# Transformer pipeline example¶

In this example, we will create a pipeline of transformers.

import matplotlib.pyplot as plt
from gemseo.api import configure_logger
from gemseo.mlearning.transform.pipeline import Pipeline
from gemseo.mlearning.transform.scaler.scaler import Scaler
from numpy import allclose
from numpy import linspace
from numpy import matmul
from numpy import sin

configure_logger()


Out:

<RootLogger root (INFO)>


## Create dataset¶

x = linspace(0, 1, 100)
data = sin(10 * x) - 3 * x


## Create transformer pipeline¶

We create a pipeline of two transformers; the first performing a shift, the second a scale (both scalers). This could also be achieved using one scaler, but we here present a pipeline doing these transformations separately for illustrative purposes.

shift = Scaler(offset=5)
scale = Scaler(coefficient=0.5)
pipeline = Pipeline(transformers=[shift, scale])


## Transform data¶

In order to use the transformer, we have to fit it to the data.

pipeline.fit(data)

# Transform data using the pipeline
transformed_data = pipeline.transform(data)

# Transform data using individual components of the pipeline
only_shifted_data = shift.transform(data)


Out:

WARNING - 07:18:59: The Scaler.fit() function does nothing; the instance of Scaler uses the coefficient and offset passed at its initialization
WARNING - 07:18:59: The Scaler.fit() function does nothing; the instance of Scaler uses the coefficient and offset passed at its initialization


## Plot data¶

plt.plot(x, data, label="Original data")
plt.plot(x, transformed_data, label="Shifted and scaled data")
plt.plot(x, only_shifted_data, label="Shifted but not scaled data")
plt.legend()
plt.show()


## Compute jacobian¶

jac = pipeline.compute_jacobian(data)
only_shift_jac = shift.compute_jacobian(data)
only_scale_jac = scale.compute_jacobian(only_shifted_data)

print(jac)
print(only_shift_jac)
print(only_scale_jac)
print(allclose(jac, matmul(only_scale_jac, only_shift_jac)))


Out:

[[0.5 0.  0.  ... 0.  0.  0. ]
[0.  0.5 0.  ... 0.  0.  0. ]
[0.  0.  0.5 ... 0.  0.  0. ]
...
[0.  0.  0.  ... 0.5 0.  0. ]
[0.  0.  0.  ... 0.  0.5 0. ]
[0.  0.  0.  ... 0.  0.  0.5]]
[[1. 0. 0. ... 0. 0. 0.]
[0. 1. 0. ... 0. 0. 0.]
[0. 0. 1. ... 0. 0. 0.]
...
[0. 0. 0. ... 1. 0. 0.]
[0. 0. 0. ... 0. 1. 0.]
[0. 0. 0. ... 0. 0. 1.]]
[[0.5 0.  0.  ... 0.  0.  0. ]
[0.  0.5 0.  ... 0.  0.  0. ]
[0.  0.  0.5 ... 0.  0.  0. ]
...
[0.  0.  0.  ... 0.5 0.  0. ]
[0.  0.  0.  ... 0.  0.5 0. ]
[0.  0.  0.  ... 0.  0.  0.5]]
True


Total running time of the script: ( 0 minutes 0.133 seconds)

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