Learning Data Mining with Python

Chapter 2
Pipelines take a list of steps as input, representing the chain of the data mining
application. The last step needs to be an Estimator, while all previous steps are
Transformers. The input dataset is altered by each Transformer, with the output of
one step being the input of the next step. Finally, the samples are classified by the last
step's estimator. In our pipeline, we have two steps:
1. Use MinMaxScaler to scale the feature values from 0 to 1
2. Use KNeighborsClassifier as the classification algorithms
Each step is then represented by a tuple ('name', step). We can then create
our pipeline:
scaling_pipeline = Pipeline([('scale', MinMaxScaler()),
('predict', KNeighborsClassifier())])
The key here is the list of tuples. The first tuple is our scaling step and the second
tuple is the predicting step. We give each step a name: the first we call scale and the
second we call predict, but you can choose your own names. The second part of the
tuple is the actual Transformer or estimator object.
Running this pipeline is now very easy, using the cross validation code from before:
scores = cross_val_score(scaling_pipeline, X_broken, y,
scoring='accuracy')
print("The pipeline scored an average accuracy for is {0:.1f}%".
format(np.mean(transformed_scores) * 100))
This gives us the same score as before (82.3 percent), which is expected, as we are
effectively running the same steps.
In later chapters, we will use more advanced testing methods, and setting
up pipelines is a great way to ensure that the code complexity does not
grow unmanageably.
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