prepnet 0.2.0

Reconstructable preprocessor library

Prepnet

Reconstructable preprocessor library.

There are concepts of this library.

• All pre-processes can save as a pickle.
• Reconstructable pre-processes for feature analysis

Example

A simple example is see examples/01_iris.ipynb There is pre-process using prepnet for iris dataset in a part of example.

import prepnet 
from sklearn import datasets

# Load dataset.
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target_names[iris.target]

# Scale by std and mean, and split 5 folds.
context = prepnet.FunctionalContext()
with context.enter('normalize'):
    # All pre-process method allow method chain.
    context[
        'sepal length (cm)',
        'sepal width (cm)',
        'petal length (cm)',
        'petal width (cm)',
    ].standardize()

# context.post is execute always after other preprocesses.
with context.enter('post'):
    context.split()

# convert python list object from prepnet.DataFrameArray.
preprocessed_df_list = list(context.encode(df))
# Concat first 4 element for train dataset.
train_df = pd.concat(preprocessed_df_list[:4], axis=0) 
# Use last element for test dataset.
test_df = preprocessed_df_list[-1]

And above preprocessor context can disable normalize easily

new_context = context.disable()
preprocessed_df_list = list(context.encode(df))
# Concat first 4 element for train dataset
nonnorm_train_df = pd.concat(preprocessed_df_list[:4], axis=0) 
# Use last element for test dataset
nonnorm_test_df = preprocessed_df_list[-1]

Do you ever remember this?

Boss: Hey, what's the difference between the new results and the old ones?

Someone: Well, some preprocesses are different.

Boss: Okay. Let me see the dataset.

Someone: Yes, sir. It's this and this.

Boss: What's the difference two datasets? The value that comes out of the difference is slightly, what's the difference in the preprocess?

Someone: Well, I just don't know.

Boss: Why? The dataset contains a commit idand you're managing source codes with git.

Someone: Even if I knew what version of the dataset it was created from. I would have commented out the details and preprocessed it...

Boss: Hey you...

Install

pip install prepnet

or

git clone https://github.com/elda27/prepnet
cd prepnet
python setup.py install

Test

python -m pytest --cov=prepnet