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    I accidentally ran a trial with objective = maximize instead of objective = minimize, can I switch it now and load from the same db so it can use the found sensitivities? I get ValueError: Cannot overwrite study direction from [<StudyDirection.MAXIMIZE: 2>] to [<StudyDirection.MINIMIZE: 1>]. when I try
    I.e. can I just overwrite the study_directions table manually to have minimize
    Philip May
    I would suggest to create a new study with right direction and then enqueue all trials from your wrong study to the new.
    Cool! Will try
    Hi, I am using xgboost 1.4.0 and optuna 2.8.0. I am using a RHEL 7.1 machine that has 64 processors. I am trying to use 60(out of 64) processors both for xgboost and optuna(study.optimize). The code did run for 8 hours and there is no output. Can someone pls help
    # Import data into xgb.DMatrix form
    dtrain = xgb.DMatrix(X_train,label=y_train)
    dtest = xgb.DMatrix(X_test,label=y_test)
    #Set parameters
    #Instantiate the stratified folds
    skfolds = StratifiedKFold(n_splits=n_splits,shuffle=True,random_state=random_state)       
    # define the search space and the objecive function
    def objective(trial):
        # Define the search space
        param_sp = {
            'base_score'            : base_score, 
            'booster'               : booster, 
            'colsample_bytree'      : trial.suggest_discrete_uniform('colsample_bytree',0.7,0.85,0.05),
            'learning_rate'         : trial.suggest_loguniform('learning_rate',0.01,0.1),
            'max_depth'             : trial.suggest_int('max_depth', 4,8,1),
            'objective'             : objective_ml, 
            'scale_pos_weight'      : trial.suggest_uniform('scale_pos_weight',1,100),        
            'subsample'             : trial.suggest_discrete_uniform('subsample',0.5,0.85,0.05),      
            'verbosity'             : verbosity, 
            'tree_method'           : tree_method,            
            'predictor'             : predictor, 
            'eval_metric'           : eval_metric,
            'grow_policy'           : grow_policy        
        #Perform Native API cross validation  
        # Set n_estimators as a trial attribute
        trial.set_user_attr("n_estimators", len(xgb_cv_results))
        #Obtain the number of estimators 
        #Print the params selected in the trial 
        #Create the params set for obtaining the cross validation score 
        'learning_rate': trial.params['learning_rate'], 
        'subsample': trial.params['subsample'],
        'colsample_bytree': trial.params['colsample_bytree'],
        'max_depth': trial.params['max_depth'],
        'scale_pos_weight': trial.params['scale_pos_weight'],
        'grow_policy': grow_policy,    
        # Specific sklearn api variables   
        #Instantiate the XGB Estimator 
        #Obtain the cross validation score - to be used by the trial to rate models
        cv_score=cross_val_score(xgb_estimator, X_train, y_train,scoring='f1',cv=skfolds,n_jobs=parallel_jobs).mean()    
        return cv_score
    #Create the Study
    study = optuna.create_study(study_name='XGB',direction='maximize',sampler=TPESampler(consider_magic_clip=True,seed=random_state,multivariate=True))
    # perform the search
    study.optimize(objective, n_trials=n_trials,n_jobs=parallel_jobs)
    4 replies
    Is there a way to have optuna print the hyperparam importances on plotly to a specific localhost port? right now when i do fig = optuna.visualization.plot_param_importances(study) fig.show(), it chooses localhost:<randomport>
    or at least print the port at which it prints to console, so I can ssh forward it
    I managed to have fig.show output to an svg, so resolved
    Is there a way to set optuna default hyperparams; i.e. optuna/optuna#1855, so at the start of an empty study it runs a trial with those hyperparams which I know are pretty good, and then searches knowing theres some pretty decent point there? would that even help the algorithm
    1 reply
    Hiroyuki Vincent Yamazaki

    As always, thanks for all the feedback and contributions. Weโ€™ve just released v2.9.0 with new features and code refactoring of the TPE sampler.

    ๐Ÿ†• Optuna from the command line without writing Python
    ๐Ÿ†• Weights & Biases integration
    ๐Ÿ”จ TPE sampler refactorings

    Check out the highlights and release notes at
    https://github.com/optuna/optuna/releases/tag/v2.9.0 or with the Tweet https://twitter.com/OptunaAutoML/status/1422086080073854983 .

    Hiroyuki Vincent Yamazaki

    We just released a patch, 2.9.1.

    It includes changes to the Ask-and-Tell CLI subcommands to exclude storage URIs from the logs since those could contain sensitive information. Please consider upgrading if you are using these subcommands.

    Sree Aurovindh Viswanathan
    I am running Optuna on Pytorch DDP with Mulitple GPUs. When i just use one GPU/ rank=0, the code runs fine. When I try with multi gpu, the code stops/freezes at trial.report(loss,epoch) . I am using https://github.com/optuna/optuna-examples/blob/main/pytorch/pytorch_distributed_simple.py#L41 as example. Any comments on how i can debug in my situation.
    1 reply
    Is there a way to use Optuna to minimize parameters? (Parameters, referring to coefficients of the network, not hyperparameters)
    1 reply
    Miguel Crispim Romao
    Hi all. Could you tell me what is the computational complexity of NSGAIISampler? I know that TPE is O(n^3) as any other GP, but I'm witnessing a massive slowdown as one approaches de 1000 trials with NSGAIISampler and I don't know why. Cheers!
    2 replies
    Zahra Taheri
    Hi everyone.
    My question is about using TPE sampler with Hyperband pruner from Optuna:
    Q1: What is the exact relation between studyโ€™s trials and brackets of Hyperband pruner? For example, if the size of a bracket is 10 (i.e. it contains 10 hyperparameter configurations), how many trials are executed for this bracket? Does Optuna consider a trial for each hyperparameter configuration?
    Q2: We set the number of epochs in training a neural network as the maximum resource of the Hyperband pruner. Based on the maximum resource value, are the number of epochs for training in each trial different?
    Q3: Does Optuna use maximum resource for the first and a few beginning Trials?
    Miguel Crispim Romao

    Hi all. Could you tell me what is the computational complexity of NSGAIISampler? I know that TPE is O(n^3) as any other GP, but I'm witnessing a massive slowdown as one approaches de 1000 trials with NSGAIISampler and I don't know why. Cheers!

    I wonder if there's any reason why the trials become progressively slower? This has been observed with RandomSampler too

    1 reply
    Hello Everyone, I am trying to use Optuna in a project which uses both Pytorch Lightning (for code organisation) anf MLFlow (for logs tracking). So, out of 10 trials only last trail is getting stored my MLFLow. But ideally I expect it to store all the trials. Has someone ever done this before? It would be really helpful if someone can provide some inputs.
    3 replies
    Screenshot 2021-08-30 at 06.59.07.pngI'm trying to understand the new format for the pareto plot with 2 objectives (v2.9.1). In the example, there are two very pale points at about (0.015, 0.195). (N.B. vertical axis is minimised, horizontal is maximised.) These are obviously on the pareto front and, for my example I would class them as the best points. How is Optuna determining which point is best on a pareto plot as, objectively, every point on the front is potentially the best? Also, what do the blue and red coloured vertical bars represent? Is there any documentation?
    2 replies
    Hello Everyone! I am new to contributing to Optuna. The issue #1618 requires to add comments but list of files in the thread already have the appropriate Raises comments present in them. Could anyone help me please? Thank you
    4 replies
    Ali Kayhan Atay
    hey everyone, can someone explain me how can i supply LightGBMPruningCallback in to the LightGBMTunerCV ? i couldnt figure out since callback expects trials Thank you
    2 replies
    Basil Kraft

    Hi everyone! After having successfully tuned HPs using optuna, I want to run 10 different trials using the best HPs, each with a different configuration. I manipulate the best trial and add it to the study like this:

    best_trial = study.best_trial
    for fold in range(10):
        cv_trial = best_trial.copy()
        cv_trial.params['fold'] = fold

    Now, if I call this in parallel, the same folds get evaluated multiple times. Is there an elegant way to deal with this, i.e., to only run each trial/fold once?

    3 replies
    Adrien RUAULT
    Hi everyone! I was wondering if it was possible to control the level of exploration of the TPESampler. Indeed I am running a 100 trials including 50 random trial. My issue is that after the 50th trial, the Sampler almost stops exploring. It always stays in the same range of hyperparameters. From my understanding of baysian optimization it should be possible to control the balance between exploitation and exploration. However I don't know if that is possible with TPE. Do you have any insihgts? :heart:
    2 replies
    Hello Everyone :) I am looking for a way to test multiple kernel initializers and how well they work. These kernel initializers do not only have different names, but do have different parameters as well. What is the best way to optimiz those kinds of problems? Is it possible to create nested trial-parameters? Thank you
    3 replies
    Crissman Loomis

    As always, thanks for all the feedback and contributions. Weโ€™ve just released v2.10.0 with new features and code refactoring.

    ๐Ÿ†• CLI tools for listing Optuna trials
    ๐Ÿ†• Multi-objective optimization support of Weights & Biases and MLflow integration

    Check out the highlights and release notes at
    https://github.com/optuna/optuna/releases/tag/v2.10.0 or with the Tweet https://twitter.com/OptunaAutoML/status/1444915450937176064.

    SungJun Cho
    Hello all! I was wondering how the parallelization is implemented in Bayesian optimization (BO) which uses samplers like TPE (and GP-EI). Optuna supports process-based optimization (link), and it seems like the trial at one node or GPU has to finish in order to run the trial at another node or GPU. I believe this is compatible to BO, since the result of previous trial is necessary to make predictions about the next sampling point, but I was not sure what advantages parallelization gives over using one computing source if trials are computed serially. Thank you!
    4 replies
    Anurag Gupta
    Hello everyone!
    I am new to optuna, and was looking to find some examples to implement unsupervised ML algorithms using optuna for their hyperparameter tuning, (Kmeans, etc), but I couldn't find any online. Can anyone help me with any link or their own example?
    6 replies
    Paras Koundal
    Hi, I am working with Graph Neural Networks. Was trying to use optuna to improve. However, I get the error Trial 0 failed, because the value None could not be cast to float. The only reply to this issue on github doesn't help
    1 reply

    Hi there =)

    I have been using Optuna for a bit (great tool big thanks!) - just started trying to combine it with Hydra using the Optuna pluging (https://hydra.cc/docs/next/plugins/optuna_sweeper/) - working well and succesfully tested storing results in an sqlite database. However I am struggling to get the Optuna-Dashboard to load results / to see the sqlite service - to note as well the python command study = optuna.load_study("test_study","sqlite:///test_study.db") raises a KeyError(NOT_FOUND_MSG) error - despite sqlite:///test_study.db storage target being sucessfully used by the Optuna Sweeper plugin...

    I am running this all in a docker container with a python:3.9.7-slim base...

    any thoughts / advice / direction for next step(s) would be greatly appreciated!!

    Hi. Is it possible in Optuna to report multiple metrics and do optimization only on one? I've been looking trough docs up and down but cannot find it.
    Or is the only option to use MLFlow?
    Oh, I see it's possible with trial.set_user_attr!