Kaggler

Kaggler is a Python package for Kaggle data science competitions and distributed under the version 3 of the GNU General Public License.

It provides online learning algorithms for classification - inspired by Kaggle user tinrtgu's code. It uses the sparse input format that handles large sparse data efficiently. Core code is optimized for speed by using Cython.

Algorithms

Currently algorithms available are as follows:

Online learning algorithms

• Stochastic Gradient Descent (SGD)
• Follow-the-Regularized-Leader (FTRL)
• Follow-the-Regularized-Leader with Factorization Machine (FTRL_FM)
• Factorization Machine (FM)
• Neural Networks (NN) - with a single (NN) or two (NN_H2) ReLU hidden layers
• Decision Tree

Batch learning algorithm

• Neural Networks (NN) - with a single hidden layer and L-BFGS optimization

Install

Using pip

Python package is available at PyPi for pip installation:

sudo pip install -U Kaggler

From source code

If you want to install it from source code:

python setup.py build_ext --inplace
sudo python setup.py install

Input Format

libsvm style sparse file format is used.

1 1:1 4:1 5:0.5
0 2:1 5:1

Example

from kaggler.online_model import SGD, FTRL, FM, NN

# SGD
clf = SGD(a=.01,                # learning rate
          l1=1e-6,              # L1 regularization parameter
          l2=1e-6,              # L2 regularization parameter
          n=2**20,              # number of hashed features
          epoch=10,             # number of epochs
          interaction=True)     # use feature interaction or not

# FTRL
clf = FTRL(a=.1,                # alpha in the per-coordinate rate
           b=1,                 # beta in the per-coordinate rate
           l1=1.,               # L1 regularization parameter
           l2=1.,               # L2 regularization parameter
           n=2**20,             # number of hashed features
           epoch=1,             # number of epochs
           interaction=True)    # use feature interaction or not

# FM
clf = FM(n=1e5,                 # number of features
         epoch=100,             # number of epochs
         dim=4,                 # size of factors for interactions
         a=.01)                 # learning rate

# NN
clf = NN(n=1e5,                 # number of features
         epoch=10,              # number of epochs
         h=16,                  # number of hidden units
         a=.1,                  # learning rate
         l2=1e-6)               # L2 regularization parameter

# online training and prediction directly with a libsvm file
for x, y in clf.read_sparse('train.sparse'):
    p = clf.predict_one(x)      # predict for an input
    clf.update_one(x, p - y)    # update the model with the target using error

for x, _ in clf.read_sparse('test.sparse'):
    p = clf.predict_one(x)

# online training and prediction with a scipy sparse matrix
from sklearn.datasets import load_svmlight_file

X, y = load_svmlight_file('train.sparse')

clf.fit(X, y)
p = clf.predict(X)

Package Documentation

Package documentation is available at here.