From b2c4f82426bb06fea9c5714af2bcdb985138da7f Mon Sep 17 00:00:00 2001 From: Vincent M Date: Mon, 18 Nov 2024 16:27:22 +0100 Subject: [PATCH] FEA Add `TextEncoder` (#1077) --- .github/workflows/testing.yml | 3 +- CHANGES.rst | 6 + doc/conf.py | 1 + doc/encoding.rst | 53 +- doc/install.rst | 62 + doc/reference/index.rst | 14 + ..._feature_interpretation_with_gapencoder.py | 117 - examples/02_text_with_string_encoders.py | 296 + pixi.lock | 6864 +++++++++++------ pyproject.toml | 23 +- skrub/__init__.py | 2 + skrub/_gap_encoder.py | 4 +- skrub/_similarity_encoder.py | 2 + skrub/_text_encoder.py | 409 + skrub/datasets/__init__.py | 2 + skrub/datasets/_fetching.py | 54 +- skrub/datasets/tests/test_fetching.py | 33 + skrub/tests/test_text_encoder.py | 175 + 18 files changed, 5818 insertions(+), 2302 deletions(-) delete mode 100644 examples/02_feature_interpretation_with_gapencoder.py create mode 100644 examples/02_text_with_string_encoders.py create mode 100644 skrub/_text_encoder.py create mode 100644 skrub/tests/test_text_encoder.py diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index d1c94033f..efb6cb48d 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -18,6 +18,7 @@ jobs: environment: [ ci-py309-min-deps, ci-py309-min-optional-deps, + ci-py311-transformers, ci-py312-latest-deps, ci-py312-latest-optional-deps ] @@ -33,7 +34,7 @@ jobs: frozen: true - name: Run tests - run: pixi run -e ${{ matrix.environment }} test -n 3 + run: pixi run -e ${{ matrix.environment }} test -n auto - name: Upload coverage reports to Codecov uses: codecov/codecov-action@v5.0.2 diff --git a/CHANGES.rst b/CHANGES.rst index e520f914d..84ac28256 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -27,6 +27,12 @@ New features Major changes ------------- +* The :class:`TextEncoder` is now available to encode string columns with + diverse entries. + It allows the representation of table entries as embeddings computed by a deep + learning language model. The weights of this model can be fetched locally + or from the HuggingFace Hub. + :pr:`1077` by :user:`Vincent Maladiere `. * :class:`AggJoiner`, :class:`AggTarget` and :class:`MultiAggJoiner` now require the `operations` argument. They do not split columns by type anymore, but diff --git a/doc/conf.py b/doc/conf.py index b71d38be0..acc437f08 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -343,6 +343,7 @@ "pandas": ("http://pandas.pydata.org/pandas-docs/stable", None), "polars": ("https://docs.pola.rs/py-polars/html", None), "seaborn": ("http://seaborn.pydata.org", None), + "sentence_transformers": ("https://sbert.net/", None), } diff --git a/doc/encoding.rst b/doc/encoding.rst index 4daa10e71..31bb03524 100644 --- a/doc/encoding.rst +++ b/doc/encoding.rst @@ -12,8 +12,11 @@ for different types of data. .. _dirty_categories: -Encoding open-ended entries and dirty categories ------------------------------------------------- +Encoding string columns +------------------------- + +Non-normalized entries and dirty categories +............................................ String columns can be seen categories for statistical analysis, but standard tools to represent categories fail if these strings are not @@ -35,7 +38,7 @@ categories, eg to replace :class:`~sklearn.preprocessing.OneHotEncoder`: Useful when there are a small number of categories, but we still want to capture the links between them (eg: "west", "north", "north-west") -.. topic:: References:: +.. topic:: References For a detailed description of the problem of encoding dirty categorical data, see `Similarity encoding for learning with dirty @@ -50,6 +53,50 @@ categories, eg to replace :class:`~sklearn.preprocessing.OneHotEncoder`: Similarity encoding for learning with dirty categorical variables. 2018. Machine Learning journal, Springer. + +Text with diverse entries +........................... + +When strings in a column are not dirty categories, but rather diverse +entries of text (names, open-ended or free-flowing text) it is useful to +use language models of various sizes to represent string columns as embeddings. +Depending on the task and dataset, this approach may lead to significant improvements +in the quality of predictions, albeit with potential increases in memory usage and computation time. + +Skrub integrates these language models as scikit-learn transformers, allowing them +to be easily plugged into :class:`TableVectorizer` and +:class:`~sklearn.pipeline.Pipeline`. + +These language models are pre-trained deep-learning encoders that have been fine-tuned +specifically for embedding tasks. Note that skrub does not provide a simple way to +fine-tune language models directly on your dataset. + +.. warning:: + + These encoders require installing additional dependencies around torch. + See the "deep learning dependencies" section in the :ref:`installation_instructions` + guide for more details. + +With :class:`TextEncoder`, a wrapper around the `sentence-transformers `_ +package, you can use any sentence embedding model available on the HuggingFace Hub +or locally stored on your disk. This means you can fine-tune a model using +the sentence-transformers library and then use it with the :class:`TextEncoder` +like any other pre-trained model. For more information, see the +`sentence-transformers fine-tuning guide `_. + +.. topic:: References + + See `Vectorizing string entries for data processing on tables: when are larger + language models better? `_ [3]_ + for a comparison between large language models and string-based encoders + (such as the :class:`MinHashEncoder`) in the context of dirty categories versus + diverse entries regimes. + +.. [3] L. Grinsztajn, M. Kim, E. Oyallon, G. Varoquaux. + Vectorizing string entries for data processing on tables: when are larger + language models better? 2023. + + Encoding dates --------------- diff --git a/doc/install.rst b/doc/install.rst index 0dd20f174..afa580e81 100644 --- a/doc/install.rst +++ b/doc/install.rst @@ -1,5 +1,7 @@ .. _installation_instructions: +.. currentmodule:: skrub + ======= Install ======= @@ -31,6 +33,21 @@ Install pip install skrub -U +| + +**Deep learning dependencies** + +Deep-learning based encoders like :class:`TextEncoder` require installing optional +dependencies to use them. The following will install +`torch `_, +`transformers `_, +and `sentence-transformers `_. + +.. code:: console + + $ pip install skrub[transformers] -U + + .. raw:: html @@ -41,6 +58,21 @@ Install conda install -c conda-forge skrub +| + +**Deep learning dependencies** + +Deep-learning based encoders like :class:`TextEncoder` require installing optional +dependencies to use them. The following will install +`torch `_, +`transformers `_, +and `sentence-transformers `_. + +.. code:: console + + $ conda install -c conda-forge skrub[transformers] + + .. raw:: html @@ -51,6 +83,21 @@ Install mamba install -c conda-forge skrub +| + +**Deep learning dependencies** + +Deep-learning based encoders like :class:`TextEncoder` require installing optional +dependencies to use them. The following will install +`torch `_, +`transformers `_, +and `sentence-transformers `_. + +.. code:: console + + $ mamba install -c conda-forge skrub[transformers] + + .. raw:: html @@ -139,6 +186,21 @@ If no errors or failures are found, your environment is ready for development! Now that you're set up, review our :ref:`implementation guidelines` and start coding! +| + +**Deep learning dependencies** + +Deep-learning based encoders like :class:`TextEncoder` require installing optional +dependencies to use them. The following will install +`torch `_, +`transformers `_, +and `sentence-transformers `_. + +.. code:: console + + $ pip install -e ".[transformers]" + + .. raw:: html diff --git a/doc/reference/index.rst b/doc/reference/index.rst index 6cf4e29c1..8d7646848 100644 --- a/doc/reference/index.rst +++ b/doc/reference/index.rst @@ -54,6 +54,20 @@ Encoding a column to_datetime +Deep Learning +------------- + +These encoders require installing additional dependencies around torch. +See the "deep learning dependencies" section in the :ref:`installation_instructions` +guide for more details. + +.. autosummary:: + :toctree: generated/ + :template: base.rst + :nosignatures: + + TextEncoder + .. _building_a_pipeline_ref: diff --git a/examples/02_feature_interpretation_with_gapencoder.py b/examples/02_feature_interpretation_with_gapencoder.py deleted file mode 100644 index 4f8a84b86..000000000 --- a/examples/02_feature_interpretation_with_gapencoder.py +++ /dev/null @@ -1,117 +0,0 @@ -""" -.. _example_interpreting_gap_encoder: - -========================================== -Feature interpretation with the GapEncoder -========================================== - -In this notebook, we will explore the output and inner workings of the -|GapEncoder|, one of the `high cardinality categorical encoders `_ -provided by skrub. - -.. |GapEncoder| replace:: - :class:`~skrub.GapEncoder` - -.. |SimilarityEncoder| replace:: - :class:`~skrub.SimilarityEncoder` -""" - -############################################################################### -# The |GapEncoder| is scalable and interpretable in terms of -# finding latent categories, as we will show. -# -# First, let's retrieve the -# `employee salaries dataset `_: - -from skrub.datasets import fetch_employee_salaries - -dataset = fetch_employee_salaries() - -# Alias X and y -X, y = dataset.X, dataset.y - -X - -############################################################################### -# Encoding job titles -# ------------------- -# -# Let's look at the job titles, the column containing dirty data we want to encode: -# -# .. topic:: Note: -# -# Dirty data, as opposed to clean, are all non-curated categorical -# columns with variations such as typos, abbreviations, duplications, -# alternate naming conventions etc. -# - -X_dirty = X["employee_position_title"] - -############################################################################### -# Let's have a look at a sample of the job titles: - -X_dirty.sort_values().tail(15) - -############################################################################### -# Then, we create an instance of the |GapEncoder| with 10 components. -# This means that the encoder will attempt to extract 10 latent topics -# from the input data: - -from skrub import GapEncoder - -enc = GapEncoder(n_components=10, random_state=1) - -############################################################################### -# Finally, we fit the model on the dirty categorical data and transform it -# in order to obtain encoded vectors of size 10: - -X_enc = enc.fit_transform(X_dirty) -X_enc.shape - -############################################################################### -# Interpreting encoded vectors -# ---------------------------- -# -# The |GapEncoder| can be understood as a continuous encoding -# on a set of latent topics estimated from the data. The latent topics -# are built by capturing combinations of substrings that frequently -# co-occur, and encoded vectors correspond to their activations. -# To interpret these latent topics, we select for each of them a few labels -# from the input data with the highest activations. -# In the example below we select 3 labels to summarize each topic. - -topic_labels = enc.get_feature_names_out(n_labels=3) -for k, labels in enumerate(topic_labels): - print(f"Topic n°{k}: {labels}") - -############################################################################### -# As expected, topics capture labels that frequently co-occur. For instance, -# the labels "firefighter", "rescuer", "rescue" appear together in -# "Firefighter/Rescuer III", or "Fire/Rescue Lieutenant". -# -# We can now understand the encoding of different samples. - -import matplotlib.pyplot as plt - -encoded_labels = enc.transform(X_dirty[:20]) -plt.figure(figsize=(8, 10)) -plt.imshow(encoded_labels) -plt.xlabel("Latent topics", size=12) -plt.xticks(range(0, 10), labels=topic_labels, rotation=50, ha="right") -plt.ylabel("Data entries", size=12) -plt.yticks(range(0, 20), labels=X_dirty[:20].to_numpy().flatten()) -plt.colorbar().set_label(label="Topic activations", size=12) -plt.tight_layout() -plt.show() - -############################################################################### -# As we can see, each dirty category encodes on a small number of topics, -# These can thus be reliably used to summarize each topic, which are in -# effect latent categories captured from the data. -# -# Conclusion -# ---------- -# -# In this notebook, we have seen how to interpret the output of the -# |GapEncoder|, and how it can be used to summarize categorical variables -# as a set of latent topics. diff --git a/examples/02_text_with_string_encoders.py b/examples/02_text_with_string_encoders.py new file mode 100644 index 000000000..ab410309b --- /dev/null +++ b/examples/02_text_with_string_encoders.py @@ -0,0 +1,296 @@ +""" +.. _example_string_encoders: + +===================================================== +Various string encoders: a sentiment analysis example +===================================================== + +In this example, we explore the performance of string and categorical encoders +available in skrub. + +.. |GapEncoder| replace:: + :class:`~skrub.GapEncoder` + +.. |MinHashEncoder| replace:: + :class:`~skrub.MinHashEncoder` + +.. |TextEncoder| replace:: + :class:`~skrub.TextEncoder` + +.. |TableReport| replace:: + :class:`~skrub.TableReport` + +.. |TableVectorizer| replace:: + :class:`~skrub.TableVectorizer` + +.. |pipeline| replace:: + :class:`~sklearn.pipeline.Pipeline` + +.. |HistGradientBoostingClassifier| replace:: + :class:`~sklearn.ensemble.HistGradientBoostingClassifier` + +.. |RandomizedSearchCV| replace:: + :class:`~sklearn.model_selection.RandomizedSearchCV` + +.. |GridSearchCV| replace:: + :class:`~sklearn.model_selection.GridSearchCV` +""" + +# %% +# The Toxicity dataset +# -------------------- +# We focus on the toxicity dataset, a corpus of 1,000 tweets, evenly balanced +# between the binary labels "Toxic" and "Not Toxic". +# Our goal is to classify each entry between these two labels, using only the +# text of the tweets as features. +from skrub.datasets import fetch_toxicity + +dataset = fetch_toxicity() +X, y = dataset.X, dataset.y +X["is_toxic"] = y + +# %% +# When it comes to displaying large chunks of text, the |TableReport| is especially +# useful! Click on any cell below to expand and read the tweet in full. +from skrub import TableReport + +TableReport(X) + +# %% +# GapEncoder +# ---------- +# First, let's vectorize our text column using the |GapEncoder|, one of the +# `high cardinality categorical encoders `_ +# provided by skrub. +# As introduced in the :ref:`previous example`, the |GapEncoder| +# performs matrix factorization for topic modeling. It builds latent topics by +# capturing combinations of substrings that frequently co-occur, and encoded vectors +# correspond to topic activations. +# +# To interpret these latent topics, we select for each of them a few labels from +# the input data with the highest activations. In the example below we select 3 labels +# to summarize each topic. +from skrub import GapEncoder + +gap = GapEncoder(n_components=30) +X_trans = gap.fit_transform(X["text"]) +# Add the original text as a first column +X_trans.insert(0, "text", X["text"]) +TableReport(X_trans) + +# %% +# We can use a heatmap to highlight the highest activations, making them more visible +# for comparison against the original text and vectors above. + +import numpy as np +from matplotlib import pyplot as plt + + +def plot_gap_feature_importance(X_trans): + x_samples = X_trans.pop("text") + + # We slightly format the topics and labels for them to fit on the plot. + topic_labels = [x.replace("text: ", "") for x in X_trans.columns] + labels = x_samples.str[:50].values + "..." + + # We clip large outliers to makes activations more visible. + X_trans = np.clip(X_trans, a_min=None, a_max=200) + + plt.figure(figsize=(10, 10), dpi=200) + plt.imshow(X_trans.T) + + plt.yticks( + range(len(topic_labels)), + labels=topic_labels, + ha="right", + size=12, + ) + plt.xticks(range(len(labels)), labels=labels, size=12, rotation=50, ha="right") + + plt.colorbar().set_label(label="Topic activations", size=13) + plt.ylabel("Latent topics", size=14) + plt.xlabel("Data entries", size=14) + plt.tight_layout() + plt.show() + + +plot_gap_feature_importance(X_trans.head()) + +# %% +# Now that we have an understanding of the vectors produced by the |GapEncoder|, +# let's evaluate its performance in toxicity classification. The |GapEncoder| excels +# at handling categorical columns with high cardinality, but here the column consists +# of free-form text. Sentences are generally longer, with more unique ngrams than +# high cardinality categories. +# +# To benchmark the performance of the |GapEncoder| against the toxicity dataset, +# we integrate it into a |TableVectorizer|, as introduced in the +# :ref:`previous example`, +# and create a |pipeline| by appending a |HistGradientBoostingClassifier|, which +# consumes the vectors produced by the |GapEncoder|. +# +# We set ``n_components`` to 30; however, to achieve the best performance, we would +# need to find the optimal value for this hyperparameter using either |GridSearchCV| +# or |RandomizedSearchCV|. We skip this part to keep the computation time for this +# example small. +# +# Recall that the ROC AUC is a metric that quantifies the ranking power of estimators, +# where a random estimator scores 0.5, and an oracle —providing perfect predictions— +# scores 1. +from sklearn.ensemble import HistGradientBoostingClassifier +from sklearn.model_selection import cross_validate +from sklearn.pipeline import make_pipeline + +from skrub import TableVectorizer + + +def plot_box_results(named_results): + fig, ax = plt.subplots() + names, scores = zip( + *[(name, result["test_score"]) for name, result in named_results] + ) + ax.boxplot(scores) + ax.set_xticks(range(1, len(names) + 1), labels=list(names), size=12) + ax.set_ylabel("ROC AUC", size=14) + plt.title( + "AUC distribution across folds (higher is better)", + size=14, + ) + plt.show() + + +results = [] + +y = X.pop("is_toxic").map({"Toxic": 1, "Not Toxic": 0}) + +gap_pipe = make_pipeline( + TableVectorizer(high_cardinality=GapEncoder(n_components=30)), + HistGradientBoostingClassifier(), +) +gap_results = cross_validate(gap_pipe, X, y, scoring="roc_auc") +results.append(("GapEncoder", gap_results)) + +plot_box_results(results) + +# %% +# MinHashEncoder +# -------------- +# We now compare these results with the |MinHashEncoder|, which is faster +# and produces vectors better suited for tree-based estimators like +# |HistGradientBoostingClassifier|. To do this, we can simply replace +# the |GapEncoder| with the |MinHashEncoder| in the previous pipeline +# using ``set_params()``. +from sklearn.base import clone + +from skrub import MinHashEncoder + +minhash_pipe = clone(gap_pipe).set_params( + **{"tablevectorizer__high_cardinality": MinHashEncoder(n_components=30)} +) +minhash_results = cross_validate(minhash_pipe, X, y, scoring="roc_auc") +results.append(("MinHashEncoder", minhash_results)) + +plot_box_results(results) + +# %% +# Remarkably, the vectors produced by the |MinHashEncoder| offer less predictive +# power than those from the |GapEncoder| on this dataset. +# +# TextEncoder +# ----------- +# Let's now shift our focus to pre-trained deep learning encoders. Our previous +# encoders are syntactic models that we trained directly on the toxicity dataset. +# To generate more powerful vector representations for free-form text and diverse +# entries, we can instead use semantic models, such as BERT, which have been trained +# on very large datasets. +# +# |TextEncoder| enables you to integrate any Sentence Transformer model from the +# Hugging Face Hub (or from your local disk) into your |pipeline| to transform a text +# column in a dataframe. By default, |TextEncoder| uses the e5-small-v2 model. +from skrub import TextEncoder + +text_encoder_pipe = clone(gap_pipe).set_params( + **{"tablevectorizer__high_cardinality": TextEncoder()} +) +text_encoder_results = cross_validate(text_encoder_pipe, X, y, scoring="roc_auc") +results.append(("TextEncoder", text_encoder_results)) + +plot_box_results(results) + +# %% +# The performance of the |TextEncoder| is significantly stronger than that of +# the syntactic encoders, which is expected. But how long does it take to load +# and vectorize text on a CPU using a Sentence Transformer model? Below, we display +# the tradeoff between predictive accuracy and training time. Note that since we are +# not training the Sentence Transformer model, the "fitting time" refers to the +# time taken for vectorization. + + +def plot_performance_tradeoff(results): + fig, ax = plt.subplots(figsize=(5, 4), dpi=200) + markers = ["s", "o", "^"] + for idx, (name, result) in enumerate(results): + ax.scatter( + result["fit_time"], + result["test_score"], + label=name, + marker=markers[idx], + ) + mean_fit_time = np.mean(result["fit_time"]) + mean_score = np.mean(result["test_score"]) + ax.scatter( + mean_fit_time, + mean_score, + color="k", + marker=markers[idx], + ) + std_fit_time = np.std(result["fit_time"]) + std_score = np.std(result["test_score"]) + ax.errorbar( + x=mean_fit_time, + y=mean_score, + yerr=std_score, + fmt="none", + c="k", + capsize=2, + ) + ax.errorbar( + x=mean_fit_time, + y=mean_score, + xerr=std_fit_time, + fmt="none", + c="k", + capsize=2, + ) + + ax.set_xlabel("Time to fit (seconds)") + ax.set_ylabel("ROC AUC") + ax.set_title("Prediction performance / training time trade-off") + + ax.annotate( + "", + xy=(1.5, 0.98), + xytext=(8.5, 0.90), + arrowprops=dict(arrowstyle="->", mutation_scale=15), + ) + ax.text(8, 0.86, "Best time / \nperformance trade-off") + ax.legend(bbox_to_anchor=(1, 0.3)) + plt.show() + + +plot_performance_tradeoff(results) + +# %% +# The black points represent the average time to fit and AUC for each vectorizer, +# and the width of the bars represents one standard deviation +# +# The green outlier dot on the right side of the plot corresponds to the first time +# the Sentence Transformers model was downloaded and loaded into memory. +# During the subsequent cross-validation iterations, the model is simply copied, +# which reduces computation time for the remaining folds. +# +# Conclusion +# ---------- +# In conclusion, |TextEncoder| provides powerful vectorization for text, but at +# the cost of longer computation times and the need for additional dependencies, +# such as torch. diff --git a/pixi.lock b/pixi.lock index 49d57a990..eaf976854 100644 --- a/pixi.lock +++ b/pixi.lock @@ -3,6 +3,7 @@ environments: ci-py309-min-deps: channels: - url: https://conda.anaconda.org/conda-forge/ + - url: https://conda.anaconda.org/pytorch/ indexes: - https://pypi.org/simple packages: @@ -90,7 +91,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libglx-1.7.0-ha4b6fd6_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.2.0-h77fa898_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.51-hbd13f7d_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.51-hbd13f7d_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-20_linux64_openblas.conda @@ -149,7 +150,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.0.0-pyhd8ed1ab_0.conda - 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"""Constructs latent topics with continuous encoding. + """Encode string columns by constructing latent topics. This encoder can be understood as a continuous encoding on a set of latent categories estimated from the data. The latent categories are built by @@ -130,6 +130,8 @@ class GapEncoder(TransformerMixin, SingleColumnTransformer): Encode string columns as a numeric array with the minhash method. SimilarityEncoder : Encode string columns as a numeric array with n-gram string similarity. + TextEncoder : + Encode string columns with a pretrained language model. deduplicate : Deduplicate data by hierarchically clustering similar strings. diff --git a/skrub/_similarity_encoder.py b/skrub/_similarity_encoder.py index 508560097..be16080c5 100644 --- a/skrub/_similarity_encoder.py +++ b/skrub/_similarity_encoder.py @@ -217,6 +217,8 @@ class SimilarityEncoder(OneHotEncoder): GapEncoder : Encodes dirty categories (strings) by constructing latent topics with continuous encoding. + TextEncoder : + Encode string columns with a pretrained language model. deduplicate : Deduplicate data by hierarchically clustering similar strings. diff --git a/skrub/_text_encoder.py b/skrub/_text_encoder.py new file mode 100644 index 000000000..36f0b1068 --- /dev/null +++ b/skrub/_text_encoder.py @@ -0,0 +1,409 @@ +import functools +import numbers +import os +import warnings +from pathlib import Path + +from sklearn.base import TransformerMixin +from sklearn.decomposition import PCA +from sklearn.utils.validation import check_is_fitted + +from . import _dataframe as sbd +from ._on_each_column import RejectColumn, SingleColumnTransformer +from ._utils import import_optional_dependency, unique_strings +from .datasets._utils import get_data_dir + + +class ModelNotFound(ValueError): + pass + + +class TextEncoder(SingleColumnTransformer, TransformerMixin): + """Encode string features by applying a pretrained language model \ + downloaded from the HuggingFace Hub. + + This is a thin wrapper around :class:`~sentence_transformers.SentenceTransformer` + that follows the scikit-learn API, making it usable within a scikit-learn pipeline. + + .. warning:: + + To use this class, you need to install the optional ``transformers`` + dependencies for skrub. See the "deep learning dependencies" section + in the :ref:`installation_instructions` guide for more details. + + This class uses a pre-trained model, so calling ``fit`` or ``fit_transform`` + will not train or fine-tune the model. Instead, the model is loaded from disk, + and a PCA is fitted to reduce the dimension of the language model's output, + if ``n_components`` is not None. + + When PCA is disabled, this class is essentially stateless, with loading the + pre-trained model from disk being the only difference between ``fit_transform`` + and ``transform``. + + Be aware that parallelizing this class (e.g., using + :class:`~skrub.TableVectorizer` with ``n_jobs`` > 1) may be computationally + expensive. This is because a copy of the pre-trained model is loaded into memory + for each thread. Therefore, we recommend you to let the default n_jobs=None + (or set to 1) of the TableVectorizer and let pytorch handle parallelism. + + If memory usage is a concern, check the characteristics of your selected model. + + Parameters + ---------- + model_name : str, default="intfloat/e5-small-v2" + + - If a filepath on disk is passed, this class loads the model from that path. + - Otherwise, it first tries to download a pre-trained + :class:`~sentence_transformers.SentenceTransformer` model. + If that fails, tries to construct a model from Huggingface models repository + with that name. + + The following models have a good performance/memory usage tradeoff: + + - ``intfloat/e5-small-v2`` + - ``all-MiniLM-L6-v2`` + - ``all-mpnet-base-v2`` + + You can find more options on the `sentence-transformers documentation + `_. + + The default model is a shrinked version of e5-v2, which has shown good + performance in the benchmark of [1]_. + + n_components : int or None, default=30, + The number of embedding dimensions. As the number of dimensions is different + accross embedding models, this class uses a :class:`~sklearn.decomposition.PCA` + to set the number of embedding to ``n_components`` during ``transform``. + Set ``n_components=None`` to skip the PCA dimension reduction mechanism. + + See [1]_ for more details on the choice of the PCA and default + ``n_components``. + + device : str, default=None + Device (e.g. "cpu", "cuda", "mps") that should be used for computation. + If None, checks if a GPU can be used. + Note that macOS ARM64 users can enable the GPU on their local machine + by setting ``device="mps"``. + + batch_size : int, default=32 + The batch size to use during ``transform``. + + token_env_variable : str, default=None + The name of the environment variable which stores your HuggingFace + authentication token to download private models. + Note that we only store the name of the variable but not the token itself. + + cache_folder : str, default=None + Path to store models. By default ``~/skrub_data``. + See :func:`skrub.datasets._utils.get_data_dir`. + Note that when unpickling ``TextEncoder`` on another machine, + the ``cache_folder`` path needs to be accessible to store the downloaded model. + + store_weights_in_pickle : bool, default=False + Whether or not to keep the loaded sentence-transformers model + in the ``TextEncoder`` when pickling. + + - When set to False, the ``_estimator`` property is removed from + the object to pickle, which significantly reduces the size of + the serialized object. Note that when the serialized object is + unpickled on another machine, the ``TextEncoder`` will try to download + the sentence-transformer model again from HuggingFace Hub. + This process could fail if, for example, the machine doesn't have + internet access. Additionally, if you use weights stored on disk + that are *not* on the HuggingFace Hub (by passing a path to + ``model_name``), these weights will not be pickled either. + Therefore you would need to copy them to the machine where you + unpickle the ``TextEncoder``. + - When set to True, the ``_estimator`` property is included in + the serialized object. Users deploying fine-tuned models stored on + disk are recommended to use this option. Note that the machine + where the ``TextEncoder`` is unpickled must have the same device than + the machine where it was pickled. + + random_state : int, RandomState instance or None, default=None + Used when the PCA dimension reduction mechanism is used, for reproducible + results across multiple function calls. + + verbose : bool, default=True + Verbose level, controls whether to show a progress bar or not during + ``transform``. + + Attributes + ---------- + input_name_ : str + The name of the fitted column. + + pca_ : sklearn.decomposition.PCA + A fitted PCA to reduce the embedding dimensionality (either PCA or truncation, + see the ``n_components`` parameter). + + n_components_ : int + The number of dimensions of the embeddings after dimensionality + reduction. + + See Also + -------- + MinHashEncoder : + Encode string columns as a numeric array with the minhash method. + GapEncoder : + Encode string columns by constructing latent topics. + SimilarityEncoder : + Encode string columns as a numeric array with n-gram string similarity. + + References + ---------- + .. [1] L. Grinsztajn, M. Kim, E. Oyallon, G. Varoquaux + "Vectorizing string entries for data processing on tables: when are larger + language models better?", 2023. + https://hal.science/hal-04345931 + + Examples + -------- + >>> import pandas as pd + >>> from skrub import TextEncoder + + Let's encode video comments using only 2 embedding dimensions: + + >>> enc = TextEncoder(model_name='intfloat/e5-small-v2', n_components=2) + >>> X = pd.Series([ + ... "The professor snatched a good interview out of the jaws of these questions.", + ... "Bookmarking this to watch later.", + ... "When you don't know the lyrics of the song except the chorus", + ... ], name='video comments') + + Fitting does not train the underlying pre-trained deep-learning model, + but ensure various checks and enable dimension reduction. + + >>> enc.fit_transform(X) # doctest: +SKIP + video comments_1 video comments_2 + 0 0.411395 0.096504 + 1 -0.105210 -0.344567 + 2 -0.306184 0.248063 + """ + + def __init__( + self, + model_name="intfloat/e5-small-v2", + n_components=30, + device=None, + batch_size=32, + token_env_variable=None, + cache_folder=None, + store_weights_in_pickle=False, + random_state=None, + verbose=False, + ): + self.model_name = model_name + self.n_components = n_components + self.device = device + self.batch_size = batch_size + self.token_env_variable = token_env_variable + self.cache_folder = cache_folder + self.store_weights_in_pickle = store_weights_in_pickle + self.random_state = random_state + self.verbose = verbose + + def fit_transform(self, column, y=None): + """Fit the TextEncoder from ``column``. + + In practice, it loads the pre-trained model from disk and returns + the embeddings of the column. + + Parameters + ---------- + column : pandas or polars Series of shape (n_samples,) + The string column to compute embeddings from. + + y : None + Unused. Here for compatibility with scikit-learn. + + Returns + ------- + X_out : pandas or polars DataFrame of shape (n_samples, n_components) + The embedding representation of the input. + """ + del y + if not sbd.is_string(column): + raise RejectColumn(f"Column {sbd.name(column)!r} does not contain strings.") + + self._check_params() + + self.input_name_ = sbd.name(column) + + X_out = self._vectorize(column) + + if self.n_components is not None: + if (min_shape := min(X_out.shape)) >= self.n_components: + self.pca_ = PCA( + n_components=self.n_components, + copy=False, + random_state=self.random_state, + ) + X_out = self.pca_.fit_transform(X_out) + else: + warnings.warn( + f"The matrix shape is {(X_out.shape)}, and its minimum is " + f"{min_shape}, which is too small to fit a PCA with " + f"n_components={self.n_components}. " + "The embeddings will be truncated by keeping the first " + f"{self.n_components} dimensions instead. " + "Set n_components=None to keep all dimensions and remove " + "this warning." + ) + # self.n_components can be greater than the number + # of dimensions of X_out. + # Therefore, self.n_components_ below stores the resulting + # number of dimensions of X_out. + X_out = X_out[:, : self.n_components] + + self.n_components_ = X_out.shape[1] + + cols = self.get_feature_names_out() + X_out = sbd.make_dataframe_like(column, dict(zip(cols, X_out.T))) + X_out = sbd.copy_index(column, X_out) + + return X_out + + def transform(self, column): + """Transform ``column`` using the TextEncoder. + + This method uses the embedding model loaded in memory during ``fit`` + or ``fit_transform``. + + Parameters + ---------- + column : pandas or polars Series of shape (n_samples,) + The string column to compute embeddings from. + + Returns + ------- + X_out : pandas or polars DataFrame of shape (n_samples, n_components) + The embedding representation of the input. + """ + check_is_fitted(self, "_estimator") + + if not sbd.is_string(column): + raise ValueError(f"Column {sbd.name(column)!r} does not contain strings.") + + X_out = self._vectorize(column) + + if hasattr(self, "pca_"): + X_out = self.pca_.transform(X_out) + elif self.n_components is not None: + X_out = X_out[:, : self.n_components] + + cols = self.get_feature_names_out() + X_out = sbd.make_dataframe_like(column, dict(zip(cols, X_out.T))) + X_out = sbd.copy_index(column, X_out) + + return X_out + + def _vectorize(self, column): + is_null = sbd.to_numpy(sbd.is_null(column)) + column = sbd.to_numpy(column) + unique_x, indices_x = unique_strings(column, is_null) + + # sentence-transformers deals with converting a torch tensor + # to a numpy array, on CPU. + return self._estimator.encode( + unique_x, + normalize_embeddings=False, + batch_size=self.batch_size, + show_progress_bar=self.verbose, + )[indices_x] + + @functools.cached_property + def _estimator(self): + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", + message=r".*IProgress not found.*", + ) + st = import_optional_dependency("sentence_transformers") + + self._cache_folder = get_data_dir( + name=self.model_name, data_home=self.cache_folder + ) + + if self.token_env_variable is not None: + token = os.getenv(self.token_env_variable) + else: + token = None + + try: + estimator = st.SentenceTransformer( + self.model_name, + device=self.device, + cache_folder=self._cache_folder, + token=token, + ) + except OSError as e: + raise ModelNotFound( + f"{self.model_name} is not a local folder and is not a valid " + "model identifier listed on 'https://huggingface.co/models'.\n " + "If this is a private repository, make sure to pass a token having " + "permission to this repo by setting this token as an environment " + "variable, and passing this variable to the TextEncoder as " + "`token_env_variable=`" + ) from e + return estimator + + def _check_params(self): + # XXX: Use sklearn _parameter_constraints instead? + if self.n_components is not None and not isinstance( + self.n_components, numbers.Integral + ): + raise ValueError( + f"Got n_components={self.n_components!r} but expected an integer " + "or None." + ) + + if not (isinstance(self.batch_size, numbers.Integral) and self.batch_size > 0): + raise ValueError( + f"Got batch_size={self.batch_size} but expected a positive integer" + ) + + if self.cache_folder is not None and not isinstance( + self.cache_folder, (str, bytes, Path) + ): + raise ValueError( + f"Got cache_folder={self.cache_folder} but expected a " + "str, bytes or Path type." + ) + + if not isinstance(self.model_name, (str, Path)): + raise ValueError( + f"Got model_name={self.model_name} but expected a str or a Path type." + ) + return + + def get_feature_names_out(self): + """Get output feature names for transformation. + + Returns + ------- + feature_names_out : list of str + Transformed feature names. + """ + check_is_fitted(self) + n_digits = len(str(self.n_components_)) + return [ + f"{self.input_name_}_{str(i).zfill(n_digits)}" + for i in range(1, self.n_components_ + 1) + ] + + def __getstate__(self): + state = self.__dict__.copy() + # Always dump self._cache_folder because it is overwritten when the model + # is loaded, and it shows an absolut path on the user machine. + # However, we have to include self.cache_folder in the serialized object + # because that is a parameter provided by the user. + remove_props = ["_cache_folder"] + if not self.store_weights_in_pickle: + remove_props.append("_estimator") + + for prop in remove_props: + if prop in state: + del state[prop] + + return state diff --git a/skrub/datasets/__init__.py b/skrub/datasets/__init__.py index ea393dad3..d6e8d1f2c 100644 --- a/skrub/datasets/__init__.py +++ b/skrub/datasets/__init__.py @@ -10,6 +10,7 @@ fetch_movielens, fetch_open_payments, fetch_road_safety, + fetch_toxicity, fetch_traffic_violations, fetch_world_bank_indicator, get_data_dir, @@ -34,6 +35,7 @@ "fetch_world_bank_indicator", "fetch_figshare", "fetch_credit_fraud", + "fetch_toxicity", "fetch_movielens", "get_data_dir", "make_deduplication_data", diff --git a/skrub/datasets/_fetching.py b/skrub/datasets/_fetching.py index cd8667c4d..0af827679 100644 --- a/skrub/datasets/_fetching.py +++ b/skrub/datasets/_fetching.py @@ -1045,6 +1045,7 @@ def fetch_world_bank_indicator( def fetch_figshare( figshare_id: str, *, + target=None, load_dataframe: bool = True, data_directory: Path | str | None = None, ) -> DatasetAll | DatasetInfoOnly: @@ -1062,7 +1063,7 @@ def fetch_figshare( source="figshare", dataset_name=f"figshare_{figshare_id!r}", dataset_id=figshare_id, - target=None, + target=target, load_dataframe=load_dataframe, data_directory=data_directory, ) @@ -1122,6 +1123,17 @@ def fetch_credit_fraud(load_dataframe=True, data_directory=None): data_directory : str, default=None The directory to which the dataset will be written during the download. If None, the directory is set to ~/skrub_data. + + Returns + ------- + bunch : sklearn.utils.Bunch + A dictionnary-like object, whose fields are: + - product : pd.DataFrame + - baskets : pd.DataFrame + - source_product : str + - source_baskets : str + - path_product : str + - path_baskets : str """ dataset_name_to_id = { "products": "49176205", @@ -1139,3 +1151,43 @@ def fetch_credit_fraud(load_dataframe=True, data_directory=None): bunch[f"path_{dataset_name}"] = dataset.path return bunch + + +def fetch_toxicity(load_dataframe=True, data_directory=None): + """Fetch the toxicity dataset from figshare. + + This is a balanced binary classification use-case, where the single table + consists in only two columns: + - `text`: the text of the comment + - `is_toxic`: whether or not the comment is toxic + + Parameters + ---------- + load_dataframe : bool, default=True + Whether or not to load the dataset in memory after download. + + data_directory : str, default=None + The directory to which the dataset will be written during the download. + If None, the directory is set to ~/skrub_data. + + Returns + ------- + dataset : DatasetAll + A dataclass object whose fields are: + - name: str + - description: str + - source: str + - target: str + - X: pd.DataFrame + - y: pd.Series + - path: Path + """ + + figshare_id = "49823901" + dataset = fetch_figshare( + figshare_id, + load_dataframe=load_dataframe, + data_directory=data_directory, + target="is_toxic", + ) + return dataset diff --git a/skrub/datasets/tests/test_fetching.py b/skrub/datasets/tests/test_fetching.py index 8cfac5661..00d781928 100644 --- a/skrub/datasets/tests/test_fetching.py +++ b/skrub/datasets/tests/test_fetching.py @@ -257,3 +257,36 @@ def test_fetch_credit_fraud(): mock_urlretrieve.assert_not_called() assert_frame_equal(bunch.products, disk_bunch.products) assert_frame_equal(bunch.baskets, disk_bunch.baskets) + + +def test_fetch_toxicity(): + pytest.importorskip("pyarrow") + with TemporaryDirectory() as temp_dir: + try: + # Valid call + bunch = _fetching.fetch_toxicity( + data_directory=temp_dir, + ) + + except (ConnectionError, URLError): + pytest.skip( + "Exception: Skipping this test because we encountered an " + "issue probably related to an Internet connection problem. " + ) + return + + assert bunch.source == "https://ndownloader.figshare.com/files/49823901" + + df = pd.concat([bunch.X, bunch.y], axis=1) + assert_frame_equal(df, pd.read_parquet(bunch.path)) + + # Now that we have verified the file is on disk, we want to test + # whether calling the function again reads it from disk (which it should) + # or queries the network again (which it shouldn't). + with mock.patch("urllib.request.urlretrieve") as mock_urlretrieve: + # Same valid call as above + disk_bunch = _fetching.fetch_toxicity( + data_directory=temp_dir, + ) + mock_urlretrieve.assert_not_called() + assert_frame_equal(bunch.X, disk_bunch.X) diff --git a/skrub/tests/test_text_encoder.py b/skrub/tests/test_text_encoder.py new file mode 100644 index 000000000..cbfefb3df --- /dev/null +++ b/skrub/tests/test_text_encoder.py @@ -0,0 +1,175 @@ +import pickle + +import pandas as pd +import pytest +from numpy.testing import assert_array_equal +from sklearn.base import clone + +import skrub._dataframe as sbd +from skrub import TableVectorizer, TextEncoder +from skrub._on_each_column import RejectColumn +from skrub._text_encoder import ModelNotFound + +pytest.importorskip("sentence_transformers") + + +@pytest.fixture +def encoder(): + """Create a fixture such that we set the common parameters of the encoder. + + Here, we have two constraints: + + - We want a really small model to not blow up the memory of the GitHub Actions + workers. + - We want to force the use of the CPU. GitHub Actions workers on macOS ARM64 will + detect the MPS backend, but due to limitations, no memory can be allocated. + See https://github.com/actions/runner-images/issues/9918 for more details. + """ + return TextEncoder( + model_name="sentence-transformers/paraphrase-albert-small-v2", + device="cpu", + ) + + +def test_missing_import_error(encoder): + try: + import sentence_transformers # noqa + except ImportError: + pass + else: + return + + st = clone(encoder) + x = pd.Series(["oh no"]) + with pytest.raises(ImportError, match="Missing optional dependency"): + st.fit(x) + + +def test_text_encoder(df_module, encoder): + X = df_module.make_column("", ["hello sir", "hola que tal"]) + encoder = clone(encoder).set_params(n_components=2) + X_out = encoder.fit_transform(X) + assert sbd.shape(X_out) == (2, 2) + + X_out_2 = encoder.fit_transform(X) + df_module.assert_frame_equal(X_out, X_out_2) + + +@pytest.mark.parametrize("X", [["hello"], "hello"]) +def test_not_a_series(X, encoder): + with pytest.raises(ValueError): + clone(encoder).fit(X) + + +def test_not_a_series_with_string(df_module, encoder): + X = df_module.make_column("", [1, 2, 3]) + with pytest.raises(RejectColumn): + clone(encoder).fit(X) + + +def test_missing_value(df_module, encoder): + X = df_module.make_column("", [None, None, "hey"]) + encoder = clone(encoder).set_params(n_components=None) + X_out = encoder.fit_transform(X) + + assert sbd.shape(X_out) == (3, 768) + X_out = X_out.to_numpy() + assert_array_equal(X_out[0, :], X_out[1, :]) + + +def test_n_components(df_module, encoder): + X = df_module.make_column("", ["hello sir", "hola que tal"]) + encoder_all = clone(encoder).set_params(n_components=None).fit(X) + for meth in ("fit_transform", "transform"): + X_out = getattr(encoder_all, meth)(X) + assert sbd.shape(X_out)[1] == 768 + assert encoder_all.n_components_ == 768 + + encoder_2 = clone(encoder).set_params(n_components=2).fit(X) + for meth in ("fit_transform", "transform"): + X_out = getattr(encoder_2, meth)(X) + assert sbd.shape(X_out)[1] == 2 + assert encoder_2.n_components_ == 2 + + encoder_30 = clone(encoder).set_params(n_components=30) + with pytest.warns(UserWarning, match="The embeddings will be truncated"): + for meth in ("fit_transform", "transform"): + X_out = getattr(encoder_30, meth)(X) + assert not hasattr(encoder_30, "pca_") + assert sbd.shape(X_out)[1] == 30 + assert encoder_30.n_components_ == 30 + + +def test_wrong_parameters(encoder): + with pytest.raises(ValueError, match="Got n_components='yes'"): + clone(encoder).set_params(n_components="yes")._check_params() + + with pytest.raises(ValueError, match="Got batch_size=-10"): + clone(encoder).set_params(batch_size=-10)._check_params() + + with pytest.raises(ValueError, match="Got model_name=1"): + clone(encoder).set_params(model_name=1)._check_params() + + with pytest.raises(ValueError, match="Got cache_folder=1"): + clone(encoder).set_params(cache_folder=1)._check_params() + + with pytest.raises(ValueError, match="Got model_name=1"): + clone(encoder).set_params(model_name=1)._check_params() + + +def test_wrong_model_name(encoder): + x = pd.Series(["Good evening Dave"]) + with pytest.raises(ModelNotFound): + clone(encoder).set_params(model_name="HAL-9000").fit(x) + + +def test_transform_equal_fit_transform(df_module, encoder): + x = df_module.make_column("", ["hello again"]) + encoder = clone(encoder).set_params(n_components=None) + X_out = encoder.fit_transform(x) + X_out_2 = encoder.transform(x) + df_module.assert_frame_equal(X_out, X_out_2) + + +def test_transform_error_on_float_data(df_module, encoder): + """Check that we raise an error when data without any string is passed at + transform.""" + x = df_module.make_column("", [1.0, 2.5, 3.7]) + + encoder = clone(encoder).set_params(n_components=None) + encoder.fit(df_module.make_column("", ["hello", "world"])) + + with pytest.raises(ValueError, match="does not contain strings"): + encoder.transform(x) + + +@pytest.mark.parametrize("n_jobs", [1, 2]) +def test_encoder_in_table_vectorizer(df_module, encoder, n_jobs): + X = df_module.make_dataframe({"msg": ["hey there", "hello"]}) + X_out = TableVectorizer(low_cardinality=encoder, n_jobs=n_jobs).fit_transform(X) + assert sbd.shape(X_out) == (2, 30) + + +def test_token_env_variable(df_module, encoder): + token_env_variable = "my_imaginary_token" + X = df_module.make_column("", ["hello", "hola"]) + encoder = clone(encoder).set_params(token_env_variable=token_env_variable).fit(X) + assert encoder.token_env_variable == token_env_variable + + +@pytest.mark.parametrize("store_weights_in_pickle", [True, False]) +def test_store_weights_in_pickle(df_module, encoder, store_weights_in_pickle): + X = df_module.make_column("", ["hello", "hola", "guttentag"]) + encoder = ( + clone(encoder) + .set_params( + store_weights_in_pickle=store_weights_in_pickle, + n_components=2, + ) + .fit(X) + ) + assert hasattr(encoder, "_estimator") + + obj = pickle.dumps(encoder) + encoder_unpickled = pickle.loads(obj) + assert ("_estimator" in encoder_unpickled.__dict__) is store_weights_in_pickle