scikit meta logo scikit meta: grouped estimation

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Notes

Again, we'll list some boilerplate imports first.

import numpy as np
import pandas as pd
import matplotlib.pylab as plt
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.metrics import mean_absolute_error, mean_squared_error

from sklego.datasets import load_chicken
from sklego.preprocessing import ColumnSelector

df = load_chicken(as_frame=True)

def plot_model(model):
    df = load_chicken(as_frame=True)
    model.fit(df[['diet', 'time']], df['weight'])
    metric_df = df[['diet', 'time', 'weight']].assign(pred=lambda d: model.predict(d[['diet', 'time']]))
    metric = mean_absolute_error(metric_df['weight'], metric_df['pred'])
    plt.figure(figsize=(12, 4))
    # plt.scatter(df['time'], df['weight'])
    for i in [1, 2, 3, 4]:
        pltr = metric_df[['time', 'diet', 'pred']].drop_duplicates().loc[lambda d: d['diet'] == i]
        plt.plot(pltr['time'], pltr['pred'], color='.rbgy'[i])
    plt.title(f"linear model per group, MAE: {np.round(metric, 2)}");

To see the effect of using the "diet" as a one-hot encoded vector you can run this pipeline. Note that the ColumnSelector is a tool from scikit-lego.

feature_pipeline = Pipeline([
    ("datagrab", FeatureUnion([
        ("discrete", Pipeline([
            ("grab", ColumnSelector("diet")),
            ("encode", OneHotEncoder(categories="auto", sparse=False))
        ])),
        ("continuous", Pipeline([
            ("grab", ColumnSelector("time")),
            ("standardize", StandardScaler())
        ]))
    ]))
])

pipe = Pipeline([
    ("transform", feature_pipeline),
    ("model", LinearRegression())
])

plot_model(pipe)

To run a model for each diet instead, you can run:

from sklego.meta import GroupedPredictor
mod = GroupedPredictor(LinearRegression(), groups=["diet"])
plot_model(mod)

Note how each line has it's own intercept and slope.