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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]) |
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