Integrate with StatsModels#

This is an introduction about how to use StatsModels for model fitting and prediction in Mars.

Installation#

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

You can install StatsModels via pip:

pip install statsmodels

Visit installation guide for StatsModels for more information.

On the other hand, if you are using Mars on a cluster, make sure StatsModels 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.model_selection import train_test_split
In [8]: X_train, X_test, y_train, y_test = \
   ...:     train_test_split(data, boston.target, train_size=0.7, random_state=0)

Training#

We can fit a model with API similar to the distributed estimation API implemented in StatsModels.

In [9]: from mars.learn.contrib import statsmodels as msm
In [10]: model = msm.MarsDistributedModel(num_partitions=5)
In [11]: results = model.fit(y_train, X_train, alpha=0.2)
In [12]: results
Out[12]: <mars.learn.contrib.statsmodels.api.MarsResults at 0x7fd47a118f70>

Arguments for DistributedModel like model_class, estimation_method and join_method can be added to the constructor of MarsDistributedModel.

Prediction#

For prediction,

In [13]: results.predict(X_test)
Out[13]:
  20.475695
  20.792441
  23.158081
   19.912593
  14.290641
         ...
   24.798897
  22.196336
   23.714524
  19.824247
  22.138279
Length: 152, dtype: float64

Distributed fitting 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 fitting and prediction shown above will be submitted to the cluster, or you can specify session=*** explicitly as well.

Take MarsDistributedModel.fit() as an example.

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

# specify partition number
model = msm.MarsDistributedModel(num_partitions=5)
# or specify factor for cluster size,
# num_partitions will be int(factor * num_cores)
model = msm.MarsDistributedModel(factor=1.2)

# fitting will submitted to cluster by default
results = model.fit(y_train, X_train, alpha=1.2)

# Or, session could be specified as well
results = model.fit(y_train, X_train, alpha=1.2, session=sess)