How to build text preprocessing pipelines with Dask?

How to build text preprocessing pipelines with Dask?

How to build text preprocessing pipelines with Dask?

This recipe helps you build text preprocessing pipelines with Dask


Recipe Objective.

How to build text preprocessing pipelines with Dask?

`dask_ml.preprocessing` have same styled transformers of **scikit-learn** that we can use in Pipelines to perform different types of data transformations as the part of the model fitting process. These transformers works very nicely on dask collections (`dask.array, dask.dataframe`), NumPy arrays, or pandas dataframes.

Step 1- Importing Libraries.

!apt install dask_ml from dask_ml.preprocessing import Categorizer, OneHotEncoder from sklearn.linear_model import LogisticRegression from sklearn.pipeline import make_pipeline import pandas as pd import dask.dataframe as dd

Step 2- Creating a DataFrame.

We will create a dataframe and then divide it to x and y to fit them in the pipeline.

df = pd.DataFrame({"A": [1, 2, 3, 4, 5, 6], "B": ["a", "b", "c", "d", "e", "f"]}) x = dd.from_pandas(df, npartitions=2) y = dd.from_pandas(pd.Series([0, 1, 1, 0]), npartitions=2)

Step 3- Creating a pipeline.

We will create a pipeline in which we process the data through Categorizer, OneHotEncoder, LogisticRegression.

pipe = make_pipeline( Categorizer(), OneHotEncoder(), LogisticRegression(solver='lbfgs') ), y) ``` Pipeline(steps=[('categorizer', Categorizer()), ('onehotencoder', OneHotEncoder()), ('logisticregression', LogisticRegression())]) ```

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