

In [1]:

    
%matplotlib inline
import pandas as pd
import sys
import shutil
from alpenglow.experiments import BatchFactorExperiment, ExternalModelExperiment
from alpenglow.evaluation import DcgScore
import numpy as np
import matplotlib
import matplotlib.pyplot as plt



In [2]:

    
data = pd.read_csv('http://info.ilab.sztaki.hu/~fbobee/alpenglow/tutorial_dataset.csv', header=None, names=['time', 'user', 'item'])

libfm model



In [3]:

    
exp = ExternalModelExperiment(
    period_length=60 * 60 * 24 * 7 * 4,
    in_name_base="batches/batch",
    mode="read",
)



In [4]:

    
res = exp.run(data)









    



running experiment...



In [5]:

    
res['dcg'] = DcgScore(res)



In [6]:

    
res['dcg'].mean()









    Out[6]:





0.01446016560516256

alpenglow batch factor model



In [7]:

    
exp = BatchFactorExperiment(
    period_length=60 * 60 * 24 * 7 * 4,
    negative_rate=5
)
res2 = exp.run(data)









    



running experiment...



In [8]:

    
res2['dcg'] = DcgScore(res2)



In [9]:

    
res2['dcg'].mean()









    Out[9]:





0.0173458838786909

plotting



In [10]:

    
plt.figure(figsize=(15,8))
res.groupby(res.time//(60*60*24)).dcg.mean().plot()
res2.groupby(res2.time//(60*60*24)).dcg.mean().plot()
plt.ylabel('daily average ndcg')
plt.legend(['libfm', 'alpenglow'])









    Out[10]:





<matplotlib.legend.Legend at 0x7f0f9ba0f1d0>



In [ ]: