import numpy as np import pandas as pd import tensorflow as tf from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import r2_score,median_absolute_error,mean_absolute_error # 读取数据 data = pd.read_csv(r'C:/Users/Ljimmy/Desktop/yyqc/peijian/销量数据rnn.csv') dataset = data.values # 数据归一化 scaler = MinMaxScaler(feature_range=(0, 1)) dataset = scaler.fit_transform(dataset) # 分割训练集和测试集 train_size = int(len(dataset) * 0.67) test_size = len(dataset) - train_size train, test = dataset[0:train_size, :], dataset[train_size:len(dataset), :] # 将数据集转化为适合GRU的数据格式 def create_dataset(dataset): X, Y = [], [] for i in range(len(dataset)-1): a = dataset[i:(i+1), :] X.append(a) Y.append(dataset[i+1, :]) return np.array(X), np.array(Y) train_X, train_Y = create_dataset(train) train_Y = train_Y[:, 2:] # 取第三列及以后的数据 test_X, test_Y = create_dataset(test) test_Y = test_Y[:, 2:] # 取第三列及以后的数据 # 定义GRU模型 model = tf.keras.Sequential([ tf.keras.layers.GRU(units=64, return_sequences=True, input_shape=(1, 3)), tf.keras.layers.GRU(units=32), tf.keras.layers.Dense(3)]) # 编译模型 model.compile(optimizer='adam', loss='mse') # 训练模型 model.fit(train_X, train_Y, epochs=100, batch_size=16, verbose=2) # 预测测试集 test_predict = model.predict(test_X) test_predict = scaler.inverse_transform(test_predict) #test_Y = scaler.inverse_transform(test_Y.reshape(-1, 1)) # 计算RMSE误差 rmse = np.sqrt(np.mean((test_predict - test_Y) ** 2)) print('Test RMSE:',rmse) # 预测下一个月的销量 last_month_sales = data.tail(1).values last_month_sales = scaler.transform(last_month_sales) next_month_sales = model.predict(np.array([last_month_sales])) next_month_sales = scaler.inverse_transform(next_month_sales) print('Next month sales:',next_month_sales[0][0])预测结果不够准确,如何增加准确率
时间: 2023-06-15 07:04:01 浏览: 163
有以下几种方式可以尝试提高准确率:
1. 增加训练数据量:可以尝试寻找更多的数据来进行训练,以提高模型的泛化能力和准确率。
2. 调整模型参数:可以尝试调整GRU模型的参数,如增加或减少GRU层的数量、增加或减少神经元的数量等,以寻找最优的模型参数组合。
3. 对数据进行更详细的分析:可以对原始数据进行更详细的分析,找到影响销量的因素,并将这些因素作为额外的特征加入到模型中,以提高模型的准确率。
4. 尝试其他类型的模型:可以尝试使用其他类型的深度学习模型,如LSTM、CNN等,以寻找更适合该数据集的模型。
相关问题
import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense, LSTM from sklearn.metrics import r2_score,median_absolute_error,mean_absolute_error # 读取数据 data = pd.read_csv(r'C:/Users/Ljimmy/Desktop/yyqc/peijian/销量数据rnn.csv') # 取出特征参数 X = data.iloc[:,2:].values # 数据归一化 scaler = MinMaxScaler(feature_range=(0, 1)) X[:, 0] = scaler.fit_transform(X[:, 0].reshape(-1, 1)).flatten() #X = scaler.fit_transform(X) #scaler.fit(X) #X = scaler.transform(X) # 划分训练集和测试集 train_size = int(len(X) * 0.8) test_size = len(X) - train_size train, test = X[0:train_size, :], X[train_size:len(X), :] # 转换为监督学习问题 def create_dataset(dataset, look_back=1): X, Y = [], [] for i in range(len(dataset) - look_back - 1): a = dataset[i:(i + look_back), :] X.append(a) Y.append(dataset[i + look_back, 0]) return np.array(X), np.array(Y) look_back = 12 X_train, Y_train = create_dataset(train, look_back) #Y_train = train[:, 2:] # 取第三列及以后的数据 X_test, Y_test = create_dataset(test, look_back) #Y_test = test[:, 2:] # 取第三列及以后的数据 # 转换为3D张量 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) # 构建LSTM模型 model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1))) model.add(LSTM(units=50)) model.add(Dense(units=1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(X_train, Y_train, epochs=5, batch_size=32) #model.fit(X_train, Y_train.reshape(Y_train.shape[0], 1), epochs=10, batch_size=32) # 预测下一个月的销量 last_month_sales = data.tail(12).iloc[:,2:].values #last_month_sales = data.tail(1)[:,2:].values last_month_sales = scaler.transform(last_month_sales) last_month_sales = np.reshape(last_month_sales, (1, look_back, 1)) next_month_sales = model.predict(last_month_sales) next_month_sales = scaler.inverse_transform(next_month_sales) print('Next month sales: %.0f' % next_month_sales[0][0]) # 计算RMSE误差 rmse = np.sqrt(np.mean((next_month_sales - last_month_sales) ** 2)) print('Test RMSE: %.3f' % rmse)IndexError Traceback (most recent call last) Cell In[1], line 36 33 X_test, Y_test = create_dataset(test, look_back) 34 #Y_test = test[:, 2:] # 取第三列及以后的数据 35 # 转换为3D张量 ---> 36 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) 37 X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) 38 # 构建LSTM模型 IndexError: tuple index out of range
这个错误提示是因为在第36行中,X_train数组的维度不符合要求。具体地说,X_train的维度为 (样本数量, 时间步长, 特征数),而在代码中reshape时将其调整为了 (样本数量, 时间步长, 1),这表示只有一个特征,但实际上在读取数据时有多个特征。
可能的解决方法是检查数据读取部分是否正确,确保X数组包含了所有的特征,并且在构建数据集和LSTM模型时正确地设置了特征数和时间步长等参数。
.使用Python的scikit-learn库来实现一个逻辑回归模型。你需要使用糖尿病数据集(Diabetes dataset)来训练模型,并预测患者是否患有糖尿病。 提示: 目标变量的值大于median的视为1,小于等于median的视为0
在Python中,我们可以使用scikit-learn库中的`LogisticRegression`类来创建逻辑回归模型。首先,需要加载所需的库和数据集。这里我们假设已经安装了`pandas`、`numpy`和`scikit-learn`。以下是实现步骤:
1. 导入所需库:
```python
import pandas as pd
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import MinMaxScaler
```
2. 加载糖尿病数据集(如果数据集未直接包含在scikit-learn中,你可以从其他地方获取数据并清洗预处理):
```python
diabetes = datasets.load_diabetes()
X = diabetes.data
y = diabetes.target
```
3. 对目标变量进行二分类转换(这里通过中位数划分0和1):
```python
median = np.median(y)
y_binary = [1 if value > median else 0 for value in y]
```
4. 划分特征(X)和标签(y_binary)到训练集和测试集:
```python
X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.2, random_state=42)
```
5. 数据标准化(为了提高模型性能):
```python
scaler = MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
```
6. 创建逻辑回归模型并拟合数据:
```python
model = LogisticRegression()
model.fit(X_train_scaled, y_train)
```
7. 预测新数据:
```python
predictions = model.predict(X_test_scaled)
```
8. 评估模型性能(如准确率,精确率,召回率等):
```python
from sklearn.metrics import accuracy_score, precision_score, recall_score
print("Accuracy:", accuracy_score(y_test, predictions))
print("Precision:", precision_score(y_test, predictions))
print("Recall:", recall_score(y_test, predictions))
```
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