from sklearn.model_selection import StratifiedKFold, KFold
import time
from hyperopt import fmin, tpe, hp, STATUS_OK, Trials
import xgboost as xgb
from sklearn.metrics import r2_score, mean_absolute_error
import os
os.environ['OMP_NUM_THREADS'] = str(2)
if __name__ == '__main__':
# init timer
t = Timer()
t.add('test')
""" DATA PREPARATION """
# load data
data, dcol = solar.load()
# select data
ly = ['y']
lx = [
'doy', 'hour', 'LCDC267', 'MCDC267', 'HCDC267', 'TCDC267',
'logAPCP267', 'RH267', 'TMP267', 'DSWRF267'
]
data = data[lx + ly]
dcol = get_dcol(data, ltarget=ly)
# select one hour data
hour = 11
idata = data[data.hour == hour]
idata.drop('hour', axis=1, inplace=True)
idcol = get_dcol(idata, ltarget=['y'])
# clean
del (data)
def main():
# init timer
t = Timer()
t.add('test')
""" DATA PREPARATION """
# load data
data, dcol = solar.load()
# select data
ly = ['y']
lx = [
'doy', 'hour', 'LCDC267', 'MCDC267', 'HCDC267', 'TCDC267',
'logAPCP267', 'RH267', 'TMP267', 'DSWRF267'
]
data = data[lx + ly]
dcol = get_dcol(data, ltarget=ly)
# select one hour data
hour = 11
idata = data[data.hour == hour]
idata.drop('hour', axis=1, inplace=True)
idcol = get_dcol(idata, ltarget=['y'])
# clean
del (data)
del (dcol)
# filtering outliers (ghi vs power)
from preprocessing.outliers import median2D
isoutlier = median2D.launch(idata['DSWRF267'].values,
idata.y.values,
percent=20.)
idata['isoutlier'] = isoutlier
idata = idata[idata.isoutlier == False]
idata.drop('isoutlier', axis=1, inplace=True)
# prepare data
X = idata[idcol['lx']].values
scaler = Scaler()
y = scaler.fit_transform(idata[idcol['ly']].values).ravel()
print('Prepared data: X: %s y: %s' % (X.shape, y.shape))
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
random_state=42)
print('Prepared data: X_train: %s y_train: %s' %
(X_train.shape, y_train.shape))
print('Prepared data: X_test: %s y_test: %s' %
(X_test.shape, y_test.shape))
# replace training dataset
X = X_train
y = y_train
""" ESTIMATOR WITH BAYESIAN TUNING """
from hpsklearn import HyperoptEstimator, xgboost_regression
from hyperopt import tpe
# Instantiate a HyperoptEstimator with the search space and number of evaluations
clf = HyperoptEstimator(regressor=xgboost_regression('my_clf'),
preprocessing=[],
algo=tpe.suggest,
max_evals=250,
trial_timeout=300)
clf.fit(X, y)
print(clf.best_model())
y_hat = clf.predict(X_test)
dscores = metrics_regression(y_test, y_hat, X.shape[1])
tf = t.since('test')
print(
'\nBayesian tuning -test: bias = %.3f mae = %.3f r2 = %.3f (time: %s)'
%
(dscores['bias'], dscores['mae'], dscores['r2'], format_duration(tf)))
# training
y_hat = clf.predict(X)
dscores = metrics_regression(y, y_hat, X.shape[1])
print(
'Bayesian tuning - train: bias = %.3f mae = %.3f r2 = %.3f (time: %s)'
%
(dscores['bias'], dscores['mae'], dscores['r2'], format_duration(tf)))