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python - 如何将附加模型精度保存在文本文件中？

问题描述

我正在比较一些模型并尝试输出具有所有模型精度的文件。但是，我将模型放在 for 循环中，并且尝试包含 file.write 选项，这只会提供最终模型的准确性。例如我的代码是这样的：

dataset= pd.read_csv('data.csv')
data = dataset.drop(["gene"],1)
df = data.iloc[:,0:26]
df = df.fillna(0)
X = MinMaxScaler().fit_transform(df)
le = preprocessing.LabelEncoder()
encoded_value = le.fit_transform(["certain", "likely", "possible", "unlikely"])
Y = le.fit_transform(data["category"])

sm = SMOTE(random_state=100)
X_res, y_res = sm.fit_resample(X, Y)

seed = 7
logreg = LogisticRegression(penalty='l1', solver='liblinear',multi_class='auto')
LR_par= {'penalty':['l1'], 'C': [0.5, 1, 5, 10], 'max_iter':[100, 200, 500, 1000]}

rfc =RandomForestClassifier(n_estimators=500)
param_grid = {"max_depth": [3],
             "max_features": ["auto"],
              "min_samples_split": [2],
              "min_samples_leaf": [1],
              "bootstrap": [False],
              "criterion": ["entropy", "gini"]}

svm = SVC(gamma="scale")
tuned_parameters = {'kernel':('linear', 'rbf'), 'C':(1,0.25,0.5,0.75)}

inner_cv = KFold(n_splits=10, shuffle=True, random_state=seed)
outer_cv = KFold(n_splits=10, shuffle=True, random_state=seed)

models = []
models.append(('RFC', GridSearchCV(rfc, param_grid, cv=inner_cv,iid=False, n_jobs=1)))
models.append(('LR', GridSearchCV(logreg, LR_par, cv=inner_cv, iid=False, n_jobs=1)))
models.append(('SVM', GridSearchCV(svm, tuned_parameters, cv=inner_cv, iid=False, n_jobs=1)))

results = []
names = []
scoring = 'accuracy'
X_train, X_test, Y_train, Y_test = train_test_split(X_res, y_res, test_size=0.2, random_state=0)


for name, model in models:
    nested_cv_results = model_selection.cross_val_score(model, X_res, y_res, cv=outer_cv, scoring=scoring)
    results.append(nested_cv_results)
    names.append(name)
    msg = "Nested CV Accuracy %s: %f (+/- %f )" % (name, nested_cv_results.mean()*100, nested_cv_results.std()*100)
    print(msg)
    model.fit(X_train, Y_train)
    print('Test set accuracy: {:.2f}'.format(model.score(X_test, Y_test)*100),  '%')
    output = msg
    file = open("accuracies.txt","w")
    file.write(output)
    file.close()

在尝试编写输出文件时，我在 for 循环中创建的文件 'accuracies.txt' 仅给出了 SVM 的准确性，我该如何更改它以提供每个模型的准确性？

我也试过：

for name, model in models:
    nested_cv_results = model_selection.cross_val_score(model, X_res, y_res, cv=2, scoring=scoring)
    results.append(nested_cv_results)
    names.append(name)
    msg = "Nested CV Accuracy %s: %f (+/- %f )" % (name, nested_cv_results.mean()*100, nested_cv_results.std()*100)
    print(msg)
    model.fit(X_train, Y_train)
    print('Test set accuracy: {:.2f}'.format(model.score(X_test, Y_test)*100),  '%')
    print("Best Parameters: \n{}\n".format(model.best_params_))
    print("Best CV Score: \n{}\n".format(model.best_score_)) #average of all cv folds for a single combination of the parameters you specify 

file = open("accuracies.txt","w")
file.write(results)
file.close()

但是，这给出了以下错误：

TypeError: write() argument must be str, not list

标签： pythoncsv

解决方案

只有 SVM 结果被写入，因为您在 for 循环的每次迭代中都覆盖了文件。尝试以下操作：

with open("accuracies.txt","w") as file: # open file before the loop
    for name, model in models:
        nested_cv_results = model_selection.cross_val_score(model, X_res, y_res, cv=outer_cv, scoring=scoring)
        results.append(nested_cv_results)
        names.append(name)
        msg = "Nested CV Accuracy %s: %f (+/- %f )" % (name, nested_cv_results.mean()*100, nested_cv_results.std()*100)
        print(msg)
        model.fit(X_train, Y_train)
        print('Test set accuracy: {:.2f}'.format(model.score(X_test, Y_test)*100),  '%')
        output = msg
        file.write(output) # write output for each iteration

对于您的第二次尝试，您正在尝试编写一个列表对象，而不是一个字符串。为此，您可以使用f.writelines而不是f.write. \n此外，如果您想要不同行上的项目，请务必添加新行。

for name, model in models:
    nested_cv_results = model_selection.cross_val_score(model, X_res, y_res, cv=2, scoring=scoring)
    results.append(nested_cv_results)
    names.append(name)
    msg = "Nested CV Accuracy %s: %f (+/- %f )\n" % (name, nested_cv_results.mean()*100, nested_cv_results.std()*100)
    print(msg)
    model.fit(X_train, Y_train)
    print('Test set accuracy: {:.2f}'.format(model.score(X_test, Y_test)*100),  '%')
    print("Best Parameters: \n{}\n".format(model.best_params_))
    print("Best CV Score: \n{}\n".format(model.best_score_)) #average of all cv folds for a single combination of the parameters you specify 

with open("accuracies.txt","w") as file:
    file.writelines(results)

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