Analysing Housing Data in Scikit-learn¶

"California Housing Data"を用いて、Linear RegressionとRandom Forestの出力を比較してみる。

Boston Dataよりデータ数が相当多いので、学習に時間がかかることに注意

In [19]:
# California Housing Data
from sklearn.datasets import fetch_california_housing
import pandas as pd
housing = fetch_california_housing()
features = pd.DataFrame(housing.data, columns=housing.feature_names)
targets = housing.target

features.head()
Out[19]:
MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude Longitude
0 8.3252 41.0 6.984127 1.023810 322.0 2.555556 37.88 -122.23
1 8.3014 21.0 6.238137 0.971880 2401.0 2.109842 37.86 -122.22
2 7.2574 52.0 8.288136 1.073446 496.0 2.802260 37.85 -122.24
3 5.6431 52.0 5.817352 1.073059 558.0 2.547945 37.85 -122.25
4 3.8462 52.0 6.281853 1.081081 565.0 2.181467 37.85 -122.25
In [10]:
# 訓練データとテストデータへの分割
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(features, targets, train_size=0.9, random_state=0)

# データの正規化
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler().fit(X_train)
X_train_scaled = pd.DataFrame(scaler.transform(X_train), index=X_train.index.values,
                             columns=X_train.columns.values)
X_test_scaled = pd.DataFrame(scaler.transform(X_test), index=X_test.index.values, columns=X_test.columns.values)
In [11]:
# モデルの選択
# (1) 重回帰
from sklearn.linear_model import LinearRegression
lm_model = LinearRegression()

# (2)ランダムフォレスト回帰
from sklearn.ensemble import RandomForestRegressor
rfr_model = RandomForestRegressor(n_estimators=500, oob_score=True, random_state=0)
In [12]:
# 学習(トレーニング)
lm_model.fit(X_train, y_train)
rfr_model.fit(X_train, y_train)

#### 学習機械によるテストデータでの予測(推定)
lm_predicted_test = lm_model.predict(X_test)
rfr_predicted_test = rfr_model.predict(X_test)


# 各種評価指数の計算
from sklearn.metrics import r2_score
from scipy.stats import spearmanr, pearsonr

# (1) 決定係数の算出
lm_test_score = r2_score(y_test, lm_predicted_test)
rfr_test_score = r2_score(y_test, rfr_predicted_test)

# (2) Spearman Correlationの算出
lm_spearman = spearmanr(y_test, lm_predicted_test)
rfr_spearman = spearmanr(y_test, rfr_predicted_test)

# (3) Pearson Correlationの算出
lm_pearson = pearsonr(y_test, lm_predicted_test)
rfr_pearson = pearsonr(y_test, rfr_predicted_test)

# 各種評価指標のプリント
print("Test data R-2 score: (Linear) %5.3f (Random Forest) %5.3f"
      % (lm_test_score, rfr_test_score))

print("Test data Spearman correlation: (Linear) %.3f (Random Forest) %.3f"
      % (lm_spearman[0], rfr_spearman[0]))

print("Test data Pearson correlation: (Linear) %.3f (Random Forest) %.3f"
      % (lm_pearson[0], rfr_pearson[0]))

Test data R-2 score: (Linear) 0.610 (Random Forest) 0.818
Test data Spearman correlation: (Linear) 0.817 (Random Forest) 0.910
Test data Pearson correlation: (Linear) 0.781 (Random Forest) 0.905
In [18]:
import matplotlib.pyplot as plt
# 予測と観測値の散布図
fig, axes = plt.subplots(1, 2, figsize=(10,5))
axes[0].scatter(lm_predicted_test, y_test)
axes[0].set_xlabel("Predicted value by Linear Model")
axes[0].set_ylabel("Observed value")
axes[0].set_title("Linear Regression")
axes[1].scatter(rfr_predicted_test, y_test)
axes[1].set_xlabel("Predicted value by Random Forest")
axes[1].set_ylabel("Observed value")
axes[1].set_title("Random Forest Regression")

# 推定値＝観測値(y=x)の補助線
x = [0.0, 5.0]
axes[0].plot(x,x, "r")
axes[1].plot(x,x, "r")

plt.show()
In [ ]: