"""
=======================================================
Estimating prediction intervals of time series forecast
=======================================================
This example uses :class:`mapie.regression.MapieRegressor` to estimate
prediction intervals associated with time series forecast. We use the
standard cross-validation approach to estimate residuals and associated
prediction intervals.

We use here the Victoria electricity demand dataset used in the book
"Forecasting: Principles and Practice" by <NAME> and <NAME>.
The electricity demand features daily and weekly seasonalities and is impacted
by the temperature, considered here as a exogeneous variable.

The data is modelled by a Random Forest model with a
:class:`sklearn.model_selection.RandomizedSearchCV` using a sequential
:class:`sklearn.model_selection.TimeSeriesSplit` cross validation, in which the
training set is prior to the validation set.
The best model is then feeded into :class:`mapie.regression.MapieRegressor`
to estimate the associated prediction intervals.
We consider the standard CV+ resampling method.

We wish to emphasize one main limitation with this example.
We use a standard cross-validation in Mapie to estimate the prediction
intervals, through the `sklearn.model_selection.KFold()` object.
Residuals are therefore estimated using models trained on data with higher
indices than the validation data, which is inappropriate for time-series data.
Howerver, using a `sklearn.model_selection.TimeSeriesSplit` cross validation
object for estimating the residuals breaks the theoretical guarantees of the
Jackknife+ and CV+ methods.
"""
import pandas as pd
from mapie.metrics import regression_coverage_score
from mapie.regression import MapieRegressor
from matplotlib import pylab as plt
from scipy.stats import randint
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import RandomizedSearchCV, TimeSeriesSplit

# Load input data and feature engineering
demand_df = pd.read_csv(
    "../data/demand_temperature.csv", parse_dates=True, index_col=0
)
demand_df["Date"] = pd.to_datetime(demand_df.index)
demand_df["Weekofyear"] = demand_df.Date.dt.isocalendar().week.astype("int64")
demand_df["Weekday"] = demand_df.Date.dt.isocalendar().day.astype("int64")
demand_df["Hour"] = demand_df.index.hour

# Train/validation/test split
num_test_steps = 24 * 7 * 2
demand_train = demand_df.iloc[:-num_test_steps, :].copy()
demand_test = demand_df.iloc[-num_test_steps:, :].copy()
X_train = demand_train.loc[:, ["Weekofyear", "Weekday", "Hour", "Temperature"]]
y_train = demand_train["Demand"]
X_test = demand_test.loc[:, ["Weekofyear", "Weekday", "Hour", "Temperature"]]
y_test = demand_test["Demand"]

# CV parameter search
n_iter = 10
n_splits = 5
tscv = TimeSeriesSplit(n_splits=n_splits)
random_state = 59
rf_model = RandomForestRegressor(random_state=random_state)
rf_params = {"max_depth": randint(2, 30), "n_estimators": randint(10, 1e3)}
cv_obj = RandomizedSearchCV(
    rf_model,
    param_distributions=rf_params,
    n_iter=n_iter,
    cv=tscv,
    scoring="neg_root_mean_squared_error",
    random_state=random_state,
    verbose=0,
    n_jobs=-1,
)
cv_obj.fit(X_train, y_train)
best_est = cv_obj.best_estimator_

# Estimate prediction intervals on test set with best estimator
# Here, a non-nested CV approach is used for the sake of computational
# time, but a nested CV approach is preferred.
# See the dedicated example in the gallery for more information.
alpha = 0.1
mapie = MapieRegressor(
    best_est, method="plus", cv=n_splits, agg_function="median", n_jobs=-1
)
mapie.fit(X_train, y_train)
y_pred, y_pis = mapie.predict(X_test, alpha=alpha)
coverage = regression_coverage_score(y_test, y_pis[:, 0, 0], y_pis[:, 1, 0])
width = (y_pis[:, 1, 0] - y_pis[:, 0, 0]).mean()

# Print results
print(
    "Coverage and prediction interval width mean for CV+: "
    f"{coverage:.3f}, {width:.3f}"
)

# Plot estimated prediction intervals on test set
fig = plt.figure(figsize=(15, 5))
ax = fig.add_subplot(1, 1, 1)
ax.set_ylabel("Hourly demand (GW)")
ax.plot(demand_test.Demand, lw=2, label="Test data", c="C1")
ax.plot(demand_test.index, y_pred, lw=2, c="C2", label="Predictions")
ax.fill_between(
    demand_test.index,
    y_pis[:, 0, 0],
    y_pis[:, 1, 0],
    color="C2",
    alpha=0.2,
    label="CV+ PIs",
)
ax.legend()
plt.show()

"""
=======================================================
Estimating prediction intervals of time series forecast
=======================================================
This example uses :class:`mapie.regression.MapieRegressor` to estimate
prediction intervals associated with time series forecast. We use the
standard cross-validation approach to estimate residuals and associated
prediction intervals.

We use here the Victoria electricity demand dataset used in the book
"Forecasting: Principles and Practice" by <NAME> and <NAME>.
The electricity demand features daily and weekly seasonalities and is impacted
by the temperature, considered here as a exogeneous variable.

The data is modelled by a Random Forest model with a
:class:`sklearn.model_selection.RandomizedSearchCV` using a sequential
:class:`sklearn.model_selection.TimeSeriesSplit` cross validation, in which the
training set is prior to the validation set.
The best model is then feeded into :class:`mapie.regression.MapieRegressor`
to estimate the associated prediction intervals.
We consider the standard CV+ resampling method.

We wish to emphasize one main limitation with this example.
We use a standard cross-validation in Mapie to estimate the prediction
intervals, through the `sklearn.model_selection.KFold()` object.
Residuals are therefore estimated using models trained on data with higher
indices than the validation data, which is inappropriate for time-series data.
Howerver, using a `sklearn.model_selection.TimeSeriesSplit` cross validation
object for estimating the residuals breaks the theoretical guarantees of the
Jackknife+ and CV+ methods.
"""
import pandas as pd
from mapie.metrics import regression_coverage_score
from mapie.regression import MapieRegressor
from matplotlib import pylab as plt
from scipy.stats import randint
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import RandomizedSearchCV, TimeSeriesSplit

# Load input data and feature engineering
demand_df = pd.read_csv(
    "../data/demand_temperature.csv", parse_dates=True, index_col=0
)
demand_df["Date"] = pd.to_datetime(demand_df.index)
demand_df["Weekofyear"] = demand_df.Date.dt.isocalendar().week.astype("int64")
demand_df["Weekday"] = demand_df.Date.dt.isocalendar().day.astype("int64")
demand_df["Hour"] = demand_df.index.hour

# Train/validation/test split
num_test_steps = 24 * 7 * 2
demand_train = demand_df.iloc[:-num_test_steps, :].copy()
demand_test = demand_df.iloc[-num_test_steps:, :].copy()
X_train = demand_train.loc[:, ["Weekofyear", "Weekday", "Hour", "Temperature"]]
y_train = demand_train["Demand"]
X_test = demand_test.loc[:, ["Weekofyear", "Weekday", "Hour", "Temperature"]]
y_test = demand_test["Demand"]

# CV parameter search
n_iter = 10
n_splits = 5
tscv = TimeSeriesSplit(n_splits=n_splits)
random_state = 59
rf_model = RandomForestRegressor(random_state=random_state)
rf_params = {"max_depth": randint(2, 30), "n_estimators": randint(10, 1e3)}
cv_obj = RandomizedSearchCV(
    rf_model,
    param_distributions=rf_params,
    n_iter=n_iter,
    cv=tscv,
    scoring="neg_root_mean_squared_error",
    random_state=random_state,
    verbose=0,
    n_jobs=-1,
)
cv_obj.fit(X_train, y_train)
best_est = cv_obj.best_estimator_

# Estimate prediction intervals on test set with best estimator
# Here, a non-nested CV approach is used for the sake of computational
# time, but a nested CV approach is preferred.
# See the dedicated example in the gallery for more information.
alpha = 0.1
mapie = MapieRegressor(
    best_est, method="plus", cv=n_splits, agg_function="median", n_jobs=-1
)
mapie.fit(X_train, y_train)
y_pred, y_pis = mapie.predict(X_test, alpha=alpha)
coverage = regression_coverage_score(y_test, y_pis[:, 0, 0], y_pis[:, 1, 0])
width = (y_pis[:, 1, 0] - y_pis[:, 0, 0]).mean()

# Print results
print(
    "Coverage and prediction interval width mean for CV+: "
    f"{coverage:.3f}, {width:.3f}"
)

# Plot estimated prediction intervals on test set
fig = plt.figure(figsize=(15, 5))
ax = fig.add_subplot(1, 1, 1)
ax.set_ylabel("Hourly demand (GW)")
ax.plot(demand_test.Demand, lw=2, label="Test data", c="C1")
ax.plot(demand_test.index, y_pred, lw=2, c="C2", label="Predictions")
ax.fill_between(
    demand_test.index,
    y_pis[:, 0, 0],
    y_pis[:, 1, 0],
    color="C2",
    alpha=0.2,
    label="CV+ PIs",
)
ax.legend()
plt.show()

======================================================= Estimating prediction intervals of time series forecast ======================================================= This example uses :class:`mapie.regression.MapieRegressor` to estimate prediction intervals associated with time series forecast. We use the standard cross-validation approach to estimate residuals and associated prediction intervals.  We use here the Victoria electricity demand dataset used in the book "Forecasting: Principles and Practice" by <NAME> and <NAME>. The electricity demand features daily and weekly seasonalities and is impacted by the temperature, considered here as a exogeneous variable.  The data is modelled by a Random Forest model with a :class:`sklearn.model_selection.RandomizedSearchCV` using a sequential :class:`sklearn.model_selection.TimeSeriesSplit` cross validation, in which the training set is prior to the validation set. The best model is then feeded into :class:`mapie.regression.MapieRegressor` to estimate the associated prediction intervals. We consider the standard CV+ resampling method.  We wish to emphasize one main limitation with this example. We use a standard cross-validation in Mapie to estimate the prediction intervals, through the `sklearn.model_selection.KFold()` object. Residuals are therefore estimated using models trained on data with higher indices than the validation data, which is inappropriate for time-series data. Howerver, using a `sklearn.model_selection.TimeSeriesSplit` cross validation object for estimating the residuals breaks the theoretical guarantees of the Jackknife+ and CV+ methods. # Load input data and feature engineering # Train/validation/test split # CV parameter search # Estimate prediction intervals on test set with best estimator # Here, a non-nested CV approach is used for the sake of computational # time, but a nested CV approach is preferred. # See the dedicated example in the gallery for more information. # Print results # Plot estimated prediction intervals on test set