对csv文件进行预测，一次一行

Question

I have a large csv file that i need to take a row of data, one at a time, and score it against a model. I have tried the code below but get an error of "X has 120839 features per sample; expecting 30" . I can run the model against the entire dataset and it makes predictions on each row. But i need to do it one line at a time, thank you.

loaded_model = joblib.load('LR_model.sav')
with open(r'fordTestA.csv', "r") as f:

for line in f:
    line = f.readlines()[1:]  ##minus headers
    result = loaded_model.predict(line)

In this scenario, it doesnt seem to split the lines as there is \\n after each row. I tried to add

line = line.rstrip('\n')

This gives an error : " 'list' object has no attribute 'rstrip'" . Thanks in advance for any feedback.

Answer 1

I'm not familiar with joblib or predict() , but:

import csv

# other code

with open(r'fordTestA.csv', 'r', newline='') as f:
    rows = csv.reader(f, delimiter=',')
    _ = next(rows) # skip headers
    for row in rows:
        line = list(map(float, row)) # convert row of str to row of float
        results = loaded_model.predict(line)
        # or if you need a ',' delimited string
        line = ','.join(row)
        results = loaded_model.predict(row)