Question

[已解决]下面的过程是我处理新数据并尝试预测但使用数据和经过训练的模型失败的地方。

首先我导入

import pandas as pd
from sklearn import preprocessing
import sklearn.model_selection as ms
from sklearn import linear_model
import sklearn.metrics as sklm
import numpy as np
import numpy.random as nr
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as ss
import math

%matplotlib inline

导入数据和数据处理

##test
##prepare test_data
x_test_data = pd.read_csv('AW_test.csv')
x_test_data.loc[:,x_test_data.dtypes==object].isnull().sum()

##dropnan
cols_of_interest = ['Title','MiddleName','Suffix','AddressLine2']
x_test_data.drop(cols_of_interest,axis=1,inplace=True)

##dropduplicate
x_test_data.drop_duplicates(subset = 'CustomerID', keep = 'first', 
inplace=True)
print(x_test_data.shape)

然后我将分类变量特征转换为一键编码矩阵

##change categorical variables to numeric variables
def encode_string(cat_features):
    enc = preprocessing.LabelEncoder()
    enc.fit(cat_features)
    enc_cat_features = enc.transform(cat_features)
    ohe = preprocessing.OneHotEncoder()
    encoded = ohe.fit(enc_cat_features.reshape(-1,1))
    return encoded.transform(enc_cat_features.reshape(-1,1)).toarray()

categorical_columns = 
['CountryRegionName','Education','Occupation','Gender','MaritalStatus']
Features = encode_string(x_test_data['CountryRegionName'])
for col in categorical_columns:
    temp = encode_string(x_test_data[col])
    Features = np.concatenate([Features, temp],axis=1)
print(Features)

然后，将其余的数字特征添加到矩阵上

##add numeric variables
Features = np.concatenate([Features, 
np.array(x_test_data[['HomeOwnerFlag','NumberCarsOwned',
'TotalChildren','YearlyIncome']])], axis=1)

接下来，我缩放特征矩阵

##scale numeric variables
with open('./lin_reg_scaler.pickle', 'rb') as file:
scaler =pickle.load(file)
Features[:,-5:] = scaler.transform(Features[:,-5:])

我在另一个文件中加载了我训练的线性回归模型（如果需要，我可以将其发布）

# Loading the saved linear regression model pickle
import pickle
loaded_model = pickle.load(open('./lin_reg_mod.pickle', 'rb'))

我将特征矩阵直接放入

#predict
loaded_model.predict(Features)

但是，这就是我所得到的

array([-5.71697209e+12, -4.64634881e+12, -4.64634881e+12, -4.64634881e+12,
   -4.64634881e+12, -4.64634881e+12, -5.71697209e+12, -4.64634881e+12,
   -5.71697209e+12, -4.64634881e+12, -5.71697209e+12, -4.64634881e+12,
   -4.64634881e+12, -4.64634881e+12, -5.71697209e+12, -4.64634881e+12,
   -4.64634881e+12, -5.71697209e+12, -5.71697209e+12, -5.71697209e+12,
   -4.64634881e+12, -4.64634881e+12, -4.64634881e+12, -4.64634881e+12,
   -4.64634881e+12, -5.71697209e+12, -4.64634881e+12, -5.71697209e+12,
   -5.71697209e+12, -4.64634881e+12, -5.71697209e+12, -5.71697209e+12,
   -4.64634881e+12, -5.71697209e+12, -4.64634881e+12, -5.71697209e+12,
   -4.64634881e+12, -4.64634881e+12, -4.64634881e+12, -4.64634881e+12,
   -5.71697209e+12, -5.71697209e+12, -4.64634881e+12, -4.64634881e+12,
   -4.64634881e+12, -4.64634881e+12, -4.64634881e+12, -5.71697209e+12,
   -4.64634881e+12, -4.64634881e+12, -4.64634881e+12, -4.64634881e+12,
   -4.64634881e+12, -4.64634881e+12, -4.64634881e+12, -4.64634881e+12,
   -4.64634881e+12, -5.71697209e+12, -4.64634881e+12, -5.71697209e+12,
   -4.64634881e+12, -4.64634881e+12, -4.64634881e+12, -5.71697209e+12,
   -5.71697209e+12, -5.71697209e+12, -5.71697209e+12, -4.64634881e+12,............

在我的另一个文件中，我已经成功地训练了模型并使用测试数据对其进行了测试。

这是我在该文件的模型中输入x_test时得到的结果（我想要得到的结果）：

[83.75482221 66.31820493 47.22211384 ... 69.65032224 88.45908874
  58.45193545]

我不知道发生了什么，有人可以帮忙吗

[UPDATE]下面是我训练模型的代码

custs = pd.read_csv('combined_custs.csv')
custs.dtypes

##avemonthspend data
ams = pd.read_csv('AW_AveMonthSpend.csv')
ams.drop_duplicates(subset='CustomerID', keep='first', inplace=True)
##merge
combined_custs=custs.merge(ams)
combined_custs.to_csv('./ams_combined_custs.csv')
combined_custs.head(20)
##change categorical variables to numeric variables
def encode_string(cat_features):
enc = preprocessing.LabelEncoder()
enc.fit(cat_features)
enc_cat_features = enc.transform(cat_features)
ohe = preprocessing.OneHotEncoder()
encoded = ohe.fit(enc_cat_features.reshape(-1,1))
return encoded.transform(enc_cat_features.reshape(-1,1)).toarray()

categorical_columns = 
['CountryRegionName','Education','Occupation','Gender','MaritalStatus']
Features = encode_string(combined_custs['CountryRegionName'])
for col in categorical_columns:
    temp = encode_string(combined_custs[col])
    Features = np.concatenate([Features, temp],axis=1)
print(Features.shape)
print(Features[:2,:])

##add numeric variables
Features = np.concatenate([Features, 


np.array(combined_custs[['HomeOwnerFlag',
'NumberCarsOwned','TotalChildren','YearlyIncome']])], axis=1)

print(Features.shape)
print(Features)

##train_test_split
nr.seed(9988)
labels = np.array(combined_custs['AveMonthSpend'])
indx = range(Features.shape[0])
indx = ms.train_test_split(indx, test_size = 300)
x_train = Features[indx[0],:]
y_train = np.ravel(labels[indx[0]])
x_test = Features[indx[1],:]
y_test = np.ravel(labels[indx[1]])
print(x_test.shape)

##scale numeric variables
scaler = preprocessing.StandardScaler().fit(x_train[:,-5:])

x_train[:,-5:] = scaler.transform(x_train[:,-5:])
x_test[:,-5:] = scaler.transform(x_test[:,-5:])
x_train[:2,]

import pickle
file = open('./lin_reg_scaler.pickle', 'wb')
pickle.dump(scaler, file)
file.close()

##define and fit the linear regression model
lin_mod = linear_model.LinearRegression(fit_intercept=False)
lin_mod.fit(x_train,y_train)
print(lin_mod.intercept_)
print(lin_mod.coef_)

import pickle
file = open('./lin_reg_mod.pickle', 'wb')
pickle.dump(lin_mod, file)
file.close()

lin_mod.predict(x_test)

对我的训练模型的预测是：

array([ 78.20673535,  91.11860042,  75.27284767,  63.69507673,
   102.10758616,  74.64252358,  92.84218321,  77.9675721 ,
   102.18989779,  96.98098962,  87.61415378,  39.37006326,
    85.81839618,  78.41392293,  45.49439829,  48.0944897 ,
    36.06024114,  70.03880373, 128.90267485,  54.63235443,
    52.20289729,  82.61123334,  41.58779815,  57.6456416 ,
    46.64014991,  78.38639454,  77.61072157,  94.5899366 ,.....

Answer 1

您在培训和测试中都使用了这种方法：

i = d.find_all("img", class_ = "image")
for item in i:
    img = item.get("src")
    print(img)

通过致电：

def encode_string(cat_features):
    enc = preprocessing.LabelEncoder()
    enc.fit(cat_features)
    enc_cat_features = enc.transform(cat_features)
    ohe = preprocessing.OneHotEncoder()
    encoded = ohe.fit(enc_cat_features.reshape(-1,1))
    return encoded.transform(enc_cat_features.reshape(-1,1)).toarray()

但是正如我在上面的评论中所说，您需要像在火车上一样对测试进行相同的预处理。

在测试过程中，根据Features = encode_string(combined_custs['CountryRegionName']) for col in categorical_columns: temp = encode_string(combined_custs[col]) Features = np.concatenate([Features, temp],axis=1)中数据的顺序，编码发生变化。因此，也许在训练期间得到数字0的字符串值现在得到数字1，并且最终x_test_data中功能的顺序也会发生变化。

要解决此问题，您需要分别为每列保存LabelEncoder和OneHotEncoder。

因此在培训期间，请执行以下操作：

Features

然后，在测试期间：

import pickle
def encode_string(cat_features):
    enc = preprocessing.LabelEncoder()
    enc.fit(cat_features)
    enc_cat_features = enc.transform(cat_features)

    # Save the LabelEncoder for this column
    encoder_file = open('./'+cat_features+'_encoder.pickle', 'wb')
    pickle.dump(lin_mod, encoder_file)
    encoder_file.close()

    ohe = preprocessing.OneHotEncoder()
    encoded = ohe.fit(enc_cat_features.reshape(-1,1))

    # Same for OHE
    ohe_file = open('./'+cat_features+'_ohe.pickle', 'wb')
    pickle.dump(lin_mod, ohe_file)
    ohe_file.close()

    return encoded.transform(enc_cat_features.reshape(-1,1)).toarray()

为什么我不能直接使用功能矩阵进行预测？

1 个答案: