質問編集履歴
1
プログラムの抜け
test
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File without changes
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test
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@@ -188,6 +188,76 @@
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print('MAE:',mae)
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191
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#特徴量に年を入れる
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#df['year'] = df['datetime'].dt.year #年の特徴量
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#月の特徴量
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df['month']=df['datetime'].dt.month
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#日の要素をデータフレームに加える
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df['day']=df['datetime'].dt.day
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# 曜日
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df['dayofweek'] = df['datetime'].dt.dayofweek
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x_pos = df['POSIX'].values
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#x =df[['POSIX','year','month','day','dayofweek']].values
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x =df[['POSIX','month','day','dayofweek']].values
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y = df['demand'].values
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x_pos=x_pos.reshape(-1,1)
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x_pos_train,x_pos_test=x_pos[:N_train+i*24],x_pos[N_train:N_train+i*24+24] #xと同じ 正解データ
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x=x
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y=y.reshape(-1,1)
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x_train, y_train = x[:N_train + i * 24], y[:N_train + i * 24] # 多分1回ごとに訓練期間が1日分長くなる
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x_test, y_test = x[N_train + i * 24:N_train + i * 24 + 24], y[N_train + i * 24:N_train + i * 24 + 24] # 多分1日予測を繰り返してる
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y_train=np.reshape(y_train,(-1)) #1次元に変換
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y_test=np.reshape(y_test,(-1)) #1次元に変換
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#機械学習パート
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rf = RandomForestRegressor(random_state=1)
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rf.fit(x_train,y_train)
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y_pred=rf.predict(x_test)
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y_pred=np.array(y_pred)
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print('y_pred',y_pred)
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print(x_test)
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plt.figure(figsize=(9,5))
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plt.plot(x_pos,y,color='gray',linestyle='dashdot')
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plt.plot(x_pos_train,y_train,'+')
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plt.plot(x_pos_test,y_pred,'--')
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plt.show()
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```
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