diff --git a/doc/_templates/index.html b/doc/_templates/index.html index 4c2e7c2ae..b76fd12e9 100644 --- a/doc/_templates/index.html +++ b/doc/_templates/index.html @@ -152,7 +152,7 @@
Inspect it, apply it to new data

- Discover the skrub DataOps → + Discover the skrub DataOps →

diff --git a/doc/data_ops.rst b/doc/data_ops.rst index 31c832f54..3d4110158 100644 --- a/doc/data_ops.rst +++ b/doc/data_ops.rst @@ -56,6 +56,18 @@ they can be saved in a file, loaded, applied to new data as easily as a single etc. . Skrub DataOps remove those limitations and add several useful features such as interactive previews and integration with Optuna. +A quick overview of DataOps +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This tutorial walks through the main components of the DataOps on a simple +example: + +.. toctree:: + :maxdepth: 1 + + auto_tutorials/1111_data_ops_quick_tour + + Data Ops basic concepts ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -64,7 +76,6 @@ Data Ops basic concepts modules/data_ops/basics/what_are_data_ops modules/data_ops/basics/building_data_ops_plan - auto_tutorials/1110_data_ops_intro modules/data_ops/basics/using_previews modules/data_ops/basics/direct_access_methods modules/data_ops/basics/control_flow diff --git a/doc/data_ops_report.py b/doc/data_ops_report.py index a8ca9abfa..9831e8f81 100644 --- a/doc/data_ops_report.py +++ b/doc/data_ops_report.py @@ -74,18 +74,16 @@ def create_employee_salaries_report(): if (output_dir / "index.html").exists(): return output_dir - dataset = skrub.datasets.fetch_employee_salaries(split="train").employee_salaries - data_var = skrub.var("data", dataset) - X = data_var.drop("current_annual_salary", axis=1).skb.mark_as_X() - y = data_var["current_annual_salary"].skb.mark_as_y() - - vectorizer = TableVectorizer() - X_vec = X.skb.apply(vectorizer) + pred = ( + skrub.var("employee_data") + .skb.apply(TableVectorizer()) + .skb.apply(HistGradientBoostingRegressor(), y=skrub.var("salary")) + ) - hgb = HistGradientBoostingRegressor() - predictor = X_vec.skb.apply(hgb, y=y) + dataset = skrub.datasets.fetch_employee_salaries(split="train") - predictor.skb.full_report( + pred.skb.full_report( + {"employee_data": dataset.X, "salary": dataset.y}, output_dir=output_dir, overwrite=True, open=False, diff --git a/doc/modules/data_ops/basics/building_data_ops_plan.rst b/doc/modules/data_ops/basics/building_data_ops_plan.rst index 00e42f316..2f9630c1f 100644 --- a/doc/modules/data_ops/basics/building_data_ops_plan.rst +++ b/doc/modules/data_ops/basics/building_data_ops_plan.rst @@ -90,5 +90,5 @@ By working only on Data Ops we ensure that all the operations done on the data are added correctly to the computational graph, which then allows the resulting learner to execute all steps as intended. -See :ref:`sphx_glr_auto_tutorials_1110_data_ops_intro.py` for an introductory +See :ref:`sphx_glr_auto_tutorials_1111_data_ops_quick_tour.py` for an introductory example on how to use skrub DataOps on a single dataframe. diff --git a/doc/modules/data_ops/basics/control_flow.rst b/doc/modules/data_ops/basics/control_flow.rst index 7cd1fc31a..193bd8175 100644 --- a/doc/modules/data_ops/basics/control_flow.rst +++ b/doc/modules/data_ops/basics/control_flow.rst @@ -168,7 +168,7 @@ Finally, there are other situations where using :func:`deferred` can be helpful: .. rubric:: Examples -- See :ref:`sphx_glr_auto_examples_data_ops_1110_data_ops_intro.py` for an introductory +- See :ref:`sphx_glr_auto_examples_data_ops_1111_data_ops_quick_tour.py` for an introductory example on how to use skrub DataOps on a single dataframe. - See :ref:`sphx_glr_auto_examples_data_ops_1120_multiple_tables.py` for an example of how skrub DataOps can be used to process multiple tables using dataframe APIs. diff --git a/doc/tutorials/1110_data_ops_intro.py b/doc/tutorials/1110_data_ops_intro.py deleted file mode 100644 index 4f3fd807e..000000000 --- a/doc/tutorials/1110_data_ops_intro.py +++ /dev/null @@ -1,210 +0,0 @@ -""" -Tutorial: Using Data Ops to build a machine-learning pipeline -======================================================================= - -.. currentmodule:: skrub - -.. |fetch_employee_salaries| replace:: :func:`datasets.fetch_employee_salaries` -.. |TableReport| replace:: :class:`TableReport` -.. |var| replace:: :func:`var` -.. |skb.mark_as_X| replace:: :meth:`DataOp.skb.mark_as_X` -.. |skb.mark_as_y| replace:: :meth:`DataOp.skb.mark_as_y` -.. |TableVectorizer| replace:: :class:`TableVectorizer` -.. |ToDatetime| replace:: :class:`ToDatetime` -.. |skb.apply| replace:: :meth:`.skb.apply() ` -.. |HistGradientBoostingRegressor| replace:: - :class:`~sklearn.ensemble.HistGradientBoostingRegressor` -.. |.skb.full_report()| replace:: :meth:`.skb.full_report() ` -.. |choose_float| replace:: :func:`choose_float` -.. |make_randomized_search| replace:: - :meth:`.skb.make_randomized_search ` - -This example shows data how we can use skrub's -:ref:`DataOps ` for building a machine learning pipeline. - -The challenge of preparing data for machine learning is the need to -apply the same data preparation and wrangling operations to new data, for prediction. - -Skrub's DataOps build pipelines that blend data wrangling and machine -learning by recording all the operations involved in pre-processing data -and training models, as well as the state of the transformers and models used to -make predictions. - -.. admonition:: What is a state? - :collapsible: closed - - The state of a transformer or model refers to the internal parameters and - attributes that are learned or set during the fitting process. For example, - in a :class:`~sklearn.preprocessing.StandardScaler`, the state would include - the mean and standard deviation calculated from the training data. - In a pre-processing transformer like |ToDatetime|, the state would include the - inferred datetime format based on the data it was fitted on. - In a machine learning model like |HistGradientBoostingRegressor|, the state - would include the fitted parameters of the model after training on the data. - -The result of building a DataOps plan is a *learner*, an object with an interface -similar to that of a scikit-learn estimator, but which contains all the steps in the -data preparation and model training process, along with the state of all the -transformers and models: this allows to save the learner, load it back later, -and use it to make predictions on new data. - -This example is meant to be an introduction to skrub DataOps, and as such it -will not cover all the features. Further examples in the gallery -:ref:`data_ops_examples_ref` go into more detail on skrub DataOps -for more complex tasks. - - -""" - -# %% -# The data -# --------- -# -# We begin by loading the employee salaries dataset, which is a regression dataset -# that contains information about employees and their current annual salaries. -# By default, the |fetch_employee_salaries| function returns the training set. -# We will load the test set later, to evaluate our model on unseen data. - -import pandas as pd - -from skrub.datasets import fetch_employee_salaries - -training_data = pd.read_csv( - fetch_employee_salaries(split="train").employee_salaries_path -) - -# %% -# We can take a look at the dataset using the |TableReport|. -# This dataset contains numerical, categorical, and datetime features. The column -# ``current_annual_salary`` is the target variable we want to predict. -# - -import skrub - -skrub.TableReport(training_data) -# %% -# Assembling our DataOps plan -# ---------------------------- -# -# Our goal is to predict the ``current_annual_salary`` of employees based on their -# other features. We will use skrub's DataOps to combine both skrub and scikit-learn -# objects into a single DataOps plan, which will allow us to preprocess the data, -# train a model, and tune hyperparameters. -# -# We begin by defining a skrub |var|, which is the entry point for our DataOps plan. - -data_var = skrub.var("data", training_data) - -# %% -# Next, we define the initial features ``X`` and the target variable ``y``. -# We use the |skb.mark_as_X| and |skb.mark_as_y| methods to mark these variables -# in the DataOps plan. This allows skrub to properly split these objects into -# training and validation steps when executing cross-validation or hyperparameter -# tuning. - -X = data_var.drop("current_annual_salary", axis=1).skb.mark_as_X() -y = data_var["current_annual_salary"].skb.mark_as_y() -# %% -# Our first step is to vectorize the features in ``X``. We will use the -# |TableVectorizer| to convert the categorical and numerical features into a -# numerical format that can be used by machine learning algorithms. -# We apply the vectorizer to ``X`` using the |skb.apply| method, which allows us to -# apply any scikit-learn compatible transformer to the skrub variable. - -from skrub import TableVectorizer - -vectorizer = TableVectorizer() - -X_vec = X.skb.apply(vectorizer) -X_vec -# %% -# By clicking on ``Show graph``, we can see the DataOps plan that has been created: -# the plan shows the steps that have been applied to the data so far. -# Now that we have the vectorized features, we can proceed to train a model. -# We use a scikit-learn |HistGradientBoostingRegressor| to predict the target variable. -# We apply the model to the vectorized features using ``.skb.apply``, and pass -# ``y`` as the target variable. -# Note that the resulting ``predictor`` variable shows prediction results on the -# preview subsample, but the model will be properly fitted when we create the learner. - -from sklearn.ensemble import HistGradientBoostingRegressor - -hgb = HistGradientBoostingRegressor() - -predictor = X_vec.skb.apply(hgb, y=y) -predictor - -# %% -# Now that we have built our entire plan, we can explore it in more detail -# with the |.skb.full_report()| method:: -# -# predictor.skb.full_report() -# -# This produces a folder on disk rather than displaying inline in a notebook so -# we do not run it here. But you can -# `see the output here <../../_static/employee_salaries_report/index.html>`_. -# -# This method evaluates each step in -# the plan and shows detailed information about the operations that are being performed. - -# %% -# Turning the DataOps plan into a learner, for later reuse -# --------------------------------------------------------- -# -# Now that we have defined the predictor, we can create a ``learner``, a -# standalone object that contains all the steps in the DataOps plan. We fit the -# learner, so that it can be used to make predictions on new data. - -trained_learner = predictor.skb.make_learner(fitted=True) - -# %% -# A big advantage of the learner is that it can be pickled and saved to disk, -# allowing us to reuse the trained model later without needing to retrain it. -# The learner contains all steps in the DataOps plan, including the fitted -# vectorizer and the trained model. We can save it using Python's ``pickle`` module. -# Here we use ``pickle.dumps`` to serialize the learner object into a byte string. - -import pickle - -saved_model = pickle.dumps(trained_learner) - -# %% -# We can now load the saved model back into memory using ``pickle.loads``. -loaded_model = pickle.loads(saved_model) - -# %% -# Now, we can make predictions on new data using the loaded model, by passing -# a dictionary with the skrub variable names as keys. -# We don't have to create a new variable, as this will be done internally by the -# learner. -# In fact, the ``learner`` is similar to a scikit-learn estimator, but rather -# than taking ``X`` and ``y`` as inputs, it takes a dictionary (the "environment") -# where each key corresponds to the name of a skrub variable in the plan (in this -# case, "data"). -# -# We can now get the test set of the employee salaries dataset: -unseen_data = pd.read_csv(fetch_employee_salaries(split="test").employee_salaries_path) - -# %% -# Then, we can use the loaded model to make predictions on the unseen data by -# passing a dictionary with the variable name as the key. - -predicted_values = loaded_model.predict({"data": unseen_data}) -predicted_values - -# %% -# We can also evaluate the model's performance using the `score` method, which -# uses the scikit-learn scoring function used by the predictor: -loaded_model.score({"data": unseen_data}) - -# %% -# Conclusion -# ---------- -# -# In this example, we have briefly introduced the skrub DataOps and how they can -# be used to build powerful machine learning pipelines. We have shown how to preprocess -# data and train a model. We have also demonstrated how to save and load the trained -# model, and how to make predictions on new data. -# -# However, skrub DataOps are significantly more powerful than what we have shown here. -# For more advanced examples, see :ref:`data_ops_examples_ref`. diff --git a/doc/tutorials/1111_data_ops_quick_tour.py b/doc/tutorials/1111_data_ops_quick_tour.py new file mode 100644 index 000000000..44dd0b555 --- /dev/null +++ b/doc/tutorials/1111_data_ops_quick_tour.py @@ -0,0 +1,281 @@ +""" +Quick overview of DataOps +========================= + +.. currentmodule:: skrub + +Here we give a bird's eye view of the DataOps workflow on a simple regression task +that we saw in an :ref:`early example `: predicting the +salaries of US Government employees. + +This dataset is so simple that it can be handled without the DataOps, using a +scikit-learn :class:`~sklearn.pipeline.Pipeline`, but we will move on to more +challenging datasets in later sections. +""" + +# %% +# Here is the dataset we will work with. The column to predict is +# ``current_annual_salary``. + +# sphinx_gallery_start_ignore +import skrub + +skrub.set_config(data_ops_open_graph_dropdown=True) +# sphinx_gallery_end_ignore + +import skrub + +train_dataset = skrub.datasets.fetch_employee_salaries(split="train") +skrub.TableReport(train_dataset.employee_salaries) + +# %% +# A first simple pipeline +# ----------------------- +# +# We start by defining our predictive pipeline. We will need to encode the +# features with a :class:`TableVectorizer` then predict with a +# :class:`~sklearn.ensemble.HistGradientBoostingRegressor`. +# +# Inputs to our pipeline are declared with :func:`var`: + +# %% +employee_data = skrub.var("employee_data") +employee_data + +# %% +# Transformation steps are added by calling methods on the intermediate +# results. An important one is :meth:`DataOp.skb.apply`, which applies a +# scikit-learn estimator: + +# %% +from sklearn.ensemble import HistGradientBoostingRegressor + +salary = skrub.var("salary") + +pred = employee_data.skb.apply(skrub.TableVectorizer()).skb.apply( + HistGradientBoostingRegressor(), y=salary +) +pred + +# %% +# Note that the methods are accessed through the special attribute ``.skb``: +# for example ``.skb.apply``. We will explain why shortly. + +# %% +# Once we have added all the steps, we create a *learner*: an object similar to a +# scikit-learn estimator with ``fit`` and ``predict`` methods. + +learner = pred.skb.make_learner() +learner.fit({"employee_data": train_dataset.X, "salary": train_dataset.y}) + +# %% +# Regular scikit-learn estimators always take the same fixed inputs: X and y. +# Skrub learners can process arbitrary data, so the signature of methods like +# ``fit`` and ``predict`` is different: we pass a dictionary of inputs. The +# keys correspond to the names of the variables we used to define our learner, +# here ``"employee_data"`` and ``"salary"``. +# +# Finally, we can use our fitted learner to make a prediction: + +# %% +test_dataset = skrub.datasets.fetch_employee_salaries(split="test") +learner.predict({"employee_data": test_dataset.X, "salary": test_dataset.y}) + +# %% +# We can generate a complete report for the execution of our DataOp by calling: +# +# .. code:: python +# +# pred.skb.full_report({"employee_data": test_dataset.X, "salary": test_dataset.y}) +# +# As the output is usually quite large, it does not display inline in a +# notebook but is instead opened in a separate browser tab. However here we +# insert it in the page for convenience. By clicking a node in the graph you +# can see its result, how long it took, and the scikit-learn estimator that was +# fitted (if any). +# +# .. raw:: html +# +# +# +# You can also visit the report +# `here <../_static/employee_salaries_report/index.html>`_. +# +# It is also possible to create report about the execution of specific methods +# of the learner like "fit" and "predict" with :meth:`SkrubLearner.report`. + +# %% +# Cross-validation +# ---------------- +# +# We now make a few refinements on the previous pipeline. DataOps can accept +# any type of input and perform all processing, so we will extend our pipeline +# so that it includes the data loading and the creation of our features +# ``employee_data`` and our target ``salary``. The input will be simply the +# path to a csv file: + +train_dataset.path + +# %% +# Therefore we declare a new variable, to represent the CSV path. +# +# We also introduce an important feature of DataOps: interactive preview +# results. If we pass a value to our variable when creating it, it is used as +# example data on which skrub runs our pipeline as we define it, so we can see what +# the result looks like every step of the way. + +# %% +csv_path = skrub.var("csv_path", train_dataset.path) +csv_path + +# %% +# Note the added "Result" section in the output, which shows what the current +# pipeline's output looks like. +# +# Similarly to ``.skb.apply`` (which applies an estimator), ``.skb.apply_func`` +# applies a function: + +# %% +import pandas as pd + +full_data = csv_path.skb.apply_func(pd.read_csv) +full_data + +# %% +# Next, from our full dataframe we extract the predictive features and the +# regression target. +# +# The following snippet of code shows 2 important aspects: +# +# - Any methods or operators we access on our DataOp ``full_data``, like +# ``drop`` or the ``[]`` operator below, are recorded in the pipeline and +# will be applied to the DataOp's result: +# ``full_data['current_annual_salary']`` is roughly equivalent to +# ``full_data.skb.apply_func(lambda df: df['curent_annual_salary'])``. This +# is why all the skrub functionality is behind the ``.skb`` prefix as +# mentioned earlier: all other attribute access will be replayed directly on +# the result that the DataOp produces. +# - Once we have defined the features and targets, we mark them with +# :meth:`DataOp.skb.mark_as_X()` and :meth:`DataOp.skb.mark_as_y()` +# respectively. This tells skrub that when performing cross-validation, those +# are the intermediate results that should be divided into training and +# testing sets. Therefore, X and y do not need to be constructed and split +# *outside* the pipeline. Instead, our pipeline can encompass the full +# processing, and we indicate where the train/test split should happen. + +employee_data = full_data.drop( + columns="current_annual_salary", errors="ignore" +).skb.mark_as_X() +# (errors='ignore' because this column could be absent at the inference stage.) + +salary = full_data["current_annual_salary"].skb.mark_as_y() +salary + +# %% +# Finally, we apply the regressor. Note that the X and y nodes, on which +# train/test split is performed, are colored differently. + +pred = employee_data.skb.apply(skrub.TableVectorizer()).skb.apply( + HistGradientBoostingRegressor(), y=salary +) +pred + +# %% +# Once we have defined our pipeline, we can tell skrub to perform the +# cross-validation with :meth:`DataOp.skb.cross_validate`. + +pred.skb.cross_validate(scoring="neg_mean_absolute_percentage_error") + +# %% +# Note that the variables used in this pipeline are different than the previous +# one: we just have ``"csv_path"`` and not ``"employee_data"`` and ``"salary"`` +# like before. + +learner = pred.skb.make_learner(fitted=True) +learner.predict({"csv_path": test_dataset.path}) + +# %% +# Tuning arbitrary choices +# ------------------------ +# +# The last feature we present in this first tutorial is hyperparameter tuning. +# +# The start of the pipeline is the same as before: + +# %% +full_data = skrub.var("csv_path", train_dataset.path).skb.apply_func(pd.read_csv) +employee_data = full_data.drop( + columns="current_annual_salary", errors="ignore" +).skb.mark_as_X() +salary = full_data["current_annual_salary"].skb.mark_as_y() + +# %% +# We use functions like :func:`choose_from` or :func:`choose_float` whenever we +# have a choice for which we want to try several options and keep the one that +# performs best on the validation data. +# +# We simply replace the value by the special "choice" object produced by skrub +# in our pipeline, and it becomes a tunable hyperparameter of our skrub +# learner. Here we want to tune: +# +# - the choice of encoder applied to high-cardinality categorical columns +# (:class:`StringEncoder` or :class:`~sklearn.preprocessing.TargetEncoder`) +# - for the StringEncoder, the number of components +# - the learning rate of +# the :class:`~sklearn.ensemble.HistGradientBoostingRegressor`. +# +# **Note:** choices are not restricted to estimators or their hyperparameters, +# we can tune any value used anywhere in a pipeline, or the choice between +# different pipelines; more details :ref:`here `. + +from sklearn.preprocessing import TargetEncoder + +n_components = skrub.choose_int(10, 80, name="n_components") # choose int in [10, 80[ + +encoder = skrub.choose_from( # choosing between 2 different estimators + { + "lse": skrub.StringEncoder(n_components=n_components), # nesting choices + "target": TargetEncoder(), + }, + name="encoder", +) + +pred = employee_data.skb.apply( + skrub.TableVectorizer(high_cardinality=encoder), y=salary +).skb.apply( + HistGradientBoostingRegressor( + learning_rate=skrub.choose_float(0.01, 0.7, log=True, name="learning_rate") + ), + y=salary, +) +print(pred.skb.describe_param_grid()) + +# %% +# To actually run the search for the best hyperparameters, we use +# :meth:`DataOp.skb.make_randomized_search` or +# :meth:`DataOp.skb.make_grid_search`. For the randomized search we can use the +# powerful Optuna library which provides features like state-of-the-art +# hyperparameter samplers, live interactive visualization of the search with +# ``optuna-dashboard``, stopping and resuming searches, etc. + +search = pred.skb.make_randomized_search( + backend="optuna", fitted=True, n_iter=16, random_state=0 +) +search.results_ + +# %% +search.plot_results() + +# %% +# The search can be used with the same interface as the :class:`SkrubLearner` +# we saw before. Alternatively, we can access its ``best_learner_`` attribute, +# which is a SkrubLearner. + +search.predict({"csv_path": test_dataset.path}) diff --git a/examples/02_data_ops/1130_choices.py b/examples/02_data_ops/1130_choices.py index 550b4981e..a57c66da2 100644 --- a/examples/02_data_ops/1130_choices.py +++ b/examples/02_data_ops/1130_choices.py @@ -66,7 +66,7 @@ # We mark the ``texts`` column as the input variable and the ``labels`` column as # the target variable. # -# See `the previous example <1110_data_ops_intro.html>`_ +# See `the previous example <1111_data_ops_quick_tour.html>`_ # for a more detailed explanation # of :func:`skrub.X` and :func:`skrub.y`. # diff --git a/pixi.lock b/pixi.lock index 04daec327..f8d67deee 100644 --- a/pixi.lock +++ b/pixi.lock @@ -565,7 +565,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py314h3de4e8d_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda - 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