optuna | A hyperparameter optimization framework | Machine Learning library
kandi X-RAY | optuna Summary
kandi X-RAY | optuna Summary
Website | Docs | Install Guide | Tutorial. Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API. Thanks to our define-by-run API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters.
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Top functions reviewed by kandi - BETA
- Optimize the study using the given function
- Optimizes an objective function
- Run a single trial
- Optimizes the objective function
- Plot the empirical distribution function
- Return True if the direction is a multi - objective
- Check that the arguments passed to the plot
- Helper function for _EDFInfo
- Mark a function as deprecated
- Adds a trial to the session
- Plot intermediate values
- Run tool
- Return trials data as a pandas DataFrame
- Plot parameter importances
- Plot the optimization history
- Plot a slice plot
- Plot a parallel coordinate
- Transform a search space into a numpy array
- Enqueues a trial
- Plot a study contour
- Create a study ask
- Plot the pareto front
- Upgrade the database
- Mark a class as deprecated
- Return set of requirements required to download
- Sample relative to study
optuna Key Features
optuna Examples and Code Snippets
Lines of Code : 140License : Permissive (MIT)
Copyfrom itertools import chain
from more_itertools import pairwise
from torch import nn
class MLP(nn.Sequential):
def __init__(self, dims: list[int]):
super().__init__(chain.from_iterable(
(
nn.Linear(in_feature !TF_CPP_MIN_LOG_LEVEL=3 xagents tune ppo --env PongNoFrameskip-v4 --study ppo-carnival --storage sqlite:///ppo-carnival.db --trial-steps 500000 --n-trials 100 --warmup-trials 3 --preprocess --n-envs 16 32 --lr 1e-5 1e-2 --opt-epsilon 1e-7 1e-4 --gamm !TF_CPP_MIN_LOG_LEVEL=3 xagents tune ppo --env PongNoFrameskip-v4 --study ppo-carnival --storage sqlite:///ppo-carnival.db --trial-steps 500000 --n-trials 100 --warmup-trials 3 --preprocess --n-envs 16 32 --lr 1e-5 1e-2 --opt-epsilon 1e-7 1e-4 --gamm import argparse
import os
import pprint
import gym
import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter
from tianshou.data import Collector, VectorReplayBuffer
from tianshou.env import DummyVectorEnv, SubprocVectorEnv
f defaults:
- datasets: data
- models: Ets
- search_spaces@hydra.sweeper.search_space: Ets
- override hydra/sweeper: optuna
- override hydra/sweeper/sampler: tpe
hydra:
run:
dir: data/outputs/${now:%Y-%m-%d}/${user.user}/${now: def forward(self, x):
x = self.linear1(x)
x = self.bn1(x)
x = self.activation(x)
x = self.linear2(x)
return x
class XGBoostQuantileRegressor(XGBRegressor):
def __init__(self, quant_alpha, max_depth=3, **kwargs):
self.quant_alpha = quant_alpha
super().__init__(max_depth=max_depth, **kwargs)
# other methods unchanged and omi1. Create model with features n. 2. Measure model's objective or accuracy for example. 3. Save accuracy, and features used. 4. If number of features is only 30 goto step 8. 5. Get feature importance. 6. Drop the feature with lowest value 7. Goto
Lines of Code : 175License : Strong Copyleft (CC BY-SA 4.0)Copy"""
References:
https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
https://github.com/DTUWindEnergy/PyWake
"""
import time
from py_wake.examples.data.hornsrev1 import V80
from py_wake.examples.dattuner = lgb.LightGBMTunerCV({"objective": "binary", 'verbose': -1},
train_set=test_dataset, num_boost_round=10,
nfold=5, stratified=True, shuffle=True, verbose_eval=None)
Community Discussions
Trending Discussions on optuna
QUESTION
I want to hyper-parameter optimize multiple time series forecasting models on the same data. I'm using the Optuna Sweeper plugin for Hydra. The different models have different hyper-parameters and therefore different search spaces. At the moment my config file looks like this:
...ANSWER
Answered 2022-Mar-07 at 19:54Here are two options:
- Use a
@packagedirective - Use a variable interpolation
In detail:
Using an@package directive
An @package directive can be used to place Ets.yaml in the hydra.sweeper.search_space package:
QUESTION
I would like to do a neural network for regression analysis using optuna based on this site. I would like to create a model with two 1D data as input and one 1D data as output in batch learning.
x is the training data and y is the teacher data.
ANSWER
Answered 2022-Mar-06 at 12:00With PyTorch, when you call y_pred = model(x) that will call the forward function which is defined in the Model class.
So, y_pred will get the result of the forward function, in your case, it returns nothing, that's why you get a None value. You can change the forward function as below:
QUESTION
I want to build a quantile regressor based on XGBRegressor, the scikit-learn wrapper class for XGBoost. I have the following two versions: the second version is simply trimmed from the first one, but it no longer works.
I am wondering why I need to put every parameters of XGBRegressor in its child class's initialization? What if I just want to take all the default parameter values except for max_depth?
(My XGBoost is of version 1.4.2.)
No.1 the full version that works as expected:
...ANSWER
Answered 2021-Dec-26 at 11:58I am not an expert with scikit-learn but it seems that one of the requirements of various objects used by this framework is that they can be cloned by calling the sklearn.base.clone method. This appears to be something that the existing XGBRegressor class does, so is something your subclass of XGBRegressor must also do.
What may help is to pass any other unexpected keyword arguments as a **kwargs parameter. In your constructor, kwargs will contain a dict of all of the other keyword parameters that weren't assigned to other constructor parameters. You can pass this dict of parameters on to the call to the superclass constructor by referring to them as **kwargs again: this will cause Python to expand them out:
QUESTION
if I tune a model with the LightGBMTunerCV I always get this massive result of the cv_agg's binary_logloss. If I do this with a bigger dataset, this (unnecessary) io slows down the performance of the optimization process.
Here is the code:
...ANSWER
Answered 2021-Nov-29 at 23:38You can pass verbose_eval parameter with value None in LightGBMTunerCV().
Example:
QUESTION
I am tuning the word2vec model hyper-parameters. Word2Vec has to many log in console that I cannot read Optuna or my custom log. Is there any trick to suppress logs generated by Word2Vec?
...ANSWER
Answered 2021-Nov-19 at 22:09Gensim's classes generally only log if you specifically turn it on, in your code, by setting either a global or module/class-specific logging level.
So, are you sure you didn't turn on more logging that you want?
Search your code for anything that sets an INFO or DEBUG level of logging - and either delete or adjust/narrow that line to either not enable, or to set a more restrictie level, on the word2vec module or Word2Vec class.
QUESTION
I am just curious for more information on what an intermediate step actually is and how to use pruning if you're using a different ml library that isn't in the tutorial section eg) XGB, Pytorch etc.
For example:
...ANSWER
Answered 2021-Nov-17 at 03:18The basic model creation can be done by passing a complete training datasets once. But there are models that can still be improved (an increase in accuracy) by re-training again on the same training datasets.
To see to it that we are not wasting resources here, we would check the accuracy after every step using the validation datasets via intermediate_score if accuracy improves, if not we prune the whole trial skipping other steps. Then we go for next trial asking another value of alpha - the hyperparameter that we are trying to determine to have the greatest accuracy on the validation datasets.
For other libraries, it is just a matter of asking ourselves what do we want with our model, accuracy for sure is a good criteria to measure the model's competency. There can be others.
Example optuna pruning, I want the model to continue re-training but only at my specific conditions. If intermediate value cannot defeat my best_accuracy and if steps are already more than half of my max iteration then prune this trial.
QUESTION
I am using optuna to tune xgboost model's hyperparameters. I find it stuck at trial 2 (trial_id=3) for a long time(244 minutes). But When I look at the SQLite database which records the trial data, I find all the trial 2 (trial_id=3) hyperparameters has been calculated except the mean squared error value of trial 2. And the optuna trial 2 (trial_id=3) seems stuck at that step. I want to know why this happened? And how to fix the issue?
Here is the code
...ANSWER
Answered 2021-Nov-16 at 20:09Although I am not 100% sure, I think I know what happened.
This issue happens because some parameters are not suitable for certain booster type and the trial will return nan as result and be stuck at the step - calculating the MSE score.
To solve the problem, you just need to delete the "booster": "dart".
In other words, using "booster": trial.suggest_categorical("booster", ["gbtree", "gblinear"]), rather than "booster": trial.suggest_categorical("booster", ["gbtree", "gblinear", "dart"]), can solve the problem.
I got the idea when I tuned my LightGBMRegressor Model. I found many trials fail because these trials returned nan and they all used the same "boosting_type"="rf". So I deleted the rf and all 100 trials were completed without any error. Then I looked for the XGBRegressor issue which I posted above. I found all the trials which were stuck had the same "booster":"dart" either. So I deleted the dart, and the XGBRegressor run normally.
QUESTION
I'm trying to optimize a custom model (no fancy ML whatsoever) that has 13 parameters, 12 of which I know to be normally distributed. I've gotten decent results using the hyperopt library:
ANSWER
Answered 2021-Nov-11 at 22:46You can cheat optuna by using uniform distribution and transforming it into normal distribution. To do that one of the method is inversed error function implemented in scipy.
Function takes uniform distribution from in range <-1, 1> and converts it to standard normal distribution
QUESTION
I would like to use [OPTUNA][1] with sklearn [MLPRegressor][1] model.
For almost all hyperparameters it is quite straightforward how to set OPTUNA for them.
For example, to set the learning rate:
learning_rate_init = trial.suggest_float('learning_rate_init ',0.0001, 0.1001, step=0.005)
My problem is how to set it for hidden_layer_sizes since it is a tuple. So let's say I would like to have two hidden layers where the first will have 100 neurons and the second will have 50 neurons. Without OPTUNA I would do:
MLPRegressor( hidden_layer_sizes =(100,50))
But what if I want OPTUNA to try different neurons in each layer? e.g., from 100 to 500, how can I set it? the MLPRegressor expects a tuple
ANSWER
Answered 2021-Nov-11 at 17:05You could set up your objective function as follows:
QUESTION
I am working on a multiclass classification problem with 3 (1, 2, 3) classes being perfectly distributed. (70 instances of each class resulting in (210, 8) dataframe).
Now my data has all the 3 classes distributed in order i.e first 70 instances are class1, next 70 instances are class 2 and last 70 instances are class 3. I know that this kind of distribution will lead to good score on train set but poor score on test set as the test set has classes that the model has not seen. So I used stratify parameter in train_test_split. Below is my code:-
ANSWER
Answered 2021-Nov-05 at 17:38Finally got the answer. The dataset I was using was the issue. The dataset was tailor made for knn algorithm and that was why I was getting perfect scores as I was using the same algorithm.
I got came to this conclusion after I performed a clustering exercise on this dataset and the K-Means algorithm perfectly predicted the clusters.
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