museotoolbox.ai.SuperLearner.__init__

SuperLearner.__init__(classifier, param_grid=None, n_jobs=1, verbose=False)[source]

SuperLearner, shortname for Supervised Learning, ease the way to learn a model via an array or a raster using Scikit-Learn algorithm. After learning a model via fit(), you can predict via predict_image() or predict_array().

Parameters

  • classifier (algorithm compatible with scikit-learn.) – For example RandomForestClassifier(n_estimators=100) from from sklearn.ensemble import RandomForestClassifier

  • param_grid (False or dict, optional (default=False)) – param_grid for the grid_search. E.g. for RandomForestClassifier : param_grid=dict(n_estimators=[10,100],max_features=[1,3])

  • n_jobs (int, default 1.) – Number of cores to be used by sklearn in grid-search.

  • verbose (bool or int, optional (default=False)) – The higher it is the more sequential will show progression.

Examples

>>> import museotoolbox as mtb
>>> from sklearn.ensemble import RandomForestClassifier
>>> X,y = mtb.datasets.load_historical_data(return_X_y=True)
>>> RS50 = mtb.cross_validation.RandomStratifiedKFold(n_splits=2,n_repeats=5,
        random_state=12,verbose=False)
>>> classifier = RandomForestClassifier()
>>> SL = mtb.ai.SuperLearner(verbose=True,classifier=classifier)
>>> SL.fit(X,y,cv=RS50,param_grid=dict(n_estimators=[100,200]))
Fitting 10 folds for each of 2 candidates, totalling 20 fits
best score : 0.966244859222
best n_estimators : 200
>>> for kappa in SL.get_stats_from_cv(confusion_matrix=False,kappa=True):
    print(kappa)
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
{'kappa': 0.94145803865870303}
{'kappa': 0.94275572196698443}
{'kappa': 0.94566553229314054}
{'kappa': 0.94210064101370472}
{'kappa': 0.94566137634353153}
{'kappa': 0.94085890364956737}
{'kappa': 0.94136385707385184}
{'kappa': 0.9383201352573155}
{'kappa': 0.93887726891376944}
{'kappa': 0.94450020549861891}
[Parallel(n_jobs=-1)]: Done  10 out of  10 | eSLsed:    8.7s finished
>>> SL.predict_image(raster,'/tmp/classification.tif')
Total number of blocks : 15
Prediction...  [########################################]100%
Saved /tmp/classification.tif using function predictArray