semvec/pypi-packages huggingface dataset:
odds 0.2.2
outlier_detection, detects outliers.
Requires Python: >=3.6, <4
Keywords: machine learning anomaly outlier novelty detection ranking


[![Build Status](https://app.travis-ci.com/jogrundy/od_dist.svg?branch=main)](https://app.travis-ci.com/jogrundy/od_dist)

# Outlier Data Detection Systems - ODDS

# As used in paper "Simple Models are Effective in Anomaly Detection in Multi-variate Time Series"

This work was funded by the grant "Early detection of contact distress for enhanced performance monitoring and predictive inspection of machines" (EP/S005463/1) from the Engineering and Physical Sciences Research Council (EPSRC), UK.

> pip install odds

The work is done by the **OD** object. Import the 'OD' object as follows:

> from odds import OD

Instantiate the object with the 'algo' argument, where a short string represents the algorithm you wish to use. In this case, 'VAR' refers to vector autoregression, a simple linear multidimensional regression algorithm. Other implemented algorithms are listed below.

> od = OD('VAR')

To use the object, you need to call the 'get_os()' function, with 'X' as its argument, where X is a data matrix, **n** samples by **p** features. **p must be 2 or greater to work**
on many of the systems, this returns a vector with n scores, one for each sample.

> outlier_scores = od.get_os(X)

The higher scores are the more outlying. you can then set a threshold if you wish, or just look at the ranking. Scores have not been sanitised, they may contain 'nan' values particularly from the 'VAE' if the data input has not been scaled. However it seems other algorithms work better without scaling, so inputs are not automatically scaled.

Hyperparameters for each of these algorithms are currently fixed to the values in my paper, however at some point I will be finishing implementing a pass though allowing you to specify the hyperparameters at instantiation. This is on my ToDo list.

To get normalised (between 0 and 1) scores, use the 'norm' keyword argument. This may result in errors if the data is not normalised, as there may be infinite values in the scores (usually only from the 'VAE').

> normalised_scores = od.get_os(X, norm=True)


Valid strings for outlier algorithms:

- 'VAR' Vector Autoregression
- 'FRO' Ordinary Feature Regression
- 'FRL' LASSO Feature Regression
- 'FRR' Ridge Feature Regression
- 'GMM' Gaussian Mixture model
- 'IF' Isolation Forest
- 'DBSCAN' Density Based Spatial Clustering and Noise
- 'OCSVM' One Class Support Vector Machine
- 'LSTM' Long Short Term Memory
- 'GRU' Gated Recurrent Unit
- 'AE' Autoencoder
- 'VAE' Variational Autoencoder
- 'OP' Outlier Pursuit
- 'GOP' Graph Regularised Outlier Pursuit
- 'RAND' Random scoring (for baseline comparison)



Hyperparameter table ![Hyperparameter table](images/table_hyperparameters.png)
imgrerite 0.1.1
A command-line tool to hide and reveal information inside images
Requires Python: >=3.6

# ImgReRite

A command line tool to hide and reveal information inside images (works for both PNGs and JPGs)

## Dependencies

- Python 3

Most of the Linux and MacOS systems comes pre-installed with Python, unfortunately this is not true for Windows. But, it can be downloaded manually from [(Python)](https://www.python.org/downloads/)

## Installation

Run ```pip install imgrerite``` in the terminal

## Usage

- On Windows

Use ```py -m imgrerite.app```

- On Linux

Use ```python3 -m imgrerite.app```
## FAQs

### But where does my resulting images get stored?

In your system's home directory, there would be a new folder now, named Imgrerite, all the images are saved there. By reverse revealing those images, you'll see the text you hid

### And what are the limitations to this tool?

You can only enter texts for now and that too without a new line, I know it sucks!
I'll improve this in the future, so that a whole text file can be hidden inside an image rather than manually typing the text 

And well, let me tell you a secret, this does not really hide stuff, I mean anyone can use this tool to read those hidden texts, right?
I'm planning to next implement a mechanism that solves this

### Okay, but why? WHY?

I was bored

### I need help in installation? / Provide Feedback / Meh! Just wanna say 'Hi!'

Drop me a mail at: [jigyasu@outlook.in](mailto:jigyasu@outlook.in?subject=[GitHub]%20Imgrerite) or if you're a Discord person: [(Imgrerite - Discord)](https://discord.gg/8YEJCWR3QU)
df-io 0.0.12
Helpers for doing IO with Pandas DataFrames

# df_io
Python helpers for doing IO with Pandas DataFrames

# Available methods
## read_df

* bzip2/gzip/zstandard compression
* passing parameters to Pandas' readers
* reading from anything, which `smart_open` supports (local files, AWS S3 etc)
* most of the available formats, Pandas supports

## write_df

This method supports:
* streaming writes
* chunked writes
* bzip2/gzip/zstandard compression
* passing parameters to Pandas' writers
* writing to anything, which `smart_open` supports (local files, AWS S3 etc)
* most of the available formats, Pandas supports

# Documentation

[API doc](https://github.com/Mikata-Project/df_io/tree/master/docs/df_io.md)

### Examples

Write a Pandas DataFrame (df) to an S3 path in CSV format (the default):

```python
import df_io

df_io.write_df(df, 's3://bucket/dir/mydata.csv')
```

The same with gzip compression:

```python
df_io.write_df(df, 's3://bucket/dir/mydata.csv.gz')
```

With zstandard compression using pickle:

```python
df_io.write_df(df, 's3://bucket/dir/mydata.pickle.zstd', fmt='pickle')
```


Using JSON lines:

```python
df_io.write_df(df, 's3://bucket/dir/mydata.json.gz', fmt='json')
```

Passing writer parameters:

```python
df_io.write_df(df, 's3://bucket/dir/mydata.json.gz', fmt='json', writer_options={'lines': False})
```

Chunked write (splitting the df into equally sized parts and creating/writing outputs for them):

```python
df_io.write_df(df, 's3://bucket/dir/mydata.json.gz', fmt='json', chunksize=10000)
```