Pyplearnr is a tool designed to easily and more elegantly build, validate (nested k-fold cross-validation), and test scikit-learn pipelines.
Pyplearnr is a tool designed to perform model selection, hyperparameter tuning, and model validation via nested k-fold cross-validation in a reproducible way.
I found GridSearchCV to be lacking. I wanted a tool that used a similar procedure to perform simultaneous hyperparameter tuning AND model selection with a clear input that summarizes exactly what scikit-learn pipeline steps and parameter combinations will used and whose results allow perfect reproducibility. So, I made my own.
See the [demo](https://nbviewer.jupyter.org/github/JaggedParadigm/pyplearnr/blob/master/pyplearnr_demo.ipynb) for more detailed use of pyplearnr with actual data.
Here are the basic steps:
#### 1) Place feature data into non-null feature matrix and target vector
#### 2) Initialize the nested k-fold cross-validation object
kfcv = ppl.NestedKFoldCrossValidation(outer_loop_fold_count=5,
#### 3) Specify the combinatorial pipeline schematic detailing all possible model/parameter combinations
Ex: Here's an example of model/parameter combinations of optional scaling of two types, a principal component analysis directly using scikit-learn's sklearn.decomposition.PCA transformer, selection of data transformed by k principal components (between 1 and 30), and the use of either a k-nearest neighbors classifier (k between 1 and 30) or random forest classifier with a maximum depth between 2 and 5 (and a specified random state for reproducibility).
pipeline_schematic = [
'k': range(1, feature_count+1)
#### 4) Run pyplearnr
# Perform nested k-fold cross-validation
kfcv.fit(X, y, pipeline_schematic=pipeline_schematic,
The core model selection and validation method is nested k-fold cross-validation (stratified if for classification). Inner-fold contests are used for model selection and outer-folds are used to cross-validate the final winning model.
Here's the basic algorithm used by pyplearnr:
- 1) Pyplearnr shuffles and divides the data into k validation outer-folds.
- 2) For each outer-fold:
- a) The remaining folds are combined to form the corresponding training set
- b) This training set is divided into k (or possibly a different number) of inner-test-folds.
- c) For each inner-test-fold:
- i) The remaining inner-test-folds are combined and used to train all pipelines/models, which are scored on the corresponding inner-test-fold
- d) The winning model/pipeline of each inner-test-fold contest is chosen as that with the best median score over all inner-test-folds
- iii) The user is alerted If there is a tie and expected to decide the winning pipeline (usually the simplest for better generalizability)
- 4) The final winning model/pipeline is chosen as that with the most number of wins from all inner-test-fold contests corresponding to each outer-fold
- e) Again, the user is expected to decide the winner If there is a tie
- 5) This final winning model/pipeline is trained on all of the training data for each outer-fold, tested on the corresponding validation set, and summary statistics are presented to the user representing expected out-of-sample performance.
Python (>= 2.7 or >= 3.3)
scikit-learn (>= 0.18.2)
numpy (>= 1.13.0)
scipy (>= 0.19.1)
pandas (>= 0.20.2)
matplotlib (>= 2.0.2)
For use in Jupyter notebooks and the conda installation, I recommend having nb_conda (>= 2.2.0).
### User installation
Install by using pip:
pip install pyplearnr
For conda, you can issue the same command above within a conda environment or you can include this in your environment.yml file:
and then either generate a new environment from the terminal using:
conda env create
or update an existing one (environment_name) using:
conda env update -n=environment_name -f=./environment.yml
Another option is to simply clone the respository, link to the location in your code, and import it.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size pyplearnr-18.104.22.168-py2-none-any.whl (34.3 kB)||File type Wheel||Python version py2||Upload date||Hashes View|
|Filename, size pyplearnr-22.214.171.124.tar.gz (28.6 kB)||File type Source||Python version None||Upload date||Hashes View|
Hashes for pyplearnr-126.96.36.199-py2-none-any.whl