7장 – 앙상블 학습과 랜덤 포레스트

이 노트북은 7장에 있는 모든 샘플 코드와 연습문제 해답을 가지고 있습니다.

설정

먼저 몇 개의 모듈을 임포트합니다. 맷플롯립 그래프를 인라인으로 출력하도록 만들고 그림을 저장하는 함수를 준비합니다. 또한 파이썬 버전이 3.5 이상인지 확인합니다(파이썬 2.x에서도 동작하지만 곧 지원이 중단되므로 파이썬 3을 사용하는 것이 좋습니다). 사이킷런 버전이 0.20 이상인지도 확인합니다.

In [1]:
# 파이썬 ≥3.5 필수
import sys
assert sys.version_info >= (3, 5)

# 사이킷런 ≥0.20 필수
import sklearn
assert sklearn.__version__ >= "0.20"

# 공통 모듈 임포트
import numpy as np
import os

# 노트북 실행 결과를 동일하게 유지하기 위해
np.random.seed(42)

# 깔끔한 그래프 출력을 위해
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)

# 그림을 저장할 위치
PROJECT_ROOT_DIR = "."
CHAPTER_ID = "ensembles"
IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID)
os.makedirs(IMAGES_PATH, exist_ok=True)

def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300):
    path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension)
    print("그림 저장:", fig_id)
    if tight_layout:
        plt.tight_layout()
    plt.savefig(path, format=fig_extension, dpi=resolution)

투표기반 분류기

In [2]:
heads_proba = 0.51
coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)
cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)
In [3]:
plt.figure(figsize=(8,3.5))
plt.plot(cumulative_heads_ratio)
plt.plot([0, 10000], [0.51, 0.51], "k--", linewidth=2, label="51%")
plt.plot([0, 10000], [0.5, 0.5], "k-", label="50%")
plt.xlabel("Number of coin tosses")
plt.ylabel("Heads ratio")
plt.legend(loc="lower right")
plt.axis([0, 10000, 0.42, 0.58])
save_fig("law_of_large_numbers_plot")
plt.show()
그림 저장: law_of_large_numbers_plot
In [4]:
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_moons

X, y = make_moons(n_samples=500, noise=0.30, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

노트: 향후 버전을 위해 사이킷런에서 기본 값이 될 solver="lbfgs", n_estimators=100, gamma="scale"로 지정합니다.

In [5]:
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC

log_clf = LogisticRegression(solver="lbfgs", random_state=42)
rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)
svm_clf = SVC(gamma="scale", random_state=42)

voting_clf = VotingClassifier(
    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],
    voting='hard')
In [6]:
voting_clf.fit(X_train, y_train)
Out[6]:
VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),
                             ('rf', RandomForestClassifier(random_state=42)),
                             ('svc', SVC(random_state=42))])
In [7]:
from sklearn.metrics import accuracy_score

for clf in (log_clf, rnd_clf, svm_clf, voting_clf):
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))
LogisticRegression 0.864
RandomForestClassifier 0.896
SVC 0.896
VotingClassifier 0.912

간접 투표:

In [8]:
log_clf = LogisticRegression(solver="lbfgs", random_state=42)
rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)
svm_clf = SVC(gamma="scale", probability=True, random_state=42)

voting_clf = VotingClassifier(
    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],
    voting='soft')
voting_clf.fit(X_train, y_train)
Out[8]:
VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),
                             ('rf', RandomForestClassifier(random_state=42)),
                             ('svc', SVC(probability=True, random_state=42))],
                 voting='soft')
In [9]:
from sklearn.metrics import accuracy_score

for clf in (log_clf, rnd_clf, svm_clf, voting_clf):
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))
LogisticRegression 0.864
RandomForestClassifier 0.896
SVC 0.896
VotingClassifier 0.92

배깅 앙상블

In [10]:
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier

bag_clf = BaggingClassifier(
    DecisionTreeClassifier(random_state=42), n_estimators=500,
    max_samples=100, bootstrap=True, random_state=42)
bag_clf.fit(X_train, y_train)
y_pred = bag_clf.predict(X_test)
In [11]:
from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, y_pred))
0.904
In [12]:
tree_clf = DecisionTreeClassifier(random_state=42)
tree_clf.fit(X_train, y_train)
y_pred_tree = tree_clf.predict(X_test)
print(accuracy_score(y_test, y_pred_tree))
0.856
In [13]:
from matplotlib.colors import ListedColormap

def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.45, -1, 1.5], alpha=0.5, contour=True):
    x1s = np.linspace(axes[0], axes[1], 100)
    x2s = np.linspace(axes[2], axes[3], 100)
    x1, x2 = np.meshgrid(x1s, x2s)
    X_new = np.c_[x1.ravel(), x2.ravel()]
    y_pred = clf.predict(X_new).reshape(x1.shape)
    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])
    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)
    if contour:
        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])
        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)
    plt.plot(X[:, 0][y==0], X[:, 1][y==0], "yo", alpha=alpha)
    plt.plot(X[:, 0][y==1], X[:, 1][y==1], "bs", alpha=alpha)
    plt.axis(axes)
    plt.xlabel(r"$x_1$", fontsize=18)
    plt.ylabel(r"$x_2$", fontsize=18, rotation=0)
In [14]:
fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)
plt.sca(axes[0])
plot_decision_boundary(tree_clf, X, y)
plt.title("Decision Tree", fontsize=14)
plt.sca(axes[1])
plot_decision_boundary(bag_clf, X, y)
plt.title("Decision Trees with Bagging", fontsize=14)
plt.ylabel("")
save_fig("decision_tree_without_and_with_bagging_plot")
plt.show()
그림 저장: decision_tree_without_and_with_bagging_plot

랜덤 포레스트

In [15]:
bag_clf = BaggingClassifier(
    DecisionTreeClassifier(max_leaf_nodes=16, random_state=42),
    n_estimators=500, max_samples=1.0, bootstrap=True, random_state=42)
In [16]:
bag_clf.fit(X_train, y_train)
y_pred = bag_clf.predict(X_test)
In [17]:
from sklearn.ensemble import RandomForestClassifier

rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, random_state=42)
rnd_clf.fit(X_train, y_train)

y_pred_rf = rnd_clf.predict(X_test)
In [18]:
np.sum(y_pred == y_pred_rf) / len(y_pred)  # 거의 에측이 동일합니다.
Out[18]:
0.976
In [19]:
from sklearn.datasets import load_iris
iris = load_iris()
rnd_clf = RandomForestClassifier(n_estimators=500, random_state=42)
rnd_clf.fit(iris["data"], iris["target"])
for name, score in zip(iris["feature_names"], rnd_clf.feature_importances_):
    print(name, score)
sepal length (cm) 0.11249225099876375
sepal width (cm) 0.02311928828251033
petal length (cm) 0.4410304643639577
petal width (cm) 0.4233579963547682
In [20]:
rnd_clf.feature_importances_
Out[20]:
array([0.11249225, 0.02311929, 0.44103046, 0.423358  ])
In [21]:
plt.figure(figsize=(6, 4))

for i in range(15):
    tree_clf = DecisionTreeClassifier(max_leaf_nodes=16, random_state=42 + i)
    indices_with_replacement = np.random.randint(0, len(X_train), len(X_train))
    tree_clf.fit(X[indices_with_replacement], y[indices_with_replacement])
    plot_decision_boundary(tree_clf, X, y, axes=[-1.5, 2.45, -1, 1.5], alpha=0.02, contour=False)

plt.show()

OOB 평가

In [22]:
bag_clf = BaggingClassifier(
    DecisionTreeClassifier(random_state=42), n_estimators=500,
    bootstrap=True, oob_score=True, random_state=40)
bag_clf.fit(X_train, y_train)
bag_clf.oob_score_
Out[22]:
0.8986666666666666
In [23]:
bag_clf.oob_decision_function_
Out[23]:
array([[0.32275132, 0.67724868],
       [0.34117647, 0.65882353],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [0.09497207, 0.90502793],
       [0.31147541, 0.68852459],
       [0.01754386, 0.98245614],
       [0.97109827, 0.02890173],
       [0.97765363, 0.02234637],
       [0.74404762, 0.25595238],
       [0.        , 1.        ],
       [0.7173913 , 0.2826087 ],
       [0.85026738, 0.14973262],
       [0.97222222, 0.02777778],
       [0.0625    , 0.9375    ],
       [0.        , 1.        ],
       [0.97837838, 0.02162162],
       [0.94642857, 0.05357143],
       [1.        , 0.        ],
       [0.01704545, 0.98295455],
       [0.39473684, 0.60526316],
       [0.88700565, 0.11299435],
       [1.        , 0.        ],
       [0.97790055, 0.02209945],
       [0.        , 1.        ],
       [0.99428571, 0.00571429],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.62569832, 0.37430168],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [0.13402062, 0.86597938],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.38251366, 0.61748634],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.27093596, 0.72906404],
       [0.34146341, 0.65853659],
       [1.        , 0.        ],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.00531915, 0.99468085],
       [0.98843931, 0.01156069],
       [0.91428571, 0.08571429],
       [0.97282609, 0.02717391],
       [0.98019802, 0.01980198],
       [0.        , 1.        ],
       [0.07361963, 0.92638037],
       [0.98019802, 0.01980198],
       [0.0052356 , 0.9947644 ],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [0.97790055, 0.02209945],
       [0.8       , 0.2       ],
       [0.42424242, 0.57575758],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.66477273, 0.33522727],
       [1.        , 0.        ],
       [1.        , 0.        ],
       [0.86781609, 0.13218391],
       [1.        , 0.        ],
       [0.56725146, 0.43274854],
       [0.1576087 , 0.8423913 ],
       [0.66492147, 0.33507853],
       [0.91709845, 0.08290155],
       [0.        , 1.        ],
       [0.16759777, 0.83240223],
       [0.87434555, 0.12565445],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.995     , 0.005     ],
       [0.        , 1.        ],
       [0.07878788, 0.92121212],
       [0.05418719, 0.94581281],
       [0.29015544, 0.70984456],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.83040936, 0.16959064],
       [0.01092896, 0.98907104],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [0.21465969, 0.78534031],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [0.94660194, 0.05339806],
       [0.77094972, 0.22905028],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.16574586, 0.83425414],
       [0.65306122, 0.34693878],
       [0.        , 1.        ],
       [0.02564103, 0.97435897],
       [0.50555556, 0.49444444],
       [1.        , 0.        ],
       [0.03208556, 0.96791444],
       [0.99435028, 0.00564972],
       [0.23699422, 0.76300578],
       [0.49509804, 0.50490196],
       [0.9947644 , 0.0052356 ],
       [0.00555556, 0.99444444],
       [0.98963731, 0.01036269],
       [0.26153846, 0.73846154],
       [0.92972973, 0.07027027],
       [1.        , 0.        ],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [0.80113636, 0.19886364],
       [1.        , 0.        ],
       [0.0106383 , 0.9893617 ],
       [1.        , 0.        ],
       [1.        , 0.        ],
       [1.        , 0.        ],
       [0.98181818, 0.01818182],
       [1.        , 0.        ],
       [0.01036269, 0.98963731],
       [0.97752809, 0.02247191],
       [0.99453552, 0.00546448],
       [0.01960784, 0.98039216],
       [0.17857143, 0.82142857],
       [0.98387097, 0.01612903],
       [0.29533679, 0.70466321],
       [0.98295455, 0.01704545],
       [0.        , 1.        ],
       [0.00561798, 0.99438202],
       [0.75690608, 0.24309392],
       [0.38624339, 0.61375661],
       [0.40625   , 0.59375   ],
       [0.87368421, 0.12631579],
       [0.92462312, 0.07537688],
       [0.05181347, 0.94818653],
       [0.82802548, 0.17197452],
       [0.01546392, 0.98453608],
       [0.        , 1.        ],
       [0.02298851, 0.97701149],
       [0.9726776 , 0.0273224 ],
       [1.        , 0.        ],
       [1.        , 0.        ],
       [0.01041667, 0.98958333],
       [0.        , 1.        ],
       [0.03804348, 0.96195652],
       [0.02040816, 0.97959184],
       [1.        , 0.        ],
       [1.        , 0.        ],
       [0.94915254, 0.05084746],
       [1.        , 0.        ],
       [1.        , 0.        ],
       [0.99462366, 0.00537634],
       [0.        , 1.        ],
       [0.39378238, 0.60621762],
       [0.33152174, 0.66847826],
       [0.00609756, 0.99390244],
       [0.        , 1.        ],
       [0.3172043 , 0.6827957 ],
       [1.        , 0.        ],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.00588235, 0.99411765],
       [0.        , 1.        ],
       [0.98924731, 0.01075269],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.62893082, 0.37106918],
       [0.92344498, 0.07655502],
       [0.        , 1.        ],
       [0.99526066, 0.00473934],
       [1.        , 0.        ],
       [0.98888889, 0.01111111],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.06989247, 0.93010753],
       [1.        , 0.        ],
       [0.03608247, 0.96391753],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.02185792, 0.97814208],
       [1.        , 0.        ],
       [0.95808383, 0.04191617],
       [0.78362573, 0.21637427],
       [0.56650246, 0.43349754],
       [0.        , 1.        ],
       [0.18023256, 0.81976744],
       [1.        , 0.        ],
       [0.93121693, 0.06878307],
       [0.97175141, 0.02824859],
       [1.        , 0.        ],
       [0.00531915, 0.99468085],
       [0.        , 1.        ],
       [0.43010753, 0.56989247],
       [0.85858586, 0.14141414],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.00558659, 0.99441341],
       [0.        , 1.        ],
       [0.96923077, 0.03076923],
       [0.        , 1.        ],
       [0.21649485, 0.78350515],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [0.98477157, 0.01522843],
       [0.8       , 0.2       ],
       [0.99441341, 0.00558659],
       [0.        , 1.        ],
       [0.09497207, 0.90502793],
       [0.99492386, 0.00507614],
       [0.01714286, 0.98285714],
       [0.        , 1.        ],
       [0.02747253, 0.97252747],
       [1.        , 0.        ],
       [0.77005348, 0.22994652],
       [0.        , 1.        ],
       [0.90229885, 0.09770115],
       [0.98387097, 0.01612903],
       [0.22222222, 0.77777778],
       [0.20348837, 0.79651163],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [0.20338983, 0.79661017],
       [0.98181818, 0.01818182],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.98969072, 0.01030928],
       [0.        , 1.        ],
       [0.48663102, 0.51336898],
       [1.        , 0.        ],
       [0.00529101, 0.99470899],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [0.08379888, 0.91620112],
       [0.12352941, 0.87647059],
       [0.99415205, 0.00584795],
       [0.03517588, 0.96482412],
       [1.        , 0.        ],
       [0.39790576, 0.60209424],
       [0.05434783, 0.94565217],
       [0.53191489, 0.46808511],
       [0.51898734, 0.48101266],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [0.60869565, 0.39130435],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.24157303, 0.75842697],
       [0.81578947, 0.18421053],
       [0.08717949, 0.91282051],
       [0.99453552, 0.00546448],
       [0.82142857, 0.17857143],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [0.11904762, 0.88095238],
       [0.04188482, 0.95811518],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.89150943, 0.10849057],
       [0.19230769, 0.80769231],
       [0.95238095, 0.04761905],
       [0.00515464, 0.99484536],
       [0.59375   , 0.40625   ],
       [0.07692308, 0.92307692],
       [0.99484536, 0.00515464],
       [0.83684211, 0.16315789],
       [0.        , 1.        ],
       [0.99484536, 0.00515464],
       [0.95360825, 0.04639175],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.26395939, 0.73604061],
       [0.98461538, 0.01538462],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.00574713, 0.99425287],
       [0.85142857, 0.14857143],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.75301205, 0.24698795],
       [0.8969697 , 0.1030303 ],
       [1.        , 0.        ],
       [0.75555556, 0.24444444],
       [0.48863636, 0.51136364],
       [0.        , 1.        ],
       [0.92473118, 0.07526882],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.87709497, 0.12290503],
       [1.        , 0.        ],
       [1.        , 0.        ],
       [0.74752475, 0.25247525],
       [0.09146341, 0.90853659],
       [0.42268041, 0.57731959],
       [0.22395833, 0.77604167],
       [0.        , 1.        ],
       [0.87046632, 0.12953368],
       [0.78212291, 0.21787709],
       [0.00507614, 0.99492386],
       [1.        , 0.        ],
       [1.        , 0.        ],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.02884615, 0.97115385],
       [0.96      , 0.04      ],
       [0.93478261, 0.06521739],
       [1.        , 0.        ],
       [0.50731707, 0.49268293],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.01604278, 0.98395722],
       [1.        , 0.        ],
       [1.        , 0.        ],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.96987952, 0.03012048],
       [0.        , 1.        ],
       [0.05172414, 0.94827586],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.99494949, 0.00505051],
       [0.01675978, 0.98324022],
       [1.        , 0.        ],
       [0.14583333, 0.85416667],
       [0.        , 1.        ],
       [0.00546448, 0.99453552],
       [0.        , 1.        ],
       [0.41836735, 0.58163265],
       [0.13095238, 0.86904762],
       [0.22110553, 0.77889447],
       [1.        , 0.        ],
       [0.97647059, 0.02352941],
       [0.21195652, 0.78804348],
       [0.98882682, 0.01117318],
       [0.        , 1.        ],
       [0.        , 1.        ],
       [1.        , 0.        ],
       [0.96428571, 0.03571429],
       [0.34554974, 0.65445026],
       [0.98235294, 0.01764706],
       [1.        , 0.        ],
       [0.        , 1.        ],
       [0.99465241, 0.00534759],
       [0.        , 1.        ],
       [0.06043956, 0.93956044],
       [0.98214286, 0.01785714],
       [1.        , 0.        ],
       [0.03108808, 0.96891192],
       [0.58854167, 0.41145833]])
In [24]:
from sklearn.metrics import accuracy_score
y_pred = bag_clf.predict(X_test)
accuracy_score(y_test, y_pred)
Out[24]:
0.912

특성 중요도

In [25]:
from sklearn.datasets import fetch_openml

mnist = fetch_openml('mnist_784', version=1)
mnist.target = mnist.target.astype(np.uint8)
In [26]:
rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)
rnd_clf.fit(mnist["data"], mnist["target"])
Out[26]:
RandomForestClassifier(random_state=42)
In [27]:
def plot_digit(data):
    image = data.reshape(28, 28)
    plt.imshow(image, cmap = mpl.cm.hot,
               interpolation="nearest")
    plt.axis("off")
In [28]:
plot_digit(rnd_clf.feature_importances_)

cbar = plt.colorbar(ticks=[rnd_clf.feature_importances_.min(), rnd_clf.feature_importances_.max()])
cbar.ax.set_yticklabels(['Not important', 'Very important'])

save_fig("mnist_feature_importance_plot")
plt.show()
그림 저장: mnist_feature_importance_plot

에이다부스트

In [29]:
from sklearn.ensemble import AdaBoostClassifier

ada_clf = AdaBoostClassifier(
    DecisionTreeClassifier(max_depth=1), n_estimators=200,
    algorithm="SAMME.R", learning_rate=0.5, random_state=42)
ada_clf.fit(X_train, y_train)
Out[29]:
AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1),
                   learning_rate=0.5, n_estimators=200, random_state=42)
In [30]:
plot_decision_boundary(ada_clf, X, y)
In [31]:
m = len(X_train)

fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)
for subplot, learning_rate in ((0, 1), (1, 0.5)):
    sample_weights = np.ones(m)
    plt.sca(axes[subplot])
    for i in range(5):
        svm_clf = SVC(kernel="rbf", C=0.05, gamma="scale", random_state=42)
        svm_clf.fit(X_train, y_train, sample_weight=sample_weights)
        y_pred = svm_clf.predict(X_train)
        sample_weights[y_pred != y_train] *= (1 + learning_rate)
        plot_decision_boundary(svm_clf, X, y, alpha=0.2)
        plt.title("learning_rate = {}".format(learning_rate), fontsize=16)
    if subplot == 0:
        plt.text(-0.7, -0.65, "1", fontsize=14)
        plt.text(-0.6, -0.10, "2", fontsize=14)
        plt.text(-0.5,  0.10, "3", fontsize=14)
        plt.text(-0.4,  0.55, "4", fontsize=14)
        plt.text(-0.3,  0.90, "5", fontsize=14)
    else:
        plt.ylabel("")

save_fig("boosting_plot")
plt.show()
그림 저장: boosting_plot
In [32]:
list(m for m in dir(ada_clf) if not m.startswith("_") and m.endswith("_"))
Out[32]:
['base_estimator_',
 'classes_',
 'estimator_errors_',
 'estimator_weights_',
 'estimators_',
 'feature_importances_',
 'n_classes_',
 'n_features_in_']

그레이디언트 부스팅

In [33]:
np.random.seed(42)
X = np.random.rand(100, 1) - 0.5
y = 3*X[:, 0]**2 + 0.05 * np.random.randn(100)
In [34]:
from sklearn.tree import DecisionTreeRegressor

tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)
tree_reg1.fit(X, y)
Out[34]:
DecisionTreeRegressor(max_depth=2, random_state=42)
In [35]:
y2 = y - tree_reg1.predict(X)
tree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=42)
tree_reg2.fit(X, y2)
Out[35]:
DecisionTreeRegressor(max_depth=2, random_state=42)
In [36]:
y3 = y2 - tree_reg2.predict(X)
tree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=42)
tree_reg3.fit(X, y3)
Out[36]:
DecisionTreeRegressor(max_depth=2, random_state=42)
In [37]:
X_new = np.array([[0.8]])
In [38]:
y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))
In [39]:
y_pred
Out[39]:
array([0.75026781])
In [40]:
def plot_predictions(regressors, X, y, axes, label=None, style="r-", data_style="b.", data_label=None):
    x1 = np.linspace(axes[0], axes[1], 500)
    y_pred = sum(regressor.predict(x1.reshape(-1, 1)) for regressor in regressors)
    plt.plot(X[:, 0], y, data_style, label=data_label)
    plt.plot(x1, y_pred, style, linewidth=2, label=label)
    if label or data_label:
        plt.legend(loc="upper center", fontsize=16)
    plt.axis(axes)
In [41]:
plt.figure(figsize=(11,11))

plt.subplot(321)
plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label="$h_1(x_1)$", style="g-", data_label="Training set")
plt.ylabel("$y$", fontsize=16, rotation=0)
plt.title("Residuals and tree predictions", fontsize=16)

plt.subplot(322)
plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label="$h(x_1) = h_1(x_1)$", data_label="Training set")
plt.ylabel("$y$", fontsize=16, rotation=0)
plt.title("Ensemble predictions", fontsize=16)

plt.subplot(323)
plot_predictions([tree_reg2], X, y2, axes=[-0.5, 0.5, -0.5, 0.5], label="$h_2(x_1)$", style="g-", data_style="k+", data_label="Residuals")
plt.ylabel("$y - h_1(x_1)$", fontsize=16)

plt.subplot(324)
plot_predictions([tree_reg1, tree_reg2], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label="$h(x_1) = h_1(x_1) + h_2(x_1)$")
plt.ylabel("$y$", fontsize=16, rotation=0)

plt.subplot(325)
plot_predictions([tree_reg3], X, y3, axes=[-0.5, 0.5, -0.5, 0.5], label="$h_3(x_1)$", style="g-", data_style="k+")
plt.ylabel("$y - h_1(x_1) - h_2(x_1)$", fontsize=16)
plt.xlabel("$x_1$", fontsize=16)

plt.subplot(326)
plot_predictions([tree_reg1, tree_reg2, tree_reg3], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label="$h(x_1) = h_1(x_1) + h_2(x_1) + h_3(x_1)$")
plt.xlabel("$x_1$", fontsize=16)
plt.ylabel("$y$", fontsize=16, rotation=0)

save_fig("gradient_boosting_plot")
plt.show()
그림 저장: gradient_boosting_plot
In [42]:
from sklearn.ensemble import GradientBoostingRegressor

gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=3, learning_rate=1.0, random_state=42)
gbrt.fit(X, y)
Out[42]:
GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,
                          random_state=42)
In [43]:
gbrt_slow = GradientBoostingRegressor(max_depth=2, n_estimators=200, learning_rate=0.1, random_state=42)
gbrt_slow.fit(X, y)
Out[43]:
GradientBoostingRegressor(max_depth=2, n_estimators=200, random_state=42)
In [44]:
fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)

plt.sca(axes[0])
plot_predictions([gbrt], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label="Ensemble predictions")
plt.title("learning_rate={}, n_estimators={}".format(gbrt.learning_rate, gbrt.n_estimators), fontsize=14)
plt.xlabel("$x_1$", fontsize=16)
plt.ylabel("$y$", fontsize=16, rotation=0)

plt.sca(axes[1])
plot_predictions([gbrt_slow], X, y, axes=[-0.5, 0.5, -0.1, 0.8])
plt.title("learning_rate={}, n_estimators={}".format(gbrt_slow.learning_rate, gbrt_slow.n_estimators), fontsize=14)
plt.xlabel("$x_1$", fontsize=16)

save_fig("gbrt_learning_rate_plot")
plt.show()
그림 저장: gbrt_learning_rate_plot

조기 종료를 사용한 그래디언트 부스팅

In [45]:
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

X_train, X_val, y_train, y_val = train_test_split(X, y, random_state=49)

gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=120, random_state=42)
gbrt.fit(X_train, y_train)

errors = [mean_squared_error(y_val, y_pred)
          for y_pred in gbrt.staged_predict(X_val)]
bst_n_estimators = np.argmin(errors) + 1

gbrt_best = GradientBoostingRegressor(max_depth=2, n_estimators=bst_n_estimators, random_state=42)
gbrt_best.fit(X_train, y_train)
Out[45]:
GradientBoostingRegressor(max_depth=2, n_estimators=56, random_state=42)
In [46]:
min_error = np.min(errors)
In [47]:
plt.figure(figsize=(10, 4))

plt.subplot(121)
plt.plot(errors, "b.-")
plt.plot([bst_n_estimators, bst_n_estimators], [0, min_error], "k--")
plt.plot([0, 120], [min_error, min_error], "k--")
plt.plot(bst_n_estimators, min_error, "ko")
plt.text(bst_n_estimators, min_error*1.2, "Minimum", ha="center", fontsize=14)
plt.axis([0, 120, 0, 0.01])
plt.xlabel("Number of trees")
plt.ylabel("Error", fontsize=16)
plt.title("Validation error", fontsize=14)

plt.subplot(122)
plot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.1, 0.8])
plt.title("Best model (%d trees)" % bst_n_estimators, fontsize=14)
plt.ylabel("$y$", fontsize=16, rotation=0)
plt.xlabel("$x_1$", fontsize=16)

save_fig("early_stopping_gbrt_plot")
plt.show()
그림 저장: early_stopping_gbrt_plot
In [48]:
gbrt = GradientBoostingRegressor(max_depth=2, warm_start=True, random_state=42)

min_val_error = float("inf")
error_going_up = 0
for n_estimators in range(1, 120):
    gbrt.n_estimators = n_estimators
    gbrt.fit(X_train, y_train)
    y_pred = gbrt.predict(X_val)
    val_error = mean_squared_error(y_val, y_pred)
    if val_error < min_val_error:
        min_val_error = val_error
        error_going_up = 0
    else:
        error_going_up += 1
        if error_going_up == 5:
            break  # early stopping
In [49]:
print(gbrt.n_estimators)
61
In [50]:
print("Minimum validation MSE:", min_val_error)
Minimum validation MSE: 0.002712853325235463

XGBoost 사용하기

In [51]:
try:
    import xgboost
except ImportError as ex:
    print("에러: xgboost 라이브러리 설치되지 않았습니다.")
    xgboost = None
In [52]:
if xgboost is not None:  # 책에 없음
    xgb_reg = xgboost.XGBRegressor(random_state=42)
    xgb_reg.fit(X_train, y_train)
    y_pred = xgb_reg.predict(X_val)
    val_error = mean_squared_error(y_val, y_pred) # 책에 없음
    print("Validation MSE:", val_error)           # 책에 없음
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
Validation MSE: 0.0028512559726563943
In [53]:
if xgboost is not None:  # 책에 없음
    xgb_reg.fit(X_train, y_train,
                eval_set=[(X_val, y_val)], early_stopping_rounds=2)
    y_pred = xgb_reg.predict(X_val)
    val_error = mean_squared_error(y_val, y_pred)  # 책에 없음
    print("Validation MSE:", val_error)            # 책에 없음
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[0]	validation_0-rmse:0.286719
Will train until validation_0-rmse hasn't improved in 2 rounds.
[1]	validation_0-rmse:0.258221
[2]	validation_0-rmse:0.232634
[3]	validation_0-rmse:0.210526
[4]	validation_0-rmse:0.190232
[5]	validation_0-rmse:0.172196
[6]	validation_0-rmse:0.156394
[7]	validation_0-rmse:0.142241
[8]	validation_0-rmse:0.129789
[9]	validation_0-rmse:0.118752
[10]	validation_0-rmse:0.108388
[11]	validation_0-rmse:0.100155
[12]	validation_0-rmse:0.09208
[13]	validation_0-rmse:0.084791
[14]	validation_0-rmse:0.078699
[15]	validation_0-rmse:0.073248
[16]	validation_0-rmse:0.069391
[17]	validation_0-rmse:0.066277
[18]	validation_0-rmse:0.063458
[19]	validation_0-rmse:0.060326
[20]	validation_0-rmse:0.0578
[21]	validation_0-rmse:0.055643
[22]	validation_0-rmse:0.053943
[23]	validation_0-rmse:0.053138
[24]	validation_0-rmse:0.052415
[25]	validation_0-rmse:0.051821
[26]	validation_0-rmse:0.051226
[27]	validation_0-rmse:0.051135
[28]	validation_0-rmse:0.05091
[29]	validation_0-rmse:0.050893
[30]	validation_0-rmse:0.050725
[31]	validation_0-rmse:0.050471
[32]	validation_0-rmse:0.050285
[33]	validation_0-rmse:0.050492
[34]	validation_0-rmse:0.050348
Stopping. Best iteration:
[32]	validation_0-rmse:0.050285

Validation MSE: 0.002528626115371327
In [54]:
%timeit xgboost.XGBRegressor().fit(X_train, y_train) if xgboost is not None else None
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:54] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
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[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[20:53:57] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1572314959925/work/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
3.57 ms ± 10.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [55]:
%timeit GradientBoostingRegressor().fit(X_train, y_train)
19.7 ms ± 36.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

연습문제 해답

1. to 7.

부록 A 참조.

8. 투표 기반 분류기

문제: MNIST 데이터를 불러들여 훈련 세트, 검증 세트, 테스트 세트로 나눕니다(예를 들면 훈련에 40,000개 샘플, 검증에 10,000개 샘플, 테스트에 10,000개 샘플).

MNIST 데이터셋은 앞에서 로드했습니다.

In [56]:
from sklearn.model_selection import train_test_split
In [57]:
X_train_val, X_test, y_train_val, y_test = train_test_split(
    mnist.data, mnist.target, test_size=10000, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(
    X_train_val, y_train_val, test_size=10000, random_state=42)

문제: 그런 다음 랜덤 포레스트 분류기, 엑스트라 트리 분류기, SVM 같은 여러 종류의 분류기를 훈련시킵니다.

In [58]:
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
from sklearn.svm import LinearSVC
from sklearn.neural_network import MLPClassifier
In [59]:
random_forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)
extra_trees_clf = ExtraTreesClassifier(n_estimators=100, random_state=42)
svm_clf = LinearSVC(random_state=42)
mlp_clf = MLPClassifier(random_state=42)
In [60]:
estimators = [random_forest_clf, extra_trees_clf, svm_clf, mlp_clf]
for estimator in estimators:
    print("Training the", estimator)
    estimator.fit(X_train, y_train)
Training the RandomForestClassifier(random_state=42)
Training the ExtraTreesClassifier(random_state=42)
Training the LinearSVC(random_state=42)
/home/haesun/anaconda3/envs/homl2/lib/python3.7/site-packages/sklearn/svm/_base.py:975: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
Training the MLPClassifier(random_state=42)
In [61]:
[estimator.score(X_val, y_val) for estimator in estimators]
Out[61]:
[0.9692, 0.9715, 0.8695, 0.9639]

선형 SVM이 다른 분류기보다 성능이 많이 떨어집니다. 그러나 투표 기반 분류기의 성능을 향상시킬 수 있으므로 그대로 두겠습니다.

문제: 그리고 검증 세트에서 개개의 분류기보다 더 높은 성능을 내도록 이들을 간접 또는 직접 투표 분류기를 사용하는 앙상블로 연결해보세요.

In [62]:
from sklearn.ensemble import VotingClassifier
In [63]:
named_estimators = [
    ("random_forest_clf", random_forest_clf),
    ("extra_trees_clf", extra_trees_clf),
    ("svm_clf", svm_clf),
    ("mlp_clf", mlp_clf),
]
In [64]:
voting_clf = VotingClassifier(named_estimators)
In [65]:
voting_clf.fit(X_train, y_train)
/home/haesun/anaconda3/envs/homl2/lib/python3.7/site-packages/sklearn/svm/_base.py:975: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
Out[65]:
VotingClassifier(estimators=[('random_forest_clf',
                              RandomForestClassifier(random_state=42)),
                             ('extra_trees_clf',
                              ExtraTreesClassifier(random_state=42)),
                             ('svm_clf', LinearSVC(random_state=42)),
                             ('mlp_clf', MLPClassifier(random_state=42))])
In [66]:
voting_clf.score(X_val, y_val)
Out[66]:
0.9706
In [67]:
[estimator.score(X_val, y_val) for estimator in voting_clf.estimators_]
Out[67]:
[0.9692, 0.9715, 0.8695, 0.9639]

SVM 모델을 제거해서 성능이 향상되는지 확인해 보죠. 다음과 같이 set_params()를 사용하여 None으로 지정하면 특정 예측기를 제외시킬 수 있습니다:

In [68]:
voting_clf.set_params(svm_clf=None)
Out[68]:
VotingClassifier(estimators=[('random_forest_clf',
                              RandomForestClassifier(random_state=42)),
                             ('extra_trees_clf',
                              ExtraTreesClassifier(random_state=42)),
                             ('svm_clf', None),
                             ('mlp_clf', MLPClassifier(random_state=42))])

예측기 목록이 업데이트되었습니다:

In [69]:
voting_clf.estimators
Out[69]:
[('random_forest_clf', RandomForestClassifier(random_state=42)),
 ('extra_trees_clf', ExtraTreesClassifier(random_state=42)),
 ('svm_clf', None),
 ('mlp_clf', MLPClassifier(random_state=42))]

하지만 훈련된 예측기 목록은 업데이트되지 않습니다:

In [70]:
voting_clf.estimators_
Out[70]:
[RandomForestClassifier(random_state=42),
 ExtraTreesClassifier(random_state=42),
 LinearSVC(random_state=42),
 MLPClassifier(random_state=42)]

VotingClassifier를 다시 훈련시키거나 그냥 훈련된 예측기 목록에서 SVM 모델을 제거할 수 있습니다:

In [71]:
del voting_clf.estimators_[2]

VotingClassifier를 다시 평가해 보죠:

In [72]:
voting_clf.score(X_val, y_val)
Out[72]:
0.9736

훨씬 나아졌네요! SVM 모델이 성능을 저하시켰습니다. 이제 간접 투표 분류기를 사용해 보죠. 분류기를 다시 훈련시킬 필요는 없고 voting"soft"로 지정하면 됩니다:

In [73]:
voting_clf.voting = "soft"
In [74]:
voting_clf.score(X_val, y_val)
Out[74]:
0.97

이 경우는 직접 투표 방식이 낫네요.

앙상블을 얻고 나면 테스트 세트로 확인해보세요. 개개의 분류기와 비교해서 성능이 얼마나 향상되나요?

In [75]:
voting_clf.voting = "hard"
voting_clf.score(X_test, y_test)
Out[75]:
0.9704
In [76]:
[estimator.score(X_test, y_test) for estimator in voting_clf.estimators_]
Out[76]:
[0.9645, 0.9691, 0.9604]

여기서는 투표 기반 분류기가 최선의 모델의 오차율을 아주 조금만 감소시킵니다.

9. 스태킹 앙상블

문제: 이전 연습문제의 각 분류기를 실행해서 검증 세트에서 예측을 만들고 그 결과로 새로운 훈련 세트를 만들어보세요. 각 훈련 샘플은 하나의 이미지에 대한 전체 분류기의 예측을 담은 벡터고 타깃은 이미지의 클래스입니다. 새로운 훈련 세트에 분류기 하나를 훈련시켜 보세요.

In [77]:
X_val_predictions = np.empty((len(X_val), len(estimators)), dtype=np.float32)

for index, estimator in enumerate(estimators):
    X_val_predictions[:, index] = estimator.predict(X_val)
In [78]:
X_val_predictions
Out[78]:
array([[5., 5., 5., 5.],
       [8., 8., 8., 8.],
       [2., 2., 2., 2.],
       ...,
       [7., 7., 7., 7.],
       [6., 6., 6., 6.],
       [7., 7., 7., 7.]], dtype=float32)
In [79]:
rnd_forest_blender = RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)
rnd_forest_blender.fit(X_val_predictions, y_val)
Out[79]:
RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)
In [80]:
rnd_forest_blender.oob_score_
Out[80]:
0.9677

이 블렌더를 세밀하게 튜닝하거나 다른 종류의 블렌더(예를 들어, MLPClassifier)를 시도해 볼 수 있습니다. 그런 늘 하던대로 다음 교차 검증을 사용해 가장 좋은 것을 선택합니다.

문제: 축하합니다. 방금 블렌더를 훈련시켰습니다. 그리고 이 분류기를 모아서 스태킹 앙상블을 구성했습니다. 이제 테스트 세트에 앙상블을 평가해보세요. 테스트 세트의 각 이미지에 대해 모든 분류기로 예측을 만들고 앙상블의 예측 결과를 만들기 위해 블렌더에 그 예측을 주입합니다. 앞서 만든 투표 분류기와 비교하면 어떤가요?

In [81]:
X_test_predictions = np.empty((len(X_test), len(estimators)), dtype=np.float32)

for index, estimator in enumerate(estimators):
    X_test_predictions[:, index] = estimator.predict(X_test)
In [82]:
y_pred = rnd_forest_blender.predict(X_test_predictions)
In [83]:
from sklearn.metrics import accuracy_score
In [84]:
accuracy_score(y_test, y_pred)
Out[84]:
0.967

이 스태킹 앙상블은 앞서 만든 투표 기반 분류기만큼 성능을 내지는 못합니다. 최선의 개별 분류기만큼 뛰어나지는 않습니다.