X_train, y_train = df_train.iloc[:, 1:], df_train.iloc[:, 0] X_test, y_test = df_test.iloc[:, 1:], df_test.iloc[:, 0] reg = xgb.XGBRegressor(n_estimators=1000) reg.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], early_stopping_rounds=50, verbose=False) # Change verbose to True if you want to see it train f, ax = plt.subplots(figsize=(15, 10)) _ = plot_importance(reg, height=0.9, ax=ax) plt.show() df_test['Prediction'] = reg.predict(X_test) df_all = pd.concat([df_test, df_train], sort=False) f, ax = plt.subplots(1) f.set_figheight(5) f.set_figwidth(15) _ = df_all[['Global_active_power', 'Prediction']].plot(ax=ax, style=['-', '.']) ax.set_xbound(lower='12-10-2007', upper='12-20-2007') plot = plt.suptitle('Predicted Day')

def train_linear_regression(): global df, train_ratio x_train, x_test, y_train, y_test = train_test_split(df.iloc[:, :-1], df.iloc[:, -1], train_size=train_ratio) model = LinearRegression() model.fit(x_train, y_train) score = model.score(x_test, y_test) text_output.insert(tk.END, "线性回归模型测试评分：{:.2f}\n".format(score))解释每一句代码含义

- x_train, x_test, y_train, y_test = train_test_split(df.iloc[:, :-1], df.iloc[:, -1], train_size=train_ratio)：使用train_test_split函数将数据集df划分为训练集和测试集，其中训练集占比为train_...

df_train = df_log.iloc[ : -test_size] df_test = df_log.iloc[-test_size : ] df_train.shape,df_test.shape

其中 df_log 是原始数据集， iloc[] 是 Pandas 中的索引方式，[ : -test_size] 表示选取从第 0 行到倒数第 test_size 行的数据作为训练集，[-test_size : ] 表示选取从倒数第 test_size 行到最后一行的数据作为测试...

def train_decision_tree(): # global df, train_ratio # x_train, x_test, y_train, y_test = train_test_split(df.iloc[:, :-1], df.iloc[:, -1], train_size=train_ratio) # model = DecisionTreeRegressor() # model.fit(x_train, y_train) # score = model.score(x_test, y_test) # text_output.insert(tk.END, "决策树模型测试评分：{:.2f}\n".format(score))解释每一句代码含义

- x_train, x_test, y_train, y_test = train_test_split(df.iloc[:, :-1], df.iloc[:, -1], train_size=train_ratio)：使用train_test_split函数将数据集df划分为训练集和测试集，其中训练集占比为train_...

def train_random_forest(): # global df, train_ratio # x_train, x_test, y_train, y_test = train_test_split(df.iloc[:, :-1], df.iloc[:, -1], train_size=train_ratio) # model = RandomForestRegressor() # model.fit(x_train, y_train) # score = model.score(x_test, y_test) # text_output.insert(tk.END, "随机森林模型测试评分：{:.2f}\n".format(score))解释每一句代码含义

- x_train, x_test, y_train, y_test = train_test_split(df.iloc[:, :-1], df.iloc[:, -1], train_size=train_ratio)：使用train_test_split函数将数据集df划分为训练集和测试集，其中训练集占比为train_...

提取目标变量和特征变量 scaler = StandardScaler() X = df.iloc[:, 4:] # 特征数据 X = scaler.fit_transform(X) y_1 = df[[‘U(Ⅳ)浓度’]] # 目标变量1 y_2 = df[[‘U(Ⅵ)浓度’]] # 目标变量2 y_3 = df[[‘硝酸浓度’]] # 目标变量2 随机划分数据集 X_train_1, X_test_1, y_train_1, y_test_1 = train_test_split(X, y_1, test_size=0.2, random_state=42) X_train_2, X_test_2, y_train_2, y_test_2 = train_test_split(X, y_2, test_size=0.2, random_state=42) X_train_3, X_test_3, y_train_3, y_test_3 = train_test_split(X, y_3, test_size=0.2, random_state=42) 对特征变量进行标准化 scaler = StandardScaler() X_train_1_std = scaler.fit_transform(X_train_1) X_test_1_std = scaler.transform(X_test_1) X_train_2_std = scaler.fit_transform(X_train_2) X_test_2_std = scaler.transform(X_test_2) X_train_3_std = scaler.fit_transform(X_train_3) X_test_3_std = scaler.transform(X_test_3) 建立随机森林模型并进行训练 rf_1 = RandomForestRegressor(n_estimators=1000, random_state=42) rf_1.fit(X_train_1_std, y_train_1) rf_2 = RandomForestRegressor(n_estimators=1000, random_state=42) rf_2.fit(X_train_2_std, y_train_2) rf_3 = RandomForestRegressor(n_estimators=1000, random_state=42) rf_3.fit(X_train_3_std, y_train_3) 对测试集进行预测并计算准确性 accuracy_1 = rf_1.score(X_test_1_std, y_test_1) accuracy_2 = rf_2.score(X_test_2_std, y_test_2) accuracy_3 = rf_3.score(X_test_3_std, y_test_3) print(‘U(Ⅳ)浓度的预测准确度为： {:.2f}%’.format(accuracy_1 * 100)) print(‘U(Ⅵ)浓度的预测准确度为： {:.2f}%’.format(accuracy_2 * 100)) print(‘U(Ⅵ)浓度的预测准确度为： {:.2f}%’.format(accuracy_3 * 100)) 请使用代码通过绘制图表的方式说明该随机森林中决策树的生成过程，给出我相应的代码请不要使用graphviz软件

plot_tree(rf_1.estimators_[0], filled=True) plt.show() # 绘制第二棵树 plt.figure(figsize=(10, 10)) plot_tree(rf_1.estimators_[1], filled=True) plt.show() # 绘制第三棵树 plt.figure(figsize=(10, 10)) ...

请帮我评估一下，我一共有9000行训练数据，代码如下：def get_data(train_df): train_df = train_df[['user_id', 'behavior_type']] train_df=pd.pivot_table(train_df,index=['user_id'],columns=['behavior_type'],aggfunc={'behavior_type':'count'}) train_df.fillna(0,inplace=True) train_df=train_df.reset_index(drop=True) train_df.columns=train_df.columns.droplevel(0) x_train=train_df.iloc[:,:3] y_train=train_df.iloc[:,-1] type=torch.float32 x_train=torch.tensor(x_train.values,dtype=type) y_train=torch.tensor(y_train.values,dtype=type) print(x_train) print(y_train) return x_train ,y_train x_train,y_train=get_data(train_df) x_test,y_test=get_data(test_df) print(x_test) #创建模型 class Order_pre(nn.Module): def init(self): super(Order_pre, self).init() self.ln1=nn.LayerNorm(3) self.fc1=nn.Linear(3,6) self.fc2 = nn.Linear(6, 12) self.fc3 = nn.Linear(12, 24) self.fc4 = nn.Linear(24, 1) def forward(self,x): x=self.ln1(x) x=self.fc1(x) x = nn.functional.relu(x) x = self.fc2(x) x = nn.functional.relu(x) x = self.fc3(x) x = nn.functional.relu(x) x = self.fc4(x) return x #定义模型、损失函数和优化器 model=Order_pre() loss_fn=nn.MSELoss() optimizer=torch.optim.SGD(model.parameters(),lr=1) #开始跑数据 for epoch in range(1,50): #预测值 y_pred=model(x_train) #损失值 loss=loss_fn(y_pred,y_train) #反向传播 optimizer.zero_grad() loss.backward() optimizer.step() print('epoch',epoch,'loss',loss) # 开始预测y值 y_test_pred=model(x_test) y_test_pred=y_test_pred.detach().numpy() y_test=y_test.detach().numpy() y_test_pred=pd.DataFrame(y_test_pred) y_test=pd.DataFrame(y_test) dfy=pd.concat([y_test,y_test_pred],axis=1) print(dfy) dfy.to_csv('resulty.csv')

1. 数据处理部分：你的代码中首先对数据进行了透视操作，然后将数据分成了x_train和y_train两部分。x_train中只包含了前三列数据，而y_train中只包含了最后一列数据。这样处理可能会导致信息的丢失，因为你只考虑了...

import pandas as pd from sklearn.model_selection import GroupShuffleSplit df = pd.read_csv('horse_race_data.csv') gss = GroupShuffleSplit(test_size=.40, n_splits=1, \ random_state=7).split(df, groups=df['id']) # 生成训练集和验证集的索引 X_train_inds, X_test_inds = next(gss) train_data= df.iloc[X_train_inds] X_train = train_data.loc[:, ~train_data.columns.isin(['id','rank'])] y_train = train_data.loc[:, train_data.columns.isin(['rank'])] test_data= df.iloc[X_test_inds] X_test = test_data.loc[:, ~test_data.columns.isin(['rank'])] y_test = test_data.loc[:, test_data.columns.isin(['rank'])]

这段代码是使用pandas和sklearn库来处理horse_race_data.csv文件中的数据，并将其划分为训练集和验证集。...最后，将训练集和验证集的特征和标签分别存储在X_train、y_train、X_test和y_test变量中。

import numpy as np import pandas as pd import matplotlib.pyplot as plt import BPNN from sklearn import metrics from sklearn.metrics import mean_absolute_error from sklearn.metrics import mean_squared_error #导入必要的库 df1=pd.read_excel(r'D:\Users\Desktop\大数据\44.xls',0) df1=df1.iloc[:,:] #进行数据归一化 from sklearn import preprocessing min_max_scaler = preprocessing.MinMaxScaler() df0=min_max_scaler.fit_transform(df1) df = pd.DataFrame(df0, columns=df1.columns) x=df.iloc[:,:4] y=df.iloc[:,-1] #划分训练集测试集 cut=4#取最后cut=30天为测试集 x_train, x_test=x.iloc[4:],x.iloc[:4]#列表的切片操作，X.iloc[0:2400，0:7]即为1-2400行，1-7列 y_train, y_test=y.iloc[4:],y.iloc[:4] x_train, x_test=x_train.values, x_test.values y_train, y_test=y_train.values, y_test.values #神经网络搭建 bp1 = BPNN.BPNNRegression([4, 16, 1]) train_data=[[sx.reshape(4,1),sy.reshape(1,1)] for sx,sy in zip(x_train,y_train)] test_data = [np.reshape(sx,(4,1))for sx in x_test] #神经网络训练 bp1.MSGD(train_data, 1000, len(train_data), 0.2) #神经网络预测 y_predict=bp1.predict(test_data) y_pre = np.array(y_predict) # 列表转数组 y_pre=y_pre.reshape(4,1) y_pre=y_pre[:,0] #画图 #展示在测试集上的表现 draw=pd.concat([pd.DataFrame(y_test),pd.DataFrame(y_pre)],axis=1); draw.iloc[:,0].plot(figsize=(12,6)) draw.iloc[:,1].plot(figsize=(12,6)) plt.legend(('real', 'predict'),loc='upper right',fontsize='15') plt.title("Test Data",fontsize='30') #添加标题 #输出精度指标 print('测试集上的MAE/MSE') print(mean_absolute_error(y_pre, y_test)) print(mean_squared_error(y_pre, y_test) ) mape = np.mean(np.abs((y_pre-y_test)/(y_test)))*100 print('=============mape==============') print(mape,'%') # 画出真实数据和预测数据的对比曲线图 print("R2 = ",metrics.r2_score(y_test, y_pre)) # R2 运行上述程序。在下面这一步中draw=pd.concat([pd.DataFrame(y_test),pd.DataFrame(y_pre)],axis=1);我需要将归一化的数据变成真实值，输出对比图，该怎么修改程序

draw.iloc[:,0].plot(figsize=(12,6)) draw.iloc[:,1].plot(figsize=(12,6)) plt.legend(('real', 'predict'), loc='upper right', fontsize='15') plt.title("Test Data", fontsize='30') # 输出精度指标 print('...

train_data, test_data = df.iloc[:train_size], df.iloc[train_size:]

这行代码的作用是将一个DataFrame类型的数据集 df 分成训练集 train...具体而言，df.iloc[:train_size] 表示选取 df 的前 train_size 行，而 df.iloc[train_size:] 则表示选取 df 的第 train_size 行及其后面的所有行。

def get_data(train_df): train_df = train_df[['user_id', 'behavior_type']] train_df=pd.pivot_table(train_df,index=['user_id'],columns=['behavior_type'],aggfunc={'behavior_type':'count'}) train_df.fillna(0,inplace=True) train_df=train_df.reset_index(drop=True) train_df.columns=train_df.columns.droplevel(0) x_train=train_df.iloc[:,:3] y_train=train_df.iloc[:,-1] type=torch.float32 x_train=torch.tensor(x_train.values,dtype=type) y_train=torch.tensor(y_train.values,dtype=type) print(x_train) print(y_train) return x_train ,y_train x_train,y_train=get_data(train_df) x_test,y_test=get_data(test_df) print(x_test) #创建模型 class Order_pre(nn.Module): def init(self): super(Order_pre, self).init() self.ln1=nn.LayerNorm(3) self.fc1=nn.Linear(3,6) self.fc2 = nn.Linear(6, 12) self.fc3 = nn.Linear(12, 24) self.dropout=nn.Dropout(0.5) self.fc4 = nn.Linear(24, 48) self.fc5 = nn.Linear(48, 96) self.fc6 = nn.Linear(96, 1) def forward(self,x): x=self.ln1(x) x=self.fc1(x) x = nn.functional.relu(x) x = self.fc2(x) x = nn.functional.relu(x) x = self.fc3(x) x = self.dropout(x) x = nn.functional.relu(x) x = self.fc4(x) x = nn.functional.relu(x) x = self.fc5(x) x = nn.functional.relu(x) x = self.fc6(x) return x #定义模型、损失函数和优化器 model=Order_pre() loss_fn=nn.MSELoss() optimizer=torch.optim.SGD(model.parameters(),lr=0.05) #开始跑数据 for epoch in range(1,50): #预测值 y_pred=model(x_train) #损失值 loss=loss_fn(y_pred,y_train) #反向传播 optimizer.zero_grad() loss.backward() optimizer.step() print('epoch',epoch,'loss',loss) # 开始预测y值 y_test_pred=model(x_test) y_test_pred=y_test_pred.detach().numpy() y_test=y_test.detach().numpy() y_test_pred=pd.DataFrame(y_test_pred) y_test=pd.DataFrame(y_test) dfy=pd.concat([y_test,y_test_pred],axis=1) print(dfy) dfy.to_csv('resulty.csv') 如果我想要使用学习率调度器应该怎么操作

y_pred=model(x_train) # 损失值 loss=loss_fn(y_pred,y_train) # 反向传播 optimizer.zero_grad() loss.backward() # 更新学习率 scheduler.step() optimizer.step() print('epoch',epoch,'loss',loss) ...

优化代码df = df.iloc[:,:7].copy() for col_name in df.columns: # 取列名for col_name in X_copy.columns: col_data = df[[col_name]] # 根据列名拿列数据，两个方括号是因为要二维数组 stand_data = StandardScaler().fit_transform(col_data.values) # 标准化 df[col_name] = stand_data # 将数据替换成标准化后的数据 return X_copy def run_classifier(): # 将数据集分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test, random_state=random) # 训练分类器 knn.fit(X_train, y_train) # 在训练集上计算分类器的准确率 score = knn.score(X_train, y_train) # 更新结果标签的文本 result_label5.config(text="训练集分类器的准确率为：{:.2f}".format(score))

X_train, X_test, y_train, y_test = train_test_split(X_copy, y, test_size=test, random_state=random) # 训练分类器 knn.fit(X_train, y_train) # 在训练集上计算分类器的准确率 score = knn.score(X_...

将df = df.iloc[:,:7].copy() for col_name in df.columns: # 取列名for col_name in X_copy.columns: col_data = df[[col_name]] # 根据列名拿列数据，两个方括号是因为要二维数组 stand_data = StandardScaler().fit_transform(col_data.values) # 标准化 df[col_name] = stand_data # 将数据替换成标准化后的数据 return X_copy加入到 def run_classifier(): # 将数据集分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test, random_state=random) # 训练分类器 knn.fit(X_train, y_train) # 在训练集上计算分类器的准确率 score = knn.score(X_train, y_train) # 更新结果标签的文本 result_label5.config(text="训练集分类器的准确率为：{:.2f}".format(score))

首先，代码使用iloc方法取出数据集的前7列，然后使用for循环遍历数据集的所有列。对于每一列，使用StandardScaler方法将数据进行标准化处理，并将标准化后的数据替换原来的数据。接着，使用train_test_split方法将...

将两段代码合成一个完整的df = df.iloc[:,:7].copy() for col_name in df.columns: # 取列名for col_name in X_copy.columns: col_data = df[[col_name]] # 根据列名拿列数据，两个方括号是因为要二维数组 stand_data = StandardScaler().fit_transform(col_data.values) # 标准化 df[col_name] = stand_data # 将数据替换成标准化后的数据 return X_copy def run_classifier(): # 将数据集分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test, random_state=random) # 训练分类器 knn.fit(X_train, y_train) # 在训练集上计算分类器的准确率 score = knn.score(X_train, y_train) # 更新结果标签的文本 result_label5.config(text="训练集分类器的准确率为：{:.2f}".format(score))

X_train, X_test, y_train, y_test = train_test_split(X_copy, y, test_size=test, random_state=random) # 训练KNN分类器 knn = KNeighborsClassifier(n_neighbors=5) knn.fit(X_train, y_train) # 在训练集上...

from sklearn.model_selection import train_test_split X_train,X_test,y_train,y_test = train_test_split(iris_df.iloc[ : ,0:4], iris_df[ ' target ' ], random_state= 14)目的

其中，iloc[ : ,0:4]表示取iris_df中的前四列特征，即花萼长度、花萼宽度、花瓣长度、花瓣宽度。'target'表示鸢尾花的类别，是我们所要预测的目标变量。 random_state参数指定了随机数种子，保证每次运行得到的训练...

import ... iris = datasets.load_iris() from sklearn.preprocessing import MinMaxScaleriris_data=MinMaxScaler().fit_transform(iris.data)pnint(ini.s...data[0.:.5..;J) iris_df=pd.DataFrame(iris_data,columns=[ ' Sepal Length ', 'Sepal Width ', 'Petal Length ',' Petal iris_df[ 'target ' ]=iris.target fnom sklearn.model_selection import train_test_split X_train，X_test,y_train,y_test = train_test_split(iris_df.iloc[ :,0:4], inis..df[ 'target ' ], random_state=.14) from sklearn.neighbors import KNeighborsClassifierknn = KNeighborsClassifier() knn.fit(X_train, y_train) y_predicted = knn.predict(x_test) accuracy = np.mean(y_predicted == y_test) *109print( '当前分类评估器是:knn ') print( '当前Accuracy是:%.1f' %accuracy + '%')使用的算法

然后，我们使用train_test_split()函数将鸢尾花数据集划分成了训练集和测试集，其中训练集占比为75%。接着，我们创建了一个KNeighborsClassifier()类的实例knn，并使用fit()方法在训练集上拟合了K近邻模型。接...

X = df_smoted.iloc[:, :-1] y = df_smoted.iloc[:, -1] from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=123)这段代码的意思

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df_train = df_log.iloc[ : -test_size] df_test = df_log.iloc[-test_size : ] df_train.shape,df_test.shape

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from sklearn.model_selection import train_test_split X_train,X_test,y_train,y_test = train_test_split(iris_df.iloc[ : ,0:4], iris_df[ ' target ' ], random_state= 14)目的

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