from sf_runoff import create_it_matrix, create_gap
from climatology_ensemble import daily_climatology_p_et_ensemble
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy as np
from sklearn.svm import SVR
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.compose import TransformedTargetRegressor
from sklearn.model_selection import GridSearchCV,TimeSeriesSplit, KFold
from sklearn.metrics import mean_squared_error, r2_score
from sf_runoff import smape
import pdb
import seaborn as sns
def classic_CV_SVR_predict(daily_input, C, eps,gamma,t_length,t_unit, n_splits):#, radius_for_ensemble):
#compute the daily climatology and the quantile analysis
daily_clim = daily_climatology_p_et_ensemble(daily_input,0,t_unit)
#get the input-target matrix
it_matrix=create_it_matrix(daily_input,t_length,t_unit)
#split in train-test sets
tscv = KFold(n_splits=n_splits)
sets = tscv.split(it_matrix.index)
j=0;
prediction=pd.DataFrame(data=None)
for train_index, test_index in sets:
#reduce train_set to have a gap with test index and ensure independence
train_index=create_gap(train_index, test_index,t_unit)
#set up training features
X = it_matrix.drop(columns='Q').iloc[train_index]
y = it_matrix['Q'].iloc[train_index]
#set up the model according to the parameters
svr_model_tuned = SVR(kernel='rbf', gamma=gamma, C=C, epsilon=eps, cache_size=1000)
svr_model_tuned = make_pipeline(StandardScaler(),
TransformedTargetRegressor(regressor=svr_model_tuned, transformer=StandardScaler()))
#fit the model
svr_model_tuned.fit(X, y)
# get the test dates (end of the month)
test_dates = it_matrix.index[test_index]#[daily_input.index.is_month_end]
# and their day of the year
doy_test_dates = test_dates.day_of_year
# Save the true runoff values (with t_unit days rolling average)
target = {}
target['true_runoff'] = daily_input.Q.rolling(30, min_periods=30).mean().loc[test_dates]
# Compute runoff monthly climatology using the whole dataset
runoff_daily_clim = daily_input.Q.rolling(30, min_periods=30).mean()
target['runoff_clim'] = [runoff_daily_clim.loc[runoff_daily_clim.index.day_of_year == d].mean() for d in doy_test_dates]
X_trueTP = it_matrix.loc[test_dates, :].drop(columns='Q')
target['prediction'] = svr_model_tuned.predict(X_trueTP)
"""
# Predict using temperature and precipitation climatology
# predict also for 25th and 75th quantile situations.
X_climTP = X_trueTP.copy()
X_climTP_Q25=X_trueTP.copy()
X_climTP_Q75=X_trueTP.copy()
#predict till 6 months of advance
lead_times = range(1,6)
for lt in lead_times:
# modify the X matrix by substituting the climatology to the real meteo vars for lt months.
change_dest = [c for c in X_climTP.columns if c.split('_')[1] == str(-lt + 1)]
change_source = [c.split('_')[0] for c in change_dest]
X_climTP.loc[:, change_dest]=daily_clim.loc[(test_dates-np.timedelta64(t_unit*(lt-1),'D')).day_of_year][change_source].values
#predict
target[f'climTP_lt{lt}'] = svr_model_tuned.predict(X_climTP)
# modify the X matrix by substituting the climatology to the extreme (25th and 75th quantiles) meteo vars for lt months.
change_source_25 = []
change_source_75 = []
#modify the source, by taking the daily climathological data referred to the quantiles situations
for i in change_source:
change_source_25.append(i+'_Q25')
change_source_75.append((i+'_Q75'))
X_climTP_Q25.loc[:, change_dest]=daily_clim.loc[(test_dates-np.timedelta64(t_unit*(lt-1),'D')).day_of_year][change_source_25].values
target[f'climTP_lt{lt}_Q25'] = svr_model_tuned.predict(X_climTP_Q25)
X_climTP_Q75.loc[:, change_dest]=daily_clim.loc[(test_dates-np.timedelta64(t_unit*(lt-1),'D')).day_of_year][change_source_75].values
target[f'climTP_lt{lt}_Q75'] = svr_model_tuned.predict(X_climTP_Q75)
"""
target['split']= np.repeat(j,test_index.shape[0])
#add this split prediction to the
#pdb.set_trace()
prediction=prediction.append(pd.DataFrame(data=target, index=test_dates))
j=j+1
return prediction.drop(columns='split')
def classic_CV_PCA_SVR_predict(daily_input, C, eps,gamma, n, t_length,t_unit ,n_splits): #radius_for_ensemble):
#compute the daily climatology and the quantile analysis
daily_clim = daily_climatology_p_et_ensemble(daily_input,0,t_unit)
#get the input-target matrix
it_matrix=create_it_matrix(daily_input,t_length,t_unit)
#split in train-test sets
tscv = KFold(n_splits=n_splits)
sets = tscv.split(it_matrix.index)
j=0;
prediction=pd.DataFrame(data=None)
for train_index, test_index in sets:
#reduce train_set to have a gap with test index and ensure independence
train_index=create_gap(train_index, test_index,t_unit)
#set up training features
X = it_matrix.drop(columns='Q').iloc[train_index]
y = it_matrix['Q'].iloc[train_index]
#set up the model according to the parameters
svr_model_tuned = SVR(kernel='rbf', gamma=gamma, C=C, epsilon=eps, cache_size=1000)
svr_model_tuned = make_pipeline(StandardScaler(),
PCA(n_components=n),
TransformedTargetRegressor(regressor=svr_model_tuned, transformer=StandardScaler()))
#fit the model
svr_model_tuned.fit(X, y)
# get the test dates (end of the month)
test_dates = it_matrix.index[test_index]
# and their day of the year
doy_test_dates = test_dates.day_of_year
# Save the true runoff values (with t_unit days rolling average)
target = {}
target['true_runoff'] = daily_input.Q.rolling(30, min_periods=30).mean().loc[test_dates]
# Compute runoff monthly climatology using the whole dataset
runoff_daily_clim = daily_input.Q.rolling(30, min_periods=30).mean()
target['runoff_clim'] = [runoff_daily_clim.loc[runoff_daily_clim.index.day_of_year == d].mean() for d in doy_test_dates]
X_trueTP = it_matrix.loc[test_dates, :].drop(columns='Q')
target['prediction'] = svr_model_tuned.predict(X_trueTP)
"""
# Predict using temperature and precipitation climatology
# predict also for 25th and 75th quantile situations.
X_climTP = X_trueTP.copy()
X_climTP_Q25=X_trueTP.copy()
X_climTP_Q75=X_trueTP.copy()
#predict till 6 months of advance
lead_times = range(1,6)
for lt in lead_times:
# modify the X matrix by substituting the climatology to the real meteo vars for lt months.
change_dest = [c for c in X_climTP.columns if c.split('_')[1] == str(-lt + 1)]
change_source = [c.split('_')[0] for c in change_dest]
X_climTP.loc[:, change_dest]=daily_clim.loc[(test_dates-np.timedelta64(t_unit*(lt-1),'D')).day_of_year][change_source].values
#predict
target[f'climTP_lt{lt}'] = svr_model_tuned.predict(X_climTP)
# modify the X matrix by substituting the climatology to the extreme (25th and 75th quantiles) meteo vars for lt months.
change_source_25 = []
change_source_75 = []
#modify the source, by taking the daily climathological data referred to the quantiles situations
for i in change_source:
change_source_25.append(i+'_Q25')
change_source_75.append((i+'_Q75'))
X_climTP_Q25.loc[:, change_dest]=daily_clim.loc[(test_dates-np.timedelta64(t_unit*(lt-1),'D')).day_of_year][change_source_25].values
target[f'climTP_lt{lt}_Q25'] = svr_model_tuned.predict(X_climTP_Q25)
X_climTP_Q75.loc[:, change_dest]=daily_clim.loc[(test_dates-np.timedelta64(t_unit*(lt-1),'D')).day_of_year][change_source_75].values
target[f'climTP_lt{lt}_Q75'] = svr_model_tuned.predict(X_climTP_Q75)
"""
target['split']= np.repeat(j,test_index.shape[0])
#pdb.set_trace()
#add this split prediction to the
prediction=prediction.append(pd.DataFrame(data=target, index=test_dates))
j=j+1
return prediction.drop(columns='split')