firelink 0.1.1

Firelink is based on scikit-learn pipeline and adding the functionality to store the pipeline in `.yaml` or `.ember` file for production.

Firelink

Firelink is based on scikit-learn pipeline and adding the functionality to store the pipeline in .yaml or .ember file for production.

Quickstart

Installation

pip install firelink

Basic Usage

import pandas as pd
from pandas.testing import assert_frame_equal
from firelink.transform import Drop_duplicates, Filter
from firelink.pipeline import FirePipeline

df = pd.DataFrame(
    {
        "a": range(10),
        "b": range(10, 20),
        "c": range(20, 30),
        "d": ["a", "n", "d", "f", "g", "h", "h", "j", "q", "w"],
        "e": ["a", "d", "a", "d", "e", "e", "a", "a", "d", "d"],
    }
)

trans_1 = Filter(["a", "e"])
trans_2 = Drop_duplicates(["e"], keep="first")

pipe_1 = FirePipeline(
    [("filter column a and e", trans_1), ("drop duplicate for column e", trans_2)]
)

pipe_1.save_fire("pipe_1.ember", file_type="ember")
pipe_2 = FirePipeline.link_fire("pipe_1.ember")

df1 = pipe_1.fit_transform(df)
df2 = pipe_2.fit_transform(df)

assert_frame_equal(df1, df2)

Spark Usage

import pandas as pd
from pandas.testing import assert_frame_equal
from firelink.spark_transform import WithColumn
from firelink.transform import Assign
from firelink.pipeline import FirePipeline
from pyspark.sql import SparkSession, functions as F

spark = SparkSession.builder.appName("spark_session").enableHiveSupport().getOrCreate()

df = pd.DataFrame({"col1": [1, 2, 3], "col2": ["a", "b", "c"]})
sdf = spark.createDataFrame(df)

add1 = WithColumn("Country", "F.lit('Canada')")
add2 = WithColumn("City", "F.lit('Toronto')")
spark_pipe = FirePipeline([("Add Country", add1), ("Add City", add2)])

# set_config(display="diagram")
# set_config(display="text")
spark_pipe

sdf = spark_pipe.fit_transform(sdf)
sdf.show()

add1 = Assign(**{"Country": "Canada"})
add2 = Assign(**{"City": "Toronto"})
pandas_pipe = FirePipeline([("Add Country", add1), ("Add City", add2)])

pandas_pipe.fit_transform(df)

assert_frame_equal(sdf.toPandas(), pandas_pipe.fit_transform(df))

Detailed Documentation

For the detailed documentation, please go through this portal.