skip_gram_train.py

from Product2VecSkipGram import Product2VecSkipGram
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split

if __name__ == '__main__':
    np.random.seed(2017)
    products = pd.read_csv('data/products.csv')
    df = pd.read_pickle('data/prod2vec.pkl').products
    print('initial size', len(df))

    df_train, df_cv = train_test_split(df, test_size=0.1, random_state=2017)
    batch_size = 1024
    rates = {100000: 0.5,
             200000: 0.25,
             500000: 0.1}
    model = Product2VecSkipGram(df_train, df_cv, batch_size, 1, 1, np.max(products.product_id) + 1)
    model.train(120001, 20000, len(df_cv) // batch_size, rates)