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upgrade autogluon
This commit is contained in:
Michael Duong
parent
7f2f80af22
commit
ad2c4d6019
8 changed files with 46 additions and 40 deletions
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@ -14,8 +14,9 @@ default:
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output_filepath: ./data/model/allmodels/
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problem_type: regression
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eval_metric: mean_squared_error #mean_absolute_error
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time_limit: 4000
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presets: medium_quality
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time_limit: 1800
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presets: good_quality
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excluded_model_types: ['RF', 'FASTAI', 'CAT', 'NN_TORCH', 'KNN', 'XT']
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infer_limit: 0.05
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infer_limit_batch_size: 10000
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ag_args_ensemble: {'num_folds_parallel': 2}
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@ -24,7 +24,7 @@ default:
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# data_filepath: s3://retrofit-data-dev/sap_change_model/dataset_test.parquet
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data_filepath: s3://retrofit-data-dev/sap_change_model/dataset.parquet
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# data_filepath: s3://retrofit-datalake-dev/dataset_with0perm_all.parquet
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train_proportion: 1
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train_proportion: 0.9
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output_train_filepath: ./data/prepared_data/train.parquet
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output_test_filepath: ./data/prepared_data/test.parquet
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@ -25,7 +25,7 @@ def model_factory(model_type: str) -> MLModel:
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models = {
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"SKLearnLinearRegression": SKLearnLinearRegression(),
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"SKLearnSVMRegression": SKLearnSVMRegression(),
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"AutogluonAutoML": AutogluonAutoML()
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"AutogluonAutoML": AutogluonAutoML(),
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# ADD OTHER MODELS HERE
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}
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@ -151,6 +151,7 @@ class AutogluonAutoML:
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"excluded_model_types",
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"infer_limit",
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"infer_limit_batch_size",
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"ag_args_ensemble",
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]
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def load_model(self, path: Union[Path, str]) -> None:
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@ -207,6 +208,7 @@ class AutogluonAutoML:
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excluded_model_types=model_hyperparameters["excluded_model_types"],
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infer_limit=model_hyperparameters["infer_limit"],
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infer_limit_batch_size=model_hyperparameters["infer_limit_batch_size"],
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ag_args_ensemble=model_hyperparameters["ag_args_ensemble"],
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)
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def predict(
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@ -27,12 +27,12 @@ stages:
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default.prepare_data.output_dataclient_type: local
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default.prepare_data.output_test_filepath: ./data/prepared_data/test.parquet
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default.prepare_data.output_train_filepath: ./data/prepared_data/train.parquet
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default.prepare_data.train_proportion: 1
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default.prepare_data.train_proportion: 0.9
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outs:
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- path: data/prepared_data/
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hash: md5
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md5: 3c77fa10cd1cd503eb4d2540394629f6.dir
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size: 42626894
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md5: 3d1144848fce4ce50f6abfaec5235552.dir
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size: 46392840
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nfiles: 2
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build_model:
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cmd: python 2_build_model.py
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@ -43,8 +43,8 @@ stages:
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size: 4820
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- path: data/prepared_data
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hash: md5
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md5: 3c77fa10cd1cd503eb4d2540394629f6.dir
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size: 42626894
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md5: 3d1144848fce4ce50f6abfaec5235552.dir
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size: 46392840
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nfiles: 2
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params:
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configs/build_model.yaml:
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@ -61,8 +61,8 @@ stages:
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output_filepath: ./data/model/allmodels/
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problem_type: regression
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eval_metric: mean_squared_error
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time_limit: 4000
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presets: medium_quality
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time_limit: 1800
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presets: good_quality
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excluded_model_types:
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- RF
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- FASTAI
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@ -72,21 +72,23 @@ stages:
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- XT
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infer_limit: 0.05
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infer_limit_batch_size: 10000
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ag_args_ensemble:
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num_folds_parallel: 2
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outs:
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- path: data/fit_predictions/
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hash: md5
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md5: e0a11ac6e4adf69d6180c0217c639a0e.dir
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size: 3680908
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md5: 346b6611afbf2070e038bf945249a86e.dir
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size: 3384302
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nfiles: 1
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- path: data/model/
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hash: md5
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md5: bdaaf823857f9dc7b6ee2d4b88927cc1.dir
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size: 805896324
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nfiles: 31
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md5: 8e37f21728cd092660bafa8c32dc109f.dir
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size: 423840922
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nfiles: 118
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- path: metrics/fit_metrics.json
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hash: md5
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md5: 0ed5b1141bbb8bc3156e7c056b29f3cd
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size: 225
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md5: d63e1a8d31503055835ac35149554e41
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size: 223
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generate_predictions:
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cmd: python 3_generate_predictions.py
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deps:
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@ -96,13 +98,13 @@ stages:
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size: 2464
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- path: data/model
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hash: md5
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md5: bdaaf823857f9dc7b6ee2d4b88927cc1.dir
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size: 805896324
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nfiles: 31
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md5: 8e37f21728cd092660bafa8c32dc109f.dir
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size: 423840922
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nfiles: 118
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- path: data/prepared_data
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hash: md5
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md5: 3c77fa10cd1cd503eb4d2540394629f6.dir
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size: 42626894
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md5: 3d1144848fce4ce50f6abfaec5235552.dir
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size: 46392840
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nfiles: 2
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params:
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configs/settings.yaml:
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@ -114,8 +116,8 @@ stages:
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outs:
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- path: data/predictions/
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hash: md5
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md5: 38707d16ae1e2330cc03f524db9cdd60.dir
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size: 648730
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md5: d148baf508140353d62c16d6ab0fb6b7.dir
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size: 469224
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nfiles: 1
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generate_metrics:
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cmd: python 4_generate_metrics.py
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@ -126,13 +128,13 @@ stages:
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size: 3484
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- path: data/predictions
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hash: md5
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md5: 38707d16ae1e2330cc03f524db9cdd60.dir
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size: 648730
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md5: d148baf508140353d62c16d6ab0fb6b7.dir
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size: 469224
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nfiles: 1
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- path: data/prepared_data
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hash: md5
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md5: 3c77fa10cd1cd503eb4d2540394629f6.dir
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size: 42626894
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md5: 3d1144848fce4ce50f6abfaec5235552.dir
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size: 46392840
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nfiles: 2
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params:
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configs/settings.yaml:
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@ -142,8 +144,8 @@ stages:
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outs:
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- path: metrics/metrics.json
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hash: md5
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md5: 145e7ac84ab4a4407b23695a632b4d91
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size: 226
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md5: 196232f94b563ac525cf65ee5cc6d639
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size: 222
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startup_cleanup:
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cmd: python 0_startup_cleanup.py
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deps:
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@ -1,7 +1,7 @@
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joblib==1.3.2
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boto3==1.28.17
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pandas==1.5.3
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autogluon==0.8.2
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pandas==2.1.4
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autogluon==1.0.0
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dynaconf==3.2.0
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pyarrow==13.0.0
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pre-commit==3.3.3
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@ -1,7 +1,7 @@
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joblib==1.3.2
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boto3==1.28.17
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pandas==1.5.3
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autogluon==0.8.2
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pandas==2.1.4
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autogluon==1.0.0
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dynaconf==3.2.0
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pyarrow==13.0.0
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PyYAML==6.0.1
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@ -1,9 +1,10 @@
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joblib==1.3.2
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boto3==1.28.17
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pandas==1.5.3
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autogluon==0.8.2
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pandas==2.1.4
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autogluon==1.0.0
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ray==2.6.3
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dynaconf==3.2.0
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alibi==0.9.4
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alibi==0.9.5
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shap==0.42.1
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pyarrow==13.0.0
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pre-commit==3.3.3
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@ -1,4 +1,4 @@
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boto3==1.28.41
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pandas==1.5.3
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autogluon==0.8.2
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pandas==2.1.4
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autogluon==1.0.0
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dynaconf==3.2.0
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