| import sys | |
| import csv | |
| import os | |
| import yaml | |
| import pickle | |
| import numpy as np | |
| import pandas as pd | |
| import project.evaluation.metrics as m | |
| from os.path import exists | |
| from project.data.preparation import prepare_data, get_embeddings | |
| from project.recsys.helper import Helper | |
| from datetime import datetime | |
| from project.recsys.algorithms import execute_algo | |
| from project.evaluation.ResultReport import Results | |
| from keras.models import model_from_yaml | |
| def get_rnn(): | |
| model = model_from_yaml(open('training_model.yaml','r')) | |
| model.load_weights('training_weights.h5') | |
| return model | |
| def skip_all(executed, params, k): | |
| folds = executed[executed['params'] == params]['folds'] | |
| return folds.max() == k | |
| def skip_fold(executed, params, fold): | |
| folds = executed[executed['params'] == params]['folds'] | |
| return folds.max() >= fold | |
| def cross_validation(df, conf, setups): | |
| params = conf['evaluation'] | |
| r_paths = conf['results'] | |
| kfold = prepare_data(df, conf) | |
| dataset = params['dataset'] | |
| topN = int(params['topN']) | |
| k = int(params['k']) | |
| results = Results(setups, k) | |
| exec_path = r_paths['full'] | |
| pwd_rec = 'tmp/{}/rec/'.format(dataset) | |
| if not exists(pwd_rec): | |
| os.mkdir(pwd_rec) | |
| if not exists(exec_path): | |
| pd.DataFrame({},columns=['params','algo','folds','prec','rec','f1','map','ndcg@5','p@5']).to_csv(exec_path,index=None,sep='\t') | |
| executed = pd.read_csv(exec_path, sep='\t') | |
| for setup in setups: | |
| _, params, path = setup | |
| if not exists(pwd_rec + params): | |
| os.mkdir(pwd_rec + params) | |
| if skip_all(executed, params, k): | |
| continue | |
| songs = df['song'].unique().tolist() | |
| m2v, sm2v = get_embeddings(path, songs) | |
| songs = pd.DataFrame({ 'm2v': [m2v[x] for x in songs], 'sm2v': [sm2v[x] for x in songs]}, index=songs, columns=['m2v','sm2v']) | |
| fold = 1 | |
| for train, test in kfold: | |
| if skip_fold(executed, params, fold): | |
| fold+=1 | |
| continue | |
| time = datetime.now().strftime('%d/%m/%Y %H:%M') | |
| print('%s | fold-%d | Running recsys w/ k-fold with the following params: %s' % (time, fold, params)) | |
| helper = Helper(train, test, songs, dataset) | |
| m2vTN, sm2vTN, csm2vTN, csm2vUK = execute_algo(train.index, test.index, songs, topN, k, helper, pwd_rec + params) | |
| res = results.fold_results(params, m2vTN, sm2vTN, csm2vTN, csm2vUK, fold) | |
| res.to_csv(exec_path, sep='\t', mode='a', index=None, header=None) | |
| fold+=1 | |