def ridgeCV(y_ts, df_norm, keys=None, kwrgs_model=None):
'''
X contains all precursor data, incl train and test
X_train, y_train are split up by TrainIsTrue
Preciction is made for whole timeseries
'''
#%%
if keys is None:
no_data_col = ['TrainIsTrue', 'RV_mask', 'fit_model_mask']
keys = df_norm.columns
keys = [k for k in keys if k not in no_data_col]
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
# warnings.filterwarnings("ignore", category=FutureWarning)
if kwrgs_model == None:
# use Bram settings
kwrgs_model = {'fit_intercept': True, 'alphas': (.01, .1, 1.0, 10.0)}
# find parameters for gridsearch optimization
kwrgs_gridsearch = {
k: i
for k, i in kwrgs_model.items() if type(i) == list
}
# only the constant parameters are kept
kwrgs = kwrgs_model.copy()
[kwrgs.pop(k) for k in kwrgs_gridsearch.keys()]
if 'feat_sel' in kwrgs:
feat_sel = kwrgs.pop('feat_sel')
else:
feat_sel = None
# Get training years
x_fit_mask, y_fit_mask, x_pred_mask, y_pred_mask = utils.get_masks(df_norm)
X = df_norm[keys]
X = X.dropna(axis='columns') # drop only nan columns
# X = add_constant(X)
X_train = X[x_fit_mask.values]
X_pred = X[x_pred_mask.values]
RV_fit = y_ts['ts'].loc[y_fit_mask.index] # y_fit may be shortened
# because X_test was used to predict y_train due to lag, hence train-test
# leakage.
# y_ts dates may no longer align with x_fit y_fit masks
y_fit_mask = df_norm['TrainIsTrue'].loc[y_fit_mask.index].values
y_train = RV_fit[y_fit_mask].squeeze()
# if y_pred_mask is not None:
# y_dates = RV_fit[y_pred_mask.values].index
# else:
# y_dates = RV_fit.index
X = X_train
# # Create stratified random shuffle which keeps together years as blocks.
kwrgs_cv = ['kfold', 'seed']
kwrgs_cv = {k: i for k, i in kwrgs.items() if k in kwrgs_cv}
[kwrgs.pop(k) for k in kwrgs_cv.keys()]
if len(kwrgs_cv) >= 1:
cv = utils.get_cv_accounting_for_years(y_train, **kwrgs_cv)
kwrgs['store_cv_values'] = False
else:
cv = None
kwrgs['store_cv_values'] = True
model = RidgeCV(cv=cv, **kwrgs)
if feat_sel is not None:
if feat_sel['model'] is None:
feat_sel['model'] = model
model, new_features, rfecv = utils.feature_selection(
X_train, y_train.values, **feat_sel)
X_pred = X_pred[new_features]
else:
model.fit(X_train, y_train)
y_pred = model.predict(X_pred)
prediction = pd.DataFrame(y_pred, index=y_pred_mask.index, columns=[0])
model.X_pred = X_pred
model.name = 'Ridge Regression'
#%%
return prediction, model
