From 98710fd8385ccbe964acf9207f0b51da758de1af Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Frederik=20Peter=20H=C3=B8ngaard?= Date: Sun, 21 May 2023 01:18:39 +0200 Subject: [PATCH 1/2] Update dependencies --- Pipfile | 1 + Pipfile.lock | 62 +++++++++++++++++++++++++++++++--------------------- 2 files changed, 38 insertions(+), 25 deletions(-) diff --git a/Pipfile b/Pipfile index c12ab05..58bec3a 100644 --- a/Pipfile +++ b/Pipfile @@ -9,6 +9,7 @@ pandas = "==1.5.*" scikit-learn = "*" tqdm = "*" jupyter = "*" +xgboost = "*" [dev-packages] black = "==23.*" diff --git a/Pipfile.lock b/Pipfile.lock index aa905f5..a7c80e3 100644 --- a/Pipfile.lock +++ b/Pipfile.lock @@ -1,7 +1,7 @@ { "_meta": { "hash": { - "sha256": "a5dc7a8a6ec4078ce0d03261251f9452d7c5d70fca4102d184df59b32b1a8a1c" + "sha256": "79fdea66262af3a139c4e3e40343f2bccbbe3bba979276d5158119df01dc3583" }, "pipfile-spec": 6, "requires": { @@ -261,11 +261,11 @@ }, "ipykernel": { "hashes": [ - "sha256:1ae6047c1277508933078163721bbb479c3e7292778a04b4bacf0874550977d6", - "sha256:302558b81f1bc22dc259fb2a0c5c7cf2f4c0bdb21b50484348f7bafe7fb71421" + "sha256:1aba0ae8453e15e9bc6b24e497ef6840114afcdb832ae597f32137fa19d42a6f", + "sha256:77aeffab056c21d16f1edccdc9e5ccbf7d96eb401bd6703610a21be8b068aadc" ], "markers": "python_version >= '3.8'", - "version": "==6.22.0" + "version": "==6.23.1" }, "ipython": { "hashes": [ @@ -506,11 +506,11 @@ }, "nbconvert": { "hashes": [ - "sha256:78685362b11d2e8058e70196fe83b09abed8df22d3e599cf271f4d39fdc48b9e", - "sha256:d2e95904666f1ff77d36105b9de4e0801726f93b862d5b28f69e93d99ad3b19c" + "sha256:51b6c77b507b177b73f6729dba15676e42c4e92bcb00edc8cc982ee72e7d89d7", + "sha256:af5064a9db524f9f12f4e8be7f0799524bd5b14c1adea37e34e83c95127cc818" ], "markers": "python_version >= '3.7'", - "version": "==7.3.1" + "version": "==7.4.0" }, "nbformat": { "hashes": [ @@ -652,11 +652,11 @@ }, "platformdirs": { "hashes": [ - "sha256:47692bc24c1958e8b0f13dd727307cff1db103fca36399f457da8e05f222fdc4", - "sha256:7954a68d0ba23558d753f73437c55f89027cf8f5108c19844d4b82e5af396335" + "sha256:412dae91f52a6f84830f39a8078cecd0e866cb72294a5c66808e74d5e88d251f", + "sha256:e2378146f1964972c03c085bb5662ae80b2b8c06226c54b2ff4aa9483e8a13a5" ], "markers": "python_version >= '3.7'", - "version": "==3.5.0" + "version": "==3.5.1" }, "prometheus-client": { "hashes": [ @@ -1060,20 +1060,20 @@ }, "tornado": { "hashes": [ - "sha256:1285f0691143f7ab97150831455d4db17a267b59649f7bd9700282cba3d5e771", - "sha256:3455133b9ff262fd0a75630af0a8ee13564f25fb4fd3d9ce239b8a7d3d027bf8", - "sha256:5e2f49ad371595957c50e42dd7e5c14d64a6843a3cf27352b69c706d1b5918af", - "sha256:81c17e0cc396908a5e25dc8e9c5e4936e6dfd544c9290be48bd054c79bcad51e", - "sha256:90f569a35a8ec19bde53aa596952071f445da678ec8596af763b9b9ce07605e6", - "sha256:9661aa8bc0e9d83d757cd95b6f6d1ece8ca9fd1ccdd34db2de381e25bf818233", - "sha256:a27a1cfa9997923f80bdd962b3aab048ac486ad8cfb2f237964f8ab7f7eb824b", - "sha256:b4e7b956f9b5e6f9feb643ea04f07e7c6b49301e03e0023eedb01fa8cf52f579", - "sha256:d7117f3c7ba5d05813b17a1f04efc8e108a1b811ccfddd9134cc68553c414864", - "sha256:db181eb3df8738613ff0a26f49e1b394aade05034b01200a63e9662f347d4415", - "sha256:ffdce65a281fd708da5a9def3bfb8f364766847fa7ed806821a69094c9629e8a" + "sha256:05615096845cf50a895026f749195bf0b10b8909f9be672f50b0fe69cba368e4", + "sha256:0c325e66c8123c606eea33084976c832aa4e766b7dff8aedd7587ea44a604cdf", + "sha256:29e71c847a35f6e10ca3b5c2990a52ce38b233019d8e858b755ea6ce4dcdd19d", + "sha256:4b927c4f19b71e627b13f3db2324e4ae660527143f9e1f2e2fb404f3a187e2ba", + "sha256:5b17b1cf5f8354efa3d37c6e28fdfd9c1c1e5122f2cb56dac121ac61baa47cbe", + "sha256:6a0848f1aea0d196a7c4f6772197cbe2abc4266f836b0aac76947872cd29b411", + "sha256:7efcbcc30b7c654eb6a8c9c9da787a851c18f8ccd4a5a3a95b05c7accfa068d2", + "sha256:834ae7540ad3a83199a8da8f9f2d383e3c3d5130a328889e4cc991acc81e87a0", + "sha256:b46a6ab20f5c7c1cb949c72c1994a4585d2eaa0be4853f50a03b5031e964fc7c", + "sha256:c2de14066c4a38b4ecbbcd55c5cc4b5340eb04f1c5e81da7451ef555859c833f", + "sha256:c367ab6c0393d71171123ca5515c61ff62fe09024fa6bf299cd1339dc9456829" ], "markers": "python_version >= '3.8'", - "version": "==6.3.1" + "version": "==6.3.2" }, "tqdm": { "hashes": [ @@ -1134,6 +1134,18 @@ ], "markers": "python_version >= '3.7'", "version": "==4.0.7" + }, + "xgboost": { + "hashes": [ + "sha256:1d1dda6b84ea50a2ea1ed18390e93e275d57dc4cffd682014dc30ae5a116c92b", + "sha256:63474265a0194f27889c6fb54e5939ad21bcd5fcfaca7b6a89e143be42ed7ad1", + "sha256:9eed5629c9008c36d65db6869defac31de635f766f215fc4b09b6a389c637f27", + "sha256:ac17664ff24ea1c160a0d50aff521b654f0911f4684a88bbb46a074c46c9e3f1", + "sha256:af3227dbd839a8e2a215844a6276eae027d5f83a9cb501148dfcdb047a195411", + "sha256:ca9e8455343cc3f1fddc825209ad00623bc82de0364097b31d649bca6a5f8fb4" + ], + "index": "pypi", + "version": "==1.7.5" } }, "develop": { @@ -1242,11 +1254,11 @@ }, "platformdirs": { "hashes": [ - "sha256:47692bc24c1958e8b0f13dd727307cff1db103fca36399f457da8e05f222fdc4", - "sha256:7954a68d0ba23558d753f73437c55f89027cf8f5108c19844d4b82e5af396335" + "sha256:412dae91f52a6f84830f39a8078cecd0e866cb72294a5c66808e74d5e88d251f", + "sha256:e2378146f1964972c03c085bb5662ae80b2b8c06226c54b2ff4aa9483e8a13a5" ], "markers": "python_version >= '3.7'", - "version": "==3.5.0" + "version": "==3.5.1" }, "pluggy": { "hashes": [ From 5f511943dd8e9f7d57c709bfcd7f4e93e3f47d92 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Frederik=20Peter=20H=C3=B8ngaard?= Date: Sun, 21 May 2023 01:26:06 +0200 Subject: [PATCH 2/2] Update project for 0.0.3 --- README.md | 13 +++++++++++++ pyproject.toml | 2 +- 2 files changed, 14 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index c634eb5..eaf8461 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,18 @@ The aim of lazy-learn is exactly that. Given a dataset, easy-learn will analyse types and distributions of attributes, preprocess, feature-engineer and ultimately train models to be used for further evaluation or inference. +## Upcoming features + +Current stable version is 0.0.3. The upcoming updates will support: +- Abstract construction of model architectures +- XGBoost, LightGBM, Adaboost and Catboost architectures +- Time partitioning of datasets +- Automated Hyperparameter Optimisation (HPO) +- Text features +- An interface to AutoGluon +- Outlier detection and handling +- Automated suggestions of performance metrics + ## Usage Using lazy-learn revolves around the `LazyLearner` class. You can think of it as a kind of project, and it is the wrapper for any experiment within lazy-learn. @@ -17,6 +29,7 @@ lazy-learn requires: - pandas - scikit-learn +- xgboost ### User Installation ``` diff --git a/pyproject.toml b/pyproject.toml index a45714a..4b9d64b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "hatchling.build" [project] name = "lazylearn" -version = "0.0.2" +version = "0.0.3" authors = [ { name="Frederik P. Høngaard", email="mail@frederikhoengaard.com" }, ]