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Fast and memory-efficient ANN with a subset-search functionality

rii 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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Reconfigurable Inverted Index (Rii): IVFPQ-based fast and memory efficient approximate nearest neighbor search method
with a subset-search functionality.

Reference:
- [Y. Matsui](http://yusukematsui.me/), [R. Hinami](http://www.satoh-lab.nii.ac.jp/member/hinami/), and [S. Satoh](http://research.nii.ac.jp/~satoh/index.html), "**Reconfigurable Inverted Index**", ACM Multimedia 2018 (oral). [**[paper](https://dl.acm.org/ft_gateway.cfm?id=3240630)**] [**[project](http://yusukematsui.me/project/rii/rii.html)**]

## Summary of features
![](http://yusukematsui.me/project/rii/img/teaser1.png)  |  ![](http://yusukematsui.me/project/rii/img/teaser2.png)
:---:|:---:
The search can be operated for a subset of a database. | Rii remains fast even after many new items are added.
- Fast and memory efficient ANN. Rii enables you to run billion-scale search in less than 10 ms.
- You can run the search over a **subset** of the whole database
- Rii Remains fast even after many vectors are newly added (i.e., the data structure can be **reconfigured**)

## Installing
You can install the package via pip. This library works with Python 3.6+ on linux/mac/wsl/Windows10

```
pip install rii
```

<details>
  <summary>For windows (maintained by @ashleyabraham)</summary>

### Installing in Windows 10 via `pip install`
  Requires MS Visual Studio Build tools C++ 14.0 or 14.1 toolset or above to compile and install via pip install

### Pre-compiled binary for Windows 10 
  Pre-compiled binaries doesn't require MS Visual Studio Build tools

```
  #Python 3.8
  pip install https://github.com/ashleyabraham/rii/releases/download/v0.2.8/rii-0.2.8-cp38-cp38-win_amd64.whl
  ```
  ```
  #Python 3.7
  pip install https://github.com/ashleyabraham/rii/releases/download/v0.2.8/rii-0.2.8-cp37-cp37m-win_amd64.whl
```

#### OpenMP
  OpenMP requires libomp140_x86_64.dll to compile in windows, which is part of MS Visual Studio Build tools and it is not redistributable.
  
  In order to use OpenMP 3.0 /openmp:llvm flag is used which causes warnings of multiple libs loading, use at your descretion when used with other parallel processing library loadings. To supress use

`SET KMP_DUPLICATE_LIB_OK=TRUE`

#### SIMD
  The /arch:AVX2 flag is used in MSVC to set appropriate SIMD preprocessors and compiler intrinsics

</details>

## [Documentation](https://rii.readthedocs.io/en/latest/index.html)
- [Tutorial](https://rii.readthedocs.io/en/latest/source/tutorial.html)
- [Tips](https://rii.readthedocs.io/en/latest/source/tips.html)
- [API](https://rii.readthedocs.io/en/latest/source/api.html)

## Usage

### Basic ANN

```python
import rii
import nanopq
import numpy as np

N, Nt, D = 10000, 1000, 128
X = np.random.random((N, D)).astype(np.float32)  # 10,000 128-dim vectors to be searched
Xt = np.random.random((Nt, D)).astype(np.float32)  # 1,000 128-dim vectors for training
q = np.random.random((D,)).astype(np.float32)  # a 128-dim vector

# Prepare a PQ/OPQ codec with M=32 sub spaces
codec = nanopq.PQ(M=32).fit(vecs=Xt)  # Trained using Xt

# Instantiate a Rii class with the codec
e = rii.Rii(fine_quantizer=codec)

# Add vectors
e.add_configure(vecs=X)

# Search
ids, dists = e.query(q=q, topk=3)
print(ids, dists)  # e.g., [7484 8173 1556] [15.06257439 15.38533878 16.16935158]
```
Note that you can construct a PQ codec and instantiate the Rii class at the same time if you want.
```python
e = rii.Rii(fine_quantizer=nanopq.PQ(M=32).fit(vecs=Xt))
e.add_configure(vecs=X)
```
Furthermore, you can even write them in one line by chaining a function.
```python
e = rii.Rii(fine_quantizer=nanopq.PQ(M=32).fit(vecs=Xt)).add_configure(vecs=X)
```

### Subset search

```python
# The search can be conducted over a subset of the database
target_ids = np.array([85, 132, 236, 551, 694, 728, 992, 1234]) # Specified by IDs
# For windows, you must specify dtype=np.int64 as follows.
# target_ids = np.array([85, 132, 236, 551, 694, 728, 992, 1234], dtype=np.int64)

ids, dists = e.query(q=q, topk=3, target_ids=target_ids)
print(ids, dists)  # e.g., [728  85 132] [14.80522156 15.92787838 16.28690338]
```

### Data addition and reconfiguration

```python
# Add new vectors
X2 = np.random.random((1000, D)).astype(np.float32)
e.add(vecs=X2)  # Now N is 11000
e.query(q=q)  # Ok. (0.12 msec / query)

# However, if you add quite a lot of vectors, the search might become slower
# because the data structure has been optimized for the initial item size (N=10000)
X3 = np.random.random((1000000, D)).astype(np.float32) 
e.add(vecs=X3)  # A lot. Now N is 1011000
e.query(q=q)  # Slower (0.96 msec/query)

# In such case, run the reconfigure function. That updates the data structure
e.reconfigure()
e.query(q=q)  # Ok. (0.21 msec / query)
```

### I/O by pickling
```python
import pickle
with open('rii.pkl', 'wb') as f:
    pickle.dump(e, f)
with open('rii.pkl', 'rb') as f:
    e_dumped = pickle.load(f)  # e_dumped is identical to e
```

### Util functions
```python
# Print the current parameters
e.print_params()

# Delete all PQ-codes and posting lists. fine_quantizer is kept.
e.clear()

# You can switch the verbose flag
e.verbose = False

# You can merge two Rii instances if they have the same fine_quantizer
e1 = rii.Rii(fine_quantizer=codec)
e2 = rii.Rii(fine_quantizer=codec)
e1.add_configure(vecs=X1)
e2.add_configure(vecs=X2)
e1.merge(e2)  # Now e1 contains both X1 and X2

```

## [Examples](./examples)
- [Simple tag search](./examples/tag_search/simple_tag_search.ipynb)
- [Benchmark](./examples/benchmark/)

## Author
- [Yusuke Matsui](http://yusukematsui.me)

## Credits
- The logo is designed by [@richardbmx](https://github.com/richardbmx) ([#4](https://github.com/matsui528/rii/issues/4))
- The windows implementation is by [@ashleyabraham](https://github.com/ashleyabraham) ([#42](https://github.com/matsui528/rii/pull/42))
- Fixing the build ([#50](https://github.com/matsui528/rii/pull/50)) and supporting for ARM CPUs ([#58](https://github.com/matsui528/rii/pull/58)) by [@timzag](https://github.com/timzag)

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