semvec/pypi-packages huggingface dataset:
tableParser 1.0.1
It extract the table data from the pdf

TableParser

tableParser is a python program using the concept of deep learning for extract the data from tables present in the PDF. It basically takes the file name of the pdf and extrcts the tables from the PDF. This is usefull when we need tables of the reserch papers or some thing like long table. This program uses the pytorch framework (made by facebook) and tesseract for parsing the table data.

This is an Intital stage of the project. It have some bugs hopefully later release will fix this issue. 😊
behaveml 0.2.9
Machine learning for animal behavior analysis
Requires Python: >=3.6

[![codecov](https://codecov.io/gh/benlansdell/behaveml/branch/master/graph/badge.svg?token=PN52Q3UH3G)](https://codecov.io/gh/benlansdell/behaveml)

# BehaveML

Machine learning for animal behavior.

Interprets pose-tracking files (currently only from DLC) and behavior annotations (currently only from BORIS) to train a behavior classifier, perform unsupervised learning, and other common analysis tasks. 

## Installation

```
pip install behaveml
```

Can install optional extras with:

```
pip install numpy, cython
pip install behaveml[all]
```

This includes matplotlib, keras, and Linderman lab's state-space model package, [ssm](https://github.com/lindermanlab/ssm). Note that installing ssm requires cython and numpy for the build, so must be already present in the environment. 

## Quickstart

Import
```
from glob import glob 
from behaveml import VideosetDataFrame, clone_metadata
from behaveml import compute_dl_probability_features, compute_mars_features
from behaveml.io import get_sample_data_paths
```

Gather the DLC and BORIS tracking and annotation files
```
tracking_files, boris_files = get_sample_data_paths()
```

Setup some parameters
```
frame_width = 20                 # (float) length of entire horizontal shot
frame_width_units = 'in'         # (str) units frame_width is given in
fps = 30                         # (int) frames per second
resolution = (1200, 1600)        # (tuple) HxW in pixels
```

Create a parameter object and video dataset
```
metadata = clone_metadata(tracking_files, 
                          label_files = boris_files, 
                          frame_width = frame_width, 
                          fps = fps, 
                          frame_width_units = frame_width_units, 
                          resolution = resolution)

animal_renamer = {'adult': 'resident', 'juvenile':'intruder'}

dataset = VideosetDataFrame(metadata, animal_renamer=animal_renamer)
```

Now create features on this dataset
```
dataset.add_features(compute_dl_probability_features, 
                     featureset_name = '1dcnn', 
                     add_to_features = True)

dataset.add_features(compute_mars_features, 
                     featureset_name = 'MARS', 
                     add_to_features = True)
```

Now access a features table, labels, and groups for learning with `dataset.features, dataset.labels, dataset.groups`. From here it's easy to use some ML libraries to predict behavior. For example:
```
from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier
from sklearn.model_selection import cross_val_predict
from sklearn.metrics import accuracy_score

model = RandomForestClassifier()
predictions = cross_val_predict(model, dataset.features, dataset.labels, dataset.groups)
score = accuracy_score(dataset.labels, predictions)
```

## Features

* Quickly generate a movie