https://github.com/alvarouc/polyssifier
run a multitude of classifiers on you data and get an AUC report
https://github.com/alvarouc/polyssifier
auc classifier mlp regression
Last synced: 15 days ago
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run a multitude of classifiers on you data and get an AUC report
- Host: GitHub
- URL: https://github.com/alvarouc/polyssifier
- Owner: alvarouc
- License: gpl-2.0
- Created: 2015-08-11T22:13:15.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2024-02-08T21:33:53.000Z (about 2 years ago)
- Last Synced: 2024-07-14T01:43:29.056Z (over 1 year ago)
- Topics: auc, classifier, mlp, regression
- Language: Python
- Size: 5.95 MB
- Stars: 67
- Watchers: 6
- Forks: 23
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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Polyssifier
===========
Polyssifier runs a multitude of machine learning models on data. It reports scores, confusion matrices, predictions, and plots the scores ranked by classifier performance.
## Installation
```bash
pip install polyssifier
```
## How to use
### For classification
```python
from polyssifier import poly
# Load data
data = np.load("/path/to/data.npy")
label = np.load("/path/to/labels.npy")
# Run analysis
report = poly(data,label, n_folds=8)
# Plot results
report.plot_scores()
report.plot_features(ntop=10)
```
### For Regression
```python
from polyssifier import polyr
# Load data
data = np.load("/path/to/data.npy")
target = np.load("/path/to/target.npy")
# Run analysis
report = polyr(data, target, n_folds=8)
# Plot results
report.plot_scores()
report.plot_features(ntop=10)
```
### In the terminal
```bash
poly data.npy label.npy --concurrency 10
```
### Requirements
- Sklearn
- Numpy
- Pandas
### Features
- Cross validated scores.
- Report f1 score (scoring='f1') or ROC (scoring='auc') for classification
- Report MSE or R^2 for regression
- Feature ranking for compatible models (Logistic Regression, Linear SVM, Random Forest)
- Parallel processing.
- Control the number of threads with 'concurrency'.
- We recommend setting concurrency to half the number of Cores in your system.
- Saves trained models for future use in case of server malfunction.
- Set project_name for identifying a experiment.
- Activate feature selection step setting
- feature_selection=True
- Automatically scales your data with scale=True
Example: on [sample/example.ipynb](sample/example.ipynb)
It includes the following classifiers:
- Multilayer Perceptron
- Nearest Neighbors
- Linear SVM
- RBF SVM
- Decision Tree
- Random Forest
- Logistic Regression
- Naive Bayes
- Voting Classifier
and the following regressors:
- Linear Regression
- Bayesian Ridge
- PassiveAggressiveRegressor
- GaussianProcessRegressor
- Ridge
- Lasso
- Lars
- LassoLars
- OrthogonalMatchingPursuit
- ElasticNet
You can exclude some of this models by providing a list of names as follows:
```python
from polyssifier import poly
report = poly(data,label, n_folds=8,
exclude=['Multilayer Perceptron'])
```