Python ensemble.AdaBoostRegressor方法代碼示例

# 需要導入模塊: from sklearn import ensemble [as 別名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 別名]
def Train(data, modelcount, censhu, yanzhgdata):
    model = AdaBoostRegressor(DecisionTreeRegressor(max_depth=censhu),
                              n_estimators=modelcount, learning_rate=0.8)

    model.fit(data[:, :-1], data[:, -1])
    # 給出訓練數據的預測值
    train_out = model.predict(data[:, :-1])
    # 計算MSE
    train_mse = mse(data[:, -1], train_out)

    # 給出驗證數據的預測值
    add_yan = model.predict(yanzhgdata[:, :-1])
    # 計算MSE
    add_mse = mse(yanzhgdata[:, -1], add_yan)
    print(train_mse, add_mse)
    return train_mse, add_mse

# 最終確定組合的函數 

# 需要導入模塊: from sklearn import ensemble [as 別名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 別名]
def test_gridsearch():
    # Check that base trees can be grid-searched.
    # AdaBoost classification
    boost = AdaBoostClassifier(base_estimator=DecisionTreeClassifier())
    parameters = {'n_estimators': (1, 2),
                  'base_estimator__max_depth': (1, 2),
                  'algorithm': ('SAMME', 'SAMME.R')}
    clf = GridSearchCV(boost, parameters)
    clf.fit(iris.data, iris.target)

    # AdaBoost regression
    boost = AdaBoostRegressor(base_estimator=DecisionTreeRegressor(),
                              random_state=0)
    parameters = {'n_estimators': (1, 2),
                  'base_estimator__max_depth': (1, 2)}
    clf = GridSearchCV(boost, parameters)
    clf.fit(boston.data, boston.target) 

# 需要導入模塊: from sklearn import ensemble [as 別名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 別名]
def test_sample_weight_adaboost_regressor():
    """
    AdaBoostRegressor should work without sample_weights in the base estimator
    The random weighted sampling is done internally in the _boost method in
    AdaBoostRegressor.
    """
    class DummyEstimator(BaseEstimator):

        def fit(self, X, y):
            pass

        def predict(self, X):
            return np.zeros(X.shape[0])

    boost = AdaBoostRegressor(DummyEstimator(), n_estimators=3)
    boost.fit(X, y_regr)
    assert_equal(len(boost.estimator_weights_), len(boost.estimator_errors_)) 

# 需要導入模塊: from sklearn import ensemble [as 別名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 別名]
def test_multidimensional_X():
    """
    Check that the AdaBoost estimators can work with n-dimensional
    data matrix
    """

    from sklearn.dummy import DummyClassifier, DummyRegressor

    rng = np.random.RandomState(0)

    X = rng.randn(50, 3, 3)
    yc = rng.choice([0, 1], 50)
    yr = rng.randn(50)

    boost = AdaBoostClassifier(DummyClassifier(strategy='most_frequent'))
    boost.fit(X, yc)
    boost.predict(X)
    boost.predict_proba(X)

    boost = AdaBoostRegressor(DummyRegressor())
    boost.fit(X, yr)
    boost.predict(X) 

# 需要導入模塊: from sklearn import ensemble [as 別名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 別名]
def run_sklearn():
  n_trees = 100
  n_folds = 3

  # https://www.analyticsvidhya.com/blog/2015/06/tuning-random-forest-model/
  alg_list = [
      ['lreg',LinearRegression()],
      ['rforest',RandomForestRegressor(n_estimators=1000, n_jobs=-1, max_depth=3)],
      ['extree',ExtraTreesClassifier(n_estimators = 1000,max_depth=2)],
      ['adaboost',AdaBoostRegressor(base_estimator=None, n_estimators=600, learning_rate=1.0)],
      ['knn', sklearn.neighbors.KNeighborsRegressor(n_neighbors=5)]
  ]

  start_time = time.time()
  for name,alg in alg_list:
      train = jhkaggle.train_sklearn.TrainSKLearn("1",name,alg,False)
      train.run()
      train = None
  elapsed_time = time.time() - start_time
  print("Elapsed time: {}".format(jhkaggle.util.hms_string(elapsed_time))) 

# 需要導入模塊: from sklearn import ensemble [as 別名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 別名]
def sample_1031_4():
    """
    10.3.1_4 豬老三使用回歸預測股價：使用集成學習算法預測股價AdaBoost與RandomForest
    :return:
    """
    train_x, train_y_regress, train_y_classification, pig_three_feature, \
    test_x, test_y_regress, test_y_classification, kl_another_word_feature_test = sample_1031_1()

    # AdaBoost
    from sklearn.ensemble import AdaBoostRegressor

    estimator = AdaBoostRegressor(n_estimators=100)
    regress_process(estimator, train_x, train_y_regress, test_x,
                    test_y_regress)
    plt.show()
    # RandomForest
    from sklearn.ensemble import RandomForestRegressor

    estimator = RandomForestRegressor(n_estimators=100)
    regress_process(estimator, train_x, train_y_regress, test_x, test_y_regress)
    plt.show() 

# 需要導入模塊: from sklearn import ensemble [as 別名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 別名]
def test_sample_weight_adaboost_regressor():
    """
    AdaBoostRegressor should work without sample_weights in the base estimator

    The random weighted sampling is done internally in the _boost method in
    AdaBoostRegressor.
    """
    class DummyEstimator(BaseEstimator):

        def fit(self, X, y):
            pass

        def predict(self, X):
            return np.zeros(X.shape[0])

    boost = AdaBoostRegressor(DummyEstimator(), n_estimators=3)
    boost.fit(X, y_regr)
    assert_equal(len(boost.estimator_weights_), len(boost.estimator_errors_)) 

# 需要導入模塊: from sklearn import ensemble [as 別名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 別名]
def Adaboost_First(self, data, max_depth=5, n_estimators=320):
        model = AdaBoostRegressor(DecisionTreeRegressor(max_depth=max_depth),
                                  n_estimators=n_estimators, learning_rate=0.8)
        model.fit(data['train'][:, :-1], data['train'][:, -1])
        # 注意存儲驗證數據集結果和預測數據集結果的不同
        # 訓練數據集的預測結果
        xul = model.predict(data['train'][:, :-1])
        # 驗證的預測結果
        yanre = model.predict(data['test'][:, :-1])
        # 預測的預測結果
        prer = model.predict(data['predict'][:, :-1])
        # 儲存
        self.yanzhneg_pr.append(yanre)
        self.predi.append(prer)
        # 分別計算訓練、驗證、預測的誤差
        # 每計算一折後，要計算訓練、驗證、預測數據的誤差
        xx = self.RMSE(xul, data['train'][:, -1])
        yy = self.RMSE(yanre, data['test'][:, -1])
        pp = self.RMSE(prer, data['predict'][:, -1])
        # 儲存誤差
        self.error_dict['AdaBoost'] = [xx, yy, pp]
        # 驗證數據集的真實輸出結果
        self.yanzhneg_real = data['test'][:, -1]

        # 預測數據集的真實輸出結果
        self.preal = data['predict'][:, -1]
        return print('1層中的AdaBoost運行完畢')

    # GBDT 

# 需要導入模塊: from sklearn import ensemble [as 別名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 別名]
def recspre(exstr, predata, datadict, zhe, count=100):
    tree, te = exstr.split('-')
    model = AdaBoostRegressor(DecisionTreeRegressor(max_depth=int(te)),
                              n_estimators=int(tree), learning_rate=0.8)
    model.fit(datadict[zhe]['train'][:, :-1], datadict[zhe]['train'][:, -1])

    # 預測
    yucede = model.predict(predata[:, :-1])
    # 為了便於展示，選100條數據進行展示
    zongleng = np.arange(len(yucede))
    randomnum = np.random.choice(zongleng, count, replace=False)

    yucede_se = list(np.array(yucede)[randomnum])

    yuce_re = list(np.array(predata[:, -1])[randomnum])

    # 對比
    plt.figure(figsize=(17, 9))
    plt.subplot(2, 1, 1)
    plt.plot(list(range(len(yucede_se))), yucede_se, 'r--', label='預測', lw=2)
    plt.scatter(list(range(len(yuce_re))), yuce_re, c='b', marker='.', label='真實', lw=2)
    plt.xlim(-1, count + 1)
    plt.legend()
    plt.title('預測和真實值對比[最大樹數%d]' % int(tree))

    plt.subplot(2, 1, 2)
    plt.plot(list(range(len(yucede_se))), np.array(yuce_re) - np.array(yucede_se), 'k--', marker='s', label='真實-預測', lw=2)
    plt.legend()
    plt.title('預測和真實值相對誤差')

    plt.savefig(r'C:\Users\GWT9\Desktop\duibi.jpg')
    return '預測真實對比完畢'

# 主函數 

# 需要導入模塊: from sklearn import ensemble [as 別名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 別名]
def test_regression_toy():
    # Check classification on a toy dataset.
    clf = AdaBoostRegressor(random_state=0)
    clf.fit(X, y_regr)
    assert_array_equal(clf.predict(T), y_t_regr) 

# 需要導入模塊: from sklearn import ensemble [as 別名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 別名]
def test_boston():
    # Check consistency on dataset boston house prices.
    reg = AdaBoostRegressor(random_state=0)
    reg.fit(boston.data, boston.target)
    score = reg.score(boston.data, boston.target)
    assert score > 0.85

    # Check we used multiple estimators
    assert len(reg.estimators_) > 1
    # Check for distinct random states (see issue #7408)
    assert_equal(len(set(est.random_state for est in reg.estimators_)),
                 len(reg.estimators_)) 

# 需要導入模塊: from sklearn import ensemble [as 別名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 別名]
def test_pickle():
    # Check pickability.
    import pickle

    # Adaboost classifier
    for alg in ['SAMME', 'SAMME.R']:
        obj = AdaBoostClassifier(algorithm=alg)
        obj.fit(iris.data, iris.target)
        score = obj.score(iris.data, iris.target)
        s = pickle.dumps(obj)

        obj2 = pickle.loads(s)
        assert_equal(type(obj2), obj.__class__)
        score2 = obj2.score(iris.data, iris.target)
        assert_equal(score, score2)

    # Adaboost regressor
    obj = AdaBoostRegressor(random_state=0)
    obj.fit(boston.data, boston.target)
    score = obj.score(boston.data, boston.target)
    s = pickle.dumps(obj)

    obj2 = pickle.loads(s)
    assert_equal(type(obj2), obj.__class__)
    score2 = obj2.score(boston.data, boston.target)
    assert_equal(score, score2) 

# 需要導入模塊: from sklearn import ensemble [as 別名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 別名]
def test_sample_weight_missing():
    from sklearn.cluster import KMeans

    clf = AdaBoostClassifier(KMeans(), algorithm="SAMME")
    assert_raises(ValueError, clf.fit, X, y_regr)

    clf = AdaBoostRegressor(KMeans())
    assert_raises(ValueError, clf.fit, X, y_regr) 

# 需要導入模塊: from sklearn import ensemble [as 別名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 別名]
def setClf(self):
#         min_samples_split = 3
        self.clf = AdaBoostRegressor()
        return 

# 需要導入模塊: from sklearn import ensemble [as 別名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 別名]
def __init__(self, options):
        self.handle_options(options)
        params = options.get('params', {})
        out_params = convert_params(
            params,
            strs=['loss', 'max_features'],
            floats=['learning_rate'],
            ints=['n_estimators'],
        )

        self.estimator = _AdaBoostRegressor(**out_params)