Integrate with LightGBM

This is an introduction about how to use LightGBM for training and prediction in Mars.

Installation

If you are trying to use Mars on a single machine e.g. on your laptop, make sure LightGBM is installed.

You can install LightGBM via pip:

pip install lightgbm

Visit installation guide for LightGBM for more information.

On the other hand, if you are using Mars on a cluster, make sure LightGBM is installed on each worker.

Prepare data

First, we use scikit-learn to load the Boston Housing dataset.

In [1]: from sklearn.datasets import load_boston
In [2]: boston = load_boston()

Then create Mars DataFrame from the dataset.

In [3]: import mars.dataframe as md
In [4]: data = md.DataFrame(boston.data, columns=boston.feature_names)

Explore the top 5 rows data of the DataFrame.

In [5]: data.head().execute()
Out[5]:
      CRIM    ZN  INDUS  CHAS    NOX  ...  RAD    TAX  PTRATIO       B  LSTAT
0  0.00632  18.0   2.31   0.0  0.538  ...  1.0  296.0     15.3  396.90   4.98
1  0.02731   0.0   7.07   0.0  0.469  ...  2.0  242.0     17.8  396.90   9.14
2  0.02729   0.0   7.07   0.0  0.469  ...  2.0  242.0     17.8  392.83   4.03
3  0.03237   0.0   2.18   0.0  0.458  ...  3.0  222.0     18.7  394.63   2.94
4  0.06905   0.0   2.18   0.0  0.458  ...  3.0  222.0     18.7  396.90   5.33

[5 rows x 13 columns]

mars.dataframe.DataFrame.describe() gives summary statistics of the columns.

In [6]: data.describe().execute()
Out[6]:
             CRIM          ZN       INDUS  ...     PTRATIO           B       LSTAT
count  506.000000  506.000000  506.000000  ...  506.000000  506.000000  506.000000
mean     3.613524   11.363636   11.136779  ...   18.455534  356.674032   12.653063
std      8.601545   23.322453    6.860353  ...    2.164946   91.294864    7.141062
min      0.006320    0.000000    0.460000  ...   12.600000    0.320000    1.730000
25%      0.082045    0.000000    5.190000  ...   17.400000  375.377500    6.950000
50%      0.256510    0.000000    9.690000  ...   19.050000  391.440000   11.360000
75%      3.677083   12.500000   18.100000  ...   20.200000  396.225000   16.955000
max     88.976200  100.000000   27.740000  ...   22.000000  396.900000   37.970000

[8 rows x 13 columns]

We can shuffle the sequence of the data, and separate the data into train and test parts.

In [7]: from mars.learn.utils import shuffle
In [8]: X, y = shuffle(data, boston.target)
In [9]: train_size = int(X.shape[0] * 0.7)
In [10]: X_train, X_test = X[:train_size], X[train_size:]
In [11]: y_train, y_test = y[:train_size], y[train_size:]

Training

We can train data with scikit-learn API similar to the API implemented in LightGBM.

In [12]: from mars.learn.contrib import lightgbm as lgb
In [13]: lg_reg = lgb.LGBMRegressor(colsample_bytree=0.3, learning_rate=0.1,
    ...:                            max_depth=5, reg_alpha=10, n_estimators=10)

In [14]: lg_reg.fit(X_train, y_train)
Out[14]:
LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=0.3,
              importance_type='split', learning_rate=0.1, max_depth=5,
              min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,
              n_estimators=10, n_jobs=-1, num_leaves=31, objective=None,
              out_dtype_=dtype('float64'), random_state=None, reg_alpha=10,
              reg_lambda=0.0, silent=True, subsample=1.0,
              subsample_for_bin=200000, subsample_freq=0)

Prediction

For prediction, scikit-learn API can also be used.

In [15]: lg_reg.predict(X_test)
Out[15]:
476    20.044731
490    18.540266
304    26.985506
216    21.946456
256    34.913612
         ...
250    24.234580
224    34.980905
500    21.376179
134    19.605267
248    23.253156
Name: predictions, Length: 152, dtype: float64

Distributed training and prediction

Refer to Run on Clusters section for deployment, or Run on Kubernetes section for running Mars on Kubernetes.

Once a cluster exists, you can either set the session as default, the training and prediction shown above will be submitted to the cluster, or you can specify session=*** explicitly as well.

Take LGBMRegressor.fit() as an example.

# A cluster has been configured, and web UI is started on <web_ip>:<web_port>
from mars.session import new_session
# set the session as the default one
sess = new_session('http://<web_ip>:<web_port>').as_default()

lg_reg = lgb.LGBMRegressor()

# training will submitted to cluster by default
lg_reg.fit(X_train)

# Or, session could be specified as well
lg_reg.fit(X_train, session=sess)