Python MultiOutputRegressor.predict方法代码示例

# 需要导入模块: from sklearn.multioutput import MultiOutputRegressor [as 别名]
# 或者: from sklearn.multioutput.MultiOutputRegressor import predict [as 别名]
def test_multi_target_sparse_regression():
    X, y = datasets.make_regression(n_targets=3)
    X_train, y_train = X[:50], y[:50]
    X_test = X[50:]

    for sparse in [sp.csr_matrix, sp.csc_matrix, sp.coo_matrix, sp.dok_matrix, sp.lil_matrix]:
        rgr = MultiOutputRegressor(Lasso(random_state=0))
        rgr_sparse = MultiOutputRegressor(Lasso(random_state=0))

        rgr.fit(X_train, y_train)
        rgr_sparse.fit(sparse(X_train), y_train)

        assert_almost_equal(rgr.predict(X_test), rgr_sparse.predict(sparse(X_test)))

# 需要导入模块: from sklearn.multioutput import MultiOutputRegressor [as 别名]
# 或者: from sklearn.multioutput.MultiOutputRegressor import predict [as 别名]
def test_multi_target_sample_weight_partial_fit():
    # weighted regressor
    X = [[1, 2, 3], [4, 5, 6]]
    y = [[3.141, 2.718], [2.718, 3.141]]
    w = [2., 1.]
    rgr_w = MultiOutputRegressor(SGDRegressor(random_state=0))
    rgr_w.partial_fit(X, y, w)

    # weighted with different weights
    w = [2., 2.]
    rgr = MultiOutputRegressor(SGDRegressor(random_state=0))
    rgr.partial_fit(X, y, w)

    assert_not_equal(rgr.predict(X)[0][0], rgr_w.predict(X)[0][0])

# 需要导入模块: from sklearn.multioutput import MultiOutputRegressor [as 别名]
# 或者: from sklearn.multioutput.MultiOutputRegressor import predict [as 别名]
def test_multi_target_sample_weights():
    # weighted regressor
    Xw = [[1, 2, 3], [4, 5, 6]]
    yw = [[3.141, 2.718], [2.718, 3.141]]
    w = [2., 1.]
    rgr_w = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
    rgr_w.fit(Xw, yw, w)

    # unweighted, but with repeated samples
    X = [[1, 2, 3], [1, 2, 3], [4, 5, 6]]
    y = [[3.141, 2.718], [3.141, 2.718], [2.718, 3.141]]
    rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
    rgr.fit(X, y)

    X_test = [[1.5, 2.5, 3.5], [3.5, 4.5, 5.5]]
    assert_almost_equal(rgr.predict(X_test), rgr_w.predict(X_test))

# 需要导入模块: from sklearn.multioutput import MultiOutputRegressor [as 别名]
# 或者: from sklearn.multioutput.MultiOutputRegressor import predict [as 别名]
    def test_multioutput(self):

        # http://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py

        from sklearn.multioutput import MultiOutputRegressor
        from sklearn.ensemble import RandomForestRegressor

        # Create a random dataset
        rng = np.random.RandomState(1)
        X = np.sort(200 * rng.rand(600, 1) - 100, axis=0)
        y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T
        y += (0.5 - rng.rand(*y.shape))

        df = pdml.ModelFrame(X, target=y)

        max_depth = 30

        rf1 = df.ensemble.RandomForestRegressor(max_depth=max_depth,
                                                random_state=self.random_state)
        reg1 = df.multioutput.MultiOutputRegressor(rf1)

        rf2 = RandomForestRegressor(max_depth=max_depth,
                                    random_state=self.random_state)
        reg2 = MultiOutputRegressor(rf2)

        df.fit(reg1)
        reg2.fit(X, y)

        result = df.predict(reg2)
        expected = pd.DataFrame(reg2.predict(X))
        tm.assert_frame_equal(result, expected)

# 需要导入模块: from sklearn.multioutput import MultiOutputRegressor [as 别名]
# 或者: from sklearn.multioutput.MultiOutputRegressor import predict [as 别名]
y += (0.5 - rng.rand(*y.shape))

X_train, X_test, y_train, y_test = train_test_split(X, y,
                                                    train_size=400,
                                                    random_state=4)

max_depth = 30
regr_multirf = MultiOutputRegressor(RandomForestRegressor(max_depth=max_depth,
                                                          random_state=0))
regr_multirf.fit(X_train, y_train)

regr_rf = RandomForestRegressor(max_depth=max_depth, random_state=2)
regr_rf.fit(X_train, y_train)

# Predict on new data
y_multirf = regr_multirf.predict(X_test)
y_rf = regr_rf.predict(X_test)

# Plot the results
plt.figure()
s = 50
a = 0.4
plt.scatter(y_test[:, 0], y_test[:, 1], edgecolor='k',
            c="navy", s=s, marker="s", alpha=a, label="Data")
plt.scatter(y_multirf[:, 0], y_multirf[:, 1], edgecolor='k',
            c="cornflowerblue", s=s, alpha=a,
            label="Multi RF score=%.2f" % regr_multirf.score(X_test, y_test))
plt.scatter(y_rf[:, 0], y_rf[:, 1], edgecolor='k',
            c="c", s=s, marker="^", alpha=a,
            label="RF score=%.2f" % regr_rf.score(X_test, y_test))
plt.xlim([-6, 6])