train_test_split 不拆分数据

Question

There is a dataframe which consists of 14 columns in total, the last column is the target label with integer values = 0 or 1.

I have defined -

1. X = df.iloc[:,1:13] ---- this consists of the feature values
2. Ly = df.iloc[:,-1] ------ this consists of the corresponding labels

Both have same length as desired, X is the dataframe that consists of 13 columns, shape (159880, 13), y is an array type with shape(159880,)

But when i perform train_test_split on X,y - the function is not working properly.

Below is the straightforward code -

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

After this split, both X_train and X_test have shape (119910,13). y_train is having shape (39970,13) and y_test is having shape (39970,)

This is weird, even after defining test_size parameter, the results stay same.

Please advise, what could have been going wrong.

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)

Answer 1

你把train_test_split的结果搞混了，应该是

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=0)

Answer 2

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' )