"""
Searching over betting markets
==============================

This example shows BettorGridSearchCV. The markets to bet on are a hyperparameter, so you can search over them like any
other.
"""

# Author: Georgios Douzas <gdouzas@icloud.com>
# Licence: MIT

import matplotlib.pyplot as plt
import pandas as pd
from sklearn.compose import make_column_transformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import TimeSeriesSplit
from sklearn.multioutput import MultiOutputClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder

from sportsbet.dataloaders import DataLoader
from sportsbet.evaluation import BettorGridSearchCV, ClassifierBettor, backtest
from sportsbet.sources import SampleSoccerOdds, SampleSoccerStats

# %%
# Extracting the data
# -------------------

dataloader = DataLoader(
    param_grid={'league': ['England', 'Spain']},
    stats=SampleSoccerStats(),
    odds=SampleSoccerOdds(),
)
X_train, Y_train, O_train = dataloader.extract_train_data(odds_type='market_maximum')

# %%
# The model
# ---------

classifier = make_pipeline(
    make_column_transformer(
        (OneHotEncoder(handle_unknown='ignore'), ['league', 'home_team', 'away_team']),
        remainder='passthrough',
    ),
    SimpleImputer(),
    MultiOutputClassifier(LogisticRegression(solver='liblinear', random_state=7, class_weight='balanced')),
)

# %%
# The markets are a hyperparameter
# --------------------------------
#
# Which market to bet is a choice, just as the regularisation of the classifier is a choice. There is no reason to set
# one by hand and search the other. Put both in the grid and let the search decide.

bettor = BettorGridSearchCV(
    estimator=ClassifierBettor(classifier, init_cash=10000.0, stake=50.0),
    param_grid={
        'classifier__multioutputclassifier__estimator__C': [0.1, 1.0],
        'betting_markets': [['home_win'], ['draw'], ['away_win'], ['home_win', 'draw', 'away_win']],
    },
    cv=TimeSeriesSplit(2),
)
_ = bettor.fit(X_train, Y_train, O_train)

# %%
# What it chose:

bettor.best_params_

# %%
# Backtesting the search
# ----------------------
#
# The search re-runs inside every fold. So it chooses the markets on the training part of the fold and judges them on
# the part it has not seen. Choosing them once, on everything, would choose them on the answers.

results = backtest(bettor, X_train, Y_train, O_train, cv=TimeSeriesSplit(3))
results

# %%
# What the search saw
# -------------------
#
# The search scored every set of markets on the same footing as the regularisation. The chart below shows the best
# score for each set of markets. That score is how the search came to prefer one set over the others. The bar it picked
# is the choice you would otherwise have made by hand, or worse, by peeking at the answers.

scores = pd.DataFrame(bettor.cv_results_)
scores['markets'] = scores['param_betting_markets'].apply(' + '.join)
best_per_markets = scores.groupby('markets')['mean_test_score'].max().sort_values()
chosen = ' + '.join(bettor.best_params_['betting_markets'])
colours = ['tab:orange' if markets == chosen else 'tab:blue' for markets in best_per_markets.index]

fig, ax = plt.subplots()
ax.barh(best_per_markets.index, best_per_markets.to_numpy(), color=colours)
ax.axvline(0, color='black', linewidth=0.8)
ax.set_title('The markets are scored, not assumed (orange is the choice)')
ax.set_xlabel('best cross-validated return')