Pythonではじめる教師なし学習 ―機械学習の可能性を広げるラベルなしデータの利用

3.9

 

t-SNE

87

# Locally Linear Embedding (LLE)

from sklearn.manifold import LocallyLinearEmbedding

n_neighbors = 10

n_components = 2

method = 'modified'

n_jobs = 4

random_state = 2018

lle = LocallyLinearEmbedding(n_neighbors=n_neighbors, \

n_components=n_components, method=method, \

random_state=random_state, n_jobs=n_jobs)

lle.fit(X_train.loc[0:5000,:])

X_train_lle = lle.transform(X_train)

X_train_lle = pd.DataFrame(data=X_train_lle, index=train_index)

X_validation_lle = lle.transform(X_validation)

X_validation_lle = pd.DataFrame(data=X_validation_lle, index=validation_index)

scatterPlot(X_train_lle, y_train, "Locally Linear Embedding")

図 3-13 に、

LLE

による

2

次元