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3ecb175 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | """Predictor sets and feature engineering for the yield-prediction models.
M1, M2, M3 are linear regressions on increasingly rich plot-level
features. RF is a Random Forest baseline that uses a seven-feature
predictor set including squared and total-area aggregate features.
"""
from __future__ import annotations
import pandas as pd
PREDICTORS_M1: tuple[str, ...] = ("total_area_in2",)
PREDICTORS_M2: tuple[str, ...] = (
"area_B_in2",
"area_A1_in2",
"area_A2_in2",
"area_OV_in2",
)
PREDICTORS_M3: tuple[str, ...] = (
"area_B_in2",
"area_A1_in2",
"area_A2_in2",
"area_OV_in2",
"mean_area",
)
# Wider feature set for the Random Forest baseline: four class areas plus
# mean_area, total_area_sq, and total_area_in2.
PREDICTORS_RF: tuple[str, ...] = (
"area_B_in2",
"area_A1_in2",
"area_A2_in2",
"area_OV_in2",
"mean_area",
"total_area_sq",
"total_area_in2",
)
RESPONSE: str = "obs_sum_B_A1_A2_OV"
RF_KWARGS: dict = {
"n_estimators": 500,
"random_state": 42,
"max_depth": None,
"min_samples_split": 2,
"min_samples_leaf": 1,
"n_jobs": -1,
}
def add_model_features(df: pd.DataFrame) -> pd.DataFrame:
"""Recompute total_area_in2 from class areas, add mean_area and total_area_sq.
total_area_in2 is overwritten with the sum of the four class-area
columns (this can differ from the upstream aggregate if any tubers
have a NaN class). mean_area = total_area_in2 / n_tubers, and
total_area_sq = total_area_in2 ** 2.
"""
df = df.copy()
df["total_area_in2"] = df[list(PREDICTORS_M2)].sum(axis=1)
df["mean_area"] = df["total_area_in2"] / df["n_tubers"]
df["total_area_sq"] = df["total_area_in2"] ** 2
return df
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