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from utils import metrics, Snv, No_transformation, KF_CV, sel_ratio
class Regmodel(object):
from hyperopt import fmin, hp, tpe, Trials, space_eval, STATUS_OK, anneal
def __init__(self, train, test, n_iter, add_hyperparams = None, nfolds = 3, **kwargs):
from hyperopt import fmin, hp, tpe, Trials, space_eval, STATUS_OK, anneal
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self.SCORE = 100000000
self._xc, self._xt, self._ytrain, self._ytest = train[0], test[0], train[1], test[1]
self._nc, self._nt, self._p = train[0].shape[0], test[0].shape[0], train[0].shape[1]
self._model, self._best = None, None
self._yc, self._ycv, self._yt = None, None, None
self._cv_df = DataFrame()
self._sel_ratio = DataFrame()
self._nfolds = nfolds
self._selected_bands = DataFrame(index = ['from', 'to'])
self.important_features = None
self._hyper_params = {'polyorder': hp.choice('polyorder', [0, 1, 2]),
'deriv': hp.choice('deriv', [0, 1, 2]),
'window_length': hp.choice('window_length', [15, 21, 27, 33]),
'normalization': hp.choice('normalization', ['Snv', 'No_transformation'])}
if add_hyperparams is not None:
self._hyper_params.update(add_hyperparams)
self._best = None
trials = Trials()
best_params = fmin(fn=self.objective,
space=self._hyper_params,
algo=tpe.suggest, # Tree of Parzen Estimators’ (tpe) which is a Bayesian approach
max_evals=n_iter,
trials=trials,
verbose=1)
@property
def train_data_(self):
return [self._xc, self._ytrain]
@property
def test_data_(self):
return [self._xt, self._ytest]
@property
def pretreated_spectra_(self):
return self.pretreated
@property
def get_params_(self):### This method return the search space where the optimization algorithm will search for optimal subset of hyperparameters
return self._hyper_params
def objective(self, params):
pass
@property
def best_hyperparams_(self): ### This method returns the subset of selected hyperparametes
return self._best
@property
def best_hyperparams_print(self):### This method returns a sentence telling what signal preprocessing method was applied
if self._best['normalization'] == 'Snv':
a = 'Standard Normal Variate (SNV)'
elif self._best['normalization'] == 'No_transformation':
a = " No transformation was performed"
SG = f'- Savitzky-Golay derivative parameters \:(Window_length:{self._best['window_length']}; polynomial order: {self._best['polyorder']}; Derivative order : {self._best['deriv']})'
Norm = f'- Spectral Normalization \: {a}'
return SG+"\n"+Norm
@property
def model_(self): # This method returns the developed model
return self._model
@property
def pred_data_(self): ## this method returns the predicted data in training and testing steps
return self._yc, self._yt
@property
def cv_data_(self): ## Cross validation data
return self._ycv
@property
def CV_results_(self):
return self._cv_df
@property
def important_features_(self):
return self.important_features
@property
def selected_features_(self):
return self._selected_bands
@property
def sel_ratio_(self):
return self._sel_ratio
########################################### PLSR #########################################
class Plsr(Regmodel):
def __init__(self, train, test, n_iter = 10, cv = 3):
super().__init__(train, test, n_iter, nfolds = cv, add_hyperparams = {'n_components': hp.randint('n_components', 1,20)})
### parameters in common
def objective(self, params):
params['n_components'] = int(params['n_components'])
x0 = [self._xc, self._xt]
x1 = [eval(str(params['normalization'])+"(x0[i])") for i in range(2)]
a, b, c = params['deriv'], params['polyorder'], params['window_length']
if a > b or b > c:
if self._best is not None:
a, b, c = self._best['deriv'], self._best['polyorder'], self._best['window_length']
else:
a, b, c = 0, 0, 1
params['deriv'], params['polyorder'], params['window_length'] = a, b, c
x2 = [savgol_filter(x1[i], polyorder=params['polyorder'], deriv=params['deriv'], window_length = params['window_length']) for i in range(2)]
model = PLSRegression(scale = False, n_components = params['n_components'])
folds = KF_CV().CV(x = x2[0], y = np.array(self._ytrain), n_folds = self._nfolds)
yp = KF_CV().cross_val_predictor(model = model, folds = folds, x = x2[0], y = np.array(self._ytrain))
self._cv_df = KF_CV().metrics_cv(y = np.array(self._ytrain), ypcv = yp, folds =folds)[1]
score = self._cv_df.loc["cv",'rmse']
Model = PLSRegression(scale = False, n_components = params['n_components'])
Model.fit(x2[0], self._ytrain)
if self.SCORE > score:
self.SCORE = score
self._ycv = KF_CV().meas_pred_eq(y = np.array(self._ytrain), ypcv=yp, folds=folds)
self._yc = Model.predict(x2[0])
self._yt = Model.predict(x2[1])
self._model = Model
for key,value in params.items():
try: params[key] = int(value)
except (TypeError, ValueError): params[key] = value
self._best = params
self.pretreated = DataFrame(x2[0])
self._sel_ratio = sel_ratio(Model, x2[0])
return score
############################################ iplsr #########################################
class TpeIpls(Regmodel):
def __init__(self, train, test, n_iter = 10, n_intervall = 5, cv = 3):
self.n_intervall = n_intervall
self.n_arrets = self.n_intervall*2
r = {'n_components': hp.randint('n_components', 1,20)}
r.update({f'v{i}': hp.randint(f'v{i}', 0, train[0].shape[1]) for i in range(1,self.n_arrets+1)})
super().__init__(train, test, n_iter, add_hyperparams = r, nfolds = cv)
### parameters in common
def objective(self, params):
### wevelengths index
self.idx = [params[f'v{i}'] for i in range(1,self.n_arrets+1)]
self.idx.sort()
arrays = [np.arange(self.idx[2*i],self.idx[2*i+1]+1) for i in range(self.n_intervall)]
id = np.unique(np.concatenate(arrays, axis=0), axis=0)
### Preprocessing
x0 = [self._xc, self._xt]
x1 = [eval(str(params['normalization'])+"(x0[i])") for i in range(2)]
a, b, c = params['deriv'], params['polyorder'], params['window_length']
if a > b or b > c:
if self._best is not None:
a, b, c = self._best['deriv'], self._best['polyorder'], self._best['window_length']
else:
a, b, c = 0, 0, 1
params['deriv'], params['polyorder'], params['window_length'] = a, b, c
x2 = [savgol_filter(x1[i], polyorder=params['polyorder'], deriv=params['deriv'], window_length = params['window_length']) for i in range(2)]
prepared_data = [x2[i][:,id] for i in range(2)]
### Modelling
folds = KF_CV().CV(x = prepared_data[0], y = np.array(self._ytrain), n_folds = self._nfolds)
try:
model = PLSRegression(scale = False, n_components = params['n_components'])
yp = KF_CV().cross_val_predictor(model = model, folds = folds, x = prepared_data[0], y = np.array(self._ytrain))
self._cv_df = KF_CV().metrics_cv(y = np.array(self._ytrain), ypcv = yp, folds =folds)[1]
except ValueError as ve:
params["n_components"] = 1
model = PLSRegression(scale = False, n_components = params["n_components"])
yp = KF_CV().cross_val_predictor(model = model, folds = folds, x = prepared_data[0], y = np.array(self._ytrain))
self._cv_df = KF_CV().metrics_cv(y = np.array(self._ytrain), ypcv = yp, folds =folds)[1]
score = self._cv_df.loc['cv','rmse']
Model = PLSRegression(scale = False, n_components = model.n_components)
Model.fit(prepared_data[0], self._ytrain)
if self.SCORE > score:
self.SCORE = score
self._ycv = KF_CV().meas_pred_eq(y = np.array(self._ytrain), ypcv=yp, folds=folds)
self._yc = Model.predict(prepared_data[0])
self._yt = Model.predict(prepared_data[1])
self._model = Model
for key,value in params.items():
try: params[key] = int(value)
except (TypeError, ValueError): params[key] = value
self._best = params
self.pretreated = DataFrame(x2[0])
self.segments = arrays
for i in range(len(self.segments)):
self._selected_bands[f'band{i+1}'] = [self.segments[i][0], self.segments[i][self.segments[i].shape[0]-1]]
self._selected_bands.index = ['from','to']
return score
########################################### LWPLSR #########################################
############################################ Pcr #########################################
class Pcr(Regmodel):
def __init__(self, train, test, n_iter = 10, n_val = 5):
super.__init__()
{f'pc{i}': hp.randint(f'pc{i+1}', 0, train[0].shape[1]) for i in range(self.n_val)}