semvec/pypi-packages huggingface dataset:
deciplot 0.0.1
A handy tool that helps you better visualize your data by plotting 2D decision boundaries, using any sklearn classifier models.

# Deciplot
This is a handy tool that helps you better visualize your data by plotting 2D decision boundaries, using any sklearn classifier models.

## Installation
Run the following to install:
```python
pip install deciplot
```

## Usage
```python
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from deciplot import DeciPlot2D

# Get iris dataset from sklearn
iris = load_iris()
X, y, feature_names, target_names = iris['data'], iris['target'], iris['feature_names'], iris['target_names']

# Initialize a classifier model for plotting decision boundaries
dtree = DecisionTreeClassifier()

# Initialize an DecPlot2D object
dp2d = DeciPlot2D(X, y, feature_names, target_names, dtree)

# Plot and visualize decision boundaries
dp2d.plot(figsize=(20, 10))
```
After running this code, we'll get this beautiful plot:
<p align="center">
<img src='https://user-images.githubusercontent.com/16115992/97473857-8391fb00-195c-11eb-9e14-d6c022c2aec4.png' alt='Decision boundary plot' height=400px/>
</p>
coinstac-pyprofiler 0.1.0
A wrapper for some existing python profilers
Requires Python: >=3.6

# coinstac python profiler
Profile your python code (includes code running using coinstac-simulator). This primarily uses pyinstrument profiler, but can also be extended to include other python profilers

## Prerequisites
    Python 3.6+
    Other packages listed in Requirements.txt
    
# Usage
NOTE: The code currently implements profiling based on pyinstrument profiler. Other profilier (such as cprofile) can be included based on requirements. 
## First way: 
Use the following line above the method definition which needs to be profiled.

```python
from coinstac_pyprofiler import custom_profiler as cprof
@cprof.profile(type="pyinstrument", output_file_prefix=output_file_prefix)
```

Decorator class to profile any method.
Note: 'output_file_prefix' should include its (absolute) directory path


## Second way: 
Create object of Profile class in custom_profiler.py and use start() and stop() methods to control profiling. 

```python
from coinstac_pyprofiler import custom_profiler as cprof
@cprof.profile(type="pyinstrument", output_file_prefix=output_file_prefix)
profile = cprof.Profile(type='pyinstrument',  output_file_prefix=<your_dir_path>/<some_file_prefix>")
profile.start()
<your code to profile>
profile.stop()
profile.persist_log()
 ```

# Merging multiple profile output files
Merges the json profiler output files generated using pyinstrument profiling and saves merged output.

## Use-case:
For a computation in coinstac-simulator, some computation has many iterations and every iteration of python call generates a separate profile json output file. All such json files can be merged separately for each client/remote using this call.

## Example
An example usage is included in tests/examples/profiler_usage.py which demonstrates the usage of the above mentioned profiling methods and also provides examples to merge multiple profile output files.

Happy profiling!!
decplot 0.0.1
A handy tool that helps you better visualize your data by plotting 2D decision boundaries, using any sklearn classifier models.

# Decplot
This is a handy tool that helps you better visualize your data by plotting 2D decision boundaries, using any sklearn classifier models.

## Installation
Run the following to install:
```python
pip install decplot
```

## Usage
```python
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from decplot import DecPlot2D

# Get iris dataset from sklearn
iris = load_iris()
X, y, feature_names, target_names = iris['data'], iris['target'], iris['feature_names'], iris['target_names']

# Initialize a classifier model for plotting decision boundaries
dtree = DecisionTreeClassifier()

# Initialize an DecPlot2D object
dp2d = DecPlot2D(X, y, feature_names, target_names, dtree)

# Plot and visualize decision boundaries
dp2d.plot(figsize=(20, 10))
```
After running this code, we'll get this beautiful plot:
<p align="center">
<img src='https://user-images.githubusercontent.com/16115992/97473857-8391fb00-195c-11eb-9e14-d6c022c2aec4.png' alt='Decision boundary plot' height=400px/>
</p>