Quickstart

In the following steps we will show a short guide to help you getting started with ml-stars.

We will be executing a single primitive for data transformation.

1. Load the primitive

The first step in order to run a primitive is to load it.

This will be done using the mlstars.load_primitive function, which will load the indicated primitive as an MLBlock Object from MLBlocks.

In this case, we will load the sklearn.preprocessing.MinMaxScaler primitive.

In [1]: from mlstars import load_primitive

In [2]: primitive = load_primitive('sklearn.preprocessing.MinMaxScaler')

2. Load some data

The StandardScaler is a transformation primitive which scales your data into a given range.

To use this primtives, we generate a synthetic data with some numeric values.

In [3]: import numpy as np

In [4]: data = np.array([10, 1, 3, -1, 5, 6, 0, 4, 13, 4]).reshape(-1, 1)

The data is a list of integers where their original range is between [-1, 13].

3. Fit the primitive

In order to run our primitive, we first need to fit it.

This is the process where it analyzes the data to detect what is the original range of the data.

This is done by calling its fit method and passing the data as X.

In [5]: primitive.fit(X=data)

4. Produce results

Once the pipeline is fit, we can process the data by calling the produce method of the primitive instance and passing agin the data as X.

In [6]: transformed = primitive.produce(X=data)

In [7]: transformed
Out[7]: 
array([[0.78571429],
       [0.14285714],
       [0.28571429],
       [0.        ],
       [0.42857143],
       [0.5       ],
       [0.07142857],
       [0.35714286],
       [1.        ],
       [0.35714286]])

We can see how the transformed data contains the transformed values and the data is now in [0, 1] range.

Installation Basic Concepts