質問編集履歴
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```ここに言語名を入力
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#モジュール読み込み
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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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import time
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import winsound
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from sklearn.preprocessing import StandardScaler
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from sklearn.decomposition import KernelPCA as KPCA
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import LeaveOneOut
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from sklearn.pipeline import make_pipeline
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from sklearn.model_selection import GridSearchCV
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start=time.time()
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#データ読み込み
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file_name='data_class2.xlsx'
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sheet_name='Sheet7'
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df = pd.read_excel(file_name,sheet_name=sheet_name)
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df=df.drop('results',axis=1)
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df_X=df.copy()
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df_Y=df.copy()
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features_df=df_X.drop(['Target','Target2'],axis=1)
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drop_idx=['HR-rest',\
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'HR-task',\
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'HR-after',\
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'HF-rest',\
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'HF-task',\
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'HF-after',\
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'LH-rest',\
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'LH-task',\
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'LH-after',\
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'Target2']
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drop_idx2=['HR-rest',\
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'HR-task',\
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'HR-after',\
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'HF-rest',\
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'HF-task',\
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'HF-after',\
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'LH-rest',\
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'LH-task',\
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'LH-after',\
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'Target']
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mod_excel_num=92
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con_mod_index=(mod_excel_num-1)
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targets_df=df_Y.drop(drop_idx,axis=1)
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targets2_df=df_Y.drop(drop_idx2,axis=1)
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targets_array=np.array(targets_df)
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targets2_array=np.array(targets2_df.iloc[:con_mod_index])
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features1=np.array(features_df)
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features2=np.array(features_df.iloc[:con_mod_index])
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targets1=np.ravel(targets_array)
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targets2=np.ravel(targets2_array)
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#LOO交差検定によるグリッドサーチでのハイパーパラメータの探索
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2
改善
test
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test
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@@ -236,7 +236,7 @@
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predicted_label1 = pipe_lr1.predict(test_data)
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predicted_label1 = pipe_lr1.predict(test_data)#この部分が更新されません
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predicted_labels1.append(predicted_label1)
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predicted_label2 = pipe_lr2.predict(test_data)
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predicted_label2 = pipe_lr2.predict(test_data)#この部分が更新されません
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predicted_labels2.append(predicted_label2)
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誤字
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二値分類を二回行い3クラスに分類するロジスティック回帰についてです。デバックした結果predicted_label1 = pipe_
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二値分類を二回行い3クラスに分類するロジスティック回帰についてです。デバックした結果predicted_label1 = pipe_lr1.predict(test_data)とpredicted_label2 = pipe_lr2.predict(test_data)の部分がループのたびに更新されていませんでした。なにが間違えているのでしょうか？
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