diff --git a/AUTHORS.rst b/AUTHORS.rst index 236746766..66d7761fe 100644 --- a/AUTHORS.rst +++ b/AUTHORS.rst @@ -43,6 +43,7 @@ Contributors * Ambros Marzetta * Carl McBride Ellis * Baptiste Calot +* Damien Bouet * Leonardo Garma * Mohammed Jawhar * Syed Affan diff --git a/HISTORY.rst b/HISTORY.rst index 7f20e9e97..59a583430 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -5,6 +5,10 @@ History 0.9.x (2024-xx-xx) ------------------ +* Add `SplitCPRegressor`, based on new `SplitCP` abstract class, to support the new CCP method +* Add `GaussianCCP`, `PolynomialCCP` and `CustomCCP` based on `CCPCalibrator` to implement the Conditional CP method +* Add the `StandardCalibrator`, to reproduce standard CP and make sure that the `SplitCPRegressor` is implemented correctly. +* Add the CCP documentation, tutorial and demo notebooks * Fix issue 525 in contribution guidelines with syntax errors in hyperlinks and other formatting issues. * Bump wheel version to avoid known security vulnerabilities * Fix issue 495 to center correctly the prediction intervals diff --git a/README.rst b/README.rst index f117b4036..2e3f00af9 100644 --- a/README.rst +++ b/README.rst @@ -229,6 +229,8 @@ and with the financial support from Région Ile de France and Confiance.ai. [12] Angelopoulos, Anastasios N., Stephen, Bates, Emmanuel J. Candès, et al. "Learn Then Test: Calibrating Predictive Algorithms to Achieve Risk Control." (2022). +[13] Isaac Gibbs, John J. Cherian, and Emmanuel J. Candès, "Conformal Prediction With Conditional Guarantees" (2023). + 📝 License ========== diff --git a/doc/api.rst b/doc/api.rst index ce411d3e4..460814212 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -1,3 +1,6 @@ + +.. _api: + ######### MAPIE API ######### @@ -109,9 +112,33 @@ Resampling subsample.BlockBootstrap subsample.Subsample +New Split CP class +=================== + +.. autosummary:: + :toctree: generated/ + :template: class.rst + + future.split.base.SplitCP + future.split.SplitCPRegressor + future.split.SplitCPClassifier + +Calibrators +=========== + +.. autosummary:: + :toctree: generated/ + :template: class.rst + + future.calibrators.base.BaseCalibrator + future.calibrators.StandardCalibrator + future.calibrators.ccp.CCPCalibrator + future.calibrators.ccp.CustomCCP + future.calibrators.ccp.PolynomialCCP + future.calibrators.ccp.GaussianCCP Mondrian -========== +======== .. autosummary:: :toctree: generated/ diff --git a/doc/index.rst b/doc/index.rst index 73926b81c..68fca97a5 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -32,6 +32,16 @@ examples_classification/index notebooks_classification +.. toctree:: + :maxdepth: 2 + :hidden: + :caption: CONDITIONAL CP + + theoretical_description_ccp + theoretical_description_calibrators + examples_regression/4-tutorials/plot_ccp_tutorial + examples_classification/4-tutorials/plot_ccp_class_tutorial + .. toctree:: :maxdepth: 2 :hidden: diff --git a/doc/notebooks_regression.rst b/doc/notebooks_regression.rst index 24b8ce12e..1ee05bbd9 100755 --- a/doc/notebooks_regression.rst +++ b/doc/notebooks_regression.rst @@ -16,3 +16,6 @@ This section lists a series of Jupyter notebooks hosted on the MAPIE Github repo ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- +4. Leverage CCP method to have adaptative prediction intervals on Communities and Crime Dataset : `ccp_CandC_notebook `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + diff --git a/doc/theoretical_description_calibrators.rst b/doc/theoretical_description_calibrators.rst new file mode 100644 index 000000000..8f76d1fff --- /dev/null +++ b/doc/theoretical_description_calibrators.rst @@ -0,0 +1,80 @@ +.. title:: Calibrators : contents + +.. _theoretical_description_calibrators: + +############### +Calibrators +############### + +In Mapie, the conformalisation step is done directly inside +:class:`~mapie.regression.MapieRegressor` or :class:`~mapie.classification.MapieClassifier`, +depending on the ``method`` argument. +However, when implementing the new CCP method, we decided to externalize the conformalisation +step into a new object named ``calibrator``, to have more freedom and possible customisation. + +The new classes (:class:`~mapie.future.split.SplitCPRegressor` and :class:`~mapie.future.split.SplitCPClassifier`) have 3 steps: + +1. ``fit_predictor``, which fit the sklearn estimator +2. ``fit_calibrator``, which do the conformalisation (calling ``calibrator.fit``) +3. ``predict``, which compute the predictions and call ``calibrator.predict`` to create the prediction intervals + +Thus, the calibrators, based on :class:`~mapie.future.calibrators.base.BaseCalibrator`, +must have the two methods: ``fit`` and ``predict``. + +Mapie currently implements calibrators for the CCP method (and the standard method), +but any conformal prediction method can be implemented by the user as +a subclass of :class:`~mapie.future.calibrators.base.BaseCalibrator`. + +Example of standard split CP: +------------------------------ + +For instance, the :class:`~mapie.future.calibrators.StandardCalibrator` implements +the :ref:`standard split method`: + +* ``.fit`` computes :math:`\hat{q}_{n, \alpha}^+`, the :math:`(1-\alpha)` quantile of the distribution +* ``.predict`` comptues the prediction intervals with: :math:`\hat{\mu}(X_{n+1}) \pm \hat{q}_{n, \alpha}^+` + + +The CCP calibrators: +--------------------- +For the CCP method (see :ref:`theoretical description`), +:class:`~mapie.future.calibrators.ccp.CCPCalibrator` implements: + +* ``.fit`` solve the optimization problem (see :ref:`step 2`) to find the optimal :math:`\hat{g}` +* ``.predict`` comptues the prediction intervals using :math:`\hat{g}` (see :ref:`step 3`) + +We just need a way to define our :math:`\Phi` function (see :ref:`step 1`). + +Multiple subclasses are implemented to facilitate the definition of the :math:`\Phi` function, +but other could be implemented by the user as a subclass of :class:`~mapie.future.calibrators.ccp.CCPCalibrator`. + +1. :class:`~mapie.future.calibrators.ccp.CustomCCP` + + This class allows to define by hand the :math:`\Phi` function, as a + concatenation of other functions which create features of ``X`` (or potentially ``y_pred`` or any exogenous variable ``z``) + + It can also be used to concatenate other :class:`~mapie.future.calibrators.ccp.CCPCalibrator` instances. + +2. :class:`~mapie.future.calibrators.ccp.PolynomialCCP` + + It create some polynomial features of ``X`` (or potentially ``y_pred`` or any exogenous variable ``z``). + It could be created by hand using `CustomCCP`, it is just a way simplify the creation of :math:`\Phi`. + +3. :class:`~mapie.future.calibrators.ccp.GaussianCCP` + + It create gaussian kernels, as done in the method's paper :ref:`[1]`. + It samples random points from the :math:`\{ X_i \}_i`, then compute gaussian distances + between each point and :math:`X_{n+1}` with a given standard deviation :math:`\sigma` + (which can be optimized using cross-validation), following the formula: + + .. math:: + \forall j \in \{ \text{sampled index} \}, \quad \Phi(X)_j = exp \left( -\frac{(X_{n+1} - X_j)^2}{2\sigma ^2} \right) + + +.. _theoretical_description_calibrators_references: + +References +========== + +[1] Isaac Gibbs, John J. Cherian, and Emmanuel J. Candès, +"Conformal Prediction With Conditional Guarantees", `arXiv `_, 2023. diff --git a/doc/theoretical_description_ccp.rst b/doc/theoretical_description_ccp.rst new file mode 100644 index 000000000..c7c501b3e --- /dev/null +++ b/doc/theoretical_description_ccp.rst @@ -0,0 +1,201 @@ +.. title:: Theoretical Description : contents + +.. _theoretical_description_ccp: + +######################## +Theoretical Description +######################## + +The Conditional Conformal Prediction (CCP) method :ref:`[1]` is a model agnostic conformal prediction method which +can create adaptative prediction intervals. + +In MAPIE, this method has a lot of advantages: + +- It is model agnostic (it doesn’t depend on the model but only on the predictions, unlike CQR) +- It can create very adaptative intervals (with a varying width which truly reflects the model uncertainty) +- while providing coverage guarantee on all sub-groups of interest (avoiding biases) +- with the possibility to inject prior knowledge about the data or the model + +However, we will also see its disadvantages: +- The adaptativity depends on the calibrator we use: It can be difficult to choose the correct calibrator, +with the best parameters. +- The calibration and even more the inference are much longer than for the other methods. +We can reduce the inference time using ``unsafe_approximation=True``, +but we lose the strong theoretical guarantees and risk a small miscoverage +(even if, most of the time, the coverage is achieved). + +To conclude, it can create more adaptative intervals than the other methods, +but it can be difficult to find the best settings (calibrator type and parameters) +and can have a big computational time. + +How does it works? +==================== + +Method's intuition +-------------------- + +We recall that the `standard split method` estimates the absolute residuals by a constant :math:`\hat{q}_{n, \alpha}^+` +(which is the quantile of :math:`{|Y_i-\hat{\mu}(X_i)|}_{1 \leq i \leq n}`). Then, the prediction interval is: + +.. math:: \hat{C}_{n, \alpha}^{\textrm split}(X_{n+1}) = \hat{\mu}(X_{n+1}) \pm \hat{q}_{n, \alpha}^+ + +The idea of the `CCP` method, is to learn, not a constant, but a function :math:`q(X)`, +to have a different interval width depending on the :math:`X` value. Then, we would have: + +.. math:: \hat{C}_{n, \alpha}^{\textrm CCP}(X_{n+1}) = \hat{\mu}(X_{n+1}) \pm \hat{q}(X_{n+1}) + +To be able to find the best function, while having some coverage guarantees, +we should select this function inside some defined class of functions :math:`\mathcal{F}`. + +This method is motivated by the following equivalence: + +.. math:: + \begin{array}{c} + \mathbb{P}(Y_{n+1} \in \hat{C} \; | \; X_{n+1}=x) = 1 - \alpha, \quad \text{for all x} \\ + \textstyle \Longleftrightarrow \\ + \mathbb{E} \left[ f(X_{n+1}) \mathbb{I} \left\{ Y_{n+1} \in \hat{C}(X_{n+1}) \right\} \right] = 0, \quad \text{for all measurable f} \\ + \end{array} + +This is the equation corresponding to the perfect conditional coverage, which is theoretically impossible to obtain. +Then, relaxing this objective by replacing "all measurable f" with "all f belonging to some class :math:`\mathcal{F}`" +seems a way to get close to the perfect conditional coverage. + + +.. _theoretical_description_ccp_control_steps: + +The method follow 3 steps: +---------------------------- + +1. Choose a class of functions. The simple approach is to choose a class a finite dimension :math:`d \in \mathbb{N}`, + using, for any :math:`\Phi \; : \; \mathbb{R}^d \to \mathbb{R}` (chosen by the user) + + .. math:: + \mathcal{F} = \left\{ \Phi (\cdot)^T \beta : \beta \in \mathbb{R}^d \right\} + +2. Find the best function of this class by solving the following optimization problem: + + .. note:: It is actually a quantile regression between the transformation :math:`\Phi (X)` and the conformity scores `S`. + + Considering an upper bound :math:`M` of the conformity scores, + such as :math:`S_{n+1} < M`: + + .. math:: + \hat{g}_M^{n+1} := \text{arg}\min_{g \in \mathcal{F}} \; \frac{1}{n+1} \sum_{i=1}^n{l_{\alpha} (g(X_i), S_i)} \; + \frac{1}{n+1}l_{\alpha} (g(X_{n+1}), M) + + .. warning:: + In the :ref:`API`, we use by default :math:`M=max(\{S_i\}_{i\leq n})`, + the maximum conformity score of the calibration set, + but you can specify it yourself if a bound is known, considering your data, + model and conformity score. + + Moreover, it means that there is still small computations which are done + for each test point :math:`X_{n+1}`. If you want to avoid that, you can + use ``unsafe_approximation=True``, which only consider: + + .. math:: + \hat{g} := \text{arg}\min_{g \in \mathcal{F}} \; \frac{1}{n} \sum_{i=1}^n{l_{\alpha} (g(X_i), S_i)} + + However, it may result in a small miscoverage. + It is recommanded to empirically check the resulting coverage on the test set. + +3. We use this optimized function :math:`\hat{g}_M^{n+1}` to compute the prediction intervals: + + .. math:: + \hat{C}_M^{n+1}(X_{n+1}) = \{ y : S(X_{n+1}, \: y) \leq \hat{g}_M^{n+1}(X_{n+1}) \} + + .. note:: The formulas are generic and work with all conformity scores. But in the case of the absolute residuals, we get: + + .. math:: + \hat{C}(X_{n+1}) = \hat{\mu}(X_{n+1}) \pm \hat{g}_M^{n+1}(X_{n+1}) + +.. _theoretical_description_ccp_control_coverage: + +Coverage guarantees: +----------------------- + +.. warning:: + The following guarantees assume that the approximation described above is not used, and that + the chosen bound M is indeed such as :math:`\forall \text{ test index }i, \; S_i < M` + +Following this steps, we have the coverage guarantee: +:math:`\forall f \in \mathcal{F},` + +.. math:: + \mathbb{P}_f(Y_{n+1} \in \hat{C}_M^{n+1}(X_{n+1})) \geq 1 - \alpha + +.. math:: + \text{and} \quad \left | \mathbb{E} \left[ f(X_{n+1}) \left(\mathbb{I} \left\{ Y_{n+1} \in \hat{C}_M^{n+1}(X_{n+1}) \right\} - (1 - \alpha) \right) \right] \right | + \leq \frac{d}{n+1} \mathbb{E} \left[ \max_{1 \leq i \leq n+1} \left|f(X_i)\right| \right] + +.. note:: + If we want to have a homogenous coverage on some given groups in :math:`\mathcal{G}`, we can use + :math:`\mathcal{F} = \{ x \mapsto \sum _{G \in \mathcal{G}} \; \beta_G \mathbb{I} \{ x \in G \} : \beta_G \in \mathbb{R} \}`, + then we have :math:`\forall G \in \mathcal{G}`: + + .. math:: + 1 - \alpha + \leq \mathbb{P} \left( Y_{n+1} \in \hat{C}_M^{n+1}(X_{n+1}) \; | \; X_{n+1} \in G \right) + \leq 1- \alpha + \frac{|\mathcal{G}|}{(n+1) \mathbb{P}(X_{n+1} \in G)} \\ + = 1- \alpha + \frac{\text{number of groups in } \mathcal{G}}{\text{number of samples of } \{X_i\} \text{ in G}} + +How to use it in practice? +============================ + +Creating a class a function adapted to our needs +-------------------------------------------------- + +The following will provide some tips on how to use the method (for more practical examples, see +:doc:`examples_regression/4-tutorials/plot_ccp_tutorial` or +`How to leverage the CCP method on real data +`_ +). + +1. If you want a generally adaptative interval and you don't have prior + knowledge about your data, you can use gaussian kernels, implemented in Mapie + in :class:`~mapie.future.calibrators.ccp.GaussianCCP`. See the API doc for more information. + +2. If you want to avoid bias on sub-groups and ensure an homogenous coverage on those, + you can add indicator functions corresponding to those groups. + +3. You can inject prior knowledge in the method using :class:`~mapie.future.calibrators.ccp.CustomCCP`, + if you have information about the conformity scores distribution + (domains with different biavior, expected model uncertainty depending on a given feature, etc). + +4. Empirically test obtained coverage on a test set, to make sure that the expected coverage is achieved. + + +Avoid miscoverage +-------------------- + +- | To guarantee marginal coverage, you need to have an intercept term in the :math:`\Phi` function (meaning, a feature equal to :math:`1` for all :math:`X_i`). + | It correspond, in the :ref:`API`, to ``bias=True``. + +- | Some miscoverage can come from the optimization process, which is + solved with numerical methods, and may fail to find the global minimum. + If the target coverage is not achieved, you can try adding regularization, + to help the optimization process. You can also try reducing the number of dimensions :math:`d` + or using a smoother :math:`\Phi` function, such as with gaussian kernels + (indeed, using only indicator functions makes the optimization difficult). + + .. warning:: + Adding some regularization will theoretically induce a miscoverage, + as the objective function will slightly increase, to minimize the regularization term. + + In practice, it may increase the coverage (as it helps the optimization convergence), + but it can also decrease it. Always empirically check the resulting coverage + and avoid too big regularization terms (below :math:`10^{-4}` is usually recommanded). + + +- | Finally, if you have coverage issues because the optimisation is difficult, + you can artificially enforce higher coverage by reducing the value of :math:`\alpha`. + Evaluating the best adjusted :math:`\alpha` using cross-validation will ensure + the same coverage on the test set (subject to variability due to the finite number of samples). + + +.. _theoretical_description_ccp_references: + +References +========== + +[1] Isaac Gibbs, John J. Cherian, and Emmanuel J. Candès, +"Conformal Prediction With Conditional Guarantees", `arXiv `_, 2023. \ No newline at end of file diff --git a/doc/theoretical_description_classification.rst b/doc/theoretical_description_classification.rst index 445fcfe42..f26815694 100644 --- a/doc/theoretical_description_classification.rst +++ b/doc/theoretical_description_classification.rst @@ -31,6 +31,9 @@ for at least :math:`90 \%` of the new test data points. Note that the guarantee is possible only on the marginal coverage, and not on the conditional coverage :math:`P \{Y_{n+1} \in \hat{C}_{n, \alpha}(X_{n+1}) | X_{n+1} = x_{n+1} \}` which depends on the location of the new test point in the distribution. + +.. _theoretical_description_classification_lac: + 1. LAC ------ diff --git a/doc/theoretical_description_conformity_scores.rst b/doc/theoretical_description_conformity_scores.rst index 5ec0aee4d..8fc69a7c5 100644 --- a/doc/theoretical_description_conformity_scores.rst +++ b/doc/theoretical_description_conformity_scores.rst @@ -6,7 +6,7 @@ Theoretical Description for Conformity Scores ############################################# -The :class:`mapie.conformity_scores.ConformityScore` class implements various +The :class:`~mapie.conformity_scores.ConformityScore` class implements various methods to compute conformity scores for regression. We give here a brief theoretical description of the scores included in the module. Note that it is possible for the user to create any conformal scores that are not @@ -27,7 +27,7 @@ and the other on the left side. 1. The absolute residual score ------------------------------ -The absolute residual score (:class:`mapie.conformity_scores.AbsoluteConformityScore`) +The absolute residual score (:class:`~mapie.conformity_scores.AbsoluteConformityScore`) is the simplest and most commonly used conformal score, it translates the error of the model : in regression, it is called the residual. @@ -46,7 +46,7 @@ This score is by default symmetric (*see above for definition*). 2. The gamma score ------------------ -The gamma score [2] (:class:`mapie.conformity_scores.GammaConformityScore`) adds a +The gamma score [2] (:class:`~mapie.conformity_scores.GammaConformityScore`) adds a notion of adaptivity with the normalization of the residuals by the predictions. .. math:: \frac{|Y-\hat{\mu}(X)|}{\hat{\mu}(X)} @@ -71,7 +71,7 @@ in use cases where we want greater uncertainty when the prediction is high. 3. The residual normalized score -------------------------------- -The residual normalized score [1] (:class:`mapie.conformity_scores.ResidualNormalisedScore`) +The residual normalized score [1] (:class:`~mapie.conformity_scores.ResidualNormalisedScore`) is slightly more complex than the previous scores. The normalization of the residual is now done by the predictions of an additional model :math:`\hat\sigma` which learns to predict the base model residuals from :math:`X`. @@ -99,7 +99,7 @@ it is not proportional to the uncertainty. Key takeaways ------------- -- The absolute residual score is the basic conformity score and gives constant intervals. It is the one used by default by :class:`mapie.regression.MapieRegressor`. +- The absolute residual score is the basic conformity score and gives constant intervals. It is the one used by default by :class:`~mapie.regression.MapieRegressor`. - The gamma conformity score adds a notion of adaptivity by giving intervals of different sizes and is proportional to the uncertainty. - The residual normalized score is a conformity score that requires an additional model diff --git a/doc/theoretical_description_regression.rst b/doc/theoretical_description_regression.rst index 09c55e74c..f83a6bd09 100644 --- a/doc/theoretical_description_regression.rst +++ b/doc/theoretical_description_regression.rst @@ -6,7 +6,7 @@ Theoretical Description ####################### -The :class:`mapie.regression.MapieRegressor` class uses various +The :class:`~mapie.regression.MapieRegressor` class uses various resampling methods based on the jackknife strategy recently introduced by Foygel-Barber et al. (2020) [1]. They allow the user to estimate robust prediction intervals with any kind of @@ -57,6 +57,8 @@ The figure below illustrates the naive method. :width: 200 :align: center +.. _theoretical_description_regression_standard: + 2. The split method =================== @@ -293,7 +295,7 @@ hypothesis". It means that the probability law of data should not change up to reordering. This hypothesis is not relevant in many cases, notably for dynamical times series. That is why a specific class is needed, namely -:class:`mapie.time_series_regression.MapieTimeSeriesRegressor`. +:class:`~mapie.time_series_regression.MapieTimeSeriesRegressor`. Its implementation looks like the jackknife+-after-bootstrap method. The leave-one-out (LOO) estimators are approximated thanks to a few boostraps. @@ -398,4 +400,4 @@ International Conference on Machine Learning (ICML, 2021). [5] Jing Lei, Max G’Sell, Alessandro Rinaldo, Ryan J Tibshirani, and Larry Wasserman. "Distribution-free predictive inference for regression". -Journal of the American Statistical Association, 113(523):1094–1111, 2018. \ No newline at end of file +Journal of the American Statistical Association, 113(523):1094–1111, 2018. diff --git a/examples/classification/4-tutorials/plot_ccp_class_tutorial.py b/examples/classification/4-tutorials/plot_ccp_class_tutorial.py new file mode 100644 index 000000000..05260b7f0 --- /dev/null +++ b/examples/classification/4-tutorials/plot_ccp_class_tutorial.py @@ -0,0 +1,394 @@ +""" +============================================ +Tutorial: Conditional CP for classification +============================================ + +The tutorial will explain how to use the CCP method for classification +and will wompare it with the other methods available in MAPIE. The CCP method +implements the method described in the Gibbs et al. (2023) paper [1]. + +In this tutorial, the classifier will be +:class:`~sklearn.linear_model.LogisticRegression`. +We will use a synthetic toy dataset. + +We will compare the CCP method (using +:class:`~mapie.future.split.SplitCPRegressor`, +:class:`~mapie.future.calibrators.ccp.CustomCCP` and +:class:`~mapie.future.calibrators.ccp.GaussianCCP`), with the +standard method, using for both, the LAC conformity score +(:class:`~mapie.conformity_scores.LACConformityScore`). + +Recall that the ``LAC`` method consists on applying a threshold on the +predicted softmax, to keep all the classes above the threshold +(``alpha`` is ``1 - target coverage``). + +Warning: +In this tutorial, we use ``unsafe_approximation=True`` to have a faster +computation (because Read The Docs examples require fast computation). +This mode use an approximation, which make the inference (``predict``) faster, +but induce a small miscoverage. It is recommanded not to use it, or be +very careful and empirically check the coverage and a test set. + +[1] Isaac Gibbs, John J. Cherian, and Emmanuel J. Candès, +"Conformal Prediction With Conditional Guarantees", +`arXiv `_, 2023. +""" + +import warnings + +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.patches import Patch +from sklearn.model_selection import ShuffleSplit +from sklearn.linear_model import LogisticRegression + +from mapie.future.calibrators import CustomCCP, GaussianCCP +from mapie.classification import MapieClassifier +from mapie.conformity_scores import LACConformityScore +from mapie.future.split.classification import SplitCPClassifier + +warnings.filterwarnings("ignore") + +random_state = 1 +np.random.seed(random_state) + +ALPHA = 0.2 +UNSAFE_APPROXIMATION = True +N_CLASSES = 5 + +############################################################################## +# 1. Data generation +# -------------------------------------------------------------------------- +# Let's start by creating some synthetic data with 5 gaussian distributions +# +# We are going to use 5000 samples for training, 3000 for calibration and +# 10000 for testing (to have a good conditional coverage evaluation). + + +def create_toy_dataset(n_samples=1000): + centers = [(0, 3.5), (-3, 0), (0, -2), (4, -1), (3, 1)] + covs = [ + np.diag([1, 1]), np.diag([2, 2]), np.diag([3, 2]), + np.diag([3, 3]), np.diag([2, 2]), + ] + n_per_class = ( + np.linspace(0, n_samples, N_CLASSES + 1)[1:] + - np.linspace(0, n_samples, N_CLASSES + 1)[: -1].astype(int) + ).astype(int) + X = np.vstack([ + np.random.multivariate_normal(center, cov, n) + for center, cov, n in zip(centers, covs, n_per_class) + + ]) + y = np.hstack([np.full(n_per_class[i], i) for i in range(N_CLASSES)]) + + return X, y + + +def generate_data(seed=1, n_train=2000, n_calib=2000, n_test=2000, ): + np.random.seed(seed) + x_train, y_train = create_toy_dataset(n_train) + x_calib, y_calib = create_toy_dataset(n_calib) + x_test, y_test = create_toy_dataset(n_test) + + return x_train, y_train, x_calib, y_calib, x_test, y_test + +############################################################################## +# Let's visualize the data and its distribution + + +x_train, y_train, *_ = generate_data(seed=None, n_train=1000) + +for c in range(N_CLASSES): + plt.scatter(x_train[y_train == c, 0], x_train[y_train == c, 1], + c=f"C{c}", s=1.5, label=f'Class {c}') +plt.legend() +plt.show() + + +############################################################################## +# 2. Plotting and adaptativity comparison functions +# -------------------------------------------------------------------------- + + +def run_exp( + mapies, names, alpha, + n_train=1000, n_calib=1000, n_test=1000, + grid_step=100, plot=True, seed=1, max_display=2000 +): + ( + x_train, y_train, x_calib, y_calib, x_test, y_test + ) = generate_data( + seed=seed, n_train=n_train, n_calib=n_calib, n_test=n_test + ) + + if max_display: + display_ind = np.random.choice(np.arange(0, len(x_test)), max_display) + else: + display_ind = np.arange(0, len(x_test)) + + color_map = plt.cm.get_cmap("Purples", N_CLASSES + 1) + + if plot: + fig = plt.figure() + fig.set_size_inches(6 * (len(mapies) + 1), 7) + grid = plt.GridSpec(1, len(mapies) + 1) + + x_min = np.min(x_train) + x_max = np.max(x_train) + step = (x_max - x_min) / grid_step + + xx, yy = np.meshgrid( + np.arange(x_min, x_max, step), np.arange(x_min, x_max, step) + ) + X_test_mesh = np.stack([xx.ravel(), yy.ravel()], axis=1) + + scores = np.zeros((len(mapies), N_CLASSES+1)) + for i, (mapie, name) in enumerate(zip(mapies, names)): + if isinstance(mapie, MapieClassifier): + mapie.fit( + np.vstack([x_train, x_calib]), np.hstack([y_train, y_calib]) + ) + _, y_ps_test = mapie.predict(x_test, alpha=alpha) + if plot: + y_pred_mesh, y_ps_mesh = mapie.predict( + X_test_mesh, alpha=alpha + ) + elif isinstance(mapie, SplitCPClassifier): + mapie.fit( + np.vstack([x_train, x_calib]), np.hstack([y_train, y_calib]) + ) + _, y_ps_test = mapie.predict( + x_test, unsafe_approximation=UNSAFE_APPROXIMATION + ) + if plot: + y_pred_mesh, y_ps_mesh = mapie.predict(X_test_mesh) + else: + raise + + if plot: + if i == 0: + ax1 = fig.add_subplot(grid[0, 0]) + + ax1.scatter( + X_test_mesh[:, 0], X_test_mesh[:, 1], + c=[f"C{x}" for x in y_pred_mesh], alpha=1, marker="s", + edgecolor="none", s=220 * step + ) + ax1.fill_between( + x=[min(X_test_mesh[:, 0]) - step] + list(X_test_mesh[:, 0]) + + [max(X_test_mesh[:, 0]) + step], + y1=min(X_test_mesh[:, 1]) - step, + y2=max(X_test_mesh[:, 1]) + step, + color="white", alpha=0.6 + ) + ax1.scatter( + x_test[display_ind, 0], x_test[display_ind, 1], + c=[f"C{x}" for x in y_test[display_ind]], + alpha=1, marker=".", edgecolor="black", s=80 + ) + + ax1.set_title("Predictions", fontsize=22, pad=12) + ax1.set_xlim([-6, 8]) + ax1.set_ylim([-6, 8]) + legend_labels = [f"Class {i}" for i in range(N_CLASSES)] + handles = [ + plt.Line2D([0], [0], marker='.', color='w', + markerfacecolor=f"C{i}", markersize=10) + for i in range(N_CLASSES) + ] + ax1.legend(handles, legend_labels, title="Classes", + fontsize=18, title_fontsize=20) + + y_ps_sums = y_ps_mesh[:, :, 0].sum(axis=1) + + ax = fig.add_subplot(grid[0, i + 1]) + + scatter = ax.scatter( + X_test_mesh[:, 0], + X_test_mesh[:, 1], + c=y_ps_sums, + marker='s', + edgecolor="none", + s=220 * step, + alpha=1, + cmap=color_map, + vmin=0, + vmax=N_CLASSES, + ) + ax.scatter(x_test[display_ind, 0], x_test[display_ind, 1], + c=[f"C{x}" for x in y_test[display_ind]], + alpha=0.6, marker=".", edgecolor="gray", s=50) + + colorbar = plt.colorbar(scatter, ax=ax) + colorbar.ax.set_ylabel("Set size", fontsize=20) + colorbar.ax.tick_params(labelsize=18) + ax.set_title(name, fontsize=22, pad=12) + ax.set_xlim([-6, 8]) + ax.set_ylim([-6, 8]) + + if isinstance(mapie, SplitCPClassifier): + centers = [] + for f in mapie.calibrator_.functions_ + [mapie.calibrator_]: + if hasattr(f, "points_"): + centers += list(f.points_) + if len(centers) > 0: + centers = np.stack(centers) + else: + centers = None + + if centers is not None: + ax.scatter(centers[:, 0], centers[:, 1], c="gold", + alpha=1, edgecolors="black", s=50) + + scores[i, 1:] = [ + y_ps_test[(y_test == c), c, 0].astype(int).sum(axis=0) + / len(y_ps_test[(y_test == c), :, 0]) + for c in range(N_CLASSES) + ] + scores[i, 0] = np.mean(scores[i, 1:]) + + if plot: + fig.tight_layout() + plt.show() + else: + return scores + + +def plot_cond_coverage(scores, names): + labels = [f"Class {i}" for i in range(N_CLASSES)] + labels.insert(0, "marginal") + x = np.arange(len(labels)) + width = 0.2 + + fig, ax = plt.subplots(figsize=(10, 6)) + for i in range(len(mapies)): + ax.boxplot( + scores[:, i, :], positions=x + width * (i-1), widths=width, + patch_artist=True, boxprops=dict(facecolor=f"C{i}"), + medianprops=dict(color="black"), labels=labels + ) + ax.axhline(y=1-ALPHA, color='red', linestyle='--', label=f'alpha={ALPHA}') + ax.axvline(x=0.5, color='black', linestyle='--') + + ax.set_ylabel('Coverage') + ax.set_title('Coverage on each class') + ax.set_xticks(x) + ax.set_xticklabels(labels) + ax.set_ylim([0.6, 1]) + + custom_handles = [Patch(facecolor=f"C{i}", edgecolor='black', + label=names[i]) for i in range(len(mapies))] + handles, labels = ax.get_legend_handles_labels() + + # Update the legend with the combined handles and labels + ax.legend(handles + custom_handles, labels + names, loc="lower left") + + plt.show() + + +############################################################################## +# 3. Creation of Mapie instances +# -------------------------------------------------------------------------- +# We are going to compare the standard ``LAC`` method with: +# +# - The ``CCP`` method using the predicted classes as groups (to have a +# homogenous coverage on each class). +# - The ``CCP`` method with gaussian kernels, to have adaptative prediction +# sets, without prior knowledge or information +# (:class:`~mapie.future.calibrators.ccp.GaussianCCP`). + + +n_train = 5000 +n_calib = 3000 +n_test = 10000 + +cv = ShuffleSplit(n_splits=1, test_size=n_calib/(n_train + n_calib), + random_state=random_state) + +# =========================== Standard LAC =========================== +mapie_lac = MapieClassifier(LogisticRegression(), method="lac", cv=cv) + + +# ============= CCP indicator groups on predicted classes ============= +mapie_ccp_y_pred = SplitCPClassifier( + LogisticRegression(), + calibrator=CustomCCP(lambda y_pred: y_pred), + alpha=ALPHA, cv=cv, conformity_score=LACConformityScore() +) + +# ======================== CCP Gaussian kernels ======================== +mapie_ccp_gauss = SplitCPClassifier( + LogisticRegression(), + calibrator=GaussianCCP(40, 1, bias=True), + alpha=ALPHA, cv=cv, conformity_score=LACConformityScore() +) + +mapies = [mapie_lac, mapie_ccp_y_pred, mapie_ccp_gauss] +names = ["Standard LAC", "CCP predicted class groups", "CCP Gaussian kernel"] + + +############################################################################## +# 4. Generate the prediction sets +# -------------------------------------------------------------------------- + +run_exp(mapies, names, ALPHA, n_train=n_train, n_calib=n_calib, n_test=n_test) + +############################################################################## +# We can see that the ``CCP`` method seems to create better +# prediction sets than the standard method. Indeed, where the +# classes distributions overlap (especially for class 3 and 4), +# the size of the sets should increase, to correctly represente the model +# uncertainty on those samples. +# +# The middle of all the classes distributions, where points could +# belong to any class, should have the biggest prediction sets (with almost +# all the clases in the sets, as we are very uncertain). The calibrator +# with gaussian kernels perfectly represented this uncertainty, with big sets +# for the middle points (the dark purple being sets with 4 classes). +# +# Thus, between the two ``CCP`` methods, the one using gaussian kernels +# (:class:`~mapie.future.calibrators.ccp.GaussianCCP`) seems the most +# adaptative. +# +# This modelisation of uncertainty is not visible at all in the standard +# method, where we have, in the opposite, empty sets where the distributions +# overlap. + + +############################################################################## +# 5. Evaluate the adaptativity +# -------------------------------------------------------------------------- +# If we can, at first, assess the adaptativity of the methods just looking at +# the prediction sets, the most accurate way is to look if the coverage is +# homogenous on sub parts of the data (on each class for instance). + + +N_TRIALS = 6 +scores = np.zeros((N_TRIALS, len(mapies), N_CLASSES+1)) +for i in range(N_TRIALS): + scores[i, :, :] = run_exp( + mapies, names, ALPHA, n_train=n_train, n_calib=n_calib, n_test=n_test, + plot=False, seed=i + ) + +plot_cond_coverage(scores, names) + +############################################################################## +# A pefectly adaptative method whould result in a homogenous coverage +# for all classes. We can see that the ``CCP`` method, with the predicted +# classes as groups, is more adaptative than the standard method. The +# over-coverage of the standard method on class 1 was corrected in the ``CCP`` +# method, and the under-coverage on class 4 was also slightly corrected. +# +# However, the ``CCP`` with a gaussian calibrator +# (:class:`~mapie.future.calibrators.ccp.GaussianCCP`), is clearly the +# most adaptative method, with no under-coverage neither for the class 2 and 4. +# +# To conclude, the ``CCP`` method offer adaptative perdiction sets. +# We can inject prior knowledge or groups on which we want to avois bias +# (We tried to do this with the classes, but it was not perfect because we only +# had access to the predictions, not the true classes). +# Using gaussian kernels, with a correct sigma parameter +# (which can be optimized using cross-validation if needed), can be the easiest +# and best solution to have very adaptative prdiction sets. diff --git a/examples/regression/3-scientific-articles/plot_gibbs2023_simulations.py b/examples/regression/3-scientific-articles/plot_gibbs2023_simulations.py new file mode 100644 index 000000000..0f7692c05 --- /dev/null +++ b/examples/regression/3-scientific-articles/plot_gibbs2023_simulations.py @@ -0,0 +1,368 @@ +""" +====================================================================== +Reproduction of part of the paper experiments of Gibbs et al. (2023) +====================================================================== + +:class:`~mapie.regression.MapieCCPRegressor` is used to reproduce a +part of the paper experiments of Gibbs et al. (2023) in their article [1] +which we argue is a good procedure to get adaptative prediction intervals (PI) +and a guaranteed coverage on all sub groups of interest. + +For a given model, the simulation adjusts the MAPIE regressors using the +``CCP`` method, on a synthetic dataset first considered by Romano et al. (2019) +[2], and compares the bounds of the PIs with the standard split CP. + +In order to reproduce the results of the standard split conformal prediction +(Split CP), we reuse the Mapie implementation in +:class:`~mapie.regression.MapieRegressor`. + +This simulation is carried out to check that the CCP method implemented in +MAPIE gives the same results as [1], and that the bounds of the PIs are +obtained. + +It is important to note that we are checking here if the adaptativity property +of the prediction intervals are well obtained. However, the paper do this +computations with the full conformal prediction approach, whereas we +implemented the faster but more conservatice split method. Thus, the results +may vary a little. + +[1] Isaac Gibbs, John J. Cherian, Emmanuel J. Candès (2023). +Conformal Prediction With Conditional Guarantees + +[2] Yaniv Romano, Evan Patterson, Emmanuel J. Candès (2019). +Conformalized Quantile Regression. +33rd Conference on Neural Information Processing Systems (NeurIPS 2019). +""" +import warnings + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from scipy.stats import norm +from sklearn.linear_model import LinearRegression +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import PolynomialFeatures + +from mapie.future.calibrators.ccp import CustomCCP, GaussianCCP +from mapie.conformity_scores import AbsoluteConformityScore +from mapie.regression import MapieRegressor +from mapie.future.split import SplitCPRegressor + +warnings.filterwarnings("ignore") + +random_state = 1 +np.random.seed(random_state) + + +############################################################################### +# 1. Global model parameters +# ----------------------------------------------------------------------------- + +def init_model(): + # the degree of the polynomial regression + degree = 4 + + model = Pipeline( + [ + ("poly", PolynomialFeatures(degree=degree)), + ("linear", LinearRegression()) + ] + ) + return model + +############################################################################### +# 2. Generate and present data +# ----------------------------------------------------------------------------- + + +def generate_data(n_train=2000, n_calib=2000, n_test=500): + def f(x): + ax = 0*x + for i in range(len(x)): + ax[i] = (np.random.poisson(np.sin(x[i])**2 + 0.1) + + 0.03*x[i]*np.random.randn(1)) + ax[i] += 25*(np.random.uniform(0, 1, 1) < 0.01)*np.random.randn(1) + return ax.astype(np.float32) + + # training features + X_train = np.random.uniform(0, 5.0, size=n_train).astype(np.float32) + X_calib = np.random.uniform(0, 5.0, size=n_calib).astype(np.float32) + X_test = np.random.uniform(0, 5.0, size=n_test).astype(np.float32) + + # generate labels + y_train = f(X_train) + y_calib = f(X_calib) + y_test = f(X_test) + + # reshape the features + X_train = X_train.reshape(-1, 1) + X_calib = X_calib.reshape(-1, 1) + X_test = X_test.reshape(-1, 1) + + return X_train, y_train, X_calib, y_calib, X_test, y_test + + +X_train, y_train, X_calib, y_calib, X_test, y_test = generate_data() + +fig = plt.figure(figsize=(12, 5)) +ax1 = fig.add_subplot(1, 2, 1) +ax1.scatter(X_train[:, 0], y_train, s=1.5, alpha=0.6, label="Train Data") +ax1.set_xlabel("X") +ax1.set_ylabel("Y") +ax1.set_title("Train Data") +ax1.legend() + +ax2 = fig.add_subplot(1, 2, 2) +ax2.scatter(X_train[:, 0], y_train, s=1.5, alpha=0.6, label="Train Data") +ax2.set_ylim([-2, 6]) +ax2.set_xlabel("X") +ax2.set_ylabel("Y") +ax2.set_title("Zoom") +ax2.legend() + +plt.show() + +############################################################################## +# 3. Prepare model and show predictions +# ----------------------------------------------------------------------------- + +model = init_model() + +model.fit(X_train, y_train) + +sort_order = np.argsort(X_test[:, 0]) +x_test_s = X_test[sort_order] +y_pred_s = model.predict(x_test_s) + +plt.figure(figsize=(6, 5)) +plt.scatter(X_test[:, 0], y_test, s=1.5, alpha=0.6, label="Test Data") +plt.plot(x_test_s, y_pred_s, "-k", label="Prediction") +plt.ylim([-2, 6]) +plt.xlabel("X") +plt.ylabel("Y") +plt.title("Test Data (Zoom)") +plt.legend() +plt.show() + + +############################################################################## +# 4. Prepare Experiments +# ----------------------------------------------------------------------------- +# In this experiment, we will use the +# :class:`~mapie.regression.MapieRegressor` and +# :class:`~mapie.regression.MapieCCPRegressor` to compute prediction intervals +# with the basic Split CP method and the paper CCP method. +# The coverages was computed, in the paper, on 500 different dataset +# generations, to have a good idea of the true value. +# Indeed, the empirical coverage of a single +# experiment is stochastic, because of the finite number of calibration and +# test samples. +# We will only compute 50 trials, because of the documentation +# computational power limitations. + +ALPHA = 0.1 + + +def estimate_coverage(mapie_split, mapie_ccp, group_functs=[]): + _, _, X_calib, y_calib, X_test, y_test = generate_data() + + mapie_split.fit(X_calib, y_calib) + _, y_pi_split = mapie_split.predict(X_test, alpha=ALPHA) + + mapie_ccp.fit_calibrator(X_calib, y_calib) + _, y_pi_ccp = mapie_ccp.predict(X_test) + + cover_split = np.logical_or(y_test < y_pi_split[:, 0, 0], + y_test > y_pi_split[:, 1, 0]) + cover_ccp = np.logical_or(y_test < y_pi_ccp[:, 0, 0], + y_test > y_pi_ccp[:, 1, 0]) + group_covers = [] + marginal_cover = np.asarray((cover_split.mean(), cover_ccp.mean())) + for funct in group_functs: + group_cover = np.zeros((2,)) + group_cover[0] = (funct(X_test).flatten() + * cover_split).sum() / funct(X_test).sum() + group_cover[1] = (funct(X_test).flatten() + * cover_ccp).sum() / funct(X_test).sum() + group_covers.append(group_cover) + return marginal_cover, np.array(group_covers) + + +def plot_results(X_test, y_test, n_trials=10, + experiment="Groups", split_sym=True): + + # Split CP + mapie_split = MapieRegressor( + model, method="base", cv="prefit", + conformity_score=AbsoluteConformityScore(sym=split_sym) + ) + mapie_split.conformity_score.eps = 1e-5 + mapie_split.fit(X_calib, y_calib) + _, y_pi_split = mapie_split.predict(X_test, alpha=ALPHA) + + if experiment == "Groups": + # CCP Groups + calibrator_groups = CustomCCP([ + lambda X, t=t: np.logical_and(X >= t, X < t + 0.5).astype(int) + for t in np.arange(0, 5.5, 0.5) + ]) + mapie_ccp = SplitCPRegressor( + model, calibrator=calibrator_groups, alpha=ALPHA, cv="prefit", + conformity_score=AbsoluteConformityScore(sym=False), + random_state=None + ) + mapie_ccp.conformity_score.eps = 1e-5 + mapie_ccp.fit(X_calib, y_calib) + _, y_pi_ccp = mapie_ccp.predict(X_test) + else: + # CCP Shifts + eval_locs = [1.5, 3.5] + eval_scale = 0.2 + other_locs = [0.5, 2.5, 4.5] + other_scale = 1 + + calibrator_shifts = GaussianCCP( + points=( + np.array(eval_locs+other_locs).reshape(-1, 1), + [eval_scale]*len(eval_locs) + [other_scale]*len(other_locs), + ), + bias=True, + normalized=False, + ) + mapie_ccp = SplitCPRegressor( + model, calibrator=calibrator_shifts, alpha=ALPHA, cv="prefit", + conformity_score=AbsoluteConformityScore(sym=False), + random_state=None + ) + mapie_ccp.conformity_score.eps = 1e-5 + mapie_ccp.fit(X_calib, y_calib) + _, y_pi_ccp = mapie_ccp.predict(X_test) + + # =========== n_trials run to get average marginal coverage ============ + if experiment == "Groups": + eval_functions = [ + lambda X, a=a, b=b: np.logical_and(X >= a, X <= b).astype(int) + for a, b in zip([1, 3], [2, 4]) + ] + eval_names = ["[1, 2]", "[3, 4]"] + else: + eval_functions = [ + lambda x: norm.pdf(x, loc=1.5, scale=0.2).reshape(-1, 1), + lambda x: norm.pdf(x, loc=3.5, scale=0.2).reshape(-1, 1) + ] + eval_names = ["f1", "f2"] + + marginal_cov = np.zeros((n_trials, 2)) + group_cov = np.zeros((len(eval_functions), n_trials, 2)) + for j in range(n_trials): + marginal_cov[j], group_cov[:, j, :] = estimate_coverage( + mapie_split, mapie_ccp, eval_functions + ) + + coverageData = pd.DataFrame() + + for group, cov in zip(["Marginal"]+eval_names, + [marginal_cov] + list(group_cov)): + for i, name in enumerate(["Split", "CCP"]): + coverageData = pd.concat( + [coverageData, + pd.DataFrame({'Method': [name] * len(cov), + 'Range': [group] * len(cov), + 'Miscoverage': np.asarray(cov)[:, i]})], + axis=0 + ) + + # ================== results plotting ================== + cp = plt.get_cmap('tab10').colors + + # Set font and style + plt.rcParams['font.family'] = 'DejaVu Sans' + plt.rcParams['axes.grid'] = False + + fig = plt.figure() + fig.set_size_inches(17, 6) + + sort_order = np.argsort(X_test[:, 0]) + x_test_s = X_test[sort_order] + y_test_s = y_test[sort_order] + y_pred_s = model.predict(x_test_s) + + ax1 = fig.add_subplot(1, 3, 1) + ax1.plot(x_test_s, y_test_s, '.', alpha=0.2) + ax1.plot(x_test_s, y_pred_s, lw=1, color='k') + ax1.plot(x_test_s, y_pi_split[sort_order, 0, 0], color=cp[0], lw=2) + ax1.plot(x_test_s, y_pi_split[sort_order, 1, 0], color=cp[0], lw=2) + ax1.fill_between(x_test_s.flatten(), y_pi_split[sort_order, 0, 0], + y_pi_split[sort_order, 1, 0], + color=cp[0], alpha=0.4, label='split prediction interval') + ax1.set_ylim(-2, 6.5) + ax1.tick_params(axis='both', which='major', labelsize=14) + ax1.set_xlabel("$X$", fontsize=16, labelpad=10) + ax1.set_ylabel("$Y$", fontsize=16, labelpad=10) + ax1.set_title("Split calibration", fontsize=18, pad=12) + + if experiment == 'Groups': + ax1.axvspan(1, 2, facecolor='grey', alpha=0.25) + ax1.axvspan(3, 4, facecolor='grey', alpha=0.25) + else: + for loc in eval_locs: + ax1.plot(x_test_s, norm.pdf(x_test_s, loc=loc, scale=eval_scale), + color='grey', ls='--', lw=3) + + ax2 = fig.add_subplot(1, 3, 2, sharex=ax1, sharey=ax1) + ax2.plot(x_test_s, y_test_s, '.', alpha=0.2) + ax2.plot(x_test_s, y_pred_s, color='k', lw=1) + ax2.plot(x_test_s, y_pi_ccp[sort_order, 0, 0], color=cp[1], lw=2) + ax2.plot(x_test_s, y_pi_ccp[sort_order, 1, 0], color=cp[1], lw=2) + ax2.fill_between(x_test_s.flatten(), y_pi_ccp[sort_order, 0, 0], + y_pi_ccp[sort_order, 1, 0], color=cp[1], alpha=0.4, + label='conditional calibration') + ax2.tick_params(axis='both', which='major', direction='out', labelsize=14) + ax2.set_xlabel("$X$", fontsize=16, labelpad=10) + ax2.set_ylabel("$Y$", fontsize=16, labelpad=10) + ax2.set_title("Conditional calibration", fontsize=18, pad=12) + + if experiment == 'Groups': + ax2.axvspan(1, 2, facecolor='grey', alpha=0.25) + ax2.axvspan(3, 4, facecolor='grey', alpha=0.25) + else: + for loc in eval_locs: + ax2.plot(x_test_s, norm.pdf(x_test_s, loc=loc, scale=eval_scale), + color='grey', ls='--', lw=3) + + ax3 = fig.add_subplot(1, 3, 3) + + ranges = coverageData['Range'].unique() + methods = coverageData['Method'].unique() + bar_width = 0.8 / len(methods) + for i, method in enumerate(methods): + method_data = coverageData[coverageData['Method'] == method] + x = np.arange(len(ranges)) + i * bar_width + ax3.bar(x, method_data.groupby("Range")['Miscoverage'].mean(), + width=bar_width, label=method, color=cp[i]) + + ax3.set_xticks(np.arange(len(ranges)) + bar_width * (len(methods) - 1) / 2) + ax3.set_xticklabels(ranges) + + ax3.axhline(0.1, color='red') + ax3.legend() + ax3.set_ylabel("Miscoverage", fontsize=18, labelpad=10) + ax3.set_xlabel(experiment, fontsize=18, labelpad=10) + ax3.set_ylim(0., 0.2) + ax3.tick_params(axis='both', which='major', labelsize=14) + + plt.tight_layout(pad=2) + plt.show() + + +############################################################################## +# 5. Reproduce experiment and results +# ----------------------------------------------------------------------------- + +plot_results(X_test, y_test, 20, experiment="Groups") + +plot_results(X_test, y_test, 20, experiment="Shifts") + + +############################################################################## +# We succesfully reproduced the experiement of the Gibbs et al. paper [1]. diff --git a/examples/regression/4-tutorials/plot_ccp_tutorial.py b/examples/regression/4-tutorials/plot_ccp_tutorial.py new file mode 100644 index 000000000..b2a556c8e --- /dev/null +++ b/examples/regression/4-tutorials/plot_ccp_tutorial.py @@ -0,0 +1,682 @@ +""" +============================================ +Tutorial: Conditional CP for regression +============================================ + +The tutorial will explain how to use the CCP method, and +will compare it with the other methods available in MAPIE. The CCP method +implements the method described in the Gibbs et al. (2023) paper [1]. + +We will see in this tutorial how to use the method. It has a lot of advantages: + +- It is model agnostic (it doesn't depend on the model but only on the + predictions, unlike `CQR`) +- It can create very adaptative intervals (with a varying width which truly + reflects the model uncertainty) +- while providing coverage guarantee on all sub-groups of interest + (avoiding biases) +- with the possibility to inject prior knowledge about the data or the model + +However, we will also see its disadvantages: + +- The adaptativity depends on the calibrator we use: It can be difficult to + choose the correct calibrator, + with the best parameters (this tutorial will try to help you with this task). +- The calibration and even more the inference are much longer than for the + other methods. We can reduce the inference time using + ``unsafe_approximation=True``, but we lose the strong theoretical guarantees + and risk a small miscoverage + (even if, most of the time, the coverage is achieved). + +Conclusion on the method: + +It can create more adaptative intervals than the other methods, but it can be +difficult to find the best settings (calibrator type and parameters) +and can have a big computational time. + +---- + +In this tutorial, we will use a synthetic toy dataset. +The estimator will be :class:`~sklearn.pipeline.Pipeline` +with :class:`~sklearn.preprocessing.PolynomialFeatures` and +:class:`~sklearn.linear_model.LinearRegression` (or +:class:`~sklearn.linear_model.QuantileRegressor` for CQR). + +We will compare the different available calibrators ( +:class:`~mapie.future.calibrators.ccp.CustomCCP`, +:class:`~mapie.future.calibrators.ccp.GaussianCCP` +and :class:`~mapie.future.calibrators.ccp.PolynomialCCP`) of the CCP method +(using :class:`~mapie.future.split.SplitCPRegressor`), with the +standard split-conformal method, the CV+ method +(:class:`~mapie.regression.MapieRegressor`) and CQR +(:class:`~mapie.regression.MapieQuantileRegressor`) + +Recall that the ``alpha`` is ``1 - target coverage``. + +Warning: + +In this tutorial, we use ``unsafe_approximation=True`` to have a faster +computation (because Read The Docs examples require fast computation). +This mode use an approximation, which make the inference (``predict``) faster, +but induce a small miscoverage. It is recommanded not to use it, or be +very careful and empirically check the coverage and a test set. + +[1] Isaac Gibbs, John J. Cherian, and Emmanuel J. Candès, +"Conformal Prediction With Conditional Guarantees", +`arXiv `_, 2023. +""" + +import warnings + +import matplotlib.colors as mcolors +import matplotlib.pyplot as plt +import numpy as np +from scipy.stats import norm +from sklearn.linear_model import LinearRegression, QuantileRegressor +from sklearn.model_selection import ShuffleSplit +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import PolynomialFeatures + +from mapie.future.calibrators import CustomCCP, GaussianCCP, PolynomialCCP +from mapie.future.calibrators.ccp import CCPCalibrator +from mapie.future.split import SplitCPRegressor +from mapie.regression import MapieQuantileRegressor, MapieRegressor + +warnings.filterwarnings("ignore") + +random_state = 42 +np.random.seed(random_state) + +ALPHA = 0.1 +UNSAFE_APPROXIMATION = True + +############################################################################## +# 1. Data generation +# -------------------------------------------------------------------------- +# Let's start by creating some synthetic data with different domains and +# distributions to evaluate the adaptativity of the methods: +# - baseline distribution of ``x*sin(x)`` +# - Add noise : +# - between -1 and 0: uniform distribution of the points around the baseline +# - between 0 and 5: normal distribution with a noise value which +# increase with ``x`` +# +# We are going to use 5000 samples for training, 5000 for calibration and +# 5000 for testing. + + +def x_sinx(x): + """One-dimensional x*sin(x) function.""" + return x*np.sin(x) + + +def get_1d_data_with_heteroscedastic_noise( + funct, min_x, max_x, n_samples, noise, power +): + """ + Generate 1D noisy data uniformely from the given function + and standard deviation for the noise. + """ + X = np.linspace(min_x, max_x, n_samples) + np.random.shuffle(X) + y = ( + funct(X) + + (np.random.normal(0, noise, len(X)) * ((X)/max_x)**power*max_x) + + (np.random.uniform(-noise*3, noise*3, len(X))) * (X < 0) + ) + true_pi = np.hstack([x_sinx(X).reshape(-1, 1)]*2) + true_pi[X < 0, 0] += noise*3*(1-ALPHA) + true_pi[X < 0, 1] -= noise*3*(1-ALPHA) + true_pi[X >= 0, 0] += norm.ppf(1 - ALPHA/2) * noise * ( + ((X[X >= 0])/max_x)**power*max_x) + true_pi[X >= 0, 1] -= norm.ppf(1 - ALPHA/2) * noise * ( + ((X[X >= 0])/max_x)**power*max_x) + return X.reshape(-1, 1), y, true_pi + + +def generate_data(n_train=10000, n_test=5000, noise=0.8, power=2): + X, y, true_pi = get_1d_data_with_heteroscedastic_noise( + x_sinx, -1, 5, n_train + n_test, noise, power) + indexes = list(range(len(X))) + train_indexes = np.random.choice(indexes, n_train) + indexes = list(set(indexes) - set(train_indexes)) + test_indexes = np.random.choice(indexes, n_test) + return (X[train_indexes, :], y[train_indexes], + X[test_indexes, :], y[test_indexes], + true_pi[train_indexes, :], true_pi[test_indexes, :]) + + +X_train, y_train, X_test, y_test, train_pi, test_pi = generate_data() + + +############################################################################## +# Let's visualize the data and its distribution + +plt.scatter(X_train, y_train, color="C0", alpha=0.5, s=3, + label="Training data") +sort_order = np.argsort(X_train[:, 0]) +x_sorted = X_train[sort_order, :] +plt.plot(x_sorted, train_pi[sort_order, 0], "k--", + label=f"True interval (alpha={ALPHA})") +plt.plot(x_sorted, train_pi[sort_order, 1], "k--", linestyle='--') +plt.plot(x_sorted, x_sinx(x_sorted), "k-", label="baseline") +plt.xlabel("x") +plt.ylabel("y") +plt.title("Data") +plt.legend() +plt.show() + + +############################################################################## +# 2. Model: Polynomial regression +# -------------------------------------------------------------------------- + +polynomial_degree = 4 +quantile_estimator = Pipeline([ + ("poly", PolynomialFeatures(degree=polynomial_degree)), + ("linear", QuantileRegressor(solver="highs", alpha=0)) +]) +estimator = Pipeline([ + ("poly", PolynomialFeatures(degree=polynomial_degree)), + ("linear", LinearRegression()) +]) + + +############################################################################## +# 3. Plotting and adaptativity comparison functions +# -------------------------------------------------------------------------- + +def plot_subplot(ax, X, y, mapie, y_pred, upper_pi, lower_pi, color_rgb, + show_transform=False, ax_transform=None): + """ + Plot the prediction interval and calibrator's features of a mapie instance + """ + sort_order = np.argsort(X[:, 0]) + lw = 1 + color = mcolors.rgb2hex(color_rgb) + x_test_sorted = X[sort_order] + y_test_sorted = y[sort_order] + y_pred_sorted = y_pred[sort_order] + upper_pi_sorted = upper_pi[sort_order] + lower_pi_sorted = lower_pi[sort_order] + sample = np.random.choice(list(range(len(X))), min(4000, len(X))) + # Plot test data + ax.scatter(x_test_sorted[sample, 0], y_test_sorted[sample], s=1, alpha=0.3, + color='darkblue', label="Test Data") + # Plot prediction + ax.plot(x_test_sorted[:, 0], y_pred_sorted, lw=lw, + color='black', label="Prediction") + # Plot prediction interval + ax.fill_between(x_test_sorted[:, 0], upper_pi_sorted, lower_pi_sorted, + color=color, alpha=0.3, label="Prediction interval") + # Plot upper and lower prediction intervals + ax.plot(x_test_sorted[:, 0], upper_pi_sorted, lw=lw, color=color) + ax.plot(x_test_sorted[:, 0], lower_pi_sorted, lw=lw, color=color) + # Plot true prediction interval + ax.plot(x_test_sorted[:, 0], test_pi[sort_order, 0], "--k", + lw=lw*1.5, label='True Interval') + ax.plot(x_test_sorted[:, 0], test_pi[sort_order, 1], "--k", lw=lw*1.5) + + if ( + show_transform and isinstance(mapie, SplitCPRegressor) + and isinstance(mapie.calibrator_, CCPCalibrator) + ): + transform = mapie.calibrator_.transform(x_test_sorted)\ + * mapie.calibrator_.beta_up_[0] + for i in range(transform.shape[1]): + ax_transform.plot( + x_test_sorted[:, 0], + transform[:, i], + lw=lw, color=color + ) + + +def has_ccp_calibrator(mapie): + """ + Whether or not, the ``mapie`` instance has a ``CCPCalibrator`` calibrator + """ + if ( + not isinstance(mapie, SplitCPRegressor) + or not isinstance(mapie.calibrator_, CCPCalibrator) + ): + return False + for calibrator in list(mapie.calibrator_.functions_) + [mapie.calibrator_]: + if isinstance(calibrator, CCPCalibrator): + return True + return False + + +def plot_figure(mapies, y_preds, y_pis, titles, show_components=False): + """ + Plot the prediction interval of mapie instances. + Also plot the features of the calibrator, if ``show_transform=True`` + """ + cp = plt.get_cmap('tab10').colors + ncols = min(3, len(titles)) + nrows = int(np.ceil(len(titles) / ncols)) + ax_need_transform = np.zeros((nrows, ncols)) + if show_components: + for i, mapie in enumerate(mapies): + ax_need_transform[i//ncols, i % ncols] = has_ccp_calibrator(mapie) + row_need_transform = np.max(ax_need_transform, axis=1) + height_ratio = np.array([ + item for x in row_need_transform + for item in ([3] if x == 0 else [3, 1]) + ]) + fig, axes = plt.subplots( + nrows=nrows + int(sum(row_need_transform)), ncols=ncols, + figsize=(ncols*3.6, nrows*3.6 + int(sum(row_need_transform))*1.8), + height_ratios=height_ratio + ) + + transform_axes = np.full((nrows, ncols), None) + transform_axes[row_need_transform == 1, :] = axes[height_ratio == 1, :] + transform_axes = transform_axes.flatten() + main_axes = axes[height_ratio == 3, :].flatten() + else: + fig, axes = plt.subplots(nrows=nrows, ncols=ncols, + figsize=(ncols*4, nrows*4)) + main_axes = axes.flatten() + transform_axes = np.full(main_axes.shape, None) + + for i in range(len(mapies), len(main_axes)): + fig.delaxes(main_axes[i]) + if transform_axes[i] is not None: + fig.delaxes(transform_axes[i]) + + for i, (m_ax, t_ax, mapie, y_pred, y_pi, title) in enumerate( + zip(main_axes, transform_axes, mapies, y_preds, y_pis, titles) + ): + lower_bound = y_pi[:, 0, 0] + upper_bound = y_pi[:, 1, 0] + + plot_subplot( + m_ax, X_test, y_test, mapie, y_pred, upper_bound, lower_bound, + cp[i], show_transform=ax_need_transform.flatten()[i], + ax_transform=t_ax + ) + m_ax.set_title(title) + if i % 3 == 0: + m_ax.set_ylabel('Y') + if t_ax is not None: + t_ax.set_title("Components of the PI") + if i >= len(titles) - ncols: + t_ax.set_xlabel('X') + if i % 3 == 0: + t_ax.set_ylabel('component value') + m_ax.set_xlabel('X') + m_ax.legend() + + fig.tight_layout() + plt.show() + + +def compute_conditional_coverage(X_test, y_test, y_pis, bins_width=0.25): + """ + Compute the conditional coverage on ``X_test``, using discret bins + """ + bin_edges = np.arange(np.min(X_test), np.max(X_test) + bins_width, + bins_width) + coverage = np.zeros(len(bin_edges) - 1) + + for i in range(len(bin_edges) - 1): + in_bin = np.logical_and(X_test[:, 0] >= bin_edges[i], + X_test[:, 0] < bin_edges[i + 1]) + coverage[i] = np.mean(np.logical_and( + y_test[in_bin] >= y_pis[in_bin, 0, 0], + y_test[in_bin] <= y_pis[in_bin, 1, 0] + )) + + bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2 + return bin_centers, coverage + + +def plot_evaluation(titles, y_pis, X_test, y_test): + """ + Plot the conditional coverages + """ + sort_order = np.argsort(X_test[:, 0]) + cp = plt.get_cmap('tab10').colors + + num_plots = len(titles) + num_rows = (num_plots + 2) // 3 + + fig, axs = plt.subplots(nrows=num_rows, ncols=2, + figsize=(10, 3.7*num_rows)) + if len(axs.shape) == 1: + axs = axs.reshape(1, -1) + axs = axs.flatten() # Flatten to make indexing easier + + cov_lim = [1, 0] + width_lim = [np.inf, 0] + for i in range(num_rows): + for j, pi in enumerate(y_pis[3*i: 3*(i+1)]): + c = mcolors.rgb2hex(cp[i*3+j]) + # Conditionnal coverage + bin_centers, coverage = compute_conditional_coverage( + X_test, y_test, pi + ) + axs[i * 2].plot(bin_centers, coverage, lw=2, color=c) + axs[i * 2].axhline( + y=np.mean(coverage), color=c, linestyle="--", + label=f"Coverage={round(np.mean(coverage)*100, 1)}%" + ) + axs[i * 2].axhline( + y=1-ALPHA, color='black', linestyle="--", + label=( + f"alpha={ALPHA}" if j == len(y_pis[3*i: 3*(i+1)]) - 1 + else None + ) + ) + cov_lim[0] = min(cov_lim[0], min(coverage)) + cov_lim[1] = max(cov_lim[1], max(coverage)) + # Interval width + width = pi[sort_order, 1, 0] - pi[sort_order, 0, 0] + axs[i * 2 + 1].plot( + X_test[sort_order, 0], + width, + lw=2, color=c, label=titles[i*3+j] + ) + + width_lim[0] = min(width_lim[0], min(width)) + width_lim[1] = max(width_lim[1], max(width)) + perfect_width = test_pi[sort_order, 0] - test_pi[sort_order, 1] + axs[i * 2 + 1].plot( + X_test[sort_order, 0], + perfect_width, + lw=2, color='black', linestyle="--", label="Perfect Width" + ) + width_lim[0] = min(width_lim[0], min(perfect_width)) + width_lim[1] = max(width_lim[1], max(perfect_width)) + + axs[i * 2 + 1].legend(fontsize=10) + axs[i * 2 + 1].set_title("Prediction Interval Width") + axs[i * 2 + 1].set_xlabel("X") + axs[i * 2 + 1].set_ylabel("Width") + axs[i * 2].legend(fontsize=10) + axs[i * 2].set_title("Conditional Coverage") + axs[i * 2].set_xlabel("X (bins of 0.5 width)") + axs[i * 2].set_ylabel("Coverage") + + # Remove unused subplots + for j in range(num_plots * 2, len(axs)): + fig.delaxes(axs[j]) + + for ax_cov, ax_width in zip(axs[::2], axs[1::2]): + ax_cov.set_ylim([cov_lim[0]*0.95, cov_lim[1]*1.05]) + ax_width.set_ylim([width_lim[0]*0.95, width_lim[1]*1.05]) + + plt.tight_layout() + plt.show() + + +############################################################################## +# 4. Creation of Mapie instances +# -------------------------------------------------------------------------- +# We are going to test different methods : ``CV+``, ``CQR`` and ``CCP`` +# (with default parameters) + +cv = ShuffleSplit(n_splits=1, test_size=0.5, random_state=random_state) + +# ================== Basic Split-conformal ================== +mapie_split = MapieRegressor(estimator, method="base", cv=cv) +mapie_split.fit(X_train, y_train) +y_pred_split, y_pi_split = mapie_split.predict(X_test, alpha=ALPHA) + +# ================== CV+ ================== +# MapieRegressor defaults to method='plus' and cv=5 +mapie_cv = MapieRegressor(estimator) +mapie_cv.fit(X_train, y_train) +y_pred_cv, y_pi_cv = mapie_cv.predict(X_test, alpha=ALPHA) + +# ================== CQR ================== +mapie_cqr = MapieQuantileRegressor(quantile_estimator, alpha=ALPHA) +mapie_cqr.fit(X_train, y_train) +y_pred_cqr, y_pi_cqr = mapie_cqr.predict(X_test) + +# ================== CCP ================== +# `SplitCPRegressor` defaults to `calibrator=GaussianCCP()`` +mapie_ccp = SplitCPRegressor(estimator, calibrator=GaussianCCP(), + alpha=ALPHA, cv=cv) +mapie_ccp.fit(X_train, y_train) +y_pred_ccp, y_pi_ccp = mapie_ccp.predict( + X_test, unsafe_approximation=UNSAFE_APPROXIMATION +) + +# ================== PLOT ================== +mapies = [mapie_split, mapie_cv, mapie_cqr, mapie_ccp] +y_preds = [y_pred_split, y_pred_cv, y_pred_cqr, y_pred_ccp] +y_pis = [y_pi_split, y_pi_cv, y_pi_cqr, y_pi_ccp] +titles = ["Basic Split", "CV+", "CQR", "CCP (default)"] + +plot_figure(mapies, y_preds, y_pis, titles) +plot_evaluation(titles, y_pis, X_test, y_test) + + +############################################################################## +# The :class:`~mapie.future.split.regression.SplitCPRegressor` has is +# a very adaptative method, even with default +# parameters values. If the dataset is more complex, the default parameters +# may not be enough to get the best performances. In this case, we can use +# more advanced settings, described below. + + +############################################################################## +# 5. How to improve the results? +# -------------------------------------------------------------------------- +# +# 5.1. How does the ``CCP`` method works ? +# -------------------------------------------------------------------------- +# The CCP method is based on a function which create some features(vector of +# d dimensions), based on ``X`` (and potentially the prediction ``y_pred``). +# +# These features should be able to represente the distribuion of the +# conformity scores, which is here (by default) the absolute residual: +# ``|y_true - y_pred|`` + +############################################################################## +# Examples of basic functions: +# -------------------------------------------------------------------------- +# +############################################################################## + +############################################################################## +# 1) ``f : X -> (1)``, will try to estimate the absolute residual with a +# constant, and will results in a prediction interval of constant width +# (like the basic split CP) +# +# 2) ``f : X -> (1, X)``, will result in a prediction interval of width +# equal to: a constant + a value proportional to the value of ``X`` +# (it seems a good idea here, as the uncertainty increase with ``X``) +# +# 3) ``f : X, y_pred -> (y_pred)``, will result in a prediction interval +# of width proportional to the prediction (Like the basic split CP with a +# gamma conformity score). + + +############################################################################## +# Using custom definition +# -------------------------------------------------------------------------- +# +############################################################################## + + +calibrator1 = CustomCCP([lambda X: np.ones(len(X))]) +calibrator1_bis = CustomCCP(bias=True) +# calibrator1_bis is equivalent to calibrator1, +# as bias=True adds a column of ones +calibrator2 = CustomCCP([lambda X: X], bias=True) +calibrator3 = CustomCCP([lambda y_pred: y_pred]) + + +############################################################################## +# Or using :class:`~mapie.future.calibrators.ccp.PolynomialCCP` class: +# -------------------------------------------------------------------------- +# +############################################################################## + +calibrator1 = PolynomialCCP(0) +calibrator2 = PolynomialCCP(1) # degree=1 is equivalent to degree=[0, 1] +calibrator3 = PolynomialCCP([1], variable="y_pred") +# Note: adding '0' in the 'degree' argument list +# is equivalent tohaving bias=True, as X^0=1 + + +############################################################################## +# 5.2. Improve the performances without prior knowledge: :class:`GaussianCCP` +# -------------------------------------------------------------------------- +# If we don't know anything about the data, we can use +# :class:`~mapie.future.calibrators.ccp.GaussianCCP`, +# which will sample random points, and apply gaussian kernels +# with a given standard deviation ``sigma``. +# +# Basically, the conformity score of a given point ``x_test``, +# will be estimated based on the conformity scores +# of calibration samples which are closed to ``x_test``. +# It result in a globally good adaptativity. +# +# The ``sigma`` hyperparameter can be optimized using cross-validation. +# It is defined by default based on the standard deviaiton of ``X``. + +calibrator_gauss1 = GaussianCCP(np.arange(-1, 6).reshape(-1, 1), 1) +calibrator_gauss2 = GaussianCCP(30, 0.05) +calibrator_gauss3 = GaussianCCP(30, 0.25, random_sigma=True) + +# # ================== CCP 1 ================== +mapie_ccp_1 = SplitCPRegressor(estimator, calibrator=calibrator_gauss1, + cv=cv, alpha=ALPHA) +mapie_ccp_1.fit(X_train, y_train) +y_pred_ccp_1, y_pi_ccp_1 = mapie_ccp_1.predict( + X_test, unsafe_approximation=UNSAFE_APPROXIMATION +) + +# # ================== CCP 2 ================== +mapie_ccp_2 = SplitCPRegressor(estimator, calibrator=calibrator_gauss2, + cv=cv, alpha=ALPHA) +mapie_ccp_2.fit(X_train, y_train) +y_pred_ccp_2, y_pi_ccp_2 = mapie_ccp_2.predict( + X_test, unsafe_approximation=UNSAFE_APPROXIMATION +) + +# # ================== CCP 3 ================== +mapie_ccp_3 = SplitCPRegressor(estimator, calibrator=calibrator_gauss3, + cv=cv, alpha=ALPHA) +mapie_ccp_3.fit(X_train, y_train) +y_pred_ccp_3, y_pi_ccp_3 = mapie_ccp_3.predict( + X_test, unsafe_approximation=UNSAFE_APPROXIMATION +) + + +mapies = [mapie_split, mapie_cv, mapie_cqr, + mapie_ccp_1, mapie_ccp_2, mapie_ccp_3] +y_preds = [y_pred_split, y_pred_cv, y_pred_cqr, + y_pred_ccp_1, y_pred_ccp_2, y_pred_ccp_3] +y_pis = [y_pi_split, y_pi_cv, y_pi_cqr, + y_pi_ccp_1, y_pi_ccp_2, y_pi_ccp_3] +titles = ["Basic Split", "CV+", "CQR", + "CCP 1: 6 points, s=1 (under-fit)", + "CCP 2: 30 points, s=0.05 (over-fit)", + "CCP 3: 30 points, s=0.25 (good calibrator)"] + +plot_figure(mapies, y_preds, y_pis, titles, show_components=True) +plot_evaluation(titles, y_pis, X_test, y_test) + +############################################################################## +# --> Using gaussian distances (with correct sigma value) from randomly +# sampled points is a good solution to have an overall good adaptativity. + +############################################################################## +# 5.3. Improve the performances using what we know about the data +# -------------------------------------------------------------------------- +# To improve the results, we need to analyse the data +# and the conformity scores we chose (here, the absolute residuals). +# +# 1) We can see that the residuals (error with the prediction) +# increase with X, for X > 0. +# +# 2) For X < 0, the points seem uniformly distributed around +# the base distribution. +# +# --> It should be a good idea to inject in the calibrator the two groups +# ( X < 0 and X > 0). We can use on each group +# :class:`~mapie.future.calibrators.ccp.GaussianCCP` +# (or :class:`~mapie.future.calibrators.ccp.PolynomialCCP`, +# as it seems adapted in this example) + +calibrator1 = CustomCCP( + [lambda X: X < 0, (lambda X: X >= 0)*PolynomialCCP(3)] +) +calibrator2 = CustomCCP( + [ + (lambda X: X < 0)*PolynomialCCP(3), + (lambda X: X >= 0)*PolynomialCCP(3) + ] +) +calibrator3 = CustomCCP( + [ + (lambda X: X < 0)*GaussianCCP(5), + (lambda X: X >= 0)*GaussianCCP(30) + ], + normalized=True, +) + +# ================== CCP 1 ================== +mapie_ccp_1 = SplitCPRegressor(estimator, calibrator=calibrator1, + cv=cv, alpha=ALPHA) +mapie_ccp_1.fit(X_train, y_train) +y_pred_ccp_1, y_pi_ccp_1 = mapie_ccp_1.predict( + X_test, unsafe_approximation=UNSAFE_APPROXIMATION +) + +# ================== CCP 2 ================== +mapie_ccp_2 = SplitCPRegressor(estimator, calibrator=calibrator2, + cv=cv, alpha=ALPHA) +mapie_ccp_2.fit(X_train, y_train) +y_pred_ccp_2, y_pi_ccp_2 = mapie_ccp_2.predict( + X_test, unsafe_approximation=UNSAFE_APPROXIMATION +) + +# ================== CCP 3 ================== +mapie_ccp_3 = SplitCPRegressor(estimator, calibrator=calibrator3, + cv=cv, alpha=ALPHA) +mapie_ccp_3.fit(X_train, y_train) +y_pred_ccp_3, y_pi_ccp_3 = mapie_ccp_3.predict( + X_test, unsafe_approximation=UNSAFE_APPROXIMATION +) + +mapies = [mapie_split, mapie_cv, mapie_cqr, + mapie_ccp_1, mapie_ccp_2, mapie_ccp_3] +y_preds = [y_pred_split, y_pred_cv, y_pred_cqr, + y_pred_ccp_1, y_pred_ccp_2, y_pred_ccp_3] +y_pis = [y_pi_split, y_pi_cv, y_pi_cqr, + y_pi_ccp_1, y_pi_ccp_2, y_pi_ccp_3] +titles = ["Basic Split", "CV+", "CQR", + "CCP 1: const (X<0) / poly (X>0)", + "CCP 2: poly (X<0) / poly (X>0)", + "CCP: gauss (X<0) / gauss (X>0)"] + + +plot_figure(mapies, y_preds, y_pis, titles, show_components=True) +plot_evaluation(titles, y_pis, X_test, y_test) + +############################################################################## +# 6. Conclusion: +# -------------------------------------------------------------------------- +# The goal is to get prediction intervals which are the most adaptative +# possible. Perfect adaptativity whould result in a perfectly constant +# conditional coverage. +# +# Considering this adaptativity criteria, the most adaptative interval is +# this last brown one, with the two groups +# and the gaussian calibrators. In this example, the polynomial +# calibrator (in purple) also worked well, but the gaussian one is more generic +# (It usually work with any dataset, assuming we use the correct parameters, +# whereas the polynomial features are not always adapted). +# +# This is the power of the ``CCP`` method: combining prior knowledge and +# generic features (gaussian kernelsl) to have a great overall adaptativity. +# +# However, it can be difficult to find the best calibrator and parameters. +# Sometimes, a simpler method (standard ``split`` with ``GammaConformityScore`` +# for example) can be enough. Don't forget to try at first the simpler method, +# and move on with the more advanced if it is necessary. diff --git a/mapie/conformity_scores/sets/utils.py b/mapie/conformity_scores/sets/utils.py index 5912607fb..0833d8ec8 100644 --- a/mapie/conformity_scores/sets/utils.py +++ b/mapie/conformity_scores/sets/utils.py @@ -124,12 +124,15 @@ def get_last_index_included( y_pred_proba_cumsum: NDArray of shape (n_samples, n_classes) Cumsumed probabilities in the original order. - threshold: NDArray of shape (n_alpha,) or shape (n_samples_train,) + threshold: NDArray of shape (n_alpha,), (n_samples, n_alpha) + or (n_samples_train,) Threshold to compare with y_proba_last_cumsum, can be either: - the quantiles associated with alpha values when ``cv`` == "prefit", ``cv`` == "split" or ``agg_scores`` is "mean" + (or a quantile value for each sample, + with shape (n_samples, n_alpha)) - the conformity score from training samples otherwise (i.e., when ``cv`` is a CV splitter and @@ -144,17 +147,22 @@ def get_last_index_included( NDArray of shape (n_samples, n_alpha) Index of the last included sorted probability. """ + if len(threshold.shape) == 1: + formatted_threshold = threshold[np.newaxis, :] + else: + formatted_threshold = threshold + if include_last_label or include_last_label == 'randomized': y_pred_index_last = ( np.ma.masked_less( y_pred_proba_cumsum - - threshold[np.newaxis, :], + - formatted_threshold, -EPSILON ).argmin(axis=1) ) else: max_threshold = np.maximum( - threshold[np.newaxis, :], + formatted_threshold, np.min(y_pred_proba_cumsum, axis=1) ) y_pred_index_last = np.argmax( diff --git a/mapie/future/__init__.py b/mapie/future/__init__.py new file mode 100644 index 000000000..c7a8ed6ce --- /dev/null +++ b/mapie/future/__init__.py @@ -0,0 +1,11 @@ +from .split import SplitCPRegressor, SplitCPClassifier +from .calibrators import CustomCCP, GaussianCCP, PolynomialCCP + + +__all__ = [ + "SplitCPRegressor", + "SplitCPClassifier", + "CustomCCP", + "PolynomialCCP", + "GaussianCCP", +] diff --git a/mapie/future/calibrators/__init__.py b/mapie/future/calibrators/__init__.py new file mode 100644 index 000000000..f2183d87e --- /dev/null +++ b/mapie/future/calibrators/__init__.py @@ -0,0 +1,9 @@ +from .ccp import CustomCCP, GaussianCCP, PolynomialCCP +from .standard import StandardCalibrator + +__all__ = [ + "StandardCalibrator", + "CustomCCP", + "PolynomialCCP", + "GaussianCCP", +] diff --git a/mapie/future/calibrators/base.py b/mapie/future/calibrators/base.py new file mode 100644 index 000000000..9d609a112 --- /dev/null +++ b/mapie/future/calibrators/base.py @@ -0,0 +1,88 @@ +from __future__ import annotations + +from abc import ABCMeta, abstractmethod +from typing import List, Optional + +from sklearn.base import BaseEstimator + +from mapie._typing import ArrayLike, NDArray + + +class BaseCalibrator(BaseEstimator, metaclass=ABCMeta): + """ + Base abstract class for the calibrators used in ``SplitCPRegressor`` + or ``SplitCPClassifier`` to estimate the conformity scores. + + The ``BaseCalibrator`` subclasses should have at least two methods: + + - ``fit`` : Fit the calibrator to estimate the conformity scores + quantiles. + + - ``predict`` : Predict the conformity score quantiles. + + Attributes + ---------- + fit_attributes: Optional[List[str]] + Name of attributes set during the ``fit`` method, and required to call + ``predict``. + """ + + fit_attributes: List[str] + sym: bool + alpha: Optional[float] + random_state: Optional[int] + + @abstractmethod + def fit( + self, + X_calib: ArrayLike, + conformity_scores_calib: NDArray, + **kwargs, + ) -> BaseCalibrator: + """ + Fit the calibrator to estimate the conformity scores + quantiles. The method can take as arguments any of : + ``X, y, sample_weight, groups, y_pred_calib, conformity_scores_calib, + X_train, y_train, z_train, sample_weight_train, train_index, + X_calib, y_calib, z_calib, sample_weight_calib, calib_index``, + any attributes of the ``SplitCP`` instance, + or any other argument, which the user will have to pass as + ``**calib_kwargs``. + + Parameters + ---------- + X_calib: ArrayLike of shape (n_samples, n_features) + Calibration data. + + conformity_scores_calib: ArrayLike of shape (n_samples,) + Calibration conformity scores + + Returns + ------- + BaseCalibrator + Fitted self + """ + + @abstractmethod + def predict( + self, + X: ArrayLike, + **kwargs, + ) -> NDArray: + """ + Predict the conformity score quantiles. + The method can take as arguments any of : ``X, y_pred``, + any attributes of the ``SplitCP`` instance, + or any other argument, which the user will have to pass as + ``**kwargs``. + + Parameters + ---------- + X : ArrayLike of shape (n_samples, n_features) + Observed samples + + Returns + ------- + NDArray of shape (n_samples,) + Prediction + """ diff --git a/mapie/future/calibrators/ccp/__init__.py b/mapie/future/calibrators/ccp/__init__.py new file mode 100644 index 000000000..c278032e3 --- /dev/null +++ b/mapie/future/calibrators/ccp/__init__.py @@ -0,0 +1,11 @@ +from .base import CCPCalibrator +from .custom import CustomCCP +from .gaussian import GaussianCCP +from .polynomial import PolynomialCCP + +__all__ = [ + "CCPCalibrator", + "CustomCCP", + "PolynomialCCP", + "GaussianCCP", +] diff --git a/mapie/future/calibrators/ccp/base.py b/mapie/future/calibrators/ccp/base.py new file mode 100644 index 000000000..a93d151ce --- /dev/null +++ b/mapie/future/calibrators/ccp/base.py @@ -0,0 +1,694 @@ +from __future__ import annotations + +import warnings +from abc import ABCMeta, abstractmethod +from typing import Callable, Iterable, List, Optional, Tuple, Union, cast + +import numpy as np +from scipy.optimize import minimize, OptimizeResult +from sklearn.base import clone +from sklearn.utils import _safe_indexing +from sklearn.utils.validation import _num_samples, check_is_fitted + +from mapie._typing import ArrayLike, NDArray +from mapie.future.calibrators.base import BaseCalibrator +from mapie.future.calibrators.ccp.utils import ( + calibrator_optim_objective, check_multiplier, + check_custom_calibrator_functions, concatenate_functions, + check_required_arguments, dynamic_arguments_call +) + + +class CCPCalibrator(BaseCalibrator, metaclass=ABCMeta): + """ + Base abstract class for the calibrators used in + :class:`~mapie.future.split.SplitCPRegressor` or + :class:`~mapie.future.split.SplitCPClassifier` + to estimate the conformity scores. + It corresponds to the adaptative conformal prediction method proposed by + Gibbs et al. (2023) in "Conformal Prediction With Conditional Guarantees". + + The goal is to learn the quantile of the conformity scores distribution, + to built the prediction interval, not with a constant ``q`` (as it is the + case in the standard CP), but with a function ``q(X)`` which is adaptative + as it depends on ``X``. + + See the examples and the documentation to build a + :class:`~mapie.future.calibrators.ccp.CCPCalibrator` + adaptated to your dataset and constraints. + + Parameters + ---------- + functions: Optional[Union[Callable, Iterable[Callable]]] + List of functions (or + :class:`~mapie.future.calibrators.ccp.CCPCalibrator` objects) + or single function. + + Each function can take a combinaison of the following arguments: + + - ``X``: Input dataset, of shape (n_samples, ``n_in``) + - ``y_pred``: estimator prediction, of shape (n_samples,) + - ``z``: exogenous variable, of shape (n_samples, n_features). + It should be given in the ``fit`` and ``predict`` methods. + + The results of each functions will be concatenated to build the final + result of the transformation, of shape ``(n_samples, n_out)``, which + will be used to estimate the conformity scores quantiles. + + By default ``None``. + + bias: bool + Add a column of ones to the features, + (to make sure that the marginal coverage is guaranteed). + If the ``CCPCalibrator`` object definition covers all the dataset + (meaning, for all calibration and test samples, the resulting + ``calibrator.predict(X, y_pred, z)`` is never all zeros), + this column of ones is not necessary to obtain marginal coverage. + In this case, you can set this argument to ``False``. + + If you are not sure, use ``bias=True`` to garantee the marginal + coverage. + + By default ``False``. + + normalized: bool + Whether or not to normalized the resulting + ``calibrator.predict(X, y_pred, z)``. Normalization + will result in a bounded interval prediction width, avoiding the width + to explode to +inf or crash to zero. It is particularly intersting when + you know that the conformity scores are bounded. It also prevent the + interval to have a width of zero for out-of-distribution samples. + On the opposite, it is not recommended if the conformity + scores can vary a lot. + + By default ``False`` + + init_value: Optional[ArrayLike] + Optimization initialisation value. + If ``None``, the initial vector is sampled from a normal distribution. + + By default ``None``. + + reg_param: Optional[float] + Float to monitor the ridge regularization + strength. ``reg_param`` must be a non-negative + float i.e. in ``[0, inf)``. + + .. warning:: + A too strong regularization may compromise the guaranteed + marginal coverage. If ``calibrator.normalize=True``, it is usually + recommanded to use ``reg_param < 1e-3``. + + If ``None``, no regularization is used. + + By default ``None``. + + Attributes + ---------- + transform_attributes: Optional[List[str]] + Name of attributes set during the ``fit`` method, and required to call + ``transform``. + + fit_attributes: Optional[List[str]] + Name of attributes set during the ``fit`` method, and required to call + ``predict``. + + n_in: int + Number of features of ``X`` + + n_out: int + Number of features of ``calibrator.transform(X, y_pred, z)`` + + beta_up_: Tuple[NDArray, bool] + Calibration fitting results, used to build the upper bound of the + prediction intervals. + beta_up_[0]: Array of shape (calibrator.n_out, ) + beta_up_[1]: Whether the optimization process converged or not + (cover is not guaranteed if the optimisation has failed) + + beta_low_: Tuple[NDArray, bool] + Same as ``beta_up_``, but for the lower bound + + References + ---------- + Isaac Gibbs and John J. Cherian and Emmanuel J. Candès. + "Conformal Prediction With Conditional Guarantees", 2023 + """ + transform_attributes: List[str] = ["functions_"] + fit_attributes: List[str] = ["beta_up_", "beta_low_"] + + def __init__( + self, + functions: Optional[Union[Callable, Iterable[Callable]]] = None, + bias: bool = False, + normalized: bool = False, + init_value: Optional[ArrayLike] = None, + reg_param: Optional[float] = None, + ) -> None: + self.functions = functions + self.bias = bias + self.normalized = normalized + self.init_value = init_value + self.reg_param = reg_param + + self._multipliers: Optional[List[Callable]] = None + + @abstractmethod + def _check_transform_parameters( + self, + X: ArrayLike, + y_pred: Optional[ArrayLike] = None, + z: Optional[ArrayLike] = None, + ) -> None: + """ + Check the parameters required to call ``transform``. + In particular, check that the ``functions`` + attribute is valid and set the ``functions_`` argument. + + Parameters + ---------- + X: ArrayLike of shape (n_samples, n_features) + Training data. + + y: ArrayLike of shape (n_samples,) + Training labels. + + By default ``None`` + + z: Optional[ArrayLike] of shape (n_calib_samples, n_exog_features) + Exogenous variables + + By default ``None`` + """ + + def _check_init_value( + self, init_value: Optional[ArrayLike], n_out: int + ) -> ArrayLike: + """ + Set the ``init_value_`` attribute depending on ``init_value`` argument. + If ``init_value = None``, ``init_value_`` is set to + ``np.random.normal(0, 1, n_out)``. + + Parameters + ---------- + init_value : Optional[ArrayLike] + Optimization initialisation value, set at ``CCPCalibrator`` + initialisation. + + n_out : int + Number of dimensions of the ``CCPCalibrator`` transformation. + + Returns + ------- + ArrayLike + Optimization initialisation value + """ + if init_value is None: + return np.random.normal(0, 1, n_out) + else: + return init_value + + def _check_optimization_success( + self, *optimization_results: OptimizeResult + ) -> None: + """ + Check that all the ``optimization_results`` have successfully + converged. + + Parameters + ---------- + *optimization_resutls: OptimizeResult + Scipy optimization outputs + """ + for res in optimization_results: + if not res.success: + warnings.warn( + "WARNING: The optimization process " + f"failed with the following error: \n" + f"{res.message}\n" + "The returned prediction interval may be inaccurate." + ) + + def _transform_params( + self, + X: ArrayLike, + y_pred: Optional[ArrayLike] = None, + z: Optional[ArrayLike] = None, + ) -> CCPCalibrator: + """ + Set all the necessary attributes to be able to transform + ``(X, y_pred, z)`` into the expected array of features. + + It should set all the attributes of ``transform_attributes`` + (i.e. ``functions_``). It should also set, once fitted, ``n_in``, + ``n_out`` and ``init_value_``. + + Parameters + ---------- + X: ArrayLike of shape (n_samples, n_features) + Training data. + + y_pred: ArrayLike of shape (n_samples,) + Training labels. + + By default ``None`` + + z: Optional[ArrayLike] of shape (n_calib_samples, n_exog_features) + Exogenous variables + + By default ``None`` + """ + # Fit the calibrator + self._check_transform_parameters(X, y_pred, z) + # Do some checks + check_multiplier(self._multipliers, X, y_pred, z) + result = self.transform(X, y_pred, z) + + self.n_in = len(_safe_indexing(X, 0)) + self.n_out = len(_safe_indexing(result, 0)) + self.init_value_ = self._check_init_value(self.init_value, self.n_out) + return self + + def fit( + self, + X_calib: ArrayLike, + conformity_scores_calib: NDArray, + y_pred_calib: Optional[ArrayLike] = None, + z_calib: Optional[ArrayLike] = None, + **optim_kwargs, + ) -> CCPCalibrator: + """ + Fit the calibrator. It should set all the ``transform_attributes`` + and ``fit_attributes``. + + Parameters + ---------- + X_calib: ArrayLike of shape (n_samples, n_features) + Calibration data with not-null weights. + + conformity_scores_calib: ArrayLike of shape (n_samples,) + Calibration conformity scores with not-null weights. + + y_pred_calib: ArrayLike of shape (n_samples,) + Calibration target with not-null weights. + + z_calib: Optional[ArrayLike] of shape + (n_calib_samples, n_exog_features) + Exogenous variables with not-null weights. + + By default ``None``. + + optim_kwargs: Dict + Other argument, used in sklear.optimize.minimize. + Can be any of : ``method, jac, hess, hessp, bounds, constraints, + tol, callback, options`` + + By default, we use ``method='SLSQP'`` and + ``options={'maxiter: 1000}``. + """ + check_required_arguments(self.alpha) + self.alpha = cast(float, self.alpha) + + if self.sym: + q = 1 - self.alpha + else: + q = 1 - self.alpha / 2 + + if self.random_state is not None: + np.random.seed(self.random_state) + + self._transform_params(X_calib, y_pred_calib, z_calib) + + cs_features = self.transform(X_calib, y_pred_calib, z_calib) + + self._check_unconsistent_features(cs_features) + + not_nan_index = np.where(~np.isnan(conformity_scores_calib))[0] + # Some conf. score values may be nan (ex: with ResidualNormalisedScore) + + if "method" not in optim_kwargs: + optim_kwargs["method"] = "SLSQP" + if "options" not in optim_kwargs: + optim_kwargs["options"] = {} + if "maxiter" not in optim_kwargs["options"]: + optim_kwargs["options"]["maxiter"] = 1000 + + self.calib_cs_features = cs_features[not_nan_index, :] + self.conformity_scores_calib = conformity_scores_calib[not_nan_index] + self.q = q + self.reg_param + + self.optim_kwargs = optim_kwargs + + optimal_beta_up = cast(OptimizeResult, minimize( + calibrator_optim_objective, self.init_value_, + args=( + np.vstack( + [ + cs_features[not_nan_index, :], + cs_features[not_nan_index[0], :] + ] + ), + np.hstack( + [ + conformity_scores_calib[not_nan_index], + conformity_scores_calib[not_nan_index[0]] + ] + ), + q, + self.reg_param, + ), + **optim_kwargs, + )) + + if not self.sym: + optimal_beta_low = cast(OptimizeResult, minimize( + calibrator_optim_objective, self.init_value_, + args=( + np.vstack( + [ + cs_features[not_nan_index, :], + cs_features[not_nan_index[0], :] + ] + ), + np.hstack( + [ + - conformity_scores_calib[not_nan_index], + - conformity_scores_calib[not_nan_index[0]] + ] + ), + q, + self.reg_param, + ), + **optim_kwargs, + )) + else: + optimal_beta_low = optimal_beta_up + + self._check_optimization_success(optimal_beta_up, optimal_beta_low) + + self.beta_up_ = cast(Tuple[NDArray, bool], + (optimal_beta_up.x, optimal_beta_up.success)) + self.beta_low_ = cast(Tuple[NDArray, bool], + (optimal_beta_low.x, + optimal_beta_low.success)) + + return self + + def _check_unconsistent_features(self, cs_features: NDArray) -> None: + """ + Check if the ``cs_features`` array has rows full of zeros. + """ + if np.any(np.all(cs_features == 0, axis=1)): + warnings.warn("WARNING: At least one row of the transformation " + "calibrator.transform(X, y_pred, z) is full of " + "zeros. It will result in a prediction interval of " + "zero width. Consider changing the `CCPCalibrator` " + "definintion.\nFix: Use `bias=True` " + "in the `CCPCalibrator` definition.") + + def transform( + self, + X: Optional[ArrayLike] = None, + y_pred: Optional[ArrayLike] = None, + z: Optional[ArrayLike] = None, + ) -> NDArray: + """ + Transform ``(X, y_pred, z)`` into an array of shape + ``(n_samples, n_out)`` which represent features to estimate the + conformity scores. + + Parameters + ---------- + X : ArrayLike + Observed samples + + y_pred : ArrayLike + Target prediction + + z : ArrayLike + Exogenous variable + + Returns + ------- + NDArray + features + """ + check_is_fitted(self, self.transform_attributes) + + params_mapping = {"X": X, "y_pred": y_pred, "z": z} + cs_features = concatenate_functions(self.functions_, params_mapping) + + if self.normalized: + norm = cast(NDArray, + np.linalg.norm(cs_features, axis=1)).reshape(-1, 1) + # the rows full of zeros are replace by rows of ones + cs_features[(abs(norm) == 0)[:, 0], :] = np.ones( + cs_features.shape[1]) + norm[abs(norm) == 0] = 1 + cs_features /= norm + + # Multiply the result by each multiplier function + if self._multipliers is not None: + for f in self._multipliers: + cs_features *= dynamic_arguments_call(f, params_mapping) + + return cs_features + + def _get_cs_bound( + self, + conformity_scores: NDArray, + ) -> Tuple[float, float]: + """ + Create a valid up and down conformity score bound, based on + ``cs_bound`` + + Parameters + ---------- + cs_bound: Optional[Union[float, Tuple[float, float]]] + Bound of the conformity scores, such as for all conformity score S + corresponding to ``X`` and ``y_pred``: + + - If the conformity score has ``sym=True``: + ``cs_bound`` is a ``float`` and ``|S| <= cs_bound`` + + - If the conformity score has ``sym=False``: + ``cs_bound`` is a ``Tuple[float, float]`` and + ``cs_bound[0] <= S <= cs_bound[1]`` + + If ``cs_bound=None``, + the maximum (and minimum if ``sym=False``) value + of the calibration conformity scores is used. + + By default ``None`` + + sym : bool + Whether or not the computed prediction intervals should be + symetrical or not + + conformity_scores: NDArray + Conformity scores, used to estimate the bounds if ``cs_bound=None`` + + Returns + ------- + Tuple[float, float] + (cs_bound_up, cs_bound_low) + """ + + cs_bound_up = max(conformity_scores) + cs_bound_low = min(conformity_scores) + + return cs_bound_up, cs_bound_low + + def predict( + self, + X: ArrayLike, + y_pred: Optional[ArrayLike] = None, + z: Optional[ArrayLike] = None, + unsafe_approximation: bool = False, + **kwargs, + ) -> NDArray: + """ + Predict the conformity scores estimation by: + - Transforming ``(X, y_pred, z)`` into an array of features of shape + ``(n_samples, n_out)`` + - computing the dot product with the optimized beta values. + + Parameters + ---------- + X : ArrayLike + Observed samples + + y_pred : Optional[ArrayLike] + Target prediction + + z : Optional[ArrayLike] + Exogenous variable + + cs_bound: Optional[Union[float, Tuple[float, float]]] + Bound of the conformity scores, such as for all conformity score S + corresponding to ``X`` and ``y_pred``: + + - If the conformity score has ``sym=True``: + ``cs_bound`` is a ``float`` and ``|S| <= cs_bound`` + + - If the conformity score has ``sym=False``: + ``cs_bound`` is a ``Tuple[float, float]`` and + ``cs_bound[0] <= S <= cs_bound[1]`` + + If ``cs_bound=None``, + the maximum (and minimum if ``sym=False``) value + of the calibration conformity scores is used. + + By default ``None`` + + unsafe_approximation: Bool + The most of the computation is done during the calibration phase + (``fit`` method). + However, the theoretical guarantees of the method rely on a small + adjustment of the calibration for each test point. It will induce + a conservatice interval prediction (potentially with over-coverage) + and a long inference time, depending on the numbere of test points. + + Using ``unsafe_approximation = True`` will desactivate this + correction, providing the interval predictions almost instantly. + However, it can result in a small miss-coverage, as the previous + guarantees don't hold anymore. + + By default, ``False`` + + Returns + ------- + NDArray + Transformation + """ + check_required_arguments(y_pred) + + check_is_fitted(self, self.transform_attributes + self.fit_attributes) + + cs_features = self.transform(X, y_pred, z) + + self._check_unconsistent_features(cs_features) + + if unsafe_approximation: + y_pred_low = -cs_features.dot(self.beta_low_[0][:, np.newaxis]) + y_pred_up = cs_features.dot(self.beta_up_[0][:, np.newaxis]) + else: + cs_bound_up, cs_bound_low = self._get_cs_bound( + self.conformity_scores_calib + ) + + y_pred_up = np.zeros((_num_samples(X), 1)) + y_pred_low = np.zeros((_num_samples(X), 1)) + for i in range(len(y_pred_up)): + corrected_beta_up = cast(OptimizeResult, minimize( + calibrator_optim_objective, self.beta_up_[0], + args=( + np.vstack( + [self.calib_cs_features, cs_features[[i], :]] + ), + np.hstack( + [self.conformity_scores_calib, [cs_bound_up]] + ), + self.q, + self.reg_param, + ), + **self.optim_kwargs, + )) + + if not self.sym: + corrected_beta_low = cast(OptimizeResult, minimize( + calibrator_optim_objective, self.beta_low_[0], + args=( + np.vstack( + [self.calib_cs_features, cs_features[[i], :]] + ), + -np.hstack( + [self.conformity_scores_calib, [cs_bound_low]] + ), + self.q, + self.reg_param, + ), + **self.optim_kwargs, + )) + + else: + corrected_beta_low = corrected_beta_up + + self._check_optimization_success( + corrected_beta_up, corrected_beta_low + ) + + y_pred_up[[i]] = cs_features[[i], :].dot( + corrected_beta_up.x[:, np.newaxis] + ) + y_pred_low[[i]] = -cs_features[[i], :].dot( + corrected_beta_low.x[:, np.newaxis] + ) + + return np.hstack([y_pred_low, y_pred_up]) + + def __call__( + self, + X: Optional[ArrayLike] = None, + y_pred: Optional[ArrayLike] = None, + z: Optional[ArrayLike] = None, + ) -> NDArray: + """ + Call the ``transform`` method. + + Parameters + ---------- + X : ArrayLike + Observed samples + + y_pred : ArrayLike + Target prediction + + z : ArrayLike + Exogenous variable + + Returns + ------- + NDArray + features + """ + return self.transform(X, y_pred, z) + + def __mul__(self, funct: Optional[Callable]) -> CCPCalibrator: + """ + Multiply a ``CCPCalibrator`` with another function. + This other function should return an array of shape (n_samples, 1) + or (n_samples, ). + + The output of the ``transform`` method of the resulting + ``CCPCalibrator`` instance will be multiplied by the ``funct`` values. + + Parameters + ---------- + funct : Optional[Callable] + function which should return an array of shape (n_samples, 1) + or (n_samples, ) + + Returns + ------- + CCPCalibrator + self, with ``funct`` append in the ``_multipliers`` argument list. + """ + if funct is None: + return self + else: + check_custom_calibrator_functions([funct]) + old_multipliers = self._multipliers + new_calibrator = cast(CCPCalibrator, clone(self)) + if old_multipliers is None: + new_calibrator._multipliers = [funct] + else: + new_calibrator._multipliers = old_multipliers + [funct] + return new_calibrator + + def __rmul__(self, other) -> CCPCalibrator: + """ + Do the same as ``__mul__`` + """ + return self.__mul__(other) diff --git a/mapie/future/calibrators/ccp/custom.py b/mapie/future/calibrators/ccp/custom.py new file mode 100644 index 000000000..efc924597 --- /dev/null +++ b/mapie/future/calibrators/ccp/custom.py @@ -0,0 +1,243 @@ +from __future__ import annotations + +from typing import Callable, Iterable, List, Optional, Union + +from sklearn.utils import _safe_indexing + +from mapie._typing import ArrayLike + +from .base import CCPCalibrator +from .utils import (check_multiplier, check_custom_calibrator_functions, + format_functions) + + +class CustomCCP(CCPCalibrator): + """ + Calibrator based on :class:`~mapie.future.calibrators.ccp.CCPCalibrator`, + used in :class:`~mapie.future.split.SplitCPRegressor` or + :class:`~mapie.future.split.SplitCPClassifier` + to estimate the conformity scores. + + It corresponds to the adaptative conformal prediction method proposed by + Gibbs et al. (2023) in + "Conformal Prediction With Conditional Guarantees" [1]. + + The goal is to learn the quantile of the conformity scores distribution, + to built the prediction interval, not with a constant ``q`` (as it is the + case in the standard CP), but with a function ``q(X)`` which is adaptative + as it depends on ``X``. + + This class builds a :class:`~mapie.future.calibrators.ccp.CCPCalibrator` + object with custom features, function of ``X``, ``y_pred`` or ``z``, + defined as a list of functions in ``functions`` argument. + + This class can be used to concatenate + :class:`~mapie.future.calibrators.ccp.CCPCalibrator` instances. + + See the examples and the documentation to build a + :class:`~mapie.future.calibrators.ccp.CCPCalibrator` + adaptated to your dataset and constraints. + + Parameters + ---------- + functions: Optional[Union[Callable, Iterable[Callable]]] + List of functions (or + :class:`~mapie.future.calibrators.ccp.CCPCalibrator` objects) + or single function. + + Each function can take a combinaison of the following arguments: + + - ``X``: Input dataset, of shape (n_samples, ``n_in``) + - ``y_pred``: estimator prediction, of shape (n_samples,) + - ``z``: exogenous variable, of shape (n_samples, n_features). + It should be given in the ``fit`` and ``predict`` methods. + + The results of each functions will be concatenated to build the final + result of the transformation, of shape ``(n_samples, n_out)``, which + will be used to estimate the conformity scores quantiles. + + By default ``None``. + + bias: bool + Add a column of ones to the features, + (to make sure that the marginal coverage is guaranteed). + If the ``CCPCalibrator`` object definition covers all the dataset + (meaning, for all calibration and test samples, the resulting + ``calibrator.predict(X, y_pred, z)`` is never all zeros), + this column of ones is not necessary to obtain marginal coverage. + In this case, you can set this argument to ``False``. + + If you are not sure, use ``bias=True`` to garantee the marginal + coverage. + + By default ``False``. + + normalized: bool + Whether or not to normalized the resulting + ``calibrator.predict(X, y_pred, z)``. Normalization + will result in a bounded interval prediction width, avoiding the width + to explode to +inf or crash to zero. It is particularly intersting when + you know that the conformity scores are bounded. It also prevent the + interval to have a width of zero for out-of-distribution samples. + On the opposite, it is not recommended if the conformity + scores can vary a lot. + + By default ``False`` + + init_value: Optional[ArrayLike] + Optimization initialisation value. + If ``None``, the initial vector is sampled from a normal distribution. + + By default ``None``. + + reg_param: Optional[float] + Float to monitor the ridge regularization + strength. ``reg_param`` must be a non-negative + float i.e. in ``[0, inf)``. + + .. warning:: + A too strong regularization may compromise the guaranteed + marginal coverage. If ``calibrator.normalize=True``, it is usually + recommanded to use ``reg_param < 1e-3``. + + If ``None``, no regularization is used. + + By default ``None``. + + Attributes + ---------- + transform_attributes: Optional[List[str]] + Name of attributes set during the ``fit`` method, and required to call + ``transform``. + + fit_attributes: Optional[List[str]] + Name of attributes set during the ``fit`` method, and required to call + ``predict``. + + n_in: int + Number of features of ``X`` + + n_out: int + Number of features of ``calibrator.transform(X, y_pred, z)`` + + beta_up_: Tuple[NDArray, bool] + Calibration fitting results, used to build the upper bound of the + prediction intervals. + beta_up_[0]: Array of shape (calibrator.n_out, ) + beta_up_[1]: Whether the optimization process converged or not + (cover is not guaranteed if the optimisation has failed) + + beta_low_: Tuple[NDArray, bool] + Same as ``beta_up_``, but for the lower bound + + References + ---------- + [1]: + Isaac Gibbs and John J. Cherian and Emmanuel J. Candès. + "Conformal Prediction With Conditional Guarantees", 2023 + + Examples + -------- + >>> import numpy as np + >>> from mapie.future.calibrators import CustomCCP + >>> from mapie.future.split import SplitCPRegressor + >>> from mapie.conformity_scores import AbsoluteConformityScore + >>> np.random.seed(1) + >>> X_train = np.linspace(0, 3.14, 1001).reshape(-1, 1) + >>> y_train = np.random.rand(len(X_train))*np.sin(X_train[:,0]) + >>> calibrator = CustomCCP( + ... functions=[ + ... lambda X: np.sin(X[:,0]), + ... ], + ... bias=True, + ... ) + >>> mapie = SplitCPRegressor( + ... calibrator=calibrator, alpha=0.1, random_state=1, + ... conformity_score=AbsoluteConformityScore(sym=False) + ... ).fit(X_train, y_train) + >>> y_pred, y_pi = mapie.predict(X_train) + """ + transform_attributes: List[str] = ["functions_", "is_transform_fitted_"] + + def __init__( + self, + functions: Optional[Union[Callable, Iterable[Callable]]] = None, + bias: bool = False, + normalized: bool = False, + init_value: Optional[ArrayLike] = None, + reg_param: Optional[float] = None, + ) -> None: + super().__init__(functions, bias, normalized, init_value, reg_param) + + def _check_transform_parameters( + self, + X: ArrayLike, + y_pred: Optional[ArrayLike] = None, + z: Optional[ArrayLike] = None, + ) -> None: + """ + Check the parameters required to call ``transform``. + In particular, check that the ``functions`` + attribute is valid and set the ``functions_`` argument. + + Parameters + ---------- + X: ArrayLike of shape (n_samples, n_features) + Training data. + + y: ArrayLike of shape (n_samples,) + Training labels. + + By default ``None`` + + z: Optional[ArrayLike] of shape (n_calib_samples, n_exog_features) + Exogenous variables + + By default ``None`` + """ + self.functions_ = format_functions(self.functions, self.bias) + check_custom_calibrator_functions(self.functions_) + + def _transform_params( + self, + X: ArrayLike, + y_pred: Optional[ArrayLike] = None, + z: Optional[ArrayLike] = None, + ) -> CustomCCP: + """ + Set all the necessary attributes to be able to transform + ``(X, y_pred, z)`` into the expected array of features. + + It should set all the attributes of ``transform_attributes`` + (i.e. ``functions_``). It should also set, once fitted, ``n_in``, + ``n_out`` and ``init_value_``. + + Parameters + ---------- + X: ArrayLike of shape (n_samples, n_features) + Training data. + + y_pred: ArrayLike of shape (n_samples,) + Training labels. + + By default ``None`` + + z: Optional[ArrayLike] of shape (n_calib_samples, n_exog_features) + Exogenous variables + + By default ``None`` + """ + check_multiplier(self._multipliers, X, y_pred, z) + self._check_transform_parameters(X, y_pred, z) + + for phi in self.functions_: + if isinstance(phi, CCPCalibrator): + phi._transform_params(X, y_pred, z) + check_multiplier(phi._multipliers, X, y_pred, z) + self.is_transform_fitted_ = True + + result = self.transform(X, y_pred, z) + self.n_in = len(_safe_indexing(X, 0)) + self.n_out = len(_safe_indexing(result, 0)) + self.init_value_ = self._check_init_value(self.init_value, self.n_out) + return self diff --git a/mapie/future/calibrators/ccp/gaussian.py b/mapie/future/calibrators/ccp/gaussian.py new file mode 100644 index 000000000..380bba89e --- /dev/null +++ b/mapie/future/calibrators/ccp/gaussian.py @@ -0,0 +1,310 @@ +from __future__ import annotations + +from typing import Callable, List, Optional, Tuple, Union + +import numpy as np +from sklearn.utils import _safe_indexing +from sklearn.utils.validation import _num_samples + +from mapie._typing import ArrayLike +from .base import CCPCalibrator + +from .utils import compute_sigma, format_functions, sample_points + + +class GaussianCCP(CCPCalibrator): + """ + Calibrator based on :class:`~mapie.future.calibrators.ccp.CCPCalibrator`, + used in :class:`~mapie.future.split.SplitCPRegressor` or + :class:`~mapie.future.split.SplitCPClassifier` + to estimate the conformity scores. + + It corresponds to the adaptative conformal prediction method proposed by + Gibbs et al. (2023) in "Conformal Prediction With Conditional Guarantees". + + The goal is to learn the quantile of the conformity scores distribution, + to built the prediction interval, not with a constant ``q`` (as it is the + case in the standard CP), but with a function ``q(X)`` which is adaptative + as it depends on ``X``. + + This class builds a :class:`~mapie.future.calibrators.ccp.CCPCalibrator` + object with gaussian kernel features, + which computes the gaussian distance between ``X`` and some points, + randomly sampled in the dataset or set by the user. + + See the examples and the documentation to build a + :class:`~mapie.future.calibrators.ccp.CCPCalibrator` + adaptated to your dataset and constraints. + + Parameters + ---------- + points : Union[int, ArrayLike, Tuple[ArrayLike, ArrayLike]] + If Array: List of data points, used as centers to compute + gaussian distances. Should be an array of shape (n_points, n_in). + + If integer, the points will be sampled randomly from the ``X`` + dataset, where ``X`` is the data give to the + ``GaussianCCP.fit`` method, which usually correspond to + the ``X`` argument of the ``fit`` or ``fit_calibrator`` method + of a ``SplitCP`` instance. + + You can pass a Tuple[ArrayLike, ArrayLike], to have a different + ``sigma`` value for each point. The two elements of the + tuple should be: + - Data points: 2D array of shape (n_points, n_in) + - Sigma values 2D array of shape (n_points, n_in) or (n_points, 1) + In this case, the ``sigma``, ``random_sigma`` and ``X`` argument are + ignored. + + By default, ``20`` + + sigma : Optional[Union[float, ArrayLike]] + Standard deviation value used to compute the guassian distances, + with the formula: + np.exp(-0.5 * ((X - point) / ``sigma``) ** 2) + - It can be an integer + - It can be a 1D array of float with as many + values as dimensions in the dataset + + If you want different standard deviation values of each points, + you can indicate the sigma value of each point in the ``points`` + argument. + + If ``None``, ``sigma`` will default to a float equal to + ``np.std(X)/(n**0.5)*d`` + - where ``X`` is the calibration data, passed to ``GaussianCCP.fit`` + method, which usually correspond to the ``X`` argument of the ``fit`` + or ``fit_calibrator`` method of a ``SplitCP`` instance. + - ``n`` is the number of points used as gaussian centers. + - ``d`` is the number of dimensions of ``X`` (i.e. ``n_in``). + + By default, ``None`` + + random_sigma : bool + Whether to apply to the standard deviation values, a random multiplier, + different for each point, equal to: + + ``2**np.random.normal(0, 1*2**(-2+np.log10(len(points))))`` + + Exemple: + - For 10 points, the sigma value will be, in general, + multiplied by a value between 0.7 and 1.4 + - For 100 points, the sigma value will be, in general, + multiplied by a value between 0.5 and 2 + + .. note:: + This is a default suggestion of randomization, + which allow to have in the same time wide and narrow gaussians. + + You can use fully custom sigma values, buy passing to the + ``points`` argument, a different sigma value for each point. + + By default, ``False`` + + bias: bool + Add a column of ones to the features, + (to make sure that the marginal coverage is guaranteed). + If the ``CCPCalibrator`` object definition covers all the dataset + (meaning, for all calibration and test samples, the resulting + ``calibrator.predict(X, y_pred, z)`` is never all zeros), + this column of ones is not necessary to obtain marginal coverage. + In this case, you can set this argument to ``False``. + + If you are not sure, use ``bias=True`` to garantee the marginal + coverage. + + ..note:: + In this case, with ``GaussianCCP``, if ``normalized`` is + ``True`` (it is, by default), the result of + ``calibrator.predict(X, y_pred, z)`` will never + be all zeros, so this ``bias`` is not required, + to have a guaranteed coverage. + + By default ``False``. + + normalized: bool + Whether or not to normalized the resulting + ``calibrator.predict(X, y_pred, z)``. Normalization + will result in a bounded interval prediction width, avoiding the width + to explode to +inf or crash to zero. It is particularly intersting when + you know that the conformity scores are bounded. It also prevent the + interval to have a width of zero for out-of-distribution samples. + On the opposite, it is not recommended if the conformity + scores can vary a lot. + + .. note:: + To make sure that for too small ``sigma`` values, + or for out-of-distribution samples, the interval width doesn't + crash to zero, we set by default ``normalized = True``. + By doing so, even the samples which were in any gaussian tild, + will still be linked to the closest one. + + By default ``True`` + + init_value: Optional[ArrayLike] + Optimization initialisation value. + If ``None``, the initial vector is sampled from a normal distribution. + + By default ``None``. + + reg_param: Optional[float] + Float to monitor the ridge regularization + strength. ``reg_param`` must be a non-negative + float i.e. in ``[0, inf)``. + + .. warning:: + A too strong regularization may compromise the guaranteed + marginal coverage. If ``calibrator.normalize=True``, it is usually + recommanded to use ``reg_param < 1e-3``. + + If ``None``, no regularization is used. + + By default ``None``. + + Attributes + ---------- + transform_attributes: Optional[List[str]] + Name of attributes set during the ``fit`` method, and required to call + ``transform``. + + fit_attributes: Optional[List[str]] + Name of attributes set during the ``fit`` method, and required to call + ``predict``. + + n_in: int + Number of features of ``X`` + + n_out: int + Number of features of ``calibrator.transform(X, y_pred, z)`` + + points_: NDArray + Array of shape (n_points, n_in), corresponding to the points used to + compute the gaussian distanes. + + sigmas_: NDArray of shape (len(points), 1) or (len(points), n_in) + Standard deviation values + + beta_up_: Tuple[NDArray, bool] + Calibration fitting results, used to build the upper bound of the + prediction intervals. + beta_up_[0]: Array of shape (calibrator.n_out, ) + beta_up_[1]: Whether the optimization process converged or not + (the coverage is not garantied if the optimization fail) + + beta_low_: Tuple[NDArray, bool] + Same as beta_up, but for the lower bound + + References + ---------- + Isaac Gibbs and John J. Cherian and Emmanuel J. Candès. + "Conformal Prediction With Conditional Guarantees", 2023 + + Examples + -------- + >>> import numpy as np + >>> from mapie.future.calibrators import GaussianCCP + >>> from mapie.future.split import SplitCPRegressor + >>> np.random.seed(1) + >>> X_train = np.arange(0,400, 2).reshape(-1, 1) + >>> y_train = 1 + 2*X_train[:,0] + np.random.rand(len(X_train)) + >>> mapie = SplitCPRegressor( + ... calibrator=GaussianCCP(2), alpha=0.1, random_state=1, + ... ).fit(X_train, y_train) + >>> y_pred, y_pi = mapie.predict(X_train) + """ + transform_attributes: List[str] = ["points_", "sigmas_", "functions_"] + + def __init__( + self, + points: Union[int, ArrayLike, Tuple[ArrayLike, ArrayLike]] = 20, + sigma: Optional[Union[float, ArrayLike]] = None, + random_sigma: bool = False, + bias: bool = False, + normalized: bool = True, + init_value: Optional[ArrayLike] = None, + reg_param: Optional[float] = None, + ) -> None: + self.points = points + self.sigma = sigma + self.random_sigma = random_sigma + self.bias = bias + self.normalized = normalized + self.init_value = init_value + self.reg_param = reg_param + + self._multipliers: Optional[List[Callable]] = None + + def _check_points_sigma( + self, points: ArrayLike, sigmas: ArrayLike + ) -> None: + """ + Take 2D arrays of points and standard deviations and check + compatibility + + Parameters + ---------- + points : ArrayLike + 2D array of shape (n_points, n_in) + sigmas : ArrayLike + 2D array of shape (n_points, 1) or (n_points, n_in) + + Raises + ------ + ValueError + - If ``points`` and ``sigmas`` don't have the same number of rows + - If ``sigmas``is not of shape (n_points, 1) or (n_points, n_in) + """ + if _num_samples(points) != _num_samples(sigmas): + raise ValueError("There should have as many points as " + "standard deviation values") + + if len(_safe_indexing(sigmas, 0)) not in [ + 1, len(_safe_indexing(points, 0)) + ]: + raise ValueError("The standard deviation 2D array should be of " + "shape (n_points, 1) or (n_points, n_in).\n" + f"Got sigma of shape: ({_num_samples(sigmas)}, " + f"{len(_safe_indexing(points, 0))}).") + + def _check_transform_parameters( + self, + X: ArrayLike, + y_pred: Optional[ArrayLike] = None, + z: Optional[ArrayLike] = None, + ) -> None: + """ + Check the parameters required to call ``transform``. + In particular, set the ``points_`` and ``sigmas_`` attributes, based + on the ``points``, ``sigma`` and ``random_sigma`` arguments. + Then, the ``functions_`` attributes is set, with functions to compute + all the gaussian distances. + + Parameters + ---------- + X: ArrayLike of shape (n_samples, n_features) + Training data. + + y: ArrayLike of shape (n_samples,) + Training labels. + + By default ``None`` + + z: Optional[ArrayLike] of shape (n_calib_samples, n_exog_features) + Exogenous variables + + By default ``None`` + """ + self.points_ = sample_points(X, self.points, self._multipliers) + self.sigmas_ = compute_sigma( + X, self.points, self.points_, self.sigma, + self.random_sigma, self._multipliers + ) + self._check_points_sigma(self.points_, self.sigmas_) + + functions = [ + lambda X, mu=_safe_indexing(self.points_, i), + sigma=_safe_indexing(self.sigmas_, i): + np.exp(-0.5 * np.sum(((X - mu) / sigma) ** 2, axis=1)) + for i in range(_num_samples(self.points_)) + ] + self.functions_ = format_functions(functions, self.bias) diff --git a/mapie/future/calibrators/ccp/polynomial.py b/mapie/future/calibrators/ccp/polynomial.py new file mode 100644 index 000000000..a098d046c --- /dev/null +++ b/mapie/future/calibrators/ccp/polynomial.py @@ -0,0 +1,280 @@ +from __future__ import annotations + +from typing import Callable, List, Optional, Tuple, Union + +from mapie._typing import ArrayLike + +from .base import CCPCalibrator +from .utils import format_functions + + +class PolynomialCCP(CCPCalibrator): + """ + Calibrator based on :class:`~mapie.future.calibrators.ccp.CCPCalibrator`, + used in :class:`~mapie.future.split.SplitCPRegressor` or + :class:`~mapie.future.split.SplitCPClassifier` + to estimate the conformity scores. + + It corresponds to the adaptative conformal prediction method proposed by + Gibbs et al. (2023) in "Conformal Prediction With Conditional Guarantees". + + The goal is to learn the quantile of the conformity scores distribution, + to built the prediction interval, not with a constant ``q`` (as it is the + case in the standard CP), but with a function ``q(X)`` which is adaptative + as it depends on ``X``. + + This class builds a :class:`~mapie.future.calibrators.ccp.CCPCalibrator` + object with polynomial features of ``X``, ``y_pred`` or ``z``. + + See the examples and the documentation to build a + :class:`~mapie.future.calibrators.ccp.CCPCalibrator` + adaptated to your dataset and constraints. + + Parameters + ---------- + degree: Union[int, List[int]] + If ``degree`` is an integer, it correspond to the degree of the + polynomial features transformer. It will create the features + ``1``, ``variable``, ``variable``**2, ..., ``variable``**``degree``. + + If ``degree`` is a list of integers, it will create the features + ``variable``**d, for all integer d in ``degree`` + + ``variable`` may be ``X``, ``y_pred`` or ``z``, depending on the + ``variable`` argument value. + + If ``None``, it will default to ``degree=1``. + + .. note:: + if ``0`` is in the considered exponents (if ``degree`` is an + integer, or if ``0 in degree`` if it is a list), it is not + ``variable**0`` of shape ``(n_samples, n_in)`` which is added, + but only one feature of ones, of shape ``(n_samples, 1)``. + It is actually equivalent to ``bias=True``. + + By default ``None``. + + variable: Literal["X", "y_pred", "z"] + String, used to choose which argument between ``X``, ``y_pred`` and + ``z`` is used to build the polynomial features. + + By default ``"X"`` + + bias: bool + Add a column of ones to the features, + (to make sure that the marginal coverage is guaranteed). + If the ``CCPCalibrator`` object definition covers all the dataset + (meaning, for all calibration and test samples, the resulting + ``calibrator.predict(X, y_pred, z)`` is never all zeros), + this column of ones is not necessary to obtain marginal coverage. + In this case, you can set this argument to ``False``. + + If you are not sure, use ``bias=True`` to garantee the marginal + coverage. + + By default ``False``. + + normalized: bool + Whether or not to normalized the resulting + ``calibrator.predict(X, y_pred, z)``. Normalization + will result in a bounded interval prediction width, avoiding the width + to explode to +inf or crash to zero. It is particularly intersting when + you know that the conformity scores are bounded. It also prevent the + interval to have a width of zero for out-of-distribution samples. + On the opposite, it is not recommended if the conformity + scores can vary a lot. + + By default ``False`` + + init_value: Optional[ArrayLike] + Optimization initialisation value. + If ``None``, is sampled from a normal distribution. + + By default ``None``. + + reg_param: Optional[float] + Float to monitor the ridge regularization + strength. ``reg_param`` must be a non-negative + float i.e. in ``[0, inf)``. + + .. warning:: + A too strong regularization may compromise the guaranteed + marginal coverage. If ``calibrator.normalize=True``, it is usually + recommanded to use ``reg_param < 1e-3``. + + If ``None``, no regularization is used. + + By default ``None``. + + Attributes + ---------- + transform_attributes: Optional[List[str]] + Name of attributes set during the ``fit`` method, and required to call + ``transform``. + + fit_attributes: Optional[List[str]] + Name of attributes set during the ``fit`` method, and required to call + ``predict``. + + n_in: int + Number of features of ``X`` + + n_out: int + Number of features of ``calibrator.transform(X, y_pred, z)`` + + exponents: List[int] + List of exponents of the built polynomial features + + beta_up_: Tuple[NDArray, bool] + Calibration fitting results, used to build the upper bound of the + prediction intervals. + beta_up_[0]: Array of shape (calibrator.n_out, ) + beta_up_[1]: Whether the optimization process converged or not + (the coverage is not garantied if the optimization fail) + + beta_low_: Tuple[NDArray, bool] + Same as beta_up, but for the lower bound + + References + ---------- + Isaac Gibbs and John J. Cherian and Emmanuel J. Candès. + "Conformal Prediction With Conditional Guarantees", 2023 + + Examples + -------- + >>> import numpy as np + >>> from mapie.future.calibrators import PolynomialCCP + >>> from mapie.future.split import SplitCPRegressor + >>> np.random.seed(1) + >>> X_train = np.arange(0,400, 2).reshape(-1, 1) + >>> y_train = 1 + 2*X_train[:,0] + np.random.rand(len(X_train)) + >>> mapie = SplitCPRegressor( + ... calibrator=PolynomialCCP(1), alpha=0.1, random_state=1, + ... ).fit(X_train, y_train) + >>> y_pred, y_pi = mapie.predict(X_train) + """ + def __init__( + self, + degree: Optional[Union[int, List[int]]] = None, + variable: str = "X", + bias: bool = False, + normalized: bool = False, + init_value: Optional[ArrayLike] = None, + reg_param: Optional[float] = None, + ) -> None: + self.degree = degree + self.variable = variable + self.bias = bias + self.normalized = normalized + self.init_value = init_value + self.reg_param = reg_param + + self._multipliers: Optional[List[Callable]] = None + + def _convert_degree( + self, degree: Optional[Union[int, List[int]]], bias: bool + ) -> Tuple[List[int], bool]: + """ + Convert ``degree`` argument into a list of exponents + + Parameters + ---------- + degree: Union[int, List[int]] + If ``degree``is an integer, it correspond to the degree of the + polynomial features. It will create the features + ``1``, ``variable``, ``variable``**2, ..., + ``variable``**``degree``. + + If ``degree``is an iterable of integers, it will create the + features ``variable``**d, for all integer d in ``degree`` + + ``variable`` may be ``X``, ``y_pred`` or ``z``, depending on the + ``variable``argument value. + + If ``None``, it will default to ``degree=1``. + + By default ``None``. + + bias: bool + Add a column of ones to the features, + (to make sure that the marginal coverage is guaranteed). + If the ``CCPCalibrator`` object definition covers all the dataset + (meaning, for all calibration and test samples, the resulting + ``calibrator.predict(X, y_pred, z)`` is never all zeros), + this column of ones is not necessary to obtain marginal coverage. + In this case, you can set this argument to ``False``. + + If you are not sure, use ``bias=True`` to garantee the marginal + coverage. + + Returns + ------- + Tuple[List[int], bool] + - List of exponents (the exponent ``0`` will be replaced by + ``bias=True``, which is equivalent. It is useless to add as many + columns of ones as dimensions of ``X``. Only one is enough.) + - new ``bias`` value. + """ + if degree is None: + exponents = [0, 1] + elif isinstance(degree, int): + exponents = list(range(degree+1)) + else: + exponents = degree + + return exponents, (0 in exponents) or bias + + def _create_functions( + self, exponents: List[int], variable: str + ) -> List[Callable]: + """ + Create the list of lambda functions, based on the list ``exponents`` + and the ``variable`` value. + + Parameters + ---------- + exponents: List[int] + List of exponents to apply on the ``variable``` + + variable: Literal["X", "y_pred", "z"] + Variable on which to apply the exponents. + """ + if variable == "X": + return [lambda X, d=d: X**d for d in exponents if d != 0] + elif variable == "y_pred": + return [lambda y_pred, d=d: y_pred**d for d in exponents if d != 0] + elif variable == "z": + return [lambda z, d=d: z**d for d in exponents if d != 0] + else: + raise ValueError("variable must be 'X', 'y_pred' or 'z'") + + def _check_transform_parameters( + self, + X: ArrayLike, + y_pred: Optional[ArrayLike] = None, + z: Optional[ArrayLike] = None, + ) -> None: + """ + Check the parameters required to call ``transform``. + In particular, check that the ``functions`` + attribute is valid and set the ``functions_`` argument. + + Parameters + ---------- + X: ArrayLike of shape (n_samples, n_features) + Training data. + + y: ArrayLike of shape (n_samples,) + Training labels. + + By default ``None`` + + z: Optional[ArrayLike] of shape (n_calib_samples, n_exog_features) + Exogenous variables + + By default ``None`` + """ + self.exponents, self.bias = self._convert_degree( + self.degree, self.bias) + functions = self._create_functions(self.exponents, self.variable) + self.functions_ = format_functions(functions, self.bias) diff --git a/mapie/future/calibrators/ccp/utils.py b/mapie/future/calibrators/ccp/utils.py new file mode 100644 index 000000000..4cb15a0a8 --- /dev/null +++ b/mapie/future/calibrators/ccp/utils.py @@ -0,0 +1,543 @@ +from __future__ import annotations + +import inspect +from typing import Callable, Dict, Iterable, List, Optional, Tuple, Union, cast + +import numpy as np +from sklearn.utils import _safe_indexing +from sklearn.utils.validation import _num_samples + +from mapie._typing import ArrayLike, NDArray + + +def format_functions( + functions: Optional[Union[Callable, Iterable[Callable]]], + bias: bool, +) -> List[Callable]: + """ + Validate ``functions`` and add a column of ones, as a lambda function + if ``bias=True``. + + Parameters + ---------- + functions: Optional[Union[Callable, Iterable[Callable]]] + List of functions (or CCPCalibrator objects) or single function. + + Each function can take a combinaison of the following arguments: + - ``X``: Input dataset, of shape (n_samples, ``n_in``) + - ``y_pred``: estimator prediction, of shape (n_samples,) + - ``z``: exogenous variable, of shape (n_samples, n_features). + It should be given in the ``fit`` and ``predict`` methods. + The results of each functions will be concatenated to build the final + result of the transformation, of shape ``(n_samples, n_out)``, which + will be used to estimate the conformity scores quantiles. + + If ``None``, return an empty list. + + bias: bool + Whether or not to add a column of ones to the features. + + Returns + ------- + List[Callable] + ``functions`` as a not empty list + + Raises + ------ + ValueError + If ``functions`` is empty or ``None`` and ``bias=False``. + """ + if functions is None: + functions = [] + elif isinstance(functions, Iterable): + functions = list(functions) + else: + functions = [functions] + + if bias: + functions.append(lambda X: np.ones((_num_samples(X), 1))) + if (len(functions) == 0): + raise ValueError("You need to define the `functions` argument " + "with a function or a list of functions, " + "or keep bias argument to True.") + return functions + + +def check_custom_calibrator_functions( + functions: List[Callable] +) -> None: + """ + Raise errors if the elements in ``functions`` have + unexpected arguments. + + Raises + ------ + ValueError + If functions contain unknown required arguments. + + Notes + ----- + This method ensures that the provided functions only use recognized + arguments ('X', 'y_pred', 'z'). Unknown optional arguments are allowed, + but will always use their default values. + """ + error_ind: Dict[str, List[int]] = {} + for i, funct in enumerate(functions): + assert callable(funct) + params = inspect.signature(funct).parameters + + for param, arg in params.items(): + if ( + param not in ["X", "y_pred", "z"] + and arg.default is inspect.Parameter.empty + ): + if param in error_ind: + error_ind[param].append(i) + else: + error_ind[param] = [i] + + if len(error_ind) > 0: + error_msg = "" + for param, inds in error_ind.items(): + error_msg += ( + f"The functions at index ({', '.join(map(str, inds))}) " + + "of the 'functions' argument, has an unknown required " + + f"argument '{param}'.\n" + ) + raise ValueError( + "Forbidden required argument in `CustomCCP` calibrator.\n" + f"{error_msg}" + "The only allowed required argument are : 'X', " + "'y_pred' and 'z'.\n" + "Note: You can use optional arguments if you want " + "to. They will act as parameters, as it is always " + "their default value which will be used." + ) + + +def sample_points( + X: ArrayLike, + points: Union[int, ArrayLike, Tuple[ArrayLike, ArrayLike]], + multipliers: Optional[List[Callable]] = None, +) -> NDArray: + """ + Generate the ``points_`` attribute from the ``points`` and ``X`` arguments. + Only the samples which have weights (the value for each ``multipliers`` + function) different from ``0`` can be sampled. + + Parameters + ---------- + X : ArrayLike + Samples + + points : Union[int, ArrayLike, Tuple[ArrayLike, ArrayLike]] + If Array: List of data points, used as centers to compute + gaussian distances. Should be an array of shape (n_points, n_in). + + If integer, the points will be sampled randomly from the ``X`` + dataset, where ``X`` is the data give to the + ``GaussianCCP.fit`` method, which usually correspond to + the ``X`` argument of the ``fit`` or ``fit_calibrator`` method + of a ``SplitCP`` instance. + + You can pass a Tuple[ArrayLike, ArrayLike], to have a different + ``sigma`` value for each point. The two elements of the + tuple should be: + - Data points: 2D array of shape (n_points, n_in) + - Sigma values 2D array of shape (n_points, n_in) or (n_points, 1) + In this case, the ``sigma``, ``random_sigma`` and ``X`` argument are + ignored. + + If ``None``, default to ``20``. + + multipliers: Optional[List[Callable]] + List of functions which should return an array of shape (n_samples, 1) + or (n_samples, ) used to weight the sample. + + Returns + ------- + NDArray + 2D NDArray of points + + Raises + ------ + ValueError + If ``points`` is an invalid argument. + """ + if isinstance(points, int): + if multipliers is None: + not_null_index = list(range(_num_samples(X))) + else: # Only sample points which have a not null multiplier value + test = np.ones((_num_samples(X), 1)).astype(bool) + for f in multipliers: + multi = f(X) + if len(multi.shape) == 1: + multi = multi.reshape(-1, 1) + test = test & (multi != 0) + not_null_index = [i for i in range(_num_samples(X)) if test[i, 0]] + if len(not_null_index) < points: + if _num_samples(X) > points: + raise ValueError("There are not enough samples with a " + "multiplier value different from zero " + f"to sample the {points} points.") + else: + raise ValueError("There is not enough valid samples from " + f"which to sample the {points} points.") + points_index = np.random.choice( + not_null_index, size=points, replace=False + ) + points_ = _safe_indexing(X, points_index) + elif isinstance(points, tuple): + points_ = np.array(points[0]) + elif len(np.array(points).shape) == 2: + points_ = np.array(points) + else: + raise ValueError("Invalid `points` argument. The points argument" + "should be an integer, " + "a 2D array or a tuple of two 2D arrays.") + return points_ + + +def compute_sigma( + X: ArrayLike, + points: Optional[Union[int, ArrayLike, Tuple[ArrayLike, ArrayLike]]], + points_: NDArray, + sigma: Optional[Union[float, ArrayLike]], + random_sigma: bool, + multipliers: Optional[List[Callable]] = None, + +) -> NDArray: + """ + Generate the ``sigmas_`` attribute from the ``points``, ``sigma``, ``X`` + arguments and the fitted ``points_``. + + Parameters + ---------- + X : ArrayLike + Samples + + points : Optional[Union[int, ArrayLike, Tuple[ArrayLike, ArrayLike]]] + Input ``points`` argument of ``GaussianCCP`` calibrator. + + points_ : NDArray + Fitted 2D arrray of points + + sigma : Optional[Union[float, ArrayLike]] + Standard deviation value used to compute the guassian distances, + with the formula: + ``np.exp(-0.5 * ((X - point) / sigma) ** 2)`` + - It can be an integer + - It can be a 1D array of float with as many + values as dimensions in the dataset + + If you want different standard deviation values of each points, + you can indicate the sigma value of each point in the ``points`` + argument. + + If ``None``, ``sigma`` will default to a float equal to + ``np.std(X)/(n**0.5)*d`` + - where ``X`` is the calibration data, + passed to ``GaussianCCP.fit`` method, through + ``SplitCPRegressor.fit/fit_calibrate`` method. + - ``n`` is the number of points (``len(points)``). + - ``d`` is the number of dimensions of ``X``. + + random_sigma : bool + Whether to apply to the standard deviation values, a random multiplier, + different for each point, equal to: + + ``2**np.random.normal(0, 1*2**(-2+np.log10(len(points))))`` + + Exemple: + - For 10 points, the sigma value will, in general, + be multiplied by a value between 0.7 and 1.4 + - For 100 points, the sigma value will, in general, + be multiplied by a value between 0.5 and 2 + + multipliers: Optional[List[Callable]] + List of functions which should return an array of shape (n_samples, 1) + or (n_samples, ) used to weight the sample. + + Returns + ------- + sigmas_ + 2D NDArray of standard deviation values + """ + # If each point has a corresponding sigma value + if isinstance(points, tuple): + sigmas_ = np.array(points[1], dtype=float) + if len(sigmas_.shape) == 1: + sigmas_ = sigmas_.reshape(-1, 1) + # If sigma is not defined + elif sigma is None: + # We get the X indexes which correspond to a not zero multiplier value + if multipliers is None: + not_null_index = list(range(_num_samples(X))) + else: + test = np.ones((_num_samples(X), 1)).astype(bool) + for f in multipliers: + multi = f(X) + if len(multi.shape) == 1: + multi = multi.reshape(-1, 1) + test = test & (multi != 0) + not_null_index = [i for i in range(_num_samples(X)) if test[i, 0]] + + points_std = np.std(_safe_indexing(X, not_null_index), axis=0)\ + / (_num_samples(points_)**0.5)\ + * _num_samples(_safe_indexing(X, 0)) + + sigmas_ = np.ones((_num_samples(points_), 1))*points_std + # If sigma is defined + elif isinstance(points, int): + sigmas_ = _init_sigmas(sigma, points) + else: + sigmas_ = _init_sigmas(sigma, _num_samples(points)) + + if random_sigma: + n = _num_samples(points_) + sigmas_ *= ( + 2**np.random.normal(0, 1*2**(-2+np.log10(n)), n) + .reshape(-1, 1) + ) + return cast(NDArray, sigmas_) + + +def _init_sigmas( + sigma: Union[float, ArrayLike], + n_points: int, +) -> NDArray: + """ + If ``sigma`` is not ``None``, take a sigma value, and set ``sigmas_`` + to a standard deviation 2D array of shape (n_points, n_sigma), + n_sigma being 1 or ``n_in``. + + Parameters + ---------- + sigma : Union[float, ArrayLike] + standard deviation, as float or 1D array of length ``n_in`` + (number of dimensins of the dataset) + + n_points : int + Number of points user for gaussian distances calculation + + Raises + ------ + ValueError + If ``sigma`` is not None, a float or a 1D array + """ + if isinstance(sigma, (float, int)): + return np.ones((n_points, 1))*sigma + else: + if len(np.array(sigma).shape) != 1: + raise ValueError("sigma argument should be a float " + "or a 1D array of floats.") + return np.ones((n_points, 1))*np.array(sigma) + + +def dynamic_arguments_call(f: Callable, params_mapping: Dict) -> NDArray: + """ + Call the function ``f``, with the correct arguments + + Parameters + ---------- + f : Callable + function to call + + params_mapping : Dict + Dictionnary of argument names / values + + Returns + ------- + NDArray + result as 2D array + """ + + params = inspect.signature(f).parameters + used_params = { + p: params_mapping[p] for p in params + if p in params_mapping and params_mapping[p] is not None + } + res = np.array(f(**used_params), dtype=float) + if len(res.shape) == 1: + res = np.expand_dims(res, axis=1) + + return res + + +def concatenate_functions( + functions: List[Callable], params_mapping: Dict, +) -> NDArray: + """ + Call the function of ``functions``, with the + correct arguments, and concatenate the results, multiplied by each + ``multipliers`` functions values. + + Parameters + ---------- + functions : List[Callable] + List of functions to call + + params_mapping : Dict + Dictionnary of argument names / values + + Returns + ------- + NDArray + Concatenated result + """ + # Compute phi(X, y_pred, z) + result = np.hstack([ + dynamic_arguments_call(f, params_mapping) for f in functions + ]) + return result + + +def check_multiplier( + multipliers: Optional[List[Callable]], + X: Optional[ArrayLike] = None, + y_pred: Optional[ArrayLike] = None, + z: Optional[ArrayLike] = None, +) -> None: + """ + Check if ``multipliers`` is a valid ``multiplier`` argument for + ``CCPCalibrator``. + + Parameters + ---------- + multipliers : List[Callable] + function which sould return an array of shape (n_samples, 1) or + (n_samples, ) + + X : ArrayLike + Observed samples + + y_pred : ArrayLike + Target prediction + + z : ArrayLike + Exogenous variable + """ + if multipliers is None: + return + params_mapping = {"X": X, "y_pred": y_pred, "z": z} + for f in multipliers: + res = dynamic_arguments_call(f, params_mapping) + if res.shape != (_num_samples(X), 1): + raise ValueError("The function used as multiplier should return an" + "array of shape n_samples, 1) or (n_samples, ).\n" + f"Got shape = {res.shape}.") + + +def fast_mean_pinball_loss( + y_true: NDArray, + y_pred: NDArray, + *, + sample_weight: Optional[NDArray] = None, + alpha: float = 0.5 +) -> float: + """ + Pinball loss for quantile regression. + It does the same as ``sklearn.metric.mean_minball_loss``, but without + the checks on the ``y_true`` and ``y_pred`` arrays, for faster computation. + + Parameters + ---------- + y_true : NDArray of shape (n_samples,) or (n_samples, n_outputs) + Ground truth (correct) target values. + + y_pred : NDArray of shape (n_samples,) or (n_samples, n_outputs) + Estimated target values. + + sample_weight : NDArray of shape (n_samples,), default=None + Sample weights. + + alpha : float, slope of the pinball loss, default=0.5, + This loss is equivalent to :ref:`mean_absolute_error` when `alpha=0.5`, + `alpha=0.95` is minimized by estimators of the 95th percentile. + + Returns + ------- + loss : float + Weighted average of all output errors. + The pinball loss output is a non-negative floating point. The best + value is 0.0. + """ + diff = y_true - y_pred + sign = (diff >= 0).astype(diff.dtype) + loss = alpha * sign * diff - (1 - alpha) * (1 - sign) * diff + output_errors = np.average(loss, weights=sample_weight, axis=0) + + return np.mean(output_errors) + + +def calibrator_optim_objective( + beta: NDArray, calibrator_preds: NDArray, conformity_scores: NDArray, + q: float, reg_param: Optional[float], +) -> float: + """ + Objective funtcion to minimize to get the estimation of + the conformity scores ``q`` quantile, caracterized by + the scalar parameters in the ``beta`` vector. + + Parameters + ---------- + beta : NDArray + Parameters to optimize to minimize the objective function + + calibrator_preds : NDArray + Transformation of the data X using the ``CCPCalibrator``. + + conformity_scores : NDArray + Conformity scores of X + + q : float + Between ``0.0`` and ``1.0``, represents the quantile, being + ``1-alpha`` if ``alpha`` is the risk level of the confidence interval. + + reg_param: Optional[float] + Float to monitor the ridge regularization + strength. ``reg_param`` must be a non-negative + float i.e. in ``[0, inf)``. + + .. warning:: + A too strong regularization may compromise the guaranteed + marginal coverage. If ``calibrator.normalize=True``, it is usually + recommanded to use ``reg_param < 1e-3``. + + If ``None``, no regularization is used. + + By default ``None``. + + Returns + ------- + float + Scalar value to minimize, being the sum of the pinball losses. + """ + if reg_param is not None: + reg_val = float(reg_param * np.linalg.norm(beta, ord=1)) + else: + reg_val = 0 + return fast_mean_pinball_loss( + y_true=conformity_scores, y_pred=calibrator_preds.dot(beta), + alpha=q, + ) + reg_val + + +def check_required_arguments(*args) -> None: + """ + Make sure that the ``args`` arguments are not ``None``. + + It is used in calibrators based on ``BaseCalibrator``. + Their ``fit`` and ``predict`` methods must have their custom + arguments as optional (even the required ones), to match the base class + signature. So we have to check that the required arguments + are not ``None``. + + Raises + ------ + ValueError + If one of the passed argument is ``None``. + """ + if any(arg is None for arg in args): + raise ValueError("One of the required arguments is None." + "Fix the calibrator method definition.") diff --git a/mapie/future/calibrators/standard.py b/mapie/future/calibrators/standard.py new file mode 100644 index 000000000..9e13a1669 --- /dev/null +++ b/mapie/future/calibrators/standard.py @@ -0,0 +1,107 @@ +from __future__ import annotations + +from typing import List, cast + +import numpy as np +from sklearn.utils.validation import _num_samples + +from mapie._typing import ArrayLike, NDArray +from mapie.future.calibrators.base import BaseCalibrator +from .ccp.utils import check_required_arguments +from mapie.conformity_scores.interface import BaseConformityScore + + +class StandardCalibrator(BaseCalibrator): + """ + Calibrator used to get the standard conformal prediciton. It is strictly + equivalent to ``MapieRegressor`` with ``method='base'``. + + Attributes + ---------- + fit_attributes: Optional[List[str]] + Name of attributes set during the ``fit`` method, and required to call + ``predict``. + + q_up_: float + Calibration fitting results, used to build the upper bound of the + prediction intervals. It correspond to the quantile of the calibration + conformity scores. + + q_low_: float + Same as q_up_, but for the lower bound + """ + fit_attributes: List[str] = ["q_up_", "q_low_"] + + def __init__(self) -> None: + return + + def fit( + self, + X_calib: ArrayLike, + conformity_scores_calib: NDArray, + allow_infinite_bounds: bool = False, + **kwargs, + ) -> BaseCalibrator: + """ + Fit the calibrator instance + + Parameters + ---------- + X_calib: ArrayLike of shape (n_samples, n_features) + Calibration data. + + conformity_scores_calib: ArrayLike of shape (n_samples,) + Calibration conformity scores + + allow_infinite_bounds: bool + Allow infinite prediction intervals to be produced. + """ + check_required_arguments(self.alpha) + self.alpha = cast(float, self.alpha) + + # TODO: Partial copy paste of the BaseConformityScore.get_bounds method + if self.sym: + alpha_ref = 1-self.alpha + quantile_ref = BaseConformityScore.get_quantile( + conformity_scores_calib[..., np.newaxis], + np.array([alpha_ref]), axis=0 + )[0, 0] + self.q_low_, self.q_up_ = -quantile_ref, quantile_ref + + else: + alpha_low, alpha_up = self.alpha/2, 1 - self.alpha/2 + + self.q_low_ = BaseConformityScore.get_quantile( + conformity_scores_calib[..., np.newaxis], + np.array([alpha_low]), axis=0, reversed=True, + unbounded=allow_infinite_bounds + )[0, 0] + self.q_up_ = BaseConformityScore.get_quantile( + conformity_scores_calib[..., np.newaxis], + np.array([alpha_up]), axis=0, + unbounded=allow_infinite_bounds + )[0, 0] + + return self + + def predict( + self, + X: ArrayLike, + **kwargs, + ) -> NDArray: + """ + Predict the conformity scores estimation + + Parameters + ---------- + X : ArrayLike + Observed samples + + Returns + ------- + NDArray + prediction + """ + return np.ones((_num_samples(X), 2)) * np.array([ + self.q_low_, self.q_up_ + ]) diff --git a/mapie/future/calibrators/utils.py b/mapie/future/calibrators/utils.py new file mode 100644 index 000000000..7523ff181 --- /dev/null +++ b/mapie/future/calibrators/utils.py @@ -0,0 +1,39 @@ +from __future__ import annotations + +from typing import Optional + +from .base import BaseCalibrator +from .ccp import GaussianCCP + + +def check_calibrator( + calibrator: Optional[BaseCalibrator], +) -> BaseCalibrator: + """ + Check if ``calibrator`` is a ``BaseCalibrator`` instance. + + Parameters + ---------- + calibrator: Optional[BaseCalibrator] + A ``BaseCalibrator`` instance used to estimate the conformity scores + quantiles. + + If ``None``, use as default a ``GaussianCCP`` instance. + + Returns + ------- + BaseCalibrator + ``calibrator`` if defined, a ``GaussianCCP`` instance otherwise. + + Raises + ------ + ValueError + If ``calibrator`` is not ``None`` nor a ``BaseCalibrator`` instance. + """ + if calibrator is None: + return GaussianCCP() + elif isinstance(calibrator, BaseCalibrator): + return calibrator + else: + raise ValueError("Invalid `calibrator` argument. It must be `None` " + "or a `BaseCalibrator` instance.") diff --git a/mapie/future/split/__init__.py b/mapie/future/split/__init__.py new file mode 100644 index 000000000..c91f9b621 --- /dev/null +++ b/mapie/future/split/__init__.py @@ -0,0 +1,7 @@ +from .classification import SplitCPClassifier +from .regression import SplitCPRegressor + +__all__ = [ + "SplitCPClassifier", + "SplitCPRegressor", +] diff --git a/mapie/future/split/base.py b/mapie/future/split/base.py new file mode 100644 index 000000000..aa0679f5f --- /dev/null +++ b/mapie/future/split/base.py @@ -0,0 +1,687 @@ +from __future__ import annotations + +import inspect +import warnings +from abc import ABCMeta, abstractmethod +from typing import Any, Callable, Dict, Optional, Tuple, Union, cast + +import numpy as np +from sklearn.base import BaseEstimator +from sklearn.model_selection import (BaseCrossValidator, + PredefinedSplit, ShuffleSplit) +from sklearn.utils.validation import _num_samples, check_is_fitted + +from mapie._typing import ArrayLike, NDArray +from mapie.future.calibrators.base import BaseCalibrator +from mapie.conformity_scores.interface import BaseConformityScore +from mapie.utils import _sample_non_null_weight, fit_estimator + + +class SplitCP(BaseEstimator, metaclass=ABCMeta): + """ + Base abstract class for Split Conformal Prediction + + Parameters + ---------- + predictor: Optional[BaseEstimator] + Any estimator from scikit-learn API. + (i.e. with ``fit`` and ``predict`` methods). + + If ``None``, will default to a value defined by the subclass + + By default ``None``. + + calibrator: Optional[BaseCalibrator] + A ``BaseCalibrator`` instance used to estimate the conformity scores. + + If ``None``, defaults to a ``GaussianCCP`` calibrator. + + By default ``None``. + + cv: Optional[Union[int, str, ShuffleSplit, PredefinedSplit]] + The splitting strategy for computing conformity scores. + Choose among: + + - Any splitter (``ShuffleSplit`` or ``PredefinedSplit``) + with ``n_splits=1``. + - ``"prefit"``, assumes that ``predictor`` has been fitted already. + All data provided in the ``calibrate`` method is then used + for the calibration. + The user has to take care manually that data used for model fitting + and calibration (the data given in the ``calibrate`` method) + are disjoint. + - ``"split"`` or ``None``: divide the data into training and + calibration subsets (using the default ``calib_size``=0.3). + The splitter used is the following: + ``sklearn.model_selection.ShuffleSplit`` with ``n_splits=1``. + + By default ``None``. + + conformity_score: Optional[BaseConformityScore] + ``BaseConformityScore`` instance. + It defines the link between the observed values, the predicted ones + and the conformity scores. + + - Can be any ``BaseConformityScore`` class + - ``None`` is associated with a default value defined by the subclass + + By default ``None``. + + alpha: Optional[float] + Between ``0.0`` and ``1.0``, represents the risk level of the + confidence interval. + Lower ``alpha`` produce larger (more conservative) prediction + intervals. + ``alpha`` is the complement of the target coverage level. + + By default ``None`` + + random_state: Optional[int] + Integer used to set the numpy seed, to get reproducible calibration + results. + If ``None``, the prediction intervals will be stochastics, and will + change if you refit the calibration (even if no arguments have change). + + WARNING: If ``random_state`` is not ``None``, ``np.random.seed`` will + be changed, which will reset the seed for all the other random + number generators. It may have an impact on the rest of your code. + + By default ``None``. + """ + + default_sym_ = True + fit_attributes = ["predictor_"] + calib_attributes = ["calibrator_"] + + cv: Optional[Union[int, str, ShuffleSplit, PredefinedSplit]] + alpha: Optional[float] + + @abstractmethod + def __init__( + self, + predictor: Optional[BaseEstimator] = None, + calibrator: Optional[BaseCalibrator] = None, + cv: Optional[Union[int, str, ShuffleSplit, PredefinedSplit]] = None, + alpha: Optional[float] = None, + conformity_score: Optional[BaseConformityScore] = None, + random_state: Optional[int] = None, + ) -> None: + """ + Initialisation + """ + + @abstractmethod + def _check_fit_parameters(self) -> BaseEstimator: + """ + Check and replace default value of ``predictor`` and ``cv`` arguments. + """ + + @abstractmethod + def _check_calibrate_parameters(self) -> Tuple[ + BaseConformityScore, BaseCalibrator + ]: + """ + Check and replace default ``conformity_score``, ``alpha`` and + ``calibrator`` arguments. + """ + + def _check_cv( + self, + cv: Optional[Union[int, str, ShuffleSplit, PredefinedSplit]] = None, + test_size: Optional[Union[int, float]] = None, + ) -> Union[str, ShuffleSplit, PredefinedSplit]: + """ + Check if ``cv`` is ``None``, ``"prefit"``, ``"split"``, + or ``ShuffleSplit``/``PredefinedSplit`` with ``n_splits=1``. + + Return a ``ShuffleSplit`` instance with ``n_splits=1`` + if ``None`` or ``"split"``. + + Else raise error. + + Parameters + ---------- + cv: Optional[Union[str, BaseCrossValidator, BaseShuffleSplit]] + Cross-validator to check, by default ``None``. + + test_size: float + If float, should be between 0.0 and 1.0 and represent the + proportion of the dataset to include in the test split. + If cv is not ``"split"``, ``test_size`` is ignored. + + By default ``None``. + + Returns + ------- + Union[str, PredefinedSplit, ShuffleSplit] + The cast `cv` parameter. + + Raises + ------ + ValueError + If the cross-validator is not valid. + """ + if cv is None or cv == "split": + return ShuffleSplit( + n_splits=1, test_size=test_size, random_state=self.random_state + ) + elif (isinstance(cv, (PredefinedSplit, ShuffleSplit)) + and cv.get_n_splits() == 1): + return cv + elif cv == "prefit": + return cv + else: + raise ValueError( + "Invalid cv argument. Allowed values are None, 'prefit', " + "'split' or a `ShuffleSplit/PredefinedSplit` object with " + "`n_splits=1`." + ) + + def _check_alpha(self, alpha: Optional[float] = None) -> None: + """ + Check the ``alpha`` parameter. + + Parameters + ---------- + alpha: Optional[float] + Can be a float between 0 and 1, represent the uncertainty + of the confidence interval. Lower alpha produce + larger (more conservative) prediction intervals. + alpha is the complement of the target coverage level. + + Raises + ------ + ValueError + If alpha is not ``None`` or a float between 0 and 1. + """ + if alpha is None: + return + if isinstance(alpha, float): + alpha = alpha + else: + raise ValueError( + "Invalid alpha. Allowed values are float." + ) + + if alpha < 0 or alpha > 1: + raise ValueError("Invalid alpha. " + "Allowed values are between 0 and 1.") + + def _get_method_arguments( + self, method: Callable, local_vars: Dict[str, Any], + kwargs: Optional[Dict], + ) -> Dict: + """ + Return a dictionnary of the ``method`` arguments. + + The arguments of ``method`` must be attributes of ``self``, in + ``local_vars``, or in ``kwargs``. + + Parameters + ---------- + method: Callable + method for which to check the signature + + local_vars : Dict[str, Any] + Dictionnary of available variables + + kwargs : Optional[Dict] + Other arguments + + exclude_args : Optional[List[str]] + Arguments to exclude + + Returns + ------- + Dict + dictinnary of arguments + """ + self_attrs = {k: v for k, v in self.__dict__.items()} + sig = inspect.signature(method) + + method_kwargs: Dict[str, Any] = {} + for param in sig.parameters.values(): + # We ignore the arguments like *args and **kwargs of the method + if param.kind in (inspect.Parameter.POSITIONAL_OR_KEYWORD, + inspect.Parameter.KEYWORD_ONLY): + param_name = param.name + if kwargs is not None and param_name in kwargs: + method_kwargs[param_name] = kwargs[param_name] + elif param_name in self_attrs: + method_kwargs[param_name] = self_attrs[param_name] + elif param_name in local_vars: + method_kwargs[param_name] = local_vars[param_name] + + return method_kwargs + + def _check_conformity_scores(self, conformity_scores: NDArray) -> NDArray: + """ + Check the conformity scores shape + + Parameters + ---------- + conformity_scores : NDArray of shape (n_samples,) or (n_sampels, 1) + Conformity scores + + Returns + ------- + NDArray: + Conformity scores as 1D-array of shape (n_samples,) + """ + if len(conformity_scores.shape) == 1: + return conformity_scores + if conformity_scores.shape[1] == 1: + return conformity_scores[:, 0] + else: + raise ValueError( + "Invalid conformity scores. The `get_conformity_scores`" + "method of the calibrator, should return an array of shape" + "(n_samples,) or (n_samples, 1)." + f"Got {conformity_scores.shape}." + ) + + def fit_predictor( + self, + X: ArrayLike, + y: ArrayLike, + sample_weight: Optional[ArrayLike] = None, + groups: Optional[ArrayLike] = None, + **fit_kwargs, + ) -> SplitCP: + """ + Fit the predictor if ``cv`` argument is not ``"prefit"`` + + Parameters + ---------- + X: ArrayLike of shape (n_samples, n_features) + Training data. + + y: ArrayLike of shape (n_samples,) + Training labels. + + sample_weight: Optional[ArrayLike] of shape (n_samples,) + Sample weights used in the predictor fitting. + If ``None``, then samples are equally weighted. + If some weights are null, + their corresponding observations are removed + before the fitting process and hence have no residuals. + If weights are non-uniform, residuals are still uniformly weighted. + + By default ``None``. + + groups: Optional[ArrayLike] of shape (n_samples,) + Group labels for the samples, used while splitting the dataset into + train/test set. + + By default ``None``. + + **fit_kwargs: dict + Additional fit parameters for the predictor. + + Returns + ------- + SplitCP + self + """ + predictor = self._check_fit_parameters() + + if self.cv != 'prefit': + self.cv = cast(BaseCrossValidator, self.cv) + + train_index, _ = list(self.cv.split(X, y, groups))[0] + + ( + X_train, y_train, _, sample_weight_train, _ + ) = _sample_non_null_weight(X, y, sample_weight, train_index) + + self.predictor_ = fit_estimator( + predictor, X_train, y_train, + sample_weight=sample_weight_train, **fit_kwargs + ) + else: + self.predictor_ = predictor + return self + + def fit_calibrator( + self, + X: ArrayLike, + y: ArrayLike, + sample_weight: Optional[ArrayLike] = None, + groups: Optional[ArrayLike] = None, + **calib_kwargs, + ) -> SplitCP: + """ + Fit the calibrator. Arguments of the calibrator's ``fit`` method + that are not in the following list: + ``X, y, z, sample_weight, groups, y_pred_calib, + conformity_scores_calib, + X_train, y_train, z_train, sample_weight_train, train_index, + X_calib, y_calib, z_calib, sample_weight_calib, calib_index`` + nor attributes of the ``SplitCP`` instance, + must be given by the user in ``**calib_kwargs``. + + Parameters + ---------- + X: ArrayLike of shape (n_samples, n_features) + Data + + y: ArrayLike of shape (n_samples,) + Target + + sample_weight: Optional[ArrayLike] of shape (n_samples,) + Sample weights of the data, used as weights in the + calibration process. + + By default ``None``. + + groups: Optional[ArrayLike] of shape (n_samples,) + Group labels for the samples used while splitting the dataset into + train/test set. + + By default ``None``. + + calib_kwargs: dict + Additional fit parameters for the calibrator, used as kwargs. + See the calibrator ``.fit`` method documentation to have more + information about the required arguments. + + .. note:: + if the calibrator need exogenous variables (``z_train`` or + ``z_calib``), you should pass ``z`` in ``calib_kwargs`` + + Returns + ------- + SplitCP + self + """ + self._check_fit_parameters() + self.conformity_score_, calibrator = self._check_calibrate_parameters() + check_is_fitted(self, self.fit_attributes) + + if self.alpha is None: + warnings.warn("No calibration is done, because alpha is None.") + return self + + # Get training and calibration sets + if self.cv != 'prefit': + self.cv = cast(BaseCrossValidator, self.cv) + + train_index, calib_index = list(self.cv.split(X, y, groups))[0] + else: + train_index, calib_index = (np.array([], dtype=int), + np.arange(_num_samples(X))) + + z = cast(Optional[ArrayLike], calib_kwargs.get("z", None)) + ( + X_train, y_train, z_train, sample_weight_train, train_index + ) = _sample_non_null_weight(X, y, sample_weight, train_index, z) + ( + X_calib, y_calib, z_calib, sample_weight_calib, calib_index + ) = _sample_non_null_weight(X, y, sample_weight, calib_index, z) + + # Compute conformity scores + y_pred_calib = self.predict_score(X_calib) + + y_calib = cast(NDArray, y_calib) + X_calib = cast(NDArray, X_calib) + + conformity_scores_calib = self.get_conformity_scores( + self.conformity_score_, X_calib, y_calib, + y_pred_calib, sample_weight_calib, groups + ) + + conformity_scores_calib = self._check_conformity_scores( + conformity_scores_calib + ) + + # Get the calibrator arguments + dict_arguments = dict(zip([ + "X", "y", "z", "sample_weight", "groups", + "y_pred_calib", "conformity_scores_calib", + "X_train", "y_train", "z_train", + "sample_weight_train", "train_index", + "X_calib", "y_calib", "z_calib", + "sample_weight_calib", "calib_index", + ], + [ + X, y, z, sample_weight, groups, + y_pred_calib, conformity_scores_calib, + X_train, y_train, z_train, sample_weight_train, train_index, + X_calib, y_calib, z_calib, sample_weight_calib, calib_index, + ])) + calib_arguments = self._get_method_arguments( + calibrator.fit, + dict_arguments, + calib_kwargs + ) + + self.calibrator_ = calibrator.fit( + **calib_arguments, + **( + { + key: calib_kwargs[key] for key in calib_kwargs + if key not in dict_arguments + } + if calib_kwargs is not None + else {} + ) + ) + + return self + + def fit( + self, + X: ArrayLike, + y: ArrayLike, + sample_weight: Optional[ArrayLike] = None, + groups: Optional[ArrayLike] = None, + fit_kwargs: Optional[Dict] = None, + calib_kwargs: Optional[Dict] = None + ) -> SplitCP: + """ + Fit the predictor (if ``cv`` is not ``"prefit"``) + and fit the calibrator. + + Parameters + ---------- + X: ArrayLike of shape (n_samples, n_features) + Data + + y: ArrayLike of shape (n_samples,) + Target + + sample_weight: Optional[ArrayLike] of shape (n_samples,) + Sample weights used in the predictor fitting. + If ``None``, then samples are equally weighted. + If some weights are null, + their corresponding observations are removed + before the fitting process and hence have no residuals. + If weights are non-uniform, residuals are still uniformly weighted. + + By default ``None``. + + groups: Optional[ArrayLike] of shape (n_samples,) + Group labels for the samples used while splitting the dataset into + train/test set. + + By default ``None``. + + fit_kwargs: dict + Additional fit parameters for the predictor, used as kwargs. + + calib_kwargs: dict + Additional fit parameters for the calibrator, used as kwargs. + See the calibrator ``.fit`` method documentation to have more + information about the required arguments. + + .. note:: + if the calibrator need exogenous variables (``z_train`` or + ``z_calib``), you should pass ``z`` in ``calib_kwargs`` + + Returns + ------- + SplitCP + self + """ + self.fit_predictor(X, y, sample_weight, groups, + **(fit_kwargs if fit_kwargs is not None else {})) + self.fit_calibrator(X, y, sample_weight, groups, + **(calib_kwargs + if calib_kwargs is not None else {})) + return self + + def predict( + self, + X: ArrayLike, + **kwargs, + ) -> Union[NDArray, Tuple[NDArray, NDArray]]: + """ + Predict target on new samples with prediction intervals. + + Parameters + ---------- + X: ArrayLike of shape (n_samples, n_features) + Test data. + + kwargs: dict + Additional predict parameters for the calibrator, used as kwargs. + See the calibrator ``.predict`` method documentation to have more + information about the required arguments. + + Returns + ------- + Union[NDArray, Tuple[NDArray, NDArray]] + - Predictions : NDArray of shape (n_samples,) + if ``alpha`` is ``None``. + - Prediction intervals + if ``alpha`` is not ``None``. + """ + check_is_fitted(self, self.fit_attributes) + y_pred = self.predict_score(X) + + if self.alpha is None: + return self.predict_best(y_pred) + + check_is_fitted(self, self.calib_attributes) + + # Fit the calibrator + bounds_arguments = self._get_method_arguments( + self.calibrator_.predict, {}, kwargs, + ) + + y_bounds = self.predict_bounds(X, y_pred, **bounds_arguments) + + return self.predict_best(y_pred), y_bounds + + @abstractmethod + def get_conformity_scores( + self, + conformity_score: BaseConformityScore, + X: NDArray, + y: NDArray, + y_pred: NDArray, + sample_weight: Optional[ArrayLike] = None, + groups: Optional[ArrayLike] = None, + **kwargs, + ) -> NDArray: + """ + Return the conformity scores of the data + + Parameters + ---------- + conformity_score: BaseRegressionScore + Score function that handle all that is related + to conformity scores. + + X: NDArray of shape (n_samples, n_features) + Data + + y: NDArray of shape (n_samples,) + Target + + y_pred: NDArray of shape (n_samples,) + Predictions + + sample_weight: Optional[ArrayLike] of shape (n_samples,) + Sample weights of the data, used as weights in the + calibration process. + + By default ``None``. + + groups: Optional[ArrayLike] of shape (n_samples,) + Group labels for the samples used while splitting the dataset into + train/test set. + + By default ``None``. + + Returns + ------- + NDArray of shape (n_samples,) + Conformity scores. + """ + + @abstractmethod + def predict_score( + self, X: ArrayLike + ) -> NDArray: + """ + Compute the predictor prediction, used to compute the + conformity scores. + + Parameters + ---------- + X: ArrayLike + Observed values. + + Returns + ------- + NDArray + Scores (usually ``y_pred`` in regression and ``y_pred_proba`` + in classification) + """ + + @abstractmethod + def predict_bounds( + self, + X: ArrayLike, + y_pred: NDArray, + **predict_kwargs, + ) -> NDArray: + """ + Compute the bounds, using the fitted ``calibrator_``. + + Parameters + ---------- + X: ArrayLike + Observed values. + + y_pred: 2D NDArray + Predicted scores (target) + + z: Optional[ArrayLike] + Exogenous variables + + Returns + ------- + NDArray + Bounds (or prediction set in classification) + """ + + @abstractmethod + def predict_best(self, y_pred: NDArray) -> NDArray: + """ + Compute the best prediction, in an array of shape (n_samples, ) + + Parameters + ---------- + y_pred: NDArray + Prediction scores (can be the prediction, the probas, ...) + + z: Optional[ArrayLike] + Exogenous variables + + Returns + ------- + NDArray + predictions + """ diff --git a/mapie/future/split/classification.py b/mapie/future/split/classification.py new file mode 100644 index 000000000..2c7dcd4dc --- /dev/null +++ b/mapie/future/split/classification.py @@ -0,0 +1,384 @@ +from __future__ import annotations + +from typing import List, Optional, Tuple, Union, cast + +import numpy as np +from sklearn.base import ClassifierMixin +from sklearn.linear_model import LogisticRegression +from sklearn.model_selection import PredefinedSplit, ShuffleSplit +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import LabelEncoder +from sklearn.utils.validation import check_is_fitted + +from mapie._typing import ArrayLike, NDArray +from mapie.future.calibrators.utils import check_calibrator +from mapie.conformity_scores import BaseClassificationScore +from mapie.conformity_scores.interface import BaseConformityScore +from mapie.conformity_scores.utils import check_classification_conformity_score +from mapie.estimator.classifier import EnsembleClassifier +from mapie.future.split.base import BaseCalibrator, SplitCP + + +class SplitCPClassifier(SplitCP): + """ + Class to compute Conformal Predictions in a ``"split"`` approach for + classification tasks. + It is based on a predictor (a sklearn estimator), and a calibrator + (``Calibrator`` object). + + Parameters + ---------- + predictor: Optional[ClassifierMixin] + Any classifier from scikit-learn API. + (i.e. with ``fit`` and ``predict`` methods). + If ``None``, ``predictor`` defaults to a ``LogisticRegression`` + instance. + + By default ``"None"``. + + calibrator: Optional[BaseCalibrator] + A ``BaseCalibrator`` instance used to estimate the conformity scores. + + If ``None``, use as default a ``StandardCalibrator`` instance. + + By default ``None``. + + cv: Optional[Union[int, str, ShuffleSplit, PredefinedSplit]] + The splitting strategy for computing conformity scores. + Choose among: + + - Any splitter (``ShuffleSplit`` or ``PredefinedSplit``) + with ``n_splits=1``. + - ``"prefit"``, assumes that ``predictor`` has been fitted already. + All data provided in the ``calibrate`` method is then used + for the calibration. + The user has to take care manually that data used for model fitting + and calibration (the data given in the ``calibrate`` method) + are disjoint. + - ``"split"`` or ``None``: divide the data into training and + calibration subsets (using the default ``calib_size=0.3``). + The splitter used is the following: + ``sklearn.model_selection.ShuffleSplit`` with ``n_splits=1``. + + By default ``None``. + + conformity_score: Optional[BaseClassificationScore] + ``BaseClassificationScore`` instance. + It defines the link between the observed values, the predicted ones + and the conformity scores. For instance, the default ``None`` value + correspondonds to a conformity score which assumes + y_obs = y_pred + conformity_score. + + - ``None``, to use the default ``AbsoluteBaseClassificationScore`` + symetrical conformity score + - Any ``BaseClassificationScore`` class + + By default ``None``. + + alpha: Optional[float] + Between ``0.0`` and ``1.0``, represents the risk level of the + confidence interval. + Lower ``alpha`` produce larger (more conservative) prediction + intervals. + ``alpha`` is the complement of the target coverage level. + + By default ``None`` + + random_state: Optional[int] + Integer used to set the numpy seed, to get reproducible calibration + results. + If ``None``, the prediction intervals will be stochastic, and will + change if you refit the calibration (even if no arguments have change). + + WARNING: If ``random_state``is not ``None``, ``np.random.seed`` will + be changed, which will reset the seed for all the other random + number generators. It may have an impact on the rest of your code. + + By default ``None``. + + Examples + -------- + >>> import numpy as np + >>> from mapie.future.split import SplitCPClassifier + >>> np.random.seed(1) + >>> X_train = np.arange(0,400,2).reshape(-1, 1) + >>> y_train = np.array([0]*50 + [1]*50 + [2]*50 + [3]*50) + >>> mapie_reg = SplitCPClassifier(alpha=0.1, random_state=1) + >>> mapie_reg = mapie_reg.fit(X_train, y_train) + >>> y_pred, y_pis = mapie_reg.predict(X_train) + """ + def __init__( + self, + predictor: Optional[ + Union[ + ClassifierMixin, + Pipeline, + List[Union[ClassifierMixin, Pipeline]] + ] + ] = None, + calibrator: Optional[BaseCalibrator] = None, + cv: Optional[Union[str, PredefinedSplit, ShuffleSplit]] = None, + alpha: Optional[float] = None, + conformity_score: Optional[BaseClassificationScore] = None, + random_state: Optional[int] = None, + ) -> None: + self.random_state = random_state + self.cv = cv + self.predictor = predictor + self.conformity_score = conformity_score + self.calibrator = calibrator + self.alpha = alpha + + def _check_estimator_fit_predict_predict_proba( + self, estimator: ClassifierMixin + ) -> None: + """ + Check that the estimator has a fit and precict method. + + Parameters + ---------- + estimator: ClassifierMixin + Estimator to train. + + Raises + ------ + ValueError + If the estimator does not have a fit or predict or predict_proba + attribute. + """ + if not (hasattr(estimator, "fit") and hasattr(estimator, "predict") + and hasattr(estimator, "predict_proba")): + raise ValueError( + "Invalid estimator. " + "Please provide a classifier with fit," + "predict, and predict_proba methods." + ) + + def _check_estimator_classification( + self, + estimator: Optional[ClassifierMixin] = None, + cv: Optional[Union[str, PredefinedSplit, ShuffleSplit]] = None, + ) -> ClassifierMixin: + """ + Check if estimator is ``None``, + and returns a ``LogisticRegression`` instance if necessary. + If the ``cv`` attribute is ``"prefit"``, + check if estimator is indeed already fitted. + + Parameters + ---------- + estimator: Optional[ClassifierMixin] + Estimator to check, by default ``None``. + + Returns + ------- + ClassifierMixin + The estimator itself or a default ``LogisticRegression`` instance. + + Raises + ------ + ValueError + If the estimator is not ``None`` + and has no ``fit`` nor ``predict`` nor ``predict_proba`` methods. + + NotFittedError + If the estimator is not fitted + and ``cv`` attribute is ``"prefit"``. + """ + if estimator is None: + estimator = LogisticRegression(multi_class="multinomial") + + if isinstance(estimator, Pipeline): + est = estimator[-1] + else: + est = estimator + self._check_estimator_fit_predict_predict_proba(est) + + if cv == "prefit": + check_is_fitted(est) + if not hasattr(est, "classes_"): + raise AttributeError( + "Fitted classifier must contain 'classes_' attribute." + ) + return est + + def _check_fit_parameters(self) -> ClassifierMixin: + """ + Check and replace default value of ``predictor`` and ``cv`` arguments. + Copy the ``predictor`` in ``predictor_`` attribute if ``cv="prefit"``. + """ + self.cv = self._check_cv(self.cv) + predictor = self._check_estimator_classification(self.predictor, + self.cv) + return predictor + + def _check_calibrate_parameters(self) -> Tuple[ + BaseClassificationScore, BaseCalibrator + ]: + """ + Check and replace default ``conformity_score``, ``alpha`` and + ``calibrator`` arguments. + """ + conformity_score_ = check_classification_conformity_score( + self.conformity_score, None + ) + calibrator = check_calibrator(self.calibrator) + calibrator.sym = True + calibrator.alpha = self.alpha + calibrator.random_state = self.random_state + self._check_alpha(self.alpha) + return conformity_score_, calibrator + + def get_conformity_scores( + self, + conformity_score: BaseConformityScore, + X: NDArray, + y: NDArray, + y_pred: NDArray, + sample_weight: Optional[ArrayLike] = None, + groups: Optional[ArrayLike] = None, + **kwargs, + ) -> NDArray: + """ + Return the conformity scores of the data + + Parameters + ---------- + conformity_score: BaseRegressionScore + Score function that handle all that is related + to conformity scores. + + X: NDArray of shape (n_samples, n_features) + Data + + y: NDArray of shape (n_samples,) + Target + + y_pred: NDArray of shape (n_samples,) + Predictions + + sample_weight: Optional[ArrayLike] of shape (n_samples,) + Sample weights of the data, used as weights in the + calibration process. + + By default ``None``. + + groups: Optional[ArrayLike] of shape (n_samples,) + Group labels for the samples used while splitting the dataset into + train/test set. + + By default ``None``. + + Returns + ------- + NDArray of shape (n_samples,) + Conformity scores. + """ + y_enc = LabelEncoder().fit(self.predictor_.classes_).transform(y) + conformity_score = cast(BaseClassificationScore, conformity_score) + + conformity_score.set_external_attributes( + classes=self.predictor_.classes_, + random_state=self.random_state, + ) + + return conformity_score.get_conformity_scores( + y, y_pred, y_enc=y_enc, X=X, + sample_weight=sample_weight, groups=groups + ) + + def predict_score( + self, X: ArrayLike + ) -> NDArray: + """ + Compute the predicted probas, used to compute the + conformity scores. + + Parameters + ---------- + X: ArrayLike + Observed values. + + Returns + ------- + NDArray of shape (n_samples, n_classes) + Predicted probas + """ + return self.predictor_.predict_proba(X) + + def predict_bounds( + self, + X: ArrayLike, + y_pred: NDArray, + **kwargs, + ) -> NDArray: + """ + Compute the prediction sets, using the fitted ``calibrator_``. + + Parameters + ---------- + X: ArrayLike + Observed values. + + y_pred: 2D NDArray + Observed Target + + z: ArrayLike + Exogenous variables + + Returns + ------- + NDArray + Prediction sets, as a 3D array of shape (n_samples, n_classes, 1) + for compatibility reason with ``MapieClassifier``. + """ + # Classification conformity scores always have ``sym=True``, so + # the calibrator_.predict result is a 2D array with + # column 1 = -1 * column 2, So the true values are in res[:, 1] + predict_kwargs = self._get_method_arguments( + self.calibrator_.predict, + dict(zip(["X", "y_pred"], [X, y_pred])), + kwargs, + ) + conformity_score_pred = self.calibrator_.predict(**predict_kwargs) + + self.conformity_score_ = cast( + BaseClassificationScore, self.conformity_score_ + ) + + self.conformity_score_.quantiles_ = conformity_score_pred[:, [1]][ + :, :, np.newaxis + ] + + y_pred_set = self.conformity_score_.get_prediction_sets( + y_pred_proba=y_pred[:, :, np.newaxis], + conformity_scores=np.array([None]), # never used in split + alpha_np=np.array([self.alpha]), + estimator=EnsembleClassifier( # For compatibility. Only need cv + self.predictor_, + n_classes=len(np.unique(self.predictor_.classes_)), + cv="prefit", + n_jobs=-1, + random_state=self.random_state, + test_size=0.1, + verbose=0, + ) + ) + + return y_pred_set + + def predict_best(self, y_pred: NDArray) -> NDArray: + """ + Compute the prediction from the probas, using ``numpy.argmax``. + + Parameters + ---------- + y_pred: NDArray + Prediction scores (can be the prediction, the probas, ...) + + Returns + ------- + NDArray + best predictions + """ + return np.argmax(y_pred, axis=1) diff --git a/mapie/future/split/regression.py b/mapie/future/split/regression.py new file mode 100644 index 000000000..595f2e13f --- /dev/null +++ b/mapie/future/split/regression.py @@ -0,0 +1,281 @@ +from __future__ import annotations + +from typing import Optional, Tuple, Union, cast + +import numpy as np +from sklearn.base import RegressorMixin +from sklearn.model_selection import PredefinedSplit, ShuffleSplit + +from mapie._typing import ArrayLike, NDArray +from mapie.future.calibrators.base import BaseCalibrator +from mapie.future.calibrators.utils import check_calibrator +from mapie.conformity_scores import BaseRegressionScore +from mapie.conformity_scores.interface import BaseConformityScore +from mapie.conformity_scores.utils import check_regression_conformity_score +from mapie.future.split.base import SplitCP +from mapie.utils import check_estimator_regression, check_lower_upper_bounds + + +class SplitCPRegressor(SplitCP): + """ + Class to implement Conformal Prediction in ``"split"`` approach for + regression tasks, based on :class:`~future.split.base.SplitCP`. + It uses a predictor (``RegressorMixin`` object), + and a calibrator (``BaseCalibrator`` object). + + Parameters + ---------- + predictor: Optional[RegressorMixin] + Any regressor from scikit-learn API. + (i.e. with ``fit`` and ``predict`` methods). + If ``None``, ``predictor`` defaults to a ``LinearRegressor`` instance. + + By default ``"None"``. + + calibrator: Optional[BaseCalibrator] + A ``BaseCalibrator`` instance used to estimate the conformity scores. + + If ``None``, use as default a ``GaussianCCP`` instance. + + By default ``None``. + + cv: Optional[Union[int, str, ShuffleSplit, PredefinedSplit]] + The splitting strategy for computing conformity scores. + Choose among: + + - Any splitter (``ShuffleSplit`` or ``PredefinedSplit``) + with ``n_splits=1``. + - ``"prefit"``, assumes that ``predictor`` has been fitted already. + All data provided in the ``calibrate`` method is then used + for the calibration. + The user has to take care manually that data used for model fitting + and calibration (the data given in the ``calibrate`` method) + are disjoint. + - ``"split"`` or ``None``: divide the data into training and + calibration subsets (using the default ``calib_size``=0.3). + The splitter used is the following: + ``sklearn.model_selection.ShuffleSplit`` with ``n_splits=1``. + + By default ``None``. + + conformity_score: Optional[BaseRegressionScore] + BaseRegressionScore instance. + It defines the link between the observed values, the predicted ones + and the conformity scores. For instance, the default ``None`` value + correspondonds to a conformity score which assumes + y_obs = y_pred + conformity_score. + + - ``None``, to use the default ``AbsoluteBaseRegressionScore`` + symetrical conformity score + - Any ``BaseRegressionScore`` class + + By default ``None``. + + alpha: Optional[float] + Between ``0.0`` and ``1.0``, represents the risk level of the + confidence interval. + Lower ``alpha`` produce larger (more conservative) prediction + intervals. + ``alpha`` is the complement of the target coverage level. + + By default ``None`` + + random_state: Optional[int] + Integer used to set the numpy seed, to get reproducible calibration + results. + If ``None``, the prediction intervals may be stochastics and + change if you refit the calibration (even if no arguments have change). + + .. warning:: + Some methods, as the CCP method + (:class:`~mapie.future.calibrators.ccp.CCPCalibrator`), + have a stochastic behavior. To have reproductible results, + use an integer ``random_state`` value. + + However, if ``random_state`` is not ``None``, ``np.random.seed`` + will be changed, which will reset the seed for all the other random + number generators. It may have an impact on the rest of your code. + + By default ``None``. + + Examples + -------- + >>> import numpy as np + >>> from mapie.future.split import SplitCPRegressor + >>> np.random.seed(1) + >>> X_train = np.arange(0,400, 2).reshape(-1, 1) + >>> y_train = 2*X_train[:,0] + np.random.rand(len(X_train)) + >>> mapie_reg = SplitCPRegressor(alpha=0.1, random_state=1) + >>> mapie_reg = mapie_reg.fit(X_train, y_train) + >>> y_pred, y_pis = mapie_reg.predict(X_train) + """ + def __init__( + self, + predictor: Optional[RegressorMixin] = None, + calibrator: Optional[BaseCalibrator] = None, + cv: Optional[Union[int, str, ShuffleSplit, PredefinedSplit]] = None, + alpha: Optional[float] = None, + conformity_score: Optional[BaseRegressionScore] = None, + random_state: Optional[int] = None, + ) -> None: + self.random_state = random_state + self.cv = cv + self.predictor = predictor + self.conformity_score = conformity_score + self.calibrator = calibrator + self.alpha = alpha + + def _check_fit_parameters(self) -> RegressorMixin: + """ + Check and replace default value of ``predictor`` and ``cv`` arguments. + Copy the ``predictor`` in ``predictor_`` attribute if ``cv="prefit"``. + """ + self.cv = self._check_cv(self.cv) + predictor = check_estimator_regression(self.predictor, self.cv) + return predictor + + def _check_calibrate_parameters(self) -> Tuple[ + BaseRegressionScore, BaseCalibrator + ]: + """ + Check and replace default ``conformity_score``, ``alpha`` and + ``calibrator`` arguments. + """ + conformity_score_ = check_regression_conformity_score( + self.conformity_score, self.default_sym_ + ) + calibrator = check_calibrator(self.calibrator) + self._check_alpha(self.alpha) + calibrator.sym = conformity_score_.sym + calibrator.alpha = self.alpha + calibrator.random_state = self.random_state + return conformity_score_, calibrator + + def get_conformity_scores( + self, + conformity_score: BaseConformityScore, + X: NDArray, + y: NDArray, + y_pred: NDArray, + sample_weight: Optional[ArrayLike] = None, + groups: Optional[ArrayLike] = None, + **kwargs, + ) -> NDArray: + """ + Return the conformity scores of the data + + Parameters + ---------- + conformity_score: BaseRegressionScore + Score function that handle all that is related + to conformity scores. + + X: NDArray of shape (n_samples, n_features) + Data + + y: NDArray of shape (n_samples,) + Target + + y_pred: NDArray of shape (n_samples,) + Predictions + + sample_weight: Optional[ArrayLike] of shape (n_samples,) + Sample weights of the data, used as weights in the + calibration process. + + By default ``None``. + + groups: Optional[ArrayLike] of shape (n_samples,) + Group labels for the samples used while splitting the dataset into + train/test set. + + By default ``None``. + + Returns + ------- + NDArray of shape (n_samples,) + Conformity scores. + """ + + return conformity_score.get_conformity_scores( + y, y_pred, X=X, + ) + + def predict_score( + self, X: ArrayLike + ) -> NDArray: + """ + Compute the predictor prediction, used to compute the + conformity scores. + + Parameters + ---------- + X: ArrayLike + Observed values. + + Returns + ------- + NDArray of shape (n_samples, ) + predictions + """ + return self.predictor_.predict(X) + + def predict_bounds( + self, + X: ArrayLike, + y_pred: NDArray, + **kwargs, + ) -> NDArray: + """ + Compute the bounds, using the fitted ``calibrator_``. + + Parameters + ---------- + X: ArrayLike + Observed values. + + y_pred: 2D NDArray + Observed Target + + Returns + ------- + NDArray + Bounds, as a 3D array of shape (n_samples, 2, 1) + (because we only have 1 alpha value) + """ + predict_kwargs = self._get_method_arguments( + self.calibrator_.predict, + dict(zip(["X", "y_pred"], [X, y_pred])), + kwargs, + ) + conformity_score_pred = self.calibrator_.predict(**predict_kwargs) + + self.conformity_score_ = cast( + BaseRegressionScore, self.conformity_score_ + ) + y_pred_low = self.conformity_score_.get_estimation_distribution( + y_pred[:, np.newaxis], conformity_score_pred[:, [0]], X=X, + ) + y_pred_up = self.conformity_score_.get_estimation_distribution( + y_pred[:, np.newaxis], conformity_score_pred[:, [1]], X=X, + ) + + check_lower_upper_bounds(y_pred_low, y_pred_up, y_pred) + + return np.stack([y_pred_low, y_pred_up], axis=1) + + def predict_best(self, y_pred: NDArray) -> NDArray: + """ + Compute the prediction, in an array of shape (n_samples, ) + + Parameters + ---------- + y_pred: NDArray + Prediction scores + + Returns + ------- + NDArray + Predictions + """ + return y_pred diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py index f0191d4ab..8b06f3d8b 100644 --- a/mapie/regression/regression.py +++ b/mapie/regression/regression.py @@ -4,9 +4,7 @@ import numpy as np from sklearn.base import BaseEstimator, RegressorMixin -from sklearn.linear_model import LinearRegression from sklearn.model_selection import BaseCrossValidator -from sklearn.pipeline import Pipeline from sklearn.utils import check_random_state from sklearn.utils.validation import _check_y, check_is_fitted, indexable @@ -16,7 +14,7 @@ from mapie.conformity_scores.utils import check_regression_conformity_score from mapie.estimator.regressor import EnsembleRegressor from mapie.utils import (check_alpha, check_alpha_and_n_samples, - check_cv, check_estimator_fit_predict, + check_cv, check_estimator_regression, check_n_features_in, check_n_jobs, check_null_weight, check_verbose, get_effective_calibration_samples, check_predict_params) @@ -325,46 +323,6 @@ def _check_agg_function( else: return "mean" - def _check_estimator( - self, estimator: Optional[RegressorMixin] = None - ) -> RegressorMixin: - """ - Check if estimator is ``None``, - and returns a ``LinearRegression`` instance if necessary. - If the ``cv`` attribute is ``"prefit"``, - check if estimator is indeed already fitted. - - Parameters - ---------- - estimator: Optional[RegressorMixin] - Estimator to check, by default ``None``. - - Returns - ------- - RegressorMixin - The estimator itself or a default ``LinearRegression`` instance. - - Raises - ------ - ValueError - If the estimator is not ``None`` - and has no ``fit`` nor ``predict`` methods. - - NotFittedError - If the estimator is not fitted - and ``cv`` attribute is ``"prefit"``. - """ - if estimator is None: - return LinearRegression() - else: - check_estimator_fit_predict(estimator) - if self.cv == "prefit": - if isinstance(estimator, Pipeline): - check_is_fitted(estimator[-1]) - else: - check_is_fitted(estimator) - return estimator - def _check_ensemble( self, ensemble: bool, ) -> None: @@ -433,7 +391,7 @@ def _check_fit_parameters( if self.cv in ["split", "prefit"] and \ self.method in ["naive", "plus", "minmax"]: self.method = "base" - estimator = self._check_estimator(self.estimator) + estimator = check_estimator_regression(self.estimator, cv) agg_function = self._check_agg_function(self.agg_function) cs_estimator = check_regression_conformity_score( self.conformity_score, self.default_sym_ diff --git a/mapie/tests/test_ccp_calibrator.py b/mapie/tests/test_ccp_calibrator.py new file mode 100644 index 000000000..b90fade7d --- /dev/null +++ b/mapie/tests/test_ccp_calibrator.py @@ -0,0 +1,350 @@ +from __future__ import annotations + +from typing import Any, Dict, List + +import numpy as np +import pytest +from sklearn.base import clone +from sklearn.datasets import make_regression +from sklearn.utils.validation import check_is_fitted +from sklearn.model_selection import ShuffleSplit + +from mapie.future.calibrators.ccp import (CCPCalibrator, CustomCCP, + GaussianCCP, PolynomialCCP) +from mapie.future.calibrators.ccp.utils import check_required_arguments +from mapie.future.split import SplitCPRegressor + +random_state = 1 +np.random.seed(random_state) + +X, y = make_regression( + n_samples=500, n_features=10, noise=1.0, random_state=random_state +) +z = X[:, -2:] + +PHI = [ + CustomCCP(lambda X: np.ones((len(X), 1))), + CustomCCP(None, bias=True), + CustomCCP([lambda X: X]), + CustomCCP([lambda X: X, lambda z: z]), + CustomCCP([lambda X: X, lambda y_pred: y_pred]), + PolynomialCCP(2, "X", bias=True), + PolynomialCCP([1, 2], "X", bias=True), + PolynomialCCP([1, 4, 5], "y_pred", bias=False), + PolynomialCCP([0, 1, 4, 5], "y_pred", bias=False), + PolynomialCCP([0, 1, 3], "z", bias=False), + GaussianCCP(4), + CustomCCP([lambda X: X, PolynomialCCP(2)]), + CustomCCP([lambda X: X, GaussianCCP(2)]), + CustomCCP([ + lambda X: X, PolynomialCCP([1, 2], bias=False) + ]), + (lambda X: (X[:, 0] < 3))*CustomCCP([lambda X: X]), + CustomCCP([lambda X: X])*(lambda X: (X[:, 0] < 3)), + CustomCCP([lambda X: X])*None, + CustomCCP([lambda X: X])*(lambda X: (X[:, 0] < 3))*( + lambda X: (X[:, [0]] > 0)), +] + +# n_out without bias +N_OUT = [1, 1, 10, 12, 11, 21, 21, 3, 4, 5, 4, 31, 12, 30, 10, 10, 10, 10] + +GAUSS_NEED_FIT_SETTINGS: List[Dict[str, Any]] = [ + { + "points": 10, + "sigma": 1, + }, + { + "points": 10, + "sigma": None, + }, + { + "points": 10, + "sigma": None, + "random_sigma": True, + }, + { + "points": 10, + "sigma": None, + "random_sigma": False, + }, + { + "points": np.ones((2, X.shape[1])), + "sigma": None, + }, +] + +GAUSS_NO_NEED_FIT_SETTINGS: List[Dict[str, Any]] = [ + { + "points": np.ones((2, X.shape[1])), + "sigma": np.ones(X.shape[1]), + }, + { + "points": (np.ones((2, X.shape[1])), [1, 2]), + "sigma": None, + }, + { + "points": (np.ones((2, X.shape[1])), np.ones((2, X.shape[1]))), + "sigma": None, + }, +] + + +# ======== CustomCCP ========= +@pytest.mark.parametrize("calibrator", PHI) +def test_custom_ccp_calibrator(calibrator: Any) -> None: + """Test that initialization does not crash.""" + mapie = SplitCPRegressor(calibrator=calibrator, alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + mapie.predict(X, z=z) + + +@pytest.mark.parametrize("calibrator, n_out_raw", zip(PHI, N_OUT)) +def test_ccp_calibrator_n_attributes( + calibrator: CCPCalibrator, n_out_raw: int +) -> None: + """ + Test that the n_in and n_out attributes are corrects + """ + mapie = SplitCPRegressor(calibrator=clone(calibrator), alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + assert mapie.calibrator_.n_in == 10 + assert mapie.calibrator_.n_out == n_out_raw + + +def test_invalid_multiplication() -> None: + with pytest.raises(ValueError, match="The function used as multiplier "): + mapie = SplitCPRegressor( + calibrator=CustomCCP([lambda X: X])*( + lambda X: (X[:, [0, 1]] > 0)), + alpha=0.1, + ) + mapie.fit(X, y, calib_kwargs={"z": z}) + + +@pytest.mark.parametrize("functions", [ + [lambda X, other: X + other, lambda X, other: X - other], + [lambda X, other: X + other] +]) +def test_custom_functions_error(functions: Any) -> None: + """ + Test that creating a CCPCalibrator object with functions which have + required arguments different from 'X', 'y_pred' or 'z' raise an error. + """ + for f in functions: # For coverage + f(np.ones((10, 1)), np.ones((10, 1))) + with pytest.raises( + ValueError, + match=r"Forbidden required argument in `CustomCCP` calibrator." + ): + mapie = SplitCPRegressor(calibrator=CustomCCP(functions), alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + + +@pytest.mark.parametrize("functions", [ + [lambda X, d=1: X + d, lambda X, d=2: X - d], + [lambda X, c=1, d=1: X + c*d] +]) +def test_custom_functions_optional_arg(functions: Any) -> None: + """ + Test that creating a CCPCalibrator object with functions which have + optional arguments doesn't raise an error. + """ + for f in functions: # For coverage + f(np.ones((10, 1))) + mapie = SplitCPRegressor(calibrator=CustomCCP(functions), alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + + +def test_empty_custom_calibrator() -> None: + """ + Test that creating a CCPCalibrator object with functions which have + required arguments different from 'X', 'y_pred' or 'z' raise an error. + """ + with pytest.raises(ValueError): + mapie = SplitCPRegressor(calibrator=CustomCCP([], bias=False), + alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + + +# ======== PolynomialCCP ========= +def test_poly_calibrator_default_init() -> None: + """Test that initialization does not crash.""" + mapie = SplitCPRegressor(calibrator=PolynomialCCP(), alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + + +@pytest.mark.parametrize("degree", [2, [0, 1, 3]]) +@pytest.mark.parametrize("variable", ["X", "y_pred", "z"]) +@pytest.mark.parametrize("bias", [True, False]) +@pytest.mark.parametrize("normalized", [True, False]) +def test_poly_calibrator_init_other( + degree: Any, variable: Any, bias: bool, normalized: bool +) -> None: + """Test that initialization does not crash.""" + mapie = SplitCPRegressor(calibrator=PolynomialCCP( + degree, variable, bias, normalized), alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + + +@pytest.mark.parametrize("var", ["other", 1, np.ones((10, 1))]) +def test_invalid_variable_value(var: Any) -> None: + """ + Test that invalid variable value raise error + """ + with pytest.raises(ValueError): + mapie = SplitCPRegressor(calibrator=PolynomialCCP(variable=var), + alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + + +# ======== GaussianCCP ========= +def test_gauss_calibrator_default_init() -> None: + """Test that initialization does not crash.""" + mapie = SplitCPRegressor(calibrator=GaussianCCP(), alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + + +@pytest.mark.parametrize("points", [3, [X[0, :], X[3, :], X[7, :]], + ([[1], [2], [3]], [1, 2, 3])]) +@pytest.mark.parametrize("sigma", [None, 1, list(range(X.shape[1]))]) +@pytest.mark.parametrize("random_sigma", [True, False]) +@pytest.mark.parametrize("bias", [True, False]) +@pytest.mark.parametrize("normalized", [True, False]) +def test_poly_gauss_init_other( + points: Any, sigma: Any, random_sigma: Any, bias: bool, normalized: bool +) -> None: + """Test that initialization does not crash.""" + mapie = SplitCPRegressor(calibrator=GaussianCCP( + points, sigma, random_sigma, bias, normalized), alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + + +@pytest.mark.parametrize("points", [np.ones((10)), + np.ones((10, 2, 2))]) +def test_invalid_gauss_points(points: Any) -> None: + """ + Test that invalid ``GaussianCCP`` ``points``argument values raise + an error + """ + with pytest.raises(ValueError, match="Invalid `points` argument."): + mapie = SplitCPRegressor(calibrator=GaussianCCP(points), alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + + +def test_invalid_gauss_points_2() -> None: + """ + Test that invalid ``GaussianCCP`` ``points``argument values raise + an error + """ + with pytest.raises(ValueError, match="There should have as many points"): + mapie = SplitCPRegressor(calibrator=GaussianCCP( + points=(np.ones((10, 3)), np.ones((8, 3)))), alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + + +def test_invalid_gauss_points_3() -> None: + """ + Test that invalid ``GaussianCCP`` ``points``argument values raise + an error + """ + with pytest.raises(ValueError, match="The standard deviation 2D array"): + mapie = SplitCPRegressor(calibrator=GaussianCCP( + points=(np.ones((10, 3)), np.ones((10, 2)))), alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + + +@pytest.mark.parametrize("sigma", ["1", + np.ones((10, 2)), + np.ones((8, 1)), + np.ones(8)]) +def test_invalid_gauss_sigma(sigma: Any) -> None: + """ + Test that invalid ``GaussianCCP`` ``sigma``argument values raise an + error + """ + with pytest.raises(ValueError): + mapie = SplitCPRegressor(calibrator=GaussianCCP(3, sigma), + alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + + +@pytest.mark.parametrize("ind", range(len(GAUSS_NEED_FIT_SETTINGS))) +def test_gauss_need_calib(ind: int) -> None: + """ + Test that ``GaussianCCP`` arguments that require later completion + have ``_need_x_calib`` = ``True`` + """ + mapie = SplitCPRegressor(calibrator=GaussianCCP( + **GAUSS_NEED_FIT_SETTINGS[ind]), alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + check_is_fitted(mapie.calibrator_, mapie.calibrator_.fit_attributes) + + +@pytest.mark.parametrize("ind", range(len(GAUSS_NO_NEED_FIT_SETTINGS))) +def test_gauss_no_need_calib(ind: int) -> None: + """ + Test that ``GaussianCCP`` arguments that don't require later + completion have ``_need_x_calib`` = ``False`` + """ + mapie = SplitCPRegressor(calibrator=GaussianCCP( + **GAUSS_NEED_FIT_SETTINGS[ind]), alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + check_is_fitted(mapie.calibrator_, mapie.calibrator_.fit_attributes) + + +@pytest.mark.parametrize("arg1", ["a", None, 1]) +@pytest.mark.parametrize("arg2", ["a", None, 1]) +def test_check_required_arguments(arg1: Any, arg2: Any) -> None: + """ + Test that a ValueError is raised if any of the given argument is ``None``. + """ + if arg1 is None or arg2 is None: + with pytest.raises(ValueError): + check_required_arguments(arg1, arg2) + else: + check_required_arguments(arg1, arg2) + + +@pytest.mark.parametrize("calibrator", [ + GaussianCCP(20)*(lambda X: X[:, 0] > 0), + (lambda X: X > 0)*GaussianCCP(20), +]) +def test_gaussian_sampling_with_multiplier(calibrator: CCPCalibrator): + """ + Test that the points sampled (for the gaussian centers), are sampled + within the points which have a not null multiplier value + """ + mapie = SplitCPRegressor(calibrator=calibrator, alpha=0.1) + mapie.fit(np.linspace(-100, 100, 1000).reshape(-1, 1), np.ones(1000)) + + assert all(mapie.calibrator_.points_[i] > 0 for i in range(20)) + + +@pytest.mark.parametrize("calibrator", [ + GaussianCCP(15)*(lambda X: X[:, 0] > 0), +]) +def test_gaussian_sampling_error_not_enough_points(calibrator: CCPCalibrator): + """ + Test that the calibration samples with a not null multiplier value + to sample the ``points`` points. + """ + mapie = SplitCPRegressor(calibrator=calibrator, alpha=0.1, + cv=ShuffleSplit(1, test_size=0.5)) + + with pytest.raises(ValueError, match="There are not enough samples with"): + mapie.fit(np.linspace(-10, 10, 40).reshape(-1, 1), np.ones(40)) + + +@pytest.mark.parametrize("calibrator", [ + GaussianCCP(30), +]) +def test_gaussian_sampling_error_not_enough_points2(calibrator: CCPCalibrator): + """ + Test that the calibration samples to sample the ``points`` points. + """ + mapie = SplitCPRegressor(calibrator=calibrator, alpha=0.1, + cv=ShuffleSplit(1, test_size=0.5)) + + with pytest.raises(ValueError, match="There is not enough valid samples"): + mapie.fit(np.linspace(-10, 10, 40).reshape(-1, 1), np.ones(40)) diff --git a/mapie/tests/test_futur_classification.py b/mapie/tests/test_futur_classification.py new file mode 100644 index 000000000..5ef7fc515 --- /dev/null +++ b/mapie/tests/test_futur_classification.py @@ -0,0 +1,596 @@ +from __future__ import annotations + +from inspect import signature +from typing import Any, Callable, cast + +import numpy as np +import pytest +from sklearn.base import ClassifierMixin, clone +from sklearn.datasets import make_classification +from sklearn.dummy import DummyClassifier +from sklearn.ensemble import GradientBoostingClassifier +from sklearn.exceptions import NotFittedError +from sklearn.linear_model import LogisticRegression +from sklearn.model_selection import (KFold, LeaveOneOut, LeavePOut, + PredefinedSplit, RepeatedKFold, + ShuffleSplit, TimeSeriesSplit, + train_test_split) +from sklearn.pipeline import make_pipeline + +from mapie._typing import NDArray +from mapie.future.calibrators.ccp import (CCPCalibrator, CustomCCP, + GaussianCCP, PolynomialCCP) +from mapie.conformity_scores import LACConformityScore, APSConformityScore +from mapie.conformity_scores import BaseClassificationScore +from mapie.metrics import classification_coverage_score +from mapie.future.split import SplitCPClassifier + +random_state = 1 +np.random.seed(random_state) + +N_CLASSES = 4 +X, y = make_classification( + n_samples=200, n_features=10, + n_informative=N_CLASSES, n_classes=N_CLASSES, + random_state=random_state +) +z = X[:, -2:] + +CV = ["prefit", "split"] + +PHI = [ + CustomCCP([lambda X: np.ones((len(X), 1))]), + PolynomialCCP([0, 1]), + GaussianCCP(5), +] +WIDTHS = { + "split": 1.835, + "prefit": 1.835, +} + +COVERAGES = { + "split": 0.885, + "prefit": 0.885, +} + + +# ======== MapieCCPRegressor ========= +def test_initialized() -> None: + """Test that initialization does not crash.""" + SplitCPClassifier(alpha=0.1) + + +def test_fit_predictor() -> None: + """Test that fit_predictor raises no errors.""" + mapie = SplitCPClassifier(alpha=0.1) + mapie.fit_predictor(X, y) + + +@pytest.mark.parametrize("z", [None, z]) +def test_fit_calibrator(z: Any) -> None: + """Test that fit_calibrator raises no errors.""" + mapie = SplitCPClassifier(alpha=0.1) + mapie.fit_predictor(X, y) + mapie.fit_calibrator(X, y, z=z) + + +@pytest.mark.parametrize("z", [None, z]) +def test_fit(z: Any) -> None: + """Test that fit raises no errors.""" + mapie = SplitCPClassifier(alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + + +@pytest.mark.parametrize("z", [None, z]) +def test_fit_predictor_fit_calibrator_predict(z: Any) -> None: + """Test that fit-calibrate-predict raises no errors.""" + mapie = SplitCPClassifier(alpha=0.1) + mapie.fit_predictor(X, y) + mapie.fit_calibrator(X, y, z=z) + mapie.predict(X, z=z) + + +@pytest.mark.parametrize("z", [None, z]) +def test_fit_predict(z: Any) -> None: + """Test that fit-predict raises no errors.""" + mapie = SplitCPClassifier(alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + mapie.predict(X, z=z) + + +@pytest.mark.parametrize("z", [None, z]) +def test_fit_predict_reg(z: Any) -> None: + """Test that fit-predict raises no errors.""" + mapie = SplitCPClassifier(calibrator=GaussianCCP(reg_param=0.1), + alpha=0.1) + mapie.fit(X, y, calib_kwargs={"z": z}) + mapie.predict(X, z=z) + + +def test_not_fitted_predictor_fit_calibrator() -> None: + """Test that calibrate before fit raises errors.""" + mapie = SplitCPClassifier(alpha=0.1) + with pytest.raises(NotFittedError): + mapie.fit_calibrator(X, y) + + +def test_calib_not_complete_phi() -> None: + """Test that a not complete calibrator definition raises a warning""" + with pytest.warns(UserWarning, match="WARNING: At least one row of the"): + mapie = SplitCPClassifier( + alpha=0.1, + calibrator=CustomCCP([lambda X: (X[:, 0] > 0).astype(int)], + bias=False) + ) + mapie.fit(X, y) + + +def test_predict_not_complete_phi() -> None: + """Test that a not complete calibrator definition raises a warning""" + with pytest.warns(UserWarning, match="WARNING: At least one row of the"): + mapie = SplitCPClassifier( + alpha=0.1, + calibrator=CustomCCP([lambda X: (X[:, 0] > 0).astype(int)], + bias=False) + ) + mapie.fit(X[X[:, 0] < 0], y[X[:, 0] < 0]) + mapie.predict(X) + + +def test_no_fit_predict() -> None: + """Test that predict before fit raises errors.""" + mapie = SplitCPClassifier(alpha=0.1) + with pytest.raises(NotFittedError): + mapie.predict(X) + + +def test_no_calibrate_predict() -> None: + """Test that predict before fit raises errors.""" + mapie = SplitCPClassifier(alpha=0.1) + mapie.fit_predictor(X, y) + with pytest.raises(NotFittedError): + mapie.predict(X) + + +def test_default_sample_weight() -> None: + """Test default sample weights.""" + mapie = SplitCPClassifier(alpha=0.1) + assert ( + signature(mapie.fit_predictor).parameters["sample_weight"].default + is None + ) + + +@pytest.mark.parametrize("predictor", [0, "a", KFold(), ["a", "b"]]) +def test_invalid_predictor( + predictor: Any +) -> None: + """Test that invalid predictors raise errors.""" + with pytest.raises(ValueError, match=r".*Invalid estimator.*"): + mapie = SplitCPClassifier(predictor=predictor, alpha=0.1) + mapie.fit_predictor(X, y) + + +@pytest.mark.parametrize("predictor", [ + LogisticRegression(), + make_pipeline(LogisticRegression()), +]) +def test_invalid_prefit_predictor_calibrate( + predictor: ClassifierMixin, +) -> None: + """Test that non-fitted predictor with prefit cv raise errors when + calibrate is called""" + with pytest.raises(NotFittedError): + mapie = SplitCPClassifier(predictor=predictor, cv="prefit", + alpha=0.1) + mapie.fit_calibrator(X, y) + + +@pytest.mark.parametrize("predictor", [ + LogisticRegression(), + make_pipeline(LogisticRegression()), +]) +def test_invalid_prefit_predictor_fit( + predictor: ClassifierMixin, +) -> None: + """Test that non-fitted predictor with prefit cv raise errors when fit + is called.""" + with pytest.raises(NotFittedError): + mapie = SplitCPClassifier(predictor=predictor, cv="prefit", + alpha=0.1) + mapie.fit_predictor(X, y) + + +def test_default_parameters() -> None: + """Test default values of input parameters.""" + mapie = SplitCPClassifier(random_state=random_state, alpha=0.1) + mapie.fit(X, y) + assert isinstance(mapie.predictor_, ClassifierMixin) + assert isinstance(mapie.calibrator_, GaussianCCP) + assert isinstance(mapie.cv, ShuffleSplit) + assert mapie.alpha == 0.1 + assert isinstance(mapie.conformity_score_, BaseClassificationScore) + assert isinstance(mapie.random_state, int) + + +@pytest.mark.parametrize( + "alpha", ["a", 0, 2, 1.5, -0.3] +) +def test_invalid_alpha(alpha: Any) -> None: + with pytest.raises(ValueError): + mapie = SplitCPClassifier(alpha=alpha) + mapie.fit(X, y) + + +@pytest.mark.parametrize( + "calibrator", [1, "some_string"] +) +def test_invalid_phi(calibrator: Any) -> None: + with pytest.raises(ValueError): + mapie = SplitCPClassifier(calibrator=calibrator) + mapie.fit(X, y) + + +def test_valid_predictor() -> None: + """Test that valid predictors are not corrupted""" + mapie = SplitCPClassifier( + predictor=DummyClassifier(), + random_state=random_state, + alpha=0.1, + ) + mapie.fit_predictor(X, y) + assert isinstance(mapie.predictor, DummyClassifier) + + +@pytest.mark.parametrize( + "cv", [None, ShuffleSplit(n_splits=1), + PredefinedSplit( + test_fold=[1]*(len(X)//2) + [-1]*(len(X)-len(X)//2) + ), "prefit", "split"] +) +@pytest.mark.parametrize("predictor", [ + LogisticRegression(), + make_pipeline(LogisticRegression()), +]) +def test_valid_cv(cv: Any, predictor: ClassifierMixin) -> None: + """Test that valid cv raise no errors.""" + predictor.fit(X, y) + mapie = SplitCPClassifier(predictor, CustomCCP(bias=True), cv=cv, + alpha=0.1, random_state=random_state) + mapie.fit(X, y) + mapie.predict(X) + + +@pytest.mark.parametrize( + "cv", ["dummy", 0, 1, 1.5] + [ # Cross val splitters + 3, -1, KFold(n_splits=5), LeaveOneOut(), + RepeatedKFold(n_splits=5, n_repeats=2), ShuffleSplit(n_splits=5), + TimeSeriesSplit(), LeavePOut(p=2), + PredefinedSplit(test_fold=[0]*(len(X)//4) + [1]*(len(X)//4) + + [-1]*(len(X)-len(X)//2)), + ] +) +def test_invalid_cv(cv: Any) -> None: + """Test that invalid agg_functions raise errors.""" + with pytest.raises(ValueError, match="Invalid cv argument."): + mapie = SplitCPClassifier(cv=cv, alpha=0.1, + random_state=random_state) + mapie.fit_predictor(X, y) + + +@pytest.mark.parametrize("alpha", [0.2]) +@pytest.mark.parametrize("calibrator", PHI) +@pytest.mark.parametrize("cv", CV) +@pytest.mark.parametrize("predictor", [ + LogisticRegression(), + make_pipeline(LogisticRegression()), +]) +def test_fit_calibrate_combined_equivalence( + alpha: Any, cv: Any, calibrator: CCPCalibrator, predictor: ClassifierMixin +) -> None: + """Test predict output shape.""" + predictor_1 = clone(predictor) + predictor_2 = clone(predictor) + if cv == "prefit": + predictor_1.fit(X, y) + predictor_2.fit(X, y) + + np.random.seed(random_state) + mapie_1 = SplitCPClassifier( + predictor=predictor_1, calibrator=calibrator, + cv=cv, alpha=alpha, random_state=random_state + ) + np.random.seed(random_state) + mapie_2 = SplitCPClassifier( + predictor=predictor_2, calibrator=calibrator, + cv=cv, alpha=alpha, random_state=random_state + ) + mapie_1.fit(X, y, calib_kwargs={"z": z}) + mapie_2.fit_predictor(X, y) + mapie_2.fit_calibrator(X, y, z=z) + y_pred_1, y_pis_1 = mapie_1.predict(X, z=z) + y_pred_2, y_pis_2 = mapie_2.predict(X, z=z) + np.testing.assert_allclose(y_pred_1, y_pred_2) + np.testing.assert_allclose(y_pis_1[:, 0, 0], y_pis_2[:, 0, 0]) + np.testing.assert_allclose(y_pis_1[:, 1, 0], y_pis_2[:, 1, 0]) + + +@pytest.mark.parametrize("calibrator", PHI) +@pytest.mark.parametrize("cv", CV) +@pytest.mark.parametrize("predictor", [ + LogisticRegression(), + make_pipeline(LogisticRegression()), +]) +def test_predict_output_shape_alpha( + cv: Any, calibrator: CCPCalibrator, predictor: ClassifierMixin +) -> None: + """Test predict output shape.""" + if cv == "prefit": + predictor.fit(X, y) + + mapie = SplitCPClassifier( + predictor=predictor, calibrator=calibrator, + cv=cv, alpha=0.1, random_state=random_state + ) + mapie.fit(X, y, calib_kwargs={"z": z}) + y_pred, y_pis = mapie.predict(X, z=z) + assert y_pred.shape == (X.shape[0],) + assert y_pis.shape == (X.shape[0], N_CLASSES, 1) + + +@pytest.mark.parametrize("calibrator", PHI) +@pytest.mark.parametrize("cv", CV) +@pytest.mark.parametrize("predictor", [ + LogisticRegression(), + make_pipeline(LogisticRegression()), +]) +def test_predict_output_shape_no_alpha( + cv: Any, calibrator: CCPCalibrator, predictor: ClassifierMixin +) -> None: + """Test predict output shape.""" + if cv == "prefit": + predictor.fit(X, y) + + mapie = SplitCPClassifier( + predictor=predictor, calibrator=calibrator, cv=cv, + alpha=None, random_state=random_state + ) + mapie.fit(X, y, calib_kwargs={"z": z}) + y_pred = mapie.predict(X, z=z) + assert np.array(y_pred).shape == (X.shape[0],) + + +@pytest.mark.parametrize("template", PHI) +@pytest.mark.parametrize("predictor", [ + LogisticRegression(), + make_pipeline(LogisticRegression()), +]) +def test_same_results_prefit_split( + template: CCPCalibrator, + predictor: ClassifierMixin, +) -> None: + """ + Test checking that if split and prefit method have exactly + the same data split, then we have exactly the same results. + """ + cv = ShuffleSplit(n_splits=1, test_size=0.1, random_state=random_state) + train_index, _ = list(cv.split(X))[0] + test_fold = np.ones(len(X)) + test_fold[train_index] = -1 + + pred_cv = PredefinedSplit(test_fold) + train_index, val_index = list(pred_cv.split(X, y))[0] + X_train, X_calib = X[train_index], X[val_index] + y_train, y_calib = y[train_index], y[val_index] + z_calib = z[val_index] + + calibrator = cast(CCPCalibrator, clone(template)) + calibrator._transform_params(X, y, z) + calibrator.init_value = calibrator.init_value_ + if isinstance(calibrator, GaussianCCP): + calibrator.points = (calibrator.points_, calibrator.sigmas_) + + mapie_1 = SplitCPClassifier( + clone(predictor), clone(calibrator), pred_cv, alpha=0.1, + random_state=random_state, + ) + + fitted_predictor = clone(predictor).fit(X_train, y_train) + mapie_2 = SplitCPClassifier( + fitted_predictor, clone(calibrator), cv="prefit", alpha=0.1, + random_state=random_state, + ) + + mapie_1.fit(X, y, calib_kwargs={"z": z}) + mapie_2.fit(X_calib, y_calib, calib_kwargs={"z": z_calib}) + + y_pred_1, y_pis_1 = mapie_1.predict(X, z=z) + y_pred_2, y_pis_2 = mapie_2.predict(X, z=z) + + np.testing.assert_allclose(y_pred_1, y_pred_2) + np.testing.assert_allclose(y_pis_1[:, 0, 0], y_pis_2[:, 0, 0]) + np.testing.assert_allclose(y_pis_1[:, 1, 0], y_pis_2[:, 1, 0]) + + +@pytest.mark.parametrize("calibrator", PHI) +@pytest.mark.parametrize("cv", CV) +@pytest.mark.parametrize("predictor", [ + LogisticRegression(), + make_pipeline(LogisticRegression()), +]) +def test_results_for_ordered_alpha( + cv: Any, calibrator: CCPCalibrator, predictor: ClassifierMixin +) -> None: + """ + Test that prediction intervals lower (upper) bounds give + consistent results for ordered alphas. + """ + if cv == "prefit": + predictor.fit(X, y) + + calibrator._transform_params(X) + + mapie_reg_1 = SplitCPClassifier(predictor, clone(calibrator), cv=cv, + alpha=0.05, random_state=random_state) + mapie_reg_2 = SplitCPClassifier(predictor, clone(calibrator), cv=cv, + alpha=0.1, random_state=random_state) + + mapie_reg_1.fit(X, y, calib_kwargs={"z": z}) + _, y_pis_1 = mapie_reg_1.predict(X, z=z) + mapie_reg_2.fit(X, y, calib_kwargs={"z": z}) + _, y_pis_2 = mapie_reg_1.predict(X, z=z) + + assert (y_pis_1[:, 0, 0] <= y_pis_2[:, 0, 0]).all() + assert (y_pis_1[:, 1, 0] >= y_pis_2[:, 1, 0]).all() + + +def test_results_split() -> None: + """Test prefit results on a standard train/validation/test split.""" + cv = ShuffleSplit(1, test_size=0.5, random_state=random_state) + predictor = LogisticRegression() + mapie = SplitCPClassifier( + predictor=predictor, calibrator=clone(PHI[0]), cv=cv, alpha=0.2, + random_state=random_state + ) + mapie.fit(X, y) + _, y_ps = mapie.predict(X) + width_mean = y_ps.sum(axis=1).mean() + coverage = classification_coverage_score(y, y_ps[:, :, 0]) + np.testing.assert_allclose(width_mean, WIDTHS["split"], rtol=1e-2) + np.testing.assert_allclose(coverage, COVERAGES["split"], rtol=1e-2) + + +def test_results_prefit() -> None: + """Test prefit results on a standard train/validation/test split.""" + X_train, X_calib, y_train, y_calib = train_test_split( + X, y, test_size=0.5, random_state=1 + ) + predictor = LogisticRegression().fit(X_train, y_train) + mapie = SplitCPClassifier( + predictor=predictor, calibrator=clone(PHI[0]), cv="prefit", alpha=0.2, + random_state=random_state + ) + mapie.fit(X_calib, y_calib) + _, y_ps = mapie.predict(X) + width_mean = y_ps.sum(axis=1).mean() + coverage = classification_coverage_score(y, y_ps[:, :, 0]) + np.testing.assert_allclose(width_mean, WIDTHS["prefit"], rtol=1e-2) + np.testing.assert_allclose(coverage, COVERAGES["prefit"], rtol=1e-2) + + +@pytest.mark.parametrize("calibrator", PHI) +@pytest.mark.parametrize("cv", CV) +@pytest.mark.parametrize("predictor", [ + LogisticRegression(), + make_pipeline(LogisticRegression()), +]) +@pytest.mark.parametrize( + "conformity_score", [LACConformityScore(), APSConformityScore()] +) +def test_conformity_score( + cv: Any, + calibrator: CCPCalibrator, + predictor: ClassifierMixin, + conformity_score: BaseClassificationScore, +) -> None: + """Test that any conformity score function with MAPIE raises no error.""" + + if cv == "prefit": + predictor.fit(X, y) + + mapie = SplitCPClassifier( + predictor=predictor, + calibrator=calibrator, + cv=cv, + alpha=0.1, + conformity_score=conformity_score, + random_state=random_state, + ) + mapie.fit(X, y, calib_kwargs={"z": z}) + mapie.predict(X, z=z) + + +def test_fit_parameters_passing() -> None: + """ + Test passing fit parameters, here early stopping at iteration 3. + Checks that underlying GradientBoosting predictors have used 3 iterations + only during boosting, instead of default value for n_predictors (=100). + """ + gb = GradientBoostingClassifier(random_state=random_state) + + mapie = SplitCPClassifier(predictor=gb, alpha=0.1, + random_state=random_state) + + def early_stopping_monitor(i, est, locals): + """Returns True on the 3rd iteration.""" + if i == 2: + return True + else: + return False + + mapie.fit(X, y, fit_kwargs={"monitor": early_stopping_monitor}) + + assert cast(ClassifierMixin, mapie.predictor).estimators_.shape[0] == 3 + + +@pytest.mark.parametrize("custom_method", [ + lambda local_arg: local_arg, + lambda self_arg: self_arg, + lambda kwarg_arg: kwarg_arg, + lambda local_arg, *args, **kwargs: local_arg, + lambda self_arg, *args, **kwargs: self_arg, + lambda kwarg_arg, *args, **kwargs: kwarg_arg, +]) +def test_get_method_arguments(custom_method: Callable) -> None: + mapie = SplitCPClassifier(alpha=0.1) + mapie.self_arg = 1 + local_vars = {"local_arg": 1} + kwarg_args = {"kwarg_arg": 1} + + arguments = mapie._get_method_arguments(custom_method, local_vars, + kwarg_args) + custom_method(**arguments) + + +@pytest.mark.parametrize("conformity_scores", [ + np.random.rand(200, 1), + np.random.rand(200), +]) +def test_check_conformity_scores(conformity_scores: NDArray) -> None: + mapie = SplitCPClassifier() + assert mapie._check_conformity_scores(conformity_scores).shape == (200,) + + +def test_check_conformity_scores_error() -> None: + mapie = SplitCPClassifier() + with pytest.raises(ValueError, match="Invalid conformity scores."): + mapie._check_conformity_scores(np.random.rand(200, 5)) + + +def test_invalid_classifier(): + """ + Fitted classifier must contain the ``classes_`` attribute + """ + class Custom(ClassifierMixin): + def __init__(self) -> None: + self.fitted_ = True + + def fit(self): + pass + + def predict(self): + pass + + def predict_proba(self): + pass + + invalid_cls = Custom() + # for coverage: + invalid_cls.fit() + invalid_cls.predict() + invalid_cls.predict_proba() + + mapie = SplitCPClassifier(invalid_cls, cv="prefit", alpha=0.1) + with pytest.raises(AttributeError, + match="Fitted classifier must contain 'classes_' attr"): + mapie.fit(X, y) diff --git a/mapie/tests/test_futur_regression.py b/mapie/tests/test_futur_regression.py new file mode 100644 index 000000000..b0e637bd2 --- /dev/null +++ b/mapie/tests/test_futur_regression.py @@ -0,0 +1,719 @@ +from __future__ import annotations + +import warnings +from inspect import signature +from typing import Any, Callable, Tuple, cast + +import numpy as np +import pytest +from sklearn.base import RegressorMixin, clone +from sklearn.datasets import make_regression +from sklearn.dummy import DummyRegressor +from sklearn.ensemble import GradientBoostingRegressor +from sklearn.exceptions import NotFittedError +from sklearn.linear_model import LinearRegression +from sklearn.model_selection import (KFold, LeaveOneOut, LeavePOut, + PredefinedSplit, RepeatedKFold, + ShuffleSplit, TimeSeriesSplit, + train_test_split) +from sklearn.pipeline import make_pipeline + +from mapie._typing import NDArray +from mapie.future.calibrators.ccp import (CCPCalibrator, CustomCCP, + GaussianCCP, PolynomialCCP) +from mapie.conformity_scores import (AbsoluteConformityScore, + GammaConformityScore, + ResidualNormalisedScore) +from mapie.conformity_scores import BaseRegressionScore +from mapie.metrics import regression_coverage_score +from mapie.future.split import SplitCPRegressor + +random_state = 1 +np.random.seed(random_state) + +X_toy = np.linspace(0, 10, num=200).reshape(-1, 1) +y_toy = 2*X_toy[:, 0] + (max(X_toy)/10)*np.random.rand(len(X_toy)) +z_toy = np.linspace(0, 10, num=len(X_toy)).reshape(-1, 1) + +X, y = make_regression( + n_samples=200, n_features=10, noise=1.0, random_state=random_state +) +z = X[:, -2:] + + +CV = ["prefit", "split"] + +PHI = [ + CustomCCP([lambda X: np.ones((len(X), 1))]), + PolynomialCCP([0, 1]), + GaussianCCP(5), +] +WIDTHS = { + "safe": { + "split": 4.823, + "prefit": 4.823, + }, + "unsafe": { + "split": 3.867, + "prefit": 3.867, + }, +} + +COVERAGES = { + "safe": { + "split": 0.98, + "prefit": 0.98, + }, + "unsafe": { + "split": 0.965, + "prefit": 0.965, + }, +} + + +# ======== MapieCCPRegressor ========= +def test_initialized() -> None: + """Test that initialization does not crash.""" + SplitCPRegressor(alpha=0.1) + + +def test_fit_predictor() -> None: + """Test that fit_predictor raises no errors.""" + mapie_reg = SplitCPRegressor(alpha=0.1) + mapie_reg.fit_predictor(X_toy, y_toy) + + +@pytest.mark.parametrize("z", [None, z_toy]) +def test_fit_calibrator(z: Any) -> None: + """Test that fit_calibrator raises no errors.""" + mapie_reg = SplitCPRegressor(alpha=0.1) + mapie_reg.fit_predictor(X_toy, y_toy) + mapie_reg.fit_calibrator(X_toy, y_toy, z=z) + + +@pytest.mark.parametrize("z", [None, z_toy]) +def test_fit(z: Any) -> None: + """Test that fit raises no errors.""" + mapie_reg = SplitCPRegressor(alpha=0.1) + mapie_reg.fit(X_toy, y_toy, calib_kwargs={"z": z}) + + +@pytest.mark.parametrize("z", [None, z_toy]) +def test_fit_predictor_fit_calibrator_predict(z: Any) -> None: + """Test that fit-calibrate-predict raises no errors.""" + mapie_reg = SplitCPRegressor(alpha=0.1) + mapie_reg.fit_predictor(X_toy, y_toy) + mapie_reg.fit_calibrator(X_toy, y_toy, z=z) + mapie_reg.predict(X_toy, z=z) + + +@pytest.mark.parametrize("z", [None, z_toy]) +def test_fit_predict(z: Any) -> None: + """Test that fit-predict raises no errors.""" + mapie_reg = SplitCPRegressor(alpha=0.1) + mapie_reg.fit(X_toy, y_toy, calib_kwargs={"z": z}) + mapie_reg.predict(X_toy, z=z) + + +@pytest.mark.parametrize("z", [None, z_toy]) +def test_fit_predict_reg(z: Any) -> None: + """Test that fit-predict raises no errors.""" + mapie_reg = SplitCPRegressor(calibrator=GaussianCCP(reg_param=0.1), + alpha=0.1) + mapie_reg.fit(X_toy, y_toy, calib_kwargs={"z": z}) + mapie_reg.predict(X_toy, z=z) + + +def test_not_fitted_predictor_fit_calibrator() -> None: + """Test that calibrate before fit raises errors.""" + mapie_reg = SplitCPRegressor(alpha=0.1) + with pytest.raises(NotFittedError): + mapie_reg.fit_calibrator(X_toy, y_toy) + + +def test_calib_not_complete_phi() -> None: + """Test that a not complete calibrator definition raises a warning""" + with pytest.warns(UserWarning, match="WARNING: At least one row of the"): + mapie_reg = SplitCPRegressor( + alpha=0.1, + calibrator=CustomCCP([lambda X: (X < 5).astype(int)], bias=False) + ) + mapie_reg.fit(X_toy, y_toy) + + +def test_predict_not_complete_phi() -> None: + """Test that a not complete calibrator definition raises a warning""" + with pytest.warns(UserWarning, match="WARNING: At least one row of the"): + mapie_reg = SplitCPRegressor( + alpha=0.1, + calibrator=CustomCCP([lambda X: (X < 5).astype(int)], bias=False) + ) + mapie_reg.fit(X_toy[X_toy[:, 0] < 5], y_toy[X_toy[:, 0] < 5]) + mapie_reg.predict(X_toy) + + +def test_no_fit_predict() -> None: + """Test that predict before fit raises errors.""" + mapie_reg = SplitCPRegressor(alpha=0.1) + with pytest.raises(NotFittedError): + mapie_reg.predict(X_toy) + + +def test_no_calibrate_predict() -> None: + """Test that predict before fit raises errors.""" + mapie_reg = SplitCPRegressor(alpha=0.1) + mapie_reg.fit_predictor(X_toy, y_toy) + with pytest.raises(NotFittedError): + mapie_reg.predict(X_toy) + + +def test_default_sample_weight() -> None: + """Test default sample weights.""" + mapie_reg = SplitCPRegressor(alpha=0.1) + assert ( + signature(mapie_reg.fit_predictor).parameters["sample_weight"].default + is None + ) + + +@pytest.mark.parametrize("predictor", [0, "a", KFold(), ["a", "b"]]) +def test_invalid_predictor( + predictor: Any +) -> None: + """Test that invalid predictors raise errors.""" + with pytest.raises(ValueError, match=r".*Invalid estimator.*"): + mapie = SplitCPRegressor(predictor=predictor, alpha=0.1) + mapie.fit_predictor(X, y) + + +@pytest.mark.parametrize("predictor", [ + LinearRegression(), + make_pipeline(LinearRegression()), +]) +def test_invalid_prefit_predictor_calibrate( + predictor: RegressorMixin, +) -> None: + """Test that non-fitted predictor with prefit cv raise errors when + calibrate is called""" + with pytest.raises(NotFittedError): + mapie = SplitCPRegressor(predictor=predictor, cv="prefit", + alpha=0.1) + mapie.fit_calibrator(X, y) + + +@pytest.mark.parametrize("predictor", [ + LinearRegression(), + make_pipeline(LinearRegression()), +]) +def test_invalid_prefit_predictor_fit( + predictor: RegressorMixin, +) -> None: + """Test that non-fitted predictor with prefit cv raise errors when fit + is called.""" + with pytest.raises(NotFittedError): + mapie = SplitCPRegressor(predictor=predictor, cv="prefit", + alpha=0.1) + mapie.fit_predictor(X, y) + + +def test_default_parameters() -> None: + """Test default values of input parameters.""" + mapie_reg = SplitCPRegressor(random_state=random_state, alpha=0.1) + mapie_reg.fit(X, y) + assert isinstance(mapie_reg.predictor_, RegressorMixin) + assert isinstance(mapie_reg.calibrator_, GaussianCCP) + assert isinstance(mapie_reg.cv, ShuffleSplit) + assert mapie_reg.alpha == 0.1 + assert isinstance(mapie_reg.conformity_score_, BaseRegressionScore) + assert isinstance(mapie_reg.random_state, int) + + +@pytest.mark.parametrize( + "alpha", ["a", 0, 2, 1.5, -0.3] +) +def test_invalid_alpha(alpha: Any) -> None: + with pytest.raises(ValueError): + mapie = SplitCPRegressor(alpha=alpha) + mapie.fit(X, y) + + +@pytest.mark.parametrize( + "calibrator", [1, "some_string"] +) +def test_invalid_phi(calibrator: Any) -> None: + with pytest.raises(ValueError): + mapie = SplitCPRegressor(calibrator=calibrator) + mapie.fit(X, y) + + +def test_valid_predictor() -> None: + """Test that valid predictors are not corrupted""" + mapie_reg = SplitCPRegressor( + predictor=DummyRegressor(), + random_state=random_state, + alpha=0.1, + ) + mapie_reg.fit_predictor(X_toy, y_toy) + assert isinstance(mapie_reg.predictor, DummyRegressor) + + +@pytest.mark.parametrize( + "cv", [None, ShuffleSplit(n_splits=1), + PredefinedSplit( + test_fold=[1]*(len(X_toy)//2) + [-1]*(len(X_toy)-len(X_toy)//2) + ), "prefit", "split"] +) +@pytest.mark.parametrize("predictor", [ + LinearRegression(), + make_pipeline(LinearRegression()), +]) +def test_valid_cv(cv: Any, predictor: RegressorMixin) -> None: + """Test that valid cv raise no errors.""" + predictor.fit(X_toy, y_toy) + mapie_reg = SplitCPRegressor(predictor, CustomCCP(bias=True), cv=cv, + alpha=0.1, random_state=random_state) + mapie_reg.fit(X_toy, y_toy) + mapie_reg.predict(X_toy) + + +@pytest.mark.parametrize( + "cv", ["dummy", 0, 1, 1.5] + [ # Cross val splitters + 3, -1, KFold(n_splits=5), LeaveOneOut(), + RepeatedKFold(n_splits=5, n_repeats=2), ShuffleSplit(n_splits=5), + TimeSeriesSplit(), LeavePOut(p=2), + PredefinedSplit(test_fold=[0]*(len(X_toy)//4) + [1]*(len(X_toy)//4) + + [-1]*(len(X_toy)-len(X_toy)//2)), + ] +) +def test_invalid_cv(cv: Any) -> None: + """Test that invalid agg_functions raise errors.""" + with pytest.raises(ValueError, match="Invalid cv argument."): + mapie = SplitCPRegressor(cv=cv, alpha=0.1, + random_state=random_state) + mapie.fit_predictor(X, y) + + +@pytest.mark.parametrize("dataset", [(X, y, z), (X_toy, y_toy, z_toy)]) +@pytest.mark.parametrize("alpha", [0.2]) +@pytest.mark.parametrize("calibrator", PHI) +@pytest.mark.parametrize("cv", CV) +@pytest.mark.parametrize("predictor", [ + LinearRegression(), + make_pipeline(LinearRegression()), +]) +def test_fit_calibrate_combined_equivalence( + alpha: Any, dataset: Tuple[NDArray, NDArray, NDArray], + cv: Any, calibrator: CCPCalibrator, predictor: RegressorMixin +) -> None: + """Test predict output shape.""" + (X, y, z) = dataset + + predictor_1 = clone(predictor) + predictor_2 = clone(predictor) + if cv == "prefit": + predictor_1.fit(X, y) + predictor_2.fit(X, y) + + np.random.seed(random_state) + mapie_1 = SplitCPRegressor( + predictor=predictor_1, calibrator=calibrator, + cv=cv, alpha=alpha, random_state=random_state + ) + np.random.seed(random_state) + mapie_2 = SplitCPRegressor( + predictor=predictor_2, calibrator=calibrator, + cv=cv, alpha=alpha, random_state=random_state + ) + mapie_1.fit(X, y, calib_kwargs={"z": z}) + mapie_2.fit_predictor(X, y) + mapie_2.fit_calibrator(X, y, z=z) + y_pred_1, y_pis_1 = mapie_1.predict(X, z=z) + y_pred_2, y_pis_2 = mapie_2.predict(X, z=z) + np.testing.assert_allclose(y_pred_1, y_pred_2) + np.testing.assert_allclose(y_pis_1[:, 0, 0], y_pis_2[:, 0, 0]) + np.testing.assert_allclose(y_pis_1[:, 1, 0], y_pis_2[:, 1, 0]) + + +@pytest.mark.parametrize("dataset", [(X, y, z), (X_toy, y_toy, z_toy)]) +@pytest.mark.parametrize("calibrator", PHI) +@pytest.mark.parametrize("cv", CV) +@pytest.mark.parametrize("predictor", [ + LinearRegression(), + make_pipeline(LinearRegression()), +]) +def test_predict_output_shape_alpha( + dataset: Tuple[NDArray, NDArray, NDArray], + cv: Any, calibrator: CCPCalibrator, predictor: RegressorMixin +) -> None: + """Test predict output shape.""" + (X, y, z) = dataset + if cv == "prefit": + predictor.fit(X, y) + + mapie_reg = SplitCPRegressor( + predictor=predictor, calibrator=calibrator, + cv=cv, alpha=0.1, random_state=random_state + ) + mapie_reg.fit(X, y, calib_kwargs={"z": z}) + y_pred, y_pis = mapie_reg.predict(X, z=z) + assert y_pred.shape == (X.shape[0],) + assert y_pis.shape == (X.shape[0], 2, 1) + + +@pytest.mark.parametrize("dataset", [(X, y, z), (X_toy, y_toy, z_toy)]) +@pytest.mark.parametrize("calibrator", PHI) +@pytest.mark.parametrize("cv", CV) +@pytest.mark.parametrize("predictor", [ + LinearRegression(), + make_pipeline(LinearRegression()), +]) +def test_predict_output_shape_no_alpha( + dataset: Tuple[NDArray, NDArray, NDArray], + cv: Any, calibrator: CCPCalibrator, predictor: RegressorMixin +) -> None: + """Test predict output shape.""" + (X, y, z) = dataset + if cv == "prefit": + predictor.fit(X, y) + + mapie_reg = SplitCPRegressor( + predictor=predictor, calibrator=calibrator, cv=cv, + alpha=None, random_state=random_state + ) + mapie_reg.fit(X, y, calib_kwargs={"z": z}) + y_pred = mapie_reg.predict(X, z=z) + assert np.array(y_pred).shape == (X.shape[0],) + + +@pytest.mark.parametrize("dataset", [(X, y, z), (X_toy, y_toy, z_toy)]) +@pytest.mark.parametrize("template", PHI) +@pytest.mark.parametrize("predictor", [ + LinearRegression(), + make_pipeline(LinearRegression()), +]) +def test_same_results_prefit_split( + dataset: Tuple[NDArray, NDArray, NDArray], template: CCPCalibrator, + predictor: RegressorMixin, +) -> None: + """ + Test checking that if split and prefit method have exactly + the same data split, then we have exactly the same results. + """ + (X, y, z) = dataset + cv = ShuffleSplit(n_splits=1, test_size=0.1, random_state=random_state) + train_index, _ = list(cv.split(X))[0] + test_fold = np.ones(len(X)) + test_fold[train_index] = -1 + + pred_cv = PredefinedSplit(test_fold) + train_index, val_index = list(pred_cv.split(X, y))[0] + X_train, X_calib = X[train_index], X[val_index] + y_train, y_calib = y[train_index], y[val_index] + z_calib = z[val_index] + + calibrator = cast(CCPCalibrator, clone(template)) + calibrator._transform_params(X, y, z) + calibrator.init_value = calibrator.init_value_ + if isinstance(calibrator, GaussianCCP): + calibrator.points = (calibrator.points_, calibrator.sigmas_) + + mapie_1 = SplitCPRegressor( + clone(predictor), clone(calibrator), pred_cv, alpha=0.1, + random_state=random_state, + ) + + fitted_predictor = clone(predictor).fit(X_train, y_train) + mapie_2 = SplitCPRegressor( + fitted_predictor, clone(calibrator), cv="prefit", alpha=0.1, + random_state=random_state, + ) + + mapie_1.fit(X, y, calib_kwargs={"z": z}) + mapie_2.fit(X_calib, y_calib, calib_kwargs={"z": z_calib}) + + y_pred_1, y_pis_1 = mapie_1.predict(X, z=z) + y_pred_2, y_pis_2 = mapie_2.predict(X, z=z) + + np.testing.assert_allclose(y_pred_1, y_pred_2) + np.testing.assert_allclose(y_pis_1[:, 0, 0], y_pis_2[:, 0, 0]) + np.testing.assert_allclose(y_pis_1[:, 1, 0], y_pis_2[:, 1, 0]) + + +@pytest.mark.parametrize("dataset", [(X, y, z), (X_toy, y_toy, z_toy)]) +@pytest.mark.parametrize("calibrator", PHI) +@pytest.mark.parametrize("cv", CV) +@pytest.mark.parametrize("predictor", [ + LinearRegression(), + make_pipeline(LinearRegression()), +]) +def test_results_for_ordered_alpha( + dataset: Tuple[NDArray, NDArray, NDArray], cv: Any, + calibrator: CCPCalibrator, predictor: RegressorMixin +) -> None: + """ + Test that prediction intervals lower (upper) bounds give + consistent results for ordered alphas. + """ + (X, y, z) = dataset + if cv == "prefit": + predictor.fit(X, y) + + calibrator._transform_params(X) + + mapie_reg_1 = SplitCPRegressor(predictor, clone(calibrator), cv=cv, + alpha=0.05, random_state=random_state) + mapie_reg_2 = SplitCPRegressor(predictor, clone(calibrator), cv=cv, + alpha=0.1, random_state=random_state) + + mapie_reg_1.fit(X, y, calib_kwargs={"z": z}) + _, y_pis_1 = mapie_reg_1.predict(X, z=z) + mapie_reg_2.fit(X, y, calib_kwargs={"z": z}) + _, y_pis_2 = mapie_reg_1.predict(X, z=z) + + assert (y_pis_1[:, 0, 0] <= y_pis_2[:, 0, 0]).all() + assert (y_pis_1[:, 1, 0] >= y_pis_2[:, 1, 0]).all() + + +@pytest.mark.parametrize("dataset", [(X, y, z), (X_toy, y_toy, z_toy)]) +@pytest.mark.parametrize("cv", CV) +@pytest.mark.parametrize("predictor", [ + LinearRegression(), + make_pipeline(LinearRegression()), +]) +def test_results_with_constant_sample_weights( + dataset: Tuple[NDArray, NDArray, NDArray], + cv: Any, + predictor: RegressorMixin, +) -> None: + """ + Test predictions when sample weights are None + or constant with different values. + """ + (X, y, z) = dataset + if cv == "prefit": + predictor.fit(X, y) + + calibrator = cast(CCPCalibrator, clone(PHI[0])) + calibrator._transform_params(X) + calibrator.init_value = calibrator.init_value_ + + n_samples = len(X) + mapie0 = SplitCPRegressor(predictor, clone(calibrator), + cv=cv, alpha=0.1, random_state=random_state) + mapie1 = SplitCPRegressor(predictor, clone(calibrator), + cv=cv, alpha=0.1, random_state=random_state) + mapie2 = SplitCPRegressor(predictor, clone(calibrator), + cv=cv, alpha=0.1, random_state=random_state) + + mapie0.fit(X, y, sample_weight=None, calib_kwargs={"z": z}) + mapie1.fit(X, y, sample_weight=np.ones(shape=n_samples), + calib_kwargs={"z": z}) + mapie2.fit(X, y, sample_weight=np.ones(shape=n_samples) * 3, + calib_kwargs={"z": z}) + + y_pred0, y_pis0 = mapie0.predict(X, z=z) + y_pred1, y_pis1 = mapie1.predict(X, z=z) + y_pred2, y_pis2 = mapie2.predict(X, z=z) + np.testing.assert_allclose(y_pred0, y_pred1, rtol=1e-2, atol=1e-2) + np.testing.assert_allclose(y_pred0, y_pred2, rtol=1e-2, atol=1e-2) + np.testing.assert_allclose(y_pis0, y_pis1, rtol=1e-2, atol=1e-2) + np.testing.assert_allclose(y_pis0, y_pis2, rtol=1e-2, atol=1e-2) + + +@pytest.mark.parametrize("dataset", [ + (X, y, z), (X_toy, y_toy, z_toy), + (np.arange(0, 100).reshape(-1, 1), np.arange(0, 100), None) +]) +@pytest.mark.parametrize("calibrator", PHI) +@pytest.mark.parametrize("cv", CV) +@pytest.mark.parametrize("alpha", [0.2, 0.1, 0.05]) +@pytest.mark.parametrize("predictor", [ + LinearRegression(), + make_pipeline(LinearRegression()), +]) +def test_prediction_between_low_up( + dataset: Tuple[NDArray, NDArray, NDArray], + cv: Any, + calibrator: CCPCalibrator, + alpha: float, + predictor: RegressorMixin +) -> None: + """Test that prediction lies between low and up prediction intervals.""" + (X, y, z) = dataset + + if cv == "prefit": + predictor.fit(X, y) + + mapie = SplitCPRegressor(predictor=predictor, calibrator=calibrator, + cv=cv, alpha=alpha, random_state=random_state) + mapie.fit(X, y, calib_kwargs={"z": z}) + + with warnings.catch_warnings(record=True) as record: + y_pred, y_pis = mapie.predict(X, z=z) + + # Check if the warning was issued + warning_issued = any("The predictions are ill-sorted." in str(w.message) + for w in record) + + # Perform assertions based on whether the warning was issued + if not warning_issued: + assert (y_pred >= y_pis[:, 0, 0]).all() + assert (y_pred <= y_pis[:, 1, 0]).all() + + +@pytest.mark.parametrize("predict_mode", [ + "safe", "unsafe" +]) +def test_linear_regression_results(predict_mode: str) -> None: + """ + Test that the CCPCalibrator method in the case of a constant + calibrator = x -> np.ones(len(x)), on a multivariate linear regression + problem with fixed random state, is strictly equivalent to the regular + CP method (base, jacknife and cv) + """ + + mapie = SplitCPRegressor( + calibrator=clone(PHI[0]), + cv=ShuffleSplit(n_splits=1, test_size=0.5, random_state=random_state), + alpha=0.05, + random_state=random_state + ) + mapie.fit(X, y) + _, y_pis = mapie.predict( + X, unsafe_approximation=bool(predict_mode == "unsafe") + ) + y_pred_low, y_pred_up = y_pis[:, 0, 0], y_pis[:, 1, 0] + width_mean = (y_pred_up - y_pred_low).mean() + coverage = regression_coverage_score(y, y_pred_low, y_pred_up) + np.testing.assert_allclose( + width_mean, WIDTHS[predict_mode]["split"], rtol=1e-2 + ) + np.testing.assert_allclose( + coverage, COVERAGES[predict_mode]["split"], rtol=1e-2 + ) + + +@pytest.mark.parametrize("predict_mode", [ + "safe", "unsafe" +]) +def test_results_prefit(predict_mode: str) -> None: + """Test prefit results on a standard train/validation/test split.""" + X_train, X_calib, y_train, y_calib = train_test_split( + X, y, test_size=0.5, random_state=1 + ) + predictor = LinearRegression().fit(X_train, y_train) + mapie_reg = SplitCPRegressor( + predictor=predictor, calibrator=clone(PHI[0]), cv="prefit", alpha=0.05, + random_state=random_state + ) + mapie_reg.fit(X_calib, y_calib) + _, y_pis = mapie_reg.predict( + X, unsafe_approximation=bool(predict_mode == "unsafe") + ) + y_pred_low, y_pred_up = y_pis[:, 0, 0], y_pis[:, 1, 0] + width_mean = (y_pred_up - y_pred_low).mean() + coverage = regression_coverage_score(y, y_pred_low, y_pred_up) + np.testing.assert_allclose( + width_mean, WIDTHS[predict_mode]["prefit"], rtol=1e-2 + ) + np.testing.assert_allclose( + coverage, COVERAGES[predict_mode]["prefit"], rtol=1e-2 + ) + + +@pytest.mark.parametrize("calibrator", PHI) +@pytest.mark.parametrize("cv", CV) +@pytest.mark.parametrize("predictor", [ + LinearRegression(), + make_pipeline(LinearRegression()), +]) +@pytest.mark.parametrize( + "conformity_score", [AbsoluteConformityScore(), GammaConformityScore(), + ResidualNormalisedScore()] +) +def test_conformity_score( + cv: Any, + calibrator: CCPCalibrator, + predictor: RegressorMixin, + conformity_score: BaseRegressionScore, +) -> None: + """Test that any conformity score function with MAPIE raises no error.""" + + if cv == "prefit": + predictor.fit(X, y + 1e3) + + mapie_reg = SplitCPRegressor( + predictor=predictor, + calibrator=calibrator, + cv=cv, + alpha=0.1, + conformity_score=conformity_score, + random_state=random_state, + ) + mapie_reg.fit(X, y + 1e3, calib_kwargs={"z": z}) + mapie_reg.predict(X, z=z) + + +def test_fit_parameters_passing() -> None: + """ + Test passing fit parameters, here early stopping at iteration 3. + Checks that underlying GradientBoosting predictors have used 3 iterations + only during boosting, instead of default value for n_predictors (=100). + """ + gb = GradientBoostingRegressor(random_state=random_state) + + mapie_reg = SplitCPRegressor(predictor=gb, alpha=0.1, + random_state=random_state) + + def early_stopping_monitor(i, est, locals): + """Returns True on the 3rd iteration.""" + if i == 2: + return True + else: + return False + + mapie_reg.fit(X, y, fit_kwargs={"monitor": early_stopping_monitor}) + + assert cast(RegressorMixin, mapie_reg.predictor).estimators_.shape[0] == 3 + + +@pytest.mark.parametrize("custom_method", [ + lambda local_arg: local_arg, + lambda self_arg: self_arg, + lambda kwarg_arg: kwarg_arg, + lambda local_arg, *args, **kwargs: local_arg, + lambda self_arg, *args, **kwargs: self_arg, + lambda kwarg_arg, *args, **kwargs: kwarg_arg, +]) +def test_get_method_arguments(custom_method: Callable) -> None: + mapie = SplitCPRegressor(alpha=0.1) + mapie.self_arg = 1 + local_vars = {"local_arg": 1} + kwarg_args = {"kwarg_arg": 1} + + arguments = mapie._get_method_arguments(custom_method, local_vars, + kwarg_args) + custom_method(**arguments) + + +@pytest.mark.parametrize("conformity_scores", [ + np.random.rand(200, 1), + np.random.rand(200), +]) +def test_check_conformity_scores(conformity_scores: NDArray) -> None: + mapie = SplitCPRegressor() + assert mapie._check_conformity_scores(conformity_scores).shape == (200,) + + +def test_check_conformity_scores_error() -> None: + mapie = SplitCPRegressor() + with pytest.raises(ValueError, match="Invalid conformity scores."): + mapie._check_conformity_scores(np.random.rand(200, 5)) + + +def test_optim_kwargs(): + mapie = SplitCPRegressor(alpha=0.1) + with pytest.warns(UserWarning, match="Iteration limit reached"): + mapie.fit( + X, y, calib_kwargs={"method": "SLSQP", "options": {"maxiter": 2}} + ) diff --git a/mapie/tests/test_standard_calibrator.py b/mapie/tests/test_standard_calibrator.py new file mode 100644 index 000000000..2da44fb23 --- /dev/null +++ b/mapie/tests/test_standard_calibrator.py @@ -0,0 +1,61 @@ +from __future__ import annotations + +import numpy as np +import pytest +from sklearn.datasets import make_regression + +from sklearn.linear_model import LinearRegression +from sklearn.model_selection import train_test_split + +from mapie.future.calibrators import StandardCalibrator +from mapie.conformity_scores import AbsoluteConformityScore +from mapie.future.split import SplitCPRegressor +from mapie.regression import MapieRegressor + +random_state = 1 +np.random.seed(random_state) + +X, y = make_regression( + n_samples=500, n_features=10, noise=1.0, random_state=random_state +) +z = X[:, -2:] + + +@pytest.mark.parametrize("sym", [True, False]) +def test_calibrator_fit(sym: bool) -> None: + """Test that calibrator has correct sym parameter""" + mapie = SplitCPRegressor(calibrator=StandardCalibrator(), alpha=0.1, + conformity_score=AbsoluteConformityScore(sym=sym)) + mapie.fit(X, y, calib_kwargs={"z": z}) + assert mapie.calibrator_.sym == sym + + +@pytest.mark.parametrize("sym", [True, False]) +def test_calibrator_fit_predict(sym: bool) -> None: + """Test that initialization does not crash.""" + mapie = SplitCPRegressor(calibrator=StandardCalibrator(), alpha=0.1, + conformity_score=AbsoluteConformityScore(sym=sym)) + mapie.fit(X, y, calib_kwargs={"z": z}) + mapie.predict(X, z=z) + + +def test_standard_equivalence() -> None: + """ + Check that ``SplitCPRegressor`` with ``StandardCalibrator`` gives the + same results as ``MapieRegressor`` with ``method='base'``. + """ + X_train, X_calib, y_train, y_calib = train_test_split( + X, y, test_size=0.5, random_state=1 + ) + predictor = LinearRegression().fit(X_train, y_train) + mapie_ccp = SplitCPRegressor(predictor, calibrator=StandardCalibrator(), + cv="prefit", alpha=0.1) + mapie_ccp.fit(X_calib, y_calib) + y_pred_ccp, y_pi_ccp = mapie_ccp.predict(X) + + mapie_split = MapieRegressor(predictor, method="base", cv="prefit") + mapie_split.fit(X_calib, y_calib) + y_pred_split, y_pi_split = mapie_split.predict(X, alpha=0.1) + + np.testing.assert_allclose(y_pred_ccp, y_pred_split) + np.testing.assert_allclose(y_pi_ccp, y_pi_split) diff --git a/mapie/utils.py b/mapie/utils.py index 23d69c438..be21a4c7b 100644 --- a/mapie/utils.py +++ b/mapie/utils.py @@ -4,15 +4,16 @@ import numpy as np from sklearn.base import ClassifierMixin, RegressorMixin -from sklearn.linear_model import LogisticRegression +from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.model_selection import (BaseCrossValidator, BaseShuffleSplit, KFold, LeaveOneOut, ShuffleSplit, train_test_split) from sklearn.pipeline import Pipeline from sklearn.utils import _safe_indexing from sklearn.utils.multiclass import type_of_target -from sklearn.utils.validation import (_check_sample_weight, _num_features, - check_is_fitted, column_or_1d) +from sklearn.utils.validation import (_check_sample_weight, _check_y, + _num_features, _num_samples, + check_is_fitted, column_or_1d, indexable) from ._compatibility import np_quantile from ._typing import ArrayLike, NDArray @@ -21,7 +22,8 @@ def check_null_weight( - sample_weight: Optional[ArrayLike], X: ArrayLike, y: ArrayLike + sample_weight: Optional[ArrayLike], + X: ArrayLike, y: ArrayLike ) -> Tuple[Optional[NDArray], ArrayLike, ArrayLike]: """ Check sample weights and remove samples with null sample weights. @@ -75,6 +77,77 @@ def check_null_weight( return sample_weight, X, y +def _sample_non_null_weight( + X: ArrayLike, + y: ArrayLike, + sample_weight: Optional[ArrayLike], + index: ArrayLike, + z: Optional[ArrayLike] = None, +) -> Tuple[ArrayLike, ArrayLike, Optional[ArrayLike], + Optional[NDArray], ArrayLike]: + """ + Perform several checks on class parameters. + + Parameters + ---------- + X: ArrayLike + Observed values. + + y: ArrayLike + Target values. + + sample_weight: Optional[NDArray] of shape (n_samples,) + Non-null sample weights. + + index: ArrayLike + Indexes of the training set. + + z: Optional[ArrayLike] + Exogenous varible + + Returns + ------- + Tuple[ArrayLike, ArrayLike, Optional[ArrayLike], Optional[NDArray]] + - ArrayLike of observed values + - ArrayLike of target values + - Optional[ArrayLike] of exogenous varible + - Optional[NDArray] of sample_weight + - ArrayLike of index of non-null weights + """ + if _num_samples(index) == 0: + return np.array([]), np.array([]), np.array([]), np.array([]), index + X_select = _safe_indexing(X, index) + y_select = _safe_indexing(y, index) + z_select = _safe_indexing(z, index) if z is not None else None + + if sample_weight is not None: + sample_weight_select = _safe_indexing( + sample_weight, index) + else: + sample_weight_select = None + + index = _safe_indexing(index, sample_weight_select != 0) + + X_select, y_select, z_select = indexable(X_select, y_select, z_select) + y_select = _check_y(y_select) + + if sample_weight_select is not None: + sample_weight_select = _check_sample_weight(sample_weight_select, + X_select) + non_null_weight = sample_weight_select != 0 + X_select = _safe_indexing(X_select, non_null_weight) + y_select = _safe_indexing(y_select, non_null_weight) + if z_select is not None: + z_select = _safe_indexing(z_select, non_null_weight) + sample_weight_select = _safe_indexing( + sample_weight_select, non_null_weight) + sample_weight_select = cast(NDArray, sample_weight_select) + + sample_weight_select = cast(Optional[NDArray], sample_weight_select) + + return X_select, y_select, z_select, sample_weight_select, index + + def fit_estimator( estimator: Union[RegressorMixin, ClassifierMixin], X: ArrayLike, @@ -694,6 +767,48 @@ def check_estimator_fit_predict( ) +def check_estimator_regression( + estimator: Optional[RegressorMixin] = None, + cv: Optional[Union[str, BaseCrossValidator, BaseShuffleSplit]] = None, +) -> RegressorMixin: + """ + Check if estimator is ``None``, + and returns a ``LinearRegression`` instance if necessary. + If the ``cv`` attribute is ``"prefit"``, + check if estimator is indeed already fitted. + + Parameters + ---------- + estimator: Optional[RegressorMixin] + Estimator to check, by default ``None``. + + Returns + ------- + RegressorMixin + The estimator itself or a default ``LinearRegression`` instance. + + Raises + ------ + ValueError + If the estimator is not ``None`` + and has no ``fit`` nor ``predict`` methods. + + NotFittedError + If the estimator is not fitted + and ``cv`` attribute is ``"prefit"``. + """ + if estimator is None: + estimator = LinearRegression() + + check_estimator_fit_predict(estimator) + if cv == "prefit": + if isinstance(estimator, Pipeline): + check_is_fitted(estimator[-1]) + else: + check_is_fitted(estimator) + return estimator + + def check_alpha_and_last_axis(vector: NDArray, alpha_np: NDArray): """Check when the dimension of vector is 3 that its last axis size is the same than the number of alphas. @@ -804,31 +919,31 @@ def get_calib_set( ( X_train, X_calib, y_train, y_calib ) = train_test_split( - X, - y, - test_size=calib_size, - random_state=random_state, - shuffle=shuffle, - stratify=stratify + X, + y, + test_size=calib_size, + random_state=random_state, + shuffle=shuffle, + stratify=stratify ) sample_weight_train = sample_weight sample_weight_calib = None else: ( - X_train, - X_calib, - y_train, - y_calib, - sample_weight_train, - sample_weight_calib, + X_train, + X_calib, + y_train, + y_calib, + sample_weight_train, + sample_weight_calib, ) = train_test_split( - X, - y, - sample_weight, - test_size=calib_size, - random_state=random_state, - shuffle=shuffle, - stratify=stratify + X, + y, + sample_weight, + test_size=calib_size, + random_state=random_state, + shuffle=shuffle, + stratify=stratify ) X_train, X_calib = cast(ArrayLike, X_train), cast(ArrayLike, X_calib) y_train, y_calib = cast(ArrayLike, y_train), cast(ArrayLike, y_calib) diff --git a/notebooks/regression/tutorial_ccp_CandC.ipynb b/notebooks/regression/tutorial_ccp_CandC.ipynb new file mode 100644 index 000000000..f0c1dbe04 --- /dev/null +++ b/notebooks/regression/tutorial_ccp_CandC.ipynb @@ -0,0 +1,839 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "10d4c771", + "metadata": {}, + "source": [ + "# Using ``SplitCPRegressor`` and ``CCPCalibrator`` to get adaptative prediction intervals\n", + "## Tutorial and comparison with other methods on \"Communities and Crimes\" Dataset." + ] + }, + { + "cell_type": "markdown", + "id": "502511f8", + "metadata": {}, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/scikit-learn-contrib/MAPIE/blob/master/notebooks/regression/tutorial_ccp_CandC.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d7990401", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: mapie in /Users/damien.brouet/Documents/Repo Mapie/MAPIE (0.8.3)\n", + "Requirement already satisfied: scikit-learn in /opt/miniconda3/envs/mapie_env/lib/python3.8/site-packages (from mapie) (1.3.2)\n", + "Requirement already satisfied: scipy in /opt/miniconda3/envs/mapie_env/lib/python3.8/site-packages (from mapie) (1.10.1)\n", + "Requirement already satisfied: numpy>=1.21 in /opt/miniconda3/envs/mapie_env/lib/python3.8/site-packages (from mapie) (1.22.3)\n", + "Requirement already satisfied: packaging in /opt/miniconda3/envs/mapie_env/lib/python3.8/site-packages (from mapie) (23.2)\n", + "Requirement already satisfied: joblib>=1.1.1 in /opt/miniconda3/envs/mapie_env/lib/python3.8/site-packages (from scikit-learn->mapie) (1.3.2)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/miniconda3/envs/mapie_env/lib/python3.8/site-packages (from scikit-learn->mapie) (3.3.0)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: ucimlrepo in /opt/miniconda3/envs/mapie_env/lib/python3.8/site-packages (0.0.7)\n", + "Requirement already satisfied: pandas>=1.0.0 in /opt/miniconda3/envs/mapie_env/lib/python3.8/site-packages (from ucimlrepo) (1.3.5)\n", + "Requirement already satisfied: certifi>=2020.12.5 in /opt/miniconda3/envs/mapie_env/lib/python3.8/site-packages (from ucimlrepo) (2024.2.2)\n", + "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/miniconda3/envs/mapie_env/lib/python3.8/site-packages (from pandas>=1.0.0->ucimlrepo) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2017.3 in /opt/miniconda3/envs/mapie_env/lib/python3.8/site-packages (from pandas>=1.0.0->ucimlrepo) (2024.1)\n", + "Requirement already satisfied: numpy>=1.20.0 in /opt/miniconda3/envs/mapie_env/lib/python3.8/site-packages (from pandas>=1.0.0->ucimlrepo) (1.22.3)\n", + "Requirement already satisfied: six>=1.5 in /opt/miniconda3/envs/mapie_env/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas>=1.0.0->ucimlrepo) (1.16.0)\n" + ] + } + ], + "source": [ + "install_mapie = True\n", + "install_ucimlrepo = True\n", + "if install_mapie:\n", + " !pip install mapie\n", + "if install_ucimlrepo:\n", + " !pip install ucimlrepo" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c5438c1b", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "import matplotlib.patches as mpatches\n", + "from tqdm import tqdm\n", + "\n", + "from lightgbm import LGBMRegressor\n", + "from mapie.future.calibrators import CustomCCP, GaussianCCP, PolynomialCCP\n", + "from mapie.future.split import SplitCPRegressor\n", + "from mapie.conformity_scores import AbsoluteConformityScore\n", + "from mapie.regression import MapieQuantileRegressor, MapieRegressor\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import PredefinedSplit, RandomizedSearchCV\n", + "from ucimlrepo import fetch_ucirepo\n", + " \n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", + "\n", + "random_state = 1\n", + "np.random.seed(random_state)" + ] + }, + { + "cell_type": "markdown", + "id": "665ea4be", + "metadata": {}, + "source": [ + "## Getting the data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4da3ba44", + "metadata": {}, + "outputs": [], + "source": [ + "# fetch dataset \n", + "communities_and_crime = fetch_ucirepo(name=\"Communities and Crime\") \n", + " \n", + "# data (as pandas dataframes) \n", + "X = communities_and_crime.data.features\n", + "y = communities_and_crime.data.targets \n", + "\n", + "X = X.drop(columns=[\"communityname\"])\n", + "# We remove columns with missing values\n", + "X = X[X.columns[(X.isna().sum()==0)&((X==\"?\").sum()==0)]]\n", + "\n", + "col_names = list(X.columns)\n", + "X = X.values\n", + "y = y.values[:,0]\n", + "\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X)" + ] + }, + { + "cell_type": "markdown", + "id": "e3ca073e", + "metadata": {}, + "source": [ + "We normalize the data, to simplify the following (even if the used model doesn't requires it)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8184e7fe", + "metadata": {}, + "outputs": [], + "source": [ + "def generate_data(seed=1, n_train=1000,n_calib=1000,n_test=500):\n", + " \"\"\"\n", + " Return a new split (x_train, y_train, x_calib, y_calib, x_test, y_test)\n", + " of the dataset, based on the ``seed`` value.\n", + " \"\"\"\n", + " np.random.seed(seed)\n", + " if n_train+n_calib+n_test > len(X):\n", + " raise ValueError(\n", + " f\"n_train + n_calib + n_test = {n_train} + {n_calib} + {n_test}\"\n", + " f\" = {n_train+n_calib+n_test} > len(total_dataset) = {len(X)}\")\n", + " \n", + " indexes = list(range(len(X)))\n", + " train_indexes = np.random.choice(indexes, n_train, replace=False)\n", + " indexes = list(set(indexes) - set(train_indexes))\n", + " calib_indexes = np.random.choice(indexes, n_calib, replace=False)\n", + " indexes = list(set(indexes) - set(calib_indexes))\n", + " test_indexes = np.random.choice(indexes, n_test, replace=False)\n", + "\n", + " scaler = StandardScaler()\n", + " X_scaled = scaler.fit_transform(X)\n", + " \n", + " return X_scaled[train_indexes,:], y[train_indexes], X_scaled[calib_indexes,:], y[calib_indexes], X_scaled[test_indexes,:], y[test_indexes]" + ] + }, + { + "cell_type": "markdown", + "id": "17abf40f", + "metadata": {}, + "source": [ + "## The goal:" + ] + }, + { + "cell_type": "markdown", + "id": "8b2ef22b", + "metadata": {}, + "source": [ + "- We will try to have an adaptative prediction interval using the ``CCP`` method (using ``CCPCalibrator``). We will compare it with standard ``Split`` CP (``MapieRegressor`` with ``method='base'``), and ``CQR`` (with ``MapieQuantileRegressor``).\n", + "\n", + "- The adaptativity will be evaluated by looking at the conditional coverage over groups of target values, and groups on features of interest.\n", + "\n", + "- The groups are the 10 target groups (see the histogram below), and the 4 quantiles (with thresholds at Q1, Q2 and Q3) on features of interest (``'racepctblack', 'racePctWhite', 'racePctAsian', 'racePctHisp'``).\n", + "Those features were chosen to make sure there is no bias toward one or the other ethnicity. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "afb83741", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "thres = [0] + [round(x, 2) for x in np.sort(y)[[int(len(y)/10*i) for i in range(1,10)]]] + [1]\n", + "fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n", + "for t in thres:\n", + " ax.axvline(t, linestyle=\"--\", c=\"r\", label=\"10% quantiles\"*int(t==0))\n", + "ax.hist(y, bins=60)\n", + "ax.set_xlabel(\"Normalized Per Capita Violent Crime\")\n", + "ax.set_title(\"Histogram\")\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0e049e14", + "metadata": {}, + "source": [ + "By doing so, we create 10 groups based on the target value, where each group has the same number of samples." + ] + }, + { + "cell_type": "markdown", + "id": "3d7fba42", + "metadata": {}, + "source": [ + "## Evaluation functions" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1c8eddff", + "metadata": {}, + "outputs": [], + "source": [ + "def estimate_scores(mapies, alpha, group_functions, score_functions,\n", + " n_train=2000,n_calib=2000,n_test=500, seed=1):\n", + " \"\"\"\n", + " Sample a new data split, train the estimator on the training set, then\n", + " fit the calibration on the new calibration set. The scores corresponding\n", + " to ``score_functions`` are computing on each group of ``group_functions``.\n", + " \"\"\"\n", + " (mapie_split, mapie_cqr, mapie_ccp) = mapies\n", + " \n", + " x_train, y_train, x_calib, y_calib, x_test, y_test = generate_data(\n", + " seed=seed,\n", + " n_train=n_train,n_calib=n_calib,n_test=n_test\n", + " )\n", + "\n", + " mapie_split.fit(np.vstack([x_train, x_calib]), np.hstack([y_train, y_calib]))\n", + " _, y_pis_split = mapie_split.predict(x_test, alpha=alpha)\n", + " \n", + " mapie_cqr.fit(x_train, y_train, X_calib=x_calib, y_calib=y_calib)\n", + " _, y_pis_cqr = mapie_cqr.predict(x_test)\n", + " \n", + " mapie_ccp.fit(np.vstack([x_train, x_calib]), np.hstack([y_train, y_calib]))\n", + " _, y_pis_ccp = mapie_ccp.predict(x_test)\n", + " \n", + " scores = np.zeros((3, len(score_functions), len(group_functions)))\n", + "\n", + " for i, y_pi in enumerate([y_pis_split, y_pis_cqr, y_pis_ccp]):\n", + " for group_num, group_fn in enumerate(group_functions):\n", + " x_filter = group_fn(x_test, y_test)\n", + " for score_num, score_fn in enumerate(score_functions):\n", + " scores[i,score_num, group_num] = score_fn(\n", + " y=y_test[x_filter],\n", + " lower=y_pi[:, 0, 0][x_filter],\n", + " upper=y_pi[:, 1, 0][x_filter]\n", + " )\n", + " \n", + " return scores\n", + "\n", + "\n", + "def get_scores_n_trials(\n", + " mapies, alpha, n_trials, group_functions, group_names,\n", + " score_functions, score_names, n_train=2000, n_calib=2000, n_test=500,\n", + " ):\n", + " \"\"\"\n", + " Compute ``n_trials`` evaluation scores on different dataset splits.\n", + " \"\"\"\n", + "\n", + " scores = np.zeros((n_trials, 3, len(score_functions), len(group_functions)))\n", + "\n", + " for trial in tqdm(range(n_trials)):\n", + " scores[trial,:,:,:] = estimate_scores(\n", + " mapies, alpha, group_functions, score_functions,\n", + " n_train, n_calib, n_test, trial\n", + " )\n", + " \n", + " method_names = [\"Split\", \"CQR\", \"CCP\"]\n", + " \n", + " scores_df = pd.DataFrame()\n", + " for group_num, group_name in enumerate([e for g in group_names for e in g]):\n", + " for method_num, method_name in enumerate(method_names):\n", + " temp_df = pd.DataFrame(\n", + " {\n", + " 'Method': [method_name] * n_trials, \n", + " 'Group name' : [group_name] * n_trials, \n", + " }\n", + " )\n", + " for score_num, score_name in enumerate(score_names):\n", + " temp_df[score_name] = scores[:,method_num, score_num, group_num] \n", + "\n", + " scores_df = pd.concat([scores_df,temp_df], axis=0)\n", + " \n", + " return scores_df.reset_index(drop=True)" + ] + }, + { + "cell_type": "markdown", + "id": "416c3a11", + "metadata": {}, + "source": [ + "## Plotting functions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d2fc2079", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_subplot(ax, y_test_sorted, y_pred_sorted, upper_pi, lower_pi, lw,\n", + " color_rgb, xlabel, ylabel, title, showlegend=False):\n", + " color = mcolors.rgb2hex(color_rgb)\n", + " ax.plot(y_test_sorted, y_pred_sorted, lw=lw, color='black', label=\"Prediction\" if showlegend else \"\")\n", + " ax.fill_between(y_test_sorted, upper_pi, lower_pi, color=color, alpha=0.3, label='Prediction interval' if showlegend else \"\")\n", + " ax.plot(y_test_sorted, upper_pi, lw=lw, color=color)\n", + " ax.plot(y_test_sorted, lower_pi, lw=lw, color=color)\n", + " ax.plot([0, 1], [0, 1], lw=lw, color='black', linestyle='--', label=\"Perfect Prediction\" if showlegend else \"\")\n", + " ax.set_ylim([-0.1, 1.1])\n", + " ax.set_xlabel(xlabel)\n", + " ax.set_ylabel(ylabel)\n", + " ax.set_title(title)\n", + "\n", + "\n", + "def plot_score_boxplot(ax, df, score_name, group_names, color_discrete_map):\n", + " flatten_group_names = [item for sub in group_names for item in sub]\n", + " for i, method in enumerate([\"Split\", \"CQR\", \"CCP\"]):\n", + " df_method = df[df[\"Method\"] == method]\n", + " color = color_discrete_map[method]\n", + " \n", + " ax.boxplot(\n", + " [df_method[df_method[\"Group name\"] == g][score_name] for g in flatten_group_names],\n", + " positions=np.arange(len(flatten_group_names)) + (i-1) * 0.2,\n", + " widths=0.2, patch_artist=True,\n", + " boxprops=dict(facecolor=color), medianprops=dict(color=\"black\"),\n", + " labels=[g if i == 1 else \"\" for g in flatten_group_names]\n", + " )\n", + "\n", + " for g in group_names[1:]:\n", + " ax.axvline(x=flatten_group_names.index(g[0]) - 0.5, color='black', linewidth=2)\n", + " ax.tick_params(axis='x', rotation=-45)\n", + " ax.set_xticks(np.arange(len(flatten_group_names)))\n", + " ax.set_xticklabels(flatten_group_names, ha='left', rotation_mode='anchor')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ee58ce3d", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_results(mapies, alpha, n_trials, group_functions, group_names, score_functions, score_names, n_train=2000, n_calib=2000, n_test=500):\n", + " (mapie_split, mapie_cqr, mapie_ccp) = mapies\n", + " x_train, y_train, x_calib, y_calib, x_test, y_test = generate_data(n_train=n_train, n_calib=n_calib, n_test=n_test)\n", + "\n", + " sort_order = np.argsort(y_test)\n", + " x_test_sorted = x_test[sort_order, :]\n", + " y_test_sorted = y_test[sort_order]\n", + "\n", + " cp = plt.get_cmap('tab10').colors\n", + " plt.rcParams['font.family'] = 'DejaVu Sans'\n", + " plt.rcParams['axes.grid'] = False\n", + "\n", + " fig, axes = plt.subplots(1, 3, figsize=(20, 5))\n", + " # ========================== Split ==========================\n", + " mapie_split.fit(np.vstack([x_train, x_calib]), np.hstack([y_train, y_calib]))\n", + " y_pred_split, y_pis_split = mapie_split.predict(x_test_sorted, alpha=alpha)\n", + " split_lower = y_pis_split[:, 0, 0]\n", + " split_upper = y_pis_split[:, 1, 0]\n", + " plot_subplot(axes[0], y_test_sorted, y_pred_split, split_upper, split_lower, 1, cp[0], \"True Price\", \"Predicted Price\", \"Split\", showlegend=True)\n", + "\n", + " # ========================== CQR ==========================\n", + " mapie_cqr.fit(x_train, y_train, X_calib=x_calib, y_calib=y_calib)\n", + " y_pred_cqr, y_pi_cqr = mapie_cqr.predict(x_test_sorted)\n", + " cqr_lower = y_pi_cqr[:, 0, 0]\n", + " cqr_upper = y_pi_cqr[:, 1, 0]\n", + " plot_subplot(axes[1], y_test_sorted, y_pred_cqr, cqr_upper, cqr_lower, 1, cp[1], \"True Price\", \"Predicted Price\", \"CQR\")\n", + "\n", + " # ========================== CCP ==========================\n", + " mapie_ccp.fit(np.vstack([x_train, x_calib]), np.hstack([y_train, y_calib]))\n", + " y_pred_ccp, y_pis_ccp = mapie_ccp.predict(x_test_sorted)\n", + " ccp_lower = y_pis_ccp[:, 0, 0]\n", + " ccp_upper = y_pis_ccp[:, 1, 0]\n", + " plot_subplot(axes[2], y_test_sorted, y_pred_ccp, ccp_upper, ccp_lower, 1, cp[2], \"True Price\", \"Predicted Price\", \"CCP\")\n", + "\n", + " lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes]\n", + " lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]\n", + " fig.legend(lines, labels, loc=\"upper right\")\n", + "\n", + " plt.subplots_adjust(top=0.95, right=0.9)\n", + " plt.show()\n", + "\n", + " if n_trials:\n", + " # ========================== Compute Scores on many tries ==========================\n", + " scores_df = get_scores_n_trials(mapies, alpha, n_trials, group_functions, group_names, score_functions, score_names, n_train, n_calib, n_test)\n", + " # ============================ Plot results ============================\n", + " for score_name in score_names:\n", + " fig, ax = plt.subplots(figsize=(30, 10))\n", + "\n", + " color_discrete_map = dict(zip([\"Split\", \"CQR\", \"CCP\"], [mcolors.rgb2hex(c) for c in cp[:3]]))\n", + "\n", + " plot_score_boxplot(ax, scores_df, score_name, group_names, color_discrete_map)\n", + "\n", + " if score_name == \"Coverage\":\n", + " ax.axhline(1 - alpha, color='red', linewidth=3)\n", + "\n", + " ax.set_title(score_name, fontsize=22)\n", + " ax.set_xlabel(\"Groups\", fontsize=20)\n", + " ax.set_ylabel(score_name, fontsize=20)\n", + " ax.tick_params(axis='both', which='major', labelsize=20)\n", + " \n", + " legend_handles = [mpatches.Patch(color=color, label=method) for method, color in color_discrete_map.items()]\n", + " fig.legend(handles=legend_handles, loc='upper right', fontsize=18)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " return scores_df" + ] + }, + { + "cell_type": "markdown", + "id": "4f7526a0", + "metadata": {}, + "source": [ + "## Evaluation methods and configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "47a9f9d4", + "metadata": {}, + "outputs": [], + "source": [ + "# Number of trials, to reduce stochasticity in the evaluation\n", + "N_TRIALS = 10\n", + "\n", + "# scores functions\n", + "coverage_funct = lambda y, lower, upper : np.mean((lower <= y) & (y <= upper))\n", + "width_funct = lambda y, lower, upper : np.mean(np.abs(upper-lower))\n", + "score_functions = [coverage_funct, width_funct]\n", + "score_names = [\"Coverage\", \"Width\"]\n", + "\n", + "# Groups functions: the scores will be evaluated on each one of these groups.\n", + "thres = thres = [0] + [round(x, 2) for x in np.sort(y)[[int(len(y)/10*i) for i in range(1,10)]]] + [1]\n", + "\n", + "# index of the 4 columns of interest:\n", + "# 'racepctblack', 'racePctWhite', 'racePctAsian', 'racePctHisp'\n", + "group_cols = [4, 5, 6, 7]\n", + "\n", + "group_functions = (\n", + " # all dataset, for marginal evaluation\n", + " [lambda x, y: np.ones(len(x)).astype(bool)]\n", + " # 10 target groups\n", + " + [lambda x, y, i=i : np.logical_and(y>=thres[i], y <= thres[i+1]) for i in range(10)]\n", + " # groups on ethnicity features\n", + " + [lambda x, y, c=c, q1=q1, q2=q2 : np.logical_and(\n", + " x[:, c] >= np.sort(X_scaled[:,c])[int(len(X_scaled)*q1)],\n", + " x[:, c] <= np.sort(X_scaled[:,c])[int(len(X_scaled)*q2)-1])\n", + " for c in group_cols\n", + " for (q1, q2) in zip([0, 0.25, 0.5, 0.75], [0.25, 0.5, 0.75, 1])\n", + " ]\n", + ")\n", + "group_names = (\n", + " [[\"MARGINAL\"]] \n", + " + [[f\"Crime: {thres[i]} - {thres[i+1]}\" for i in range(10)]]\n", + " + [\n", + " [\n", + " f\"{col_names[c]} : {q1}-{q2}%\" \n", + " for (q1, q2) in zip([0, 25, 50, 75], [25, 50, 75, 100])\n", + " ]\n", + " for c in group_cols\n", + " ]\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cbd5a78b", + "metadata": {}, + "source": [ + "## Model used for predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b62c8ad5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters found: {'num_leaves': 28, 'n_estimators': 200, 'max_depth': 8, 'learning_rate': 0.05}\n", + "Best cross-validation score: 0.0856611994572579\n" + ] + } + ], + "source": [ + "# Define the model\n", + "estimator = LGBMRegressor(\n", + " objective='quantile',\n", + " alpha=0.5,\n", + " random_state=random_state,\n", + " verbose=-1,\n", + ")\n", + "\n", + "# Define the parameter grid\n", + "param_grid = {\n", + " 'learning_rate': [0.05, 0.1, 0.2],\n", + " 'max_depth': [8, 12, 16],\n", + " 'n_estimators': [50, 100, 150, 200],\n", + " 'num_leaves': [14, 21, 28, 35]\n", + "}\n", + "\n", + "# Setup the RandomizedSearchCV\n", + "random_search = RandomizedSearchCV(\n", + " estimator,\n", + " param_distributions=param_grid,\n", + " n_iter=30,\n", + " cv=5,\n", + " n_jobs=-1,\n", + " scoring='neg_mean_absolute_error',\n", + " random_state=random_state\n", + ")\n", + "\n", + "# Perform the random search\n", + "random_search.fit(X_scaled, y)\n", + "\n", + "# Print the best parameters and the corresponding score\n", + "print(\"Best parameters found: \", random_search.best_params_)\n", + "print(\"Best cross-validation score: \", -random_search.best_score_)\n", + "\n", + "estimator = random_search.best_estimator_" + ] + }, + { + "cell_type": "markdown", + "id": "92245e47", + "metadata": {}, + "source": [ + "# Experiments and results:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d9162ab5", + "metadata": {}, + "outputs": [], + "source": [ + "ALPHA = 0.2\n", + "n_train, n_calib = 650, 650\n", + "\n", + "# PredefinedSplit is used to make sure that each method is trained\n", + "# and calibrated on the same data, to have a fair comparison\n", + "cv = PredefinedSplit([-1]*n_train + [1]*n_calib)\n", + "\n", + "# ================= Split =================\n", + "mapie_split = MapieRegressor(\n", + " estimator, method=\"base\", cv=cv,\n", + " conformity_score=AbsoluteConformityScore(sym=False)\n", + ")\n", + "\n", + "# ================= CQR =================\n", + "mapie_cqr = MapieQuantileRegressor(estimator, alpha=ALPHA)" + ] + }, + { + "cell_type": "markdown", + "id": "e3a08161", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "e57f92fb", + "metadata": {}, + "source": [ + "## 1. Using ``GaussianCCP`` calibrator for adaptativity without prior knowledge on the dataset or biases" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "45ef86a4", + "metadata": {}, + "outputs": [], + "source": [ + "calibrator_1 = GaussianCCP(40, sigma=7)\n", + "\n", + "mapie_ccp = SplitCPRegressor(\n", + " estimator, calibrator_1, cv=cv, alpha=ALPHA,\n", + " conformity_score=AbsoluteConformityScore(sym=False),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a827bd88", + "metadata": {}, + "source": [ + "### Plotting the result" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c0d5742b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10/10 [01:34<00:00, 9.43s/it]\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = plot_results(\n", + " [mapie_split, mapie_cqr, mapie_ccp], ALPHA, N_TRIALS,\n", + " group_functions, group_names, score_functions, score_names,\n", + " n_train=n_train, n_calib=n_calib, n_test=1994-n_train-n_calib\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "eb894e2f", + "metadata": {}, + "source": [ + "- The method which is the more adaptative is the one with the most constant coverage.\n", + "- Here, the ``CCP`` method is the best one. We can see that the basic ``Split`` method has a strong over-coverage for small target values, and under-coverage for big target values. Moreover, it seems to have a strong bias on the ``'racepctblack'`` and ``'racePctWhite'``.\n", + "- The ``CQR`` method is better than the ``Split`` but suffers from the same issues.\n", + "\n", + "$\\to$ We managed, with the ``CCP`` method, to have almost a homogenous coverage on the target value, and a much smaller bias on the ethnicity groups.\n", + "\n", + "$\\to$ However:\n", + "- the ``CCP`` method needs few iterations (or cross-val optimisation) to find the best parameters (especially for ``sigma``)\n", + "- its calibration is much longer than the other CP methods. The computational time can increase a lot if you have a lot of calibration points and if you use a lot of dimensions in the calibrator (here, we used ``40``)." + ] + }, + { + "cell_type": "markdown", + "id": "d101bb74", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "b2921ce7", + "metadata": {}, + "source": [ + "## 2. Using ``CustomCCP`` calibrator for adaptativity with prior knowledge about the biases we want to avoid\n", + "We saw previously, that there was a strong bias on the ethnicity features (with over or under coverage for some values).\n", + "\n", + "$\\to$ We can use this information in the ``CCP`` calibrator, to fix it. Let's use a ``CustomCCP`` calibrator, with those features, to guarantee a homogenous coverage on those.\n", + "We could just add, as custom functions definition, indicatrice functions for each of the 4 groups (split using Q1, mediane and Q3 values), for each ethnicity feature. \n", + "\n", + "However, as the coverage seems to be proportional to the ethnicity value, we will also pass the specific ``X`` value." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "32943795", + "metadata": {}, + "outputs": [], + "source": [ + "calibrator_2 = CustomCCP(\n", + " [ # We add the ethnicity feature value for each of the 4 ethnicity, to make sure there is no bias.\n", + " lambda X, c=c, q1=q1, q2=q2 : X[:, c] * np.logical_and(\n", + " X[:, c] >= np.sort(X_scaled[:, c])[int(len(X_scaled)*q1)],\n", + " X[:,c] <= np.sort(X_scaled[:, c])[int(len(X_scaled)*q2)-1]\n", + " ) \n", + " for c in group_cols\n", + " for (q1, q2) in zip([0, 0.25, 0.5, 0.75], [0.25, 0.5, 0.75, 1])\n", + " ],\n", + " normalized=True,\n", + " bias=True,\n", + ")\n", + "\n", + "mapie_ccp = SplitCPRegressor(\n", + " estimator, calibrator_2, cv=cv, alpha=ALPHA,\n", + " conformity_score=AbsoluteConformityScore(sym=False),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9638f969", + "metadata": {}, + "source": [ + "### Plotting the result" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "656e48c0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10/10 [00:30<00:00, 3.03s/it]\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = plot_results( \n", + " [mapie_split, mapie_cqr, mapie_ccp], ALPHA, N_TRIALS,\n", + " group_functions, group_names, score_functions, score_names,\n", + " n_train=n_train, n_calib=n_calib, n_test=1994-n_train-n_calib\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e2098fd1", + "metadata": {}, + "source": [ + "As we expected, the coverage is now homogenous on the ethnicity groups. To achieve it, the prediction intervals are now even wider than before for previously under-covered samples, and smaller on previously over-covered samples. \n", + "\n", + "$\\to$ The ``CCP`` method can guarantee a homogenous coverage on groups of interest (thus remove bias), by giving to the calibrator those groups, using ``CustomCCP`` calibrators.\n", + "\n", + "$\\to$ Fixing this bias, almost fixed the non-homogeneity of the coverage, on the target value.\n", + "\n", + "Next steps: the only issue to achieve an almost perfect adaptativity, is to fix the under-coverage for the biggest 10% target crime values. One idea may be to combine the two approachs we used (with indicator functions to avoid the biases and gaussian kernels for overall adaptativity), or add a new column to the calibrator, with the ``y_pred`` value (example: adding ``Polynomial([4], variable=\"y_pred\")``) to have a bigger interval for high predictions, without changing too much the smaller predictions." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}