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Code for Kaggle Data Science Competitions.

Project description

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Kaggler is a Python package for lightweight online machine learning algorithms and utility functions for ETL and data analysis. It is distributed under the MIT License.

Its online learning algorithms are 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.



Python packages required are listed in requirements.txt

  • cython
  • h5py
  • hyperopt
  • lightgbm
  • ml_metrics
  • numpy/scipy
  • pandas
  • scikit-learn

Using pip

Python package is available at PyPi for pip installation:

pip install -U Kaggler

If installation fails because it cannot find MurmurHash3.h, please add . to LD_LIBRARY_PATH as described here.

From source code

If you want to install it from source code:

python build_ext --inplace
python install

Feature Engineering

One-Hot, Label, Target, Frequency, and Embedding Encoders for Categorical Features

import pandas as pd
from kaggler.preprocessing import OneHotEncoder, LabelEncoder, TargetEncoder, FrequencyEncoder, EmbeddingEncoder

trn = pd.read_csv('train.csv')
target_col = trn.columns[-1]
cat_cols = [col for col in trn.columns if trn[col].dtype == 'object']

ohe = OneHotEncoder(min_obs=100) # grouping all categories with less than 100 occurences
lbe = LabelEncoder(min_obs=100)  # grouping all categories with less than 100 occurences
te = TargetEncoder()			 # replacing each category with the average target value of the category
fe = FrequencyEncoder()	         # replacing each category with the frequency value of the category
ee = EmbeddingEncoder()          # mapping each category to a vector of real numbers

X_ohe = ohe.fit_transform(trn[cat_cols])	    # X_ohe is a scipy sparse matrix
trn[cat_cols] = lbe.fit_transform(trn[cat_cols])
trn[cat_cols] = te.fit_transform(trn[cat_cols])
trn[cat_cols] = fe.fit_transform(trn[cat_cols])
X_ee = ee.fit_transform(trn[cat_cols], trn[target_col])          # X_ee is a numpy matrix

tst = pd.read_csv('test.csv')
X_ohe = ohe.transform(tst[cat_cols])
tst[cat_cols] = lbe.transform(tst[cat_cols])
tst[cat_cols] = te.transform(tst[cat_cols])
tst[cat_cols] = fe.transform(tst[cat_cols])
X_ee = ee.transform(tst[cat_cols])

Denoising AutoEncoder (DAE)

For reference for DAE, please check out Vincent et al. (2010), "Stacked Denoising Autoencoders".

import pandas as pd
from kaggler.preprocessing import DAE

trn = pd.read_csv('train.csv')
tst = pd.read_csv('test.csv')
target_col = trn.columns[-1]
cat_cols = [col for col in trn.columns if trn[col].dtype == 'object']
num_cols = [col for col in trn.columns if col not in cat_cols + [target_col]]

# Default DAE with only the swapping noise and a single encoder/decoder pair.
dae = DAE(cat_cols=cat_cols, num_cols=num_cols, n_encoding=128)
X = dae.fit_transform(pd.concat([trn, tst], axis=0))    # encoding input features into the encoding vectors with size of 128

# Stacked DAE with the Gaussian noise, swapping noise and zero masking in 3 pairs of the encoder/decoder.
sdae = DAE(cat_cols=cat_cols, num_cols=num_cols, n_encoding=128, n_layer=3,
           noise_std=.05, swap_prob=.2, mask_prob=.1)
X = sdae.fit_transform(pd.concat([trn, tst], axis=0))

# Supervised DAE with the Gaussian noise, swapping noise and zero masking in 3 encoders in the encoder/decoder pair.
sdae = SDAE(cat_cols=cat_cols, num_cols=num_cols, n_encoding=128, n_encoder=3,
           noise_std=.05, swap_prob=.2, mask_prob=.1)
X = sdae.fit_transform(trn, trn[target_col])


Feature Selection & Hyperparameter Tuning

import pandas as pd
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from kaggler.metrics import auc
from kaggler.model import AutoLGB

N_OBS = 10000

X, y = make_classification(n_samples=N_OBS,
X = pd.DataFrame(X, columns=['x{}'.format(i) for i in range(X.shape[1])])
y = pd.Series(y)

X_trn, X_tst, y_trn, y_tst = train_test_split(X, y,

model = AutoLGB(objective='binary', metric='auc')
model.tune(X_trn, y_trn), y_trn)
p = model.predict(X_tst)
print('AUC: {:.4f}'.format(auc(y_tst, p)))


Netflix Blending

import numpy as np
from kaggler.ensemble import netflix
from kaggler.metrics import rmse

# Load the predictions of input models for ensemble
p1 = np.loadtxt('model1_prediction.txt')
p2 = np.loadtxt('model2_prediction.txt')
p3 = np.loadtxt('model3_prediction.txt')

# Calculate RMSEs of model predictions and all-zero prediction.
# At a competition, RMSEs (or RMLSEs) of submissions can be used.
y = np.loadtxt('target.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, l=0.0001) # l is an optional regularization parameter.


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


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

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

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

Data I/O

Kaggler supports CSV (.csv), LibSVM (.sps), and HDF5 (.h5) file formats:

# CSV format: target,feature1,feature2,...

# 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')


Package documentation is available at here

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