Kaggler 0.6.0

Code for Kaggle Data Science Competitions.

# Kaggler
Kaggler is a Python package for lightweight online machine learning algorithms and utility functions for ETL and data analysis. It is distributed under the version 3 of the GNU General Public License.

Its online learning algorithms are inspired by Kaggle user [tinrtgu's code](http://goo.gl/K8hQBx). It uses the sparse input format that handles large sparse data efficiently. Core code is optimized for speed by using Cython.


## Installation

### Dependencies
Python packages required are listed in `requirements.txt`
* cython
* h5py
* numpy/scipy
* pandas
* scikit-learn
* ml_metrics

### Using pip
Python package is available at PyPi for pip installation:
```
sudo pip install -U Kaggler
```
If installation fails because it cannot find `MurmurHash3.h`, please add `.` to
`LD_LIBRARY_PATH` as described [here](https://github.com/jeongyoonlee/Kaggler/issues/32).

### From source code
If you want to install it from source code:
```
python setup.py build_ext --inplace
sudo python setup.py install
```

## Data I/O
Kaggler supports CSV (`.csv`), LibSVM (`.sps`), and HDF5 (`.h5`) file formats:
```
# CSV format: target,feature1,feature2,...
1,1,0,0,1,0.5
0,0,1,0,0,5

# LibSVM format: target feature-index1:feature-value1 feature-index2:feature-value2
1 1:1 4:1 5:0.5
0 2:1 5:1

# HDF5
- issparse: binary flag indicating whether it stores sparse data or not.
- target: stores a target variable as a numpy.array
- shape: available only if issparse == 1. shape of scipy.sparse.csr_matrix
- indices: available only if issparse == 1. indices of scipy.sparse.csr_matrix
- indptr: available only if issparse == 1. indptr of scipy.sparse.csr_matrix
- data: dense feature matrix if issparse == 0 else data of scipy.sparse.csr_matrix
```

```
from kaggler.data_io import load_data, save_data

X, y = load_data('train.csv') # use the first column as a target variable
X, y = load_data('train.h5') # load the feature matrix and target vector from a HDF5 file.
X, y = load_data('train.sps') # load the feature matrix and target vector from LibSVM file.

save_data(X, y, 'train.csv')
save_data(X, y, 'train.h5')
save_data(X, y, 'train.sps')
```


## Feature Engineering

### One-hot and label encoding with grouping infrequent categories
```
import numpy as np
import pandas as pd
from kaggler.preprocessing import OneHotEncoder, LabelEncoder

df = pd.read_csv('train.csv')
cat_cols = [col for col in df.columns if df[col].dtype == np.object]

ohe = OneHotEncoder(min_obs=100) # grouping all categories with less than 100 occurences
lbe = LabelEncoder(min_obs=0.01) # grouping all categories with less than 1% occurences

X_cat = ohe.fit_transform(df[cat_cols].values) # X_cat is a scipy sparse matrix
df.loc[:, cat_cols] = lbe.fit_transform(df[cat_cols].values)
```

## Ensemble

### Netflix Blending
```
import numpy as np
from kaggler.ensemble import netflix
from kaggler.metrics import rmse

y = np.loadtxt('target.txt')
p1 = np.loadtxt('model1_prediction.txt')
p2 = np.loadtxt('model2_prediction.txt')
p3 = np.loadtxt('model3_prediction.txt')

e0 = rmse(y, np.zeros_like(y))
e1 = rmse(y, p1)
e2 = rmse(y, p2)
e3 = rmse(y, p3)

p, w = netflix([e1, e2, e3], [p1, p2, p3], e0)
```


## Algorithms
Currently algorithms available are as follows:

### Online learning algorithms
* Stochastic Gradient Descent (SGD)
* Follow-the-Regularized-Leader (FTRL)
* 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

### Examples
```python
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 kaggler import load_data

X, y = load_data('train.sps')

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

## Documentation
Package documentation is available at [here](http://pythonhosted.org//Kaggler).