example.py

import coalitional_methods as coal
import drawing
import random
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.datasets import load_iris, load_diabetes
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor


### CLASSIFICATION

problem_type = "Classification"

dataset = load_iris()
X, y = (
    pd.DataFrame(dataset.data, columns=dataset.feature_names),
    pd.Series(dataset.target),
)

model = RandomForestClassifier()
model.fit(X.values, y.values.flatten())

spearman_infs_comp25 = coal.coalitional_method(
    X,
    y,
    model,
    0.25,
    problem_type=problem_type,
    fvoid=None,
    look_at=None,
    method="spearman",
    complexity=False,
)

abs(spearman_infs_comp25).mean().sort_values(ascending=True).plot(
    kind="barh", color="dimgray"
)
plt.show()

drawing.draw_avg_influence_per_class(abs(spearman_infs_comp25), y, dataset.target_names)

rand_int = random.randrange(X.shape[0])
drawing.draw_influence_instance(
    spearman_infs_comp25, y, dataset.target_names, rand_int, problem_type=problem_type
)


### REGRESSION

problem_type = "Regression"

dataset = load_diabetes()
X, y = (
    pd.DataFrame(dataset.data, columns=dataset.feature_names),
    pd.Series(dataset.target),
)

model = RandomForestRegressor()
model.fit(X.values, y.values.flatten())

spearman_infs_comp25 = coal.coalitional_method(
    X,
    y,
    model,
    0.25,
    problem_type=problem_type,
    fvoid=None,
    look_at=None,
    method="spearman",
    complexity=False,
)

spearman_infs_comp25.abs().mean().sort_values(ascending=True).plot(
    kind="barh", color="dimgray"
)
plt.show()

rand_int = random.randrange(X.shape[0])
drawing.draw_influence_instance(
    spearman_infs_comp25, y, None, rand_int, problem_type=problem_type
)