From 4a6f1540f25650a1c4fc980d7d2004cd44525e99 Mon Sep 17 00:00:00 2001 From: Vincent M Date: Mon, 28 Oct 2024 13:30:06 +0100 Subject: [PATCH 01/24] remove intermediary folders (#1123) --- doc/reference/building_a_pipeline.rst | 21 ---- doc/reference/cleaning_a_dataframe.rst | 12 -- doc/reference/downloading_a_dataset.rst | 25 ----- doc/reference/encoding_a_column.rst | 24 ---- doc/reference/generating_a_report.rst | 22 ---- doc/reference/index.rst | 141 ++++++++++++++++++++++-- doc/reference/joining_dataframes.rst | 23 ---- 7 files changed, 133 insertions(+), 135 deletions(-) delete mode 100644 doc/reference/building_a_pipeline.rst delete mode 100644 doc/reference/cleaning_a_dataframe.rst delete mode 100644 doc/reference/downloading_a_dataset.rst delete mode 100644 doc/reference/encoding_a_column.rst delete mode 100644 doc/reference/generating_a_report.rst delete mode 100644 doc/reference/joining_dataframes.rst diff --git a/doc/reference/building_a_pipeline.rst b/doc/reference/building_a_pipeline.rst deleted file mode 100644 index 674a6e2a1..000000000 --- a/doc/reference/building_a_pipeline.rst +++ /dev/null @@ -1,21 +0,0 @@ -.. _building_a_pipeline_ref: -.. currentmodule:: skrub - -Building a pipeline -=================== - -.. autosummary:: - :toctree: generated/ - :template: base.rst - :nosignatures: - - tabular_learner - -.. autosummary:: - :toctree: generated/ - :template: base.rst - :nosignatures: - - TableVectorizer - SelectCols - DropCols diff --git a/doc/reference/cleaning_a_dataframe.rst b/doc/reference/cleaning_a_dataframe.rst deleted file mode 100644 index 7cbf2dc88..000000000 --- a/doc/reference/cleaning_a_dataframe.rst +++ /dev/null @@ -1,12 +0,0 @@ -.. _cleaning_a_dataframe_ref: -.. currentmodule:: skrub - -Cleaning a dataframe -==================== - -.. autosummary:: - :toctree: generated/ - :template: base.rst - :nosignatures: - - deduplicate diff --git a/doc/reference/downloading_a_dataset.rst b/doc/reference/downloading_a_dataset.rst deleted file mode 100644 index 3bbb423f5..000000000 --- a/doc/reference/downloading_a_dataset.rst +++ /dev/null @@ -1,25 +0,0 @@ -.. _downloading_a_dataset_ref: -.. currentmodule:: skrub.datasets - -Downloading a dataset -===================== - -.. autosummary:: - :toctree: generated/ - :template: base.rst - :nosignatures: - - fetch_employee_salaries - fetch_medical_charge - fetch_midwest_survey - fetch_open_payments - fetch_road_safety - fetch_traffic_violations - fetch_drug_directory - fetch_world_bank_indicator - fetch_movielens - fetch_credit_fraud - fetch_ken_table_aliases - fetch_ken_types - fetch_ken_embeddings - make_deduplication_data diff --git a/doc/reference/encoding_a_column.rst b/doc/reference/encoding_a_column.rst deleted file mode 100644 index ace5402a7..000000000 --- a/doc/reference/encoding_a_column.rst +++ /dev/null @@ -1,24 +0,0 @@ -.. _encoding_a_column_ref: -.. currentmodule:: skrub - -Encoding a column -================= - -.. autosummary:: - :toctree: generated/ - :template: base.rst - :nosignatures: - - GapEncoder - MinHashEncoder - SimilarityEncoder - DatetimeEncoder - ToCategorical - ToDatetime - -.. autosummary:: - :toctree: generated/ - :template: base.rst - :nosignatures: - - to_datetime diff --git a/doc/reference/generating_a_report.rst b/doc/reference/generating_a_report.rst deleted file mode 100644 index e837608d9..000000000 --- a/doc/reference/generating_a_report.rst +++ /dev/null @@ -1,22 +0,0 @@ -.. currentmodule:: skrub - -.. _generating_a_report_ref: - -Generating an HTML report -========================= - -.. autosummary:: - :toctree: generated/ - :template: base.rst - :nosignatures: - - TableReport - -.. autosummary:: - :toctree: generated/ - :template: base.rst - :nosignatures: - - patch_display - unpatch_display - column_associations diff --git a/doc/reference/index.rst b/doc/reference/index.rst index b4fca26ba..6cf4e29c1 100644 --- a/doc/reference/index.rst +++ b/doc/reference/index.rst @@ -1,15 +1,140 @@ +.. currentmodule:: skrub + ### API ### This page lists all available functions and classes of `skrub`. -.. toctree:: - :maxdepth: 2 +.. _joining_dataframes_ref: + +Joining dataframes +================== + +.. autosummary:: + :toctree: generated/ + :template: base.rst + :nosignatures: + + Joiner + AggJoiner + MultiAggJoiner + AggTarget + InterpolationJoiner + +.. autosummary:: + :toctree: generated/ + :template: base.rst + :nosignatures: + + fuzzy_join + + +.. _encoding_a_column_ref: + +Encoding a column +================= + +.. autosummary:: + :toctree: generated/ + :template: base.rst + :nosignatures: + + GapEncoder + MinHashEncoder + SimilarityEncoder + DatetimeEncoder + ToCategorical + ToDatetime + +.. autosummary:: + :toctree: generated/ + :template: base.rst + :nosignatures: + + to_datetime + + +.. _building_a_pipeline_ref: + +Building a pipeline +=================== + +.. autosummary:: + :toctree: generated/ + :template: base.rst + :nosignatures: + + TableVectorizer + SelectCols + DropCols + +.. autosummary:: + :toctree: generated/ + :template: base.rst + :nosignatures: + + tabular_learner + + +.. _generating_a_report_ref: + +Generating an HTML report +========================= + +.. autosummary:: + :toctree: generated/ + :template: base.rst + :nosignatures: + + TableReport + +.. autosummary:: + :toctree: generated/ + :template: base.rst + :nosignatures: + + patch_display + unpatch_display + column_associations + + +.. _cleaning_a_dataframe_ref: + +Cleaning a dataframe +==================== + +.. autosummary:: + :toctree: generated/ + :template: base.rst + :nosignatures: + + deduplicate + + +.. _downloading_a_dataset_ref: + +.. currentmodule:: skrub.datasets + +Downloading a dataset +===================== + +.. autosummary:: + :toctree: generated/ + :template: base.rst + :nosignatures: - joining_dataframes - encoding_a_column - building_a_pipeline - generating_a_report - cleaning_a_dataframe - downloading_a_dataset + fetch_employee_salaries + fetch_medical_charge + fetch_midwest_survey + fetch_open_payments + fetch_road_safety + fetch_traffic_violations + fetch_drug_directory + fetch_world_bank_indicator + fetch_movielens + fetch_credit_fraud + fetch_ken_table_aliases + fetch_ken_types + fetch_ken_embeddings + make_deduplication_data diff --git a/doc/reference/joining_dataframes.rst b/doc/reference/joining_dataframes.rst deleted file mode 100644 index bb534f2db..000000000 --- a/doc/reference/joining_dataframes.rst +++ /dev/null @@ -1,23 +0,0 @@ -.. _joining_dataframes_ref: -.. currentmodule:: skrub - -Joining dataframes -================== - -.. autosummary:: - :toctree: generated/ - :template: base.rst - :nosignatures: - - Joiner - AggJoiner - MultiAggJoiner - AggTarget - InterpolationJoiner - -.. autosummary:: - :toctree: generated/ - :template: base.rst - :nosignatures: - - fuzzy_join From cb391c55d185bef35b740766a5eef9c3024b94f9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=B4me=20Dock=C3=A8s?= Date: Wed, 6 Nov 2024 14:45:38 +0100 Subject: [PATCH 02/24] update lock files (#1129) * Update CI lock files [doc build] * fix flaky test --------- Co-authored-by: Lock file bot --- pixi.lock | 5324 +++++++++++++------------ skrub/_dataframe/tests/test_common.py | 4 +- 2 files changed, 2741 insertions(+), 2587 deletions(-) diff --git a/pixi.lock b/pixi.lock index b643f8185..0f3f9c13e 100644 --- a/pixi.lock +++ b/pixi.lock @@ -21,7 +21,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py39h15c3d72_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.6.3-py39h9399b63_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.6.4-py39h9399b63_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_0.conda @@ -59,14 +59,14 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lame-3.100-h166bdaf_1003.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.16-hb7c19ff_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/libasprintf-0.22.5-he8f35ee_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libasprintf-devel-0.22.5-he8f35ee_3.conda - 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- conda: https://conda.anaconda.org/conda-forge/linux-64/libllvm15-15.0.7-hb3ce162_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libllvm19-19.1.2-ha7bfdaf_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libogg-1.3.5-h4ab18f5_0.conda @@ -104,10 +103,10 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.44-hadc24fc_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libpq-16.4-h2d7952a_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.46.1-hadc24fc_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.47.0-hadc24fc_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-14.2.0-hc0a3c3a_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-14.2.0-h4852527_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-256.7-h2774228_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-256.7-h2774228_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.0-he137b08_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.bz2 @@ -121,12 +120,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py39h9399b63_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.4.3-py39hf3d152e_2.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.4.3-py39h2fa2bec_2.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/mysql-common-9.0.1-h266115a_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/mysql-libs-9.0.1-he0572af_1.conda + - 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__glibc >=2.17,<3.0.a0 + - libgcc >=13 + - python >=3.12,<3.13.0a0 + - python_abi 3.12.* *_cp312 + license: Apache-2.0 + license_family: Apache + purls: + - pkg:pypi/unicodedata2?source=hash-mapping + size: 368550 + timestamp: 1729704685856 - kind: conda name: uri-template version: 1.3.0 diff --git a/skrub/_dataframe/tests/test_common.py b/skrub/_dataframe/tests/test_common.py index 35accaeab..907b9a3a8 100644 --- a/skrub/_dataframe/tests/test_common.py +++ b/skrub/_dataframe/tests/test_common.py @@ -607,8 +607,8 @@ def test_max(df_module): def test_std(df_module): - assert ns.std(df_module.example_column) == np.nanstd( - ns.to_numpy(df_module.example_column), ddof=1 + assert float(ns.std(df_module.example_column)) == pytest.approx( + float(np.nanstd(ns.to_numpy(df_module.example_column), ddof=1)) ) From ac21c8e50f1a014c17ad9bd85816a045b9f74147 Mon Sep 17 00:00:00 2001 From: skrub-bot Date: Tue, 12 Nov 2024 10:05:48 +0100 Subject: [PATCH 03/24] Update CI lock files [doc build] (#1136) --- pixi.lock | 7873 +++++++++++++++++++++++++++-------------------------- 1 file changed, 3965 insertions(+), 3908 deletions(-) diff --git a/pixi.lock b/pixi.lock index 0f3f9c13e..ac953df24 100644 --- a/pixi.lock +++ b/pixi.lock @@ -12,7 +12,7 @@ environments: - 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vc14_runtime >=14.29.30139 + license: BSD-3-Clause + license_family: BSD + purls: + - pkg:pypi/statsmodels?source=hash-mapping + size: 11727420 + timestamp: 1727987445149 - kind: conda name: tabulate version: 0.9.0 @@ -27912,13 +27945,13 @@ packages: timestamp: 1728401055788 - kind: conda name: virtualenv - version: 20.27.0 + version: 20.27.1 build: pyhd8ed1ab_0 subdir: noarch noarch: python - url: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.27.0-pyhd8ed1ab_0.conda - sha256: 18bae5ff9f02793ca56d295f0a5f1d4443623ee3be09a6805eb7d4b18245968c - md5: a6ed1227ba6ec37cfc2b25e6512f729f + url: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.27.1-pyhd8ed1ab_0.conda + sha256: 189b935224732267df10dc116bce0835bd76fcdb20c30f560591c92028d513b0 + md5: dae21509d62aa7bf676279ced3edcb3f depends: - distlib <1,>=0.3.7 - filelock <4,>=3.12.2 @@ -27928,8 +27961,8 @@ packages: license_family: MIT purls: - pkg:pypi/virtualenv?source=hash-mapping - size: 2952166 - timestamp: 1729243861344 + size: 2965442 + timestamp: 1730204927840 - kind: conda name: vs2015_runtime version: 14.40.33810 @@ -28345,12 +28378,12 @@ packages: timestamp: 1727884600744 - kind: conda name: xorg-libxcursor - version: 1.2.2 + version: 1.2.3 build: hb9d3cd8_0 subdir: linux-64 - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxcursor-1.2.2-hb9d3cd8_0.conda - sha256: 7262935568963836efd05e0c68d5c787246578465b7a66c8bd7f0ba361d6a105 - md5: bb2638cd7fbdd980b1cff9a99a6c1fa8 + url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxcursor-1.2.3-hb9d3cd8_0.conda + sha256: 832f538ade441b1eee863c8c91af9e69b356cd3e9e1350fff4fe36cc573fc91a + md5: 2ccd714aa2242315acaf0a67faea780b depends: - __glibc >=2.17,<3.0.a0 - libgcc >=13 @@ -28360,8 +28393,8 @@ packages: license: MIT license_family: MIT purls: [] - size: 31804 - timestamp: 1727796817007 + size: 32533 + timestamp: 1730908305254 - kind: conda name: xorg-libxdamage version: 1.1.6 @@ -28817,21 +28850,21 @@ packages: timestamp: 1728364170966 - kind: conda name: zipp - version: 3.20.2 + version: 3.21.0 build: pyhd8ed1ab_0 subdir: noarch noarch: python - url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.2-pyhd8ed1ab_0.conda - sha256: 1e84fcfa41e0afdd87ff41e6fbb719c96a0e098c1f79be342293ab0bd8dea322 - md5: 4daaed111c05672ae669f7036ee5bba3 + url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_0.conda + sha256: 232a30e4b0045c9de5e168dda0328dc0e28df9439cdecdfb97dd79c1c82c4cec + md5: fee389bf8a4843bd7a2248ce11b7f188 depends: - python >=3.8 license: MIT license_family: MIT purls: - pkg:pypi/zipp?source=hash-mapping - size: 21409 - timestamp: 1726248679175 + size: 21702 + timestamp: 1731262194278 - kind: conda name: zlib version: 1.3.1 @@ -29053,6 +29086,30 @@ packages: - pkg:pypi/zstandard?source=hash-mapping size: 419552 timestamp: 1725305670210 +- kind: conda + name: zstandard + version: 0.23.0 + build: py313h574b89f_1 + build_number: 1 + subdir: win-64 + url: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py313h574b89f_1.conda + sha256: 1d2744ec0e91da267ce749e19142081472539cb140a7dad0646cd249246691fe + md5: 8e017aca933f4dd25491151edd3e7820 + depends: + - cffi >=1.11 + - python >=3.13.0rc1,<3.14.0a0 + - python_abi 3.13.* *_cp313 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + - zstd >=1.5.6,<1.5.7.0a0 + - zstd >=1.5.6,<1.6.0a0 + license: BSD-3-Clause + license_family: BSD + purls: + - pkg:pypi/zstandard?source=hash-mapping + size: 325703 + timestamp: 1725305947138 - kind: conda name: zstandard version: 0.23.0 From e0c740e4ef888bcca46468def2ee6c882d25096f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20Jolivet?= <57430673+TheooJ@users.noreply.github.com> Date: Thu, 14 Nov 2024 10:06:50 +0100 Subject: [PATCH 04/24] [ENH] Dispatch aggregate, refactor AggJoiner & AggTarget (#1116) --- CHANGES.rst | 8 + codecov.yml | 2 + skrub/_agg_joiner.py | 553 +++++++++++------------ skrub/_dataframe/_pandas.py | 243 ---------- skrub/_dataframe/_polars.py | 161 ------- skrub/_dataframe/tests/test_pandas.py | 91 +--- skrub/_dataframe/tests/test_polars.py | 60 +-- skrub/_join_utils.py | 2 +- skrub/_multi_agg_joiner.py | 50 +-- skrub/tests/test_agg_joiner.py | 618 +++++++++++++++++++------- skrub/tests/test_multi_agg_joiner.py | 71 +-- 11 files changed, 822 insertions(+), 1037 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 35f3ce9e1..f55388ab1 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -28,6 +28,14 @@ New features Major changes ------------- +* :class:`AggJoiner`, :class:`AggTarget` and :class:`MultiAggJoiner` now require + the `operations` argument. They do not split columns by type anymore, but + apply `operations` on all selected cols. "median" is now supported, "hist" and + "value_counts" are no longer supported. :pr:`1116` by :user:`Théo Jolivet `. + +* The :class:`AggTarget` no longer supports `y` inputs of type list. :pr:`1116` + by :user:`Théo Jolivet `. + Minor changes ------------- diff --git a/codecov.yml b/codecov.yml index 76339efd1..a1f2d73a4 100644 --- a/codecov.yml +++ b/codecov.yml @@ -2,6 +2,8 @@ comment: false coverage: + wait_for_ci: true + status: project: default: diff --git a/skrub/_agg_joiner.py b/skrub/_agg_joiner.py index 6bce73301..60a7edf4f 100644 --- a/skrub/_agg_joiner.py +++ b/skrub/_agg_joiner.py @@ -6,57 +6,173 @@ table with the main table. """ +from itertools import product + import numpy as np +import pandas as pd from sklearn.base import BaseEstimator, TransformerMixin -from sklearn.utils.multiclass import type_of_target from sklearn.utils.validation import check_is_fitted from skrub import _dataframe as sbd -from skrub import _join_utils -from skrub._dataframe._namespace import get_df_namespace, is_pandas, is_polars -from skrub._dataframe._pandas import _parse_argument -from skrub._utils import atleast_1d_or_none +from skrub import _join_utils, _utils +from skrub import _selectors as s +from skrub._dispatch import dispatch from ._check_input import CheckInputDataFrame -NUM_OPERATIONS = ["sum", "mean", "std", "min", "max", "hist", "value_counts"] -CATEG_OPERATIONS = ["mode", "count", "value_counts"] -ALL_OPS = NUM_OPERATIONS + CATEG_OPERATIONS +try: + import polars as pl +except ImportError: + pass + +SUPPORTED_OPS = ["count", "mode", "min", "max", "sum", "median", "mean", "std"] +# summing strings works in pandas, not in polars +NUM_ONLY_OPS = ["sum", "median", "mean", "std"] + + +def aggregate(table, key, cols_to_agg, operations, suffix): + """Aggregate the `table` by `key` and compute statistics for `cols_to_agg`. + + Parameters + ---------- + table : DataFrame of shape (n_samples, n_features) + The input dataframe to aggregate. + + key : list of str + The columns used as keys to aggregate on. + + cols_to_agg : iterable of str + The columns to aggregate. + + operations : iterable of str + The reduction functions to apply on columns in ``cols_to_agg`` + during the aggregation. + + Supported operations are "count", "mode", "min", "max", "sum", "median", + "mean", "std". The operations "sum", "median", "mean", "std" are reserved + to numeric type columns. + + suffix : str + The suffix appended to output columns. Will only be applied + to columns created by the aggregations. + + Returns + ------- + DataFrame of shape ``(n_aggregated_samples, k)`` + where ``k = n_operations * n_cols_to_agg`` + The aggregated output. + """ + table_to_agg = s.select(table, s.make_selector(key) | s.make_selector(cols_to_agg)) + + # Don't check the ID column, as it's not the one we aggregate on + table_to_check = s.select(table_to_agg, ~s.cols(*key)) + not_numeric_cols = (~s.numeric()).expand(table_to_check) + + num_only_op = list(set(operations).intersection(set(NUM_ONLY_OPS))) + + if not_numeric_cols and num_only_op: + raise AttributeError( + f"The operations {NUM_ONLY_OPS} are restricted to numeric columns." + f" \nConsider removing the following columns: {not_numeric_cols} or the" + f" following operations: {num_only_op}." + ) + + aggregated = perform_groupby(table_to_agg, key, cols_to_agg, operations) + + new_col_names = [ + f"{col}{suffix}" if col not in key else col + for col in sbd.column_names(aggregated) + ] + aggregated = sbd.set_column_names(aggregated, new_col_names) + + return aggregated + + +@dispatch +def perform_groupby(table, key, cols_to_agg, operations): + raise NotImplementedError() + + +@perform_groupby.specialize("pandas", argument_type="DataFrame") +def _perform_groupby_pandas(table, key, cols_to_agg, operations): + # Pandas does not allow the keyword "mode" for aggregating + # its ``DataFrameGroupBy`` objects, this is a workaround + pandas_aggfuncs = { + "mode": pd.Series.mode, + } + named_agg = {} + for col, operation in product(cols_to_agg, operations): + aggfunc = pandas_aggfuncs.get(operation, operation) + output_key = f"{col}_{operation}" + named_agg[output_key] = (col, aggfunc) + + aggregated = table.groupby(key).agg(**named_agg).reset_index(drop=False) + + return aggregated -def split_num_categ_operations(operations): - """Separate aggregator operators input by their type. +@perform_groupby.specialize("polars", argument_type="DataFrame") +def _perform_groupby_polars(table, key, cols_to_agg, operations): + aggfuncs = [] + for col, operation in product(cols_to_agg, operations): + polars_aggfuncs = { + "count": pl.col(col).count(), + "median": pl.col(col).median(), + "mean": pl.col(col).mean(), + "std": pl.col(col).std(), + "sum": pl.col(col).sum(), + "min": pl.col(col).min(), + "max": pl.col(col).max(), + "mode": pl.col(col).mode().first(), + } + output_key = f"{col}_{operation}" + aggfunc = polars_aggfuncs[operation].alias(output_key) + aggfuncs.append(aggfunc) + + aggregated = table.group_by(key).agg(aggfuncs) + + return aggregated + + +def check_other_inputs(operations, suffix): + """Check operations and suffix inputs. Parameters ---------- - operations : list of str - The input operators names. + operations : str or list of str + The operations to check. + suffix : str + The suffix to check. Returns ------- - num_operations, categ_operations : Tuple of list of str - List of operator names. + The checked inputs. """ - num_operations, categ_operations = [], [] - for operation in operations: - # hist(5) -> hist - op_root, _ = _parse_argument(operation) - if op_root in NUM_OPERATIONS: - num_operations.append(operation) - if op_root in CATEG_OPERATIONS: - categ_operations.append(operation) - if op_root not in ALL_OPS: - raise ValueError(f"operations options are {ALL_OPS}, got: {operation=!r}.") + operations = np.atleast_1d(operations).tolist() + if not all([isinstance(op, str) for op in operations]) or operations == []: + raise ValueError( + "`operations` must be a string or an iterable of strings, got" + f" {operations}." + ) - return num_operations, categ_operations + unsupported_ops = set(operations).difference(SUPPORTED_OPS) + if unsupported_ops: + raise ValueError( + f"`operations` options are {SUPPORTED_OPS}, but {unsupported_ops} are" + " not supported." + ) + + if not isinstance(suffix, str): + raise ValueError(f"'suffix' must be a string. Got {suffix}") + + return operations, suffix class AggJoiner(TransformerMixin, BaseEstimator): """Aggregate an auxiliary dataframe before joining it on a base dataframe. Apply numerical and categorical aggregation operations on the columns (i.e. `cols`) - to aggregate, selected by dtypes. See the list of supported operations - at the parameter `operations`. + to aggregate. See the list of supported operations at the parameter `operations`. If `cols` is not provided, `cols` are all columns from `aux_table`, except `aux_key`. @@ -70,6 +186,13 @@ class AggJoiner(TransformerMixin, BaseEstimator): The placeholder string "X" can be provided to perform self-aggregation on the input data. + operations : str or iterable of str + Aggregation operations to perform on the auxiliary table. + + Supported operations are "count", "mode", "min", "max", "sum", "median", + "mean", "std". The operations "sum", "median", "mean", "std" are reserved + to numeric type columns. + key : str, default=None The column name to use for both `main_key` and `aux_key` when they are the same. Provide either `key` or both `main_key` and `aux_key`. @@ -88,16 +211,7 @@ class AggJoiner(TransformerMixin, BaseEstimator): cols : str or iterable of str, default=None Select the columns from the auxiliary dataframe to use as values during the aggregation operations. - If set to `None`, `cols` are all columns from `aux_table`, except `aux_key`. - - operations : str or iterable of str, default=None - Aggregation operations to perform on the auxiliary table. - - - numerical : {"sum", "mean", "std", "min", "max", "hist", "value_counts"} - "hist" and "value_counts" accept an integer argument to parametrize - the binning. - - categorical : {"mode", "count", "value_counts"} - - If set to `None` (the default), ["mean", "mode"] will be used. + By default, `cols` are all columns from `aux_table`, except `aux_key`. suffix : str, default="" Suffix to append to the `aux_table`'s column names. You can use it @@ -130,89 +244,65 @@ class AggJoiner(TransformerMixin, BaseEstimator): ... }) >>> agg_joiner = AggJoiner( ... aux_table=aux, + ... operations="mean", ... main_key="airportId", ... aux_key="from_airport", - ... cols=["total_passengers", "company"], - ... operations=["mean", "mode"], + ... cols="total_passengers", ... ) >>> agg_joiner.fit_transform(main) - airportId airportName company_mode total_passengers_mean - 0 1 Paris CDG AF 103.33... - 1 2 NY JFK DL 80.00... + airportId airportName total_passengers_mean + 0 1 Paris CDG 103.33... + 1 2 NY JFK 80.00... """ def __init__( self, aux_table, + operations, *, key=None, main_key=None, aux_key=None, cols=None, - operations=None, suffix="", ): self.aux_table = aux_table + self.operations = operations self.key = key self.main_key = main_key self.aux_key = aux_key self.cols = cols - self.operations = operations self.suffix = suffix - def _check_dataframes(self, X, aux_table): - """Check dataframes input types. - - Raises an error if frames aren't both Pandas or Polars dataframes, - or if there is a Polars lazyframe. - Alternatively, allows `aux_table` to be "X". + def fit_transform(self, X, y=None): + """Aggregate auxiliary table based on the main keys. - Parameters - ---------- - X : DataFrameLike - The main table to augment. - aux_table : DataFrameLike or "X" - The auxiliary table. + Parameters + ---------- + X : DataFrameLike + Input data, based table on which to left join the + auxiliary table. + y : None + Unused, only here for compatibility. Returns ------- - X, aux_table: DataFrameLike - The validated main and auxiliary dataframes. + DataFrame + The augmented input. """ - # Polars lazyframes will raise an error here. - if not hasattr(X, "__dataframe__"): - raise TypeError(f"'X' must be a dataframe, got {type(X)}.") - if isinstance(aux_table, str): - if aux_table == "X": - return X, X - raise ValueError("'aux_table' must be a dataframe or the string 'X'.") - elif not hasattr(aux_table, "__dataframe__"): - raise TypeError( + if isinstance(self.aux_table, str) and self.aux_table == "X": + self._aux_table = X + elif not sbd.is_dataframe(self.aux_table): + raise ValueError( "'aux_table' must be a dataframe or the string 'X', got" - f" {type(aux_table)}. If you have more than one 'aux_table'," + f" {type(self.aux_table)}. If you have more than one 'aux_table'," " use the MultiAggJoiner instead." ) - - if (is_pandas(X) and not is_pandas(aux_table)) or ( - is_polars(X) and not is_polars(aux_table) - ): - raise TypeError( - "'X' and 'aux_table' must be of the same dataframe type, got" - f"{type(X)} and {type(aux_table)}" - ) - - return X, aux_table - - def _check_inputs(self, X): - """Check inputs before fitting. - - Parameters - ---------- - X : DataFrameLike - Input data, based table on which to left join the - auxiliary table. - """ - X, self._aux_table = self._check_dataframes(X, self.aux_table) + else: + self._aux_table = self.aux_table + self._aux_table = CheckInputDataFrame().fit_transform(self._aux_table) + self._main_check_input = CheckInputDataFrame() + X = self._main_check_input.fit_transform(X) self._main_key, self._aux_key = _join_utils.check_key( self.main_key, self.aux_key, self.key @@ -220,65 +310,26 @@ def _check_inputs(self, X): _join_utils.check_missing_columns(X, self._main_key, "'X' (the main table)") _join_utils.check_missing_columns(self._aux_table, self._aux_key, "'aux_table'") + self._cols = _utils.atleast_1d_or_none(self.cols) # If no `cols` provided, all columns but `aux_key` are used. if self.cols is None: self._cols = list(set(self._aux_table.columns) - set(self._aux_key)) - elif isinstance(self.cols, str): - self._cols = [ - self.cols, - ] - else: - self._cols = self.cols _join_utils.check_missing_columns(self._aux_table, self._cols, "'aux_table'") - if self.operations is None: - self._operations = ["mean", "mode"] - elif isinstance(self.operations, str): - self._operations = [ - self.operations, - ] - else: - self._operations = self.operations - - self.num_operations, self.categ_operations = split_num_categ_operations( - self._operations + self._operations, self._suffix = check_other_inputs( + self.operations, self.suffix ) - if not isinstance(self.suffix, str): - raise ValueError(f"'suffix' must be a string. Got {self.suffix}") - - def fit_transform(self, X, y=None): - """Aggregate auxiliary table based on the main keys. - - Parameters - ---------- - X : DataFrameLike - Input data, based table on which to left join the - auxiliary table. - y : None - Unused, only here for compatibility. - - Returns - ------- - DataFrame - The augmented input. - """ - self._check_inputs(X) - self._main_check_input = CheckInputDataFrame() - X = self._main_check_input.fit_transform(X) - - skrub_px, _ = get_df_namespace(self._aux_table) - self.aux_table_ = skrub_px.aggregate( + self.aggregated_aux_table_ = aggregate( self._aux_table, self._aux_key, self._cols, - self.num_operations, - self.categ_operations, - suffix=self.suffix, + self._operations, + suffix=self._suffix, ) result = _join_utils.left_join( X, - right=self.aux_table_, + right=self.aggregated_aux_table_, left_on=self._main_key, right_on=self._aux_key, ) @@ -317,13 +368,12 @@ def transform(self, X): DataFrame The augmented input. """ - check_is_fitted(self, "aux_table_") - X, _ = self._check_dataframes(X, self.aux_table_) + check_is_fitted(self, "aggregated_aux_table_") X = self._main_check_input.transform(X) result = _join_utils.left_join( X, - right=self.aux_table_, + right=self.aggregated_aux_table_, left_on=self._main_key, right_on=self._aux_key, ) @@ -331,9 +381,20 @@ def transform(self, X): result = sbd.set_column_names(result, self.all_outputs_) return result + def get_feature_names_out(self): + """Get output feature names for transformation. + + Returns + ------- + List of str + Transformed feature names. + """ + check_is_fitted(self, "aggregated_aux_table_") + return self.all_outputs_ + class AggTarget(TransformerMixin, BaseEstimator): - """Aggregate a target ``y`` before joining its aggregation on a base dataframe. + """Aggregate a target `y` before joining its aggregation on a base dataframe. Accepts :obj:`pandas.DataFrame` or :class:`polars.DataFrame` inputs. @@ -346,25 +407,19 @@ class AggTarget(TransformerMixin, BaseEstimator): If `main_key` refer to a single column, a single aggregation for this key will be generated and a single join will be performed. - Otherwise, if `main_key` is a list of keys, the target will be - aggregated using each key separately, then each aggregation of - the target will be joined on the main table. + If `main_key` is a list of keys, a multi-column aggregation will be performed + on the target. - operation : str or iterable of str, optional + operations : str or iterable of str Aggregation operations to perform on the target. - numerical : {"sum", "mean", "std", "min", "max", "hist", "value_counts"} - 'hist' and 'value_counts' accept an integer argument to parametrize - the binning. - - categorical : {"mode", "count", "value_counts"} + Supported operations are "count", "mode", "min", "max", "sum", "median", + "mean", "std". The operations "sum", "median", "mean", "std" are reserved + to numeric type targets. - If set to None (the default), ["mean", "mode"] will be used. - - suffix : str, optional + suffix : str, default="_target" The suffix to append to the columns of the target table if the join results in duplicates columns. - If set to None, "_target" is used. See Also -------- @@ -390,30 +445,31 @@ class AggTarget(TransformerMixin, BaseEstimator): >>> y = np.array([1, 1, 0, 0, 1, 1]) >>> agg_target = AggTarget( ... main_key="company", - ... operation=["mean", "max"], + ... operations=["mean", "max"], ... ) >>> agg_target.fit_transform(X, y) - flightId from_airport ... y_0_max_target y_0_mean_target - 0 1 1 ... 1 0.666667 - 1 2 1 ... 1 0.500000 - 2 3 1 ... 1 0.500000 - 3 4 2 ... 1 0.666667 - 4 5 2 ... 1 0.666667 - 5 6 2 ... 1 1.000000 + flightId from_airport ... y_0_mean_target y_0_max_target + 0 1 1 ... 0.666667 1 + 1 2 1 ... 0.500000 1 + 2 3 1 ... 0.500000 1 + 3 4 2 ... 0.666667 1 + 4 5 2 ... 0.666667 1 + 5 6 2 ... 1.000000 1 """ def __init__( self, main_key, - operation=None, - suffix=None, + operations, + *, + suffix="_target", ): self.main_key = main_key - self.operation = operation + self.operations = operations self.suffix = suffix def fit_transform(self, X, y): - """Aggregate the target ``y`` based on keys from ``X``. + """Aggregate the target `y` based on keys from `X`. Parameters ---------- @@ -421,52 +477,73 @@ def fit_transform(self, X, y): Must contains the columns names defined in `main_key`. y : DataFrameLike or SeriesLike or ArrayLike - `y` length must match `X` length, with matching indices. + `y` length must match `X` length. The target can be continuous or discrete, with multiple columns. - If the target is continuous, only numerical operations, - listed in `num_operations`, can be applied. - - If the target is discrete, only categorical operations, - listed in `categ_operations`, can be applied. - - Note that the target type is determined by - :func:`sklearn.utils.multiclass.type_of_target`. - Returns ------- Dataframe The augmented input. """ - y_ = self.check_inputs(X, y) self._main_check_input = CheckInputDataFrame() X = self._main_check_input.fit_transform(X) - skrub_px, _ = get_df_namespace(X, y_) + + self._main_key = np.atleast_1d(self.main_key).tolist() + _join_utils.check_missing_columns(X, self._main_key, "'X' (the main table)") + + if sbd.is_dataframe(y): + y_ = y + # If `y` is a series, we convert it to a dataframe + elif sbd.is_column(y): + name = sbd.name(y) if sbd.name(y) else "y_0" + y_ = sbd.make_dataframe_like(y, {name: y}) + elif isinstance(y, np.ndarray): + # 1d arrays need to be reshaped + if y.ndim == 1: + y = y.reshape(-1, 1) + + cols = {f"y_{i}": y[:, i] for i in range(y.shape[1])} + y_ = sbd.make_dataframe_like(X, cols) + else: + raise TypeError( + f"`y` must be a dataframe, a series or a numpy array, got {y}." + ) + + # Check lengths + if sbd.shape(y_)[0] != sbd.shape(X)[0]: + raise ValueError( + f"X and y length must match, got {sbd.shape(X)[0]} and" + f" {sbd.shape(y_)[0]}." + ) + + self._cols = sbd.column_names(y_) + + self._operations, self._suffix = check_other_inputs( + self.operations, self.suffix + ) # Add the main key on the target - y_[self.main_key_] = X[self.main_key_] + y_ = sbd.with_columns(y_, **{k: sbd.col(X, k) for k in self._main_key}) - num_operations, categ_operations = split_num_categ_operations(self.operation_) - self.y_ = skrub_px.aggregate( + self.aggregated_y_ = aggregate( y_, - key=self.main_key_, - cols_to_agg=self.cols_, - num_operations=num_operations, - categ_operations=categ_operations, - suffix=self.suffix_, + key=self._main_key, + cols_to_agg=self._cols, + operations=self._operations, + suffix=self._suffix, ) result = _join_utils.left_join( X, - right=self.y_, - left_on=self.main_key_, - right_on=self.main_key_, + right=self.aggregated_y_, + left_on=self._main_key, + right_on=self._main_key, ) self.all_outputs_ = sbd.column_names(result) return result def fit(self, X, y): - """Aggregate the target ``y`` based on keys from ``X``. + """Aggregate the target `y` based on keys from `X`. Parameters ---------- @@ -474,18 +551,9 @@ def fit(self, X, y): Must contains the columns names defined in `main_key`. y : DataFrameLike or SeriesLike or ArrayLike - `y` length must match `X` length, with matching indices. + `y` length must match `X` length. The target can be continuous or discrete, with multiple columns. - If the target is continuous, only numerical operations, - listed in `num_operations`, can be applied. - - If the target is discrete, only categorical operations, - listed in `categ_operations`, can be applied. - - Note that the target type is determined by - :func:`sklearn.utils.multiclass.type_of_target`. - Returns ------- AggTarget @@ -495,100 +563,37 @@ def fit(self, X, y): return self def transform(self, X): - """Left-join pre-aggregated table on `X`. + """Left-join pre-aggregated target on `X`. Parameters ---------- - X : DataFrameLike, + X : DataFrameLike The input data to transform. Returns ------- - X_transformed : DataFrameLike, + X_transformed : DataFrameLike The augmented input. """ - check_is_fitted(self, "y_") + check_is_fitted(self, "aggregated_y_") X = self._main_check_input.transform(X) result = _join_utils.left_join( X, - right=self.y_, - left_on=self.main_key_, - right_on=self.main_key_, + right=self.aggregated_y_, + left_on=self._main_key, + right_on=self._main_key, ) result = sbd.set_column_names(result, self.all_outputs_) return result - def check_inputs(self, X, y): - """Perform a check on column names data type and suffixes. - - Parameters - ---------- - X : DataFrameLike - The raw input to check. - y : DataFrameLike or SeriesLike or ArrayLike - The raw target to check. + def get_feature_names_out(self): + """Get output feature names for transformation. Returns ------- - y_ : DataFrameLike, - Transformation of the target. + List of str + Transformed feature names. """ - if not hasattr(X, "__dataframe__"): - raise TypeError(f"X must be a dataframe, got {type(X)}") - - self.main_key_ = atleast_1d_or_none(self.main_key) - - self.suffix_ = "_target" if self.suffix is None else self.suffix - - if not isinstance(self.suffix_, str): - raise ValueError(f"'suffix' must be a string, got {self.suffix_!r}") - - _join_utils.check_missing_columns(X, self.main_key_, "'X' (the main table)") - - # If y is not a dataframe, we convert it. - if hasattr(y, "__dataframe__"): - # Need to copy since we add columns in place - # during fit. - y_ = y.copy() - elif sbd.is_column(y) and sbd.name(y) is not None: - y_ = sbd.make_dataframe_like(y, {sbd.name(y): y}) - else: - if sbd.is_column(y): - y = sbd.to_numpy(y) - y_ = np.atleast_2d(y) - - # If y is Series or an array derived from a - # Series, we need to transpose it. - if len(y_) == 1 and len(y) != 1: - y_ = y_.T - - _, px = get_df_namespace(X) - y_ = px.DataFrame(y_) - - if hasattr(y, "name"): - # y is a Series - cols = [y.name] - else: - cols = [f"y_{col}" for col in y_.columns] - y_.columns = cols - - # Check lengths - if y_.shape[0] != X.shape[0]: - raise ValueError( - f"X and y length must match, got {X.shape[0]} and {y_.shape[0]}." - ) - - self.cols_ = y_.columns - - if self.operation is None: - y_type = type_of_target(y_) - if y_type in ["continuous", "continuous-multioutput"]: - operation = ["mean"] - else: - operation = ["mode"] - else: - operation = np.atleast_1d(self.operation).tolist() - self.operation_ = operation - - return y_ + check_is_fitted(self, "aggregated_y_") + return self.all_outputs_ diff --git a/skrub/_dataframe/_pandas.py b/skrub/_dataframe/_pandas.py index 4c461c196..76f6bf42a 100644 --- a/skrub/_dataframe/_pandas.py +++ b/skrub/_dataframe/_pandas.py @@ -2,253 +2,10 @@ Pandas specialization of the aggregate and join operations. """ -import re -from itertools import product - -import numpy as np -import pandas as pd - -from skrub._utils import atleast_1d_or_none - # TODO: _dataframe._pandas is temporary; all code in this module should be moved # elsewhere and use the dispatch mechanism. -def aggregate( - table, - key, - cols_to_agg, - num_operations=("mean",), - categ_operations=("mode",), - suffix=None, -): - """Aggregates a :obj:`pandas.DataFrame`. - - This function uses the ``dataframe.groupby(key).agg`` method from Pandas. - - Parameters - ---------- - table : pd.DataFrame - The input dataframe to aggregate. - - key : str or Iterable[str] - The columns used as keys to aggregate on. - - cols_to_agg : str or Iterable[str] - The columns to aggregate. - - num_operations : str or Iterable[str], default=("mean",) - The reduction functions to apply on numerical columns - in ``cols_to_agg`` during the aggregation. - - categ_operations : str or Iterable[str], default=("mode",) - The reduction functions to apply on categorical columns - in ``cols_to_agg`` during the aggregation. - - suffix : str, optional - The suffix appended to output columns. - - Returns - ------- - group : pd.DataFrame - The aggregated output. - """ - if not isinstance(table, pd.DataFrame): - raise TypeError(f"'table' must be a pandas dataframe, got {type(table)!r}.") - - key = atleast_1d_or_none(key) - cols_to_agg = atleast_1d_or_none(cols_to_agg) - num_operations = atleast_1d_or_none(num_operations) - categ_operations = atleast_1d_or_none(categ_operations) - suffix = "" if suffix is None else suffix - - num_cols, categ_cols = split_num_categ_cols(table[cols_to_agg]) - - num_named_agg, num_value_counts = get_named_agg(table, num_cols, num_operations) - categ_named_agg, categ_value_counts = get_named_agg( - table, categ_cols, categ_operations - ) - - named_agg = {**num_named_agg, **categ_named_agg} - if named_agg: - base_group = table.groupby(key).agg(**named_agg).reset_index(drop=False) - else: - base_group = None - - # 'histogram' and 'value_counts' requires a pivot - value_counts = {**num_value_counts, **categ_value_counts} - for output_key, (col_to_agg, kwargs) in value_counts.items(): - serie_group = table.groupby(key)[col_to_agg].value_counts(**kwargs) - serie_group.name = output_key - pivot = ( - serie_group.reset_index() - .pivot(index=key, columns=col_to_agg) - .reset_index() - .fillna(0) - ) - cols = pivot.columns.droplevel(0) - index_cols = np.atleast_1d(key).tolist() - feature_cols = (f"{col_to_agg}_" + cols[len(index_cols) :].astype(str)).tolist() - cols = [*index_cols, *feature_cols] - pivot.columns = cols - - if base_group is None: - base_group = pivot - else: - base_group = base_group.merge(pivot, on=key, how="left") - - if base_group is None: - raise ValueError("No aggregation to perform.") - - base_group.columns = [ - f"{col}{suffix}" if col not in key else col for col in base_group.columns - ] - sorted_cols = sorted(base_group.columns) - - return base_group[sorted_cols] - - -def get_named_agg(table, cols, operations): - """Map aggregation tuples to their output key. - - The dictionary has the form: output_key = (column, aggfunc). - This is used as input for the ``dataframe.agg`` method from Pandas. - - 'value_counts' and 'hist' operations require to pivot - the tables and treated in a separate mapping. - - Parameters - ---------- - table : pd.DataFrame - Input dataframe, only used to compute bins values if - 'value_counts' or 'hist' are operations. - - cols : list - The columns to aggregate. - - operations : list - The reduce operations to perform. - - Returns - ------- - named_agg : dict - Named aggregation mapping. - - value_counts : dict - ``value_counts`` operations mapping. - """ - named_agg, value_counts = {}, {} - for col, operation in product(cols, operations): - op_root, bin_args = _parse_argument(operation) - aggfunc, bin_args = _get_aggfunc(table[col], op_root, bin_args) - - output_key = f"{col}_{op_root}" - # 'value_counts' changes the index of the resulting frame - # and must be treated separately. - if aggfunc == "value_counts": - value_counts[output_key] = (col, bin_args) - else: - named_agg[output_key] = (col, aggfunc) - - return named_agg, value_counts - - -def _parse_argument(operation): - """Split a text input into a function name and its argument. - - Parameters - ---------- - operation : str - The operation to parse. - - Returns - ------- - operation_root : str - The name of the operation before parenthesis, if any. - - bin_args : int - The number of bin to create for 'hist' or 'value_counts'. - - Examples - -------- - >>> _parse_argument("hist(10)") - ('hist', 10) - """ - split = re.split("\\(.+\\)", operation) - op_root = split[0] - if len(split) > 1: - # remove op_root - bin_args = re.split(f"^{op_root}", operation) - bin_args = bin_args[1] - # remove parenthesis - bin_args = re.sub("\\(|\\)", "", bin_args) - bin_args = int(bin_args) - return op_root, bin_args - else: - return op_root, None - - -PANDAS_OPS_MAPPING = { - "mode": pd.Series.mode, - "quantile": pd.Series.quantile, - "hist": "value_counts", -} - - -def _get_aggfunc(serie, op_root, n_bins): - """Map operation roots to their pandas agg functions. - - When args is provided for histogram or value_counts, - we create args - - Parameters - ---------- - serie : pd.Series - Input series, used to compute the bins if `n_bins` is provided. - - op_root : str - Operation root, the operation without the bin argument, if any. - - n_bins : int - The number of bin to create when 'value_counts' or 'hist' operation are used. - - Returns - ------- - aggfunc : str or callable - The pandas agg functions to perform. - - bins_args : dict - The bins to create when using 'value_counts' or 'hist'. - """ - aggfunc = PANDAS_OPS_MAPPING.get(op_root, op_root) - - if n_bins is not None: - # histogram and value_counts - if aggfunc == "value_counts": - # If bins is a number, we need to set a fix bin range, - # otherwise bins edges will be defined dynamically for - # each rows. - min_, max_ = serie.min(), serie.max() - bins = np.linspace(min_, max_, n_bins + 1) - bins_args = dict(bins=bins) - else: - raise ValueError( - f"Operator {op_root!r} doesn't take any argument, got {n_bins!r}" - ) - else: - bins_args = {} - - return aggfunc, bins_args - - -def split_num_categ_cols(table): - """Split dataframe columns between numerical and categorical.""" - num_cols = table.select_dtypes("number").columns - categ_cols = table.select_dtypes(["object", "string", "category"]).columns - - return num_cols, categ_cols - - def rename_columns(dataframe, renaming_function): return dataframe.rename( columns={c: renaming_function(c) for c in dataframe.columns} diff --git a/skrub/_dataframe/_polars.py b/skrub/_dataframe/_polars.py index 04af24156..04247648a 100644 --- a/skrub/_dataframe/_polars.py +++ b/skrub/_dataframe/_polars.py @@ -1,171 +1,10 @@ """ Polars specialization of the aggregate and join operations. """ -try: - import polars as pl - import polars.selectors as cs - -except ImportError: - pass - -from itertools import product - -from skrub._utils import atleast_1d_or_none # TODO: _dataframe._polars is temporary; all code in this module should be moved # elsewhere and use the dispatch mechanism. -def aggregate( - table, - key, - cols_to_agg, - num_operations=("mean",), - categ_operations=("mode",), - suffix=None, -): - """Aggregate a :obj:`polars.DataFrame` or :obj:`polars.LazyFrame`. - - This function uses the ``dataframe.group_by(key).agg`` method from Polars. - - Parameters - ---------- - table : pl.DataFrame or pl.LazyFrame - The input dataframe to aggregate. - - key : str or Iterable[str] - The columns used as keys to aggregate on. - - cols_to_agg : str or Iterable[str] - The columns to aggregate. - - num_operations : str or Iterable[str], default=("mean",) - The reduction functions to apply on numerical columns - in ``cols_to_agg`` during the aggregation. - - categ_operations : str or Iterable[str], default=("mode",) - The reduction functions to apply on categorical columns - in ``cols_to_agg`` during the aggregation. - - suffix : str, - The suffix appended to output columns. - - Returns - ------- - group : pl.DataFrame or pl.LazyFrame, - The aggregated output. - """ - if not isinstance(table, (pl.DataFrame, pl.LazyFrame)): - raise TypeError( - f"'table' must be a polars dataframe or lazyframe, got {type(table)!r}." - ) - - key = atleast_1d_or_none(key) - cols_to_agg = atleast_1d_or_none(cols_to_agg) - num_operations = atleast_1d_or_none(num_operations) - categ_operations = atleast_1d_or_none(categ_operations) - suffix = "" if suffix is None else suffix - - num_cols, categ_cols = split_num_categ_cols(table.select(cols_to_agg)) - - num_aggfuncs, num_mode_cols = get_aggfuncs(num_cols, num_operations) - categ_aggfuncs, categ_mode_cols = get_aggfuncs(categ_cols, categ_operations) - - aggfuncs = [*num_aggfuncs, *categ_aggfuncs] - # If aggfuncs is empty, the output will be a series of index. - table = table.group_by(key).agg(aggfuncs) - - # flattening post-processing of mode() cols - flatten_ops = [] - for col in [*num_mode_cols, *categ_mode_cols]: - flatten_ops.append(pl.col(col).list[0].alias(col)) - # add columns, no-op if 'flatten_ops' is empty. - table = table.with_columns(flatten_ops) - - cols_renaming = {col: f"{col}{suffix}" for col in table.columns if col not in key} - table = table.rename(cols_renaming) - sorted_cols = sorted(table.columns) - - return table.select(sorted_cols) - - -def get_aggfuncs(cols, operations): - """List Polars aggregation functions. - - The list is used as input for the ``dataframe.group_by().agg()`` method from Polars. - The 'mode' operation needs a flattening post-processing. - - Parameters - ---------- - cols : list - The columns to aggregate. - - operations : list - The reduce operations to perform. - - Returns - ------- - aggfuncs : list - Named aggregation list. - - mode_cols : list - Output keys to post-process after 'mode' aggregation. - """ - aggfuncs, mode_cols = [], [] - for col, operation in product(cols, operations): - output_key = f"{col}_{operation}" - aggfunc = _polars_ops_mapping(col, operation, output_key) - aggfuncs.append(aggfunc) - - if operation == "mode": - mode_cols.append(output_key) - - return aggfuncs, mode_cols - - -def _polars_ops_mapping(col, operation, output_key): - """Map an operation to its Polars expression. - - Parameters - ---------- - col : str - Name of the column to aggregate. - operation : str - Name of the reduce function. - output_key : str - Name of the reduced column. - - Returns - ------- - aggfunc: polars.Expression - The expression to apply. - """ - polars_aggfuncs = { - "mean": pl.col(col).mean(), - "std": pl.col(col).std(), - "sum": pl.col(col).sum(), - "min": pl.col(col).min(), - "max": pl.col(col).max(), - "mode": pl.col(col).mode(), - } - aggfunc = polars_aggfuncs.get(operation, None) - - if aggfunc is None: - raise ValueError( - f"Polars operation {operation!r} is not supported. Available:" - f" {list(polars_aggfuncs)}" - ) - - return aggfunc.alias(output_key) - - -def split_num_categ_cols(table): - """Split a dataframe columns between numerical and categorical.""" - num_cols = table.select(cs.numeric()).columns - categ_cols = table.select(cs.string()).columns - - return num_cols, categ_cols - - def rename_columns(dataframe, renaming_function): return dataframe.rename({c: renaming_function(c) for c in dataframe.columns}) diff --git a/skrub/_dataframe/tests/test_pandas.py b/skrub/_dataframe/tests/test_pandas.py index 06db7f1ea..39fefcc69 100644 --- a/skrub/_dataframe/tests/test_pandas.py +++ b/skrub/_dataframe/tests/test_pandas.py @@ -1,95 +1,6 @@ import pandas as pd -import pytest -from pandas.testing import assert_frame_equal -from skrub._dataframe._pandas import ( - aggregate, - rename_columns, -) - -main = pd.DataFrame( - { - "userId": [1, 1, 1, 2, 2, 2], - "movieId": [1, 3, 6, 318, 6, 1704], - "rating": [4.0, 4.0, 4.0, 3.0, 2.0, 4.0], - "genre": ["drama", "drama", "comedy", "sf", "comedy", "sf"], - } -) - - -def test_simple_agg(): - aggregated = aggregate( - table=main, - key="movieId", - cols_to_agg=["rating", "genre"], - num_operations="mean", - categ_operations="mode", - ) - aggfunc = { - "genre_mode": ("genre", pd.Series.mode), - "rating_mean": ("rating", "mean"), - } - expected = main.groupby("movieId").agg(**aggfunc).reset_index() - expected = expected.loc[:, sorted(expected.columns)] - assert_frame_equal(aggregated, expected) - - -def test_value_counts_agg(): - aggregated = aggregate( - table=main, - key="userId", - cols_to_agg="rating", - num_operations="value_counts", - categ_operations=None, - suffix="_user", - ) - expected = pd.DataFrame( - { - "rating_2.0_user": [0.0, 1.0], - "rating_3.0_user": [0.0, 1.0], - "rating_4.0_user": [3.0, 1.0], - "userId": [1, 2], - } - ) - assert_frame_equal(aggregated, expected) - - aggregated = aggregate( - table=main, - key="userId", - cols_to_agg="rating", - num_operations="hist(2)", - categ_operations=None, - suffix="_user", - ) - expected = pd.DataFrame( - { - "rating_(1.999, 3.0]_user": [0, 2], - "rating_(3.0, 4.0]_user": [3, 1], - "userId": [1, 2], - } - ) - assert_frame_equal(aggregated, expected) - - -def test_incorrect_dataframe_inputs(): - with pytest.raises(TypeError, match=r"(?=.*pandas dataframe)(?=.*array)"): - aggregate( - table=main.values, - key="movieId", - cols_to_agg="rating", - num_operations="mean", - ) - - -def test_no_agg_operation(): - with pytest.raises(ValueError, match=r"(?=.*No aggregation)"): - aggregate( - table=main, - key="movieId", - cols_to_agg="rating", - num_operations=None, - categ_operations=None, - ) +from skrub._dataframe._pandas import rename_columns def test_rename_columns(): diff --git a/skrub/_dataframe/tests/test_polars.py b/skrub/_dataframe/tests/test_polars.py index 2a2a2fa16..1c8e7ae61 100644 --- a/skrub/_dataframe/tests/test_polars.py +++ b/skrub/_dataframe/tests/test_polars.py @@ -1,69 +1,13 @@ -import pandas as pd import pytest -from skrub._dataframe._polars import ( - aggregate, - rename_columns, -) +from skrub._dataframe._polars import rename_columns from skrub.conftest import _POLARS_INSTALLED if _POLARS_INSTALLED: import polars as pl - from polars.testing import assert_frame_equal - - main = pl.DataFrame( - { - "userId": [1, 1, 1, 2, 2, 2], - "movieId": [1, 3, 6, 318, 6, 1704], - "rating": [4.0, 4.0, 4.0, 3.0, 2.0, 4.0], - "genre": ["drama", "drama", "comedy", "sf", "comedy", "sf"], - } - ) -else: - POLARS_MISSING_MSG = "Polars is not available" - pytest.skip(reason=POLARS_MISSING_MSG, allow_module_level=True) - - -def test_simple_agg(): - aggregated = aggregate( - table=main, - key="movieId", - cols_to_agg="rating", - num_operations="mean", - ) - aggfunc = pl.col("rating").mean().alias("rating_mean") - expected = main.group_by("movieId").agg(aggfunc) - # As group_by parallizes threads, the row order of its output isn't - # deterministic. Hence, we need to set check_row_order to False. - assert_frame_equal(aggregated, expected, check_row_order=False) - - -def test_mode_agg(): - aggregated = aggregate( - table=main, - key="movieId", - cols_to_agg="genre", - categ_operations=["mode"], - ) - expected = pl.DataFrame( - { - "genre_mode": ["drama", "drama", "sf", "sf", "comedy"], - "movieId": [3, 1, 318, 1704, 6], - } - ) - assert_frame_equal(aggregated, expected, check_row_order=False) - - -def test_incorrect_dataframe_inputs(): - with pytest.raises(TypeError, match=r"(?=.*polars dataframe)(?=.*pandas)"): - aggregate( - table=pd.DataFrame(main), - key="movieId", - cols_to_agg="rating", - num_operations="mean", - ) +@pytest.mark.skipif(not _POLARS_INSTALLED, reason="Polars is not available") def test_rename_columns(): df = pl.DataFrame({"a column": [1], "another": [1]}) df = rename_columns(df, str.swapcase) diff --git a/skrub/_join_utils.py b/skrub/_join_utils.py index 9c068948f..e5a563b20 100644 --- a/skrub/_join_utils.py +++ b/skrub/_join_utils.py @@ -90,7 +90,7 @@ def check_missing_columns(table, key, table_name): table_name : str Name by which to refer to `table` in the error message if necessary. """ - missing_columns = set(key) - set(table.columns) + missing_columns = set(key) - set(sbd.column_names(table)) if not missing_columns: return raise ValueError( diff --git a/skrub/_multi_agg_joiner.py b/skrub/_multi_agg_joiner.py index b2810a363..a08969f73 100644 --- a/skrub/_multi_agg_joiner.py +++ b/skrub/_multi_agg_joiner.py @@ -60,6 +60,14 @@ class MultiAggJoiner(TransformerMixin, BaseEstimator): as in ``aux_tables = [table, "X"]``. If that's the case, the second table will be replaced by the input data. + operations : iterable of iterable of str + Aggregation operations to perform on the auxiliary tables. + + Must be an iterable of `operations` for each table in `aux_tables`. + Supported operations are "count", "mode", "min", "max", "sum", "median", + "mean", "std". The operations "sum", "median", "mean", "std" are reserved + to numeric type columns. + keys : iterable of iterable of str, default=None The column names to use for both `main_keys` and `aux_key` when they are the same. Provide either `key` or both `main_keys` and `aux_keys`. @@ -106,19 +114,6 @@ class MultiAggJoiner(TransformerMixin, BaseEstimator): in `aux_tables`, the corresponding list will be all columns of that table, except the `aux_keys` associated with that table. - operations : iterable of iterable of str, default=None - Aggregation operations to perform on the auxiliary tables. - - If not `None`, there must be an iterable of `operations` for each - table in `aux_tables`. - - - numerical : {"sum", "mean", "std", "min", "max", "hist", "value_counts"} - "hist" and "value_counts" accept an integer argument to parametrize - the binning. - - categorical : {"mode", "count", "value_counts"} - - If set to `None` (the default), ["mean", "mode"] will be used - for all auxiliary tables. - suffixes : iterable of str, default=None Suffixes to append to the `aux_tables`' column names. If set to `None`, the table indexes in `aux_tables` are used, @@ -202,20 +197,20 @@ class MultiAggJoiner(TransformerMixin, BaseEstimator): def __init__( self, aux_tables, + operations, *, keys=None, main_keys=None, aux_keys=None, cols=None, - operations=None, suffixes=None, ): self.aux_tables = aux_tables + self.operations = operations self.keys = keys self.main_keys = main_keys self.aux_keys = aux_keys self.cols = cols - self.operations = operations self.suffixes = suffixes def _check_dataframes(self, X, aux_tables): @@ -386,21 +381,18 @@ def _check_operations(self): or if `operations` is not of a valid type. """ operations = self.operations - if operations is None: - operations = [["mean", "mode"]] * len(self._aux_tables) - else: - if _is_iterable_of_iterable_of_str(operations) is not True: - raise ValueError( - "Accepted inputs for `operations` are None and iterable of iterable" - " of str." - ) + if _is_iterable_of_iterable_of_str(operations) is not True: + raise ValueError( + "Accepted inputs for `operations` are None and iterable of iterable" + " of str." + ) - if len(operations) != len(self._aux_tables): - raise ValueError( - "The number of iterables in `operations` must match the number of" - f" tables in `aux_tables`. Got {len(operations)} iterables in" - f" operations and {len(self._aux_tables)} auxiliary tables." - ) + if len(operations) != len(self._aux_tables): + raise ValueError( + "The number of iterables in `operations` must match the number of" + f" tables in `aux_tables`. Got {len(operations)} iterables in" + f" operations and {len(self._aux_tables)} auxiliary tables." + ) return operations def _check_suffixes(self): diff --git a/skrub/tests/test_agg_joiner.py b/skrub/tests/test_agg_joiner.py index 75e2b83ff..67e462fa1 100644 --- a/skrub/tests/test_agg_joiner.py +++ b/skrub/tests/test_agg_joiner.py @@ -1,99 +1,257 @@ +import re + +import numpy as np import pandas as pd +import pandas.testing import pytest +from sklearn.exceptions import NotFittedError from skrub import _dataframe as sbd -from skrub._agg_joiner import AggJoiner, AggTarget, split_num_categ_operations -from skrub.conftest import _POLARS_INSTALLED +from skrub._agg_joiner import AggJoiner, AggTarget, aggregate @pytest.fixture -def main_table(): - df = pd.DataFrame( +def main_table(df_module): + return df_module.make_dataframe( { "userId": [1, 1, 1, 2, 2, 2], "movieId": [1, 3, 6, 318, 6, 1704], - "rating": [4.0, 4.0, 4.0, 3.0, 2.0, 4.0], + "rating": [4.1, 4.1, 4.1, 3.1, 2.1, 4.1], "genre": ["drama", "drama", "comedy", "sf", "comedy", "sf"], } ) - return df -def test_split_num_categ_operations(): - "Check that the operations are correctly separated." +def test_aggregate_single_operation(df_module, main_table): + aggregated = aggregate( + main_table, + operations=["mean"], + key=["userId"], + cols_to_agg=["rating"], + suffix="", + ) + # Sorting rows because polars doesn't necessarily maintain order in group_by + if df_module.name == "polars": + aggregated = sbd.sort(aggregated, by="userId") + # In that order because columns are sorted in ``aggregate`` + expected = df_module.make_dataframe({"userId": [1, 2], "rating_mean": [4.1, 3.1]}) + df_module.assert_frame_equal(aggregated, expected) + + aggregated = aggregate( + main_table, + operations=["mode"], + key=["userId"], + cols_to_agg=["genre"], + suffix="", + ) + if df_module.name == "polars": + aggregated = sbd.sort(aggregated, by="userId") + expected = df_module.make_dataframe( + {"userId": [1, 2], "genre_mode": ["drama", "sf"]} + ) + df_module.assert_frame_equal(aggregated, expected) + + +def test_aggregate_multiple_operations(df_module, main_table): + aggregated = aggregate( + main_table, + operations=["mean", "sum"], + key=["userId"], + cols_to_agg=["rating"], + suffix="", + ) + if df_module.name == "polars": + aggregated = sbd.sort(aggregated, by="userId") + expected = df_module.make_dataframe( + {"userId": [1, 2], "rating_mean": [4.1, 3.1], "rating_sum": [12.3, 9.3]} + ) + df_module.assert_frame_equal(aggregated, expected) + + # Test that the order of the operations is kept in output columns + aggregated = aggregate( + main_table, + operations=["sum", "mean"], + key=["userId"], + cols_to_agg=["rating"], + suffix="", + ) + if df_module.name == "polars": + aggregated = sbd.sort(aggregated, by="userId") + expected = df_module.make_dataframe( + {"userId": [1, 2], "rating_sum": [12.3, 9.3], "rating_mean": [4.1, 3.1]} + ) + df_module.assert_frame_equal(aggregated, expected) + + +def test_aggregate_multiple_columns(df_module): + main_table = df_module.make_dataframe( + { + "userId": [1, 1, 1, 2, 2, 2], + "apples": [1, 2, 3, 4, 5, 6], + "oranges": [10, 20, 30, 40, 50, 60], + } + ) + + aggregated = aggregate( + main_table, + operations=["sum"], + key=["userId"], + cols_to_agg=["apples", "oranges"], + suffix="", + ) + if df_module.name == "polars": + aggregated = sbd.sort(aggregated, by="userId") + expected = df_module.make_dataframe( + {"userId": [1, 2], "apples_sum": [6, 15], "oranges_sum": [60, 150]} + ) + df_module.assert_frame_equal(aggregated, expected) + + +def test_aggregate_boolean_columns(df_module): + """Boolean columns are considered non numeric by selectors.""" + main_table = df_module.make_dataframe( + {"userId": [1, 1, 1, 2, 2, 2], "flag": [False, False, True, False, True, True]} + ) + aggregated = aggregate( + main_table, + operations=["mode"], + key=["userId"], + cols_to_agg=["flag"], + suffix="", + ) + if df_module.name == "polars": + aggregated = sbd.sort(aggregated, by="userId") + expected = df_module.make_dataframe({"userId": [1, 2], "flag_mode": [False, True]}) + df_module.assert_frame_equal(aggregated, expected) + - # Check split ops - num_ops, categ_ops = split_num_categ_operations( - ["mean", "std", "min", "mode", "max", "sum"] +def test_aggregate_suffix(df_module, main_table): + main_table = df_module.DataFrame(main_table) + aggregated = aggregate( + main_table, + operations=["mean"], + key=["userId"], + cols_to_agg=["rating"], + suffix="_custom_suffix", ) - assert num_ops == ["mean", "std", "min", "max", "sum"] - assert categ_ops == ["mode"] + assert sbd.column_names(aggregated) == ["userId", "rating_mean_custom_suffix"] + + +def test_aggregate_wrong_operation_type(df_module, main_table): + main_table = df_module.DataFrame(main_table) + with pytest.raises( + AttributeError, + match=( + r"(removing the following columns: \[\'genre\'\] or the following" + r" operations: \[\'std\'\])" + ), + ): + aggregate( + main_table, + operations=["std"], + key=["userId"], + cols_to_agg=["genre"], + suffix="", + ) @pytest.mark.parametrize("use_X_placeholder", [False, True]) -def test_simple_fit_transform(df_module, main_table, use_X_placeholder): +def test_agg_joiner_simple_fit_transform(df_module, main_table, use_X_placeholder): "Check the general behaviour of the `AggJoiner`." - X = df_module.DataFrame(main_table) - aux = X if not use_X_placeholder else "X" + aux = main_table if not use_X_placeholder else "X" agg_joiner_user = AggJoiner( aux_table=aux, + operations="mode", aux_key="userId", main_key="userId", - cols=["rating", "genre"], + cols="genre", suffix="_user", ) - main_user = agg_joiner_user.fit_transform(X) + main_user = agg_joiner_user.fit_transform(main_table) expected_user = df_module.make_dataframe( { "userId": [1, 1, 1, 2, 2, 2], "movieId": [1, 3, 6, 318, 6, 1704], - "rating": [4.0, 4.0, 4.0, 3.0, 2.0, 4.0], + "rating": [4.1, 4.1, 4.1, 3.1, 2.1, 4.1], "genre": ["drama", "drama", "comedy", "sf", "comedy", "sf"], "genre_mode_user": ["drama", "drama", "drama", "sf", "sf", "sf"], - "rating_mean_user": [4.0, 4.0, 4.0, 3.0, 3.0, 3.0], } ) - df_module.assert_frame_equal( - sbd.pandas_convert_dtypes(main_user), sbd.pandas_convert_dtypes(expected_user) - ) + df_module.assert_frame_equal(main_user, expected_user) agg_joiner_movie = AggJoiner( aux_table=aux, + operations="mean", aux_key="movieId", main_key="movieId", - cols=["rating"], + cols="rating", suffix="_movie", ) - main_movie = agg_joiner_movie.fit_transform(X) + main_movie = agg_joiner_movie.fit_transform(main_table) expected_movie = df_module.make_dataframe( { "userId": [1, 1, 1, 2, 2, 2], "movieId": [1, 3, 6, 318, 6, 1704], - "rating": [4.0, 4.0, 4.0, 3.0, 2.0, 4.0], + "rating": [4.1, 4.1, 4.1, 3.1, 2.1, 4.1], "genre": ["drama", "drama", "comedy", "sf", "comedy", "sf"], - "rating_mean_movie": [4.0, 4.0, 3.0, 3.0, 3.0, 4.0], + "rating_mean_movie": [4.1, 4.1, 3.1, 3.1, 3.1, 4.1], } ) - df_module.assert_frame_equal( - sbd.pandas_convert_dtypes(main_movie), sbd.pandas_convert_dtypes(expected_movie) + df_module.assert_frame_equal(main_movie, expected_movie) + + +def test_agg_joiner_wrong_operations(df_module, main_table): + "Check that a useful error is raised when `operations` is not supported" + + # Test operations is string not in list + agg_joiner = AggJoiner( + aux_table=main_table, + operations="wrong_op", + key="userId", ) + with pytest.raises( + ValueError, + match=r"(`operations` options are)", + ): + agg_joiner.fit(main_table) + # Test operations is None + agg_joiner = AggJoiner( + aux_table=main_table, + operations=None, + key="userId", + ) + with pytest.raises( + ValueError, + match=r"(`operations` must be a string or an iterable of strings)", + ): + agg_joiner.fit(main_table) -def test_correct_keys(df_module, main_table): + # Test operations is int + agg_joiner = AggJoiner( + aux_table=main_table, + operations=2, + key="userId", + ) + with pytest.raises( + ValueError, + match=r"(`operations` must be a string or an iterable of strings)", + ): + agg_joiner.fit(main_table) + + +def test_agg_joiner_correct_keys(df_module, main_table): "Check that expected `key` parameters for the `AggJoiner` are working." - main_table = df_module.DataFrame(main_table) # Check only key agg_joiner = AggJoiner( - aux_table=main_table, - key="userId", - cols=["rating", "genre"], + aux_table=main_table, operations="mode", key="userId", cols=["rating", "genre"] ) agg_joiner.fit(main_table) assert agg_joiner._main_key == ["userId"] @@ -102,6 +260,7 @@ def test_correct_keys(df_module, main_table): # Check multiple key agg_joiner = AggJoiner( aux_table=main_table, + operations="mode", key=["userId", "movieId"], cols=["rating", "genre"], ) @@ -113,19 +272,20 @@ def test_correct_keys(df_module, main_table): main_key=["userId", "movieId"], aux_key=["userId", "movieId"], cols=["rating", "genre"], + operations="mode", ) agg_joiner.fit(main_table) assert agg_joiner._main_key == ["userId", "movieId"] assert agg_joiner._aux_key == ["userId", "movieId"] -def test_wrong_keys(df_module, main_table): +def test_agg_joiner_wrong_keys(df_module, main_table): "Check that wrong `key` parameters for the `AggJoiner` raise an error." - main_table = df_module.DataFrame(main_table) # Check too many main_key agg_joiner = AggJoiner( aux_table=main_table, + operations="mode", main_key=["userId", "movieId"], aux_key="userId", cols=["rating", "genre"], @@ -138,6 +298,7 @@ def test_wrong_keys(df_module, main_table): # Check too many aux_key agg_joiner = AggJoiner( aux_table=main_table, + operations="mode", main_key="userId", aux_key=["userId", "movieId"], cols=["rating", "genre"], @@ -150,6 +311,7 @@ def test_wrong_keys(df_module, main_table): # Check providing key and extra main_key agg_joiner = AggJoiner( aux_table=main_table, + operations="mode", key="userId", main_key="userId", cols=["rating", "genre"], @@ -160,6 +322,7 @@ def test_wrong_keys(df_module, main_table): # Check providing key and extra aux_key agg_joiner = AggJoiner( aux_table=main_table, + operations="mode", key="userId", aux_key="userId", cols=["rating", "genre"], @@ -170,6 +333,7 @@ def test_wrong_keys(df_module, main_table): # Check main_key doesn't exist in table agg_joiner = AggJoiner( aux_table=main_table, + operations="mode", main_key="wrong_key", aux_key="userId", cols=["rating", "genre"], @@ -181,6 +345,7 @@ def test_wrong_keys(df_module, main_table): # Check aux_key doesn't exist in table agg_joiner = AggJoiner( aux_table=main_table, + operations="mode", main_key="userId", aux_key="wrong_key", cols=["rating", "genre"], @@ -190,27 +355,24 @@ def test_wrong_keys(df_module, main_table): agg_joiner.fit(main_table) -def test_default_suffix(df_module, main_table): +def test_agg_joiner_default_suffix(df_module, main_table): "Check that the default `suffix` of `AggJoiner` is ''." - main_table = df_module.DataFrame(main_table) # Check no suffix agg_joiner = AggJoiner( - aux_table=main_table, - key="userId", - cols=["rating", "genre"], + aux_table=main_table, operations="mode", key="userId", cols=["rating", "genre"] ) agg_joiner.fit(main_table) assert agg_joiner.suffix == "" -def test_too_many_suffixes(df_module, main_table): +def test_agg_joiner_too_many_suffixes(main_table): "Check that providing more than one `suffix` for the `AggJoiner` raises an error." - main_table = df_module.DataFrame(main_table) # Check inconsistent number of suffixes agg_joiner = AggJoiner( aux_table=main_table, + operations="mean", key="userId", cols="rating", suffix=["_user", "_movie", "_tag"], @@ -219,26 +381,43 @@ def test_too_many_suffixes(df_module, main_table): agg_joiner.fit(main_table) -def test_default_cols(df_module, main_table): +def test_agg_joiner_duplicate_col_name_after_suffix(main_table): + "Check that ``__skrub___`` is added for duplicate column names." + + main_table = sbd.with_columns( + main_table, **{"rating_mean": [4.1, 4.1, 4.1, 3.1, 2.1, 4.1]} + ) + # Check inconsistent number of suffixes + agg_joiner = AggJoiner( + aux_table=main_table, + operations="mean", + key="userId", + cols="rating", + suffix="", + ) + aggregated = agg_joiner.fit_transform(main_table) + assert aggregated.shape == (6, 6) + assert re.match(r"rating_mean__skrub_[0-9a-f]+__", sbd.column_names(aggregated)[5]) + + +def test_agg_joiner_default_cols(main_table): "Check that by default, `cols` are all the columns of `aux_table` except `aux_key`." - main_table = df_module.DataFrame(main_table) # Check no cols agg_joiner = AggJoiner( - aux_table=main_table, - key=["movieId", "userId"], + aux_table=main_table, operations="mode", key=["movieId", "userId"] ) agg_joiner.fit(main_table) agg_joiner._cols == ["rating", "genre"] -def test_correct_cols(df_module, main_table): +def test_agg_joiner_correct_cols(df_module, main_table): "Check that expected `cols` parameters for the `AggJoiner` are working." - main_table = df_module.DataFrame(main_table) # Check one col agg_joiner = AggJoiner( aux_table=main_table, + operations="mean", key=["movieId", "userId"], cols=["rating"], ) @@ -246,28 +425,28 @@ def test_correct_cols(df_module, main_table): agg_joiner._cols == ["rating"] -def test_wrong_cols(df_module, main_table): +def test_agg_joiner_wrong_cols(main_table): "Check that providing a column that's not in `aux_table` does not work." - main_table = df_module.DataFrame(main_table) # Check missing agg or keys cols in tables agg_joiner = AggJoiner( aux_table=main_table, + operations="mean", key="userId", cols="unknown_col", ) - match = r"(?=.*columns cannot be used because they do not exist)" + match = r"(columns cannot be used because they do not exist)" with pytest.raises(ValueError, match=match): agg_joiner.fit(main_table) -def test_input_multiple_tables(df_module, main_table): +def test_agg_joiner_input_multiple_tables(main_table): "Check that providing too many auxiliary tables in `AggJoiner` raises an error." - main_table = df_module.DataFrame(main_table) # Check too many aux_table agg_joiner = AggJoiner( aux_table=[main_table, main_table], + operations="mean", main_key="userId", aux_key=["userId", "userId"], cols=[["rating"], ["rating"]], @@ -276,77 +455,45 @@ def test_input_multiple_tables(df_module, main_table): r"(?=.*must be a dataframe or the string 'X')" r"(?=.*use the MultiAggJoiner instead)" ) - with pytest.raises(TypeError, match=error_msg): + with pytest.raises(ValueError, match=error_msg): agg_joiner.fit_transform(main_table) -def test_default_operations(df_module, main_table): - "Check that the default `operations` of `AggJoiner` are ['mean', 'mode']." - main_table = df_module.DataFrame(main_table) - - # Check default operations - agg_joiner = AggJoiner( - aux_table=main_table, - key="userId", - cols=["rating", "genre"], - ) - agg_joiner.fit(main_table) - assert agg_joiner._operations == ["mean", "mode"] - - -def test_correct_operations_input(df_module, main_table): +def test_agg_joiner_correct_operations_input(main_table): "Check that expected `operations` parameters for the `AggJoiner` are working." - main_table = df_module.DataFrame(main_table) # Check invariant operations input agg_joiner = AggJoiner( aux_table=main_table, + operations=["min", "max", "mode"], key="userId", cols=["rating", "genre"], - operations=["min", "max", "mode"], ) agg_joiner.fit(main_table) assert agg_joiner._operations == ["min", "max", "mode"] -@pytest.mark.skipif(not _POLARS_INSTALLED, reason="Polars is not available") -def test_polars_unavailable_operation(main_table): - "Check that the 'value_counts' operation is not supported for Polars." - import polars as pl - - agg_joiner = AggJoiner( - aux_table="X", - main_key="userId", - aux_key="movieId", - cols="rating", - operations=["value_counts"], - ) - with pytest.raises(ValueError, match=r"(?=.*value_counts)(?=.*supported)"): - agg_joiner.fit(pl.DataFrame(main_table)) - - -def test_not_supported_operations(df_module, main_table): +def test_agg_joiner_not_supported_operations(main_table): "Check that calling an unsupported operation raises an error." - main_table = df_module.DataFrame(main_table) # Check not supported operations agg_joiner = AggJoiner( aux_table=main_table, + operations=["nunique", "mode"], key="userId", cols=["rating", "genre"], - operations=["nunique", "mode"], ) - match = r"(?=.*operations options are)" + match = r"(?=.*`operations` options are)" with pytest.raises(ValueError, match=match): agg_joiner.fit(main_table) -def test_wrong_string_placeholder(df_module, main_table): +def test_agg_joiner_wrong_string_placeholder(main_table): "Check that `aux_table='Y'` is not a valid string placeholder." - main_table = df_module.DataFrame(main_table) agg_joiner = AggJoiner( aux_table="Y", + operations="mean", key="userId", cols="genre", ) @@ -354,19 +501,41 @@ def test_wrong_string_placeholder(df_module, main_table): agg_joiner.fit(main_table) -def test_not_fitted_dataframe(df_module, main_table): +def test_agg_joiner_get_feature_names_out(main_table): + agg_joiner = AggJoiner( + aux_table=main_table, + operations="count", + key="userId", + cols="genre", + ) + with pytest.raises(NotFittedError): + agg_joiner.get_feature_names_out() + + agg_joiner.fit(main_table) + assert agg_joiner.get_feature_names_out() == [ + "userId", + "movieId", + "rating", + "genre", + "genre_count", + ] + + +def test_agg_joiner_not_fitted_dataframe(df_module, main_table): """ Check that calling `transform` on a dataframe not containing the columns seen during `fit` raises an error. """ - main_table = df_module.DataFrame(main_table) + not_main_table = df_module.make_dataframe( {"wrong": [1, 2, 3], "dataframe": [4, 5, 6]} ) agg_joiner = AggJoiner( aux_table=main_table, + operations="mean", key="userId", + cols="rating", ) agg_joiner.fit(main_table) error_msg = r"Columns of dataframes passed to fit\(\) and transform\(\) differ" @@ -374,104 +543,241 @@ def test_not_fitted_dataframe(df_module, main_table): agg_joiner.transform(not_main_table) -y = pd.DataFrame(dict(rating=[4.0, 4.0, 4.0, 3.0, 2.0, 4.0])) +def test_agg_joiner_duplicate_columns(main_table): + joiner = AggJoiner( + aux_table=main_table, operations="mean", key="userId", cols="rating" + ) + X = sbd.with_columns(main_table, rating_mean=sbd.col(main_table, "rating")) + out_1 = joiner.fit_transform(X) + out_2 = joiner.transform(X) + assert sbd.column_names(out_1) == sbd.column_names(out_2) + + +@pytest.fixture +def y_df(df_module): + return df_module.make_dataframe({"rating": [4.1, 4.1, 4.1, 3.1, 2.1, 4.1]}) + + +@pytest.fixture(params=["df", "named_column", "1d_array", "2d_array"]) +def y_col_name(df_module, request): + input_type = request.param + y = df_module.make_dataframe({"rating": [4.1, 4.1, 4.1, 3.1, 2.1, 4.1]}) + if input_type == "df": + return (y, "rating") + if input_type == "named_column": + return (sbd.col(y, "rating"), "rating") + if input_type == "1d_array": + return (np.array([4.1, 4.1, 4.1, 3.1, 2.1, 4.1]), "y_0") + if input_type == "2d_array": + return ( + np.asarray([sbd.to_numpy(c) for c in sbd.to_column_list(y)], dtype=float).T, + "y_0", + ) + + +def test_agg_target_simple_fit_transform(df_module, main_table, y_col_name): + y, col_name = y_col_name + agg_target = AggTarget(main_key="userId", operations="mean", suffix="_user") + main_transformed = agg_target.fit_transform(main_table, y) + + main_transformed_expected = df_module.make_dataframe( + { + "userId": [1, 1, 1, 2, 2, 2], + "movieId": [1, 3, 6, 318, 6, 1704], + "rating": [4.1, 4.1, 4.1, 3.1, 2.1, 4.1], + "genre": ["drama", "drama", "comedy", "sf", "comedy", "sf"], + f"{col_name}_mean_user": [4.1, 4.1, 4.1, 3.1, 3.1, 3.1], + } + ) + + if df_module.description == "pandas-nullable-dtypes": + main_transformed = sbd.pandas_convert_dtypes(main_transformed) + df_module.assert_frame_equal(main_transformed, main_transformed_expected) -@pytest.mark.parametrize( - "y, col_name", - [ - (y, "rating"), - (y["rating"], "rating"), - (y.values, "y_0"), - (y.values.tolist(), "y_0"), - (y["rating"].rename(None), "y_0"), - ], -) -def test_agg_target(main_table, y, col_name): - agg_target = AggTarget( - main_key="userId", - suffix="_user", - operation=["hist(2)", "value_counts"], +def test_agg_target_unnamed_column(): + main_table = pd.DataFrame( + { + "userId": [1, 1, 1, 2, 2, 2], + "rating": [4.1, 4.1, 4.1, 3.1, 2.1, 4.1], + } ) - main_transformed = agg_target.fit_transform(main_table, y) + y = pd.Series([4.1, 4.1, 4.1, 3.1, 2.1, 4.1], name=None) + agg_target = AggTarget(main_key="userId", operations="mean", suffix="_user") + main_transformed = agg_target.fit_transform(main_table, y) main_transformed_expected = pd.DataFrame( + { + "userId": [1, 1, 1, 2, 2, 2], + "rating": [4.1, 4.1, 4.1, 3.1, 2.1, 4.1], + "y_0_mean_user": [4.1, 4.1, 4.1, 3.1, 3.1, 3.1], + } + ) + + pandas.testing.assert_frame_equal(main_transformed, main_transformed_expected) + + +@pytest.fixture(params=["df", "array"]) +def y_2_col_names(df_module, request): + input_type = request.param + y = df_module.make_dataframe( + { + "a": [10.1, 20.1, 30.1, 40.1, 50.1, 60.1], + "b": [60.1, 50.1, 40.1, 30.1, 20.1, 10.1], + } + ) + if input_type == "df": + return (y, "a", "b") + if input_type == "array": + return ( + np.asarray([sbd.to_numpy(c) for c in sbd.to_column_list(y)], dtype=float).T, + "y_0", + "y_1", + ) + + +def test_agg_target_multiple_columns(df_module, main_table, y_2_col_names): + y, col_name_a, col_name_b = y_2_col_names + agg_target = AggTarget(main_key="userId", operations="mean", suffix="_user") + main_transformed = agg_target.fit_transform(main_table, y) + + main_transformed_expected = df_module.make_dataframe( { "userId": [1, 1, 1, 2, 2, 2], "movieId": [1, 3, 6, 318, 6, 1704], - "rating": [4.0, 4.0, 4.0, 3.0, 2.0, 4.0], + "rating": [4.1, 4.1, 4.1, 3.1, 2.1, 4.1], "genre": ["drama", "drama", "comedy", "sf", "comedy", "sf"], - f"{col_name}_(1.999, 3.0]_user": [0, 0, 0, 2, 2, 2], - f"{col_name}_(3.0, 4.0]_user": [3, 3, 3, 1, 1, 1], - f"{col_name}_2.0_user": [0.0, 0.0, 0.0, 1.0, 1.0, 1.0], - f"{col_name}_3.0_user": [0.0, 0.0, 0.0, 1.0, 1.0, 1.0], - f"{col_name}_4.0_user": [3.0, 3.0, 3.0, 1.0, 1.0, 1.0], + f"{col_name_a}_mean_user": [20.1, 20.1, 20.1, 50.1, 50.1, 50.1], + f"{col_name_b}_mean_user": [50.1, 50.1, 50.1, 20.1, 20.1, 20.1], } ) - pd.testing.assert_frame_equal(main_transformed, main_transformed_expected) + if df_module.description == "pandas-nullable-dtypes": + main_transformed = sbd.pandas_convert_dtypes(main_transformed) + df_module.assert_frame_equal(main_transformed, main_transformed_expected) + + +def test_agg_target_multiple_main_key(df_module, main_table, y_df): + agg_target = AggTarget( + main_key=["userId", "genre"], + operations="max", + ) + main_transformed = agg_target.fit_transform(main_table, y_df) + sbd.column_names(main_transformed) == [ + "userId", + "movieId", + "rating", + "genre", + "rating_max_target", + ] + if df_module.description == "pandas-nullable-dtypes": + main_transformed = sbd.pandas_convert_dtypes(main_transformed) + y_expected = df_module.make_column( + "rating_max_target", [4.1, 4.1, 4.1, 4.1, 2.1, 4.1] + ) + df_module.assert_column_equal( + sbd.col(main_transformed, "rating_max_target"), y_expected + ) -def test_agg_target_missing_operations(main_table): +def test_agg_target_wrong_target_size(main_table, y_df): agg_target = AggTarget( main_key="userId", + operations="count", suffix="_user", ) - # y is continuous - y = pd.DataFrame(dict(rating=[4.0, 4.1, 4.2, 4.3, 4.4, 4.5])) - agg_target.fit(main_table, y) - assert agg_target.operation_ == ["mean"] + match = r"(?=.*length)(?=.*match)" + with pytest.raises(ValueError, match=match): + agg_target.fit(main_table, sbd.col(y_df, "rating")[:2]) - # y is categorical - y = pd.DataFrame(dict(rating=["1", "2", "3", "1", "2", "3"])) - agg_target.fit(main_table, y) - assert agg_target.operation_ == ["mode"] +def test_agg_target_wrong_operations(main_table, y_df): + "Check that a useful error is raised when `operations` is not supported" -def test_agg_target_check_input(main_table): + # Test operations is string not in list agg_target = AggTarget( main_key="userId", - suffix="_user", + operations="wrong_op", ) - match = r"(?=.*X must be a dataframe)" - with pytest.raises(TypeError, match=match): - agg_target.fit(main_table.values, y) + with pytest.raises( + ValueError, + match=r"(`operations` options are)", + ): + agg_target.fit(main_table, y_df) - match = r"(?=.*length)(?=.*match)" - with pytest.raises(ValueError, match=match): - agg_target.fit(main_table, y["rating"][:2]) + # Test operations is None + agg_target = AggTarget( + main_key="userId", + operations=None, + ) + with pytest.raises( + ValueError, + match=r"(`operations` must be a string or an iterable of strings)", + ): + agg_target.fit(main_table, y_df) + # Test operations is int + agg_target = AggTarget( + main_key="userId", + operations=2, + ) + with pytest.raises( + ValueError, + match=r"(`operations` must be a string or an iterable of strings)", + ): + agg_target.fit(main_table, y_df) + + +def test_agg_target_too_many_suffixes(main_table, y_df): + "Check that providing more than one `suffix` for the `AggTarget` raises an error." -def test_no_aggregation_exception(main_table): + # Check inconsistent number of suffixes agg_target = AggTarget( main_key="userId", - operation=[], + operations="mean", + suffix=["_user", "_movie", "_tag"], ) - with pytest.raises(ValueError, match=r"(?=.*No aggregation)"): - agg_target.fit(main_table, y) + with pytest.raises(ValueError, match=r"(?='suffix' must be a string.*)"): + agg_target.fit(main_table, y_df) -def test_wrong_args_ops(main_table): +def test_agg_target_get_feature_names_out(main_table, y_df): agg_target = AggTarget( main_key="userId", - operation="mean(2)", + operations="count", ) - with pytest.raises(ValueError, match=r"(?=.*'mean')(?=.*argument)"): - agg_target.fit(main_table, y) + with pytest.raises(NotFittedError): + agg_target.get_feature_names_out() + agg_target.fit(main_table, y_df) + assert agg_target.get_feature_names_out() == [ + "userId", + "movieId", + "rating", + "genre", + "rating_count_target", + ] -def test_duplicate_columns(main_table): - joiner = AggJoiner(aux_table=main_table, key="userId") - X = sbd.with_columns(main_table, rating_mean=sbd.col(main_table, "rating")) - out_1 = joiner.fit_transform(X) - out_2 = joiner.transform(X) - assert sbd.column_names(out_1) == sbd.column_names(out_2) + +def test_agg_target_non_dataframe_X(y_df): + agg_target = AggTarget(main_key="userId", operations="mean") + with pytest.raises( + TypeError, match=r"Only pandas and polars DataFrames are supported" + ): + agg_target.fit("should_be_a_dataframe", y_df) + + +def test_agg_target_non_dataframe_y(main_table): + agg_target = AggTarget(main_key="userId", operations="mean") + with pytest.raises( + TypeError, match=r"`y` must be a dataframe, a series or a numpy array" + ): + agg_target.fit(main_table, "should_be_array_like") -def test_duplicate_columns_target(main_table): - joiner = AggTarget(main_key="userId", operation="mean") - y = sbd.col(main_table, "rating") +def test_agg_target_duplicate_columns(main_table, y_df): + agg_target = AggTarget(main_key="userId", operations="mean") X = sbd.with_columns(main_table, rating_mean_target=sbd.col(main_table, "rating")) - out_1 = joiner.fit_transform(X, y) - out_2 = joiner.transform(X) + out_1 = agg_target.fit_transform(X, y_df) + out_2 = agg_target.transform(X) assert sbd.column_names(out_1) == sbd.column_names(out_2) diff --git a/skrub/tests/test_multi_agg_joiner.py b/skrub/tests/test_multi_agg_joiner.py index aed0d2035..6a6b25c1d 100644 --- a/skrub/tests/test_multi_agg_joiner.py +++ b/skrub/tests/test_multi_agg_joiner.py @@ -27,9 +27,10 @@ def test_simple_fit_transform(df_module, main_table, use_X_placeholder): multi_agg_joiner = MultiAggJoiner( aux_tables=aux, + operations=[["mode"], ["mean"]], main_keys=[["userId"], ["movieId"]], aux_keys=[["userId"], ["movieId"]], - cols=[["rating", "genre"], ["rating"]], + cols=[["genre"], ["rating"]], suffixes=["_user", "_movie"], ) @@ -42,7 +43,6 @@ def test_simple_fit_transform(df_module, main_table, use_X_placeholder): "rating": [4.0, 4.0, 4.0, 3.0, 2.0, 4.0], "genre": ["drama", "drama", "comedy", "sf", "comedy", "sf"], "genre_mode_user": ["drama", "drama", "drama", "sf", "sf", "sf"], - "rating_mean_user": [4.0, 4.0, 4.0, 3.0, 3.0, 3.0], "rating_mean_movie": [4.0, 4.0, 3.0, 3.0, 3.0, 4.0], } ) @@ -60,21 +60,27 @@ def test_X_placeholder(df_module, main_table): multi_agg_joiner = MultiAggJoiner( aux_tables=["X", main_table], + operations=[["mean"], ["mean"]], keys=[["userId"], ["userId"]], + cols=[["rating"], ["rating"]], ) multi_agg_joiner.fit_transform(main_table) assert multi_agg_joiner._aux_tables == [main_table, main_table] multi_agg_joiner = MultiAggJoiner( aux_tables=["X", "X"], + operations=[["mean"], ["mean"]], keys=[["userId"], ["userId"]], + cols=[["rating"], ["rating"]], ) multi_agg_joiner.fit_transform(main_table) assert multi_agg_joiner._aux_tables == [main_table, main_table] multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table, main_table, "X", main_table], + operations=[["mean"], ["mean"], ["mean"], ["mean"]], keys=[["userId"], ["userId"], ["userId"], ["userId"]], + cols=[["rating"], ["rating"], ["rating"], ["rating"]], ) multi_agg_joiner.fit_transform(main_table) assert multi_agg_joiner._aux_tables == [ @@ -92,6 +98,7 @@ def test_wrong_aux_tables(df_module, main_table): # Check aux_tables isn't an array multi_agg_joiner = MultiAggJoiner( aux_tables=main_table, + operations=[["mean"]], keys=["userId"], ) with pytest.raises( @@ -103,6 +110,7 @@ def test_wrong_aux_tables(df_module, main_table): # Check aux_tables is not a dataframe or "X" multi_agg_joiner = MultiAggJoiner( aux_tables=[1], + operations=[["mean"]], keys=["userId"], ) with pytest.raises( @@ -119,6 +127,7 @@ def test_wrong_main_table(df_module, main_table): # Check wrong `X` multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["mean"]], keys=[["userId"]], ) with pytest.raises( @@ -142,6 +151,7 @@ def test_check_wrong_aux_table_type(main_table, df_module): # Check aux_tables is pandas when X is polars or the opposite multi_agg_joiner = MultiAggJoiner( aux_tables=[aux_table], + operations=[["mean"]], keys=[["userId"]], ) wanted_type = "Pandas" if df_module.module == pd else "Polars" @@ -158,7 +168,9 @@ def test_correct_keys(main_table, df_module): # Check only keys multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["mean"]], keys=[["userId"]], + cols=[["rating"]], ) multi_agg_joiner.fit_transform(main_table) assert multi_agg_joiner._main_keys == [["userId"]] @@ -167,22 +179,28 @@ def test_correct_keys(main_table, df_module): # Check multiple keys multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["mean"]], keys=[["userId", "movieId"]], + cols=[["rating"]], ) multi_agg_joiner.fit_transform(main_table) # Check keys multiple tables multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table, main_table], + operations=[["mean"], ["mean"]], keys=[["userId"], ["userId"]], + cols=[["rating"], ["rating"]], ) multi_agg_joiner.fit_transform(main_table) # Check multiple main_keys and aux_keys, same length multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["mean"]], main_keys=[["userId", "movieId"]], aux_keys=[["userId", "movieId"]], + cols=[["rating"]], ) multi_agg_joiner.fit_transform(main_table) @@ -194,6 +212,7 @@ def test_no_keys(main_table, df_module): # Check no keys at all multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["mean"]], ) error_msg = r"Must pass either `keys`, or \(`main_keys` and `aux_keys`\)." with pytest.raises(ValueError, match=error_msg): @@ -207,6 +226,7 @@ def test_too_many_keys(main_table, df_module): # Check too many main_keys multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["mean"]], main_keys=[["userId", "movieId"]], aux_keys=[["userId"]], ) @@ -222,6 +242,7 @@ def test_too_many_keys(main_table, df_module): # Check too many aux_keys multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["mean"]], main_keys=[["userId"]], aux_keys=[["userId", "movieId"]], ) @@ -233,6 +254,7 @@ def test_too_many_keys(main_table, df_module): # Check providing keys and extra main_keys multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["mean"]], keys=[["userId"]], main_keys=[["userId"]], ) @@ -242,6 +264,7 @@ def test_too_many_keys(main_table, df_module): # Check providing keys and extra aux_keys multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["mean"]], keys=[["userId"]], aux_keys=[["userId"]], ) @@ -256,6 +279,7 @@ def test_unknown_keys(main_table, df_module): # Check main_keys doesn't exist in table multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["count"]], main_keys=[["wrong_key"]], aux_keys=[["userId"]], ) @@ -266,6 +290,7 @@ def test_unknown_keys(main_table, df_module): # Check aux_keys doesn't exist in table multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["count"]], main_keys=[["userId"]], aux_keys=[["wrong_key"]], ) @@ -281,6 +306,7 @@ def test_wrong_keys_length(main_table, df_module): # Check wrong main_keys lenght multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table, main_table], + operations=[["count"], ["count"]], main_keys=[["userId"]], aux_keys=[["userId"], ["userId"]], ) @@ -291,6 +317,7 @@ def test_wrong_keys_length(main_table, df_module): # Check wrong aux_keys length multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table, main_table], + operations=[["count"], ["count"]], main_keys=[["userId"], ["userId"]], aux_keys=[["userId"]], ) @@ -310,6 +337,7 @@ def test_default_cols(main_table, df_module): # Check no cols multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table, main_table], + operations=[["count"], ["count"]], keys=[["userId", "movieId"], ["userId"]], ) multi_agg_joiner.fit(main_table) @@ -323,6 +351,7 @@ def test_correct_cols(main_table, df_module): # Check providing one col multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["count"]], keys=[["userId"]], cols=[["rating"]], ) @@ -332,6 +361,7 @@ def test_correct_cols(main_table, df_module): # Check providing one col for each aux_tables multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table, main_table], + operations=[["count"], ["count"]], keys=[["userId"], ["userId"]], cols=[["rating"], ["rating"]], ) @@ -346,6 +376,7 @@ def test_wrong_cols_input_type(main_table, df_module): # Check providing wrong cols type multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["count"]], keys=[["userId"]], cols=[[1]], ) @@ -364,6 +395,7 @@ def test_too_many_cols(main_table, df_module): # Check providing too many cols multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["count"]], keys=[["userId"]], cols=[["rating"], ["rating"]], ) @@ -384,31 +416,15 @@ def test_cols_not_in_table(main_table, df_module): # Check cols not in table multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["count"]], keys=[["userId"]], cols=[["wrong_col"]], ) - error_msg = r"(?=.*columns cannot be used because they do not exist)" + error_msg = r"(columns cannot be used because they do not exist)" with pytest.raises(ValueError, match=error_msg): multi_agg_joiner.fit_transform(main_table) -def test_default_operations(main_table, df_module): - """ - Check that the default `operations` in the `MultiAggJoiner` is an - iterable of ['mode', 'mean']. - """ - main_table = df_module.DataFrame(main_table) - - # Check default operations - multi_agg_joiner = MultiAggJoiner( - aux_tables=[main_table, main_table], - keys=[["userId"], ["userId"]], - cols=[["rating", "genre"], ["rating", "genre"]], - ) - multi_agg_joiner.fit(main_table) - assert multi_agg_joiner._operations == [["mean", "mode"], ["mean", "mode"]] - - def test_correct_operations(main_table, df_module): "Check that expected `operations` parameters for the `MultiAggJoiner` are working." main_table = df_module.DataFrame(main_table) @@ -416,9 +432,9 @@ def test_correct_operations(main_table, df_module): # Check one operation multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["mean"]], keys=[["userId"]], cols=[["rating"]], - operations=[["mean"]], ) multi_agg_joiner.fit_transform(main_table) assert multi_agg_joiner._operations == [["mean"]] @@ -426,9 +442,9 @@ def test_correct_operations(main_table, df_module): # Check one operation for each aux table multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table, main_table], + operations=[["mean"], ["mean"]], keys=[["userId"], ["userId"]], cols=[["rating"], ["rating"]], - operations=[["mean"], ["mean"]], ) multi_agg_joiner.fit_transform(main_table) assert multi_agg_joiner._operations == [["mean"], ["mean"]] @@ -436,9 +452,9 @@ def test_correct_operations(main_table, df_module): # Check one and two operations multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table, main_table], + operations=[["mean"], ["mean", "mode"]], keys=[["userId"], ["userId"]], cols=[["rating"], ["rating"]], - operations=[["mean"], ["mean", "mode"]], ) multi_agg_joiner.fit_transform(main_table) assert multi_agg_joiner._operations == [["mean"], ["mean", "mode"]] @@ -451,9 +467,9 @@ def test_wrong_operations(main_table, df_module): # Check list of operations multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table, main_table], + operations="mean", keys=[["userId"], ["userId"]], cols=[["rating"], ["rating"]], - operations="mean", ) error_msg = ( r"Accepted inputs for `operations` are None and iterable of iterable of str." @@ -464,9 +480,9 @@ def test_wrong_operations(main_table, df_module): # Check badly formatted operation multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table, main_table], + operations=[["mean", "mean", "mode"]], keys=[["userId"], ["userId"]], cols=[["rating"], ["rating"]], - operations=[["mean", "mean", "mode"]], ) error_msg = ( r"The number of iterables in `operations` must match the number of tables" @@ -485,6 +501,7 @@ def test_default_suffixes(main_table, df_module): # check default suffixes with multiple tables multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table, main_table], + operations=[["count"], ["count"]], keys=[["userId"], ["userId"]], cols=[["rating"], ["rating"]], ) @@ -499,6 +516,7 @@ def test_suffixes(main_table, df_module): # check suffixes when defined multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table, main_table], + operations=[["count"], ["count"]], keys=[["userId"], ["userId"]], cols=[["rating"], ["rating"]], suffixes=["_this", "_works"], @@ -514,6 +532,7 @@ def test_too_many_suffixes(main_table, df_module): # check too many suffixes multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table, main_table], + operations=[["count"], ["count"]], keys=[["userId"], ["userId"]], cols=[["rating"], ["rating"]], suffixes=["_0", "_1", "_2"], @@ -533,6 +552,7 @@ def test_non_str_suffixes(main_table, df_module): # check suffixes not str multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["count"]], keys=[["userId"]], cols=[["rating"]], suffixes=[1], @@ -597,6 +617,7 @@ def test_not_fitted_dataframe(main_table, df_module): multi_agg_joiner = MultiAggJoiner( aux_tables=[main_table], + operations=[["count"]], keys=[["userId"]], ) multi_agg_joiner.fit(main_table) From f47250d6da230b48401997b198a74c2e94a36577 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20Jolivet?= <57430673+TheooJ@users.noreply.github.com> Date: Thu, 14 Nov 2024 15:44:28 +0100 Subject: [PATCH 05/24] Fix getting started example (#1137) --- examples/00_getting_started.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/00_getting_started.py b/examples/00_getting_started.py index 5caee61a5..76678b5f2 100644 --- a/examples/00_getting_started.py +++ b/examples/00_getting_started.py @@ -160,8 +160,8 @@ aux_table=flights, main_key="airport_id", aux_key="from_airport", - cols=["total_passengers", "company"], # the cols to perform aggregation on - operations=["mean", "mode"], # the operations to compute + cols=["total_passengers"], # the cols to perform aggregation on + operations=["mean", "std"], # the operations to compute ) agg_joiner.fit_transform(airports) From 43587e5d9074ebdb7ee0158c2be532a96cbd4ca7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Th=C3=A9o=20Jolivet?= <57430673+TheooJ@users.noreply.github.com> Date: Thu, 14 Nov 2024 18:51:18 +0100 Subject: [PATCH 06/24] [MAINT] Remove backend-specific code (#1139) --- skrub/_dataframe/_namespace.py | 102 ----------------------- skrub/_dataframe/_pandas.py | 12 --- skrub/_dataframe/_polars.py | 10 --- skrub/_dataframe/tests/test_namespace.py | 42 ---------- skrub/_dataframe/tests/test_pandas.py | 9 -- skrub/_dataframe/tests/test_polars.py | 14 ---- skrub/_interpolation_joiner.py | 6 +- skrub/_join_utils.py | 6 -- skrub/_multi_agg_joiner.py | 10 +-- skrub/tests/test_join_utils.py | 8 -- 10 files changed, 10 insertions(+), 209 deletions(-) delete mode 100644 skrub/_dataframe/_namespace.py delete mode 100644 skrub/_dataframe/_pandas.py delete mode 100644 skrub/_dataframe/_polars.py delete mode 100644 skrub/_dataframe/tests/test_namespace.py delete mode 100644 skrub/_dataframe/tests/test_pandas.py delete mode 100644 skrub/_dataframe/tests/test_polars.py diff --git a/skrub/_dataframe/_namespace.py b/skrub/_dataframe/_namespace.py deleted file mode 100644 index f2b3ca174..000000000 --- a/skrub/_dataframe/_namespace.py +++ /dev/null @@ -1,102 +0,0 @@ -import sys - -import pandas as pd - -import skrub._dataframe._pandas as skrub_pd -import skrub._dataframe._polars as skrub_pl - -# TODO: _dataframe._namespace is temporary; all code in this module should be moved -# elsewhere and use the dispatch mechanism. - - -def is_pandas(dataframe): - """Check whether the input is a Pandas dataframe. - - Parameters - ---------- - dataframe : DataFrameLike - The input dataframe - - Returns - ------- - is_pandas : bool - Whether the dataframe is a Pandas dataframe or not. - """ - return isinstance(dataframe, pd.DataFrame) - - -def is_polars(dataframe): - """Check whether the input is a Polars dataframe or lazyframe. - - Parameters - ---------- - dataframe : DataFrameLike - The input dataframe - - Returns - ------- - is_polars : bool - Whether the dataframe is a Polars dataframe/lazyframe or not. - """ - if "polars" not in sys.modules: - return False - - import polars as pl - - return isinstance(dataframe, (pl.DataFrame, pl.LazyFrame)) - - -def get_df_namespace(*dfs): - """Get the namespaces of dataframes. - - Introspects dataframes and returns their skrub namespace object - ``skrub.dataframe._{pandas, polars}`` and the dataframe module - ``{polars, pandas}`` itself. - - The dataframes passed in input need to come from the same module, otherwise a - ``TypeError`` will be raised. - - The outputs of this function are denoted ``skrub_px`` and ``px`` in reference to - the array API, returning namespace (NumPy, PyTorch and CuPy) as ``nx``. - Since we deal with Polars (``pl``) and Pandas (``pd``), we use ``px`` - as a variable name. - - Parameters - ---------- - dfs : DataFrameLike | list[DataFrameLike], - The dataframes to extract modules from. - - Returns - ------- - skrub_px : ModuleType - Skrub namespace shared by dataframe objects. - - px : ModuleType - Dataframe namespace, i.e. Pandas or Polars module. - """ - # FIXME Pandas and Polars series will raise errors. - if all([is_pandas(df) for df in dfs]): - return skrub_pd, pd - - elif all([is_polars(df) for df in dfs]): - import polars as pl - - if all([isinstance(df, pl.DataFrame) for df in dfs]) or all( - [isinstance(df, pl.LazyFrame) for df in dfs] - ): - return skrub_pl, pl - else: - raise TypeError("Mixing Polars lazyframes and dataframes is not supported.") - - else: - modules = [type(df).__module__ for df in dfs] - if all([is_polars(df) or is_pandas(df) for df in dfs]): - raise TypeError( - "Mixing Pandas and Polars dataframes is not supported, " - f"got {modules=!r}." - ) - else: - raise TypeError( - "Only Pandas or Polars dataframes are currently supported, " - f"got {modules=!r}." - ) diff --git a/skrub/_dataframe/_pandas.py b/skrub/_dataframe/_pandas.py deleted file mode 100644 index 76f6bf42a..000000000 --- a/skrub/_dataframe/_pandas.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -Pandas specialization of the aggregate and join operations. -""" - -# TODO: _dataframe._pandas is temporary; all code in this module should be moved -# elsewhere and use the dispatch mechanism. - - -def rename_columns(dataframe, renaming_function): - return dataframe.rename( - columns={c: renaming_function(c) for c in dataframe.columns} - ) diff --git a/skrub/_dataframe/_polars.py b/skrub/_dataframe/_polars.py deleted file mode 100644 index 04247648a..000000000 --- a/skrub/_dataframe/_polars.py +++ /dev/null @@ -1,10 +0,0 @@ -""" -Polars specialization of the aggregate and join operations. -""" - -# TODO: _dataframe._polars is temporary; all code in this module should be moved -# elsewhere and use the dispatch mechanism. - - -def rename_columns(dataframe, renaming_function): - return dataframe.rename({c: renaming_function(c) for c in dataframe.columns}) diff --git a/skrub/_dataframe/tests/test_namespace.py b/skrub/_dataframe/tests/test_namespace.py deleted file mode 100644 index 784e31ba9..000000000 --- a/skrub/_dataframe/tests/test_namespace.py +++ /dev/null @@ -1,42 +0,0 @@ -import pandas as pd -import pytest - -import skrub._dataframe._pandas as skrub_pd -import skrub._dataframe._polars as skrub_pl -from skrub._dataframe._namespace import get_df_namespace -from skrub.conftest import _POLARS_INSTALLED - -main = pd.DataFrame( - { - "userId": [1, 1, 1, 2, 2, 2], - "movieId": [1, 3, 6, 318, 6, 1704], - "rating": [4.0, 4.0, 4.0, 3.0, 2.0, 4.0], - "genre": ["drama", "drama", "comedy", "sf", "comedy", "sf"], - } -) - - -def test_get_namespace_pandas(): - skrub_px, px = get_df_namespace(main, main) - assert skrub_px is skrub_pd - assert px is pd - - with pytest.raises(TypeError, match=r"(?=.*Only Pandas or Polars)(?=.*supported)"): - get_df_namespace(main, main.values) - - -@pytest.mark.skipif(not _POLARS_INSTALLED, reason="Polars is not available") -def test_get_namespace_polars(): - import polars as pl - - skrub_px, px = get_df_namespace(pl.DataFrame(main), pl.DataFrame(main)) - assert skrub_px is skrub_pl - assert px is pl - - with pytest.raises(TypeError, match=r"(?=.*Mixing Pandas)(?=.*Polars)"): - get_df_namespace(main, pl.DataFrame(main)) - - with pytest.raises( - TypeError, match=r"(?=.*Mixing)(?=.*lazyframes)(?=.*dataframes)" - ): - get_df_namespace(pl.DataFrame(main), pl.LazyFrame(main)) diff --git a/skrub/_dataframe/tests/test_pandas.py b/skrub/_dataframe/tests/test_pandas.py deleted file mode 100644 index 39fefcc69..000000000 --- a/skrub/_dataframe/tests/test_pandas.py +++ /dev/null @@ -1,9 +0,0 @@ -import pandas as pd - -from skrub._dataframe._pandas import rename_columns - - -def test_rename_columns(): - df = pd.DataFrame({"a column": [1], "another": [1]}) - df = rename_columns(df, str.swapcase) - assert list(df.columns) == ["A COLUMN", "ANOTHER"] diff --git a/skrub/_dataframe/tests/test_polars.py b/skrub/_dataframe/tests/test_polars.py deleted file mode 100644 index 1c8e7ae61..000000000 --- a/skrub/_dataframe/tests/test_polars.py +++ /dev/null @@ -1,14 +0,0 @@ -import pytest - -from skrub._dataframe._polars import rename_columns -from skrub.conftest import _POLARS_INSTALLED - -if _POLARS_INSTALLED: - import polars as pl - - -@pytest.mark.skipif(not _POLARS_INSTALLED, reason="Polars is not available") -def test_rename_columns(): - df = pl.DataFrame({"a column": [1], "another": [1]}) - df = rename_columns(df, str.swapcase) - assert list(df.columns) == ["A COLUMN", "ANOTHER"] diff --git a/skrub/_interpolation_joiner.py b/skrub/_interpolation_joiner.py index 3af6afa54..2337aa8b8 100644 --- a/skrub/_interpolation_joiner.py +++ b/skrub/_interpolation_joiner.py @@ -308,8 +308,12 @@ def transform(self, X): main_table, prediction_results ) predictions = [res["predictions"] for res in prediction_results] + predictions = [ - _join_utils.add_column_name_suffix(df, self.suffix) for df in predictions + sbd.set_column_names( + df, [f"{col}{self.suffix}" for col in sbd.column_names(df)] + ) + for df in predictions ] return sbd.concat_horizontal(main_table, *predictions) diff --git a/skrub/_join_utils.py b/skrub/_join_utils.py index e5a563b20..43ffc5b93 100644 --- a/skrub/_join_utils.py +++ b/skrub/_join_utils.py @@ -6,7 +6,6 @@ from skrub import _dataframe as sbd from skrub import _selectors as s from skrub import _utils -from skrub._dataframe._namespace import get_df_namespace from skrub._dispatch import dispatch @@ -99,11 +98,6 @@ def check_missing_columns(table, key, table_name): ) -def add_column_name_suffix(dataframe, suffix): - ns, _ = get_df_namespace(dataframe) - return ns.rename_columns(dataframe, f"{{}}{suffix}".format) - - def pick_column_names(suggested_names, forbidden_names=()): """Choose column names without duplicates. diff --git a/skrub/_multi_agg_joiner.py b/skrub/_multi_agg_joiner.py index a08969f73..cf5a77fbc 100644 --- a/skrub/_multi_agg_joiner.py +++ b/skrub/_multi_agg_joiner.py @@ -5,7 +5,7 @@ from sklearn.utils.validation import check_is_fitted from skrub._agg_joiner import AggJoiner -from skrub._dataframe._namespace import is_pandas, is_polars +from skrub._dataframe import _common as sbd from skrub._utils import _is_array_like @@ -250,14 +250,14 @@ def _check_dataframes(self, X, aux_tables): ) # Check that all input types are matching - if is_pandas(X): - if not all(is_pandas(aux_table) for aux_table in aux_tables): + if sbd.is_pandas(X): + if not all(sbd.is_pandas(aux_table) for aux_table in aux_tables): raise TypeError( "All `aux_tables` must be Pandas dataframes" " when `X` is a Pandas dataframe." ) - if is_polars(X): - if not all(is_polars(aux_table) for aux_table in aux_tables): + if sbd.is_polars(X): + if not all(sbd.is_polars(aux_table) for aux_table in aux_tables): raise TypeError( "All `aux_tables` must be Polars dataframes" " when `X` is a Polars dataframe." diff --git a/skrub/tests/test_join_utils.py b/skrub/tests/test_join_utils.py index 86b8c1d90..6dd1dce08 100644 --- a/skrub/tests/test_join_utils.py +++ b/skrub/tests/test_join_utils.py @@ -48,14 +48,6 @@ def test_check_key_length_mismatch(): ) -def test_add_column_name_suffix(df_module): - df = df_module.make_dataframe({"one": [], "two three": [], "x": []}) - df = _join_utils.add_column_name_suffix(df, "") - assert list(df.columns) == ["one", "two three", "x"] - df = _join_utils.add_column_name_suffix(df, "_y") - assert list(df.columns) == ["one_y", "two three_y", "x_y"] - - @pytest.fixture def left(df_module): return df_module.make_dataframe({"left_key": [1, 2, 2], "left_col": [10, 20, 30]}) From 8808526031a9904058cfcc1ba3d017e97622def5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=B4me=20Dock=C3=A8s?= Date: Fri, 15 Nov 2024 11:12:47 +0100 Subject: [PATCH 07/24] Faster svg viewbox computation in tablereport (#1138) --- CHANGES.rst | 3 +- examples/08_join_aggregation.py | 1 + skrub/_reporting/_data/templates/buttons.html | 2 +- .../_data/templates/column-summary.html | 2 +- skrub/_reporting/_data/templates/report.html | 5 +- skrub/_reporting/_data/templates/report.js | 220 ++++++++++-------- 6 files changed, 124 insertions(+), 109 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index f55388ab1..fd61f782b 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -59,7 +59,8 @@ Minor changes Moreover, when the text contains line breaks it now appears all on one line. Note this only affects the labels in the plots; the rest of the report did not have these problems. - :pr:`1097` by :user:`Jérôme Dockès `. + :pr:`1097` by :user:`Jérôme Dockès ` + and :pr:`1138` by :user:`Jérôme Dockès `. * In the TableReport it is now possible, before clicking any of the cells, to reach the dataframe sample table and activate a cell with tab key navigation. diff --git a/examples/08_join_aggregation.py b/examples/08_join_aggregation.py index eac307376..231e663a9 100644 --- a/examples/08_join_aggregation.py +++ b/examples/08_join_aggregation.py @@ -6,6 +6,7 @@ To simplify aggregate-then-join operations for machine learning, we can include the |AggJoiner| in our pipeline. + In this example, we are tackling a fraudulent loan detection use case. Because fraud is rare, this dataset is extremely imbalanced, with a prevalence of around 1.4%. diff --git a/skrub/_reporting/_data/templates/buttons.html b/skrub/_reporting/_data/templates/buttons.html index 7f8ed476d..76ba36e7b 100644 --- a/skrub/_reporting/_data/templates/buttons.html +++ b/skrub/_reporting/_data/templates/buttons.html @@ -1,8 +1,8 @@ {% macro copybutton(target) %}