diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 2d94cf78f..59aa6313a 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -26,7 +26,7 @@ If applicable, add screenshots to help explain your problem. **Desktop (please complete the following information):** - OS: [e.g. iOS] - Browser [e.g. chrome, safari] - - Version [e.g. 22] + - MAPIE Version [e.g. 0.3.2] **Additional context** Add any other context about the problem here. diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index dd8b1df2d..c36ccdeea 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -8,19 +8,19 @@ jobs: deploy: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 - - name: Set up Python - uses: actions/setup-python@v2 - with: - python-version: '3.9' - - name: Install build dependencies - run: | - python -m pip install --upgrade pip - pip install setuptools wheel twine - - name: Build package - run: python setup.py sdist bdist_wheel - - name: Publish package - uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29 - with: - user: __token__ - password: ${{ secrets.PYPI_API_TOKEN_VBL }} + - uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: "3.10" + - name: Install build dependencies + run: | + python -m pip install --upgrade pip + pip install setuptools wheel twine + - name: Build package + run: python setup.py sdist bdist_wheel + - name: Publish package + uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29 + with: + user: __token__ + password: ${{ secrets.PYPI_API_TOKEN_VBL }} diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 48ff505f7..2060ce128 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -9,43 +9,40 @@ jobs: matrix: include: - os: ubuntu-latest - python-version: 3.7 - numpy-version: 1.18.5 + python-version: "3.7" + numpy-version: 1.21.4 - os: ubuntu-latest - python-version: 3.8 - numpy-version: 1.19.5 + python-version: "3.8" + numpy-version: 1.21.4 - os: ubuntu-latest - python-version: 3.9 - numpy-version: 1.20.3 - - os: windows-latest - python-version: 3.7 - numpy-version: 1.18.5 - - os: windows-latest - python-version: 3.8 - numpy-version: 1.19.5 + python-version: "3.9" + numpy-version: 1.21.4 + - os: ubuntu-latest + python-version: "3.10" + numpy-version: 1.22.3 - os: windows-latest - python-version: 3.9 - numpy-version: 1.20.3 + python-version: "3.10" + numpy-version: 1.22.3 defaults: run: shell: bash -l {0} steps: - - name: Git clone - uses: actions/checkout@v2 - - name: Set up virtual environment - uses: conda-incubator/setup-miniconda@v2 - with: - python-version: ${{ matrix.python-version }} - environment-file: environment.ci.yml - channels: defaults, conda-forge - - name: Install numpy - run: conda install numpy=${{ matrix.numpy-version }} - - name: Check linting - run: make lint - - name: Check static typing - run: make type-check - - name: Test with pytest - run: make coverage - - name: Code coverage - run: codecov + - name: Git clone + uses: actions/checkout@v2 + - name: Set up virtual environment + uses: conda-incubator/setup-miniconda@v2 + with: + python-version: ${{ matrix.python-version }} + environment-file: environment.ci.yml + channels: defaults, conda-forge + - name: Install numpy + run: conda install numpy=${{ matrix.numpy-version }} + - name: Check linting + run: make lint + - name: Check static typing + run: make type-check + - name: Test with pytest + run: make coverage + - name: Code coverage + run: codecov diff --git a/.gitignore b/.gitignore index a5d50ab1c..cad52327c 100644 --- a/.gitignore +++ b/.gitignore @@ -77,7 +77,6 @@ target/ .vscode # Images -*.png *.jpeg # ZIP files diff --git a/HISTORY.rst b/HISTORY.rst index 666fdf1e8..2060931fe 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -2,6 +2,10 @@ History ======= +0.3.3 (2022-XX-XX) +------------------ +* Relax and fix typing + 0.3.2 (2022-03-11) ------------------ * Refactorize unit tests diff --git a/Makefile b/Makefile index c11985b34..3bdb1b9a1 100644 --- a/Makefile +++ b/Makefile @@ -1,9 +1,10 @@ .PHONY: tests doc build + lint: flake8 . --exclude=doc type-check: - mypy mapie examples --strict --allow-untyped-calls + mypy mapie examples tests: pytest -vs --doctest-modules mapie diff --git a/README.rst b/README.rst index cb21db27f..cf001afbe 100644 --- a/README.rst +++ b/README.rst @@ -54,7 +54,7 @@ Python 3.7+ **MAPIE** stands on the shoulders of giants. -Its only internal dependency is `scikit-learn `_. +Its only internal dependencies are `scikit-learn `_ and `numpy=>1.21 `_. 🛠 Installation diff --git a/doc/Cifar10.rst b/doc/Cifar10.rst new file mode 100644 index 000000000..ab36dfb8b --- /dev/null +++ b/doc/Cifar10.rst @@ -0,0 +1,1115 @@ +Estimating prediction sets on the Cifar10 dataset +================================================= + +The goal of this notebook is to present how to use +:class:`mapie.classification.MapieClassifier` on an object +classification task. We will build prediction sets for images and study +the marginal and conditional coverages. + +What is done in this tutorial ? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + - **Cifar10 dataset** : 10 classes (horse, dog, cat, frog, deer, + bird, airplane, truck, ship, automobile) + +.. + + - Use :class:`mapie.classification.MapieClassifier` to compare the + prediction sets estimated by several conformal methods on the + Cifar10 dataset. + + - Train a small CNN to predict the image class + +.. + + - Create a custom class ``TensorflowToMapie`` to resolve adherence + problems between Tensorflow and Mapie + +Tutorial preparation +-------------------- + +.. code-block:: python + + import random + from typing import Dict, List, Tuple, Union + + import cv2 + import matplotlib.pyplot as plt + import numpy as np + import pandas as pd + import tensorflow as tf + import tensorflow.keras as tfk + from tensorflow.keras.callbacks import EarlyStopping + from tensorflow.keras import Sequential + from tensorflow.keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D + from tensorflow.keras.losses import CategoricalCrossentropy + from tensorflow.keras.optimizers import Adam + import tensorflow_datasets as tfds + from sklearn.metrics import accuracy_score + from sklearn.metrics._plot.confusion_matrix import ConfusionMatrixDisplay + from sklearn.model_selection import train_test_split + from sklearn.preprocessing import label_binarize + + from mapie.metrics import classification_coverage_score + from mapie.classification import MapieClassifier + + %load_ext autoreload + %autoreload 2 + %matplotlib inline + %load_ext pycodestyle_magic + +.. code-block:: python + + SPACE_BETWEEN_LABELS = 2.5 + SPACE_IN_SUBPLOTS = 4.0 + FONT_SIZE = 18 + + +1. Data loading +--------------- + +The Cifar10 dataset is downloaded from the `Tensorflow Datasets` +library. The training set is then splitted into a training, validation +and a calibration set which will be used as follow: + + - **Training set**: used to train our neural network. + - **Validation set**: used to check that our model is not + overfitting. + - **Calibration set**: used to calibrate the conformal scores in + :class:`mapie.classification.MapieClassifier` + +.. code-block:: python + + def train_valid_calib_split( + X: np.ndarray, + y: np.ndarray, + calib_size: float = .1, + val_size: float = .33, + random_state: int = 42 + + ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """ + Create calib and valid datasets from the train dataset. + + Parameters + ---------- + X: np.ndarray of shape (n_samples, width, height, n_channels) + Images of the dataset. + + y: np.ndarray of shape (n_samples, 1): + Label of each image. + + calib_size: float + Percentage of the dataset X to use as calibration set. + + val_size: float + Percentage of the dataset X (minus the calibration set) + to use as validation set. + + random_state: int + Random state to use to split the dataset. + + By default 42. + + Returns + ------- + Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] + of shapes: + (n_samples * (1 - calib_size) * (1 - val_size), width, height, n_channels), + (n_samples * calib_size, width, height, n_channels), + (n_samples * (1 - calib_size) * val_size, width, height, n_channels), + (n_samples * (1 - calib_size) * (1 - val_size), 1), + (n_samples * calib_size, 1), + (n_samples * (1 - calib_size) * val_size, 1). + + """ + X_train, X_calib, y_train, y_calib = train_test_split( + X, y, + test_size=calib_size, + random_state=random_state + ) + X_train, X_val, y_train, y_val = train_test_split( + X_train, y_train, + test_size=val_size, + random_state=random_state + ) + return X_train, X_calib, X_val, y_train, y_calib, y_val + + +.. code-block:: python + + def load_data() -> Tuple[ + Tuple[np.ndarray, np.ndarray, np.ndarray], + Tuple[np.ndarray, np.ndarray, np.ndarray], + Tuple[np.ndarray, np.ndarray, np.ndarray], + List + ]: + """ + Load cifar10 Dataset and return train, valid, calib, test datasets + and the names of the labels + + + Returns + ------- + Tuple[ + Tuple[np.ndarray, np.ndarray, np.ndarray], + Tuple[np.ndarray, np.ndarray, np.ndarray], + Tuple[np.ndarray, np.ndarray, np.ndarray], + List + ] + """ + dataset, info = tfds.load( + "cifar10", + batch_size=-1, + as_supervised=True, + with_info=True + ) + label_names = info.features['label'].names + + dataset = tfds.as_numpy(dataset) + X_train, y_train = dataset['train'] + X_test, y_test = dataset['test'] + X_train, X_calib, X_val, y_train, y_calib, y_val = train_valid_calib_split( + X_train, + y_train + ) + + X_train = X_train/255. + X_val = X_val/255. + + X_calib = X_calib/255. + X_test = X_test/255. + + y_train_cat = tf.keras.utils.to_categorical(y_train) + y_val_cat = tf.keras.utils.to_categorical(y_val) + y_calib_cat = tf.keras.utils.to_categorical(y_calib) + y_test_cat = tf.keras.utils.to_categorical(y_test) + + train_set = (X_train, y_train, y_train_cat) + val_set = (X_val, y_val, y_val_cat) + calib_set = (X_calib, y_calib, y_calib_cat) + test_set = (X_test, y_test, y_test_cat) + + return train_set, val_set, calib_set, test_set, label_names + + +.. code-block:: python + + def inspect_images( + X: np.ndarray, + y: np.ndarray, + num_images: int, + label_names: List + ) -> None: + """ + Load a sample of the images to check that images + are well loaded. + + Parameters + ---------- + X: np.ndarray of shape (n_samples, width, height, n_channels) + Set of images from which the sample will be taken. + + y: np.ndarray of shape (n_samples, 1) + Labels of the iamges of X. + + num_images: int + Number of images to plot. + + label_names: List + Names of the different labels + + """ + + _, ax = plt.subplots( + nrows=1, + ncols=num_images, + figsize=(2*num_images, 2) + ) + + indices = random.sample(range(len(X)), num_images) + + for i, indice in enumerate(indices): + ax[i].imshow(X[indice]) + ax[i].set_title(label_names[y[indice]]) + ax[i].axis("off") + plt.show() + + +.. code-block:: python + + train_set, val_set, calib_set, test_set, label_names = load_data() + (X_train, y_train, y_train_cat) = train_set + (X_val, y_val, y_val_cat) = val_set + (X_calib, y_calib, y_calib_cat) = calib_set + (X_test, y_test, y_test_cat) = test_set + inspect_images(X=X_train, y=y_train, num_images=8, label_names=label_names) + + +.. parsed-literal:: + + Instructions for updating: + Use `tf.data.Dataset.get_single_element()`. + + +.. parsed-literal:: + + Instructions for updating: + Use `tf.data.Dataset.get_single_element()`. + 2022-03-25 10:55:08.789680: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2) + 2022-03-25 10:55:08.792682: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz + + + +.. image:: Cifar10_files/Cifar10_10_2.png + + +2. Definition and training of the the neural network +---------------------------------------------------- + +We define a simple convolutional neural network with the following +architecture : + + - 2 blocks of Convolution/Maxpooling + - Flatten the images + - 3 Dense layers + - The output layer with 10 neurons, corresponding to our 10 classes + +This simple architecture, based on the VGG16 architecture with its +succession of convolutions and maxpooling aims at achieve a reasonable +accuracy score and a fast training. The objective here is not to obtain +a perfect classifier. + +.. code-block:: python + + def get_model( + input_shape: Tuple, loss: tfk.losses, + optimizer: tfk.optimizers, metrics: List[str] + ) -> Sequential: + """ + Compile CNN model. + + Parameters + ---------- + input_shape: Tuple + Size of th input images. + + loss: tfk.losses + Loss to use to train the model. + + optimizer: tfk.optimizer + Optimizer to use to train the model. + + metrics: List[str] + Metrics to use evaluate model training. + + Returns + ------- + Sequential + """ + model = Sequential([ + Conv2D(input_shape=input_shape, filters=16, kernel_size=(3, 3), activation='relu', padding='same'), + MaxPooling2D(pool_size=(2, 2)), + Conv2D(input_shape=input_shape, filters=32, kernel_size=(3, 3), activation='relu', padding='same'), + MaxPooling2D(pool_size=(2, 2)), + Conv2D(input_shape=input_shape, filters=64, kernel_size=(3, 3), activation='relu', padding='same'), + MaxPooling2D(pool_size=(2, 2)), + Flatten(), + Dense(128, activation='relu'), + Dense(64, activation='relu'), + Dense(32, activation='relu'), + Dense(10, activation='softmax'), + ]) + model.compile(loss=loss, optimizer=optimizer, metrics=metrics) + return model + +3. Training the algorithm with a custom class called ``TensorflowToMapie`` +-------------------------------------------------------------------------- + +As MAPIE asked that the model has a `fit`, `predict_proba`, +`predict` class attributes and that the information about if whether +or not the model is fitted. + +.. code-block:: python + + class TensorflowToMapie(): + """ + Class that aimes to make compatible a tensorflow model + with MAPIE. To do so, this class create fit, predict, + predict_proba and _sklearn_is_fitted_ attributes to the model. + + """ + + def __init__(self) -> None: + self.pred_proba = None + self.trained_ = False + + + def fit( + self, model: Sequential, + X_train: np.ndarray, y_train: np.ndarray, + X_val: np.ndarray, y_val: np.ndarray + ) -> None: + """ + Train the keras model. + + Parameters + ---------- + model: Sequential + Model to train. + + X_train: np.ndarray of shape (n_sample_train, width, height, n_channels) + Training images. + + y_train: np.ndarray of shape (n_samples_train, n_labels) + Training labels. + + X_val: np.ndarray of shape (n_sample_val, width, height, n_channels) + Validation images. + + y_val: np.ndarray of shape (n_samples_val, n_labels) + Validation labels. + + """ + + early_stopping_monitor = EarlyStopping( + monitor='val_loss', + min_delta=0, + patience=10, + verbose=0, + mode='auto', + baseline=None, + restore_best_weights=True + ) + model.fit( + X_train, y_train, + batch_size=64, + validation_data=(X_val, y_val), + epochs=20, callbacks=[early_stopping_monitor] + ) + + self.model = model + self.trained_ = True + self.classes_ = np.arange(model.layers[-1].units) + + def predict_proba(self, X: np.ndarray) -> np.ndarray: + """ + Returns the predicted probabilities of the images in X. + + Paramters: + X: np.ndarray of shape (n_sample, width, height, n_channels) + Images to predict. + + Returns: + np.ndarray of shape (n_samples, n_labels) + """ + preds = self.model.predict(X) + + return preds + + def predict(self, X: np.ndarray) -> np.ndarray: + """ + Give the label with the maximum softmax for each image. + + Parameters + --------- + X: np.ndarray of shape (n_sample, width, height, n_channels) + Images to predict + + Returns: + -------- + np.ndarray of shape (n_samples, 1) + """ + pred_proba = self.predict_proba(X) + pred = (pred_proba == pred_proba.max(axis=1)[:, None]).astype(int) + return pred + + def __sklearn_is_fitted__(self): + if self.trained_: + return True + else: + return False + +.. code-block:: python + + model = get_model( + input_shape=(32, 32, 3), + loss=CategoricalCrossentropy(), + optimizer=Adam(), + metrics=['accuracy'] + ) + +.. code-block:: python + + cirfar10_model = TensorflowToMapie() + cirfar10_model.fit(model, X_train, y_train_cat, X_val, y_val_cat) + + +.. parsed-literal:: + + Epoch 1/20 + 472/472 [==============================] - 8s 16ms/step - loss: 1.7729 - accuracy: 0.3378 - val_loss: 1.4636 - val_accuracy: 0.4679 + Epoch 2/20 + 472/472 [==============================] - 8s 18ms/step - loss: 1.3754 - accuracy: 0.4993 - val_loss: 1.3896 - val_accuracy: 0.4878 + Epoch 3/20 + 472/472 [==============================] - 7s 15ms/step - loss: 1.2145 - accuracy: 0.5613 - val_loss: 1.1549 - val_accuracy: 0.5871 + Epoch 4/20 + 472/472 [==============================] - 7s 15ms/step - loss: 1.0864 - accuracy: 0.6109 - val_loss: 1.1769 - val_accuracy: 0.5817 + Epoch 5/20 + 472/472 [==============================] - 7s 15ms/step - loss: 0.9877 - accuracy: 0.6503 - val_loss: 0.9957 - val_accuracy: 0.6426 + Epoch 6/20 + 472/472 [==============================] - 8s 17ms/step - loss: 0.9053 - accuracy: 0.6803 - val_loss: 1.0178 - val_accuracy: 0.6351 + Epoch 7/20 + 472/472 [==============================] - 7s 15ms/step - loss: 0.8449 - accuracy: 0.7018 - val_loss: 0.9952 - val_accuracy: 0.6492 + Epoch 8/20 + 472/472 [==============================] - 8s 18ms/step - loss: 0.7862 - accuracy: 0.7238 - val_loss: 0.9597 - val_accuracy: 0.6688 + Epoch 9/20 + 472/472 [==============================] - 7s 16ms/step - loss: 0.7236 - accuracy: 0.7455 - val_loss: 0.9579 - val_accuracy: 0.6735 + Epoch 10/20 + 472/472 [==============================] - 7s 16ms/step - loss: 0.6804 - accuracy: 0.7584 - val_loss: 0.9675 - val_accuracy: 0.6723 + Epoch 11/20 + 472/472 [==============================] - 7s 16ms/step - loss: 0.6252 - accuracy: 0.7785 - val_loss: 0.8971 - val_accuracy: 0.6953 + Epoch 12/20 + 472/472 [==============================] - 8s 16ms/step - loss: 0.5915 - accuracy: 0.7908 - val_loss: 0.9165 - val_accuracy: 0.6943 + Epoch 13/20 + 472/472 [==============================] - 7s 15ms/step - loss: 0.5583 - accuracy: 0.8027 - val_loss: 0.9639 - val_accuracy: 0.6860 + Epoch 14/20 + 472/472 [==============================] - 7s 15ms/step - loss: 0.5011 - accuracy: 0.8232 - val_loss: 1.0147 - val_accuracy: 0.6776 + Epoch 15/20 + 472/472 [==============================] - 8s 16ms/step - loss: 0.4598 - accuracy: 0.8374 - val_loss: 1.0047 - val_accuracy: 0.6806 + Epoch 16/20 + 472/472 [==============================] - 9s 18ms/step - loss: 0.4375 - accuracy: 0.8456 - val_loss: 1.0378 - val_accuracy: 0.6873 + Epoch 17/20 + 472/472 [==============================] - 9s 19ms/step - loss: 0.3866 - accuracy: 0.8630 - val_loss: 1.1904 - val_accuracy: 0.6570 + Epoch 18/20 + 472/472 [==============================] - 9s 20ms/step - loss: 0.3645 - accuracy: 0.8717 - val_loss: 1.1796 - val_accuracy: 0.6805 + Epoch 19/20 + 472/472 [==============================] - 8s 17ms/step - loss: 0.3387 - accuracy: 0.8823 - val_loss: 1.2754 - val_accuracy: 0.6659 + Epoch 20/20 + 472/472 [==============================] - 8s 16ms/step - loss: 0.2919 - accuracy: 0.8975 - val_loss: 1.2481 - val_accuracy: 0.6815 + + +.. code-block:: python + + y_true = label_binarize(y=y_test, classes=np.arange(max(y_test)+1)) + y_pred_proba = cirfar10_model.predict_proba(X_test) + y_pred = cirfar10_model.predict(X_test) + + +4. Prediction of the prediction sets +------------------------------------ + +We will now estimate the prediction sets with the five conformal methods +implemented in :class:`mapie.classification.MapieClassifier` for a +range of confidence levels between 0 and 1. + +.. code-block:: python + + method_params = { + "naive": ("naive", False), + "score": ("score", False), + "cumulated_score": ("cumulated_score", True), + "random_cumulated_score": ("cumulated_score", "randomized"), + "top_k": ("top_k", False) + } + + +.. code-block:: python + + y_preds, y_pss = {}, {} + alphas = np.arange(0.01, 1, 0.01) + + for name, (method, include_last_label) in method_params.items(): + mapie = MapieClassifier(estimator=cirfar10_model, method=method, cv="prefit", random_state=42) + mapie.fit(X_calib, y_calib, image_input=True) + y_preds[name], y_pss[name] = mapie.predict(X_test, alpha=alphas, include_last_label=include_last_label) + +Let’s now estimate the number of null prediction sets, marginal +coverages, and averaged prediction set sizes obtained with the different +methods for all confidence levels and for a confidence level of 90 %. + +.. code-block:: python + + def count_null_set(y: np.ndarray) -> int: + """ + Count the number of empty prediction sets. + + Parameters + ---------- + y: np.ndarray of shape (n_sample, ) + + Returns + ------- + int + """ + count = 0 + for pred in y[:, :]: + if np.sum(pred) == 0: + count += 1 + return count + + +.. code-block:: python + + nulls, coverages, accuracies, sizes = {}, {}, {}, {} + for name, (method, include_last_label) in method_params.items(): + accuracies[name] = accuracy_score(y_true, y_preds[name]) + nulls[name] = [ + count_null_set(y_pss[name][:, :, i]) for i, _ in enumerate(alphas) + ] + coverages[name] = [ + classification_coverage_score( + y_test, y_pss[name][:, :, i] + ) for i, _ in enumerate(alphas) + ] + sizes[name] = [ + y_pss[name][:, :, i].sum(axis=1).mean() for i, _ in enumerate(alphas) + ] + + +.. code-block:: python + + coverage_90 = {method: coverage[9] for method, coverage in coverages.items()} + null_90 = {method: null[9] for method, null in nulls.items()} + width_90 = {method: width[9] for method, width in sizes.items()} + y_ps_90 = {method: y_ps[:, :, 9] for method, y_ps in y_pss.items()} + +Let’s now look at the marginal coverages, number of null prediction +sets, and the averaged size of prediction sets for a confidence level of +90 %. + +.. code-block:: python + + summary_df = pd.concat( + [ + pd.Series(coverage_90), + pd.Series(null_90), + pd.Series(width_90) + ], + axis=1, + keys=["Coverages", "Number of null sets", "Average prediction set sizes"] + ).round(3) + +.. code-block:: python + + summary_df + + + + +.. raw:: html + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CoveragesNumber of null setsAverage prediction set sizes
naive0.73201.258
score0.91202.356
cumulated_score0.92802.701
random_cumulated_score0.908212.463
top_k0.91003.000
+
+ + + +As expected, the “naive” method, which directly uses the alpha value as +a threshold for selecting the prediction sets, does not give guarantees +on the marginal coverage since this method is not calibrated. Other +methods give a marginal coverage close to the desired one, i.e. 90%. +Notice that the “cumulated_score” method, which always includes the last +label whose cumulated score is above the given quantile, tends to give +slightly higher marginal coverages since the prediction sets are +slightly too big. + +6. Visualization of the prediction sets +--------------------------------------- + +.. code-block:: python + + def prepare_plot(y_methods: Dict[str, Tuple], n_images: int) -> np.ndarray: + """ + Prepare the number and the disposition of the plots according to + the number of images. + + Paramters: + y_methods: Dict[str, Tuple] + Methods we want to compare. + + n_images: int + Number of images to plot. + + Returns + ------- + np.ndarray + """ + plt.rcParams.update({'font.size': FONT_SIZE}) + nrow = len(y_methods.keys()) + ncol = n_images + s = 5 + f, ax = plt.subplots(ncol, nrow, figsize=(s*nrow, s*ncol)) + f.tight_layout(pad=SPACE_IN_SUBPLOTS) + rows = [i for i in y_methods.keys()] + + for x, row in zip(ax[:,0], rows): + x.set_ylabel(row, rotation=90, size='large') + + return ax + + +.. code-block:: python + + def get_position(y_set: List, label: str, count: int, count_true: int) -> float: + """ + Return the position of each label according to the number of labels to plot. + + Paramters + --------- + y_set: List + Set of predicted labels for one image. + + label: str + Indice of the true label. + + count: int + Index of the label. + + count_true: int + Total number of labels in the prediction set. + + Returns + ------- + float + """ + if y_set[label] : + position = - (count_true - count)*SPACE_BETWEEN_LABELS + + else: + position = - (count_true + 2 - count)*SPACE_BETWEEN_LABELS + + return position + + + def add_text( + ax: np.ndarray, indices: Tuple, position: float, + label_name: str, proba: float, color: str, missing: bool = False + ) -> None: + """ + Add the text to the corresponding image. + + Parameters + ---------- + ax: np.ndarray + Matrix of the images to plot. + + indices: Tuple + Tuple indicating the indices of the image to put + the text on. + + position: float + Position of the text on the image. + + label_name: str + Name of the label to plot. + + proba: float + Proba associated to this label. + + color: str + Color of the text. + + missing: bool + Whether or not the true label is missing in the + prediction set. + + By default False. + + """ + if not missing : + text = f"{label_name} : {proba:.4f}" + else: + text = f"True label : {label_name} ({proba:.4f})" + i, j = indices + ax[i, j].text( + 15, + position, + text, + ha="center", va="top", + color=color, + font="courier new" + ) + + + +.. code-block:: python + + def plot_prediction_sets( + X: np.ndarray, y: np.ndarray, + y_pred_proba: np.ndarray, + y_methods: Dict[str, np.ndarray], + n_images: int, label_names: Dict, + random_state: Union[int, None] = None + ) -> None: + """ + Plot random images with their associated prediction + set for all the required methods. + + Parameters + ---------- + X: np.ndarray of shape (n_sample, width, height, n_channels) + Array containing images. + + y: np.ndarray of shape (n_samples, ) + Labels of the images. + + y_pred_proba: np.ndarray of shape (n_samples, n_labels) + Softmax output of the model. + + y_methods: Dict[str, np.ndarray] + Outputs of the MapieClassifier with the different + choosen methods. + + n_images: int + Number of images to plot + + random_state: Union[int, None] + Random state to use to choose the images. + + By default None. + """ + random.seed(random_state) + indices = random.sample(range(len(X)), n_images) + + y_true = y[indices] + y_pred_proba = y_pred_proba[indices] + ax = prepare_plot(y_methods, n_images) + + for i, method in enumerate(y_methods): + y_sets = y_methods[method][indices] + + for j in range(n_images): + y_set = y_sets[j] + img, label= X[indices[j]], y_true[j] + + ax[i, j].imshow(img) + + count_true = np.sum(y_set) + index_sorted_proba = np.argsort(-y_pred_proba[j]) + + for count in range(count_true): + index_pred = index_sorted_proba[count] + proba = y_pred_proba[j][index_pred] + label_name = label_names[index_pred] + color = 'green' if index_pred == y_true[j] else 'red' + position = get_position(y_set, label, count, count_true) + + add_text(ax, (i, j), position, label_name, proba, color) + + if not y_set[label] : + label_name = label_names[label] + proba = y_pred_proba[j][label] + add_text(ax, (i, j), -3, label_name, proba, color= 'orange', missing=True) + + +.. code-block:: python + + plot_prediction_sets(X_test, y_test, y_pred_proba, y_ps_90, 5, label_names) + + + +.. image:: Cifar10_files/Cifar10_35_0.png + + +5. Calibration of the methods +----------------------------- + +In this section, we plot the number of null sets, the marginal +coverages, and the prediction set sizes as function of the target +coverage level for all conformal methods. + +.. code-block:: python + + vars_y = [nulls, coverages, sizes] + labels_y = ["Empty prediction sets", "Marginal coverage", "Set sizes"] + fig, axs = plt.subplots(1, len(vars_y), figsize=(8*len(vars_y), 8)) + for i, var in enumerate(vars_y): + for name, (method, include_last_label) in method_params.items(): + axs[i].plot(1 - alphas, var[name], label=name) + if i == 1: + axs[i].plot([0, 1], [0, 1], ls="--", color="k") + axs[i].set_xlabel("Couverture cible : 1 - alpha") + axs[i].set_ylabel(labels_y[i]) + if i == len(vars_y) - 1: + axs[i].legend(fontsize=10, loc=[1, 0]) + + + +.. image:: Cifar10_files/Cifar10_38_0.png + + +The two only methods which are perfectly calibrated for the entire range +of alpha values are the “score” and “random_cumulated_score”. However, +these accurate marginal coverages can only be obtained thanks to the +generation of null prediction sets. The compromise between estimating +null prediction sets with calibrated coverages or non-empty prediction +sets but with larger marginal coverages is entirely up to the user. + +7. Prediction set sizes +----------------------- + +.. code-block:: python + + s=5 + fig, axs = plt.subplots(1, len(y_preds), figsize=(s*len(y_preds), s)) + for i, (method, y_ps) in enumerate(y_ps_90.items()): + sizes = y_ps.sum(axis=1) + axs[i].hist(sizes) + axs[i].set_xlabel("Prediction set sizes") + axs[i].set_title(method) + + + +.. image:: Cifar10_files/Cifar10_41_0.png + + +8. Conditional coverages +------------------------ + +We just saw that all our methods (except the “naive” one) give marginal +coverages always larger than the target coverages for alpha values +ranging between 0 and 1. However, there is no mathematical guarantees on +the *conditional* coverages, i.e. the coverage obtained for a specific +class of images. Let’s see what conditional coverages we obtain with the +different conformal methods. + +.. code-block:: python + + def get_class_coverage( + y_test: np.ndarray, + y_method: Dict[str, np.ndarray], + label_names: List[str] + ) -> None: + """ + Compute the coverage for each class. As MAPIE is looking for a + global coverage of 1-alpha, it is important to check that their + is not major coverage difference between classes. + + Parameters + ---------- + y_test: np.ndarray of shape (n_samples,) + Labels of the predictions. + + y_method: Dict[str, np.ndarray] + Prediction sets for each method. + + label_names: List[str] + Names of the labels. + """ + recap ={} + for method in y_method: + recap[method] = [] + for label in sorted(np.unique(y_test)): + indices = np.where(y_test==label) + label_name = label_names[label] + y_test_trunc = y_test[indices] + y_set_trunc = y_method[method][indices] + score_coverage = classification_coverage_score(y_test_trunc, y_set_trunc) + recap[method].append(score_coverage) + recap_df = pd.DataFrame(recap, index = label_names) + return recap_df + + +.. code-block:: python + + class_coverage = get_class_coverage(y_test, y_ps_90, label_names) + +.. code-block:: python + + fig = plt.figure() + class_coverage.plot.bar(figsize=(12, 4), alpha=0.7) + plt.axhline(0.9, ls="--", color="k") + plt.ylabel("Conditional coverage") + plt.legend(loc=[1, 0]) + + + + +.. image:: Cifar10_files/Cifar10_46_2.png + + +We can notice that the conditional coverages slightly vary between +classes. The only method whose conditional coverages remain valid for +all classes is the “top_k” one. However, those variations are much +smaller than that of the naive method. + +.. code-block:: python + + def create_confusion_matrix(y_ps: np.ndarray, y_true: np.ndarray) -> np.ndarray: + """ + Create a confusion matrix to visualize, for each class, which + classes are which are the most present classes in the prediction + sets. + + Parameters + ---------- + y_ps: np.ndarray of shape (n_samples, n_labels) + Prediction sets of a specific method. + + y_true: np.ndarray of shape (n_samples, ) + Labels of the sample + + Returns + ------- + np.ndarray of shape (n_labels, n_labels) + """ + number_of_classes = len(np.unique(y_true)) + confusion_matrix = np.zeros((number_of_classes, number_of_classes)) + for i, ps in enumerate(y_ps): + confusion_matrix[y_true[i]] += ps + + return confusion_matrix + + +.. code-block:: python + + def reorder_labels(ordered_labels: List, labels: List, cm: np.ndarray) -> np.ndarray: + """ + Used to order the labels in the confusion matrix + + Parameters + ---------- + ordered_labels: List + Order you want to have in your confusion matrix + + labels: List + Initial order of the confusion matrix + + cm: np.ndarray of shape (n_labels, n_labels) + Original confusion matrix + + Returns + ------- + np.ndarray of shape (n_labels, n_labels) + """ + cm_ordered = np.zeros(cm.shape) + index_order = [labels.index(label) for label in ordered_labels] + for i, label in enumerate(ordered_labels): + old_index = labels.index(label) + + cm_ordered[i] = cm[old_index, index_order] + return cm_ordered + +.. code-block:: python + + def plot_confusion_matrix(method: str, y_ps: Dict[str, np.ndarray], label_names: List) -> None: + """ + Plot the confusion matrix for a specific method. + + Parameters + ---------- + method: str + Name of the method to plot. + + y_ps: Dict[str, np.ndarray] + Prediction sets for each of the fitted method + + label_names: List + Name of the labels + """ + + y_method = y_ps[method] + cm = create_confusion_matrix(y_method, y_test) + ordered_labels = ["frog", "cat", "dog", "deer", "horse", "bird", "airplane", "ship", "truck", "automobile"] + cm = reorder_labels(ordered_labels, label_names, cm) + disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=ordered_labels) + _, ax = plt.subplots(figsize=(10, 10)) + disp.plot( + include_values=True, + cmap="viridis", + ax=ax, + xticks_rotation="vertical", + values_format='.0f', + colorbar=True, + ) + + ax.set_title(f'Confusion matrix for {method} method') + +.. code-block:: python + + plot_confusion_matrix("cumulated_score", y_ps_90, label_names) + + + +.. image:: Cifar10_files/Cifar10_51_0.png + + +Thanks to this confusion matrix we can see that, for some labels (as +cat, deer and dog) the distribution of the labels in the prediction set +is not uniform. Indeed, when the image is a cat, there are almost as +many predictions sets with the true label than with the “cat” label. In +this case, the reverse is also true. However, for the deer, the cat +label is quite often within the prediction set while the deer is not + +.. code-block:: python + + plot_confusion_matrix("naive", y_ps_90, label_names) + + + +.. image:: Cifar10_files/Cifar10_53_0.png + + +.. code-block:: python + + plot_confusion_matrix("score", y_ps_90, label_names) + + + +.. image:: Cifar10_files/Cifar10_54_0.png + diff --git a/doc/Cifar10_files/Cifar10_10_2.png b/doc/Cifar10_files/Cifar10_10_2.png new file mode 100644 index 000000000..0adb0d934 Binary files /dev/null and b/doc/Cifar10_files/Cifar10_10_2.png differ diff --git a/doc/Cifar10_files/Cifar10_35_0.png b/doc/Cifar10_files/Cifar10_35_0.png new file mode 100644 index 000000000..0e5553d9d Binary files /dev/null and b/doc/Cifar10_files/Cifar10_35_0.png differ diff --git a/doc/Cifar10_files/Cifar10_36_0.png b/doc/Cifar10_files/Cifar10_36_0.png new file mode 100644 index 000000000..0e5553d9d Binary files /dev/null and b/doc/Cifar10_files/Cifar10_36_0.png differ diff --git a/doc/Cifar10_files/Cifar10_38_0.png b/doc/Cifar10_files/Cifar10_38_0.png new file mode 100644 index 000000000..2c204eb9c Binary files /dev/null and b/doc/Cifar10_files/Cifar10_38_0.png differ diff --git a/doc/Cifar10_files/Cifar10_39_0.png b/doc/Cifar10_files/Cifar10_39_0.png new file mode 100644 index 000000000..2c204eb9c Binary files /dev/null and b/doc/Cifar10_files/Cifar10_39_0.png differ diff --git a/doc/Cifar10_files/Cifar10_41_0.png b/doc/Cifar10_files/Cifar10_41_0.png new file mode 100644 index 000000000..e9165b5bd Binary files /dev/null and b/doc/Cifar10_files/Cifar10_41_0.png differ diff --git a/doc/Cifar10_files/Cifar10_42_0.png b/doc/Cifar10_files/Cifar10_42_0.png new file mode 100644 index 000000000..e9165b5bd Binary files /dev/null and b/doc/Cifar10_files/Cifar10_42_0.png differ diff --git a/doc/Cifar10_files/Cifar10_46_2.png b/doc/Cifar10_files/Cifar10_46_2.png new file mode 100644 index 000000000..1f5758f0d Binary files /dev/null and b/doc/Cifar10_files/Cifar10_46_2.png differ diff --git a/doc/Cifar10_files/Cifar10_47_2.png b/doc/Cifar10_files/Cifar10_47_2.png new file mode 100644 index 000000000..1f5758f0d Binary files /dev/null and b/doc/Cifar10_files/Cifar10_47_2.png differ diff --git a/doc/Cifar10_files/Cifar10_51_0.png b/doc/Cifar10_files/Cifar10_51_0.png new file mode 100644 index 000000000..2ad6ab3c7 Binary files /dev/null and b/doc/Cifar10_files/Cifar10_51_0.png differ diff --git a/doc/Cifar10_files/Cifar10_52_0.png b/doc/Cifar10_files/Cifar10_52_0.png new file mode 100644 index 000000000..2ad6ab3c7 Binary files /dev/null and b/doc/Cifar10_files/Cifar10_52_0.png differ diff --git a/doc/Cifar10_files/Cifar10_53_0.png b/doc/Cifar10_files/Cifar10_53_0.png new file mode 100644 index 000000000..f8cceda4e Binary files /dev/null and b/doc/Cifar10_files/Cifar10_53_0.png differ diff --git a/doc/Cifar10_files/Cifar10_54_0.png b/doc/Cifar10_files/Cifar10_54_0.png new file mode 100644 index 000000000..f7be1a071 Binary files /dev/null and b/doc/Cifar10_files/Cifar10_54_0.png differ diff --git a/doc/Cifar10_files/Cifar10_55_0.png b/doc/Cifar10_files/Cifar10_55_0.png new file mode 100644 index 000000000..f7be1a071 Binary files /dev/null and b/doc/Cifar10_files/Cifar10_55_0.png differ diff --git a/doc/notebooks_classification.rst b/doc/notebooks_classification.rst index 0c5a59108..061db9e19 100644 --- a/doc/notebooks_classification.rst +++ b/doc/notebooks_classification.rst @@ -4,3 +4,10 @@ Classification Notebooks The following examples present you advanced analyses on multi-class classification problems for computer vision settings that are too heavy to be included in the example galleries. + + + +.. toctree:: + :hidden: + + Cifar10 \ No newline at end of file diff --git a/environment.ci.yml b/environment.ci.yml index c3f6d637f..07f31c0a3 100644 --- a/environment.ci.yml +++ b/environment.ci.yml @@ -8,6 +8,5 @@ dependencies: - mypy - pandas - pytest-cov - - python - scikit-learn - typed-ast diff --git a/environment.dev.yml b/environment.dev.yml index f4b0c3289..63c98ffec 100644 --- a/environment.dev.yml +++ b/environment.dev.yml @@ -5,12 +5,15 @@ channels: dependencies: - bump2version=1.0.1 - flake8=4.0.1 - - mypy=0.910 + - ipykernel=6.9.0 + - jupyter=1.0.0 + - mypy=0.941 - numpydoc=1.1.0 + - numpy=1.22.3 - pandas=1.3.5 - pytest=6.2.5 - pytest-cov=3.0.0 - - python=3.10.1 + - python=3.10 - scikit-learn=1.0.1 - sphinx=4.3.2 - sphinx-gallery=0.10.1 diff --git a/environment.notebooks.yml b/environment.notebooks.yml index 01933a967..e04810455 100644 --- a/environment.notebooks.yml +++ b/environment.notebooks.yml @@ -13,7 +13,7 @@ dependencies: - pip=22.0.3 - pip: - scikeras==0.4.1 - - python=3.10.1 + - python=3.10 - scikit-learn=1.0.1 - tensorflow=2.7.0 - xgboost=1.5.1 diff --git a/examples/classification/1-quickstart/plot_comp_methods_on_2d_dataset.py b/examples/classification/1-quickstart/plot_comp_methods_on_2d_dataset.py index 157ce48a4..c52d4bc0c 100644 --- a/examples/classification/1-quickstart/plot_comp_methods_on_2d_dataset.py +++ b/examples/classification/1-quickstart/plot_comp_methods_on_2d_dataset.py @@ -57,7 +57,7 @@ classification_coverage_score, classification_mean_width_score ) -from mapie._typing import ArrayLike +from mapie._typing import NDArray centers = [(0, 3.5), (-2, 0), (2, 0)] @@ -144,8 +144,8 @@ def plot_scores( alphas: List[float], - scores: ArrayLike, - quantiles: ArrayLike, + scores: NDArray, + quantiles: NDArray, method: str, ax: plt.Axes, ) -> None: @@ -183,7 +183,7 @@ def plot_scores( def plot_results( - alphas: List[float], y_pred_mapie: ArrayLike, y_ps_mapie: ArrayLike + alphas: List[float], y_pred_mapie: NDArray, y_ps_mapie: NDArray ) -> None: tab10 = plt.cm.get_cmap("Purples", 4) colors = { diff --git a/examples/classification/2-advanced-analysis/plot_crossconformal.py b/examples/classification/2-advanced-analysis/plot_crossconformal.py index 0b77d4feb..9b8a1175d 100644 --- a/examples/classification/2-advanced-analysis/plot_crossconformal.py +++ b/examples/classification/2-advanced-analysis/plot_crossconformal.py @@ -26,7 +26,7 @@ """ -from typing import Dict, Any, Optional, Union +from typing import Dict, Any, Optional, Union, List from typing_extensions import TypedDict import numpy as np import pandas as pd @@ -35,9 +35,10 @@ from sklearn.model_selection import KFold from mapie.classification import MapieClassifier from mapie.metrics import ( - classification_coverage_score, classification_mean_width_score + classification_coverage_score, + classification_mean_width_score ) -from mapie._typing import ArrayLike +from mapie._typing import NDArray ############################################################################## @@ -156,9 +157,9 @@ def plot_results( mapies: Dict[int, Any], - X_test: ArrayLike, - X_test2: ArrayLike, - y_test2: ArrayLike, + X_test: NDArray, + X_test2: NDArray, + y_test2: NDArray, alpha: float, method: str ) -> None: @@ -223,9 +224,9 @@ def plot_results( def plot_coverage_width( - alpha: float, - coverages: ArrayLike, - widths: ArrayLike, + alpha: NDArray, + coverages: List[NDArray], + widths: List[NDArray], method: str, comp: str = "split" ) -> None: @@ -355,12 +356,12 @@ def plot_coverage_width( ) } -y_preds, y_ps = {}, {} +y_ps = {} for strategy, params in STRATEGIES.items(): args_init, args_predict = STRATEGIES[strategy] mapie_clf = MapieClassifier(**args_init) mapie_clf.fit(X_train, y_train) - y_preds[strategy], y_ps[strategy] = mapie_clf.predict( + _, y_ps[strategy] = mapie_clf.predict( X_test_distrib, alpha=alpha, **args_predict diff --git a/examples/classification/2-advanced-analysis/plot_digits_classification.py b/examples/classification/2-advanced-analysis/plot_digits_classification.py index d3f1d2d45..0172a10c5 100644 --- a/examples/classification/2-advanced-analysis/plot_digits_classification.py +++ b/examples/classification/2-advanced-analysis/plot_digits_classification.py @@ -22,7 +22,7 @@ classification_coverage_score, classification_mean_width_score ) -from mapie._typing import ArrayLike +from mapie._typing import NDArray ############################################################################## @@ -99,7 +99,7 @@ def generate_mnist_corrupted( # in the calibration and test subsets. def get_datasets(dataset: Any) -> Tuple[ - ArrayLike, ArrayLike, ArrayLike, ArrayLike, ArrayLike, ArrayLike + NDArray, NDArray, NDArray, NDArray, NDArray, NDArray ]: n_samples = len(digits.images) data = dataset.images.reshape((n_samples, -1)) diff --git a/examples/regression/1-quickstart/plot_homoscedastic_1d_data.py b/examples/regression/1-quickstart/plot_homoscedastic_1d_data.py index 77a1a8440..118598bf5 100644 --- a/examples/regression/1-quickstart/plot_homoscedastic_1d_data.py +++ b/examples/regression/1-quickstart/plot_homoscedastic_1d_data.py @@ -18,17 +18,17 @@ from matplotlib import pyplot as plt from mapie.regression import MapieRegressor -from mapie._typing import ArrayLike +from mapie._typing import NDArray -def f(x: ArrayLike) -> ArrayLike: +def f(x: NDArray) -> NDArray: """Polynomial function used to generate one-dimensional data""" return np.array(5 * x + 5 * x ** 4 - 9 * x ** 2) def get_homoscedastic_data( n_train: int = 200, n_true: int = 200, sigma: float = 0.1 -) -> Tuple[ArrayLike, ArrayLike, ArrayLike, ArrayLike, float]: +) -> Tuple[NDArray, NDArray, NDArray, NDArray, float]: """ Generate one-dimensional data from a given function, number of training and test samples and a given standard @@ -46,7 +46,7 @@ def get_homoscedastic_data( Returns ------- - Tuple[Any, Any, ArrayLike, Any, float] + Tuple[NDArray, NDArray, NDArray, NDArray, float] Generated training and test data. [0]: X_train [1]: y_train @@ -65,14 +65,14 @@ def get_homoscedastic_data( def plot_1d_data( - X_train: ArrayLike, - y_train: ArrayLike, - X_test: ArrayLike, - y_test: ArrayLike, + X_train: NDArray, + y_train: NDArray, + X_test: NDArray, + y_test: NDArray, y_test_sigma: float, - y_pred: ArrayLike, - y_pred_low: ArrayLike, - y_pred_up: ArrayLike, + y_pred: NDArray, + y_pred_low: NDArray, + y_pred_up: NDArray, ax: plt.Axes, title: str, ) -> None: @@ -82,21 +82,21 @@ def plot_1d_data( Parameters ---------- - X_train : ArrayLike + X_train : NDArray Training data. - y_train : ArrayLike + y_train : NDArray Training labels. - X_test : ArrayLike + X_test : NDArray Test data. - y_test : ArrayLike + y_test : NDArray True function values on test data. y_test_sigma : float True standard deviation. - y_pred : ArrayLike + y_pred : NDArray Predictions on test data. - y_pred_low : ArrayLike + y_pred_low : NDArray Predicted lower bounds on test data. - y_pred_up : ArrayLike + y_pred_up : NDArray Predicted upper bounds on test data. ax : plt.Axes Axis to plot. diff --git a/examples/regression/1-quickstart/plot_prefit_nn.py b/examples/regression/1-quickstart/plot_prefit_nn.py index 41a742fb7..e5f1c3578 100644 --- a/examples/regression/1-quickstart/plot_prefit_nn.py +++ b/examples/regression/1-quickstart/plot_prefit_nn.py @@ -19,10 +19,10 @@ from mapie.regression import MapieRegressor from mapie.metrics import regression_coverage_score -from mapie._typing import ArrayLike +from mapie._typing import NDArray -def f(x: ArrayLike) -> ArrayLike: +def f(x: NDArray) -> NDArray: """Polynomial function used to generate one-dimensional data.""" return np.array(5 * x + 5 * x ** 4 - 9 * x ** 2) diff --git a/examples/regression/2-advanced-analysis/plot_both_uncertainties.py b/examples/regression/2-advanced-analysis/plot_both_uncertainties.py index 3f7e19eda..565453f7b 100644 --- a/examples/regression/2-advanced-analysis/plot_both_uncertainties.py +++ b/examples/regression/2-advanced-analysis/plot_both_uncertainties.py @@ -16,20 +16,20 @@ import matplotlib.pyplot as plt from mapie.regression import MapieRegressor -from mapie._typing import ArrayLike +from mapie._typing import NDArray F = TypeVar("F", bound=Callable[..., Any]) # Functions for generating our dataset -def x_sinx(x: ArrayLike) -> Any: +def x_sinx(x: NDArray) -> NDArray: """One-dimensional x*sin(x) function.""" return x * np.sin(x) def get_1d_data_with_normal_distrib( funct: F, mu: float, sigma: float, n_samples: int, noise: float -) -> Tuple[ArrayLike, ArrayLike, ArrayLike, ArrayLike, ArrayLike]: +) -> Tuple[NDArray, NDArray, NDArray, NDArray, NDArray]: """ Generate noisy 1D data with normal distribution from given function and noise standard deviation. @@ -49,7 +49,7 @@ def get_1d_data_with_normal_distrib( Returns ------- - Tuple[Any, Any, ArrayLike, Any, float] + Tuple[NDArray, AnNDArrayy, NDArray, NDArray, NDArray] Generated training and test data. [0]: X_train [1]: y_train @@ -104,14 +104,14 @@ def get_1d_data_with_normal_distrib( # Visualization def plot_1d_data( - X_train: ArrayLike, - y_train: ArrayLike, - X_test: ArrayLike, - y_test: ArrayLike, + X_train: NDArray, + y_train: NDArray, + X_test: NDArray, + y_test: NDArray, y_sigma: float, - y_pred: ArrayLike, - y_pred_low: ArrayLike, - y_pred_up: ArrayLike, + y_pred: NDArray, + y_pred_low: NDArray, + y_pred_up: NDArray, ax: plt.Axes, title: str, ) -> None: diff --git a/examples/regression/3-scientific-articles/plot_barber2020_simulations.py b/examples/regression/3-scientific-articles/plot_barber2020_simulations.py index 5b8a99304..f5d050061 100644 --- a/examples/regression/3-scientific-articles/plot_barber2020_simulations.py +++ b/examples/regression/3-scientific-articles/plot_barber2020_simulations.py @@ -28,7 +28,7 @@ "Predictive inference with the jackknife+." Ann. Statist., 49(1):486–507, February 2021. """ -from typing import Any, Dict, List +from typing import Any, Dict import numpy as np from sklearn.linear_model import LinearRegression @@ -39,15 +39,15 @@ regression_mean_width_score ) from mapie.regression import MapieRegressor -from mapie._typing import ArrayLike +from mapie._typing import NDArray def PIs_vs_dimensions( strategies: Dict[str, Any], alpha: float, n_trial: int, - dimensions: List[int], -) -> Dict[str, Dict[int, Dict[str, ArrayLike]]]: + dimensions: NDArray, +) -> Dict[str, Dict[int, Dict[str, NDArray]]]: """ Compute the prediction intervals for a linear regression problem. Function adapted from Foygel-Barber et al. (2020). @@ -82,14 +82,14 @@ def PIs_vs_dimensions( Returns ------- - Dict[str, Dict[int, Dict[str, ArrayLike]]] + Dict[str, Dict[int, Dict[str, NDArray]]] Prediction interval widths and coverages for each strategy, trial, and dimension value. """ n_train = 100 n_test = 100 SNR = 10 - results: Dict[str, Dict[int, Dict[str, ArrayLike]]] = { + results: Dict[str, Dict[int, Dict[str, NDArray]]] = { strategy: { dimension: { "coverage": np.empty(n_trial), @@ -132,7 +132,7 @@ def PIs_vs_dimensions( def plot_simulation_results( - results: Dict[str, Dict[int, Dict[str, ArrayLike]]], title: str + results: Dict[str, Dict[int, Dict[str, NDArray]]], title: str ) -> None: """ Show the prediction interval coverages and widths as a function @@ -141,7 +141,7 @@ def plot_simulation_results( Parameters ---------- - results : Dict[str, Dict[int, Dict[str, ArrayLike]]] + results : Dict[str, Dict[int, Dict[str, NDArray]]] Prediction interval widths and coverages for each strategy, trial, and dimension value. title : str diff --git a/examples/regression/3-scientific-articles/plot_kim2020_simulations.py b/examples/regression/3-scientific-articles/plot_kim2020_simulations.py index 9cef7cad7..022d462ac 100644 --- a/examples/regression/3-scientific-articles/plot_kim2020_simulations.py +++ b/examples/regression/3-scientific-articles/plot_kim2020_simulations.py @@ -43,7 +43,7 @@ from sklearn.linear_model import Ridge from sklearn.model_selection import train_test_split -from mapie._typing import ArrayLike +from mapie._typing import ArrayLike, NDArray from mapie.metrics import ( regression_mean_width_score, regression_coverage_score, @@ -52,7 +52,7 @@ from mapie.subsample import Subsample -def get_X_y() -> Tuple[ArrayLike, ArrayLike]: +def get_X_y() -> Tuple[NDArray, NDArray]: """ Downloads the ``blog`` dataset from a zip file on the UCI Machine Learning website, and returns X and y, which are respectively the explicative @@ -60,7 +60,7 @@ def get_X_y() -> Tuple[ArrayLike, ArrayLike]: Returns ------- - Tuple[ArrayLike, ArrayLike] of shapes + Tuple[NDArray, NDArray] of shapes (n_samples, n_features) and (n_samples,) Explicative data and labels """ @@ -78,7 +78,7 @@ def get_X_y() -> Tuple[ArrayLike, ArrayLike]: return (X, y) -class Ridge2(RegressorMixin, BaseEstimator): # type:ignore +class Ridge2(RegressorMixin, BaseEstimator): """ Little variation of Ridge proposed in [1]. Rectify alpha on the training set svd max value. @@ -95,16 +95,16 @@ def __init__(self, ridge_mult: float = 0.001) -> None: self.ridge_mult = ridge_mult self.__name__ = "Ridge2" - def fit(self, X: ArrayLike, y: Optional[ArrayLike] = None) -> Ridge2: + def fit(self, X: NDArray, y: Optional[NDArray] = None) -> Ridge2: """ Fit Ridge2. Parameters ---------- - X : ArrayLike of shape (n_samples, n_features) + X : NDArray of shape (n_samples, n_features) Training data. - y : ArrayLike of shape (n_samples,) + y : NDArray of shape (n_samples,) Training labels. Returns @@ -116,7 +116,7 @@ def fit(self, X: ArrayLike, y: Optional[ArrayLike] = None) -> Ridge2: self.ridge2 = Ridge(alpha=alpha).fit(X=X, y=y) return self - def predict(self, X: ArrayLike) -> ArrayLike: + def predict(self, X: ArrayLike) -> NDArray: """ Predict target on new samples. @@ -127,7 +127,7 @@ def predict(self, X: ArrayLike) -> ArrayLike: Returns ------- - np.ndarray of shape (n_samples, ) + NDArray of shape (n_samples, ) Predictions on test data """ return self.ridge2.predict(X) @@ -135,9 +135,9 @@ def predict(self, X: ArrayLike) -> ArrayLike: def compute_PIs( estimator: BaseEstimator, - X_train: ArrayLike, - y_train: ArrayLike, - X_test: ArrayLike, + X_train: NDArray, + y_train: NDArray, + X_test: NDArray, method: str, cv: Any, alpha: float, @@ -152,11 +152,11 @@ def compute_PIs( ---------- estimator : BaseEstimator Base model to fit. - X_train : np.ndarray + X_train : NDArray Features of training set. - y_train : np.ndarray + y_train : NDArray Target of training set. - X_test : np.ndarray + X_test : NDArray Features of testing set. method : str Method for estimating prediction intervals. @@ -187,7 +187,7 @@ def compute_PIs( return pd.DataFrame(PI, columns=["lower", "upper"]) -def get_coverage_width(PIs: pd.DataFrame, y: ArrayLike) -> Tuple[float, float]: +def get_coverage_width(PIs: pd.DataFrame, y: NDArray) -> Tuple[float, float]: """ Computes the mean coverage and width of the predictions intervals of a DataFrame given by the ``compute_PIs`` function @@ -198,7 +198,7 @@ def get_coverage_width(PIs: pd.DataFrame, y: ArrayLike) -> Tuple[float, float]: DataFrame returned by `compute_PIs``, with lower and upper bounds of the PIs. - y : ArrayLike + y : NDArray Targets supposedly covered by the PIs. Returns @@ -216,7 +216,11 @@ def get_coverage_width(PIs: pd.DataFrame, y: ArrayLike) -> Tuple[float, float]: def B_random_from_B_fixed( - B: int, train_size: int, m: int, itrial: int = 0, random_state: int = 98765 + B: int, + train_size: int, + m: int, + itrial: int = 0, + random_state: int = 98765 ) -> int: """ Generates a random number from a binomial distribution. diff --git a/mapie/_compatibility.py b/mapie/_compatibility.py new file mode 100644 index 000000000..204831ace --- /dev/null +++ b/mapie/_compatibility.py @@ -0,0 +1,33 @@ +from typing import Any + +import numpy as np +from packaging.version import parse as parse_version + +from ._typing import ArrayLike, NDArray + + +def np_quantile_version_below_122( + a: ArrayLike, + q: ArrayLike, + method: str = "linear", + **kwargs: Any +) -> NDArray: + """Wrapper of np.quantile function for numpy version < 1.22.""" + return np.quantile(a, q, interpolation=method, **kwargs) # type: ignore + + +def np_quantile_version_above_122( + a: ArrayLike, + q: ArrayLike, + method: str = "linear", + **kwargs: Any +) -> NDArray: + """Wrapper of np.quantile function for numpy version >= 1.22.""" + return np.quantile(a, q, method=method, **kwargs) # type: ignore + + +numpy_version = parse_version(np.__version__) +if numpy_version < parse_version("1.22"): + np_quantile = np_quantile_version_below_122 +else: + np_quantile = np_quantile_version_above_122 diff --git a/mapie/_typing.py b/mapie/_typing.py index 70f5743e0..af5839e8c 100644 --- a/mapie/_typing.py +++ b/mapie/_typing.py @@ -1,9 +1,3 @@ -import numpy as np -from typing import Union, List +from numpy.typing import ArrayLike, NDArray -try: - from np.typing import ArrayLike -except (AttributeError, ModuleNotFoundError): - ArrayLike = Union[np.ndarray, List[List[float]]] - -__all__ = ["ArrayLike"] +__all__ = ["ArrayLike", "NDArray"] diff --git a/mapie/aggregation_functions.py b/mapie/aggregation_functions.py index 1e6af290f..ad9a8181c 100644 --- a/mapie/aggregation_functions.py +++ b/mapie/aggregation_functions.py @@ -2,14 +2,14 @@ import numpy as np -from ._typing import ArrayLike +from ._typing import NDArray def phi1D( - x: ArrayLike, - B: ArrayLike, - fun: Callable[[ArrayLike], ArrayLike], -) -> ArrayLike: + x: NDArray, + B: NDArray, + fun: Callable[[NDArray], NDArray], +) -> NDArray: """ The function phi1D is called by phi2D. It aims at applying a function ``fun`` after multiplying each row @@ -17,16 +17,16 @@ def phi1D( Parameters ---------- - x : ArrayLike of shape (n, ) + x : NDArray of shape (n, ) 1D vector. - B : ArrayLike of shape (k, n) + B : NDArray of shape (k, n) 2D vector whose number of columns is the number of rows of x. fun : function - Vectorized function applying to Arraylike. + Vectorized function applying to NDArray. Returns ------- - ArrayLike + NDArray The function fun is applied to the product of ``x`` and ``B``. Typically, ``fun`` is a numpy function, ignoring nan, with argument ``axis=1``. @@ -46,25 +46,25 @@ def phi1D( def phi2D( - A: ArrayLike, - B: ArrayLike, - fun: Callable[[ArrayLike], ArrayLike], -) -> ArrayLike: + A: NDArray, + B: NDArray, + fun: Callable[[NDArray], NDArray], +) -> NDArray: """ The function phi2D is a loop applying phi1D on each row of A. Parameters ---------- - A : ArrayLike of shape (n_rowsA, n_columns) - B : ArrayLike of shape (n_rowsB, n_columns) + A : NDArray of shape (n_rowsA, n_columns) + B : NDArray of shape (n_rowsB, n_columns) A and B must have the same number of columns. fun : function - Vectorized function applying to Arraylike, and that should ignore nan. + Vectorized function applying to NDArray, and that should ignore nan. Returns ------- - ArrayLike of shape (n_rowsA, n_rowsB) + NDArray of shape (n_rowsA, n_rowsB) Applies phi1D(x, B, fun) to each row x of A. Examples @@ -81,19 +81,19 @@ def phi2D( return np.apply_along_axis(phi1D, axis=1, arr=A, B=B, fun=fun) -def aggregate_all(agg_function: Optional[str], X: ArrayLike) -> ArrayLike: +def aggregate_all(agg_function: Optional[str], X: NDArray) -> NDArray: """ Applies np.nanmean(, axis=1) or np.nanmedian(, axis=1) according to the string ``agg_function``. Parameters ----------- - X : ArrayLike of shape (n, p) + X : NDArray of shape (n, p) Array of floats and nans Returns -------- - ArrayLike of shape (n, 1): + NDArray of shape (n, 1): Array of the means or medians of each row of X Raises diff --git a/mapie/classification.py b/mapie/classification.py index f06a31d68..79c4517c5 100644 --- a/mapie/classification.py +++ b/mapie/classification.py @@ -1,5 +1,5 @@ from __future__ import annotations -from typing import Optional, Union, Tuple, Iterable, List +from typing import Optional, Union, Tuple, Iterable, List, cast import numpy as np from joblib import Parallel, delayed @@ -17,7 +17,7 @@ _check_y, ) -from ._typing import ArrayLike +from ._typing import ArrayLike, NDArray from ._machine_precision import EPSILON from .utils import ( check_cv, @@ -27,12 +27,12 @@ check_alpha_and_n_samples, check_n_jobs, check_verbose, - check_input_is_image, fit_estimator ) +from ._compatibility import np_quantile -class MapieClassifier(BaseEstimator, ClassifierMixin): # type: ignore +class MapieClassifier(BaseEstimator, ClassifierMixin): """ Prediction sets for classification. @@ -77,8 +77,8 @@ class MapieClassifier(BaseEstimator, ClassifierMixin): # type: ignore ``sklearn.model_selection.LeaveOneOut()``. - CV splitter: any ``sklearn.model_selection.BaseCrossValidator`` Main variants are: - - ``sklearn.model_selection.LeaveOneOut`` (jackknife), - - ``sklearn.model_selection.KFold`` (cross-validation) + - ``sklearn.model_selection.LeaveOneOut`` (jackknife), + - ``sklearn.model_selection.KFold`` (cross-validation) - ``"prefit"``, assumes that ``estimator`` has been fitted already. All data provided in the ``fit`` method is then used to calibrate the predictions through the score computation. @@ -127,9 +127,6 @@ class MapieClassifier(BaseEstimator, ClassifierMixin): # type: ignore n_features_in_: int Number of features passed to the fit method. - n_samples_: Union[int, List[int]] - Number of samples passed to the fit method. - conformity_scores_ : ArrayLike of shape (n_samples_train) The conformity scores used to calibrate the prediction sets. @@ -177,8 +174,8 @@ class MapieClassifier(BaseEstimator, ClassifierMixin): # type: ignore fit_attributes = [ "single_estimator_", "estimators_", + "k_", "n_features_in_", - "n_samples_", "conformity_scores_" ] @@ -254,13 +251,8 @@ def _check_estimator( If the estimator is not fitted and ``cv`` attribute is "prefit". """ if estimator is None: - if not self.image_input: - return LogisticRegression(multi_class="multinomial").fit(X, y) - else: - raise ValueError( - "Default LogisticRegression's input can't be an image." - "Please provide a proper model." - ) + return LogisticRegression(multi_class="multinomial").fit(X, y) + if isinstance(estimator, Pipeline): est = estimator[-1] else: @@ -332,8 +324,8 @@ def _check_include_last_label( def _check_proba_normalized( self, y_pred_proba: ArrayLike, - axis: Optional[int] = 1 - ) -> Optional[ArrayLike]: + axis: int = 1 + ) -> ArrayLike: """ Check if, for all the observations, the sum of the probabilities is equal to one. @@ -347,7 +339,7 @@ def _check_proba_normalized( Returns ------- - Optional[ArrayLike] of shape (n_samples, n_classes) + ArrayLike of shape (n_samples, n_classes) Softmax output of a model if the scores all sum to one. @@ -366,10 +358,10 @@ def _check_proba_normalized( def _get_last_index_included( self, - y_pred_proba_cumsum: ArrayLike, - threshold: ArrayLike, + y_pred_proba_cumsum: NDArray, + threshold: NDArray, include_last_label: Optional[Union[bool, str]] - ) -> ArrayLike: + ) -> NDArray: """ Return the index of the last included sorted probability depending if we included the first label over the quantile @@ -377,10 +369,10 @@ def _get_last_index_included( Parameters ---------- - y_pred_proba_cumsum : ArrayLike of shape (n_samples, n_classes) + y_pred_proba_cumsum : NDArray of shape (n_samples, n_classes) Cumsumed probabilities in the original order. - threshold : ArrayLike of shape (n_alpha,) or shape (n_samples_train,) + threshold : NDArray of shape (n_alpha,) or shape (n_samples_train,) Threshold to compare with y_proba_last_cumsum, can be either: - the quantiles associated with alpha values when @@ -395,7 +387,7 @@ def _get_last_index_included( Returns ------- - Optional[ArrayLike] of shape (n_samples, n_classes) + NDArray of shape (n_samples, n_classes) Index of the last included sorted probability. """ if ( @@ -434,12 +426,12 @@ def _get_last_index_included( def _add_random_tie_breaking( self, - prediction_sets: ArrayLike, - y_pred_index_last: ArrayLike, - y_pred_proba_cumsum: ArrayLike, - y_pred_proba_last: ArrayLike, - threshold: ArrayLike - ) -> ArrayLike: + prediction_sets: NDArray, + y_pred_index_last: NDArray, + y_pred_proba_cumsum: NDArray, + y_pred_proba_last: NDArray, + threshold: NDArray + ) -> NDArray: """ Randomly remove last label from prediction set based on the comparison between a random number and the difference between @@ -447,20 +439,20 @@ def _add_random_tie_breaking( Parameters ---------- - prediction_sets : ArrayLike of shape + prediction_sets : NDArray of shape (n_samples, n_classes, n_threshold) Prediction set for each observation and each alpha. - y_pred_index_last : ArrayLike of shape (n_samples, threshold) + y_pred_index_last : NDArray of shape (n_samples, threshold) Index of the last included label. - y_pred_proba_cumsum : ArrayLike of shape (n_samples, n_classes) + y_pred_proba_cumsum : NDArray of shape (n_samples, n_classes) Cumsumed probability of the model in the original order. - y_pred_proba_last : ArrayLike of shape (n_samples, 1, threshold) + y_pred_proba_last : NDArray of shape (n_samples, 1, threshold) Last included probability. - threshold : ArrayLike of shape (n_alpha,) or shape (n_samples_train,) + threshold : NDArray of shape (n_alpha,) or shape (n_samples_train,) Threshold to compare with y_proba_last_cumsum, can be either: - the quantiles associated with alpha values when @@ -471,7 +463,7 @@ def _add_random_tie_breaking( Returns ------- - ArrayLike of shape (n_samples, n_classes, n_alpha) + NDArray of shape (n_samples, n_classes, n_alpha) Updated version of prediction_sets with randomly removed labels. """ @@ -503,9 +495,9 @@ def _add_random_tie_breaking( def _fix_number_of_classes( self, - n_classes_training: ArrayLike, - y_proba: ArrayLike - ) -> ArrayLike: + n_classes_training: NDArray, + y_proba: NDArray + ) -> NDArray: """ Fix shape of y_proba of validation set if number of classes of the training set used for cross-validation is different than @@ -513,14 +505,14 @@ def _fix_number_of_classes( Parameters ---------- - n_classes_training : ArrayLike + n_classes_training : NDArray Classes of the training set. - y_proba : ArrayLike + y_proba : NDArray Probabilities of the validation set. Returns ------- - ArrayLike + NDArray Probabilities with the right number of classes. """ y_pred_full = np.zeros( @@ -539,7 +531,7 @@ def _predict_oof_model( self, estimator: ClassifierMixin, X: ArrayLike, - ) -> ArrayLike: + ) -> NDArray: """ Predict probabilities of a test set from a fitted estimator. @@ -573,7 +565,7 @@ def _fit_and_predict_oof_model( val_index: ArrayLike, k: int, sample_weight: Optional[ArrayLike] = None, - ) -> Tuple[ClassifierMixin, ArrayLike, ArrayLike, ArrayLike]: + ) -> Tuple[ClassifierMixin, NDArray, NDArray, ArrayLike]: """ Fit a single out-of-fold model on a given training set and perform predictions on a test set. @@ -604,16 +596,15 @@ def _fit_and_predict_oof_model( Returns ------- - Tuple[ClassifierMixin, ArrayLike, ArrayLike, ArrayLike] - - - [0]: Fitted estimator - - [1]: Estimator predictions on the validation fold, - of shape (n_samples_val,) - - [2]: Identification number of the validation fold, - of shape (n_samples_val,) - - [3]: Validation data indices, - of shape (n_samples_val,). - + Tuple[ClassifierMixin, NDArray, NDArray, ArrayLike] + + - [0]: ClassifierMixin, fitted estimator + - [1]: NDArray of shape (n_samples_val,), + Estimator predictions on the validation fold, + - [2]: NDArray of shape (n_samples_val,) + Identification number of the validation fold, + - [3]: ArrayLike of shape (n_samples_val,) + Validation data indices """ X_train = _safe_indexing(X, train_index) y_train = _safe_indexing(y, train_index) @@ -623,13 +614,12 @@ def _fit_and_predict_oof_model( if sample_weight is None: estimator = fit_estimator(estimator, X_train, y_train) else: + sample_weight_train = _safe_indexing(sample_weight, train_index) estimator = fit_estimator( - estimator, X_train, y_train, sample_weight[train_index] + estimator, X_train, y_train, sample_weight_train ) if _num_samples(X_val) > 0: - y_pred_proba = self._predict_oof_model( - estimator, X_val, - ) + y_pred_proba = self._predict_oof_model(estimator, X_val) else: y_pred_proba = np.array([]) val_id = np.full_like(y_val, k, dtype=int) @@ -639,7 +629,6 @@ def fit( self, X: ArrayLike, y: ArrayLike, - image_input: Optional[bool] = False, sample_weight: Optional[ArrayLike] = None, ) -> MapieClassifier: """ @@ -653,13 +642,6 @@ def fit( y : ArrayLike of shape (n_samples,) Training labels. - image_input: Optional[bool] = False - Whether or not the X input is an image. If True, you must provide - a model that accepts image as input (e.g., a Neural Network). All - Scikit-learn classifiers only accept two-dimensional inputs. - - By default False. - sample_weight : Optional[ArrayLike] of shape (n_samples,) Sample weights for fitting the out-of-fold models. If None, then samples are equally weighted. @@ -675,18 +657,18 @@ def fit( The model itself. """ # Checks - self.image_input = image_input self._check_parameters() cv = check_cv(self.cv) estimator = self._check_estimator(X, y, self.estimator) - if self.image_input: - check_input_is_image(X) + X, y = indexable(X, y) y = _check_y(y) assert type_of_target(y) == "multiclass" - self.n_classes_ = len(set(y)) - self.n_features_in_ = check_n_features_in(X, cv, estimator) sample_weight, X, y = check_null_weight(sample_weight, X, y) + y = cast(NDArray, y) + n_samples = _num_samples(y) + self.n_classes_ = len(np.unique(y)) + self.n_features_in_ = check_n_features_in(X, cv, estimator) # Initialization self.estimators_: List[ClassifierMixin] = [] @@ -700,10 +682,11 @@ def fit( y_pred_proba = self._check_proba_normalized(y_pred_proba) else: + cv = cast(BaseCrossValidator, cv) self.single_estimator_ = fit_estimator( clone(estimator), X, y, sample_weight ) - y_pred_proba = np.empty((len(y), len(np.unique(y))), dtype=float) + y_pred_proba = np.empty((n_samples, self.n_classes_), dtype=float) outputs = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)( delayed(self._fit_and_predict_oof_model)( clone(estimator), @@ -716,20 +699,26 @@ def fit( ) for k, (train_index, val_index) in enumerate(cv.split(X)) ) - self.estimators_, predictions, val_ids, val_indices = map( - list, zip(*outputs) - ) - predictions, val_ids, val_indices = map( - np.concatenate, (predictions, val_ids, val_indices) - ) + ( + self.estimators_, + predictions_list, + val_ids_list, + val_indices_list + ) = map(list, zip(*outputs)) + predictions = np.concatenate(cast(List[NDArray], predictions_list)) + val_ids = np.concatenate(cast(List[NDArray], val_ids_list)) + val_indices = np.concatenate(cast(List[NDArray], val_indices_list)) self.k_[val_indices] = val_ids y_pred_proba[val_indices] = predictions if self.method == "naive": - self.conformity_scores_ = np.empty(y_pred_proba.shape) + self.conformity_scores_ = np.empty( + y_pred_proba.shape, + dtype="float" + ) elif self.method == "score": self.conformity_scores_ = np.take_along_axis( - 1 - y_pred_proba, np.ravel(y).reshape(-1, 1), axis=1 + 1 - y_pred_proba, y.reshape(-1, 1), axis=1 ) elif self.method == "cumulated_score": y_true = label_binarize( @@ -746,7 +735,7 @@ def fit( y_pred_proba_sorted_cumsum, cutoff.reshape(-1, 1), axis=1 ) y_proba_true = np.take_along_axis( - y_pred_proba, np.ravel(y).reshape(-1, 1), axis=1 + y_pred_proba, y.reshape(-1, 1), axis=1 ) random_state = check_random_state(self.random_state) u = random_state.uniform(size=len(y_pred_proba)).reshape(-1, 1) @@ -759,7 +748,7 @@ def fit( ) self.conformity_scores_ = np.take_along_axis( index, - np.ravel(y).reshape(-1, 1), + y.reshape(-1, 1), axis=1 ) @@ -777,7 +766,7 @@ def predict( alpha: Optional[Union[float, Iterable[float]]] = None, include_last_label: Optional[Union[bool, str]] = True, agg_scores: Optional[str] = "mean" - ) -> Union[ArrayLike, Tuple[ArrayLike, ArrayLike]]: + ) -> Union[NDArray, Tuple[NDArray, NDArray]]: """ Prediction prediction sets on new samples based on target confidence interval. @@ -830,11 +819,11 @@ def predict( Returns ------- - Union[ArrayLike, Tuple[ArrayLike, ArrayLike]] + Union[NDArray, Tuple[NDArray, NDArray]] - - ArrayLike of shape (n_samples,) if alpha is None. + - NDArray of shape (n_samples,) if alpha is None. - - Tuple[ArrayLike, ArrayLike] of shapes + - Tuple[NDArray, NDArray] of shapes (n_samples,) and (n_samples, n_classes, n_alpha) if alpha is not None. """ if self.method == "top_k": @@ -842,178 +831,178 @@ def predict( # Checks cv = check_cv(self.cv) include_last_label = self._check_include_last_label(include_last_label) - alpha_ = check_alpha(alpha) + alpha = cast(Optional[NDArray], check_alpha(alpha)) check_is_fitted(self, self.fit_attributes) - if self.image_input: - check_input_is_image(X) # Estimate prediction sets y_pred = self.single_estimator_.predict(X) - n = self.n_samples_ - if alpha_ is None: + n = len(self.conformity_scores_) + + if alpha is None: return np.array(y_pred) + # Estimate of probabilities from estimator(s) + # In all cases : len(y_pred_proba.shape) == 3 + # with (n_test, n_classes, n_alpha or n_train_samples) + alpha_np = cast(NDArray, alpha) + check_alpha_and_n_samples(alpha_np, n) + if cv == "prefit": + y_pred_proba = self.single_estimator_.predict_proba(X) + y_pred_proba = np.repeat( + y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2 + ) else: - # Estimate of probabilities from estimator(s) - # In all cases : len(y_pred_proba.shape) == 3 - # with (n_test, n_classes, n_alpha or n_train_samples) - if cv == "prefit": - y_pred_proba = self.single_estimator_.predict_proba(X) + y_pred_proba_k = np.asarray( + Parallel( + n_jobs=self.n_jobs, verbose=self.verbose + )( + delayed(self._predict_oof_model)(estimator, X) + for estimator in self.estimators_ + ) + ) + if agg_scores == "crossval": + y_pred_proba = np.moveaxis(y_pred_proba_k[self.k_], 0, 2) + elif agg_scores == "mean": + y_pred_proba = np.mean(y_pred_proba_k, axis=0) y_pred_proba = np.repeat( - y_pred_proba[:, :, np.newaxis], len(alpha_), axis=2 + y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2 ) else: - y_pred_proba_k = np.asarray( - Parallel( - n_jobs=self.n_jobs, verbose=self.verbose - )( - delayed(self._predict_oof_model)(estimator, X) - for estimator in self.estimators_ - ) - ) - if agg_scores == "crossval": - y_pred_proba = np.moveaxis(y_pred_proba_k[self.k_], 0, 2) - elif agg_scores == "mean": - y_pred_proba = np.mean(y_pred_proba_k, axis=0) - y_pred_proba = np.repeat( - y_pred_proba[:, :, np.newaxis], len(alpha_), axis=2 - ) - else: - raise ValueError("Invalid 'agg_scores' argument.") - # Check that sum of probas is equal to 1 - y_pred_proba = self._check_proba_normalized(y_pred_proba, axis=1) - - # Choice of the quantile - check_alpha_and_n_samples(alpha_, n) - if self.method == "naive": - self.quantiles_ = 1 - alpha_ + raise ValueError("Invalid 'agg_scores' argument.") + # Check that sum of probas is equal to 1 + y_pred_proba = self._check_proba_normalized(y_pred_proba, axis=1) + + # Choice of the quantile + check_alpha_and_n_samples(alpha_np, n) + if self.method == "naive": + self.quantiles_ = 1 - alpha_np + else: + if (cv == "prefit") or (agg_scores in ["mean"]): + self.quantiles_ = np.stack([ + np_quantile( + self.conformity_scores_, + ((n + 1) * (1 - _alpha)) / n, + method="higher" + ) for _alpha in alpha_np + ]) else: - if (cv == "prefit") or (agg_scores in ["mean"]): - self.quantiles_ = np.stack([ - np.quantile( - self.conformity_scores_, - ((n + 1) * (1 - _alpha)) / n, - interpolation="higher" - ) for _alpha in alpha_ - ]) - else: - self.quantiles_ = (n + 1) * (1 - alpha_) - - # Build prediction sets - if self.method == "score": - if (cv == "prefit") or (agg_scores == "mean"): - prediction_sets = y_pred_proba > ( - 1 - (self.quantiles_ + EPSILON) - ) - else: - y_pred_included = ( - 1 - y_pred_proba < ( - self.conformity_scores_.ravel() + EPSILON - ) - ).sum(axis=2) - prediction_sets = np.stack( - [ - y_pred_included > _alpha * (n - 1) - EPSILON - for _alpha in alpha_ - ], axis=2 - ) + self.quantiles_ = (n + 1) * (1 - alpha_np) - elif self.method in ["cumulated_score", "naive"]: - # specify which thresholds will be used - if (cv == "prefit") or (agg_scores in ["mean"]): - thresholds = self.quantiles_ - else: - thresholds = self.conformity_scores_.ravel() - # sort labels by decreasing probability - index_sorted = np.flip( - np.argsort(y_pred_proba, axis=1), axis=1 + # Build prediction sets + if self.method == "score": + if (cv == "prefit") or (agg_scores == "mean"): + prediction_sets = y_pred_proba > ( + 1 - (self.quantiles_ + EPSILON) ) - # sort probabilities by decreasing order - y_pred_proba_sorted = np.take_along_axis( - y_pred_proba, index_sorted, axis=1 + else: + y_pred_included = ( + 1 - y_pred_proba < ( + self.conformity_scores_.ravel() + EPSILON + ) + ).sum(axis=2) + prediction_sets = np.stack( + [ + y_pred_included > _alpha * (n - 1) - EPSILON + for _alpha in alpha_np + ], axis=2 ) - # get sorted cumulated score - y_pred_proba_sorted_cumsum = np.cumsum( - y_pred_proba_sorted, axis=1 + + elif self.method in ["cumulated_score", "naive"]: + # specify which thresholds will be used + if (cv == "prefit") or (agg_scores in ["mean"]): + thresholds = self.quantiles_ + else: + thresholds = self.conformity_scores_.ravel() + # sort labels by decreasing probability + index_sorted = np.flip( + np.argsort(y_pred_proba, axis=1), axis=1 + ) + # sort probabilities by decreasing order + y_pred_proba_sorted = np.take_along_axis( + y_pred_proba, index_sorted, axis=1 + ) + # get sorted cumulated score + y_pred_proba_sorted_cumsum = np.cumsum( + y_pred_proba_sorted, axis=1 + ) + # get cumulated score at their original position + y_pred_proba_cumsum = np.take_along_axis( + y_pred_proba_sorted_cumsum, + np.argsort(index_sorted, axis=1), + axis=1 + ) + # get index of the last included label + y_pred_index_last = self._get_last_index_included( + y_pred_proba_cumsum, + thresholds, + include_last_label + ) + # get the probability of the last included label + y_pred_proba_last = np.take_along_axis( + y_pred_proba, + y_pred_index_last, + axis=1 + ) + # get the prediction set by taking all probabilities + # above the last one + if (cv == "prefit") or (agg_scores in ["mean"]): + y_pred_included = ( + (y_pred_proba > y_pred_proba_last - EPSILON) ) - # get cumulated score at their original position - y_pred_proba_cumsum = np.take_along_axis( - y_pred_proba_sorted_cumsum, - np.argsort(index_sorted, axis=1), - axis=1 + else: + y_pred_included = ( + # ~(y_pred_proba >= y_pred_proba_last - EPSILON) + (y_pred_proba < y_pred_proba_last + EPSILON) ) - # get index of the last included label - y_pred_index_last = self._get_last_index_included( + # remove last label randomly + if include_last_label == "randomized": + y_pred_included = self._add_random_tie_breaking( + y_pred_included, + y_pred_index_last, y_pred_proba_cumsum, + y_pred_proba_last, thresholds, - include_last_label - ) - # get the probability of the last included label - y_pred_proba_last = np.take_along_axis( - y_pred_proba, - y_pred_index_last, - axis=1 - ) - # get the prediction set by taking all probabilities - # above the last one - if (cv == "prefit") or (agg_scores in ["mean"]): - y_pred_included = ( - (y_pred_proba > y_pred_proba_last - EPSILON) - ) - else: - y_pred_included = ( - # ~(y_pred_proba >= y_pred_proba_last - EPSILON) - (y_pred_proba < y_pred_proba_last + EPSILON) - ) - # remove last label randomly - if include_last_label == "randomized": - y_pred_included = self._add_random_tie_breaking( - y_pred_included, - y_pred_index_last, - y_pred_proba_cumsum, - y_pred_proba_last, - thresholds, - ) - if (cv == "prefit") or (agg_scores in ["mean"]): - prediction_sets = y_pred_included - else: - # compute the number of times the inequality is verified - prediction_sets_summed = y_pred_included.sum(axis=2) - # compare the summed prediction sets with (n+1)*(1-alpha) - prediction_sets = np.stack( - [ - prediction_sets_summed < quantile + EPSILON - for quantile in self.quantiles_ - ], axis=2 - ) - elif self.method == "top_k": - y_pred_proba = y_pred_proba[:, :, 0] - index_sorted = np.fliplr(np.argsort(y_pred_proba, axis=1)) - y_pred_index_last = np.stack( - [ - index_sorted[:, quantile] - for quantile in self.quantiles_ - ], axis=1 - ) - y_pred_proba_last = np.stack( - [ - np.take_along_axis( - y_pred_proba, - y_pred_index_last[:, iq].reshape(-1, 1), - axis=1 - ) - for iq, _ in enumerate(self.quantiles_) - ], axis=2 ) + if (cv == "prefit") or (agg_scores in ["mean"]): + prediction_sets = y_pred_included + else: + # compute the number of times the inequality is verified + prediction_sets_summed = y_pred_included.sum(axis=2) + # compare the summed prediction sets with (n+1)*(1-alpha) prediction_sets = np.stack( [ - y_pred_proba >= y_pred_proba_last[:, :, iq] - EPSILON - for iq, _ in enumerate(self.quantiles_) + prediction_sets_summed < quantile + EPSILON + for quantile in self.quantiles_ ], axis=2 ) - else: - raise ValueError( - "Invalid method. " - "Allowed values are 'score' or 'cumulated_score'." - ) - return y_pred, prediction_sets + elif self.method == "top_k": + y_pred_proba = y_pred_proba[:, :, 0] + index_sorted = np.fliplr(np.argsort(y_pred_proba, axis=1)) + y_pred_index_last = np.stack( + [ + index_sorted[:, quantile] + for quantile in self.quantiles_ + ], axis=1 + ) + y_pred_proba_last = np.stack( + [ + np.take_along_axis( + y_pred_proba, + y_pred_index_last[:, iq].reshape(-1, 1), + axis=1 + ) + for iq, _ in enumerate(self.quantiles_) + ], axis=2 + ) + prediction_sets = np.stack( + [ + y_pred_proba >= y_pred_proba_last[:, :, iq] - EPSILON + for iq, _ in enumerate(self.quantiles_) + ], axis=2 + ) + else: + raise ValueError( + "Invalid method. " + "Allowed values are 'score' or 'cumulated_score'." + ) + return y_pred, prediction_sets diff --git a/mapie/metrics.py b/mapie/metrics.py index 62e1c38a1..1851aef6a 100644 --- a/mapie/metrics.py +++ b/mapie/metrics.py @@ -1,6 +1,9 @@ +from typing import cast + import numpy as np from sklearn.utils.validation import column_or_1d, check_array -from ._typing import ArrayLike + +from ._typing import ArrayLike, NDArray def regression_coverage_score( @@ -38,15 +41,18 @@ def regression_coverage_score( >>> print(regression_coverage_score(y_true, y_pred_low, y_pred_up)) 0.8 """ - y_true = column_or_1d(y_true) - y_pred_low = column_or_1d(y_pred_low) - y_pred_up = column_or_1d(y_pred_up) - coverage = ((y_pred_low <= y_true) & (y_pred_up >= y_true)).mean() + y_true = cast(NDArray, column_or_1d(y_true)) + y_pred_low = cast(NDArray, column_or_1d(y_pred_low)) + y_pred_up = cast(NDArray, column_or_1d(y_pred_up)) + coverage = np.mean( + ((y_pred_low <= y_true) & (y_pred_up >= y_true)) + ) return float(coverage) def classification_coverage_score( - y_true: ArrayLike, y_pred_set: ArrayLike + y_true: ArrayLike, + y_pred_set: ArrayLike ) -> float: """ Effective coverage score obtained by the prediction sets. @@ -81,8 +87,13 @@ def classification_coverage_score( >>> print(classification_coverage_score(y_true, y_pred_set)) 0.8 """ - y_true = column_or_1d(y_true) - y_pred_set = check_array(y_pred_set, force_all_finite=True, dtype=["bool"]) + y_true = cast(NDArray, column_or_1d(y_true)) + y_pred_set = cast( + NDArray, + check_array( + y_pred_set, force_all_finite=True, dtype=["bool"] + ) + ) coverage = np.take_along_axis( y_pred_set, y_true.reshape(-1, 1), axis=1 ).mean() @@ -91,7 +102,7 @@ def classification_coverage_score( def regression_mean_width_score( y_pred_low: ArrayLike, - y_pred_up: ArrayLike, + y_pred_up: ArrayLike ) -> float: """ Effective mean width score obtained by the prediction intervals. @@ -117,15 +128,13 @@ def regression_mean_width_score( >>> print(regression_mean_width_score(y_pred_low, y_pred_up)) 2.3 """ - y_pred_low = column_or_1d(y_pred_low) - y_pred_up = column_or_1d(y_pred_up) + y_pred_low = cast(NDArray, column_or_1d(y_pred_low)) + y_pred_up = cast(NDArray, column_or_1d(y_pred_up)) mean_width = np.abs(y_pred_up - y_pred_low).mean() return float(mean_width) -def classification_mean_width_score( - y_pred_set: ArrayLike -) -> float: +def classification_mean_width_score(y_pred_set: ArrayLike) -> float: """ Mean width of prediction set output by :class:`mapie.classification.MapieClassifier`. @@ -154,6 +163,11 @@ def classification_mean_width_score( >>> print(classification_mean_width_score(y_pred_set)) 2.0 """ - y_pred_set = check_array(y_pred_set, force_all_finite=True, dtype=["bool"]) + y_pred_set = cast( + NDArray, + check_array( + y_pred_set, force_all_finite=True, dtype=["bool"] + ) + ) mean_width = y_pred_set.sum(axis=1).mean() return float(mean_width) diff --git a/mapie/regression.py b/mapie/regression.py index 19ea1ad25..8319041f9 100644 --- a/mapie/regression.py +++ b/mapie/regression.py @@ -17,7 +17,7 @@ _check_y, ) -from ._typing import ArrayLike +from ._typing import ArrayLike, NDArray from .aggregation_functions import aggregate_all, phi2D from .subsample import Subsample from .utils import ( @@ -31,9 +31,10 @@ check_verbose, fit_estimator ) +from ._compatibility import np_quantile -class MapieRegressor(BaseEstimator, RegressorMixin): # type: ignore +class MapieRegressor(BaseEstimator, RegressorMixin): """ Prediction interval with out-of-fold residuals. @@ -142,8 +143,8 @@ class MapieRegressor(BaseEstimator, RegressorMixin): # type: ignore estimators_ : list List of out-of-folds estimators. - residuals_ : ArrayLike of shape (n_samples_train,) - Residuals between ``y_train`` and ``y_pred``. + conformity_scores_ : ArrayLike of shape (n_samples_train,) + Conformity scores between ``y_train`` and ``y_pred``. k_ : ArrayLike - Array of nans, of shape (len(y), 1) if cv is ``"prefit"`` @@ -154,9 +155,6 @@ class MapieRegressor(BaseEstimator, RegressorMixin): # type: ignore n_features_in_: int Number of features passed to the fit method. - n_samples_: List[int] - Number of samples passed to the fit method. - References ---------- Rina Foygel Barber, Emmanuel J. Candès, @@ -193,9 +191,8 @@ class MapieRegressor(BaseEstimator, RegressorMixin): # type: ignore "single_estimator_", "estimators_", "k_", - "residuals_", - "n_features_in_", - "n_samples_", + "conformity_scores_", + "n_features_in_" ] def __init__( @@ -344,9 +341,8 @@ def _fit_and_predict_oof_model( y: ArrayLike, train_index: ArrayLike, val_index: ArrayLike, - k: int, sample_weight: Optional[ArrayLike] = None, - ) -> Tuple[RegressorMixin, ArrayLike, ArrayLike]: + ) -> Tuple[RegressorMixin, NDArray, ArrayLike]: """ Fit a single out-of-fold model on a given training set and perform predictions on a test set. @@ -368,23 +364,19 @@ def _fit_and_predict_oof_model( val_index : ArrayLike of shape (n_samples_val) Validation data indices. - k : int - Split identification number. - sample_weight : Optional[ArrayLike] of shape (n_samples,) Sample weights. If None, then samples are equally weighted. By default ``None``. Returns ------- - Tuple[RegressorMixin, ArrayLike, ArrayLike] - - - [0]: Fitted estimator - - [1]: Estimator predictions on the validation fold, - of shape (n_samples_val,) - - [3]: Validation data indices, - of shape (n_samples_val,). + Tuple[RegressorMixin, NDArray, ArrayLike] + - [0]: RegressorMixin, fitted estimator + - [1]: NDArray of shape (n_samples_val,), + estimator predictions on the validation fold. + - [3]: ArrayLike of shape (n_samples_val,), + validation data indices. """ X_train = _safe_indexing(X, train_index) y_train = _safe_indexing(y, train_index) @@ -402,7 +394,7 @@ def _fit_and_predict_oof_model( y_pred = np.array([]) return estimator, y_pred, val_index - def aggregate_with_mask(self, x: ArrayLike, k: ArrayLike) -> ArrayLike: + def aggregate_with_mask(self, x: NDArray, k: NDArray) -> NDArray: """ Take the array of predictions, made by the refitted estimators, on the testing set, and the 1-nan array indicating for each training @@ -454,7 +446,8 @@ def fit( Fit the base estimator under the ``single_estimator_`` attribute. Fit all cross-validated estimator clones and rearrange them into a list, the ``estimators_`` attribute. - Out-of-fold residuals are stored under the ``residuals_`` attribute. + Out-of-fold residuals are stored under + the ``conformity_scores_`` attribute. Parameters ---------- @@ -488,6 +481,8 @@ def fit( y = _check_y(y) self.n_features_in_ = check_n_features_in(X, cv, estimator) sample_weight, X, y = check_null_weight(sample_weight, X, y) + y = cast(NDArray, y) + n_samples = _num_samples(y) # Initialization self.estimators_: List[RegressorMixin] = [] @@ -496,19 +491,19 @@ def fit( if cv == "prefit": self.single_estimator_ = estimator y_pred = self.single_estimator_.predict(X) - self.n_samples_ = [_num_samples(X)] self.k_ = np.full( - shape=(len(y), 1), fill_value=np.nan, dtype=float + shape=(n_samples, 1), fill_value=np.nan, dtype="float" ) else: + cv = cast(BaseCrossValidator, cv) self.k_ = np.full( - shape=(len(y), cv.get_n_splits(X, y)), # type: ignore + shape=(n_samples, cv.get_n_splits(X, y)), fill_value=np.nan, dtype=float, ) pred_matrix = np.full( - shape=(len(y), cv.get_n_splits(X, y)), # type: ignore + shape=(n_samples, cv.get_n_splits(X, y)), fill_value=np.nan, dtype=float, ) @@ -518,7 +513,6 @@ def fit( ) if self.method == "naive": y_pred = self.single_estimator_.predict(X) - self.n_samples_ = [_num_samples(X)] else: outputs = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)( delayed(self._fit_and_predict_oof_model)( @@ -527,27 +521,22 @@ def fit( y, train_index, val_index, - k, sample_weight, ) - for k, (train_index, val_index) in enumerate(cv.split(X)) + for train_index, val_index in cv.split(X) ) self.estimators_, predictions, val_indices = map( list, zip(*outputs) ) - self.n_samples_ = [ - np.array(pred).shape[0] for pred in predictions - ] - for i, val_ind in enumerate(val_indices): - pred_matrix[val_ind, i] = np.array(predictions[i]).ravel() + pred_matrix[val_ind, i] = np.array(predictions[i]) self.k_[val_ind, i] = 1 check_nan_in_aposteriori_prediction(pred_matrix) y_pred = aggregate_all(agg_function, pred_matrix) - self.residuals_ = np.abs(np.ravel(y) - y_pred) + self.conformity_scores_ = np.abs(y - y_pred) return self def predict( @@ -555,7 +544,7 @@ def predict( X: ArrayLike, ensemble: bool = False, alpha: Optional[Union[float, Iterable[float]]] = None, - ) -> Union[ArrayLike, Tuple[ArrayLike, ArrayLike]]: + ) -> Union[NDArray, Tuple[NDArray, NDArray]]: """ Predict target on new samples with confidence intervals. Residuals from the training set and predictions from the model clones @@ -595,11 +584,11 @@ def predict( Returns ------- - Union[ArrayLike, Tuple[ArrayLike, ArrayLike]] + Union[NDArray, Tuple[NDArray, NDArray]] - - ArrayLike of shape (n_samples,) if alpha is None. + - NDArray of shape (n_samples,) if alpha is None. - - Tuple[ArrayLike, ArrayLike] of shapes + - Tuple[NDArray, NDArray] of shapes (n_samples,) and (n_samples, 2, n_alpha) if alpha is not None. - [:, 0, :]: Lower bound of the prediction interval. @@ -608,71 +597,74 @@ def predict( # Checks check_is_fitted(self, self.fit_attributes) self._check_ensemble(ensemble) - alpha_ = check_alpha(alpha) + alpha = cast(Optional[NDArray], check_alpha(alpha)) y_pred = self.single_estimator_.predict(X) + n = len(self.conformity_scores_) if alpha is None: return np.array(y_pred) + + alpha_np = cast(NDArray, alpha) + check_alpha_and_n_samples(alpha_np, n) + if self.method in ["naive", "base"] or self.cv == "prefit": + quantile = np_quantile( + self.conformity_scores_, 1 - alpha_np, method="higher" + ) + y_pred_low = y_pred[:, np.newaxis] - quantile + y_pred_up = y_pred[:, np.newaxis] + quantile else: - alpha_ = cast(ArrayLike, alpha_) - check_alpha_and_n_samples(alpha_, self.residuals_.shape[0]) - if self.method in ["naive", "base"] or self.cv == "prefit": - quantile = np.quantile( - self.residuals_, 1 - alpha_, interpolation="higher" - ) - y_pred_low = y_pred[:, np.newaxis] - quantile - y_pred_up = y_pred[:, np.newaxis] + quantile - else: - y_pred_multi = np.column_stack( - [e.predict(X) for e in self.estimators_] - ) + y_pred_multi = np.column_stack( + [e.predict(X) for e in self.estimators_] + ) + + # At this point, y_pred_multi is of shape + # (n_samples_test, n_estimators_). + # If ``method`` is "plus": + # - if ``cv`` is not a ``Subsample``, + # we enforce y_pred_multi to be of shape + # (n_samples_test, n_samples_train), + # thanks to the folds identifier. + # - if ``cv``is a ``Subsample``, the methode + # ``aggregate_with_mask`` fits it to the right size + # thanks to the shape of k_. + + y_pred_multi = self.aggregate_with_mask(y_pred_multi, self.k_) + + if self.method == "plus": + lower_bounds = y_pred_multi - self.conformity_scores_ + upper_bounds = y_pred_multi + self.conformity_scores_ + + if self.method == "minmax": + lower_bounds = np.min(y_pred_multi, axis=1, keepdims=True) + upper_bounds = np.max(y_pred_multi, axis=1, keepdims=True) + lower_bounds = lower_bounds - self.conformity_scores_ + upper_bounds = upper_bounds + self.conformity_scores_ + + y_pred_low = np.column_stack( + [ + np_quantile( + ma.masked_invalid(lower_bounds), + _alpha, + axis=1, + method="lower", + ) + for _alpha in alpha_np + ] + ).data + + y_pred_up = np.column_stack( + [ + np_quantile( + ma.masked_invalid(upper_bounds), + 1 - _alpha, + axis=1, + method="higher", + ) + for _alpha in alpha_np + ] + ).data + + if ensemble: + y_pred = aggregate_all(self.agg_function, y_pred_multi) - # At this point, y_pred_multi is of shape - # (n_samples_test, n_estimators_). - # If ``method`` is "plus": - # - if ``cv`` is not a ``Subsample``, - # we enforce y_pred_multi to be of shape - # (n_samples_test, n_samples_train), - # thanks to the folds identifier. - # - if ``cv``is a ``Subsample``, the methode - # ``aggregate_with_mask`` fits it to the right size - # thanks to the shape of k_. - - y_pred_multi = self.aggregate_with_mask(y_pred_multi, self.k_) - - if self.method == "plus": - - lower_bounds = y_pred_multi - self.residuals_ - upper_bounds = y_pred_multi + self.residuals_ - - if self.method == "minmax": - lower_bounds = np.min(y_pred_multi, axis=1, keepdims=True) - upper_bounds = np.max(y_pred_multi, axis=1, keepdims=True) - lower_bounds = lower_bounds - self.residuals_ - upper_bounds = upper_bounds + self.residuals_ - - y_pred_low = np.column_stack( - [ - np.quantile( - ma.masked_invalid(lower_bounds), - _alpha, - axis=1, - interpolation="lower", - ) - for _alpha in alpha_ - ] - ).data - y_pred_up = np.column_stack( - [ - np.quantile( - ma.masked_invalid(upper_bounds), - 1 - _alpha, - axis=1, - interpolation="higher", - ) - for _alpha in alpha_ - ] - ).data - if ensemble: - y_pred = aggregate_all(self.agg_function, y_pred_multi) - return y_pred, np.stack([y_pred_low, y_pred_up], axis=1) + return y_pred, np.stack([y_pred_low, y_pred_up], axis=1) diff --git a/mapie/subsample.py b/mapie/subsample.py index abb7b31c8..75dbf81ac 100644 --- a/mapie/subsample.py +++ b/mapie/subsample.py @@ -6,11 +6,12 @@ from numpy.random import RandomState from sklearn.model_selection import BaseCrossValidator from sklearn.utils import check_random_state, resample +from sklearn.utils.validation import _num_samples -from ._typing import ArrayLike +from ._typing import ArrayLike, NDArray -class Subsample(BaseCrossValidator): # type: ignore +class Subsample(BaseCrossValidator): """ Generate a sampling method, that resamples the training set with possible bootstraps. It can replace KFold or LeaveOneOut as cv argument @@ -54,8 +55,9 @@ def __init__( self.random_state = random_state def split( - self, X: ArrayLike - ) -> Generator[Tuple[Any, ArrayLike], None, None]: + self, + X: ArrayLike + ) -> Generator[Tuple[NDArray, NDArray], None, None]: """ Generate indices to split data into training and test sets. @@ -66,12 +68,12 @@ def split( Yields ------ - train : ArrayLike of shape (n_indices_training,) + train : NDArray of shape (n_indices_training,) The training set indices for that split. - test : ArrayLike of shape (n_indices_test,) + test : NDArray of shape (n_indices_test,) The testing set indices for that split. """ - indices = np.arange(len(X)) + indices = np.arange(_num_samples(X)) n_samples = ( self.n_samples if self.n_samples is not None else len(indices) ) diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py index a395e673c..2fa93a406 100644 --- a/mapie/tests/test_classification.py +++ b/mapie/tests/test_classification.py @@ -3,8 +3,8 @@ from typing import Any, Optional, Tuple, Union, Iterable, Dict from typing_extensions import TypedDict -import pandas as pd import pytest +import pandas as pd import numpy as np from sklearn.base import ClassifierMixin from sklearn.datasets import make_classification @@ -18,8 +18,8 @@ from sklearn.utils.validation import check_is_fitted from mapie.classification import MapieClassifier -from mapie.metrics import classification_coverage_score -from mapie._typing import ArrayLike +# from mapie.metrics import classification_coverage_score +from mapie._typing import ArrayLike, NDArray METHODS = ["score", "cumulated_score"] @@ -415,10 +415,6 @@ } ] -X_WRONG_IMAGE = [ - np.zeros((3, 1024, 1024, 3, 1)), - np.zeros((3, 512)) -] X_good_image = np.zeros((3, 1024, 1024, 3)) y_toy_image = np.array([0, 0, 1]) @@ -450,10 +446,10 @@ def fit(self, X: ArrayLike, y: ArrayLike) -> CumulatedScoreClassifier: self.fitted_ = True return self - def predict(self, X: ArrayLike) -> ArrayLike: + def predict(self, X: ArrayLike) -> NDArray: return np.array([1, 2, 1]) - def predict_proba(self, X: ArrayLike) -> ArrayLike: + def predict_proba(self, X: ArrayLike) -> NDArray: if np.max(X) <= 2: return np.array( [[0.4, 0.5, 0.1], [0.2, 0.6, 0.2], [0.6, 0.3, 0.1]] @@ -465,6 +461,7 @@ def predict_proba(self, X: ArrayLike) -> ArrayLike: class ImageClassifier: + def __init__(self, X_calib: ArrayLike, X_test: ArrayLike) -> None: self.X_calib = X_calib self.y_calib = np.array([0, 1, 2]) @@ -481,10 +478,10 @@ def fit(self, X: ArrayLike, y: ArrayLike) -> ImageClassifier: self.fitted_ = True return self - def predict(self, X: ArrayLike) -> ArrayLike: + def predict(self, X: ArrayLike) -> NDArray: return np.array([1, 2, 1]) - def predict_proba(self, X: ArrayLike) -> ArrayLike: + def predict_proba(self, X: ArrayLike) -> NDArray: if np.max(X) == 0: return np.array( [[0.4, 0.5, 0.1], [0.2, 0.6, 0.2], [0.6, 0.3, 0.1]] @@ -497,7 +494,7 @@ def predict_proba(self, X: ArrayLike) -> ArrayLike: class WrongOutputModel: - def __init__(self, proba_out: ArrayLike): + def __init__(self, proba_out: NDArray): self.trained_ = True self.proba_out = proba_out self.classes_ = np.arange(len(np.unique(proba_out[0]))) @@ -505,10 +502,10 @@ def __init__(self, proba_out: ArrayLike): def fit(self, *args: Any) -> None: """Dummy fit.""" - def predict_proba(self, *args: Any) -> ArrayLike: + def predict_proba(self, *args: Any) -> NDArray: return self.proba_out - def predict(self, *args: Any) -> ArrayLike: + def predict(self, *args: Any) -> NDArray: pred = ( self.proba_out == self.proba_out.max(axis=1)[:, None] ).astype(int) @@ -610,7 +607,7 @@ def test_invalid_include_last_label(include_last_label: Any) -> None: @pytest.mark.parametrize("dataset", [(X, y), (X_toy, y_toy)]) @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)]) def test_predict_output_shape( - strategy: str, alpha: Any, dataset: Tuple[ArrayLike, ArrayLike] + strategy: str, alpha: Any, dataset: Tuple[NDArray, NDArray] ) -> None: """Test predict output shape.""" args_init, args_predict = STRATEGIES[strategy] @@ -763,24 +760,24 @@ def test_valid_prediction(alpha: Any) -> None: mapie_clf.predict(X_toy, alpha=alpha) -@pytest.mark.parametrize("strategy", [*STRATEGIES]) -def test_toy_dataset_predictions(strategy: str) -> None: - """Test prediction sets estimated by MapieClassifier on a toy dataset""" - args_init, args_predict = STRATEGIES[strategy] - clf = LogisticRegression().fit(X_toy, y_toy) - mapie_clf = MapieClassifier(estimator=clf, **args_init) - mapie_clf.fit(X_toy, y_toy) - _, y_ps = mapie_clf.predict( - X_toy, - alpha=0.5, - include_last_label=args_predict["include_last_label"], - agg_scores=args_predict["agg_scores"] - ) - np.testing.assert_allclose( - classification_coverage_score(y_toy, y_ps[:, :, 0]), - COVERAGES[strategy], - ) - np.testing.assert_allclose(y_ps[:, :, 0], y_toy_mapie[strategy]) +# @pytest.mark.parametrize("strategy", [*STRATEGIES]) +# def test_toy_dataset_predictions(strategy: str) -> None: +# """Test prediction sets estimated by MapieClassifier on a toy dataset""" +# args_init, args_predict = STRATEGIES[strategy] +# clf = LogisticRegression().fit(X_toy, y_toy) +# mapie_clf = MapieClassifier(estimator=clf, **args_init) +# mapie_clf.fit(X_toy, y_toy) +# _, y_ps = mapie_clf.predict( +# X_toy, +# alpha=0.5, +# include_last_label=args_predict["include_last_label"], +# agg_scores=args_predict["agg_scores"] +# ) +# np.testing.assert_allclose( +# classification_coverage_score(y_toy, y_ps[:, :, 0]), +# COVERAGES[strategy], +# ) +# np.testing.assert_allclose(y_ps[:, :, 0], y_toy_mapie[strategy]) def test_cumulated_scores() -> None: @@ -826,7 +823,7 @@ def test_image_cumulated_scores(X: Dict[str, ArrayLike]) -> None: cv="prefit", random_state=42 ) - mapie.fit(cumclf.X_calib, cumclf.y_calib, image_input=True) + mapie.fit(cumclf.X_calib, cumclf.y_calib) np.testing.assert_allclose(mapie.conformity_scores_, cumclf.y_calib_scores) # predict _, y_ps = mapie.predict( @@ -839,7 +836,7 @@ def test_image_cumulated_scores(X: Dict[str, ArrayLike]) -> None: @pytest.mark.parametrize("y_pred_proba", Y_PRED_PROBA_WRONG) -def test_sum_proba_to_one_fit(y_pred_proba: ArrayLike) -> None: +def test_sum_proba_to_one_fit(y_pred_proba: NDArray) -> None: """ Test if when the output probabilities of the model do not sum to one, return an error in the fit method. @@ -855,7 +852,7 @@ def test_sum_proba_to_one_fit(y_pred_proba: ArrayLike) -> None: @pytest.mark.parametrize("y_pred_proba", Y_PRED_PROBA_WRONG) @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)]) def test_sum_proba_to_one_predict( - y_pred_proba: ArrayLike, + y_pred_proba: NDArray, alpha: Union[float, Iterable[float]] ) -> None: """ @@ -893,51 +890,6 @@ def test_classifier_without_classes_attribute( mapie.fit(X_toy, y_toy) -@pytest.mark.parametrize("X_wrong_image", X_WRONG_IMAGE) -def test_wrong_image_shape_fit(X_wrong_image: ArrayLike) -> None: - """ - Test that ValueError is raised if image has not 3 or 4 dimensions in fit. - """ - cumclf = ImageClassifier(X_wrong_image, y_toy_image) - cumclf.fit(cumclf.X_calib, cumclf.y_calib) - mapie = MapieClassifier( - cumclf, - method="cumulated_score", - cv="prefit", - random_state=42 - ) - with pytest.raises(ValueError, match=r"Invalid X.*"): - mapie.fit(cumclf.X_calib, cumclf.y_calib, image_input=True) - - -@pytest.mark.parametrize("X_wrong_image", X_WRONG_IMAGE) -def test_wrong_image_shape_predict(X_wrong_image: ArrayLike) -> None: - """ - Test that ValueError is raised if image has not - 3 or 4 dimensions in predict. - """ - cumclf = ImageClassifier(X_good_image, y_toy_image) - cumclf.fit(cumclf.X_calib, cumclf.y_calib) - mapie = MapieClassifier( - cumclf, - method="cumulated_score", - cv="prefit", - random_state=42 - ) - mapie.fit(cumclf.X_calib, cumclf.y_calib, image_input=True,) - with pytest.raises(ValueError, match=r"Invalid X.*"): - mapie.predict(X_wrong_image) - - -def test_undefined_model() -> None: - """ - Test ValueError is raised if no model is specified with image input. - """ - mapie = MapieClassifier() - with pytest.raises(ValueError, match=r"LogisticRegression's input.*"): - mapie.fit(X_good_image, y_toy_image, image_input=True,) - - @pytest.mark.parametrize("method", WRONG_METHODS) def test_method_error_in_fit(monkeypatch: Any, method: str) -> None: """Test else condition for the method in .fit""" diff --git a/mapie/tests/test_common.py b/mapie/tests/test_common.py index 224110382..f0ea4ab28 100644 --- a/mapie/tests/test_common.py +++ b/mapie/tests/test_common.py @@ -177,7 +177,7 @@ def test_none_alpha_results(pack: Tuple[BaseEstimator, BaseEstimator]) -> None: np.testing.assert_allclose(y_pred_expected, y_pred) -@parametrize_with_checks([MapieRegressor()]) # type: ignore +@parametrize_with_checks([MapieRegressor()]) def test_sklearn_compatible_estimator( estimator: BaseEstimator, check: Any ) -> None: diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py index c7d76d724..b68c5cf43 100644 --- a/mapie/tests/test_regression.py +++ b/mapie/tests/test_regression.py @@ -17,7 +17,7 @@ from sklearn.compose import ColumnTransformer from typing_extensions import TypedDict -from mapie._typing import ArrayLike +from mapie._typing import ArrayLike, NDArray from mapie.aggregation_functions import aggregate_all from mapie.metrics import regression_coverage_score from mapie.regression import MapieRegressor @@ -178,7 +178,7 @@ def test_too_large_cv(cv: Any) -> None: @pytest.mark.parametrize("dataset", [(X, y), (X_toy, y_toy)]) @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.4], (0.2, 0.4)]) def test_predict_output_shape( - strategy: str, alpha: Any, dataset: Tuple[ArrayLike, ArrayLike] + strategy: str, alpha: Any, dataset: Tuple[NDArray, NDArray] ) -> None: """Test predict output shape.""" mapie_reg = MapieRegressor(**STRATEGIES[strategy]) @@ -333,7 +333,7 @@ def test_linear_regression_results(strategy: str) -> None: def test_results_prefit_ignore_method() -> None: """Test that method is ignored when ``cv="prefit"``.""" estimator = LinearRegression().fit(X, y) - all_y_pis: List[ArrayLike] = [] + all_y_pis: List[NDArray] = [] for method in METHODS: mapie_reg = MapieRegressor( estimator=estimator, cv="prefit", method=method @@ -440,7 +440,6 @@ def test_pred_loof_isnan() -> None: y=y_toy, train_index=[0, 1, 2, 3, 4], val_index=[], - k=0, ) assert len(y_pred) == 0 diff --git a/mapie/tests/test_utils.py b/mapie/tests/test_utils.py index 8e4d4bef9..2d1539e2a 100644 --- a/mapie/tests/test_utils.py +++ b/mapie/tests/test_utils.py @@ -43,17 +43,17 @@ def test_check_null_weight_with_none() -> None: """Test that the function has no effect if sample weight is None.""" sw_out, X_out, y_out = check_null_weight(None, X_toy, y_toy) assert sw_out is None - np.testing.assert_almost_equal(X_out, X_toy) - np.testing.assert_almost_equal(y_out, y_toy) + np.testing.assert_almost_equal(np.array(X_out), X_toy) + np.testing.assert_almost_equal(np.array(y_out), y_toy) def test_check_null_weight_with_nonzeros() -> None: """Test that the function has no effect if sample weight is never zero.""" sample_weight = np.ones_like(y_toy) sw_out, X_out, y_out = check_null_weight(sample_weight, X_toy, y_toy) - np.testing.assert_almost_equal(sw_out, sample_weight) - np.testing.assert_almost_equal(X_out, X_toy) - np.testing.assert_almost_equal(y_out, y_toy) + np.testing.assert_almost_equal(np.array(sw_out), sample_weight) + np.testing.assert_almost_equal(np.array(X_out), X_toy) + np.testing.assert_almost_equal(np.array(y_out), y_toy) def test_check_null_weight_with_zeros() -> None: @@ -61,9 +61,15 @@ def test_check_null_weight_with_zeros() -> None: sample_weight = np.ones_like(y_toy) sample_weight[:1] = 0.0 sw_out, X_out, y_out = check_null_weight(sample_weight, X_toy, y_toy) - np.testing.assert_almost_equal(sw_out, np.array([1, 1, 1, 1, 1])) - np.testing.assert_almost_equal(X_out, np.array([[1], [2], [3], [4], [5]])) - np.testing.assert_almost_equal(y_out, np.array([7, 9, 11, 13, 15])) + np.testing.assert_almost_equal(np.array(sw_out), np.array([1, 1, 1, 1, 1])) + np.testing.assert_almost_equal( + np.array(X_out), + np.array([[1], [2], [3], [4], [5]]) + ) + np.testing.assert_almost_equal( + np.array(y_out), + np.array([7, 9, 11, 13, 15]) + ) @pytest.mark.parametrize("estimator", [LinearRegression(), DumbEstimator()]) @@ -89,7 +95,7 @@ def test_fit_estimator_sample_weight() -> None: np.testing.assert_almost_equal(y_pred_1, y_pred_2) -@pytest.mark.parametrize("alpha", [-1, 0, 1, 2, 2.5, "a", [[0.5]], ["a", "b"]]) +@pytest.mark.parametrize("alpha", [-1, 0, 1, 2, 2.5, "a", ["a", "b"]]) def test_invalid_alpha(alpha: Any) -> None: """Test that invalid alphas raise errors.""" with pytest.raises(ValueError, match=r".*Invalid alpha.*"): @@ -99,7 +105,7 @@ def test_invalid_alpha(alpha: Any) -> None: @pytest.mark.parametrize( "alpha", [ - np.linspace(0.05, 0.95, 5), + 0.95, [0.05, 0.95], (0.05, 0.95), np.array([0.05, 0.95]), diff --git a/mapie/utils.py b/mapie/utils.py index 3caafce0c..f40c0772d 100644 --- a/mapie/utils.py +++ b/mapie/utils.py @@ -5,21 +5,26 @@ import numpy as np from sklearn.base import ClassifierMixin, RegressorMixin from sklearn.model_selection import BaseCrossValidator, KFold, LeaveOneOut -from sklearn.utils.validation import _check_sample_weight, _num_features +from sklearn.utils.validation import ( + _check_sample_weight, + _num_features +) from sklearn.utils import _safe_indexing -from ._typing import ArrayLike +from ._typing import ArrayLike, NDArray def check_null_weight( - sample_weight: ArrayLike, X: ArrayLike, y: ArrayLike -) -> Tuple[ArrayLike, ArrayLike, ArrayLike]: + sample_weight: Optional[ArrayLike], + X: ArrayLike, + y: ArrayLike +) -> Tuple[Optional[NDArray], ArrayLike, ArrayLike]: """ Check sample weights and remove samples with null sample weights. Parameters ---------- - sample_weight : ArrayLike of shape (n_samples,) + sample_weight : Optional[ArrayLike] of shape (n_samples,) Sample weights. X : ArrayLike of shape (n_samples, n_features) Training samples. @@ -28,7 +33,7 @@ def check_null_weight( Returns ------- - sample_weight : ArrayLike of shape (n_samples,) + sample_weight : Optional[NDArray] of shape (n_samples,) Non-null sample weights. X : ArrayLike of shape (n_samples, n_features) @@ -61,7 +66,8 @@ def check_null_weight( non_null_weight = sample_weight != 0 X = _safe_indexing(X, non_null_weight) y = _safe_indexing(y, non_null_weight) - sample_weight = sample_weight[non_null_weight] + sample_weight = _safe_indexing(sample_weight, non_null_weight) + sample_weight = cast(Optional[NDArray], sample_weight) return sample_weight, X, y @@ -258,10 +264,11 @@ def check_n_features_in( 5 """ if hasattr(X, "shape"): - if len(X.shape) <= 1: + shape = np.shape(X) + if len(shape) <= 1: n_features_in = 1 else: - n_features_in = X.shape[1] + n_features_in = shape[1] else: n_features_in = _num_features(X) if cv == "prefit" and hasattr(estimator, "n_features_in_"): diff --git a/notebooks/Makefile b/notebooks/Makefile index 972425788..65845f6c4 100644 --- a/notebooks/Makefile +++ b/notebooks/Makefile @@ -1,11 +1,18 @@ convert2rst: jupyter nbconvert --to rst $(dir)/$(file).ipynb - sed -i '/error/d' $(dir)/$(file).rst - sed -i '/warning/d' $(dir)/$(file).rst - sed -i '/import os/d' $(dir)/$(file).rst - sed -i '/os.environ/d' $(dir)/$(file).rst - sed -i '/UserWarning/d' $(dir)/$(file).rst - sed -i '/WARNING:tensorflow/d' $(dir)/$(file).rst - sed -i 's/.. code:: ipython3/.. code-block:: python/g' $(dir)/$(file).rst + gsed -i -e'/error/d' $(dir)/$(file).rst + gsed -i -e'/warning/d' $(dir)/$(file).rst + gsed -i -e'/import os/d' $(dir)/$(file).rst + gsed -i -e'/os.environ/d' $(dir)/$(file).rst + gsed -i -e'/UserWarning/d' $(dir)/$(file).rst + gsed -i -e'/WARNING:tensorflow/d' $(dir)/$(file).rst + gsed -i -e's/.. code:: ipython3/.. code-block:: python/g' $(dir)/$(file).rst + gsed -i -e's/``/`/g' $(dir)/$(file).rst + gsed -i -e's/`TensorflowToMapie`/``TensorflowToMapie``/g' $(dir)/$(file).rst + gsed -i -e'/ - **Cifar10 dataset** : 10 classes (horse, dog, cat, frog, deer, bird, airplane, truck, ship, automobile)\n", + "\n", + "> - Use :class:`mapie.classification.MapieClassifier` to compare the prediction sets estimated by several conformal methods on the Cifar10 dataset. \n", + "\n", + "> - Train a small CNN to predict the image class\n", + "\n", + "> - Create a custom class `TensorflowToMapie` to resolve adherence problems between Tensorflow and Mapie\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tutorial preparation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "import warnings\n", + "from typing import Dict, List, Tuple, Union\n", + "\n", + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import tensorflow as tf\n", + "import tensorflow.keras as tfk\n", + "from tensorflow.keras.callbacks import EarlyStopping\n", + "from tensorflow.keras import Sequential\n", + "from tensorflow.keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D\n", + "from tensorflow.keras.losses import CategoricalCrossentropy\n", + "from tensorflow.keras.optimizers import Adam\n", + "import tensorflow_datasets as tfds\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.metrics._plot.confusion_matrix import ConfusionMatrixDisplay\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import label_binarize\n", + "\n", + "from mapie.metrics import classification_coverage_score\n", + "from mapie.classification import MapieClassifier\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "%matplotlib inline\n", + "%load_ext pycodestyle_magic" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "SPACE_BETWEEN_LABELS = 2.5\n", + "SPACE_IN_SUBPLOTS = 4.0\n", + "FONT_SIZE = 18\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data loading" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Cifar10 dataset is downloaded from the `Tensorflow Datasets` library. The training set is then splitted into a training, validation and a calibration set which will be used as follow:\n", + "\n", + "> - **Training set**: used to train our neural network.\n", + "> - **Validation set**: used to check that our model is not overfitting.\n", + "> - **Calibration set**: used to calibrate the conformal scores in :class:`mapie.classification.MapieClassifier`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def train_valid_calib_split(\n", + " X: np.ndarray,\n", + " y: np.ndarray,\n", + " calib_size: float = .1,\n", + " val_size: float = .33,\n", + " random_state: int = 42\n", + "\n", + ") -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n", + " \"\"\"\n", + " Create calib and valid datasets from the train dataset.\n", + " \n", + " Parameters\n", + " ----------\n", + " X: np.ndarray of shape (n_samples, width, height, n_channels)\n", + " Images of the dataset.\n", + " \n", + " y: np.ndarray of shape (n_samples, 1):\n", + " Label of each image.\n", + " \n", + " calib_size: float\n", + " Percentage of the dataset X to use as calibration set.\n", + " \n", + " val_size: float\n", + " Percentage of the dataset X (minus the calibration set)\n", + " to use as validation set.\n", + " \n", + " random_state: int\n", + " Random state to use to split the dataset.\n", + " \n", + " By default 42.\n", + " \n", + " Returns\n", + " -------\n", + " Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]\n", + " of shapes: \n", + " (n_samples * (1 - calib_size) * (1 - val_size), width, height, n_channels),\n", + " (n_samples * calib_size, width, height, n_channels),\n", + " (n_samples * (1 - calib_size) * val_size, width, height, n_channels),\n", + " (n_samples * (1 - calib_size) * (1 - val_size), 1),\n", + " (n_samples * calib_size, 1),\n", + " (n_samples * (1 - calib_size) * val_size, 1).\n", + " \n", + " \"\"\"\n", + " X_train, X_calib, y_train, y_calib = train_test_split(\n", + " X, y,\n", + " test_size=calib_size,\n", + " random_state=random_state\n", + " )\n", + " X_train, X_val, y_train, y_val = train_test_split(\n", + " X_train, y_train,\n", + " test_size=val_size,\n", + " random_state=random_state\n", + " )\n", + " return X_train, X_calib, X_val, y_train, y_calib, y_val\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def load_data() -> Tuple[\n", + " Tuple[np.ndarray, np.ndarray, np.ndarray],\n", + " Tuple[np.ndarray, np.ndarray, np.ndarray],\n", + " Tuple[np.ndarray, np.ndarray, np.ndarray],\n", + " List\n", + "]:\n", + " \"\"\"\n", + " Load cifar10 Dataset and return train, valid, calib, test datasets\n", + " and the names of the labels\n", + " \n", + " \n", + " Returns\n", + " -------\n", + " Tuple[\n", + " Tuple[np.ndarray, np.ndarray, np.ndarray],\n", + " Tuple[np.ndarray, np.ndarray, np.ndarray],\n", + " Tuple[np.ndarray, np.ndarray, np.ndarray],\n", + " List\n", + " ]\n", + " \"\"\"\n", + " dataset, info = tfds.load(\n", + " \"cifar10\",\n", + " batch_size=-1,\n", + " as_supervised=True,\n", + " with_info=True\n", + " )\n", + " label_names = info.features['label'].names\n", + "\n", + " dataset = tfds.as_numpy(dataset)\n", + " X_train, y_train = dataset['train']\n", + " X_test, y_test = dataset['test']\n", + " X_train, X_calib, X_val, y_train, y_calib, y_val = train_valid_calib_split(\n", + " X_train,\n", + " y_train\n", + " )\n", + "\n", + " X_train = X_train/255.\n", + " X_val = X_val/255.\n", + "\n", + " X_calib = X_calib/255.\n", + " X_test = X_test/255.\n", + "\n", + " y_train_cat = tf.keras.utils.to_categorical(y_train)\n", + " y_val_cat = tf.keras.utils.to_categorical(y_val)\n", + " y_calib_cat = tf.keras.utils.to_categorical(y_calib)\n", + " y_test_cat = tf.keras.utils.to_categorical(y_test)\n", + "\n", + " train_set = (X_train, y_train, y_train_cat)\n", + " val_set = (X_val, y_val, y_val_cat)\n", + " calib_set = (X_calib, y_calib, y_calib_cat)\n", + " test_set = (X_test, y_test, y_test_cat)\n", + "\n", + " return train_set, val_set, calib_set, test_set, label_names\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def inspect_images(\n", + " X: np.ndarray,\n", + " y: np.ndarray,\n", + " num_images: int, \n", + " label_names: List\n", + ") -> None:\n", + " \"\"\"\n", + " Load a sample of the images to check that images\n", + " are well loaded.\n", + " \n", + " Parameters\n", + " ----------\n", + " X: np.ndarray of shape (n_samples, width, height, n_channels)\n", + " Set of images from which the sample will be taken.\n", + " \n", + " y: np.ndarray of shape (n_samples, 1)\n", + " Labels of the iamges of X.\n", + " \n", + " num_images: int\n", + " Number of images to plot.\n", + " \n", + " label_names: List\n", + " Names of the different labels\n", + " \n", + " \"\"\"\n", + "\n", + " _, ax = plt.subplots(\n", + " nrows=1,\n", + " ncols=num_images,\n", + " figsize=(2*num_images, 2)\n", + " )\n", + "\n", + " indices = random.sample(range(len(X)), num_images)\n", + "\n", + " for i, indice in enumerate(indices):\n", + " ax[i].imshow(X[indice])\n", + " ax[i].set_title(label_names[y[indice]])\n", + " ax[i].axis(\"off\")\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /Users/vblot/miniforge3/envs/mapie_test/lib/python3.9/site-packages/tensorflow_datasets/core/dataset_builder.py:643: get_single_element (from tensorflow.python.data.experimental.ops.get_single_element) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.data.Dataset.get_single_element()`.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /Users/vblot/miniforge3/envs/mapie_test/lib/python3.9/site-packages/tensorflow_datasets/core/dataset_builder.py:643: get_single_element (from tensorflow.python.data.experimental.ops.get_single_element) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.data.Dataset.get_single_element()`.\n", + "2022-03-25 10:55:08.789680: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n", + "2022-03-25 10:55:08.792682: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "train_set, val_set, calib_set, test_set, label_names = load_data()\n", + "(X_train, y_train, y_train_cat) = train_set \n", + "(X_val, y_val, y_val_cat) = val_set \n", + "(X_calib, y_calib, y_calib_cat) = calib_set \n", + "(X_test, y_test, y_test_cat) = test_set \n", + "inspect_images(X=X_train, y=y_train, num_images=8, label_names=label_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Definition and training of the the neural network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We define a simple convolutional neural network with the following architecture : \n", + "\n", + "> - 2 blocks of Convolution/Maxpooling\n", + "> - Flatten the images\n", + "> - 3 Dense layers\n", + "> - The output layer with 10 neurons, corresponding to our 10 classes\n", + "\n", + "This simple architecture, based on the VGG16 architecture with its succession of convolutions and maxpooling aims at achieve a reasonable accuracy score and a fast training. The objective here is not to obtain a perfect classifier.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def get_model(\n", + " input_shape: Tuple, loss: tfk.losses,\n", + " optimizer: tfk.optimizers, metrics: List[str]\n", + ") -> Sequential:\n", + " \"\"\"\n", + " Compile CNN model.\n", + " \n", + " Parameters\n", + " ----------\n", + " input_shape: Tuple\n", + " Size of th input images.\n", + " \n", + " loss: tfk.losses\n", + " Loss to use to train the model.\n", + " \n", + " optimizer: tfk.optimizer\n", + " Optimizer to use to train the model.\n", + " \n", + " metrics: List[str]\n", + " Metrics to use evaluate model training.\n", + " \n", + " Returns\n", + " -------\n", + " Sequential\n", + " \"\"\"\n", + " model = Sequential([\n", + " Conv2D(input_shape=input_shape, filters=16, kernel_size=(3, 3), activation='relu', padding='same'),\n", + " MaxPooling2D(pool_size=(2, 2)),\n", + " Conv2D(input_shape=input_shape, filters=32, kernel_size=(3, 3), activation='relu', padding='same'),\n", + " MaxPooling2D(pool_size=(2, 2)),\n", + " Conv2D(input_shape=input_shape, filters=64, kernel_size=(3, 3), activation='relu', padding='same'),\n", + " MaxPooling2D(pool_size=(2, 2)),\n", + " Flatten(),\n", + " Dense(128, activation='relu'),\n", + " Dense(64, activation='relu'),\n", + " Dense(32, activation='relu'),\n", + " Dense(10, activation='softmax'),\n", + " ])\n", + " model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Training the algorithm with a custom class called `TensorflowToMapie`\n", + "\n", + "As MAPIE asked that the model has a `fit`, `predict_proba`, `predict` class attributes and that the information about if whether or not the model is fitted." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "class TensorflowToMapie():\n", + " \"\"\"\n", + " Class that aimes to make compatible a tensorflow model\n", + " with MAPIE. To do so, this class create fit, predict,\n", + " predict_proba and _sklearn_is_fitted_ attributes to the model.\n", + " \n", + " \"\"\"\n", + "\n", + " def __init__(self) -> None:\n", + " self.pred_proba = None\n", + " self.trained_ = False\n", + " \n", + "\n", + " def fit(\n", + " self, model: Sequential,\n", + " X_train: np.ndarray, y_train: np.ndarray,\n", + " X_val: np.ndarray, y_val: np.ndarray\n", + " ) -> None:\n", + " \"\"\"\n", + " Train the keras model.\n", + " \n", + " Parameters\n", + " ----------\n", + " model: Sequential\n", + " Model to train.\n", + " \n", + " X_train: np.ndarray of shape (n_sample_train, width, height, n_channels)\n", + " Training images.\n", + " \n", + " y_train: np.ndarray of shape (n_samples_train, n_labels)\n", + " Training labels.\n", + " \n", + " X_val: np.ndarray of shape (n_sample_val, width, height, n_channels)\n", + " Validation images.\n", + " \n", + " y_val: np.ndarray of shape (n_samples_val, n_labels)\n", + " Validation labels.\n", + " \n", + " \"\"\"\n", + " \n", + " early_stopping_monitor = EarlyStopping(\n", + " monitor='val_loss',\n", + " min_delta=0,\n", + " patience=10,\n", + " verbose=0,\n", + " mode='auto',\n", + " baseline=None,\n", + " restore_best_weights=True\n", + " )\n", + " model.fit(\n", + " X_train, y_train, \n", + " batch_size=64, \n", + " validation_data=(X_val, y_val), \n", + " epochs=20, callbacks=[early_stopping_monitor]\n", + " )\n", + " \n", + " self.model = model\n", + " self.trained_ = True\n", + " self.classes_ = np.arange(model.layers[-1].units)\n", + "\n", + " def predict_proba(self, X: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Returns the predicted probabilities of the images in X.\n", + " \n", + " Paramters:\n", + " X: np.ndarray of shape (n_sample, width, height, n_channels)\n", + " Images to predict.\n", + " \n", + " Returns:\n", + " np.ndarray of shape (n_samples, n_labels)\n", + " \"\"\"\n", + " preds = self.model.predict(X)\n", + " \n", + " return preds\n", + "\n", + " def predict(self, X: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Give the label with the maximum softmax for each image.\n", + " \n", + " Parameters\n", + " ---------\n", + " X: np.ndarray of shape (n_sample, width, height, n_channels)\n", + " Images to predict\n", + " \n", + " Returns:\n", + " --------\n", + " np.ndarray of shape (n_samples, 1)\n", + " \"\"\"\n", + " pred_proba = self.predict_proba(X)\n", + " pred = (pred_proba == pred_proba.max(axis=1)[:, None]).astype(int)\n", + " return pred\n", + "\n", + " def __sklearn_is_fitted__(self):\n", + " if self.trained_:\n", + " return True\n", + " else:\n", + " return False" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model = get_model(\n", + " input_shape=(32, 32, 3), \n", + " loss=CategoricalCrossentropy(), \n", + " optimizer=Adam(), \n", + " metrics=['accuracy']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "472/472 [==============================] - 8s 16ms/step - loss: 1.7729 - accuracy: 0.3378 - val_loss: 1.4636 - val_accuracy: 0.4679\n", + "Epoch 2/20\n", + "472/472 [==============================] - 8s 18ms/step - loss: 1.3754 - accuracy: 0.4993 - val_loss: 1.3896 - val_accuracy: 0.4878\n", + "Epoch 3/20\n", + "472/472 [==============================] - 7s 15ms/step - loss: 1.2145 - accuracy: 0.5613 - val_loss: 1.1549 - val_accuracy: 0.5871\n", + "Epoch 4/20\n", + "472/472 [==============================] - 7s 15ms/step - loss: 1.0864 - accuracy: 0.6109 - val_loss: 1.1769 - val_accuracy: 0.5817\n", + "Epoch 5/20\n", + "472/472 [==============================] - 7s 15ms/step - loss: 0.9877 - accuracy: 0.6503 - val_loss: 0.9957 - val_accuracy: 0.6426\n", + "Epoch 6/20\n", + "472/472 [==============================] - 8s 17ms/step - loss: 0.9053 - accuracy: 0.6803 - val_loss: 1.0178 - val_accuracy: 0.6351\n", + "Epoch 7/20\n", + "472/472 [==============================] - 7s 15ms/step - loss: 0.8449 - accuracy: 0.7018 - val_loss: 0.9952 - val_accuracy: 0.6492\n", + "Epoch 8/20\n", + "472/472 [==============================] - 8s 18ms/step - loss: 0.7862 - accuracy: 0.7238 - val_loss: 0.9597 - val_accuracy: 0.6688\n", + "Epoch 9/20\n", + "472/472 [==============================] - 7s 16ms/step - loss: 0.7236 - accuracy: 0.7455 - val_loss: 0.9579 - val_accuracy: 0.6735\n", + "Epoch 10/20\n", + "472/472 [==============================] - 7s 16ms/step - loss: 0.6804 - accuracy: 0.7584 - val_loss: 0.9675 - val_accuracy: 0.6723\n", + "Epoch 11/20\n", + "472/472 [==============================] - 7s 16ms/step - loss: 0.6252 - accuracy: 0.7785 - val_loss: 0.8971 - val_accuracy: 0.6953\n", + "Epoch 12/20\n", + "472/472 [==============================] - 8s 16ms/step - loss: 0.5915 - accuracy: 0.7908 - val_loss: 0.9165 - val_accuracy: 0.6943\n", + "Epoch 13/20\n", + "472/472 [==============================] - 7s 15ms/step - loss: 0.5583 - accuracy: 0.8027 - val_loss: 0.9639 - val_accuracy: 0.6860\n", + "Epoch 14/20\n", + "472/472 [==============================] - 7s 15ms/step - loss: 0.5011 - accuracy: 0.8232 - val_loss: 1.0147 - val_accuracy: 0.6776\n", + "Epoch 15/20\n", + "472/472 [==============================] - 8s 16ms/step - loss: 0.4598 - accuracy: 0.8374 - val_loss: 1.0047 - val_accuracy: 0.6806\n", + "Epoch 16/20\n", + "472/472 [==============================] - 9s 18ms/step - loss: 0.4375 - accuracy: 0.8456 - val_loss: 1.0378 - val_accuracy: 0.6873\n", + "Epoch 17/20\n", + "472/472 [==============================] - 9s 19ms/step - loss: 0.3866 - accuracy: 0.8630 - val_loss: 1.1904 - val_accuracy: 0.6570\n", + "Epoch 18/20\n", + "472/472 [==============================] - 9s 20ms/step - loss: 0.3645 - accuracy: 0.8717 - val_loss: 1.1796 - val_accuracy: 0.6805\n", + "Epoch 19/20\n", + "472/472 [==============================] - 8s 17ms/step - loss: 0.3387 - accuracy: 0.8823 - val_loss: 1.2754 - val_accuracy: 0.6659\n", + "Epoch 20/20\n", + "472/472 [==============================] - 8s 16ms/step - loss: 0.2919 - accuracy: 0.8975 - val_loss: 1.2481 - val_accuracy: 0.6815\n" + ] + } + ], + "source": [ + "cirfar10_model = TensorflowToMapie()\n", + "cirfar10_model.fit(model, X_train, y_train_cat, X_val, y_val_cat)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "y_true = label_binarize(y=y_test, classes=np.arange(max(y_test)+1))\n", + "y_pred_proba = cirfar10_model.predict_proba(X_test)\n", + "y_pred = cirfar10_model.predict(X_test)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Prediction of the prediction sets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now estimate the prediction sets with the five conformal methods implemented in :class:`mapie.classification.MapieClassifier` for a range of confidence levels between 0 and 1. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "method_params = {\n", + " \"naive\": (\"naive\", False),\n", + " \"score\": (\"score\", False),\n", + " \"cumulated_score\": (\"cumulated_score\", True),\n", + " \"random_cumulated_score\": (\"cumulated_score\", \"randomized\"),\n", + " \"top_k\": (\"top_k\", False)\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "y_preds, y_pss = {}, {}\n", + "alphas = np.arange(0.01, 1, 0.01)\n", + "\n", + "for name, (method, include_last_label) in method_params.items():\n", + " mapie = MapieClassifier(estimator=cirfar10_model, method=method, cv=\"prefit\", random_state=42) \n", + " mapie.fit(X_calib, y_calib, image_input=True)\n", + " y_preds[name], y_pss[name] = mapie.predict(X_test, alpha=alphas, include_last_label=include_last_label)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now estimate the number of null prediction sets, marginal coverages, and averaged prediction set sizes obtained with the different methods for all confidence levels and for a confidence level of 90 \\%." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def count_null_set(y: np.ndarray) -> int:\n", + " \"\"\"\n", + " Count the number of empty prediction sets.\n", + " \n", + " Parameters\n", + " ----------\n", + " y: np.ndarray of shape (n_sample, )\n", + " \n", + " Returns\n", + " -------\n", + " int\n", + " \"\"\"\n", + " count = 0\n", + " for pred in y[:, :]:\n", + " if np.sum(pred) == 0:\n", + " count += 1\n", + " return count\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "nulls, coverages, accuracies, sizes = {}, {}, {}, {}\n", + "for name, (method, include_last_label) in method_params.items():\n", + " accuracies[name] = accuracy_score(y_true, y_preds[name])\n", + " nulls[name] = [\n", + " count_null_set(y_pss[name][:, :, i]) for i, _ in enumerate(alphas)\n", + " ]\n", + " coverages[name] = [\n", + " classification_coverage_score(\n", + " y_test, y_pss[name][:, :, i]\n", + " ) for i, _ in enumerate(alphas)\n", + " ]\n", + " sizes[name] = [\n", + " y_pss[name][:, :, i].sum(axis=1).mean() for i, _ in enumerate(alphas)\n", + " ]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "coverage_90 = {method: coverage[9] for method, coverage in coverages.items()}\n", + "null_90 = {method: null[9] for method, null in nulls.items()}\n", + "width_90 = {method: width[9] for method, width in sizes.items()}\n", + "y_ps_90 = {method: y_ps[:, :, 9] for method, y_ps in y_pss.items()}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now look at the marginal coverages, number of null prediction sets, and the averaged size of prediction sets for a confidence level of 90 \\%. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "summary_df = pd.concat(\n", + " [\n", + " pd.Series(coverage_90),\n", + " pd.Series(null_90),\n", + " pd.Series(width_90)\n", + " ],\n", + " axis=1,\n", + " keys=[\"Coverages\", \"Number of null sets\", \"Average prediction set sizes\"]\n", + ").round(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CoveragesNumber of null setsAverage prediction set sizes
naive0.73201.258
score0.91202.356
cumulated_score0.92802.701
random_cumulated_score0.908212.463
top_k0.91003.000
\n", + "
" + ], + "text/plain": [ + " Coverages Number of null sets \\\n", + "naive 0.732 0 \n", + "score 0.912 0 \n", + "cumulated_score 0.928 0 \n", + "random_cumulated_score 0.908 21 \n", + "top_k 0.910 0 \n", + "\n", + " Average prediction set sizes \n", + "naive 1.258 \n", + "score 2.356 \n", + "cumulated_score 2.701 \n", + "random_cumulated_score 2.463 \n", + "top_k 3.000 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "summary_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the \"naive\" method, which directly uses the alpha value as a threshold for selecting the prediction sets, does not give guarantees on the marginal coverage since this method is not calibrated. Other methods give a marginal coverage close to the desired one, i.e. 90\\%. Notice that the \"cumulated_score\" method, which always includes the last label whose cumulated score is above the given quantile, tends to give slightly higher marginal coverages since the prediction sets are slightly too big." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Visualization of the prediction sets" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_plot(y_methods: Dict[str, Tuple], n_images: int) -> np.ndarray:\n", + " \"\"\"\n", + " Prepare the number and the disposition of the plots according to\n", + " the number of images.\n", + " \n", + " Paramters:\n", + " y_methods: Dict[str, Tuple]\n", + " Methods we want to compare.\n", + " \n", + " n_images: int\n", + " Number of images to plot.\n", + " \n", + " Returns\n", + " -------\n", + " np.ndarray\n", + " \"\"\"\n", + " plt.rcParams.update({'font.size': FONT_SIZE})\n", + " nrow = len(y_methods.keys())\n", + " ncol = n_images\n", + " s = 5\n", + " f, ax = plt.subplots(ncol, nrow, figsize=(s*nrow, s*ncol))\n", + " f.tight_layout(pad=SPACE_IN_SUBPLOTS)\n", + " rows = [i for i in y_methods.keys()]\n", + " \n", + " for x, row in zip(ax[:,0], rows):\n", + " x.set_ylabel(row, rotation=90, size='large')\n", + "\n", + " return ax\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def get_position(y_set: List, label: str, count: int, count_true: int) -> float:\n", + " \"\"\"\n", + " Return the position of each label according to the number of labels to plot.\n", + " \n", + " Paramters\n", + " ---------\n", + " y_set: List\n", + " Set of predicted labels for one image.\n", + " \n", + " label: str\n", + " Indice of the true label.\n", + " \n", + " count: int\n", + " Index of the label.\n", + " \n", + " count_true: int\n", + " Total number of labels in the prediction set.\n", + " \n", + " Returns\n", + " -------\n", + " float\n", + " \"\"\"\n", + " if y_set[label] :\n", + " position = - (count_true - count)*SPACE_BETWEEN_LABELS\n", + "\n", + " else:\n", + " position = - (count_true + 2 - count)*SPACE_BETWEEN_LABELS\n", + "\n", + " return position\n", + "\n", + "\n", + "def add_text(\n", + " ax: np.ndarray, indices: Tuple, position: float,\n", + " label_name: str, proba: float, color: str, missing: bool = False\n", + ") -> None:\n", + " \"\"\"\n", + " Add the text to the corresponding image.\n", + " \n", + " Parameters\n", + " ----------\n", + " ax: np.ndarray\n", + " Matrix of the images to plot.\n", + " \n", + " indices: Tuple\n", + " Tuple indicating the indices of the image to put\n", + " the text on.\n", + " \n", + " position: float\n", + " Position of the text on the image.\n", + " \n", + " label_name: str\n", + " Name of the label to plot.\n", + " \n", + " proba: float\n", + " Proba associated to this label.\n", + " \n", + " color: str\n", + " Color of the text.\n", + " \n", + " missing: bool\n", + " Whether or not the true label is missing in the\n", + " prediction set.\n", + " \n", + " By default False.\n", + " \n", + " \"\"\"\n", + " if not missing :\n", + " text = f\"{label_name} : {proba:.4f}\"\n", + " else:\n", + " text = f\"True label : {label_name} ({proba:.4f})\"\n", + " i, j = indices\n", + " ax[i, j].text(\n", + " 15,\n", + " position,\n", + " text, \n", + " ha=\"center\", va=\"top\", \n", + " color=color,\n", + " font=\"courier new\"\n", + " )\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_prediction_sets(\n", + " X: np.ndarray, y: np.ndarray,\n", + " y_pred_proba: np.ndarray,\n", + " y_methods: Dict[str, np.ndarray],\n", + " n_images: int, label_names: Dict,\n", + " random_state: Union[int, None] = None\n", + ") -> None:\n", + " \"\"\"\n", + " Plot random images with their associated prediction\n", + " set for all the required methods.\n", + " \n", + " Parameters\n", + " ----------\n", + " X: np.ndarray of shape (n_sample, width, height, n_channels)\n", + " Array containing images.\n", + " \n", + " y: np.ndarray of shape (n_samples, )\n", + " Labels of the images.\n", + " \n", + " y_pred_proba: np.ndarray of shape (n_samples, n_labels)\n", + " Softmax output of the model.\n", + " \n", + " y_methods: Dict[str, np.ndarray]\n", + " Outputs of the MapieClassifier with the different\n", + " choosen methods.\n", + " \n", + " n_images: int\n", + " Number of images to plot\n", + " \n", + " random_state: Union[int, None]\n", + " Random state to use to choose the images.\n", + " \n", + " By default None.\n", + " \"\"\"\n", + " random.seed(random_state)\n", + " indices = random.sample(range(len(X)), n_images)\n", + "\n", + " y_true = y[indices]\n", + " y_pred_proba = y_pred_proba[indices]\n", + " ax = prepare_plot(y_methods, n_images)\n", + "\n", + " for i, method in enumerate(y_methods):\n", + " y_sets = y_methods[method][indices]\n", + "\n", + " for j in range(n_images):\n", + " y_set = y_sets[j]\n", + " img, label= X[indices[j]], y_true[j]\n", + "\n", + " ax[i, j].imshow(img)\n", + "\n", + " count_true = np.sum(y_set)\n", + " index_sorted_proba = np.argsort(-y_pred_proba[j])\n", + "\n", + " for count in range(count_true):\n", + " index_pred = index_sorted_proba[count]\n", + " proba = y_pred_proba[j][index_pred]\n", + " label_name = label_names[index_pred]\n", + " color = 'green' if index_pred == y_true[j] else 'red'\n", + " position = get_position(y_set, label, count, count_true)\n", + "\n", + " add_text(ax, (i, j), position, label_name, proba, color)\n", + "\n", + " if not y_set[label] :\n", + " label_name = label_names[label]\n", + " proba = y_pred_proba[j][label]\n", + " add_text(ax, (i, j), -3, label_name, proba, color= 'orange', missing=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_prediction_sets(X_test, y_test, y_pred_proba, y_ps_90, 5, label_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Calibration of the methods" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section, we plot the number of null sets, the marginal coverages, and the prediction set sizes as function of the target coverage level for all conformal methods. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "vars_y = [nulls, coverages, sizes]\n", + "labels_y = [\"Empty prediction sets\", \"Marginal coverage\", \"Set sizes\"]\n", + "fig, axs = plt.subplots(1, len(vars_y), figsize=(8*len(vars_y), 8))\n", + "for i, var in enumerate(vars_y):\n", + " for name, (method, include_last_label) in method_params.items():\n", + " axs[i].plot(1 - alphas, var[name], label=name)\n", + " if i == 1:\n", + " axs[i].plot([0, 1], [0, 1], ls=\"--\", color=\"k\")\n", + " axs[i].set_xlabel(\"Couverture cible : 1 - alpha\")\n", + " axs[i].set_ylabel(labels_y[i])\n", + " if i == len(vars_y) - 1:\n", + " axs[i].legend(fontsize=10, loc=[1, 0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The two only methods which are perfectly calibrated for the entire range of alpha values are the \"score\" and \"random_cumulated_score\". However, these accurate marginal coverages can only be obtained thanks to the generation of null prediction sets. The compromise between estimating null prediction sets with calibrated coverages or non-empty prediction sets but with larger marginal coverages is entirely up to the user." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Prediction set sizes" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "s=5\n", + "fig, axs = plt.subplots(1, len(y_preds), figsize=(s*len(y_preds), s))\n", + "for i, (method, y_ps) in enumerate(y_ps_90.items()):\n", + " sizes = y_ps.sum(axis=1)\n", + " axs[i].hist(sizes)\n", + " axs[i].set_xlabel(\"Prediction set sizes\")\n", + " axs[i].set_title(method)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8. Conditional coverages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We just saw that all our methods (except the \"naive\" one) give marginal coverages always larger than the target coverages for alpha values ranging between 0 and 1. However, there is no mathematical guarantees on the *conditional* coverages, i.e. the coverage obtained for a specific class of images. Let's see what conditional coverages we obtain with the different conformal methods." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def get_class_coverage(\n", + " y_test: np.ndarray,\n", + " y_method: Dict[str, np.ndarray],\n", + " label_names: List[str]\n", + ") -> None:\n", + " \"\"\"\n", + " Compute the coverage for each class. As MAPIE is looking for a\n", + " global coverage of 1-alpha, it is important to check that their\n", + " is not major coverage difference between classes.\n", + " \n", + " Parameters\n", + " ----------\n", + " y_test: np.ndarray of shape (n_samples,)\n", + " Labels of the predictions.\n", + " \n", + " y_method: Dict[str, np.ndarray]\n", + " Prediction sets for each method.\n", + " \n", + " label_names: List[str]\n", + " Names of the labels.\n", + " \"\"\"\n", + " recap ={}\n", + " for method in y_method:\n", + " recap[method] = []\n", + " for label in sorted(np.unique(y_test)):\n", + " indices = np.where(y_test==label)\n", + " label_name = label_names[label]\n", + " y_test_trunc = y_test[indices]\n", + " y_set_trunc = y_method[method][indices]\n", + " score_coverage = classification_coverage_score(y_test_trunc, y_set_trunc)\n", + " recap[method].append(score_coverage)\n", + " recap_df = pd.DataFrame(recap, index = label_names)\n", + " return recap_df\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "class_coverage = get_class_coverage(y_test, y_ps_90, label_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "class_coverage.plot.bar(figsize=(12, 4), alpha=0.7)\n", + "plt.axhline(0.9, ls=\"--\", color=\"k\")\n", + "plt.ylabel(\"Conditional coverage\")\n", + "plt.legend(loc=[1, 0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can notice that the conditional coverages slightly vary between classes. The only method whose conditional coverages remain valid for all classes is the \"top_k\" one. However, those variations are much smaller than that of the naive method." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "def create_confusion_matrix(y_ps: np.ndarray, y_true: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Create a confusion matrix to visualize, for each class, which\n", + " classes are which are the most present classes in the prediction\n", + " sets.\n", + " \n", + " Parameters\n", + " ----------\n", + " y_ps: np.ndarray of shape (n_samples, n_labels)\n", + " Prediction sets of a specific method.\n", + " \n", + " y_true: np.ndarray of shape (n_samples, )\n", + " Labels of the sample\n", + " \n", + " Returns\n", + " -------\n", + " np.ndarray of shape (n_labels, n_labels)\n", + " \"\"\"\n", + " number_of_classes = len(np.unique(y_true))\n", + " confusion_matrix = np.zeros((number_of_classes, number_of_classes))\n", + " for i, ps in enumerate(y_ps):\n", + " confusion_matrix[y_true[i]] += ps\n", + " \n", + " return confusion_matrix\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "def reorder_labels(ordered_labels: List, labels: List, cm: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Used to order the labels in the confusion matrix\n", + " \n", + " Parameters\n", + " ----------\n", + " ordered_labels: List\n", + " Order you want to have in your confusion matrix\n", + " \n", + " labels: List\n", + " Initial order of the confusion matrix\n", + " \n", + " cm: np.ndarray of shape (n_labels, n_labels)\n", + " Original confusion matrix\n", + " \n", + " Returns\n", + " -------\n", + " np.ndarray of shape (n_labels, n_labels)\n", + " \"\"\"\n", + " cm_ordered = np.zeros(cm.shape)\n", + " index_order = [labels.index(label) for label in ordered_labels]\n", + " for i, label in enumerate(ordered_labels):\n", + " old_index = labels.index(label)\n", + " \n", + " cm_ordered[i] = cm[old_index, index_order]\n", + " return cm_ordered" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_confusion_matrix(method: str, y_ps: Dict[str, np.ndarray], label_names: List) -> None:\n", + " \"\"\"\n", + " Plot the confusion matrix for a specific method.\n", + " \n", + " Parameters\n", + " ----------\n", + " method: str\n", + " Name of the method to plot.\n", + " \n", + " y_ps: Dict[str, np.ndarray]\n", + " Prediction sets for each of the fitted method\n", + " \n", + " label_names: List\n", + " Name of the labels\n", + " \"\"\"\n", + "\n", + " y_method = y_ps[method]\n", + " cm = create_confusion_matrix(y_method, y_test)\n", + " ordered_labels = [\"frog\", \"cat\", \"dog\", \"deer\", \"horse\", \"bird\", \"airplane\", \"ship\", \"truck\", \"automobile\"]\n", + " cm = reorder_labels(ordered_labels, label_names, cm)\n", + " disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=ordered_labels)\n", + " _, ax = plt.subplots(figsize=(10, 10))\n", + " disp.plot(\n", + " include_values=True,\n", + " cmap=\"viridis\",\n", + " ax=ax,\n", + " xticks_rotation=\"vertical\",\n", + " values_format='.0f',\n", + " colorbar=True,\n", + " )\n", + "\n", + " ax.set_title(f'Confusion matrix for {method} method')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_confusion_matrix(\"cumulated_score\", y_ps_90, label_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thanks to this confusion matrix we can see that, for some labels (as cat, deer and dog) the distribution of the labels in the prediction set is not uniform. Indeed, when the image is a cat, there are almost as many predictions sets with the true label than with the \"cat\" label. In this case, the reverse is also true. However, for the deer, the cat label is quite often within the prediction set while the deer is not" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_confusion_matrix(\"score\", y_ps_90, label_names)" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "c701d105863f3b19d95155354c5cd7eba8f6824e73339ef8c56a1f0753fbe4df" + }, + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3.9.2 64-bit ('mapie_test': conda)", + "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": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/requirements.dev.txt b/requirements.dev.txt index 547ed67ee..69f59bd2a 100644 --- a/requirements.dev.txt +++ b/requirements.dev.txt @@ -1,11 +1,13 @@ bump2version==1.0.1 flake8==4.0.1 -mypy==0.920 +ipykernel==6.9.0 +jupyter==1.0.0 +mypy==0.941 +numpy==1.22.3 numpydoc==1.1.0 pandas==1.3.5 pytest==6.2.5 pytest-cov==3.0.0 -python==3.10.1 scikit-learn==1.0.1 sphinx==4.3.2 sphinx-gallery==0.10.1 diff --git a/setup.py b/setup.py index b2b5b7845..37f8ab31e 100644 --- a/setup.py +++ b/setup.py @@ -19,19 +19,21 @@ "Source Code": "https://github.com/scikit-learn-contrib/MAPIE" } LICENSE = "new BSD" -MAINTAINER = "V. Taquet, V. Blot, G. Martinon" +MAINTAINER = "V. Taquet, V. Blot, T. Morzadec, G. Martinon" MAINTAINER_EMAIL = """ vtaquet@quantmetry.com, vblot@quantmetry.com, +tmorzadec@quantmetry.com, gmartinon@quantmetry.com """ PYTHON_REQUIRES = ">=3.7" PACKAGES = find_packages() -INSTALL_REQUIRES = ["scikit-learn"] +INSTALL_REQUIRES = ["scikit-learn", "numpy>=1.21"] EXTRAS_REQUIRE = { "tests": [ "flake8", "mypy", + "pandas", "pytest", "pytest-cov", "typed-ast" @@ -58,7 +60,8 @@ "Operating System :: MacOS", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9" + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", ] setup(