nd.classify package
- class nd.classify.Classifier(clf, feature_dims=[], scale=False)[source]
Bases:
object
- Parameters
clf (sklearn classifier) – An initialized classifier object as provided by
scikit-learn
. Must provide methodsfit
andpredict
.feature_dims (list, optional) – A list of additional dimensions to use as features. For example, if the dataset has a
'time'
dimension and'time'
is infeature_dims
, every time step will be treated as an independent variable for classification purposes. Otherwise, all time steps will be treated as additional data dimensions just like'x'
and'y'
.scale (bool, optional) – If True, scale the input data before clustering to zero mean and unit variance (default: False).
- fit(ds, labels=None)[source]
- Parameters
ds (xarray.Dataset) – The dataset on which to train the classifier.
labels (xarray.DataArray, optional) – The class labels to train the classifier. To be omitted if the classifier is unsupervised, such as KMeans.
- make_Xy(ds, labels=None)[source]
Generate scikit-learn compatible X and y arrays from ds and labels.
- Parameters
ds (xarray.Dataset) – The input dataset.
labels (xarray.DataArray) – The corresponding class labels.
- Returns
X and y
- Return type
tuple(np.array, np.array)
- predict(ds, func='predict')[source]
- Parameters
ds (xarray.Dataset) – The dataset for which to predict the class labels.
func (str, optional) – The method of the classifier to use for prediction (default:
'predict'
).
- Returns
The predicted class labels.
- Return type
xarray.DataArray
- score(ds, labels=None, method='accuracy')[source]
Compute the classification score.
- Parameters
ds (xarray.Dataset) – The dataset for which to compute the score.
labels (xarray.DataArray) – The corresponding true class labels.
method (str, optional) – The scoring method as implemented in scikit-learn (default: ‘accuracy’)
- Returns
The classification score.
- Return type
float
- nd.classify.class_mean(ds, labels)[source]
Replace every pixel of the dataset with the mean of its corresponding class (or cluster, or segment).
- Parameters
ds (xarray.Dataset) – The dataset.
labels (xarray.DataArray) – The labels indicating the class each pixel in
ds
belongs to. The label dimensions may be a subset of the dimensions ofds
(such as('y', 'x')
for a dataset that also contains atime
dimension but with time-independent class labels).
- Returns
A dataset with the corresponding mean class values.
- Return type
xarray.Dataset