BayesianOptimization | Python implementation of global optimization | Computer Vision library
kandi X-RAY | BayesianOptimization Summary
kandi X-RAY | BayesianOptimization Summary
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Top functions reviewed by kandi - BETA
- Updates the timeline
- Return the header for the table
- Determines if a new instance is greater than the current value
- Format key
- Optimized Bayesian Optimization
- Prime the queue
- Sample from the bounds
- Optimize the dispatcher
- Transform the bounding box
- Trim bounds
- Update the parameters
- Update the tracker
- Update tracker
- Calculate time metrics
- Evaluate the function with the given parameters
- Evaluate the constraint function
- Register new data point
- Run an optimizer
- Black box function
- Optimization using Bayesian Optimization
- Cross - validation
- Returns the maximum value for the target function
- Return whether the given constraint values are allowed
- Handles request
- Return the constraints
- Generate classification data
BayesianOptimization Key Features
BayesianOptimization Examples and Code Snippets
Lines of Code : 50
License : No License
Copydf_user_register.sample(10)
>des_user_register= df_user_register.describe(include="all")
>user_register_log = ["user_id", "register_day", "register_type", "device_type"]
>dtype_user_register = {"user_id": np.uint32, "register_day": np.uint from bayes_opt import BayesianOptimization
# Bounded region of parameter space
pbounds = {'x': (2, 4), 'y': (-3, 3)}
optimizer = BayesianOptimization(
f=black_box_function,
pbounds=pbounds,
random_state=1,
)
optimizer.maximize(
ini optimizer.probe(
params={"x": 0.5, "y": 0.7},
lazy=True,
)
optimizer.probe(
params=[-0.3, 0.1],
lazy=True,
)
# Will probe only the two points specified above
optimizer.maximize(init_points=0, n_iter=0)
| iter | target | import time
import random
from bayes_opt import BayesianOptimization
from bayes_opt.util import UtilityFunction, Colours
import asyncio
import threading
try:
import json
import tornado.ioloop
import tornado.httpserver
from tornado. from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier as RFC
from sklearn.svm import SVC
from bayes_opt import BayesianOptimization
from bayes_opt.uti import numpy as np
from bayes_opt import BayesianOptimization
from bayes_opt import UtilityFunction
def f(x):
return np.exp(-(x - 2) ** 2) + np.exp(-(x - 6) ** 2 / 10) + 1/ (x ** 2 + 1)
if __name__ == '__main__':
optimizer = BayesianOptim Community Discussions
Trending Discussions on BayesianOptimization
QUESTION
I want to optimize my HPO of my lightgbm model. I used a Bayesian Optimization process to do so. Sadly my algorithm fails to converge.
MRE
...ANSWER
Answered 2022-Mar-21 at 22:34This is related to a change in scipy 1.8.0,
One should use -np.squeeze(res.fun) instead of -res.fun[0]
https://github.com/fmfn/BayesianOptimization/issues/300
The comments in the bug report indicate reverting to scipy 1.7.0 fixes this,
It seems the fix is been proposed in the BayesianOptimization package: https://github.com/fmfn/BayesianOptimization/pull/303
But this has not been merged and released yet, so you could either:
- fall back to scipy 1.7.0
- use the forked github version of BayesianOptimization with the patch (https://github.com/samFarrellDay/BayesianOptimization)
- apply the patch in issue 303 manually on your system
QUESTION
I want to apply regression to my dataset. I am currently trying to optimize an XGBRegressor using the BayesianOptimization, but every time I run it I get the same error. I am not very familiar with machine earning, so I would really appreciate any help I can get. Here is the code :
...ANSWER
Answered 2021-Nov-18 at 14:48The GPyOpt package specifies the hyperparameter space in a more verbose (and so more flexible?) way than other popular search methods. There are examples in the documentation: https://gpyopt.readthedocs.io/en/latest/GPyOpt.core.task.html#GPyOpt.core.task.space.Design_space
QUESTION
from sklearn import ensemble
from sklearn import linear_model
def build_model(hp):
model_type = hp.Choice('model_type', ['random_forest', 'ridge'])
if model_type == 'random_forest':
with hp.conditional_scope('model_type', 'random_forest'):
model = ensemble.RandomForestClassifier(
n_estimators=hp.Int('n_estimators', 10, 50, step=10),
max_depth=hp.Int('max_depth', 3, 10))
elif model_type == 'ridge':
with hp.conditional_scope('model_type', 'ridge'):
model = linear_model.RidgeClassifier(
alpha=hp.Float('alpha', 1e-3, 1, sampling='log'))
else:
raise ValueError('Unrecognized model_type')
return model
tuner = kt.tuners.Sklearn(
oracle=kt.oracles.BayesianOptimization(
objective=kt.Objective('score', 'max'),
max_trials=10),
hypermodel=build_model,
directory=".")
X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(
X, y, test_size=0.2)
tuner.search(X_train, y_train)
best_model = tuner.get_best_models(num_models=1)[0]
ANSWER
Answered 2021-Sep-10 at 17:18Adding
import sklearn.pipeline would temporarily fix the problem.
This is a very recent issue and will be fixed in the next release.
You can find more about it here https://github.com/keras-team/keras-tuner/issues/600
QUESTION
I am working with R. I am trying to follow this tutorial over here on function optimization: https://rpubs.com/Argaadya/bayesian-optimization
For this example, I first generate some random data:
...ANSWER
Answered 2021-Jul-07 at 23:48There appear to be a few bugs in your code, e.g. I don't think your fitness function was returning data in the required format and some of your vectors were being used before they were defined.
I made some changes so your code was more inline with the tutorial, and it seems to complete without error, but I can't say whether the outcome is "correct" or whether it will be suitable for your use-case:
QUESTION
I am trying to optimize the hyperparameters of a LSTM with Bayesian Optimization. But I received the error message TypeError: only integer scalar arrays can be converted to a scalar index when I run the code. A solution I found is to convert the training data and validation data into arrays, but in my code they are already arrays not lists. Or convert them into tuples but I cannot see how I would do this
X_train shape: (946, 60, 1)
y_train shape: (946,)
X_val shape: (192, 60, 1)
y_val shape: (192,)
...ANSWER
Answered 2021-May-06 at 12:25Your code should look like:
QUESTION
I am trying to setup a Keras tuner to simultaneously tune both the number of layers and the activation function. The network attempts to warp a 2D function into another 2D function. I keep getting the error:
...ANSWER
Answered 2021-Mar-19 at 18:37Complete code which works for me:
QUESTION
I'm getting following error and I'm not able to figure out why:
RuntimeError: Model-building function did not return a valid Keras Model instance, found (, )
I have read the answers here and here which seem to telling to import keras from tensorflow instead of stand alone keras which I'm doing but still getting the error. I would very much appreciate your help in figuring this out. Below is my entire code:
ANSWER
Answered 2021-Feb-21 at 09:13RuntimeError: Model-building function did not return a valid Keras Model instance, found (, )
(, )
As you can see this a tuple of two Keras Model instance. This is output of create_autoencoder(hp, input_dim, output_dim).
QUESTION
I am using Keras tuner's BayesianOptimization to search for the optimum hyper parameters of a model, I am also using the TensorBoard callback with it to visualise the performance of each model/trial.
However, the trials from the Tuner are named/labelled weirdly (e.g. trial_1dc4838863f2e4e8a84f0e415ee1db33). Is there a way that I can have the Tuner to name the trials only as "trial_1", "trial_2", etc.? Instead of all the numbers and letters that follow it?
I couldn't find anywhere in the Keras documentations how to do it or if there's an argument for it when creating the Tuner instance.
ANSWER
Answered 2021-Feb-16 at 12:36I was able to solve this by overriding the BayesianOptimization and BayesianOptimizationOracle classes. It just names each trial "0", "1", "2", etc.
But it would be nice if this was more flexible, because I will probably end up doing this for the other hypertuner methods. as well.
QUESTION
We are running the Bayesian Optimizer for hyper parameter tuning. By the way, I get this error. The same error occurs even if you experiment with changing all of the parameter ranges. Please answer what should be done.
...ANSWER
Answered 2020-Oct-06 at 18:06I know nothing of this Bayesian stuff, but in box bounded optimization it is a no-no to provide lower bounds greater than upper bounds:
‘gamma': (1, 0.01),
Not sure if this is your issue but it took me all of 7 seconds to see it.
QUESTION
I am trying this (apologies this is not reproducible but hopefully someone can help please):
...ANSWER
Answered 2020-Jun-25 at 12:28The error handling is done by an error function, not by finally, see Exceptions handling.
The following loop runs as expected:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install BayesianOptimization
All we need to get started is to instantiate a BayesianOptimization object specifying a function to be optimized f, and its parameters with their corresponding bounds, pbounds. This is a constrained optimization technique, so you must specify the minimum and maximum values that can be probed for each parameter in order for it to work. The BayesianOptimization object will work out of the box without much tuning needed. The main method you should be aware of is maximize, which does exactly what you think it does. There are many parameters you can pass to maximize, nonetheless, the most important ones are: - n_iter: How many steps of bayesian optimization you want to perform. The more steps the more likely to find a good maximum you are. - init_points: How many steps of random exploration you want to perform. Random exploration can help by diversifying the exploration space. The best combination of parameters and target value found can be accessed via the property optimizer.max. While the list of all parameters probed and their corresponding target values is available via the property optimizer.res.
The latest release can be obtained by two ways:.
With PyPI (pip): pip install bayesian-optimization
With conda (from conda-forge channel): conda install -c conda-forge bayesian-optimization
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