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Functions2,053 in github.com/COLA-Laboratory/TransOPT

⨍ Functions 2,053◇ Types & classes 361↳ Endpoints 19
↓ 94 callersFunction_hparam
Define a hyperparameter. random_val_fn takes a RandomState and returns a random hyperparameter value.
transopt/benchmark/HPOOOD/hparams_registry.py:18
↓ 93 callersMethodlog
Log the message to the console.
transopt/agent/chat/openai_chat.py:48
↓ 85 callersMethodsum
make a new rx from all the rxs' vals
transopt/utils/sk.py:211
↓ 77 callersMethodadd
Add a multidimensional vector to the cache. Parameters: ----------- key: any hashable A unique identifie
transopt/datamanager/lsh.py:31
↓ 74 callersMethodparameters
(self)
transopt/benchmark/HPOOOD/misc.py:444
↓ 68 callersMethodget
(self, name)
transopt/agent/registry.py:20
↓ 47 callersMethodeval
(self)
transopt/benchmark/HPOOOD/algorithms.py:1596
↓ 36 callersMethod__init__
(self, input_shape, num_classes, num_domains, hparams)
transopt/benchmark/HPOOOD/algorithms.py:1040
↓ 36 callersMethodfit
( self, X: np.ndarray, Y: np.ndarray, optimize: bool = True, )
transopt/optimizer/model/rf.py:63
↓ 33 callersMethodpredict
( self, X, return_full: bool = False, with_noise: bool = False )
transopt/optimizer/model/rf.py:82
↓ 31 callersMethodclose
(self)
transopt/datamanager/database.py:99
↓ 27 callersMethodinverse_transform
(self, X = None, Y = None)
transopt/optimizer/normalizer/standerd.py:45
↓ 27 callersMethodload
Loads data from data directory as defined in config_file.data_directory
transopt/utils/openml_data_manager.py:136
↓ 24 callersMethodexecute
( self, query, params=None, fetchone=False, fetchall=False, ti
transopt/datamanager/database.py:127
↓ 23 callersMethodplot
(self)
transopt/utils/Prior.py:28
↓ 23 callersMethodwrite
pretty write set of treatments
transopt/utils/sk.py:228
↓ 22 callersMethod__init__
( self, task_name, budget_type, budget, seed, workload, **kwargs )
transopt/benchmark/synthetic/synthetic_problems.py:1006
↓ 22 callersMethodlist_names
(self)
transopt/agent/registry.py:23
↓ 21 callersMethodget_curname
(self)
transopt/benchmark/problem_base/transfer_problem.py:70
↓ 19 callersMethod__init__
(self, root, test_envs, hparams)
transopt/benchmark/HPOOOD/ooddatasets.py:206
↓ 18 callersMethodtransform
(self, X = None, Y = None)
transopt/optimizer/normalizer/standerd.py:38
↓ 15 callersMethoddevice
(self)
transopt/benchmark/HPOOOD/misc.py:362
↓ 15 callersMethodf
(self,X, indexs)
transopt/benchmark/HPOB/HpobBench.py:68
↓ 14 callersMethodquery_dataset_info
Query the dataset information of a given table.
transopt/datamanager/database.py:348
↓ 14 callersMethodregister
(self, name=None, cls=None, **kwargs)
transopt/agent/registry.py:5
↓ 13 callersMethodget
(self)
transopt/optimizer/model/deepkernel.py:50
↓ 13 callersFunctionget_normalizer
Create the optimizer object.
transopt/utils/Normalization.py:6
↓ 13 callersMethodtrain
(self)
transopt/optimizer/model/rbfn.py:78
↓ 12 callersMethodget_results_by_order
Get results from the nested dictionary based on the specified order. Args: order (list, optional): The order in which res
transopt/ResultAnalysis/AnalysisBase.py:87
↓ 12 callersMethodrun
(self)
transopt/datamanager/database.py:53
↓ 11 callersMethodget_query_num
(self)
transopt/benchmark/problem_base/transfer_problem.py:61
↓ 11 callersFunctionnormalize
Normalize the data using the given mean and standard deviation or compute them from the data if not provided. Parameters: - data (nd
transopt/utils/Normalization.py:119
↓ 11 callersMethodoptimize
(self)
transopt/optimizer/model/bohb.py:71
↓ 11 callersMethodsample
Generates samples from the kernel distribution.
transopt/benchmark/HPOOOD/misc.py:286
↓ 10 callersMethodcompute_batch
Selects the new location to evaluate the objective.
transopt/optimizer/acquisition_function/sequential.py:15
↓ 10 callersMethodset_XY
(self, Data:Dict)
transopt/optimizer/model/rgpe.py:406
↓ 10 callersMethodupdate
(self, value)
transopt/utils/Prior.py:355
↓ 9 callersMethod__init__
(self, n_inputs, n_outputs, hparams)
transopt/benchmark/HPO/networks.py:48
↓ 9 callersMethodmodel
(self, x, y=None)
transopt/benchmark/HPO/algorithms.py:204
↓ 8 callersFunctioncompile_tex
(tex_path, output_folder)
transopt/ResultAnalysis/CompileTex.py:6
↓ 8 callersMethodobjective_function
( self, configuration: Dict, fidelity: Dict = None, seed: Union[np.random.Rand
transopt/benchmark/CPD/PCM/pcm.py:75
↓ 8 callersFunctionoutput_to_ndarray
Extract function_value from each output and convert to ndarray.
transopt/utils/serialization.py:39
↓ 8 callersFunctionroll_col
Rotate columns to right by shift.
transopt/optimizer/model/rgpe.py:14
↓ 8 callersMethodsamples
Returns a set of samples of observations based on a given value of the latent variable. :param gp: latent variable
transopt/optimizer/SingleObjOptimizer/LFL.py:307
↓ 8 callersMethodselect_data
Select data in the database. Parameters ---------- table: str Name of the database table to query.
transopt/datamanager/database.py:617
↓ 8 callersFunctiontotorch
(x,device)
transopt/optimizer/model/deepkernel.py:56
↓ 7 callersMethod__init__
(self, fname, mode="a")
transopt/benchmark/HPOOOD/misc.py:215
↓ 7 callersFunctiondownload_and_extract
(url, dst, remove=True)
transopt/benchmark/HPOOOD/download.py:31
↓ 7 callersMethodget_cur_budget
(self)
transopt/benchmark/problem_base/transfer_problem.py:67
↓ 7 callersMethodget_metadata
(self, module_name)
transopt/agent/services.py:345
↓ 7 callersMethodmap_to_design_space
Maps the given values from the search space to the design space. Args: values (np.ndarray): The values to be mapped from
transopt/space/search_space.py:43
↓ 7 callersFunctionstage_path
(data_dir, name)
transopt/benchmark/HPOOOD/download.py:22
↓ 6 callersMethoddata
convert dictionary to list of treatments
transopt/utils/sk.py:191
↓ 6 callersFunctionfeatures_by_gini
(data, labels)
transopt/optimizer/MultiObjOptimizer/CauMOpt.py:21
↓ 6 callersFunctionfind_pareto_front
Find pareto front (undominated part) of the input performance data.
transopt/utils/pareto.py:32
↓ 6 callersMethodget_methods
Get the list of methods used in the analysis. Returns: list: A list of method names.
transopt/ResultAnalysis/AnalysisBase.py:198
↓ 6 callersMethodmeta_fit
( self, source_X : List[np.ndarray], source_Y : List[np.ndarray], **kwargs,
transopt/optimizer/model/rf.py:55
↓ 6 callersMethodpredict
(self, x)
transopt/benchmark/HPOOOD/algorithms.py:118
↓ 6 callersMethodset_XY
(self, X=None, Y=None)
transopt/optimizer/SingleObjOptimizer/LFL.py:289
↓ 6 callersFunctiontotorch
(x,device)
transopt/optimizer/pretrain/deepkernelpretrain.py:46
↓ 5 callersMethod__init__
( self, task_name, budget_type, budget, seed, workload, **kwargs )
transopt/benchmark/HPOOOD/hpoood.py:445
↓ 5 callersMethod__init__
(self, n_inputs, n_outputs, hparams)
transopt/benchmark/HPOOOD/networks.py:47
↓ 5 callersMethod__init__
(self, name, range_)
transopt/space/variable.py:61
↓ 5 callersMethod_construct_vector
(self, dataset_info)
transopt/datamanager/manager.py:49
↓ 5 callersMethodfit
( self, X : np.ndarray, Y : np.ndarray, optimize: bool = False, )
transopt/optimizer/model/gp.py:81
↓ 5 callersMethodget_shingles
Extract character-based shingles from text.
transopt/datamanager/minhash.py:33
↓ 5 callersMethodget_task_names
Get the list of task names used in the analysis. Returns: list: A list of task names.
transopt/ResultAnalysis/AnalysisBase.py:207
↓ 5 callersFunctionload_data
(workload, algorithm, seed)
demo/comparison/analysis_plot.py:65
↓ 5 callersMethodobserve
Feed an observation back. Parameters ---------- X : pandas DataFrame Places where the objective function has alre
transopt/optimizer/model/hebo.py:202
↓ 5 callersMethodput
(i,x)
transopt/utils/sk.py:117
↓ 5 callersMethodsample
Perform model inference. Sample functions from the posterior distribution for the given test points. Args: data: Input d
transopt/optimizer/model/rf.py:121
↓ 5 callersMethodshow
pretty print set of treatments
transopt/utils/sk.py:220
↓ 5 callersMethodsk
sort treatments and rank them
transopt/utils/sk.py:238
↓ 5 callersMethodupdate_model
(self, Data)
transopt/optimizer/SingleObjOptimizer/LFL.py:166
↓ 4 callersMethod__init__
(self)
transopt/utils/openml_data_manager.py:132
↓ 4 callersFunctioncalculate_feature_importances
Calculates and returns feature importances.
demo/importances/get_feature_importances.py:54
↓ 4 callersMethodcdf
(self, test_Xs, train_Xs)
transopt/benchmark/HPOOOD/misc.py:312
↓ 4 callersMethodcreate_dist
(self)
transopt/benchmark/HPOOOD/misc.py:397
↓ 4 callersFunctiondata_transform
(dataset_name, augmentation_name=None)
transopt/benchmark/HPO/datasets.py:26
↓ 4 callersMethodfetch_data
(self, tasks_info)
transopt/optimizer/selector/lsh_selector.py:10
↓ 4 callersMethodfingerprint
(self, text)
transopt/datamanager/minhash.py:40
↓ 4 callersFunctiongenerate_random_string
(length)
demo/experiment_lsh_validity.py:50
↓ 4 callersMethodget_color_for_method
Get the color(s) associated with a specific method or a list of methods. Args: method (str or list): The name of the method
transopt/ResultAnalysis/AnalysisBase.py:172
↓ 4 callersMethodget_dataset_info
(self, dataset_name)
transopt/datamanager/manager.py:80
↓ 4 callersMethodget_fmin
(self)
transopt/optimizer/model/rf.py:143
↓ 4 callersFunctionget_library_path
()
transopt/utils/path.py:5
↓ 4 callersMethodget_objectives
(self)
transopt/benchmark/HPO/HPO.py:361
↓ 4 callersFunctionget_shingles
(text, ngram=5)
demo/experiment_lsh_validity.py:104
↓ 4 callersMethodget_table_list
Get the list of all database tables.
transopt/datamanager/database.py:194
↓ 4 callersMethodicdf
(self, q)
transopt/benchmark/HPOOOD/misc.py:453
↓ 4 callersFunctionload_and_prepare_data
Loads JSON data and prepares a DataFrame.
demo/comparison/analysis_plot.py:37
↓ 4 callersFunctionload_and_prepare_data
Loads JSON data and prepares a DataFrame.
demo/importances/get_feature_importances.py:29
↓ 4 callersFunctionload_data
(data_folder)
tests/data_analysis.py:12
↓ 4 callersMethodloss_gap
compute gap = max_i loss_i(h) - min_j loss_j(h), return i, j, and the gap for a single batch
transopt/benchmark/HPOOOD/algorithms.py:1935
↓ 4 callersMethodpredict
(self, x)
transopt/benchmark/HPO/algorithms.py:114
↓ 4 callersMethodpredict
Predictions with the model. Returns posterior means and standard deviations at X. Note that this is different in GPy where the variances are
transopt/optimizer/SingleObjOptimizer/LFL.py:218
↓ 4 callersMethodpredict
( self, X: np.ndarray, return_full: bool = False, with_noise: bool = False )
transopt/optimizer/model/gp.py:126
↓ 4 callersMethodrandom_sample
Initialize random samples. :param num_samples: Number of random samples to generate :return: List of dictionaries, each repr
transopt/optimizer/MultiObjOptimizer/ParEGO.py:129
↓ 4 callersMethodread_data_from_kb
(self)
transopt/ResultAnalysis/AnalysisBase.py:41
↓ 4 callersMethodremove_table
(self, name)
transopt/datamanager/database.py:297
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