pipelines-1.py

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.externals import joblib
from sklearn.linear_model import LogisticRegression
from sklearn import svm
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 some pipelines
pipe_lr = Pipeline([('scl', StandardScaler()),
			('pca', PCA(n_components=2)),
			('clf', LogisticRegression(random_state=42))])

pipe_svm = Pipeline([('scl', StandardScaler()),
			('pca', PCA(n_components=2)),
			('clf', svm.SVC(random_state=42))])
			
pipe_dt = Pipeline([('scl', StandardScaler()),
			('pca', PCA(n_components=2)),
			('clf', tree.DecisionTreeClassifier(random_state=42))])

# List of pipelines for ease of iteration
pipelines = [pipe_lr, pipe_svm, pipe_dt]
			
# Dictionary of pipelines and classifier types for ease of reference
pipe_dict = {0: 'Logistic Regression', 1: 'Support Vector Machine', 2: 'Decision Tree'}

# Fit the pipelines
for pipe in pipelines:
	pipe.fit(X_train, y_train)

# Compare accuracies
for idx, val in enumerate(pipelines):
	print('%s pipeline test accuracy: %.3f' % (pipe_dict[idx], val.score(X_test, y_test)))

# Identify the most accurate model on test data
best_acc = 0.0
best_clf = 0
best_pipe = ''
for idx, val in enumerate(pipelines):
	if val.score(X_test, y_test) > best_acc:
		best_acc = val.score(X_test, y_test)
		best_pipe = val
		best_clf = idx
print('Classifier with best accuracy: %s' % pipe_dict[best_clf])

# Save pipeline to file
joblib.dump(best_pipe, 'best_pipeline.pkl', compress=1)
print('Saved %s pipeline to file' % pipe_dict[best_clf])