semvec/pypi-packages huggingface dataset:
confusion-metrics 0.1.0
A collection of metrics for analysing confusion matrices
Requires Python: >=3.0

# David's helpful metrics library

There are many different ways to evaluate a confusion matrix. 
This helpful module implements a large number of them

    * q1
    * q2 
    * q3
    * q4
    * q5
    * q6
    * q7
    * dpower 
    * agf 
    * markedness
    * bcr 
    * ber 
    * gm 
    * agm 
    * op
    * req
    * tanimoto
    * roc
    * specificity
    * fprate
    * fnrate
    * precision
    * negativepv 
    * plr 
    * nlr 
    * youden 
    * accuracy
    * fscore 
    * f2measure 
    * fmeasure 
    * f0_5measure 
    * power
    * logpower
    * bajic_k
    * chisquare
    * ctg
    * yuleY
    * yuleQ
    * ivesgibbs
    * acp
    * acc
    * gdip1
    * gdip2
    * gdip3
    * hamming
    * jaccard

The original impelmentation was in Perl around 2005 and I appear to have not 
noted many of the references. My apologies.

Details of the calcualtion are in the docstring. This module should be used as follows:

`from metrics import Metrics`

`Metrics.list_metrics() # lists method names`

`Metrics.list_metrics(verbose=True) # gives a dictionary with the docstring`

`Metrics.measure(method, tp=TP, fp=FP, tn=TN, fn=FN) # for True Positive, False Negative etc.`

You probably want to wrap this  with `try .. except` as it will show an error if inappropriate data is given.
The `measure` method will convert counts to proportional data.

Don't forget to `Metrics.cite(method)` which will give a list of citations, if available. If you wish to add to the citations then submit a pull request.

I'd like to expand the help text in due course for each metric.

Further information on many of the metrics and their behaviour can be found at  (Tharwat, Applied Computing and Informatics (2018),https://doi.org/10.1016/j.aci.2018.08.003)[https://doi.org/10.1016/j.aci.2018.08.003]


[Find this on BitBucket]( https://bitbucket.org/davidmam/metrics.git)

 q1
 q2 
 q3
 q4
 q5
 q6
 q7
 dpower 
 agf 
 markedness
 bcr 
 ber 
 gm 
 agm 
 op
 req
 tanimoto
 roc
 specificity
 fprate
 fnrate
 precision
 negativepv 
 plr 
 nlr 
 youden 
 accuracy
 fscore 
 f2measure 
 fmeasure 
 f0_5measure 
 power
 logpower
 bajic_k
 chisquare
 ctg
 yuleY
 yuleQ
 ivesgibbs
 acp
 acc
 gdip1
 gdip2
 gdip3
 hamming
 jaccard
junk-email-collector 1.0.0
Download raw Junk/Spam emails using IMAP.
Keywords: spam

# Junk Email Collector
Download raw Junk/Spam emails using IMAP.

## Requirements

[Imbox](https://github.com/martinrusev/imbox) (Python 3)

## Usage
```python3
#!/usr/bin/env python3
from junk_email_collector import JunkEmailCollector

example_junk = JunkEmailCollector("imap.example.com", "email@example.com", "password", "Junk")
example_junk.get_junk()

```

Will download all unread emails from "Junk" folder of `email@example.com`, saving the raw emails to the folder `email_example.com/`
domainbigdata 1.1.1
Python wrapper for DomainBigData.com (no API available)
Keywords: domainbigdata

# domainbigdata-python
Pull search results from DomainBigData (no API available)

Adopted from https://github.com/crits/crits_services/blob/3dff56e7111439c4156a8408f178fd484df76feb/domainbigdata_service/domainbigdata.py

### Installation
```sh
pip install --user domainbigdata
```

### Usage
```python
import domainbigdata
import json

email = "dns-admin@google.com"
email_registrations = domainbigdata.email_lookup(email)
json.dumps(email_registrations.domains)

# domains may not always have full registrant info
domain = "example.com"
domain_reg = domainbigdata.domain_lookup(domain)
json.dumps(domain_reg.registrant_info)
```