Comments (1)
Hi,
let me quickly explain how you configure Auto-PyTorch.
(1) Most importantly, you can configure it in the constructor or the fit() method by passing keyword arguments:
autoPyTorch = AutoNetClassification(log_level='info', max_runtime=300, min_budget=30, max_budget=90)
Some of these configs affect the search space, by dis/enambling components:
AutoNetClassification(networks=["resnet", "shapedresnet", "mlpnet", "shapedmlpnet"])
But not to a fine-grained level.
(2) You can configure all ranges by passing an HyperparameterSearchSpaceUpdates object:
from autoPyTorch import HyperparameterSearchSpaceUpdates
search_space_updates = HyperparameterSearchSpaceUpdates()
search_space_updates.append(node_name="NetworkSelector",
hyperparameter="shapedresnet:activation",
value_range=["relu", "sigmoid"])
search_space_updates.append(node_name="NetworkSelector",
hyperparameter="shapedresnet:blocks_per_group",
value_range=[2,5],
log=False)
autoPyTorch = AutoNetClassification(hyperparameter_search_space_updates=search_space_updates)
You can make hyperparameters constant by passing lists with only one item to value_range.
Note that BOHB currently will not filter hyperparameters that are constant. The model will not be build earlier. We are working on this.
Presets give default settings for (1) that you can overwrite in fit() or the constructor.
tiny_cs already passes an HyperparameterSearchSpaceUpdates object to Auto-PyTorch.
If you want to use tiny_cs with only batch_size modified, you need to pass a HyperparameterUpdates object containing:
(i) The modification of the batch_size
(ii) The other stuff of tiny_cs's SearchSpaceUpdates object:
CreateDataLoader batch_size [125]
InitializationSelector initializer:initialize_bias ["No"]
LearningrateSchedulerSelector cosine_annealing:T_max [10]
LearningrateSchedulerSelector cosine_annealing:T_mult [2]
NetworkSelector shapedresnet:activation ["relu"]
NetworkSelector shapedresnet:max_shake_drop_probability [0.0,0.000001]
NetworkSelector shapedresnet:resnet_shape ["brick"]
NetworkSelector shapedresnet:use_dropout [False]
NetworkSelector shapedresnet:use_shake_drop [False]
NetworkSelector shapedresnet:use_shake_shake [False]
PreprocessorSelector truncated_svd:target_dim [100]
If you want to configure Auto-PyTorch using config files refer to
autoPyTorch.utils.config.ConfigFileParser.read() for (1) and
autoPyTorch.utils.hyperparameter_search_space_update.parse_hyperparameter_search_space_updates() for (2)
For (2), each line corresponds to an update of a hyperparameter:
NodeName hyperparameter_name value_range
For a log-range of the hyperparameter:
NodeName hyperparameter_name value_range log
Hope this helps,
Matthias
from auto-pytorch.
Related Issues (20)
- [Time series Forecasting] Continuous Ranked Probablity Score (CRPS) loss for probablity network ouput type HOT 2
- Improve `include_components` documentation HOT 1
- Cannot run time-series example on GPU HOT 1
- Score function error on example code HOT 2
- Prediction on one sample produces error HOT 1
- The predict method of the base task expects to build a logger from a temporary folder
- [Feature request] time series classification HOT 2
- Question about how to maximize the search space HOT 1
- AutoPytorch selects only Dummy model. HOT 4
- Generate a leaderboard for each model?
- ImportError: cannot import name 'AutoNetClassification' from 'autoPyTorch' HOT 1
- I ran autopytrch 0.2 on multiple datasets every time it picks the Dummy model HOT 1
- Error in using command "from autoPyTorch.api.time_series_forecasting import TimeSeriesForecastingTask" HOT 2
- TypeError occurs when import TabularRegressionTask
- Image Classification: API not finished
- Can't install on Google Colab HOT 4
- Errors on running example_tabular_classification.py HOT 1
- Errors installing autoPyTorch HOT 1
- [ERROR] [Client-AutoPyTorch:RefitLogger:1] Prediction for lgb failed with run state StatusType.TIMEOUT.
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from auto-pytorch.