sklearn pipeline

from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB import numpy as np import scipy.linalg from sklearn.preprocessing import LabelEncoder, StandardScaler import optuna import scipy.linalg from sklearn.linear_model import BayesianRidge import pandas as pd from sklearn.model_selection import LeaveOneOut, cross_val_score class EmscScaler(object): def __init__(self, order=1): self.order = order self._mx = None def mlr(self, x, y): """Multiple linear regression fit of the columns of matrix x (dependent variables) to constituent vector y (independent variables) order - order of a smoothing polynomial, which can be included in the set of independent variables. If order is not specified, no background will be included. b - fit coeffs f - fit result (m x 1 column vector) r - residual (m x 1 column vector) """ if self.order > 0: s = np.ones((len(y), 1)) for j in range(self.order): s = np.concatenate((s, (np.arange(0, 1 + (1.0 / (len(y) - 1)), 1.0 / (len(y) - 1)) ** j).reshape(-1,1)[0:len(y)]),1) X = np.concatenate((x.reshape(-1,1), s), 1) else: X = x # calc fit b=fit coefficients b = np.dot(np.dot(scipy.linalg.pinv(np.dot(X.T, X)), X.T), y) f = np.dot(X, b) r = y - f return b, f, r def fit(self, X, y=None): """fit to X (get average spectrum), y is a passthrough for pipeline compatibility""" self._mx = np.mean(X, axis=0) def transform(self, X, y=None, copy=None): if type(self._mx) == type(None): print("EMSC not fit yet. run .fit method on reference spectra") else: # do fitting corr = np.zeros(X.shape) for i in range(len(X)): b, f, r = self.mlr(self._mx, X[i, :]) corr[i, :] = np.reshape((r / b[0]) + self._mx, (corr.shape[1],)) return corr def fit_transform(self, X, y=None): self.fit(X) return self.transform(X) from sklearn.base import BaseEstimator, TransformerMixin class SpectraPreprocessor(BaseEstimator, TransformerMixin): def __init__(self, emsc_order=3,X_ref=None): self.emsc_order = emsc_order self.emsc_scalers = [EmscScaler(order=emsc_order) for _ in range(4)] self.X_ref = X_ref def fit(self, X, y=None): X_ref = self.X_ref if X_ref is None: X_ref = X.copy() # Define the column ranges for each segment ranges = [(0, 251), (281, 482), (482, 683), (683, 854)] # Fit EmscScaler for each segment for i, (start, end) in enumerate(ranges): self.emsc_scalers[i].fit(X_ref[:, start:end]) return self def transform(self, X, y=None): # Define the column ranges for each segment ranges = [(0, 251), (281, 482), (482, 683), (683, 854)] # Transform each segment transformed_segments = [] for i, (start, end) in enumerate(ranges): segment = X[:, start:end] transformed_segment = self.emsc_scalers[i].transform(segment) transformed_segments.append(transformed_segment) # Concatenate all transformed segments return np.concatenate(transformed_segments, axis=1) def fit_transform(self, X, y=None): self.fit(X) return self.transform(X) def bayesian_ridge_optuna_for_emsc_data(x_train, y_train, pipeline_): def objective(trial): try: alpha_1 = trial.suggest_float('alpha_1', 0.001, 1, log=True) alpha_2 = trial.suggest_float('alpha_2', 0.001, 1, log=True) lambda_1 = trial.suggest_float('lambda_1', 0.001, 1, log=True) lambda_2 = trial.suggest_float('lambda_2', 0.001, 1, log=True) model = pipeline_.set_params( bayesian_ridge__alpha_1=alpha_1, bayesian_ridge__alpha_2=alpha_2, bayesian_ridge__lambda_1=lambda_1, bayesian_ridge__lambda_2=lambda_2 ) model.fit(x_train, y_train) score = cross_val_score(model, x_train, y_train, cv=10, n_jobs=-1, scoring='r2') return np.mean(score) except ValueError as e: return -np.inf optuna.logging.set_verbosity(optuna.logging.WARNING) pruner = optuna.pruners.MedianPruner() study = optuna.create_study(direction="maximize", pruner=pruner) study.optimize(objective, n_trials=500, show_progress_bar=True, n_jobs=1) return study.best_params def getdata(filenamex, filenamey): x = pd.read_csv(filenamex, header=None) y = pd.read_csv(filenamey) data = pd.concat([x, y], axis=1) return data name = 'test' x, y = np.random.rand(100,884), np.random.rand(100) x_ref = np.random.rand(30,884) pipeline = Pipeline([ ('preprocessor', SpectraPreprocessor(emsc_order=3, X_ref=None)), ('scaler', StandardScaler()), ('bayesian_ridge', BayesianRidge()) ]) pipeline.set_params(preprocessor__X_ref=x_ref) ############################################################################################################################################################ best_params = bayesian_ridge_optuna_for_emsc_data(x, y, pipeline) ############################################################################################################################################################ pipeline.set_params( bayesian_ridge__alpha_1=best_params['alpha_1'], bayesian_ridge__alpha_2=best_params['alpha_2'], bayesian_ridge__lambda_1=best_params['lambda_1'], bayesian_ridge__lambda_2=best_params['lambda_2'] ) pipeline.fit(x, y) y_pred = pipeline.predict(x) print(y_pred)