from sklearn.datasets import load_digits

digits = load_digits()
data, target = digits.data, digits.target

from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline

model = make_pipeline(MinMaxScaler(), LogisticRegression(max_iter=1_000))

from sklearn.model_selection import cross_val_score, KFold

cv = KFold(shuffle=False)
test_score_no_shuffling = cross_val_score(model, data, target, cv=cv, n_jobs=2)
print(
    "The average accuracy is "
    f"{test_score_no_shuffling.mean():.3f} ± "
    f"{test_score_no_shuffling.std():.3f}"
)

cv = KFold(shuffle=True)
test_score_with_shuffling = cross_val_score(
    model, data, target, cv=cv, n_jobs=2
)
print(
    "The average accuracy is "
    f"{test_score_with_shuffling.mean():.3f} ± "
    f"{test_score_with_shuffling.std():.3f}"
)

import pandas as pd

all_scores = pd.DataFrame(
    [test_score_no_shuffling, test_score_with_shuffling],
    index=["KFold without shuffling", "KFold with shuffling"],
).T

import matplotlib.pyplot as plt

all_scores.plot.hist(bins=10, edgecolor="black", alpha=0.7)
plt.xlim([0.8, 1.0])
plt.xlabel("Accuracy score")
plt.legend(bbox_to_anchor=(1.05, 0.8), loc="upper left")
_ = plt.title("Distribution of the test scores")

print(test_score_no_shuffling)

print(digits.DESCR)

target[:200]

from itertools import count
import numpy as np

# defines the lower and upper bounds of sample indices
# for each writer
writer_boundaries = [
    0,
    130,
    256,
    386,
    516,
    646,
    776,
    915,
    1029,
    1157,
    1287,
    1415,
    1545,
    1667,
    1797,
]
groups = np.zeros_like(target)
lower_bounds = writer_boundaries[:-1]
upper_bounds = writer_boundaries[1:]

for group_id, lb, up in zip(count(), lower_bounds, upper_bounds):
    groups[lb:up] = group_id

plt.plot(groups)
plt.yticks(np.unique(groups))
plt.xticks(writer_boundaries, rotation=90)
plt.xlabel("Target index")
plt.ylabel("Writer index")
_ = plt.title("Underlying writer groups existing in the target")

from sklearn.model_selection import GroupKFold

cv = GroupKFold()
test_score = cross_val_score(
    model, data, target, groups=groups, cv=cv, n_jobs=2
)
print(
    f"The average accuracy is {test_score.mean():.3f} ± {test_score.std():.3f}"
)

all_scores = pd.DataFrame(
    [test_score_no_shuffling, test_score_with_shuffling, test_score],
    index=[
        "KFold without shuffling",
        "KFold with shuffling",
        "KFold with groups",
    ],
).T

all_scores.plot.hist(bins=10, edgecolor="black", alpha=0.7)
plt.xlim([0.8, 1.0])
plt.xlabel("Accuracy score")
plt.legend(bbox_to_anchor=(1.05, 0.8), loc="upper left")
_ = plt.title("Distribution of the test scores")