fit <- CoxBoost(train[,'month'], train[,'OS'], as.matrix(train[,-c(0:2)]), stepno=200, sf.scheme=c("sigmoid"), criterion="hscore")的各参数含义和解释
时间: 2023-12-09 18:03:28 浏览: 34
- train[,'month']:存放每个样本的观察时间(时间尺度),用于计算生存时间。
- train[,'OS']:存放每个样本的生存状态(0表示存活,1表示死亡)。
- as.matrix(train[,-c(0:2)]):存放训练数据的特征矩阵,去掉第一列(样本ID)和前两列(month和OS)。
- stepno=200:指定CoxBoost算法的最大步数。
- sf.scheme=c("sigmoid"):指定生存概率分布函数为sigmoid函数。
- criterion="hscore":指定模型评价指标为Harrell's concordance index(C-index),用于衡量模型的预测准确度。
相关问题
arr0 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]) arr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]) arr3 = np.array(input("请输入连续24个月的配件销售数据,元素之间用空格隔开:").split(), dtype=float) data_array = np.vstack((arr1, arr3)) data_matrix = data_array.T data = pd.DataFrame(data_matrix, columns=['month', 'sales']) sales = data['sales'].values.astype(np.float32) sales_mean = sales.mean() sales_std = sales.std() sales = abs(sales - sales_mean) / sales_std train_data = sales[:-1] test_data = sales[-12:] def create_model(): model = tf.keras.Sequential() model.add(layers.Input(shape=(11, 1))) model.add(layers.Conv1D(filters=32, kernel_size=2, padding='causal', activation='relu')) model.add(layers.BatchNormalization()) model.add(layers.Conv1D(filters=64, kernel_size=2, padding='causal', activation='relu')) model.add(layers.BatchNormalization()) model.add(layers.Conv1D(filters=128, kernel_size=2, padding='causal', activation='relu')) model.add(layers.BatchNormalization()) model.add(layers.Conv1D(filters=256, kernel_size=2, padding='causal', activation='relu')) model.add(layers.BatchNormalization()) model.add(layers.Conv1D(filters=512, kernel_size=2, padding='causal', activation='relu')) model.add(layers.BatchNormalization()) model.add(layers.Dense(1, activation='linear')) return model model = create_model() BATCH_SIZE = 16 BUFFER_SIZE = 100 train_dataset = tf.data.Dataset.from_tensor_slices(train_data) train_dataset = train_dataset.window(11, shift=1, drop_remainder=True) train_dataset = train_dataset.flat_map(lambda window: window.batch(11)) train_dataset = train_dataset.map(lambda window: (window[:-1], window[-1:])) train_dataset = train_dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE).prefetch(1) model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss='mse') history = model.fit(train_dataset, epochs=100, verbose=0) test_input = test_data[:-1] test_input = np.reshape(test_input, (1, 11, 1)) predicted_sales = model.predict(test_input)[0][0] * sales_std + sales_mean test_prediction = model.predict(test_input) y_test=test_data[1:12] y_pred=test_prediction y_pred = test_prediction.ravel() print("预测下一个月的销量为:", predicted_sales),如何将以下代码稍作修改插入到上面的最后,def comput_acc(real,predict,level): num_error=0 for i in range(len(real)): if abs(real[i]-predict[i])/real[i]>level: num_error+=1 return 1-num_error/len(real) a=np.array(test_data[label]) real_y=a real_predict=test_predict print("置信水平:{},预测准确率:{}".format(0.2,round(comput_acc(real_y,real_predict,0.2)* 100,2)),"%")
你可以在最后添加如下代码实现 `comput_acc()` 函数的功能:
```
label = 0 # 定义标签
a = np.array(test_data[label]) # 获取测试数据的标签列
real_y = a[1:] # 实际销售数据
real_predict = y_pred[:-1] # 预测销售数据
print("置信水平:{},预测准确率:{}".format(0.2, round(comput_acc(real_y, real_predict, 0.2) * 100, 2)), "%")
```
这段代码的作用是,首先定义标签为 0,即选取测试数据中的第一列数据(即销售数据)。然后,使用 `np.array()` 函数将该列数据转换为 numpy 数组 `a`。接着,将 `a` 中的第二个元素到最后一个元素赋值给 `real_y`,这里是因为真实销售数据的第一个元素已经作为测试数据输入了模型,所以预测值中不包含该元素。然后,将 `y_pred` 中的第一个元素到倒数第二个元素赋值给 `real_predict`,这是因为预测值中的最后一个元素已经与真实值的最后一个元素相对应。最后,调用 `comput_acc()` 函数计算预测准确率,并将结果打印输出。
arr0 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]) arr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]) arr2 = np.array(input("请输入连续24个月的车辆销售数据,元素之间用空格隔开:").split(), dtype=float) arr3 = np.array(input("请输入连续24个月的配件销售数据,元素之间用空格隔开:").split(), dtype=float) data_array = np.vstack((arr0, arr1, arr2, arr3)) data_matrix = data_array.T data = pd.DataFrame(data_matrix, columns=['num', 'month', 'car sales', 'sales']) data = data[['month', 'car sales', 'sales']] train_data, test_data = train_test_split(data, test_size=0.3) scaler = MinMaxScaler(feature_range=(0, 1)) data_scaled = scaler.fit_transform(data) train_size = int(len(data_scaled) * 0.7) test_size = len(data_scaled) - train_size train, test = data_scaled[0:train_size,:], data_scaled[train_size:len(data_scaled),:] def create_dataset(dataset, look_back=1): X, Y = [], [] for i in range(len(dataset)-look_back): X.append(dataset[i:(i+look_back), :]) Y.append(dataset[i+look_back, :]) return np.array(X), np.array(Y) look_back = 3 X_train, Y_train = create_dataset(train, look_back) X_test, Y_test = create_dataset(test, look_back) model = Sequential() model.add(LSTM(4, input_shape=(look_back, 3))) model.add(Dense(3)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(X_train, Y_train, epochs=100, batch_size=1, verbose=0) train_predict = model.predict(X_train) test_predict = model.predict(X_test) train_predict = scaler.inverse_transform(train_predict) Y_train = scaler.inverse_transform(Y_train) test_predict = scaler.inverse_transform(test_predict) Y_test = scaler.inverse_transform(Y_test) last_month = data_scaled[-look_back:] last_month = last_month.reshape((1, look_back, 3))#1,12,3 next_month = model.predict(last_month) next_month = scaler.inverse_transform(next_month) print('下个月的预测结果是:', round(next_month[0][2])),如何将以下代码插入,def comput_acc(real,predict,level): num_error=0 for i in range(len(real)): if abs(real[i]-predict[i])/real[i]>level: num_error+=1 return 1-num_error/len(real) a=np.array(test_data[label]) real_y=a real_predict=test_predict print("置信水平:{},预测准确率:{}".format(0.2,round(comput_acc(real_y,real_predict,0.2)* 100,2)),"%")
可以将以下代码插入到代码中:
```python
def comput_acc(real, predict, level):
num_error = 0
for i in range(len(real)):
if abs(real[i] - predict[i]) / real[i] > level:
num_error += 1
return 1 - num_error / len(real)
a = np.array(test_data['sales'])
real_y = a
real_predict = test_predict[:, 2]
print("置信水平:{},预测准确率:{}".format(0.2, round(comput_acc(real_y, real_predict, 0.2) * 100, 2)), "%")
```
其中,`test_data['sales']` 是测试集中的实际值(即真实销售数据),`test_predict[:, 2]` 则是模型预测的销售数据。`comput_acc` 函数用于计算预测准确率。在这里,我们将置信水平设为 0.2,表示预测结果与实际结果的相对误差在 20% 以内视为准确。