代码解释:X_train, X_test, y_train, y_test = train_test_split(sample, label, test_size=0.3, random_state=42)
时间: 2023-07-24 20:46:09 浏览: 34
这行代码使用了scikit-learn库中的train_test_split函数,将数据集分为训练集和测试集。
参数“sample”是样本数据,参数“label”是对应的标签数据。
test_size=0.3表示将数据集按照3:7的比例分成测试集和训练集,其中测试集占30%。
random_state=42表示随机种子,用于保证每次运行代码时分割的结果相同,这有助于保证实验结果的可重复性。
最终结果是将原始数据集分成四个部分:X_train表示训练集的样本,y_train表示训练集的标签,X_test表示测试集的样本,y_test表示测试集的标签。
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修改和补充下列代码得到十折交叉验证的平均auc值和平均aoc曲线,平均分类报告以及平均混淆矩阵 min_max_scaler = MinMaxScaler() X_train1, X_test1 = x[train_id], x[test_id] y_train1, y_test1 = y[train_id], y[test_id] # apply the same scaler to both sets of data X_train1 = min_max_scaler.fit_transform(X_train1) X_test1 = min_max_scaler.transform(X_test1) X_train1 = np.array(X_train1) X_test1 = np.array(X_test1) config = get_config() tree = gcForest(config) tree.fit(X_train1, y_train1) y_pred11 = tree.predict(X_test1) y_pred1.append(y_pred11 X_train.append(X_train1) X_test.append(X_test1) y_test.append(y_test1) y_train.append(y_train1) X_train_fuzzy1, X_test_fuzzy1 = X_fuzzy[train_id], X_fuzzy[test_id] y_train_fuzzy1, y_test_fuzzy1 = y_sampled[train_id], y_sampled[test_id] X_train_fuzzy1 = min_max_scaler.fit_transform(X_train_fuzzy1) X_test_fuzzy1 = min_max_scaler.transform(X_test_fuzzy1) X_train_fuzzy1 = np.array(X_train_fuzzy1) X_test_fuzzy1 = np.array(X_test_fuzzy1) config = get_config() tree = gcForest(config) tree.fit(X_train_fuzzy1, y_train_fuzzy1) y_predd = tree.predict(X_test_fuzzy1) y_pred.append(y_predd) X_test_fuzzy.append(X_test_fuzzy1) y_test_fuzzy.append(y_test_fuzzy1)y_pred = to_categorical(np.concatenate(y_pred), num_classes=3) y_pred1 = to_categorical(np.concatenate(y_pred1), num_classes=3) y_test = to_categorical(np.concatenate(y_test), num_classes=3) y_test_fuzzy = to_categorical(np.concatenate(y_test_fuzzy), num_classes=3) print(y_pred.shape) print(y_pred1.shape) print(y_test.shape) print(y_test_fuzzy.shape) # 深度森林 report1 = classification_report(y_test, y_prprint("DF",report1) report = classification_report(y_test_fuzzy, y_pred) print("DF-F",report) mse = mean_squared_error(y_test, y_pred1) rmse = math.sqrt(mse) print('深度森林RMSE:', rmse) print('深度森林Accuracy:', accuracy_score(y_test, y_pred1)) mse = mean_squared_error(y_test_fuzzy, y_pred) rmse = math.sqrt(mse) print('F深度森林RMSE:', rmse) print('F深度森林Accuracy:', accuracy_score(y_test_fuzzy, y_pred)) mse = mean_squared_error(y_test, y_pred) rmse = math.sqrt(mse) print('F?深度森林RMSE:', rmse) print('F?深度森林Accuracy:', accuracy_score(y_test, y_pred))
以下是修改和补充后的代码,实现了十折交叉验证的平均auc值和平均aoc曲线,平均分类报告以及平均混淆矩阵:
```python
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc
from sklearn.model_selection import StratifiedKFold
min_max_scaler = MinMaxScaler()
X_train, X_test, y_train, y_test = [], [], [], []
X_train_fuzzy, X_test_fuzzy, y_train_fuzzy, y_test_fuzzy = [], [], [], []
y_pred, y_pred1 = [], []
y_pred_proba, y_pred_proba1 = [], []
config = get_config()
tree = gcForest(config)
skf = StratifiedKFold(n_splits=10)
for train_id, test_id in skf.split(x, y):
# split data and normalize
X_train1, X_test1 = x[train_id], x[test_id]
y_train1, y_test1 = y[train_id], y[test_id]
X_train1 = min_max_scaler.fit_transform(X_train1)
X_test1 = min_max_scaler.transform(X_test1)
X_train1 = np.array(X_train1)
X_test1 = np.array(X_test1)
# train gcForest
tree.fit(X_train1, y_train1)
# predict on test set
y_pred11 = tree.predict(X_test1)
y_pred_proba11 = tree.predict_proba(X_test1)
# append predictions and test data
y_pred1.append(y_pred11)
y_pred_proba1.append(y_pred_proba11)
X_train.append(X_train1)
X_test.append(X_test1)
y_test.append(y_test1)
y_train.append(y_train1)
# split fuzzy data and normalize
X_train_fuzzy1, X_test_fuzzy1 = X_fuzzy[train_id], X_fuzzy[test_id]
y_train_fuzzy1, y_test_fuzzy1 = y_sampled[train_id], y_sampled[test_id]
X_train_fuzzy1 = min_max_scaler.fit_transform(X_train_fuzzy1)
X_test_fuzzy1 = min_max_scaler.transform(X_test_fuzzy1)
X_train_fuzzy1 = np.array(X_train_fuzzy1)
X_test_fuzzy1 = np.array(X_test_fuzzy1)
# train gcForest on fuzzy data
tree.fit(X_train_fuzzy1, y_train_fuzzy1)
# predict on fuzzy test set
y_predd = tree.predict(X_test_fuzzy1)
y_predd_proba = tree.predict_proba(X_test_fuzzy1)
# append predictions and test data
y_pred.append(y_predd)
y_pred_proba.append(y_predd_proba)
X_test_fuzzy.append(X_test_fuzzy1)
y_test_fuzzy.append(y_test_fuzzy1)
# concatenate and convert to categorical
y_pred = to_categorical(np.concatenate(y_pred), num_classes=3)
y_pred1 = to_categorical(np.concatenate(y_pred1), num_classes=3)
y_test = to_categorical(np.concatenate(y_test), num_classes=3)
y_test_fuzzy = to_categorical(np.concatenate(y_test_fuzzy), num_classes=3)
# calculate and print average accuracy and RMSE
mse = mean_squared_error(y_test, y_pred1)
rmse = math.sqrt(mse)
print('深度森林RMSE:', rmse)
print('深度森林Accuracy:', accuracy_score(y_test, y_pred1))
mse = mean_squared_error(y_test_fuzzy, y_pred)
rmse = math.sqrt(mse)
print('F深度森林RMSE:', rmse)
print('F深度森林Accuracy:', accuracy_score(y_test_fuzzy, y_pred))
mse = mean_squared_error(y_test, y_pred)
rmse = math.sqrt(mse)
print('F?深度森林RMSE:', rmse)
print('F?深度森林Accuracy:', accuracy_score(y_test, y_pred))
# calculate and print average classification report
report1 = classification_report(y_test, y_pred1)
print("DF", report1)
report = classification_report(y_test_fuzzy, y_pred)
print("DF-F", report)
# calculate and print average confusion matrix
cm1 = confusion_matrix(y_test.argmax(axis=1), y_pred1.argmax(axis=1))
cm = confusion_matrix(y_test_fuzzy.argmax(axis=1), y_pred.argmax(axis=1))
print('DF Confusion Matrix:')
print(cm1)
print('DF-F Confusion Matrix:')
print(cm)
# calculate and print average ROC curve and AUC value
fpr1, tpr1, threshold1 = roc_curve(y_test.ravel(), y_pred_proba1.ravel())
fpr, tpr, threshold = roc_curve(y_test_fuzzy.ravel(), y_pred_proba.ravel())
roc_auc1 = auc(fpr1, tpr1)
roc_auc = auc(fpr, tpr)
print('DF ROC AUC:', roc_auc1)
print('DF-F ROC AUC:', roc_auc)
# plot average ROC curve
plt.title('Receiver Operating Characteristic')
plt.plot(fpr1, tpr1, 'b', label = 'DF AUC = %0.2f' % roc_auc1)
plt.plot(fpr, tpr, 'g', label = 'DF-F AUC = %0.2f' % roc_auc)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
```
df_1_final_test = df_1.loc[list(set(df_1.index.tolist()).difference(set(df_train_1.index.tolist())))] #df_9_final_test = df_9.copy() 使负样本验证集等于正样本的验证集 df_9_final_test = df_9.sample(round(len(df_1_final_test)), random_state=int(cfg_train_dict['random_state'])) df_9_final_test['label'] = 0 df_ft = df_1_final_test.append(df_9_final_test, sort=False) # 随机分训练集和测试集 from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(df_train.drop(['号码', 'label'], axis=1), df_train['label'], test_size=0.2, random_state=int(cfg_train_dict['random_state']))
这段代码是将数据集划分为训练集和测试集的代码。
首先,代码根据 df_1 和 df_train_1 的索引的差异,获取了 df_1 中不在训练集中的样本,并将其赋值给 df_1_final_test。
接着,代码从 df_9 数据框中随机抽样数量为 round(len(df_1_final_test)) 的样本作为负例测试集,并给其添加一个名为 'label' 的列,所有行的值都设置为 0。
然后,代码将 df_1_final_test 和 df_9_final_test 两个数据框按行合并成一个新的数据框 df_ft。
接下来,代码使用 train_test_split 函数将 df_train 数据框划分为训练集和测试集。其中,参数 df_train.drop(['号码', 'label'], axis=1) 表示训练集的特征数据,df_train['label'] 表示训练集的标签数据。test_size 参数设置了测试集的比例,这里是 0.2,即 20% 的样本被划分为测试集。random_state 参数用于设置随机种子。
最后,代码将划分好的训练集和测试集分别赋值给 x_train、x_test、y_train、y_test 变量。
这段代码的作用是将数据集划分为训练集和测试集,用于模型的训练和评估。其中,df_train 包含了正例样本和负例样本,df_ft 包含了未在训练集中出现的正例样本和负例样本。x_train、x_test、y_train、y_test 则是划分好的训练集和测试集的特征数据和标签数据。