from scalecast.Forecaster import Forecaster

f = Forecaster(
    y = array_of_values,
    current_dates = array_of_dates,
    future_dates=fcst_horizon_length,
    test_length = 0, # do you want to test all models? if so, on how many or what percent of observations?
    cis = False, # evaluate conformal confidence intervals for all models?
    metrics = ['rmse','mape','mae','r2'], # what metrics to evaluate over the validation/test sets?
)

f.set_estimator('lstm')
f.manual_forecast(
    lags=36,
    batch_size=32,
    epochs=15,
    validation_split=.2,
    activation='tanh',
    optimizer='Adam',
    learning_rate=0.001,
    lstm_layer_sizes=(100,)*3,
    dropout=(0,)*3,
)

f.auto_Xvar_select( # iterate through different combinations of covariates
    estimator = 'lasso', # what estimator?
    alpha = .2, # estimator hyperparams?
    monitor = 'ValidationMetricValue', # what metric to monitor to make decisions?
    cross_validate = True, # cross validate
    cvkwargs = {'k':3}, # 3 folds
)

from scalecast import GridGenerator

GridGenerator.get_example_grids()
models = ['ridge','lasso','xgboost','lightgbm','knn']
f.tune_test_forecast(
    models,
    limit_grid_size = .2,
    feature_importance = True, # save pfi feature importance for each model?
    cross_validate = True, # cross validate? if False, using a seperate validation set that the user can specify
    rolling = True, # rolling time series cross validation?
    k = 3, # how many folds?
)

import matplotlib.pyplot as plt

fig, ax = plt.subplots(2,1, figsize = (12,6))
f.plot_test_set(models=models,order_by='TestSetRMSE',ax=ax[0])
f.plot(models=models,order_by='TestSetRMSE',ax=ax[1])
plt.show()

from scalecast import GridGenerator
from scalecast.Pipeline import Transformer, Reverter, Pipeline
from scalecast.util import find_optimal_transformation, backtest_metrics

def forecaster(f):
    models = ['ridge','lasso','xgboost','lightgbm','knn']
    f.tune_test_forecast(
        models,
        limit_grid_size = .2, # randomized grid search on 20% of original grid sizes
        feature_importance = True, # save pfi feature importance for each model?
        cross_validate = True, # cross validate? if False, using a seperate validation set that the user can specify
        rolling = True, # rolling time series cross validation?
        k = 3, # how many folds?
    )

transformer, reverter = find_optimal_transformation(f) # just one of several ways to select transformations for your series

pipeline = Pipeline(
    steps = [
        ('Transform',transformer),
        ('Forecast',forecaster),
        ('Revert',reverter),
    ]
)

f = pipeline.fit_predict(f)
backtest_results = pipeline.backtest(f)
metrics = backtest_metrics(backtest_results)

from scalecast.auxmodels import auto_arima

f.set_estimator('lstm')
f.manual_forecast(
    lags=36,
    batch_size=32,
    epochs=15,
    validation_split=.2,
    activation='tanh',
    optimizer='Adam',
    learning_rate=0.001,
    lstm_layer_sizes=(100,)*3,
    dropout=(0,)*3,
)

f.set_estimator('prophet')
f.manual_forecast()

auto_arima(f)

# stack previously evaluated models
f.add_signals(['lstm','prophet','arima'])
f.set_estimator('catboost')
f.manual_forecast()

from scalecast.MVForecaster import MVForecaster
from scalecast.Pipeline import MVPipeline
from scalecast.util import find_optimal_transformation, backtest_metrics
from scalecast import GridGenerator

GridGenerator.get_mv_grids()

def mvforecaster(mvf):
    models = ['ridge','lasso','xgboost','lightgbm','knn']
    mvf.tune_test_forecast(
        models,
        limit_grid_size = .2, # randomized grid search on 20% of original grid sizes
        cross_validate = True, # cross validate? if False, using a seperate validation set that the user can specify
        rolling = True, # rolling time series cross validation?
        k = 3, # how many folds?
    )

mvf = MVForecaster(f1,f2,f3) # can take N Forecaster objects

transformer1, reverter1 = find_optimal_transformation(f1)
transformer2, reverter2 = find_optimal_transformation(f2)
transformer3, reverter3 = find_optimal_transformation(f3)

pipeline = MVPipeline(
    steps = [
        ('Transform',[transformer1,transformer2,transformer3]),
        ('Forecast',mvforecaster),
        ('Revert',[reverter1,reverter2,reverter3])
    ]
)

f1, f2, f3 = pipeline.fit_predict(f1, f2, f3)
backtest_results = pipeline.backtest(f1, f2, f3)
metrics = backtest_metrics(backtest_results)

f = Forecaster(...)
f.auto_Xvar_select()
f.set_estimator('xgboost')
f.cross_validate()
f.auto_forecast()

f_new = Forecaster(...) # different series than f
f_new = infer_apply_Xvar_selection(infer_from=f,apply_to=f_new)
f_new.transfer_predict(transfer_from=f,model='xgboost') # transfers the xgboost model from f to f_new