https://github.com/kalifou/ri_tme1
Information retrieval - assignments for course at UPMC - Paris 6
https://github.com/kalifou/ri_tme1
bm25 evaluation-metrics hits-algorithm information-retrieval language-model language-modeling pagerank-algorithm python
Last synced: 3 months ago
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Information retrieval - assignments for course at UPMC - Paris 6
- Host: GitHub
- URL: https://github.com/kalifou/ri_tme1
- Owner: kalifou
- Created: 2017-09-28T10:45:41.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-01-12T13:47:53.000Z (over 7 years ago)
- Last Synced: 2025-02-04T17:55:10.798Z (5 months ago)
- Topics: bm25, evaluation-metrics, hits-algorithm, information-retrieval, language-model, language-modeling, pagerank-algorithm, python
- Language: Jupyter Notebook
- Homepage: http://dac.lip6.fr/master/enseignement/ues/ri/
- Size: 23.1 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: ReadMe.md
Awesome Lists containing this project
README
## TME RI TEXTUEL
```python
from Index import Index
from ParserCACM import ParserCACM
from TextRepresenter import PorterStemmer
from Weighter import Binary, TF, TF_IDF, Log, Log_plus
from EvalMeasure import EvalIRModel
import numpy as np
import os.path
import time
```
```python
# model_type = Random | Vectoriel | Okapi | Language | PageRank | Hits | MetaModel
fname = "data/cacm/cacm.txt"
query_file = "data/cacm/cacm.qry"
relevance_file = "data/cacm/cacm.rel"
```
### Random
```python
t1 = time.time()
type = "Random"
eval_platform = EvalIRModel(fname,query_file,relevance_file,model_type=type)
models_recall, models_inter_prec, models_AP = eval_platform.eval_std()
print "Exec duration(s) : ",time.time()-t1
```
Indexing database...Done!
Evaluation of our models ...
Model : Random
AP: 0.00489365936907
Exec duration(s) : 37.8984589577
### Vectoriel
```python
t1 = time.time()
type = "Vectoriel"
eval_platform = EvalIRModel(fname,query_file,relevance_file,model_type=type)
models_recall, models_inter_prec, models_AP = eval_platform.eval_std()
print "Exec duration(s) : ",time.time()-t1
```
Indexing database...Done!
Creating models...Done!
Evaluation of our models ...
Model : Binary
AP: 0.123849729889
Model : Text Frequency
AP: 0.13215026906
Model : Text Frequency - Inverse Document Frequency
AP: 0.154529641961
Model : Log
AP: 0.120869800911
Model : Log +
AP: 0.143917859729
Exec duration(s) : 171.119966984
### Okapi : k1=2, b=0.75
```python
t1 = time.time()
type = "Okapi"
eval_platform = EvalIRModel(fname,query_file,relevance_file,model_type=type)
models_recall, models_inter_prec, models_AP = eval_platform.eval_std()
print "Exec duration(s) : ",time.time()-t1
```
Indexing database...Done!
L moy : 4925.14700285
Evaluation of our models ...
Model : Okapi
AP: 0.264364808856
Exec duration(s) : 540.410473108
### Language : Lambda =0.2
```python
t1 = time.time()
type = "Language"
eval_platform = EvalIRModel(fname,query_file,relevance_file,model_type=type)
models_recall, models_inter_prec, models_AP = eval_platform.eval_std()
print "Exec duration(s) : ",time.time()-t1
```
Indexing database...Done!
Init of Language model
Evaluation of our models ...
Model : Language Model
AP: 0.307058115209
Exec duration(s) : 580.641823053
### PageRank : N=25, K=unlimited, d=0.85
```python
t1 = time.time()
type = "PageRank"
eval_platform = EvalIRModel(fname,query_file,relevance_file,model_type=type)
models_recall, models_inter_prec, models_AP = eval_platform.eval_std()
print "Exec duration(s) : ",time.time()-t1
```
Indexing database...Done!
Evaluation of our models ...
Model : PageRank
AP: 0.133320317291
Exec duration(s) : 1294.32549
### Hits : N=25, K=unlimited
```python
type = "Hits"
eval_platform = EvalIRModel(fname,query_file,relevance_file,model_type=type)
models_recall, models_inter_prec, models_AP = eval_platform.eval_std()
```
### MetaModel
```python
t1 = time.time()
type = "MetaModel"
eval_platform = EvalIRModel(fname,query_file,relevance_file,model_type=type)
models_recall, models_inter_prec, models_AP = eval_platform.eval_std()
print "Exec duration(s) : ",time.time()-t1
```
Indexing database...Done!
Grad theta : 0.2
Training achieved with Grad_theta < 0.1 !
Number of queries required : 4
Evaluation of our models ...
Model : MetaModel
AP: 0.285386850473
Exec duration(s) : 2393.91500187
```python
for idx,f in enumerate(eval_platform.models[0].listFeaturers.listFeaturers):
print "featurers :",str(f.model)[:-25],">"
print "theta :",eval_platform.models[0].theta[idx],"\n"
```
featurers :
theta : [ 0.01994691]
featurers :
theta : [ 0.88962339]
featurers :
theta : [ 0.06195642]
featurers :
theta : [ 0.20448804]
featurers :
theta : [ 1.82525156]
featurers :
theta : [ 1.11311955]
featurers :
theta : [ 0.74651456]