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version number and doc (#497)

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* version number

* add missing tasks in documentation

* update node-forge version
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Chi Wang
2022-03-25 17:32:37 -07:00
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parent 5f97532986
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10 changed files with 28 additions and 25 deletions
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website/docs/Use-Cases/Task-Oriented-AutoML.md
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@@ -11,10 +11,13 @@
- 'classification': classification.
- 'regression': regression.
- 'ts_forecast': time series forecasting.
- 'ts_forecast_classification': time series forecasting for classification.
- 'rank': learning to rank.
- 'seq-classification': sequence classification.
- 'seq-regression': sequence regression.
- 'summarization': text summarization.
- 'token-classification': token classification.
- 'multichoice-classification': multichoice classification.
An optional input is `time_budget` for searching models and hyperparameters. When not specified, a default budget of 60 seconds will be used.
@@ -104,18 +107,18 @@ The estimator list can contain one or more estimator names, each corresponding t
#### Estimator
* Built-in estimator.
- 'lgbm': LGBMEstimator. Hyperparameters: n_estimators, num_leaves, min_child_samples, learning_rate, log_max_bin (logarithm of (max_bin + 1) with base 2), colsample_bytree, reg_alpha, reg_lambda.
- 'xgboost': XGBoostSkLearnEstimator. Hyperparameters: n_estimators, max_leaves, max_depth, min_child_weight, learning_rate, subsample, colsample_bylevel, colsample_bytree, reg_alpha, reg_lambda.
- 'rf': RandomForestEstimator. Hyperparameters: n_estimators, max_features, max_leaves, criterion (for classification only).
- 'extra_tree': ExtraTreesEstimator. Hyperparameters: n_estimators, max_features, max_leaves, criterion (for classification only).
- 'lrl1': LRL1Classifier (sklearn.LogisticRegression with L1 regularization). Hyperparameters: C.
- 'lrl2': LRL2Classifier (sklearn.LogisticRegression with L2 regularization). Hyperparameters: C.
- 'catboost': CatBoostEstimator. Hyperparameters: early_stopping_rounds, learning_rate, n_estimators.
- 'kneighbor': KNeighborsEstimator. Hyperparameters: n_neighbors.
- 'prophet': Prophet. Hyperparameters: changepoint_prior_scale, seasonality_prior_scale, holidays_prior_scale, seasonality_mode.
- 'arima': ARIMA. Hyperparameters: p, d, q.
- 'sarimax': SARIMAX. Hyperparameters: p, d, q, P, D, Q, s.
- 'transformer': Huggingface transformer models. Hyperparameters: learning_rate, num_train_epochs, per_device_train_batch_size, warmup_ratio, weight_decay, adam_epsilon, seed.
- 'lgbm': LGBMEstimator for task "classification", "regression", "rank", "ts_forecast" and "ts_forecast_classification". Hyperparameters: n_estimators, num_leaves, min_child_samples, learning_rate, log_max_bin (logarithm of (max_bin + 1) with base 2), colsample_bytree, reg_alpha, reg_lambda.
- 'xgboost': XGBoostSkLearnEstimator for task "classification", "regression", "rank", "ts_forecast" and "ts_forecast_classification". Hyperparameters: n_estimators, max_leaves, max_depth, min_child_weight, learning_rate, subsample, colsample_bylevel, colsample_bytree, reg_alpha, reg_lambda.
- 'rf': RandomForestEstimator for task "classification", "regression", "ts_forecast" and "ts_forecast_classification". Hyperparameters: n_estimators, max_features, max_leaves, criterion (for classification only).
- 'extra_tree': ExtraTreesEstimator for task "classification", "regression", "ts_forecast" and "ts_forecast_classification". Hyperparameters: n_estimators, max_features, max_leaves, criterion (for classification only).
- 'lrl1': LRL1Classifier (sklearn.LogisticRegression with L1 regularization) for task "classification". Hyperparameters: C.
- 'lrl2': LRL2Classifier (sklearn.LogisticRegression with L2 regularization) for task "classification". Hyperparameters: C.
- 'catboost': CatBoostEstimator for task "classification" and "regression". Hyperparameters: early_stopping_rounds, learning_rate, n_estimators.
- 'kneighbor': KNeighborsEstimator for task "classification" and "regression". Hyperparameters: n_neighbors.
- 'prophet': Prophet for task "ts_forecast". Hyperparameters: changepoint_prior_scale, seasonality_prior_scale, holidays_prior_scale, seasonality_mode.
- 'arima': ARIMA for task "ts_forecast". Hyperparameters: p, d, q.
- 'sarimax': SARIMAX for task "ts_forecast". Hyperparameters: p, d, q, P, D, Q, s.
- 'transformer': Huggingface transformer models for task "seq-classification", "seq-regression", "multichoice-classification", "token-classification" and "summarization". Hyperparameters: learning_rate, num_train_epochs, per_device_train_batch_size, warmup_ratio, weight_decay, adam_epsilon, seed.
* Custom estimator. Use custom estimator for:
- tuning an estimator that is not built-in;
- customizing search space for a built-in estimator.