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def _extend_prior_with_posterior_data(self, x,y): | |
self.x_init = np.append(self.x_init, np.array([x]), axis = 0) | |
self.y_init = np.append(self.y_init, np.array(y), axis = 0) | |
def optimize(self): | |
y_max_ind = np.argmax(self.y_init) | |
y_max = self.y_init[y_max_ind] | |
optimal_x = self.x_init[y_max_ind] | |
optimal_ei = None | |
for i in range(self.n_iter): |
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def _acquisition_function(self, x): | |
return -self._get_expected_improvement(x) | |
def _get_next_probable_point(self): | |
min_ei = float(sys.maxsize) | |
x_optimal = None | |
# Trial with an array of random data points | |
for x_start in (np.random.random((self.batch_size,self.x_init.shape[1])) * self.scale): |
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def _get_expected_improvement(self, x_new): | |
# Using estimate from Gaussian surrogate instead of actual function for | |
# a new trial data point to avoid cost | |
mean_y_new, sigma_y_new = self.gauss_pr.predict(np.array([x_new]), return_std=True) | |
sigma_y_new = sigma_y_new.reshape(-1,1) | |
if sigma_y_new == 0.0: | |
return 0.0 | |
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from sklearn.gaussian_process import GaussianProcessRegressor | |
from scipy.stats import norm | |
from scipy.optimize import minimize | |
import sys | |
import pandas as pd | |
class BayesianOptimizer(): | |
def __init__(self, target_func, x_init, y_init, n_iter, scale, batch_size): | |
self.x_init = x_init |
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import numpy as np | |
def costly_function(x): | |
total = np.array([]) | |
for x_i in x: | |
total = np.append(total, np.sum(np.exp(-(x_i - 5) ** 2))) | |
return total + np.random.randn() |
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import seaborn as sns | |
plt.figure(figsize=(18,5)) | |
sns.lineplot(data=pd.DataFrame({'Predicted':result,'Actual':Y_test.to_numpy()})) |
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result = model.predict(start=1, end=125, exog=X_test.to_numpy().astype(float)) |
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from statsmodels.tsa.statespace.sarimax import SARIMAX | |
model = SARIMAX(endog=Y_train.to_numpy(), exog=X_train.to_numpy().astype(float), | |
order=(0, 0, 0),seasonal_order=(2, 0, 1, 2)) | |
model = model.fit(disp=False) | |
model.summary() |
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from pmdarima.arima import auto_arima | |
auto_model = auto_arima(Y_train.to_numpy(), exogenous=X_train.to_numpy(), m=2, seasonal=True, | |
suppress_warnings = True, | |
step_wise=True, trace=True) | |
auto_model.summary() |
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from sklearn.model_selection import TimeSeriesSplit | |
tss = TimeSeriesSplit() | |
for train_index, test_index in tss.split(X,Y): | |
Y_train, Y_test = Y.iloc[train_index], Y.iloc[test_index] | |
X_train, X_test = X.iloc[train_index], X.iloc[test_index] |
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