3

有一个总共由 14 列组成的数据框，最后一列是整数值 = 0 或 1 的目标标签。

我已经定义：

  1. X = df.iloc[:,1:13]---- 这由特征值组成
  2. y = df.iloc[:,-1]------ 这由相应的标签组成

两者都具有所需的相同长度，X是由 13 列组成的数据框，形状为 (159880, 13)，y是具有形状 (159880,) 的数组类型

但是当我执行train_test_split()时X，y- 该功能无法正常工作。

下面是简单的代码：

X_train, y_train, X_test, y_test = train_test_split(X, y, random_state = 0)

拆分后，两者都X_train具有X_test形状（119910,13）。y_train具有形状 (39970,13) 并且y_test具有形状 (39970,)

这很奇怪，即使在定义test_size参数之后，结果也保持不变。

请告知，可能出了什么问题。

import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from adspy_shared_utilities import plot_feature_importances
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

def model():
    
    df = pd.read_csv('train.csv', encoding = 'ISO-8859-1')
    df = df[np.isfinite(df['compliance'])]
    df = df.fillna(0)
    df['compliance'] = df['compliance'].astype('int')
    df = df.drop(['grafitti_status', 'violation_street_number','violation_street_name','violator_name',
                  'inspector_name','mailing_address_str_name','mailing_address_str_number','payment_status',
                  'compliance_detail', 'collection_status','payment_date','disposition','violation_description',
                  'hearing_date','ticket_issued_date','mailing_address_str_name','city','state','country',
                  'violation_street_name','agency_name','violation_code'], axis=1)
    df['violation_zip_code'] = df['violation_zip_code'].replace(['ONTARIO, Canada',', Australia','M3C1L-7000'], 0)
    df['zip_code'] = df['zip_code'].replace(['ONTARIO, Canada',', Australia','M3C1L-7000'], 0)
    df['non_us_str_code'] = df['non_us_str_code'].replace(['ONTARIO, Canada',', Australia','M3C1L-7000'], 0)
    df['violation_zip_code'] = pd.to_numeric(df['violation_zip_code'], errors='coerce')
    df['zip_code'] = pd.to_numeric(df['zip_code'], errors='coerce')
    df['non_us_str_code'] = pd.to_numeric(df['non_us_str_code'], errors='coerce')
    #df.violation_zip_code = df.violation_zip_code.replace('-','', inplace=True)
    df['violation_zip_code'] = np.nan_to_num(df['violation_zip_code'])
    df['zip_code'] = np.nan_to_num(df['zip_code'])
    df['non_us_str_code'] = np.nan_to_num(df['non_us_str_code'])
    X = df.iloc[:,0:13]
    y = df.iloc[:,-1]
    X_train, y_train, X_test, y_test = train_test_split(X, y, random_state = 0)    
    print(y_train.shape)
4

2 回答 2

5

你把train_test_split的结果弄混了，应该是

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=0)
于 2018-07-01T18:45:52.517 回答
0 
if args.mode == "train":

    # Load Data
    data, labels = load_dataset('C:/Users/PC/Desktop/train/k')

    # Train ML models
    knn(data, labels,'C:/Users/PC/Desktop/train/knn.pkl' )
于 2020-06-06T21:37:54.907 回答