随机森林误差（样本数量不一致的输入变量）

Question

在阅读了这么多有 'inconsistent number of samples' 个错误的示例之后，我仍然看不出我的代码有什么问题。

在 excel 文件中，sheet 1 包含数据。 Sheet 2 包含入围变量列表。

我把sheet2中的变量保存到一个数组中。并将其提供给随机森林模型以评估其对 sheet 1 中参数的影响。

但我得到 "Found input variables with inconsistent numbers of samples: [54, 2016]"

54是sheet2中的变量个数。 2016是sheet1中的数据行数。

我想看看这 54 个变量如何影响 sheet 1 中的 'Target' 变量。

我应该如何操作我的数据才能使这项工作正常进行？

非常感谢。

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import accuracy_score

df = pd.read_excel(r'C:\Users\ngks\Desktop\TP Course\Project Module\ProjectDataSetrev2.xlsx',sheet_name=0)
df2 = pd.read_excel(r'C:\Users\ngks\Desktop\TP Course\Project Module\ProjectDataSetrev2.xlsx',sheet_name=1)

df['DateTime']=pd.to_datetime(df['Time Stamp'], format='%Y-%m-%d %H:%M:%S')
df.set_index(df['DateTime'], inplace=True)

print(len(df2.columns))

allvar = list()

for each_var in df2.columns:
    allvar.append(each_var)

allvar = np.array(allvar)
print(allvar)

target = df['(CUP) Chiller Optimization Plant Efficiency [kW/RT]']
target=target.values.reshape(len(target),1)

allvar_train,allvar_test,target_train,target_test= train_test_split(allvar,target, random_state=0, test_size=0.6)

clf = RandomForestClassifier(n_estimators=10000, random_state=0, n_jobs=-1)

clf.fit(allvar_train, target_train)

for feature in zip(feat_labels, clf.feature_importances_):
    print(feature)

Sheet 1（保存为df）看起来像这样 Sheet 1

Sheet 2（保存为df2）看起来像这样 Sheet2

错误日志如图 Error log

错误日志 2：未知标签类型：'continuous'Error Log 2

allvar_train

target train

Answer 1

问题出在 'train_test_spilt'，您只传递特征列名称而不传递数据。像这样使用列列表从 DataFrame 中获取数据。

allvar_train,allvar_test,target_train,target_test= train_test_split(df[allvar],target, random_state=0, test_size=0.6)

你不一定需要将'allvar'和'target'转换为numpy数组它可以直接用于'train_test_split'。

注：此问题与随机森林无关

Answer 2

这是适合我的代码。

  import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt

    from sklearn.metrics import mean_squared_error
    from sklearn.model_selection import train_test_split

    from sklearn.ensemble import RandomForestRegressor
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.feature_selection import SelectFromModel
    from sklearn.metrics import accuracy_score

    df = pd.read_excel(r'C:\Users\ngks\Desktop\TP Course\Project Module\ProjectDataSetrev3.xlsx',sheet_name=0)
    df2 = pd.read_excel(r'C:\Users\ngks\Desktop\TP Course\Project Module\ProjectDataSetrev3.xlsx',sheet_name=1)

    df['DateTime']=pd.to_datetime(df['Time Stamp'], format='%Y-%m-%d %H:%M:%S')
    df.set_index(df['DateTime'], inplace=True)

    print(len(df2.columns))

    allvarlist = list()

    for each_var in df2.columns:
        allvarlist.append(each_var)

    countvar = len(allvarlist)

    allvar = df[allvarlist]
    allvar = allvar.values.reshape(len(allvar),countvar)

    target = df['(CUP) Chiller Optimization Plant Efficiency [kW/RT]']
    target=target.values.reshape(len(target),1)

    allvar_train,allvar_test,target_train,target_test= train_test_split(allvar,target, random_state=0, test_size=0.7)

    clf = RandomForestRegressor(n_estimators=10000, random_state=0, n_jobs=-1)

    #print(allvar_train)
    #print(target_train)

    clf.fit(allvar_train,np.ravel(target_train))

    for feature in zip(allvarlist, clf.feature_importances_):
        print(feature)

    importances = clf.feature_importances_
    #indices = np.argsort(importances)

    plt.figure().set_size_inches(14,16)
    plt.barh(range(allvar_train.shape[1]), importances, color="r")
    plt.yticks(range(allvar_train.shape[1]),allvarlist)

随机森林误差（样本数量不一致的输入变量）

Random Forest Error (input variables with inconsistent numbers of samples)

python

feature-selection

random-forest