QuickStart¶

Let’s take a look at some code examples.

Matrix Operations¶

Making use of SystemDS, let us multiply an Matrix with an scalar:

# Import SystemDSContext
from systemds.context import SystemDSContext
# Create a context and if necessary (no SystemDS py4j instance running)
# it starts a subprocess which does the execution in SystemDS
with SystemDSContext() as sds:
    # Full generates a matrix completely filled with one number.
    # Generate a 5x10 matrix filled with 4.2
    m = sds.full((5, 10), 4.20)
    # multiply with scalar. Nothing is executed yet!
    m_res = m * 3.1
    # Do the calculation in SystemDS by calling compute().
    # The returned value is an numpy array that can be directly printed.
    print(m_res.compute())
# context will automatically be closed and process stopped

As output we get:

[[ 13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02]
 [ 13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02]
 [ 13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02]
 [ 13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02]
 [ 13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02  13.02]]

The Python SystemDS package is compatible with numpy arrays. Let us do a quick element-wise matrix multiplication of numpy arrays with SystemDS. Remember to first start up a new terminal:

import numpy as np  # import numpy

# Import SystemDSContext
from systemds.context import SystemDSContext
from systemds.matrix import Matrix

# create a random array
m1 = np.array(np.random.randint(100, size=5 * 5) + 1.01, dtype=np.double)
m1.shape = (5, 5)
# create another random array
m2 = np.array(np.random.randint(5, size=5 * 5) + 1, dtype=np.double)
m2.shape = (5, 5)

# Create a context
with SystemDSContext() as sds:
    # element-wise matrix multiplication, note that nothing is executed yet!
    m_res = Matrix(sds, m1) * Matrix(sds, m2)
    # lets do the actual computation in SystemDS! The result is an numpy array
    m_res_np = m_res.compute()
    print(m_res_np)

More complex operations¶

SystemDS provides algorithm level functions as built-in functions to simplify development. One example of this is l2SVM, a high level functions for Data-Scientists. Let’s take a look at l2svm:

# Import numpy and SystemDS matrix
import numpy as np
from systemds.context import SystemDSContext
from systemds.matrix import Matrix
from systemds.operator.algorithm import l2svm

# Set a seed
np.random.seed(0)
# Generate random features and labels in numpy
# This can easily be exchanged with a data set.
features = np.array(np.random.randint(100, size=10 * 10) + 1.01, dtype=np.double)
features.shape = (10, 10)
labels = np.zeros((10, 1))

# l2svm labels can only be 0 or 1
for i in range(10):
    if np.random.random() > 0.5:
        labels[i][0] = 1

# compute our model
with SystemDSContext() as sds:
    model = l2svm(Matrix(sds, features), Matrix(sds, labels)).compute()
    print(model)

The output should be similar to:

[[ 0.02033445]
 [-0.00324092]
 [ 0.0014692 ]
 [ 0.02649209]
 [-0.00616902]
 [-0.0095087 ]
 [ 0.01039221]
 [-0.0011352 ]
 [-0.01686351]
 [-0.03839821]]