How to use susi - 10 common examples
To help you get started, we’ve selected a few susi examples, based on popular ways it is used in public projects.
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felixriese / susi / tests / test_SOMClustering.py View on Github 
def test_transform(n_rows, n_columns, X):
som_clustering = susi.SOMClustering(
n_rows=n_rows, n_columns=n_columns)
som_clustering.fit(X)
bmus = som_clustering.transform(X)
assert(len(bmus) == X.shape[0])
assert(len(bmus[0]) == 2)def test_fit(X, n_rows, n_columns, train_mode_unsupervised, random_state,
expected):
som = susi.SOMClustering(
n_rows=n_rows,
n_columns=n_columns,
train_mode_unsupervised=train_mode_unsupervised,
random_state=random_state)
som.fit(X)
assert isinstance(som.unsuper_som_, np.ndarray)
assert som.unsuper_som_.shape == (n_rows, n_columns, X.shape[1])
assert np.allclose(som.unsuper_som_, expected, atol=1e-20)
with pytest.raises(Exception):
som = susi.SOMClustering(train_mode_unsupervised="alsdkf")
som.fit(X)def test_som_clustering_init(n_rows, n_columns):
som_clustering = susi.SOMClustering(
n_rows=n_rows, n_columns=n_columns)
assert som_clustering.n_rows == n_rows
assert som_clustering.n_columns == n_columnsdef test_mexicanhat_nbh_dist_weight_mode():
som = susi.SOMRegressor(nbh_dist_weight_mode="mexican-hat")
som.fit(X_train, y_train)
som.predict(X_test)
with pytest.raises(Exception):
som = susi.SOMRegressor(nbh_dist_weight_mode="pseudogaussian")
som.fit(X_train, y_train)def test_mexicanhat_nbh_dist_weight_mode():
som = susi.SOMRegressor(nbh_dist_weight_mode="mexican-hat")
som.fit(X_train, y_train)
som.predict(X_test)
with pytest.raises(Exception):
som = susi.SOMRegressor(nbh_dist_weight_mode="pseudogaussian")
som.fit(X_train, y_train)def test_som_regressor_init(n_rows, n_columns):
som_reg = susi.SOMRegressor(
n_rows=n_rows, n_columns=n_columns)
assert(som_reg.n_rows == n_rows)
assert(som_reg.n_columns == n_columns)def test_MultiOutputRegressor():
mor = MultiOutputRegressor(
estimator=susi.SOMRegressor(n_jobs=2),
n_jobs=2
)
mor.fit(X, y)def test_fit(train_mode_unsupervised, train_mode_supervised):
som = susi.SOMClassifier(
n_rows=8,
n_columns=8,
train_mode_unsupervised=train_mode_unsupervised,
train_mode_supervised=train_mode_supervised,
random_state=3)
som.fit(X_train, y_train)
assert(som.score(X_test, y_test) >= 0.8)def test_fit_semi(train_mode_unsupervised, train_mode_supervised):
som = susi.SOMClassifier(
n_rows=5,
n_columns=5,
train_mode_unsupervised=train_mode_unsupervised,
train_mode_supervised=train_mode_supervised,
missing_label_placeholder=-1,
random_state=3)
som.fit(X_train, y_train_semi)
assert(som.score(X_test, y_test) > 0.5)def test_modify_weight_matrix_online(n_rows, n_columns, random_state,
n_iter_unsupervised, X, learningrate,
neighborhood_func, bmu_pos, dp, expected):
som_clustering = susi.SOMClustering(
n_rows=n_rows, n_columns=n_columns,
n_iter_unsupervised=n_iter_unsupervised, random_state=random_state)
som_clustering.fit(X)
assert np.allclose(susi.modify_weight_matrix_online(
som_array=som_clustering.unsuper_som_,
learningrate=learningrate,
dist_weight_matrix=som_clustering.get_nbh_distance_weight_matrix(
neighborhood_func, bmu_pos),
true_vector=som_clustering.X_[dp]), expected, atol=1e-8)susi
Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)
