Esempi in Python per SimpleImputer

Esempio n. 1

File: feature_1d56d35b_e79d_438b_b30f_8b25aa638540.py Progetto: HDI-Project/ballet-predict-census-income

from ballet import Feature

from ballet.eng import ConditionalTransformer
from ballet.eng.sklearn import SimpleImputer

input = ["JWTR", "JWRIP", "JWMNP"]  # TODO - str or list of str


def calculate_travel_budget(df):
    if (df["JWTR"] == 1.0).all():
        return df["JWMNP"] * df["JWRIP"]
    return df["JWMNP"] * df["JWTR"]


transformer = [
    calculate_travel_budget,
    SimpleImputer(strategy="mean"),
]  # TODO - function, transformer-like, or list thereof
name = "work_travel_combined"  # TODO - str
description = "Combine data for time to travel to work with vehicle. Lower value, the most likely they have higher income"  # TODO - str
feature = Feature(input, transformer, name=name, description=description)

Esempio n. 2

File: feature_f2a48122_f489_41ae_a6e9_4c5be6df8511.py Progetto: HDI-Project/ballet-predict-census-income

from ballet import Feature
from ballet.eng.sklearn import SimpleImputer
import numpy as np

input = "RESMODE"
transformer = SimpleImputer(missing_values=np.nan, strategy="constant", fill_value=0)
name = "Imputed Response Mode"
description = "Missing response modes values filled in with 0"
feature = Feature(input, transformer, name=name, description=description)

Esempio n. 3

from ballet import Feature
from ballet.eng.sklearn import SimpleImputer

input = "Lot Frontage"
transformer = SimpleImputer(strategy="mean")
feature = Feature(input, transformer)

Esempio n. 4

from ballet import Feature
from ballet.eng.sklearn import SimpleImputer

input = ["Year Built"]
transformer = SimpleImputer(
    strategy="mean")  # TODO - function, transformer-like, or list thereof
name = "Imputed Year Built"  # TODO - str
feature = Feature(input=input, transformer=transformer, name=name)

Esempio n. 5

File: feature_c85b4154_861c_42f9_98d0_e70d3d007aa8.py Progetto: HDI-Project/ballet-predict-census-income

from ballet import Feature
from ballet.eng.sklearn import SimpleImputer
import numpy as np

input = "JWAP"  # Time of arrival at work
transformer = [
    SimpleImputer(missing_values=np.nan, strategy="constant", fill_value=0.0),
    lambda df: np.where((df >= 70) & (df <= 124), 1, 0),
]
name = "If job has a morning start time"
description = "Return 1 if the Work arrival time >=6:30AM and <=10:30AM"
feature = Feature(input, transformer, name=name, description=description)

Esempio n. 6

from ballet import Feature
from ballet.eng.sklearn import SimpleImputer

input = "ESP"  # TODO - str or list of str
transformer = SimpleImputer(
    strategy="most_frequent"
)  # TODO - function, transformer-like, or list thereof
name = "Imputed ESP"  # TODO - str
description = "Mode imputed employment status of parents"  # TODO - str
feature = Feature(input, transformer, name=name, description=description)