def test_prediction_ranger():
input_df = pd.DataFrame(
{"feat1": [10, 13, 10, 15], "prediction": [100, 200, 300, None]}
)
pred_fn, data, log = prediction_ranger(input_df, 150, 250)
expected = pd.DataFrame(
{"feat1": [10, 13, 10, 15], "prediction": [150, 200, 250, None]}
)
assert expected.equals(data)
df.columns = ["income"]
df["bill_amount"] = data_bill_amount * 10000
df["income"] = df["income"].apply(lambda x: x * 1000)
print(f"turned our test data into an income dataframe...\n {df.head()}")
# ----------------------------------------------------------------------------------------------------------------------
# Get to the actual work.
from fklearn.training.regression import linear_regression_learner
from fklearn.training.transformation import capper, floorer, prediction_ranger
# initialize several learner functions
# 1. one function to cap the input data to ignore outliers.
# 2. then a usual regression
# 3. third again we'd min/max the output of the regression
capper_fn = capper(columns_to_cap=["income"], precomputed_caps={"income": 500})
regression_fn = linear_regression_learner(features=["income"],
target="bill_amount")
ranger_fn = prediction_ranger(prediction_min=0.0, prediction_max=200.0)
# apply two by currieing them together...
from fklearn.training.pipeline import build_pipeline
learner = build_pipeline(capper_fn, regression_fn, ranger_fn)
p, df, log = learner(df)
print(
f" the returned dataframe now contains our capped prediction:\n {df.head(5)}"
)