Improved plot_splits for time series splits

[d-kleine]

Description

• added title to data splits legend
• adjusted xticklabels for Sample Index not to intersect with each other anymore
• added legend for groups (setting the groups as labels on the xaxis would also be possible, but the samples index info would be gone then)

Related issues or pull requests

fixes #1094

Pull Request Checklist

• Added a note about the modification or contribution to the ./docs/sources/CHANGELOG.md file (if applicable)
• Added appropriate unit test functions in the ./mlxtend/*/tests directories (if applicable)
• Modify documentation in the corresponding Jupyter Notebook under mlxtend/docs/sources/ (if applicable)
• Ran PYTHONPATH='.' pytest ./mlxtend -sv and make sure that all unit tests pass (for small modifications, it might be sufficient to only run the specific test file, e.g., PYTHONPATH='.' pytest ./mlxtend/classifier/tests/test_stacking_cv_classifier.py -sv)
• Checked for style issues by running flake8 ./mlxtend

[d-kleine]

Test code:

import pandas as pd
import numpy as np

from sklearn.datasets import make_regression
from sklearn.dummy import DummyRegressor
from sklearn.metrics import root_mean_squared_error, make_scorer
from sklearn.model_selection import cross_val_score

from mlxtend.evaluate import GroupTimeSeriesSplit
from mlxtend.evaluate.time_series import plot_splits

X_test, y_test = [], []

start_year = 2010
end_year = 2020

for year in np.arange(start_year, end_year):
    X_year, y_year = make_regression(n_samples=5, n_features=2, bias=0, noise=1, random_state=year)
    X_year = pd.DataFrame(X_year).rename(columns={0:'X1', 1:'X2'})
    X_year['year'] = year
    y_year = pd.Series(y_year)
    X_test.append(X_year)
    y_test.append(y_year)

X, y = pd.concat(X_test), pd.concat(y_test)

# modelisation
model = DummyRegressor(strategy="mean")
metric = root_mean_squared_error
cv_args = {"test_size": 1, 'n_splits': len(np.unique(X['year'])) - 1, 'window_type': 'rolling'}
cv = GroupTimeSeriesSplit(**cv_args)

scores = cross_val_score(model, X, y, cv=cv, groups=X['year'], scoring=make_scorer(metric))

plot_splits(X, y, X['year'], **cv_args)

[d-kleine]

@rasbt What do you think about these improvements?

[rasbt]

This looks really really good, thanks for the PR