machine_learning.py 文件源码

def decis_tree(wine_set):
    # to remember the if the wine_set red or white
    w = wine_set

    # subset data for better tree visibility
    # wine_set = wine_set[:100]

    # recode quality (response variable) into 2 groups: 0:{3,4,5}, 1:{6,7,8,9}
    recode = {3: 0, 4: 0, 5: 0, 6: 1, 7: 1, 8: 1, 9: 1}
    wine_set['quality_c'] = wine_set['quality'].map(recode)

    # round explanatory data for easier tree
    # wine_set["residual_sugar"] = wine_set["residual_sugar"].round()
    # wine_set["alcohol"] = wine_set["alcohol"].round()

    # split into training and testing sets
    predictors = wine_set[["residual_sugar", 'alcohol']]
    targets = wine_set.quality_c

    pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, test_size=.4)

    # build model on training data
    classifier = DecisionTreeClassifier()
    classifier = classifier.fit(pred_train, tar_train)

    predictions = classifier.predict(pred_test)

    # print the confusion matrix and accuracy of the model
    print(sklearn.metrics.confusion_matrix(tar_test, predictions))
    print(sklearn.metrics.accuracy_score(tar_test, predictions))

    # export the tree for viewing
    if w.equals(red):
        export_graphviz(classifier, out_file="red_decision_tree.dot")
    else:
        export_graphviz(classifier, out_file="white_decision_tree.dot")
    # to view the decision tree create a .pdf file from the created .dot file
    # by typing in the terminal from this directory: dot -Tpdf decision_tree.dot -o decision_tree.pdf
# print('----------------Decision Tree------------------------')
# call(decis_tree)


# ____________________________________Random Forests________________