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exp_4.1_plot.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Sep 8 13:36:49 2015
@author: thalita
plots exp 4.1 ( and 3)
"""
from utils import read_results, plot_metric, plot_metrics, pd_select
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import expdb
from itertools import chain
matplotlib.rcParams['ps.useafm'] = True
matplotlib.rcParams['pdf.use14corefonts'] = True
outdir = '/tmp/'
#%%
exp_db = expdb.ExperimentDB()
result = exp_db.db
colors = 'bgrcmyk'
all_metrics = ['P','R','F1','RMSE','MAE']
ir_metrics = ['P','R','F1']
trad_metrics = ['RMSE','MAE']
#%%
select = {'RStype': 'BMFRPrecommender',
'RPtype': 'sparse',
'mincoverage': 1 ,
'threshold': 3 ,
'offlinekNN': 'False',
'neighbortype': 'user',
'algorithm': 'brute',
'metric': 'cosine'}
select_BMF = dict(select)
del select_BMF['RPtype']
select_BMF['RStype'] = 'BMFrecommender'
data = pd_select(result, select_BMF)
data['RStype'] = 'BMFRPrecommender'
data['dimred'] = 1
data['RPtype'] = 'sparse'
tmp = pd.concat((result, data), ignore_index=True)
plot_metrics(all_metrics,
varpar=('dimred', [1, 0.9, 0.8, 0.75, 0.5, 0.25]),
across=('nneighbors', 'num. de vizinhos'),
dataframe=tmp, select=select,
labelfmt='%d%%|D|', labelmul=100,
suptitle='Recomendação BMF+RP esparsa - BMF 100% e limiar 3')
plt.savefig(outdir + 'BMFRP_sparse_dimred_nneighbors.eps')
#%%
select = {'RStype': 'BMFRPrecommender',
'RPtype': 'gaussian',
'mincoverage': 1 ,
'threshold': 3 ,
'offlinekNN': 'False',
'neighbortype': 'user',
'algorithm': 'brute',
'metric': 'cosine'}
select_BMF = dict(select)
del select_BMF['RPtype']
select_BMF['RStype'] = 'BMFrecommender'
data = pd_select(result, select_BMF)
data['RStype'] = 'BMFRPrecommender'
data['dimred'] = 1
data['RPtype'] = 'gaussian'
tmp = pd.concat((result, data), ignore_index=True)
plot_metrics(all_metrics,
varpar=('dimred', [1, 0.9, 0.8, 0.75, 0.5, 0.25]),
across=('nneighbors', 'num. de vizinhos'),
dataframe=tmp, select=select,
labelfmt='%d%%|D|', labelmul=100,
suptitle='Recomendação BMF+RP gaussiana - BMF 100% e limiar 3')
plt.savefig(outdir + 'BMFRP_gaussian_dimred_nneighbors.eps')
#%% Exp 4.1
select = {'RSfactory': 'factory',
'RPtype': 'gaussian',
#'mincoverage': 1 ,
'threshold': 3 ,
#'offlinekNN': 'False',
#'neighbortype': 'user',
#'algorithm': 'brute',
#'metric': 'cosine'
}
select_BMF = dict(select)
del select_BMF['RPtype']
del select_BMF['RSfactory']
select_BMF['RStype'] = 'BMFrecommender'
select_BMF.update({'mincoverage': 1 ,
'offlinekNN': 'False',
'neighbortype': 'user',
'algorithm': 'brute',
'metric': 'cosine'
})
data = pd_select(result, select_BMF)
data['RSfactory'] = 'factory'
data['RPtype'] = 'gaussian'
tmp = pd.concat((result, data), ignore_index=True)
print(pd_select(tmp, select))
plot_metrics(all_metrics,
varpar=('RStype', ['AvgRatingEnsemble',
#'MajorityEnsemble',
#'RankSumEnsemble',
'BMFrecommender'
]),
across=('nneighbors', 'num. de vizinhos'),
dataframe=tmp, select=select,
labelfmt='%s',
suptitle='Recomendação Ensemble BMF+RP gaussiana - BMF 100% e limiar 3\nRed 25,50,75,80,90%')