質問編集履歴
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```python
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import numpy as np
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y_train = Xmat['SalePrice']
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X_train = Xmat.drop(['SalePrice'], axis=1)
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```
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### 全コード
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import numpy as np
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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# データをインポート
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train = pd.read_csv(r'C:\Users\ringo\Documents\Python\House Prices\train.csv')
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test = pd.read_csv(r'C:\Users\ringo\Documents\Python\House Prices\test.csv')
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# データを調べる。
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train.shape
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# ラベル化
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from sklearn.preprocessing import LabelEncoder
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# 欠損の可視化
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import missingno as msno
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train.head(5)
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# keep ID for submission
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train_ID = train['Id']
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test_ID = test['Id']
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# split data for training
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y_train = train['SalePrice']
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X_train = train.drop(['Id','SalePrice'], axis=1)
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# dealing with missing data
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Xmat = pd.concat([X_train, X_test])
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Xmat = Xmat.drop(['LotFrontage','MasVnrArea','GarageYrBlt'], axis=1)
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# 新しい特徴量を作る。
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Xmat['TotalSF'] = Xmat['TotalBsmtSF'] + Xmat['1stFlrSF'] + Xmat['2ndFlrSF']
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# Xmatを分割して代入（分割間違えないように）
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Xmat.shape
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X_test.shape
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X_train=Xmat[0:1460]
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X_test=Xmat[1460:2920]
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# 目的変数を調べる
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ax = sns.distplot(y_train)
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plt.show()
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y_train = np.log(y_train)
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# 特徴量の重要性を調べる。
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# ランダムフォレストで調べる。
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from sklearn.ensemble import RandomForestRegressor
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rf = RandomForestRegressor(n_estimators=80, max_features='auto')
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# 重要度の高い30の特徴量を使用。
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X_train = X_train.iloc[:,ranking[:30]]
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X_test = X_test.iloc[:,ranking[:30]]
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# 重要度の高い特徴量2つを使って新しい特徴量を作る。
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X_train["Interaction"] = X_train["TotalSF"]*X_train["OverallQual"]
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X_test["Interaction"] = X_test["TotalSF"]*X_test["OverallQual"]
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# 特徴量と目的変数の相関を調べる。
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fig = plt.figure(figsize=(12,7))
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for i in np.arange(30):
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# トレインデータの外れ値を処理。
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X_train.shape
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Xmat = X_train
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Xmat.shape
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Xmat['SalePrice'] = y_train
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Xmat.shape
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Xmat = Xmat.drop(Xmat[(Xmat['TotalSF']>5) & (Xmat['SalePrice']<12.5)].index)
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Xmat.shape
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Xmat = Xmat.drop(Xmat[(Xmat['GrLivArea']>5) & (Xmat['SalePrice']<13)].index)
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y_train = Xmat['SalePrice']
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Xmat.shape
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X_train = Xmat.drop(['SalePrice'], axis=1)
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Xmat.shape
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3
情報の追加
test
CHANGED
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test
CHANGED
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における、自分の実行結果を載せます。
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### 全コード
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import numpy as np # linear algebra
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import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
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# for visualization
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import matplotlib.pyplot as plt
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import seaborn as sns
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# データをインポート
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train = pd.read_csv(r'C:\Users\ringo\Documents\Python\House Prices\train.csv')
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test = pd.read_csv(r'C:\Users\ringo\Documents\Python\House Prices\test.csv')
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# データを調べる。
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print(train.dtypes)
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train.shape
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# ラベル化
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from sklearn.preprocessing import LabelEncoder
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for i in range(train.shape[1]):
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if train.iloc[:,i].dtypes == object:
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lbl = LabelEncoder()
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lbl.fit(list(train.iloc[:,i].values) + list(test.iloc[:,i].values))
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train.iloc[:,i] = lbl.transform(list(train.iloc[:,i].values))
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test.iloc[:,i] = lbl.transform(list(test.iloc[:,i].values))
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# 欠損の可視化
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import missingno as msno
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train.head(5)
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msno.matrix(df=train, figsize=(20,14), color=(0.5,0,0))
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# keep ID for submission
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train_ID = train['Id']
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test_ID = test['Id']
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# split data for training
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y_train = train['SalePrice']
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X_train = train.drop(['Id','SalePrice'], axis=1)
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X_test = test.drop('Id', axis=1)
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# dealing with missing data
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Xmat = pd.concat([X_train, X_test])
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Xmat = Xmat.drop(['LotFrontage','MasVnrArea','GarageYrBlt'], axis=1)
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Xmat = Xmat.fillna(Xmat.median())
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# 新しい特徴量を作る。
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Xmat['TotalSF'] = Xmat['TotalBsmtSF'] + Xmat['1stFlrSF'] + Xmat['2ndFlrSF']
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# Xmatを分割して代入（分割間違えないように）
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Xmat.shape
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X_test.shape
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X_train=Xmat[0:1460]
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X_test=Xmat[1460:2920]
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X_train.shape
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X_test.shape
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# 目的変数を調べる
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ax = sns.distplot(y_train)
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plt.show()
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# logを使って目的変数の正規化
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y_train = np.log(y_train)
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ax = sns.distplot(y_train)
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plt.show()
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# 特徴量の重要性を調べる。
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# ランダムフォレストで調べる。
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from sklearn.ensemble import RandomForestRegressor
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rf = RandomForestRegressor(n_estimators=80, max_features='auto')
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rf.fit(X_train, y_train)
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print('Training done using Random Forest')
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ranking = np.argsort(-rf.feature_importances_)
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f, ax = plt.subplots(figsize=(11, 9))
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sns.barplot(x=rf.feature_importances_[ranking], y=X_train.columns.values[ranking], orient='h')
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ax.set_xlabel("feature importance")
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plt.tight_layout()
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plt.show()
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# 重要度の高い30の特徴量を使用。
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X_train = X_train.iloc[:,ranking[:30]]
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X_test = X_test.iloc[:,ranking[:30]]
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# 重要度の高い特徴量2つを使って新しい特徴量を作る。
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|
+
X_train["Interaction"] = X_train["TotalSF"]*X_train["OverallQual"]
|
|
256
|
+
|
|
257
|
+
X_test["Interaction"] = X_test["TotalSF"]*X_test["OverallQual"]
|
|
258
|
+
|
|
259
|
+
|
|
260
|
+
|
|
261
|
+
# 特徴量と目的変数の相関を調べる。
|
|
262
|
+
|
|
263
|
+
fig = plt.figure(figsize=(12,7))
|
|
264
|
+
|
|
265
|
+
for i in np.arange(30):
|
|
266
|
+
|
|
267
|
+
ax = fig.add_subplot(5,6,i+1)
|
|
268
|
+
|
|
269
|
+
sns.regplot(x=X_train.iloc[:,i], y=y_train)
|
|
270
|
+
|
|
271
|
+
|
|
272
|
+
|
|
273
|
+
plt.tight_layout()
|
|
274
|
+
|
|
275
|
+
plt.show()
|
|
276
|
+
|
|
277
|
+
|
|
278
|
+
|
|
279
|
+
# トレインデータの外れ値を処理。
|
|
280
|
+
|
|
281
|
+
X_train.shape
|
|
282
|
+
|
|
283
|
+
Xmat = X_train
|
|
284
|
+
|
|
285
|
+
Xmat.shape
|
|
286
|
+
|
|
287
|
+
Xmat['SalePrice'] = y_train
|
|
288
|
+
|
|
289
|
+
Xmat.shape
|
|
290
|
+
|
|
291
|
+
Xmat = Xmat.drop(Xmat[(Xmat['TotalSF']>5) & (Xmat['SalePrice']<12.5)].index)
|
|
292
|
+
|
|
293
|
+
Xmat.shape
|
|
294
|
+
|
|
295
|
+
Xmat = Xmat.drop(Xmat[(Xmat['GrLivArea']>5) & (Xmat['SalePrice']<13)].index)
|
|
296
|
+
|
|
297
|
+
y_train = Xmat['SalePrice']
|
|
298
|
+
|
|
299
|
+
Xmat.shape
|
|
300
|
+
|
|
301
|
+
X_train = Xmat.drop(['SalePrice'], axis=1)
|
|
302
|
+
|
|
303
|
+
X_train.shape
|
|
304
|
+
|
|
305
|
+
Xmat.shape
|
2
間違ったタグの修正
test
CHANGED
|
File without changes
|
test
CHANGED
|
File without changes
|
1
情報の補足
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -63,3 +63,11 @@
|
|
|
63
63
|
windows 10
|
|
64
64
|
|
|
65
65
|
anaconda,atom 最新アップデートを使用
|
|
66
|
+
|
|
67
|
+
|
|
68
|
+
|
|
69
|
+
7) 各Featureとターゲットの関係を調べる
|
|
70
|
+
|
|
71
|
+
における、自分の実行結果を載せます。
|
|
72
|
+
|
|
73
|
+
|