test.py

# ~~~~~~~~~~~~~~~~~~~~
# Code by Anleo YUAN
# Please visit https://www.anleo.top
# Copyrights reserved by Chongqing University
# ~~~~~~~~~~~~~~~~~~~~
from array import array
import sys
import numpy as np
import scipy
from scipy import sparse

trainName = "train.svm"
testName = "test.svm"
modelName = "train2.model"

def svm_read_problem(data_file_name, return_scipy=False):
    """
    svm_read_problem(data_file_name, return_scipy=False) -> [y, x], y: list, x: list of dictionary
    svm_read_problem(data_file_name, return_scipy=True)  -> [y, x], y: ndarray, x: csr_matrix

    Read LIBSVM-format data from data_file_name and return labels y
    and data instances x.
    """
    if scipy != None and return_scipy:
        prob_y = array('d')
        prob_x = array('d')
        row_ptr = array('l', [0])
        col_idx = array('l')
    else:
        prob_y = []
        prob_x = []
        row_ptr = [0]
        col_idx = []
    indx_start = 1
    for i, line in enumerate(open(data_file_name)):
        line = line.split(None, 1)
        # In case an instance with all zero features
        if len(line) == 1: line += ['']
        label, features = line
        prob_y.append(float(label))
        if scipy != None and return_scipy:
            nz = 0
            for e in features.split():
                ind, val = e.split(":")
                if ind == '0':
                    indx_start = 0
                val = float(val)
                if val != 0:
                    col_idx.append(int(ind)-indx_start)
                    prob_x.append(val)
                    nz += 1
            row_ptr.append(row_ptr[-1]+nz)
        else:
            xi = {}
            for e in features.split():
                ind, val = e.split(":")
                xi[int(ind)] = float(val)
            prob_x += [xi]
    if scipy != None and return_scipy:
        prob_y = np.frombuffer(prob_y, dtype='d')
        prob_x = np.frombuffer(prob_x, dtype='d')
        col_idx = np.frombuffer(col_idx, dtype='l')
        row_ptr = np.frombuffer(row_ptr, dtype='l')
        prob_x = sparse.csr_matrix((prob_x, col_idx, row_ptr))
    return (prob_y, prob_x)

if __name__ == '__main__':
    y, x = svm_read_problem(testName, return_scipy=True)