From 7fe3356c3f39e245446d4b2e9df50f3a9e6329d6 Mon Sep 17 00:00:00 2001 From: Chris Lemke <11752694+chrislemke@users.noreply.github.com> Date: Tue, 13 Dec 2022 15:10:12 +0100 Subject: [PATCH] Rename project and cleanup --- .github/workflows/build-docs.yml | 6 +- .github/workflows/code-cov.yml | 6 +- .github/workflows/deploy-package.yml | 6 +- .github/workflows/testing.yml | 12 +- .pre-commit-config.yaml | 22 +- Brewfile | 2 + docs/CONTRIBUTING.md | 2 +- docs/README.md | 41 +- docs/base_transformer-reference.md | 2 +- docs/datetime_transformer-reference.md | 2 +- docs/encoder_transformer-reference.md | 2 +- docs/generic_transformer-reference.md | 2 +- docs/number_transformer-reference.md | 2 +- docs/reviser-reference.md | 3 - docs/selector-reference.md | 3 - docs/string_transformer-reference.md | 2 +- docs/utils-reference.md | 2 +- feature_reviser/feature_selection/reviser.py | 82 - feature_reviser/feature_selection/selector.py | 142 -- mkdocs.yml | 21 +- poetry.lock | 1477 +++++++++-------- pyproject.toml | 49 +- {feature_reviser => src}/__init__.py | 10 +- .../transformer/base_transformer.py | 0 .../transformer/datetime_transformer.py | 4 +- .../transformer/encoder_transformer.py | 0 .../transformer/generic_transformer.py | 16 +- .../transformer/number_transformer.py | 6 +- .../transformer/string_transformer.py | 6 +- {feature_reviser => src}/utils.py | 0 tests/conftest.py | 2 - tests/test_feature_selection/test_reviser.py | 55 - tests/test_feature_selection/test_selector.py | 65 - .../test_datetime_transformer.py | 3 +- .../test_encoder_transformer.py | 4 +- .../test_generic_transformer.py | 4 +- .../test_number_transformer.py | 4 +- .../test_string_transformer.py | 4 +- tests/test_utils.py | 8 +- 39 files changed, 879 insertions(+), 1200 deletions(-) create mode 100644 Brewfile delete mode 100644 docs/reviser-reference.md delete mode 100644 docs/selector-reference.md delete mode 100644 feature_reviser/feature_selection/reviser.py delete mode 100644 feature_reviser/feature_selection/selector.py rename {feature_reviser => src}/__init__.py (56%) rename {feature_reviser => src}/transformer/base_transformer.py (100%) rename {feature_reviser => src}/transformer/datetime_transformer.py (95%) rename {feature_reviser => src}/transformer/encoder_transformer.py (100%) rename {feature_reviser => src}/transformer/generic_transformer.py (96%) rename {feature_reviser => src}/transformer/number_transformer.py (95%) rename {feature_reviser => src}/transformer/string_transformer.py (98%) rename {feature_reviser => src}/utils.py (100%) delete mode 100644 tests/test_feature_selection/test_reviser.py delete mode 100644 tests/test_feature_selection/test_selector.py diff --git a/.github/workflows/build-docs.yml b/.github/workflows/build-docs.yml index 5daf631..0d6ea23 100644 --- a/.github/workflows/build-docs.yml +++ b/.github/workflows/build-docs.yml @@ -12,7 +12,7 @@ jobs: - uses: actions/checkout@v2 - uses: actions/setup-python@v2 with: - python-version: 3.9 - - run: pip install poetry==1.2.1 - - run: poetry install + python-version: "3.10" + - run: pip install poetry==1.3.1 + - run: poetry install --without dev - run: poetry run mkdocs gh-deploy --force --clean --verbose diff --git a/.github/workflows/code-cov.yml b/.github/workflows/code-cov.yml index 31294c5..e463f9c 100644 --- a/.github/workflows/code-cov.yml +++ b/.github/workflows/code-cov.yml @@ -12,9 +12,9 @@ jobs: python-version: "3.10" - name: Install dependencies and project run: | - python -m pip install poetry==1.2.1 - poetry install + python -m pip install poetry==1.3.1 + poetry install --without devs --without docs - name: Run tests and collect coverage - run: poetry run pytest --cov feature_reviser --cov-report term-missing --cov-report xml + run: poetry run pytest --cov sk-transformers --cov-report term-missing --cov-report xml - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 diff --git a/.github/workflows/deploy-package.yml b/.github/workflows/deploy-package.yml index b949b14..6c27424 100644 --- a/.github/workflows/deploy-package.yml +++ b/.github/workflows/deploy-package.yml @@ -11,7 +11,7 @@ jobs: - uses: actions/checkout@v2 - uses: actions/setup-python@v2 with: - python-version: 3.9 - - run: pip install poetry==1.2.1 - - run: poetry config pypi-token.pypi ${{ secrets.FEATURE_REVISER_UPLOAD_TOKEN }} + python-version: "3.10" + - run: pip install poetry==1.3.1 + - run: poetry config pypi-token.pypi ${{ secrets.UPLOAD_TOKEN }} - run: poetry publish --build diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index c420fb3..07ed044 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -20,23 +20,23 @@ jobs: python-version: ${{ matrix.python-version }} - name: Install dependencies and project run: | - python -m pip install poetry==1.2.1 + python -m pip install poetry==1.3.1 poetry install - name: Check with isort run: | - poetry run isort --check-only ./feature_reviser ./tests + poetry run isort --check-only ./src ./tests - name: Check with black run: | - poetry run black --check ./feature_reviser ./tests + poetry run black --check ./src ./tests - name: Check with mypy run: | poetry run mypy --config-file=pyproject.toml . - name: Check with bandit run: | - poetry run bandit -r ./feature_reviser/* + poetry run bandit -r ./src/* - name: Lint with pylint run: | - poetry run pylint --rcfile=pyproject.toml ./feature_reviser ./tests + poetry run pylint --rcfile=pyproject.toml ./src ./tests - name: Test with pytest run: | - poetry run pytest --cov feature_reviser --cov-fail-under=90 --cov-report term-missing + poetry run pytest --cov src --cov-fail-under=90 --cov-report term-missing diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 852e0b3..190ca7d 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -15,28 +15,27 @@ repos: - id: check-toml - id: debug-statements - id: fix-byte-order-marker - - id: fix-encoding-pragma - id: forbid-new-submodules - repo: https://github.com/psf/black - rev: 22.10.0 + rev: 22.12.0 hooks: - id: black - repo: https://github.com/PyCQA/pylint - rev: v2.15.5 + rev: v2.15.8 hooks: - id: pylint args: ["--rcfile=pyproject.toml"] - repo: https://github.com/PyCQA/isort - rev: 5.10.1 + rev: 5.11.1 hooks: - id: isort args: ["--profile=black"] - repo: https://github.com/pre-commit/mirrors-mypy - rev: v0.982 + rev: v0.991 hooks: - id: mypy args: @@ -52,14 +51,21 @@ repos: - "-r" - repo: https://github.com/asottile/pyupgrade - rev: v2.38.0 + rev: v3.3.1 hooks: - id: pyupgrade - repo: https://github.com/python-poetry/poetry - rev: 1.2.2 + rev: 1.3.1 hooks: - id: poetry-check - id: poetry-lock - id: poetry-export - args: ["--dev", "-f", "requirements.txt", "-o", "requirements.txt"] + args: + [ + "--dev", + "--format", + "requirements.txt", + "--output", + "requirements.txt", + ] diff --git a/Brewfile b/Brewfile new file mode 100644 index 0000000..d403dff --- /dev/null +++ b/Brewfile @@ -0,0 +1,2 @@ +brew "poetry" +brew "pre-commit" diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md index db1f71e..bf7bccc 100644 --- a/docs/CONTRIBUTING.md +++ b/docs/CONTRIBUTING.md @@ -66,7 +66,7 @@ print(pipeline.fit_transform(df).head()) 1 Incredible Hulk Schikaneder 2 Tom and Jerry Futuregarden ``` -For a non-dummy examples check out the [`MathExpressionTransformer`](number_transformer-reference.md#feature_reviser.transformer.number_transformer.MathExpressionTransformer) or the [`ValueIndicatorTransformer`](generic_transformer-reference.md#feature_reviser.transformer.generic_transformer.ValueIndicatorTransformer) for a simpler example. +For a non-dummy examples check out the [`MathExpressionTransformer`](number_transformer-reference.md#sk-transformers.transformer.number_transformer.MathExpressionTransformer) or the [`ValueIndicatorTransformer`](generic_transformer-reference.md#sk-transformers.transformer.generic_transformer.ValueIndicatorTransformer) for a simpler example. ## Poetry We are using [Poetry](https://python-poetry.org/) to manage the dependencies and the virtual environment. If you have not used it before please check out the [documentation](https://python-poetry.org/docs/) to get started. diff --git a/docs/README.md b/docs/README.md index c52a4a5..919f0c9 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,31 +1,31 @@ -![The machine](https://raw.githubusercontent.com/chrislemke/feature-reviser/master/docs/assets/images/image.png) +![The machine](https://raw.githubusercontent.com/chrislemke/sk-transformers/master/docs/assets/images/image.png) -# feature-reviser +# sk-transformers ### A collection of various scikit-learn transformers for all kinds of preprocessing and feature engineering steps 🛠 -[![testing](https://github.com/chrislemke/feature-reviser/actions/workflows/testing.yml/badge.svg?branch=main)](https://github.com/chrislemke/feature-reviser/actions/workflows/testing.yml) -[![codecov](https://codecov.io/github/chrislemke/feature-reviser/branch/main/graph/badge.svg?token=LJLXQXX6M8)](https://codecov.io/github/chrislemke/feature-reviser) -[![deploy package](https://github.com/chrislemke/feature-reviser/actions/workflows/deploy-package.yml/badge.svg)](https://github.com/chrislemke/feature-reviser/actions/workflows/deploy-package.yml) -[![pypi](https://img.shields.io/pypi/v/feature-reviser)](https://pypi.org/project/feature-reviser/) -![python version](https://img.shields.io/pypi/pyversions/feature-reviser?logo=python&logoColor=yellow) -[![downloads](https://img.shields.io/pypi/dm/feature-reviser)](https://pypistats.org/packages/feature-reviser) -[![docs](https://img.shields.io/badge/docs-mkdoks%20material-blue)](https://chrislemke.github.io/feature-reviser/) -[![license](https://img.shields.io/github/license/chrislemke/feature-reviser)](https://github.com/chrislemke/feature-reviser/blob/main/LICENSE) +[![testing](https://github.com/chrislemke/sk-transformers/actions/workflows/testing.yml/badge.svg?branch=main)](https://github.com/chrislemke/sk-transformers/actions/workflows/testing.yml) +[![codecov](https://codecov.io/github/chrislemke/sk-transformers/branch/main/graph/badge.svg?token=LJLXQXX6M8)](https://codecov.io/github/chrislemke/sk-transformers) +[![deploy package](https://github.com/chrislemke/sk-transformers/actions/workflows/deploy-package.yml/badge.svg)](https://github.com/chrislemke/sk-transformers/actions/workflows/deploy-package.yml) +[![pypi](https://img.shields.io/pypi/v/sk-transformers)](https://pypi.org/project/sk-transformers/) +![python version](https://img.shields.io/pypi/pyversions/sk-transformers?logo=python&logoColor=yellow) +[![downloads](https://img.shields.io/pypi/dm/sk-transformers)](https://pypistats.org/packages/sk-transformers) +[![docs](https://img.shields.io/badge/docs-mkdoks%20material-blue)](https://chrislemke.github.io/sk-transformers/) +[![license](https://img.shields.io/github/license/chrislemke/sk-transformers)](https://github.com/chrislemke/sk-transformers/blob/main/LICENSE) [![mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/) [![black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/) [![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit) ## Introduction -Every data tabular is different. Every column needs to be treated differently. [Scikit-learn](https://scikit-learn.org/stable/index.html) has a nice [collection of dataset transformers](https://scikit-learn.org/stable/data_transforms.html). But the possibilities of data transformation are infinite - one collection is simply not enough. This project provides a brought collection of data transformers. The idea is simple. It is like a well-equipped toolbox 🧰: You always find the tool you need and sometimes you get inspired by seeing a tool you did not know before. Please feel free to [contribute](https://chrislemke.github.io/feature-reviser/CONTRIBUTING/) your tools and ideas. +Every data tabular is different. Every column needs to be treated differently. [Scikit-learn](https://scikit-learn.org/stable/index.html) has a nice [collection of dataset transformers](https://scikit-learn.org/stable/data_transforms.html). But the possibilities of data transformation are infinite - one collection is simply not enough. This project provides a brought collection of data transformers. The idea is simple. It is like a well-equipped toolbox 🧰: You always find the tool you need and sometimes you get inspired by seeing a tool you did not know before. Please feel free to [contribute](https://chrislemke.github.io/sk-transformers/CONTRIBUTING/) your tools and ideas. ## Installation If you are using [Poetry](https://python-poetry.org/), you can install the package with the following command: ```bash -poetry add feature-reviser +poetry add sk_transformers ``` If you are using [pip](https://pypi.org/project/pip/), you can install the package with the following command: ```bash -pip install feature-reviser +pip install sk_transformers ``` ## installing dependencies @@ -39,13 +39,13 @@ pip install -r requirements.txt ``` ## The transformers -Data preprocessing often involves similar processes. No matter whether it's manipulating strings or numbers, etc. [Scikit-learn's pipeline](https://scikit-learn.org/stable/modules/compose.html#combining-estimators) implementation makes it easy to structure and sequence such preprocessing processes. To take advantage of this, the [`transformer`](https://github.com/chrislemke/feature-reviser/tree/main/feature_reviser/transformer) part of the project contains multiple methods that can be easily pipelined to simplify preprocessing. The list of transformers is open and will be extended permanently. Feel free to [contribute](https://chrislemke.github.io/feature-reviser/CONTRIBUTING/)! 🛠 +Data preprocessing often involves similar processes. No matter whether it's manipulating strings or numbers, etc. [Scikit-learn's pipeline](https://scikit-learn.org/stable/modules/compose.html#combining-estimators) implementation makes it easy to structure and sequence such preprocessing processes. To take advantage of this, the [`transformers`](https://github.com/chrislemke/sk-transformers/tree/main/sk-transformers/transformer) contain multiple methods that can be easily pipelined to simplify preprocessing. The list of transformers is open and will be extended permanently. Feel free to [contribute](https://chrislemke.github.io/sk-transformers/CONTRIBUTING/)! 🛠 ### Usage Let's assume you want to use some method from [NumPy's mathematical functions](https://numpy.org/doc/stable/reference/routines.math.html), to sum up the values of column `foo` and column `bar`. You could -use the [`MathExpressionTransformer`](https://chrislemke.github.io/feature-reviser/number_transformer-reference/#feature_reviser.transformer.number_transformer.MathExpressionTransformer): +use the [`MathExpressionTransformer`](https://chrislemke.github.io/sk-transformers/number_transformer-reference/#sk-transformers.transformer.number_transformer.MathExpressionTransformer): ```python -from feature_reviser import MathExpressionTransformer +from sk_transformers import MathExpressionTransformer import pandas as pd X = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]}) transformer = MathExpressionTransformer([("foo", "np.sum", "bar", {"axis": 0})]) @@ -56,17 +56,14 @@ array([[1, 4, 5], [2, 5, 7], [3, 6, 9]]) ``` -Even if we only pass one tuple to the transformer - in this example. Like with most other transformers the idea is to simplify preprocessing by giving the possibility to operate on multiple columns at the same time. In this case, the [`MathExpressionTransformer`](https://chrislemke.github.io/feature-reviser/number_transformer-reference/#feature_reviser.transformer.number_transformer.MathExpressionTransformer) has created an extra column with the name `foo_sum_bar`. - -## The feature reviser (under construction 🚧) -Finding the best features for your model is hard. In the `feature_selection` part of the project, we try to automate this process to make it a bit easier. This part of the project is still in development and is not yet ready for use. If you want to help, you can find more information in the [contributing guide](https://chrislemke.github.io/feature-reviser/CONTRIBUTING/). +Even if we only pass one tuple to the transformer - in this example. Like with most other transformers the idea is to simplify preprocessing by giving the possibility to operate on multiple columns at the same time. In this case, the [`MathExpressionTransformer`](https://chrislemke.github.io/sk-transformers/number_transformer-reference/#sk-transformers.transformer.number_transformer.MathExpressionTransformer) has created an extra column with the name `foo_sum_bar`. ## Contributing We're all kind of in the same boat. Preprocessing/feature engineering in data science is somehow very individual - every feature is different and must be handled and processed differently. But somehow we all have the same problems: sometimes date columns have to be changed. Sometimes strings have to be formatted, sometimes durations have to be calculated, etc. There is a huge number of preprocessing possibilities but we all use the same tools. [Scikit-learns pipelines](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html) help to use formalized functions. So why not also share these so-called transformers with others? This open source project has the goal to collect useful preprocessing pipeline steps. Let us all collect what we used for preprocessing and share it with others. This way we can all benefit from each other's work and save a lot of time. So if you have a preprocessing step that you use regularly, please feel free to contribute it to this project. The idea is that this is not only a toolbox but also an inspiration for what is possible. Maybe you have not thought about this preprocessing step before. -Please check out the [guide](https://chrislemke.github.io/feature-reviser/CONTRIBUTING/) on how to contribute to this project. +Please check out the [guide](https://chrislemke.github.io/sk-transformers/CONTRIBUTING/) on how to contribute to this project. ## Further information -For further information, please refer to the [documentation](https://chrislemke.github.io/feature-reviser/). +For further information, please refer to the [documentation](https://chrislemke.github.io/sk-transformers/). diff --git a/docs/base_transformer-reference.md b/docs/base_transformer-reference.md index 9dbd839..155590b 100644 --- a/docs/base_transformer-reference.md +++ b/docs/base_transformer-reference.md @@ -1 +1 @@ -:::feature_reviser.transformer.base_transformer +:::src.transformer.base_transformer diff --git a/docs/datetime_transformer-reference.md b/docs/datetime_transformer-reference.md index b9c25bd..34399d0 100644 --- a/docs/datetime_transformer-reference.md +++ b/docs/datetime_transformer-reference.md @@ -1 +1 @@ -:::feature_reviser.transformer.datetime_transformer +:::src.transformer.datetime_transformer diff --git a/docs/encoder_transformer-reference.md b/docs/encoder_transformer-reference.md index dd688c4..606970c 100644 --- a/docs/encoder_transformer-reference.md +++ b/docs/encoder_transformer-reference.md @@ -1 +1 @@ -:::feature_reviser.transformer.encoder_transformer +:::src.transformer.encoder_transformer diff --git a/docs/generic_transformer-reference.md b/docs/generic_transformer-reference.md index ec16679..f9e41ee 100644 --- a/docs/generic_transformer-reference.md +++ b/docs/generic_transformer-reference.md @@ -1 +1 @@ -:::feature_reviser.transformer.generic_transformer +:::src.transformer.generic_transformer diff --git a/docs/number_transformer-reference.md b/docs/number_transformer-reference.md index 2b397e0..5e74add 100644 --- a/docs/number_transformer-reference.md +++ b/docs/number_transformer-reference.md @@ -1 +1 @@ -:::feature_reviser.transformer.number_transformer +:::src.transformer.number_transformer diff --git a/docs/reviser-reference.md b/docs/reviser-reference.md deleted file mode 100644 index 9979a74..0000000 --- a/docs/reviser-reference.md +++ /dev/null @@ -1,3 +0,0 @@ -The reviser uses different statistical methods to determine the quality of the features. A distinction is made between categorical/discrete and numerical/continuous data. The results of the analysis are printed and returned as a pandas dataframe. - -:::feature_reviser.feature_selection.reviser diff --git a/docs/selector-reference.md b/docs/selector-reference.md deleted file mode 100644 index 60af15d..0000000 --- a/docs/selector-reference.md +++ /dev/null @@ -1,3 +0,0 @@ -The selector uses [scikit-learn's](https://scikit-learn.org/stable/index.html) [SelectKBest](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html#sklearn.feature_selection.SelectKBest) and [SelectFromModel](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectFromModel.html#sklearn.feature_selection.SelectFromModel) functions to automatically find the most useful features for a given classifier. - -:::feature_reviser.feature_selection.selector diff --git a/docs/string_transformer-reference.md b/docs/string_transformer-reference.md index 2fc2fbc..adceb3a 100644 --- a/docs/string_transformer-reference.md +++ b/docs/string_transformer-reference.md @@ -1 +1 @@ -:::feature_reviser.transformer.string_transformer +:::src.transformer.string_transformer diff --git a/docs/utils-reference.md b/docs/utils-reference.md index a098659..2b83b4a 100644 --- a/docs/utils-reference.md +++ b/docs/utils-reference.md @@ -1 +1 @@ -:::feature_reviser.utils +:::src.utils diff --git a/feature_reviser/feature_selection/reviser.py b/feature_reviser/feature_selection/reviser.py deleted file mode 100644 index 6be95af..0000000 --- a/feature_reviser/feature_selection/reviser.py +++ /dev/null @@ -1,82 +0,0 @@ -# -*- coding: utf-8 -*- -from typing import List, Tuple - -import numpy as np -import pandas as pd -from sklearn.base import BaseEstimator -from sklearn.feature_selection import chi2, f_classif, mutual_info_classif - -from feature_reviser.utils import check_data, prepare_categorical_data - - -def revise_classifier( - clf: BaseEstimator, - X: pd.DataFrame, - y: pd.Series, - cat_features: List[Tuple[str, int]], -) -> Tuple[pd.DataFrame, pd.DataFrame]: - """ - Examens the features of `X` for a provided classifier. - It prints out different statistics and returns those as a tuple of dataframes. - - Args: - clf (BaseEstimator): The classifier used for prediction of the features. This will be used to determine the `feature_importances_`. - df (pandas.DataFrame): The dataframe containing the categorical and numerical features. - y (pandas.Series): The target variable. - cat_features (List[Tuple[str, int]]): A tuple containing the names of the categorical features and the corresponding threshold. - If the number of unique values is greater than the threshold, the feature is considered numerical and not categorical. - Raises: - ValueError: if the `cat_features` are not in the dataframe. - TypeError: If the classifier does not contain the `fit` or the `feature_importances_ attribute. - - - Returns: - Tuple[pandas.DataFrame, pandas.DataFrame]: Tuple containing the result dataframes for categorical and numerical features. - """ - - check_data(X, y) - X = prepare_categorical_data(X, cat_features) - - cat_df = X.select_dtypes(include=["category"]) - num_df = X.select_dtypes(include=[np.float64, np.int64]) - - if len(X.select_dtypes(include=["category"]).columns) == 0: - raise ValueError( - "cat_features must be in the dataframe! Check if the threshold is maybe a bit too low." - ) - - if not hasattr(clf, "fit"): - raise AttributeError("Classifier does not have fit method!") - - clf.fit(cat_df, y) - if not hasattr(clf, "feature_importances_"): - raise AttributeError("Classifier does not have feature_importances_ attribute!") - cat_feature_importances = clf.feature_importances_ - - clf.fit(num_df, y) - if not hasattr(clf, "feature_importances_"): - raise AttributeError("Classifier does not have feature_importances_ attribute!") - num_feature_importances = clf.feature_importances_ - - chi_2, chi2_p_value = chi2(cat_df, y) - mi = mutual_info_classif(cat_df, y, discrete_features=True) - f_statistic, f_classif_p_value = f_classif(num_df, y) - num_corr = ( - pd.concat([num_df, y], axis=1).corr(method="spearman").to_numpy()[:-1, -1] - ) - - result_cat_df = pd.DataFrame( - [chi_2, chi2_p_value, mi, cat_feature_importances], - columns=cat_df.columns, - index=["chi_2", "p_value", "mutal_information", "feature_importance"], - ) - results_num_df = pd.DataFrame( - [f_statistic, f_classif_p_value, num_feature_importances, num_corr], - columns=num_df.columns, - index=["f_statistic", "p_value", "feature_importance", "correlation"], - ) - - print(result_cat_df) - print(results_num_df) - - return result_cat_df, results_num_df diff --git a/feature_reviser/feature_selection/selector.py b/feature_reviser/feature_selection/selector.py deleted file mode 100644 index 27a6775..0000000 --- a/feature_reviser/feature_selection/selector.py +++ /dev/null @@ -1,142 +0,0 @@ -# -*- coding: utf-8 -*- - -from typing import List, Optional, Tuple - -import numpy as np -import pandas as pd -from feature_engine.selection import DropCorrelatedFeatures -from sklearn.base import BaseEstimator -from sklearn.feature_selection import SelectFromModel, SelectKBest, chi2, f_classif - -from feature_reviser.utils import check_data, prepare_categorical_data - - -def select_with_classifier( - clf: BaseEstimator, - X: pd.DataFrame, - y: pd.Series, - select_k_best_first: bool = False, - cat_features: Optional[List[Tuple[str, int]]] = None, - cat_k_best: Optional[int] = None, - num_k_best: Optional[int] = None, - drop_correlated: bool = False, - drop_correlated_threshold: float = 0.6, - model_select_threshold: Optional[float] = None, - max_features: Optional[int] = None, -) -> Tuple[pd.DataFrame, pd.Series]: - """ - Optionally examens the features of `X` using `SelectKBest` for categorical and numerical features before the features of `X` - are selected using `SelectFromModel` with the provided classifier. - - Args: - clf (BaseEstimator): The classifier used for examine the features. - X (pandas.DataFrame): The dataframe containing the categorical and numerical features. - y (pandas.Series): The target variable. - select_k_best_first (bool): If `True` the features are selected using `SelectKBest` first. Defaults to False. - cat_features (Optional[List[Tuple[str, int]]]): A tuple containing the names of the categorical features and the corresponding threshold. - If the number of unique values is greater than the threshold, the feature is considered numerical and not categorical. - This is needed if `select_k_best_first` is `True`. - cat_k_best (Optional[int]): The max number of categorical features to select using `SelectKBest`. - Defaults to None. This is needed if `select_k_best_first` is `True`. - num_k_best (Optional[int]): The max number of numerical features to select using `SelectKBest`. - Defaults to None. This is needed if `select_k_best_first` is `True`. - drop_correlated (bool): If `True` the correlated features are dropped. Defaults to False. - drop_correlated_threshold (float): The threshold used for dropping correlated features. Defaults to 0.6. - model_select_threshold (Optional[float]): The threshold used for `SelectFromModel`. Defaults to None. - max_features (Optional[int]): The max number of features to select using `SelectFromModel`. Defaults to None. - - Raises: - ValueError: if the `cat_features` are not in the dataframe. - ValueError: If `select_k_best_first` is `True` and `cat_features`, `num_features`, `cat_k_best` or `num_k_best` are `None`. - TypeError: If the classifier does not contain the `fit` attribute. - - Returns: - Tuple[pandas.DataFrame, pandas.Series]: Tuple containing the selected features and the target variable. - """ - X = X.copy() - check_data(X, y) - - if cat_features: - # pylint: disable=consider-using-set-comprehension - if not {f[0] for f in cat_features}.issubset(set(X.columns)): - raise ValueError("cat_features must be in the dataframe!") - X = prepare_categorical_data(X, cat_features) - - if len(X.select_dtypes(include=["category"]).columns) == 0: - raise ValueError( - "cat_features must be in the dataframe! Check if the threshold is maybe a bit too low." - ) - - if not hasattr(clf, "fit"): - raise AttributeError("Classifier does not have fit method!") - - if select_k_best_first: - if cat_features is None or cat_k_best is None or num_k_best is None: - raise ValueError( - "If `select_k_best_first` is set to `True`, `cat_features`, `num_features`, `cat_k_best`, and `num_k_best` must be provided!" - ) - - cat_df = X.select_dtypes(include=["category"]) - num_df = X.select_dtypes(include=[np.float64, np.int64]) - - print("Selecting categorical features...") - cat_transformer = SelectKBest(chi2, k=min(cat_k_best, cat_df.shape[1] - 1)).fit( - cat_df, y - ) - print("Selecting numerical features...") - num_transformer = SelectKBest( - f_classif, k=min(num_k_best, num_df.shape[1] - 1) - ).fit(num_df, y) - - cat_x = cat_transformer.transform(cat_df) - num_x = num_transformer.transform(num_df) - - columns = [ - cat_df.columns[i] for i in cat_transformer.get_support(indices=True) - ] + [num_df.columns[i] for i in num_transformer.get_support(indices=True)] - - print( - f"The following columns were selected using the `SelectKBest` algorithm: {columns}.".replace( - "[", "" - ) - .replace("]", "") - .replace("'", "") - ) - X = pd.DataFrame(data=np.column_stack((cat_x, num_x)), columns=columns) - - if drop_correlated: - print("Dropping correlated features...") - X = DropCorrelatedFeatures( - variables=list(num_df.columns), - method="pearson", - threshold=drop_correlated_threshold, - ).fit_transform(X) - X = DropCorrelatedFeatures( - variables=list(cat_df.columns), - method="spearman", - threshold=drop_correlated_threshold, - ).fit_transform(X) - - print("Selecting features with classifier...") - - selector = SelectFromModel( - estimator=clf, - threshold=model_select_threshold, - max_features=max_features or X.shape[1], - ).fit(X, y) - selected = selector.transform(X) - columns = [X.columns[i] for i in selector.get_support(indices=True)] - print( - f"The following columns were selected using the `{clf.__class__.__name__}`: {columns}.".replace( - "[", "" - ) - .replace("]", "") - .replace("'", "") - ) - return ( - pd.DataFrame( - data=selected, - columns=columns, - ), - y, - ) diff --git a/mkdocs.yml b/mkdocs.yml index c762dc7..6b61a27 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,7 +1,7 @@ -site_name: "feature-reviser" +site_name: "sk-transformers" site_dir: public docs_dir: ./docs -repo_url: https://github.com/chrislemke/feature-reviser +repo_url: https://github.com/chrislemke/sk-transformers markdown_extensions: - attr_list - md_in_html @@ -51,15 +51,12 @@ plugins: - mkdocstrings nav: - - API reference: - - Reviser: reviser-reference.md - - Selector: selector-reference.md - - Transformer: - - Generic transformer: generic_transformer-reference.md - - Encoder transformer: encoder_transformer-reference.md - - Datetime transformer: datetime_transformer-reference.md - - Number Transformer: number_transformer-reference.md - - String transformer: string_transformer-reference.md - - Base transformer: base_transformer-reference.md + - Transformer: + - Generic transformer: generic_transformer-reference.md + - Encoder transformer: encoder_transformer-reference.md + - Datetime transformer: datetime_transformer-reference.md + - Number Transformer: number_transformer-reference.md + - String transformer: string_transformer-reference.md + - Base transformer: base_transformer-reference.md - Utils: utils-reference.md - How to contribute: CONTRIBUTING.md diff --git a/poetry.lock b/poetry.lock index 174a45c..dac23be 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,15 +1,24 @@ +# This file is automatically @generated by Poetry and should not be changed by hand. + [[package]] name = "astroid" -version = "2.12.12" +version = "2.12.13" description = "An abstract syntax tree for Python with inference support." category = "dev" optional = false python-versions = ">=3.7.2" +files = [ + {file = "astroid-2.12.13-py3-none-any.whl", hash = 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keywords = [ "machine learning", "data science" ] -homepage = "https://chrislemke.github.io/feature-reviser/" -documentation = "https://chrislemke.github.io/feature-reviser/" -repository = "https://github.com/chrislemke/feature-reviser" +homepage = "https://chrislemke.github.io/sk-transformers/" +documentation = "https://chrislemke.github.io/sk-transformers/" +repository = "https://github.com/chrislemke/sk-transformers/" [tool.poetry.dependencies] -python = ">=3.8,<3.11" +python = ">=3.8,<3.12" joblib = "1.2.0" -numpy = ">=1.20.3,<=1.23.4" -scikit-learn = ">=1.0.2,<=1.1.2" -pandas = ">=1.1.3,<=1.5.0" -feature-engine = "1.4.1" -phonenumbers = "8.12.57" +numpy = "1.23.5" +scikit-learn = "1.2.0" +pandas = "1.5.2" +feature-engine = "1.5.2" +phonenumbers = "8.13.2" [tool.poetry.group.dev.dependencies] bandit = "^1.7.4" -black = "^22.10.0" -isort = "^5.10.1" -mkdocs = "1.4.0" -mkdocstrings = "0.18.1" -mkdocs-material = "8.5.6" -mypy = "^0.982" +black = "^22.12.0" +isort = "^5.11.1" +mypy = 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DurationCalculatorTransformer, TimestampTransformer, ) -from feature_reviser.transformer.encoder_transformer import MeanEncoderTransformer -from feature_reviser.transformer.generic_transformer import ( +from src.transformer.encoder_transformer import MeanEncoderTransformer +from src.transformer.generic_transformer import ( AggregateTransformer, ColumnDropperTransformer, DtypeTransformer, @@ -15,8 +15,8 @@ ValueIndicatorTransformer, ValueReplacerTransformer, ) -from feature_reviser.transformer.number_transformer import MathExpressionTransformer -from feature_reviser.transformer.string_transformer import ( +from src.transformer.number_transformer import MathExpressionTransformer +from src.transformer.string_transformer import ( EmailTransformer, IPAddressEncoderTransformer, PhoneTransformer, diff --git a/feature_reviser/transformer/base_transformer.py b/src/transformer/base_transformer.py similarity index 100% rename from feature_reviser/transformer/base_transformer.py rename to src/transformer/base_transformer.py diff --git a/feature_reviser/transformer/datetime_transformer.py b/src/transformer/datetime_transformer.py similarity index 95% rename from feature_reviser/transformer/datetime_transformer.py rename to src/transformer/datetime_transformer.py index 9921ffa..fe24d5a 100644 --- a/feature_reviser/transformer/datetime_transformer.py +++ b/src/transformer/datetime_transformer.py @@ -4,8 +4,8 @@ import pandas as pd -from feature_reviser.transformer.base_transformer import BaseTransformer -from feature_reviser.utils import check_ready_to_transform +from src.transformer.base_transformer import BaseTransformer +from src.utils import check_ready_to_transform # pylint: disable= missing-function-docstring, unused-argument diff --git a/feature_reviser/transformer/encoder_transformer.py b/src/transformer/encoder_transformer.py similarity index 100% rename from feature_reviser/transformer/encoder_transformer.py rename to src/transformer/encoder_transformer.py diff --git a/feature_reviser/transformer/generic_transformer.py b/src/transformer/generic_transformer.py similarity index 96% rename from feature_reviser/transformer/generic_transformer.py rename to src/transformer/generic_transformer.py index 17cfbbc..3b396e8 100644 --- a/feature_reviser/transformer/generic_transformer.py +++ b/src/transformer/generic_transformer.py @@ -7,8 +7,8 @@ import pandas as pd from sklearn.preprocessing import FunctionTransformer -from feature_reviser.transformer.base_transformer import BaseTransformer -from feature_reviser.utils import check_ready_to_transform +from src.transformer.base_transformer import BaseTransformer +from src.utils import check_ready_to_transform # pylint: disable=missing-function-docstring, unused-argument @@ -18,7 +18,7 @@ class DtypeTransformer(BaseTransformer): Transformer that converts a column to a different dtype. Example: - >>> from feature_reviser import DtypeTransformer + >>> from sk_transformers import DtypeTransformer >>> import numpy as np >>> import pandas as pd >>> X = pd.DataFrame({"foo": [1, 2, 3], "bar": ["a", "a", "b"]}) @@ -58,7 +58,7 @@ class AggregateTransformer(BaseTransformer): to understand how to use function for aggregation. Other than Pandas function this transformer only support functions and string-names. Example: - >>> from feature_reviser import AggregateTransformer + >>> from sk_transformers import AggregateTransformer >>> import pandas as pd >>> X = pd.DataFrame({"foo": ["mr", "mr", "ms", "ms", "ms", "mr", "mr", "mr", "mr", "ms"], "bar": [46, 32, 78, 48, 93, 68, 53, 38, 76, 56]}) >>> transformer = AggregateTransformer([("foo", "bar", ["mean"])]) @@ -132,7 +132,7 @@ class FunctionsTransformer(BaseTransformer): this transformer *does not* support the `inverse_func`, `accept_sparse`, `feature_names_out` and, `inv_kw_args` parameters. Example: - >>> from feature_reviser import FunctionsTransformer + >>> from sk_transformers import FunctionsTransformer >>> import pandas as pd >>> X = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]}) >>> transformer = FunctionsTransformer([("foo", np.log1p, None), ("bar", np.sqrt, None)]) @@ -180,7 +180,7 @@ class MapTransformer(BaseTransformer): For this it uses the `pandas.Series.map` method. Example: - >>> from feature_reviser import MapTransformer + >>> from sk_transformers import MapTransformer >>> import pandas as pd >>> X = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]}) >>> transformer = MapTransformer([("foo", lambda x: x + 1)]) @@ -281,7 +281,7 @@ class ValueIndicatorTransformer(BaseTransformer): `NaN`, `None` or `np.nan` are **Not** caught by this implementation. Example: - >>> from feature_reviser import ValueIndicatorTransformer + >>> from sk_transformers import ValueIndicatorTransformer >>> import pandas as pd >>> X = pd.DataFrame({"foo": [1, -999, 3], "bar": ["a", "-999", "c"]}) >>> transformer = NaNIndicatorTransformer([("foo", -999), ("bar", "-999")]) @@ -365,7 +365,7 @@ class ValueReplacerTransformer(BaseTransformer): original type. It may happen, that this type changing fails if the modified column is not compatible with its original type. Example: - >>> from feature_reviser import ValueReplacerTransformer + >>> from sk_transformers import ValueReplacerTransformer >>> import pandas as pd >>> X = pd.DataFrame({"foo": ["0000-01-01", "2022/01/08", "bar", "1982-12-7", "28-09-2022"]}) >>> transformer = ( diff --git a/feature_reviser/transformer/number_transformer.py b/src/transformer/number_transformer.py similarity index 95% rename from feature_reviser/transformer/number_transformer.py rename to src/transformer/number_transformer.py index 8373f3a..4416e2b 100644 --- a/feature_reviser/transformer/number_transformer.py +++ b/src/transformer/number_transformer.py @@ -8,8 +8,8 @@ import numpy as np import pandas as pd -from feature_reviser.transformer.base_transformer import BaseTransformer -from feature_reviser.utils import check_ready_to_transform +from src.transformer.base_transformer import BaseTransformer +from src.utils import check_ready_to_transform # pylint: disable= missing-function-docstring, unused-argument @@ -23,7 +23,7 @@ class MathExpressionTransformer(BaseTransformer): various NumPy methods return values which are not fitting the size of the source column. Example: - >>> from feature_reviser import MathExpressionTransformer + >>> from sk_transformers import MathExpressionTransformer >>> import pandas as pd >>> X = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]}) >>> transformer = MathExpressionTransformer([("foo", "np.sum", "bar", {"axis": 0})]) diff --git a/feature_reviser/transformer/string_transformer.py b/src/transformer/string_transformer.py similarity index 98% rename from feature_reviser/transformer/string_transformer.py rename to src/transformer/string_transformer.py index d5a98a9..66f75db 100644 --- a/feature_reviser/transformer/string_transformer.py +++ b/src/transformer/string_transformer.py @@ -11,8 +11,8 @@ import pandas as pd import phonenumbers -from feature_reviser.transformer.base_transformer import BaseTransformer -from feature_reviser.utils import check_ready_to_transform +from src.transformer.base_transformer import BaseTransformer +from src.utils import check_ready_to_transform # pylint: disable= missing-function-docstring, unused-argument @@ -292,7 +292,7 @@ class StringSlicerTransformer(BaseTransformer): the python quirk of writing a tuple with a single argument with the trailing comma. Example: - >>> from feature_reviser import StringSlicerTransformer + >>> from sk_transformers import StringSlicerTransformer >>> import pandas as pd >>> X = pd.DataFrame({"foo": ["abc", "def", "ghi"], "bar": ["jkl", "mno", "pqr"]}) >>> transformer = StringSlicerTransformer([("foo", (0, 3, 2)), ("bar", (2,))]) diff --git a/feature_reviser/utils.py b/src/utils.py similarity index 100% rename from feature_reviser/utils.py rename to src/utils.py diff --git a/tests/conftest.py b/tests/conftest.py index be7cb5e..cef692e 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,5 +1,3 @@ -# -*- coding: utf-8 -*- - import pandas as pd import pytest from sklearn.preprocessing import OrdinalEncoder, StandardScaler diff --git a/tests/test_feature_selection/test_reviser.py b/tests/test_feature_selection/test_reviser.py deleted file mode 100644 index 112444e..0000000 --- a/tests/test_feature_selection/test_reviser.py +++ /dev/null @@ -1,55 +0,0 @@ -# -*- coding: utf-8 -*- - -import pytest - -from feature_reviser.feature_selection.reviser import revise_classifier - -# pylint: disable=missing-function-docstring - - -def test_revise_classifier_for_low_threshold(clf, X, y) -> None: - with pytest.raises(ValueError) as error: - revise_classifier(clf, X, y, [("c", 1), ("d", 1), ("e", 1)]) - - assert ( - "cat_features must be in the dataframe! Check if the threshold is maybe a bit too low." - == str(error.value) - ) - - -def test_revise_classifier_for_fit_attribute(X, y) -> None: - with pytest.raises(AttributeError) as error: - revise_classifier(None, X, y, [("c", 10), ("d", 10), ("e", 10)]) - - assert "Classifier does not have fit method!" == str(error.value) - - -def test_revise_classifier_columns(clf, X, y) -> None: - result = revise_classifier(clf, X, y, [("c", 10), ("d", 10), ("e", 10)]) - assert list(result[0].columns) == ["c", "d", "e"] - assert list(result[1].columns) == ["a", "b"] - - -def test_revise_classifier_feature_importance(clf, X, y) -> None: - result = revise_classifier(clf, X, y, [("c", 10), ("d", 10), ("e", 10)]) - assert result[0].iloc[3].sum() == 1.0 - assert result[1].iloc[2].sum() == 1.0 - - -def test_revise_classifier_correlation(clf, X, y) -> None: - result = revise_classifier(clf, X, y, [("c", 10), ("d", 10), ("e", 10)]) - assert result[0].iloc[3].sum() == 1.0 - assert result[1].iloc[3].sum() == -1.7056057308448835 - - -def test_revise_classifier_chi2(clf, X, y) -> None: - result = revise_classifier(clf, X, y, [("c", 10), ("d", 10), ("e", 10)]) - assert result[0].iloc[0][0] == 3.333333333333333 - assert result[0].iloc[0][1] == 0.625 - assert result[0].iloc[0][2] == 0.1975308641975312 - - -def test_revise_classifier_f_statistic(clf, X, y) -> None: - result = revise_classifier(clf, X, y, [("c", 10), ("d", 10), ("e", 10)]) - assert result[1].iloc[0][0] == 21.333333333333332 - assert result[1].iloc[0][1] == 23.9456209150327 diff --git a/tests/test_feature_selection/test_selector.py b/tests/test_feature_selection/test_selector.py deleted file mode 100644 index 34007c0..0000000 --- a/tests/test_feature_selection/test_selector.py +++ /dev/null @@ -1,65 +0,0 @@ -# -*- coding: utf-8 -*- - -import pandas as pd -import pytest - -from feature_reviser.feature_selection.selector import select_with_classifier - -# pylint: disable=missing-function-docstring - - -def test_select_with_classifier_for_low_threshold(clf, X, y): - with pytest.raises(ValueError) as error: - select_with_classifier( - clf, X, y, True, [("c", 1), ("d", 1), ("e", 1)], 2, 1, 0, 2 - ) - - assert ( - "cat_features must be in the dataframe! Check if the threshold is maybe a bit too low." - == str(error.value) - ) - - -def test_select_with_classifier_for_fit_attribute(X, y) -> None: - with pytest.raises(AttributeError) as error: - select_with_classifier(None, X, y) - - assert "Classifier does not have fit method!" == str(error.value) - - -def test_select_with_classifier_return_types(clf, X, y) -> None: - result = select_with_classifier(clf, X, y) - assert isinstance(result[0], pd.DataFrame) - assert isinstance(result[1], pd.Series) - - -def test_select_with_classifier_shape_without_k_best(clf, X, y) -> None: - result = select_with_classifier(clf, X, y) - assert result[0].shape == (10, 1) - assert result[1].shape == (10,) - - -def test_select_with_classifier_for_missing_parameter(clf, X, y) -> None: - with pytest.raises(ValueError) as error: - select_with_classifier(clf, X, y, True) - - assert ( - "If `select_k_best_first` is set to `True`, `cat_features`, `num_features`, `cat_k_best`, and `num_k_best` must be provided!" - == str(error.value) - ) - - -def test_select_with_classifier_column_shape_with_k_best(clf, X, y) -> None: - result = select_with_classifier( - clf=clf, - X=X, - y=y, - select_k_best_first=True, - cat_features=[("c", 10), ("d", 10), ("e", 10)], - cat_k_best=2, - num_k_best=1, - model_select_threshold=0, - max_features=2, - ) - assert result[0].shape == (10, 2) - assert result[1].shape == (10,) diff --git a/tests/test_transformer/test_datetime_transformer.py b/tests/test_transformer/test_datetime_transformer.py index c2b61f5..c357b93 100644 --- a/tests/test_transformer/test_datetime_transformer.py +++ b/tests/test_transformer/test_datetime_transformer.py @@ -1,9 +1,8 @@ -# -*- coding: utf-8 -*- import numpy as np import pytest from sklearn.pipeline import make_pipeline -from feature_reviser import DurationCalculatorTransformer, TimestampTransformer +from src import DurationCalculatorTransformer, TimestampTransformer # pylint: disable=missing-function-docstring, missing-class-docstring diff --git a/tests/test_transformer/test_encoder_transformer.py b/tests/test_transformer/test_encoder_transformer.py index 0c4afe0..7ef507c 100644 --- a/tests/test_transformer/test_encoder_transformer.py +++ b/tests/test_transformer/test_encoder_transformer.py @@ -1,9 +1,7 @@ -# -*- coding: utf-8 -*- - import numpy as np from sklearn.pipeline import make_pipeline -from feature_reviser.transformer.encoder_transformer import MeanEncoderTransformer +from src.transformer.encoder_transformer import MeanEncoderTransformer # pylint: disable=missing-function-docstring, missing-class-docstring diff --git a/tests/test_transformer/test_generic_transformer.py b/tests/test_transformer/test_generic_transformer.py index 738349e..2499c86 100644 --- a/tests/test_transformer/test_generic_transformer.py +++ b/tests/test_transformer/test_generic_transformer.py @@ -1,10 +1,8 @@ -# -*- coding: utf-8 -*- - import numpy as np import pytest from sklearn.pipeline import make_pipeline -from feature_reviser.transformer.generic_transformer import ( +from src.transformer.generic_transformer import ( AggregateTransformer, ColumnDropperTransformer, DtypeTransformer, diff --git a/tests/test_transformer/test_number_transformer.py b/tests/test_transformer/test_number_transformer.py index ae49cce..c926042 100644 --- a/tests/test_transformer/test_number_transformer.py +++ b/tests/test_transformer/test_number_transformer.py @@ -1,10 +1,8 @@ -# -*- coding: utf-8 -*- - import numpy as np import pytest from sklearn.pipeline import make_pipeline -from feature_reviser import MathExpressionTransformer +from src import MathExpressionTransformer # pylint: disable=missing-function-docstring, missing-class-docstring diff --git a/tests/test_transformer/test_string_transformer.py b/tests/test_transformer/test_string_transformer.py index a7c7c7f..44dd832 100644 --- a/tests/test_transformer/test_string_transformer.py +++ b/tests/test_transformer/test_string_transformer.py @@ -1,10 +1,8 @@ -# -*- coding: utf-8 -*- - import numpy as np import pandas as pd from sklearn.pipeline import make_pipeline -from feature_reviser import ( +from src import ( EmailTransformer, IPAddressEncoderTransformer, PhoneTransformer, diff --git a/tests/test_utils.py b/tests/test_utils.py index 47591ca..022694c 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,14 +1,8 @@ -# -*- coding: utf-8 -*- - import numpy as np import pandas as pd import pytest -from feature_reviser.utils import ( - check_data, - check_ready_to_transform, - prepare_categorical_data, -) +from src.utils import check_data, check_ready_to_transform, prepare_categorical_data # pylint: disable=missing-function-docstring