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learn_lv.py
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learn_lv.py
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import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import pickle
from collections import defaultdict
import sys
from torch.utils.data import Dataset, DataLoader
from Mmetrics import *
import LTR
import datautil
import permutationgraph
import DTR
import EEL
import PPG
import PL
def df2ds(df_path):
with open(df_path, 'rb') as f:
df = pickle.load(f)
ds = df.to_dict(orient='list')
for k in ds:
ds[k] = np.array(ds[k])
ds['dlr'] = np.concatenate([np.zeros(1), np.where(np.diff(ds['qid'])==1)[0]+1, np.array([ds['qid'].shape[0]])]).astype(int)
return type('ltr', (object,), ds)
def dict2ds(df_path):
with open(df_path, 'rb') as f:
ds = pickle.load(f)
return type('ltr', (object,), ds)
ds2019 = df2ds('LTR2019.df')
ds2020 = df2ds('LTR2020.df')
sds2019 = dict2ds('s_LTR2019.df')
sds2020 = dict2ds('s_LTR2020.df')
epochs = 50
alg = 'PPG'
if len(sys.argv) > 1:
alg = sys.argv[1]
metric = 'EEL'
if len(sys.argv) > 2:
metric = sys.argv[2]
intra = False
suffix = 'nointra_'
if len(sys.argv) > 3:
if sys.argv[3] == 'intra':
intra = True
suffix='intra_'
sessions_cnt=20
if len(sys.argv) > 4:
sessions_cnt = eval(sys.argv[4])
suffix += f'{alg}_{sessions_cnt}_{sys.argv[5]}'
exposure2020 = np.array([1./np.log2(2+i) for i in range(1,np.diff(ds2020.dlr).max()+2)])
exposure2019 = np.array([1./np.log2(2+i) for i in range(1,np.diff(ds2019.dlr).max()+2)])
def learn_one_PPG(metric, qid, verbose, y_pred, g, dlr, epochs, lr, exposure, grade_levels, samples_cnt, sessions_cnt):
s, e = dlr[qid:qid+2]
y_pred_s, g_s, sorted_docs_s, dlr_s = \
EEL.copy_sessions(y=y_pred[s:e], g=g[s:e], sorted_docs=y_pred[s:e].argsort()[::-1], sessions=sessions_cnt)
if metric == 'EEL':
objective_ins = EEL.EEL(y_pred = y_pred_s, g = g_s, dlr = dlr_s, exposure=exposure, grade_levels = grade_levels)
else:
objective_ins = DTR.DTR(y_pred = y_pred_s, g = g_s, dlr = dlr_s, exposure=exposure)
learner = PPG.Learner( PPG_mat=None, samples_cnt=samples_cnt,
objective_ins=objective_ins,
sorted_docs = sorted_docs_s,
dlr = dlr_s,
intra = g_s if intra else np.arange(g_s.shape[0]),
inter = np.repeat(dlr_s[:-1], np.diff(dlr_s)))
vals = learner.fit(epochs, lr, verbose=verbose)
return vals
def learn_all_PPG(metric, y_pred, g, dlr, epochs, lr, exposure, grade_levels, samples_cnt, sessions_cnt):
sorted_docs = []
# for qid in trange(dlr.shape[0] - 1, leave=False):
for qid in range(dlr.shape[0] - 1):
min_b = learn_one_PPG(metric, qid, 0, y_pred, g, dlr, epochs, lr, exposure, grade_levels, samples_cnt, sessions_cnt)
sorted_docs.append(min_b)
# print(ndcg_dtr(exposure, lv, np.concatenate(y_rerank), dlr, g, query_counts))
return sorted_docs
def learn_one_PL(metric, qid, verbose, y_pred, g, dlr, epochs, lr, exposure, grade_levels, samples_cnt, sessions_cnt):
s, e = dlr[qid:qid+2]
if metric == 'EEL':
objective_ins = EEL.EEL(y_pred = y_pred[s:e], g = g[s:e], dlr = np.array([0,e-s]), exposure=exposure, grade_levels = grade_levels)
else:
objective_ins = DTR.DTR(y_pred = y_pred[s:e], g = g[s:e], dlr = np.array([0,e-s]), exposure=exposure)
learner = PL.Learner(logits=y_pred[s:e], samples_cnt=samples_cnt,
objective_ins=objective_ins, sessions_cnt=sessions_cnt)
vals = learner.fit(epochs, lr, verbose=verbose)
return vals
def learn_all_PL(metric, y_pred, g, dlr, epochs, lr, exposure, grade_levels, samples_cnt, sessions_cnt):
sorted_docs = []
# for qid in trange(dlr.shape[0] - 1, leave=False):
for qid in range(dlr.shape[0] - 1):
min_b = learn_one_PL(metric, qid, 0, y_pred, g, dlr, epochs, lr, exposure, grade_levels, samples_cnt, sessions_cnt)
sorted_docs.append(min_b)
# print(ndcg_dtr(exposure, lv, np.concatenate(y_rerank), dlr, g, query_counts))
return sorted_docs
def estimated_evaluate_one(metric, qid, y_pred, g, dlr, output_permutation, exposure, sessions_cnt):
s, e = dlr[qid:qid+2]
permutation = output_permutation[qid]
lv_s, g_s, sorted_docs_s, dlr_s = \
EEL.copy_sessions(y=y_pred[s:e], g=g[s:e], sorted_docs=y_pred[s:e].argsort()[::-1], sessions=sessions_cnt)
if metric == 'EEL':
objective_ins = EEL.EEL(y_pred = lv_s, g = g_s, dlr = dlr_s, exposure=exposure, grade_levels = 2)
else:
objective_ins = DTR.DTR(y_pred = lv_s, g = g_s, dlr = dlr_s, exposure=exposure)
return objective_ins.eval(permutation)
def estimated_evaluate_all(metric, y_pred, g, dlr, output_permutation, exposure, sessions_cnt):
eel_res = []
for qid in range(dlr.shape[0] - 1):
s,e = dlr[qid:qid+2]
if len(np.unique(g[s:e])) == 1:
continue
eel = estimated_evaluate_one(metric, qid, y_pred, g, dlr, output_permutation, exposure, sessions_cnt)
eel_res.append(eel)
return np.array(eel_res).mean()
learn_fn = eval(f'learn_all_{alg}')
res = {}
def find_best(y_pred, sds, exposure, sessions_cnt):
best_lr = 0
best_samples_cnt = 0
best_fairness = np.inf
for learning_rate in ['0.01', '0.1']:
for samples_cnt in [4,8,16,32]:
output_permutation = learn_fn(metric, y_pred, sds.g, sds.dlr,
epochs, eval(learning_rate), exposure=exposure,
grade_levels=5, samples_cnt=samples_cnt, sessions_cnt=sessions_cnt)
fairness = estimated_evaluate_all(metric, y_pred, sds.g, sds.dlr,
output_permutation, exposure, sessions_cnt)
if fairness < best_fairness:
best_lr = learning_rate
best_samples_cnt = samples_cnt
best_fairness = fairness
return best_lr, best_samples_cnt
for run_i in range(2):
learning_rate, samples_cnt = 0.01, 16
res[f'lv_2020_{learning_rate}_{samples_cnt}_{run_i}'] = \
learn_fn(metric, ds2020.lv, ds2020.g, ds2020.dlr, epochs, eval(learning_rate), exposure=exposure2020,
grade_levels=5, samples_cnt=samples_cnt, sessions_cnt=sessions_cnt)
with open(f'/ivi/ilps/personal/avardas/_data/PPG/33/{suffix}_{metric}_results.pkl', 'wb') as f:
pickle.dump(res, f)
res[f'lv_2019_{learning_rate}_{samples_cnt}_{run_i}'] = \
learn_fn(metric, ds2019.lv, ds2019.g, ds2019.dlr, epochs, eval(learning_rate), exposure=exposure2019,
grade_levels=5, samples_cnt=samples_cnt, sessions_cnt=sessions_cnt)
with open(f'/ivi/ilps/personal/avardas/_data/PPG/33/{suffix}_{metric}_results.pkl', 'wb') as f:
pickle.dump(res, f)