更新：np.arange(min(indp),max(indp),0.01) ValueError：具有多个元素的数组的真值不明确。使用 a.any() 或 a.all()

Question

我有一个用于作物产量预测的随机森林回归器，具有与回归器相关的 5 个特征

['Precipitation'    ,'Min_Temp' ,'Cloud_Cover'  ,'Vapour_pressure'  ,'Area']

，对于给定的数据集，我的因变量是

Production

，此代码给出了以下错误

 x_grid = np.arange(min(indp),max(indp),0.01)
 ValueError :  The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

代码：

from numpy.core.fromnumeric import reshape
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
from sklearn.ensemble import RandomForestRegressor


df = pd.read_csv('dataset.csv')



X = df[['Precipitation' ,'Min_Temp' ,'Cloud_Cover'  ,'Vapour_pressure'  ,'Area']] 
Y = df['Production']
 

def Preprocessing(df,drop_indx_list):
    for indx in  drop_indx_list:
        df=df.drop(indx)
        df.reset_index()
    df.loc[df.Min_Temp>40,'Min_Temp']=df.Min_Temp.mean()
    return df

def DataVisualization(chart_list):
    colors = ['blue','red','green','black','yellow']
    c = 0
    for chrt in chart_list:
        t1,x,t2 = chrt.split()
        plt.scatter(df[t1],df[t2],color=colors[c])
        plt.title(chrt)
        plt.xlabel(t1)
        plt.ylabel(t2)
        plt.grid()
        plt.show()
        c+=1

drop_indx_list = [4]
df =  Preprocessing(df,drop_indx_list)
viusal_list = ['Precipitation Vs Production','Min_Temp Vs Production','Cloud_Cover Vs Production',
'Vapour_pressure  Vs Production','Area Vs Production']

x_train,x_test,y_train,y_test=train_test_split(X,Y, test_size=0.2, random_state=1)
reg=linear_model.LinearRegression()
reg.fit(x_train,y_train)
  




# prediction
y_pred=reg.predict(x_test)
for i in range(len(y_pred)):
    print('data point number : ',i," prediction : ",y_pred[i],'\n')

# Coefficients
print('\nCoefficients: ', reg.coef_,'\n')

# R-squared score
print('\nR-squared score: ', r2_score(y_test,y_pred),'\n')

# DataVisualization(viusal_list)

# Random forest
indp = df.iloc[:,1:6].values



dep = df.iloc[:,6].values

reg = RandomForestRegressor(n_estimators=10,random_state=0)
reg.fit(indp,dep)
y_pred_reg = reg.predict(indp) #fix1

x_grid = np.arange(min(indp),max(indp),0.01) # current error
x_grid = x_grid.reshape((len(x_grid),1))
plt.scatter(indp,dep,color = 'red')
plt.plot(x_grid,reg.predict(x_grid),color ='blue')
plt.title('Random forest regression')
plt.xlabel('X-axis')
plt.ylabel('Prodution')

数据集：

Dist,Precipitation,Min_Temp,Cloud_Cover,Vapour_pressure,Area,Production
Bidar,622.438,27.643,35.241,17.953,4709,9043
Bangalore,748.194,25.263,49.134,21.56,18790,20981
Belgaum,1334.194,21.254,39.728,22.5509,4398,6054
Bellary,574.325,26.407,38.466,20.008,3768,5903
Bengalore Rural,733.003,25.228000,47.620000,21.241000,140213,534214
Kolar,724.545,25.464,47.029,20.63,2278,2759
Dharwad,1623.548,26.148,38.267,23.652,8395,10986
Koppal,724.545,26.871005,41.039,19.992,3084,3952
Chikmagalur,1923.742,26.459,44.842,24.717,1650,2958
Chitradurga,674.17,25.214,41.364,20.82,3026,3325
Haveri,1473.343,25.817,41.292,23.168,10659,9865
Chamrajanagar,1334.754,25.089,50.77,23.079,3485,4120
Mandya,1477.249,24.567,49.54775,22.234,11349,18957
Mysore,2242.378,25.76766667,50.57941667,24.64266667,3462,4539
Raichur,450.113,27.42241667,35.76258333,18.93741667,4586,6145
Kodaku,1691.933,25.426,46.353,23.975,17856,15362
Hassan,2200.349,25.348,47.12,24.008,10487,7586
Devanagare,1060.343,25.509,40.929,22.042,2459,1865
Gulbarga,525.402,27.851,35.109,18.662,10487,7895

我尝试了这个，但没有帮助类似查询链接

编辑：reg.predict(indp) 修正而不是 [[6.5]]

但是现在

 np.arange(min(indp),max(indp),0.01)

行给了我另一个错误：具有多个元素的数组的真值不明确。使用 a.any() 或 a.all()

Answer 1

使用 - np.arange(np.min(indp),np.max(indp),0.1)