from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn import tree

# Load and split the data
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

# Construct pipeline
pipe = Pipeline([('scl', StandardScaler()),
			('pca', PCA(n_components=2)),
			('clf', tree.DecisionTreeClassifier(random_state=42))])

# Fit the pipeline
pipe.fit(X_train, y_train)

# Pipeline test accuracy
print('Test accuracy: %.3f' % pipe.score(X_test, y_test))

# Pipeline estimator params; estimator is stored as step 3 ([2]), second item ([1])			
print('\nModel hyperparameters:\n', pipe.steps[2][1].get_params())