>> >>> le = LabelEncoder() >>> >>> labels = gpd.read_file(l8_224078_20200518_polygons) >>> labels['lc'] = le.fit(labels.name).transform(labels.name) >>> >>> # Use supervised classification pipeline >>> pl = Pipeline([('scaler', StandardScaler()), >>> ('pca', PCA()), >>> ('clf', GaussianNB())]) >>> >>> with gw.open(l8_224078_20200518) as src: >>> X, Xy, clf = fit(src, pl, labels, col='lc')
>>> # Fit an unsupervised classifier >>> cl = Pipeline([('pca', PCA()), >>> ('cst', KMeans()))]) >>> with gw.open(l8_224078_20200518) as src: >>> X, Xy, clf = fit(src, cl)