upgini
Data search & enrichment library for Machine Learning → Easily find and add relevant features to your ML & AI pipeline from hundreds of public and premium external data sources, including open & commercial LLMs
파일 탐색기
최종 버전 다운로드 (.zip)- config.yml
- codeql.yml
- static.yml
- FUNDING.yml
- custom.js
- postBuild
- requirements.txt
- visitorid.js
- bench_data_2007.parquet
- bench_data_2008.parquet
- bench_data_2009.parquet
- bench_data_2010.parquet
- bench_data_2011.parquet
- bench_data_2012.parquet
- bench_data_2013.parquet
- bench_data_2014.parquet
- bench_data_2015.parquet
- bench_data_2016.parquet
- bench_data_2017.parquet
- bench_data_2018.parquet
- Partitioned_parquet_reading_bench.ipynb
- us_zip_elections.ipynb
- us_zip_elections.parquet
- demo_salary.csv.zip
- glassdoor_eda_data.csv.zip
- kaggle_example.ipynb
- train.csv.zip
- Upgini_Features_search&generation.ipynb
- __init__.py
- ads_manager.py
- __init__.py
- base.py
- cross.py
- delta.py
- lag.py
- roll.py
- trend.py
- volatility.py
- __init__.py
- all_operators.py
- binary.py
- date.py
- feature.py
- groupby.py
- operand.py
- operator.py
- unary.py
- utils.py
- vector.py
- __init__.py
- data_source_publisher.py
- __init__.py
- context.py
- __init__.py
- normalize_utils.py
- __init__.py
- exceptions.py
- strings.properties
- strings_widget.properties
- __init__.py
- base.py
- random_under_sampler.py
- utils.py
- __init__.py
- base_search_key_detector.py
- blocked_time_series.py
- config.py
- country_utils.py
- cpu_utils.py
- custom_loss_utils.py
- cv_utils.py
- datetime_utils.py
- deduplicate_utils.py
- display_utils.py
- email_utils.py
- fallback_progress_bar.py
- feature_info.py
- features_validator.py
- format.py
- hash_utils.py
- ip_utils.py
- mstats.py
- one_hot_encoder.py
- phone_utils.py
- postal_code_utils.py
- progress_bar.py
- psi.py
- pyarrow_utils.py
- Roboto-Regular.ttf
- sample_utils.py
- sklearn_ext.py
- sort.py
- target_utils.py
- track_info.py
- ts_utils.py
- warning_counter.py
- __about__.py
- __init__.py
- ads.py
- dataset.py
- errors.py
- features_enricher.py
- http.py
- metadata.py
- metrics.py
- search_task.py
- spinner.py
- version_validator.py
- test_autofe_registry.py
- test_bin.py
- test_cross.py
- test_date_diff.py
- test_delta.py
- test_distance.py
- test_ewma_vol.py
- test_feature.py
- test_lag.py
- test_norm.py
- test_operand.py
- test_operator_registry.py
- test_outlier_distance.py
- test_percentile.py
- test_roll.py
- test_rolling_vol.py
- test_rolling_vol2.py
- test_sim.py
- test_trend.py
- test_unary.py
- test_vector.py
- test_volatility_base.py
- test_volatility_ratio.py
- __init__.py
- test_phone_normalizer.py
- blocked_ts_logic.csv
- data.csv
- data.csv.gz
- data2.csv.gz
- data_with_time.parquet
- expected_prepared.parquet
- expected_prepared_imbalanced.parquet
- expected_prepared_with_entity_system_record_id.parquet
- features_imbalanced.parquet
- features_imbalanced_with_entity_system_record_id.parquet
- features_imbalanced_with_entity_system_record_id_validation.parquet
- initial_eval1_imbalanced.parquet
- initial_eval2_imbalanced.parquet
- initial_train_imbalanced.parquet
- mock_features.csv.gz
- mock_features.parquet
- mock_features_with_entity_system_record_id.parquet
- valid_data.parquet
- validation_df.parquet
- validation_features.parquet
- validation_features_v1.parquet
- validation_features_v1_with_entity_system_record_id.parquet
- validation_features_v2.parquet
- validation_features_v2_with_entity_system_record_id.parquet
- validation_features_v3.parquet
- validation_features_v3_with_entity_system_record_id.parquet
- data.csv.gz
- data.csv.gz
- file_meta.json
- provider_meta.json
- x_enriched.parquet
- x_sampled.parquet
- y_sampled.parquet
- test_features_enricher.csv
- test_features_enricher_features_info.csv
- test_features_enricher_features_info_after_metrics.csv
- test_features_enricher_with_complex_feature_names_features_info.csv
- test_features_enricher_with_complex_feature_names_features_info_after_metrics.csv
- test_features_enricher_with_complex_feature_names_metrics.csv
- test_features_enricher_with_datetime_metrics.csv
- test_features_enricher_with_demo_key_features_info.csv
- test_features_enricher_with_demo_key_features_info_after_metrics.csv
- test_features_enricher_with_demo_key_metrics.csv
- test_features_enricher_with_imbalanced_dataset_metrics.csv
- test_features_enricher_with_index_column_features_info.csv
- test_features_enricher_with_index_column_features_info_after_metrics.csv
- test_features_enricher_with_index_column_metrics.csv
- test_features_enricher_with_named_index_features_info.csv
- test_features_enricher_with_named_index_features_info_after_metrics.csv
- test_features_enricher_with_named_index_metrics.csv
- test_features_enricher_with_numpy_features_info.csv
- test_features_enricher_with_numpy_features_info_after_metrics.csv
- test_features_enricher_with_numpy_metrics.csv
- test_features_enricher_with_select_features_metrics.csv
- test_features_enricher_with_select_features_metrics_2.csv
- test_features_enricher_without_external_features_info.csv
- test_features_enricher_without_external_features_info_after_metrics.csv
- test_features_enricher_without_external_metrics.csv
- test_filter_by_importance_metrics.csv
- test_saved_features_enricher.csv
- test_saved_features_enricher_features_info.csv
- test_saved_features_enricher_features_info_after_metrics.csv
- test_saved_features_enricher_imbalanced_target.csv
- test_blocked_timeseries_rmsle.csv
- test_catboost_metric_binary.csv
- test_catboost_metric_binary_with_cat_features.csv
- test_default_metric_binary.csv
- test_default_metric_binary_custom_loss.csv
- test_default_metric_binary_shuffled.csv
- test_default_metric_binary_with_string_feature.csv
- test_default_metric_multiclass.csv
- test_default_metric_regression_with_outliers.csv
- test_demo_metrics.csv
- test_lightgbm_metric_binary.csv
- test_lightgbm_metric_binary_with_cat_features.csv
- test_real_case_metric_binary.csv
- test_rf_metric_rmse.csv
- features.csv.gz
- features.parquet
- features_regression_date_country_postal.parquet
- features_with_entity_system_record_id.parquet
- input.csv
- input.parquet
- input_multiclass.csv
- input_with_cat.csv
- input_with_cat.parquet
- input_with_string_feature.parquet
- metadata_regression_date_country_postal.json
- target_outliers_regression_date_country_postal.parquet
- tds_regression_date_country_postal.parquet
- validation_features.parquet
- validation_features_with_entity_system_record_id.parquet
- features.parquet
- tds.parquet
- complex_feature_name_features.parquet
- complex_feature_name_features_with_entity_system_record_id.parquet
- complex_feature_name_tds.parquet
- real_enriched_eval_x.parquet
- real_enriched_x.parquet
- real_test.parquet
- real_test_df.parquet
- real_train.parquet
- real_train_df.parquet
- df_for_psi.parquet
- test_deduplicate_utils.py
- test_one_hot_detector.py
- test_psi.py
- __init__.py
- conftest.py
- test_binary_dataset.py
- test_blocked_time_series.py
- test_categorical_dataset.py
- test_continuous_dataset.py
- test_country_utils.py
- test_custom_loss_utils.py
- test_datasource_publisher.py
- test_datetime_utils.py
- test_deterministic_digest.py
- test_email_utils.py
- test_etalon_validation.py
- test_eval_sets.py
- test_features_enricher.py
- test_metadata.py
- test_metrics.py
- test_phone_utils.py
- test_postal_code_utils.py
- test_sample_utils.py
- test_shap_metrics.py
- test_target_utils.py
- test_ts_utils.py
- test_widget.py
- utils.py
- .gitignore
- .gitpod.yml
- CITATION.cff
- CODE_OF_CONDUCT.md
- CODEOWNERS
- Dockerfile
- error_status.txt
- gitpod_init.sh
- LICENSE
- pyproject.toml
- README.md
- requirements-dev.txt
- tox.ini
# 설치 가이드
1. 코드 내려받기
git clone https://github.com/upgini/upgini
깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.
cd upgini
방금 내려받은 프로젝트 폴더 안으로 이동합니다.
2. Docker
쉬움 추천사전 준비물
- Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
- Docker Desktop 컨테이너를 빌드하고 실행하려면 필요합니다. 설치 후 실행해서 백그라운드에 켜두세요.
🐳 <b>Docker-way</b>
이 명령어를 터미널에 그대로 입력해 실행하세요.
터미널에 docker compose ps 를 입력해 컨테이너들이 Up 상태인지 확인하세요. README에 포트 번호가 적혀있다면 브라우저에서 http://localhost:포트번호 로 접속해보세요.
이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.
// repository documentation
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