我非常刚开始学习机器学习，因此我决定使用Pokemon数据集编写一个测试程序，以根据“总计”数据预测“捕获率”。我想对训练数据使用线性回归。但是当我运行程序时，出现以下错误：
预期的2D数组，取而代之的是1D数组：array = ['190''90''45''125' '190''75''45''120''200''45''190''60''225''90''3''45''150' '120''45''3''3'255''90''45''45''45''255''225''190''190' '255''90''45''45''30''45''45''90''190''90''45''90''60' '45''60''75''55''75''45''45''3''255''45''3''45''90''190' '60''190''200''225''75''45''45''45''200''120''120''255' '60''45''45''75''60''60''190''75''45''120''190''200''235' '45''45''90''30''45''45''170''235''45''190''60''75''180' '45''235''190''45''120''45''75''190''45''45''45''45''45' '75''45''45''190''45''75''3''45''60''200''45''45''255' '255''120''45''255''125''120''60''45''45''60''255''45' '180''60''45''60''3''25''120''45''3''3''45''75''30''45' '255''30''75''255''255''180''255''45''45''120''255''75' '30''45''75''45''255''120''45''45''45''190''45''75''45' '45''3''60''30''60''200''45''75''120''25''255''45''255' '200''190''190''120''45''90''170''45''75''60''100''45' '45''90''45''45''45''255''60''90''140''45''90''75''200' '45''45''255''120''3''45''75''200''255''225''120''120' '200''45''45''50''190''45''45''45''45''45''45''30''3''3' '255''45''45''255''120''225''45''75''75''45''60''255''60' '60''45''120''255''45''225''255''45''45''3'255''190''30' '190''45''45''120''75''25''75''255''45''120''100''3''65' '45''75''180''45''45''3''255''45''45''90''225''190''45' '255''3''190''70''3''120''45''45''50''200''190''255''55' '150''45''3''25''60''45''120''45''205''60''45''45''255' '30''120''75''45''90''45''45''60''190''45''45''90''45' '3''75''90''200''180''45''45''75''90''45''3''120''45' '45''45''45''75''45''155''45''55''45''30''45''150''255' '45''75''180''15''190''255''75''190''45''190''90''255' '45''45''45''190''3''60''45''60''60''255''25''145''45' '45''120''50''45''120''45''255''45''45''45''50''225''30' '75''120''3''45''120''30''45''255''90''3''3''120''45' '127''120''200''255''25''45''75''120''255''190''220''45' '65''45''90''60''200''190''190''120''190''90''45''120' '75''190''75''90''120''90''75''45''190''45''100''60''3' '45''90''190''255''45''190''45''45''25''60''60''45''190' '45''190''30''190''45''190''255''45''45''3''120''3''45' '35''120''190''255''190''45''45''45''45''255''190''45' '190''225''45''190''255''45''190''45''255''75''45''90' '120''30''180''190''100''255''235''75''60''190''160''45' '3''120''45''3''120''45''45''45''127''75''190''140''75' '225''60''45''75''120''190''190''90''3''45''150''120''30' '50'，'45'，'60'，'190'，'255'，'125'，'120'，'75'，'60'，'90'，'140']。
如果数据只有一个，则使用array.reshape（-1，1）重塑数据 feature或array.reshape（1，-1）（如果它包含单个样本）。
要解决我的错误，我尝试重塑我的x_train列表，因为这似乎是上面提到的那个，但是我仍然遇到相同的错误。也许我的语法关闭了？我从另一个发现的建议中尝试了x_train.reshape(-1, 1)和x_train = x_train.reshape(-1, 1)，但没有成功。
这是我到目前为止编写的（粗略）代码：
from sklearn import cross_validation
from sklearn import svm
from sklearn.feature_selection import RFE
from sklearn.model_selection import train_test_split
from sklearn import linear_model
import numpy as np
import matplotlib as plt
import csv
# Create linear regression object
regr = linear_model.LinearRegression()
# Create lists and append data -- we want to predict the catch rate!
total = []
catch_rate = []
with open("pokemon.csv") as f:
reader = csv.reader(f)
next(reader) # skip header
for row in reader:
total.append(row[5])
catch_rate.append(row[21])
x_train, x_test, y_train, y_test =
cross_validation.train_test_split(catch_rate, total, test_size=0.25,
random_state=0)
# Train the model using the training sets
regr.fit(x_train, y_train)
# Make predictions using the testing set
pokemon_y_pred = regr.predict(x_test)
# Plot outputs
plt.scatter(x_test, y_test, color='black')
plt.plot(x_test, pokemon_y_pred, color='blue', linewidth=3)
plt.xticks(())
plt.yticks(())
plt.show()
也许在我对代码的理解中我忽略了其他东西？同样，我正在自学，所以我将不胜感激任何帮助。
答案 0 :(得分：0)
使用pandas数据框而不是list类型。
还要注意，train_test_split函数的第一个元素必须是至少包含两列的数据框。
因此，假设您的csv文件如下所示：
Id,Name,Type_1,Type_2,Total,HP,Attack,Defense,Sp_Atk,Sp_Def,Speed,Generation,isLegendary,Color,hasGender,Pr_Male,Egg_Group_1,Egg_Group_2,hasMegaEvolution,Height_m,Weight_kg,Catch_Rate
1,Bulbasaur,Grass,Poison,318,45,49,49,65,65,45,1,False,Green,True,0.875,Monster,Grass,False,0.71,6.9,45
2,Ivysaur,Grass,Poison,405,60,62,63,80,80,60,1,False,Green,True,0.875,Monster,Grass,False,0.99,13,45
3,Venusaur,Grass,Poison,525,80,82,83,100,100,80,1,False,Green,True,0.875,Monster,Grass,True,2.01,100,45
4,Charmander,Fire,,309,39,52,43,60,50,65,1,False,Red,True,0.875,Monster,Dragon,False,0.61,8.5,45
5,Charmeleon,Fire,,405,58,64,58,80,65,80,1,False,Red,True,0.875,Monster,Dragon,False,1.09,19,45
并使用以下代码：
from sklearn import cross_validation
from sklearn import svm
from sklearn.feature_selection import RFE
from sklearn.model_selection import train_test_split
from sklearn import linear_model
import numpy as np
import matplotlib as plt
import pandas as pd #import pandas
# Create linear regression object
regr = linear_model.LinearRegression()
#load csv file with pandas
df = pd.read_csv("pokemon.csv")
#remove all string columns
df = df.drop(['Name', 'Type_1','Type_2','Color','Egg_Group_1','Egg_Group_2'], axis=1)
y= df.Catch_Rate
x_train, x_test, y_train, y_test = cross_validation.train_test_split(df, y, test_size=0.25, random_state=0)
# Train the model using the training sets
regr.fit(x_train, y_train)
# Make predictions using the testing set
pokemon_y_pred = regr.predict(x_test)
print pokemon_y_pred
# [ code continuation ...]
您将获得：
[ 45. 45.]