diff --git a/doc/index_binary_classification.rst b/doc/index_binary_classification.rst index 278d0c5db..f09adf33c 100644 --- a/doc/index_binary_classification.rst +++ b/doc/index_binary_classification.rst @@ -4,5 +4,5 @@ The binary classification case .. toctree:: :maxdepth: 2 - examples_classification/4-tutorials/plot_main-tutorial-binary-classification + examples_classification/2-advanced-analysis/plot_main-tutorial-binary-classification theoretical_description_binary_classification \ No newline at end of file diff --git a/doc/index_classification.rst b/doc/index_classification.rst index 2d8e76f5f..dc4bc6455 100644 --- a/doc/index_classification.rst +++ b/doc/index_classification.rst @@ -5,9 +5,6 @@ Prediction sets (classification) :maxdepth: 2 choosing_the_right_algorithm_classification - examples_classification/4-tutorials/plot_main-tutorial-classification - examples_classification/4-tutorials/plot_crossconformal examples_classification/index - notebooks_classification theoretical_description_classification index_binary_classification \ No newline at end of file diff --git a/examples/classification/1-quickstart/plot_comp_methods_on_2d_dataset.py b/examples/classification/2-advanced-analysis/plot_comp_methods_on_2d_dataset.py similarity index 99% rename from examples/classification/1-quickstart/plot_comp_methods_on_2d_dataset.py rename to examples/classification/2-advanced-analysis/plot_comp_methods_on_2d_dataset.py index 789bcf390..ab973589b 100644 --- a/examples/classification/1-quickstart/plot_comp_methods_on_2d_dataset.py +++ b/examples/classification/2-advanced-analysis/plot_comp_methods_on_2d_dataset.py @@ -1,6 +1,6 @@ """ ====================================================== -Comparing prediction sets on a two-dimensional dataset +LAC and APS methods explained ====================================================== In this tutorial, we compare the prediction sets estimated by diff --git a/examples/classification/4-tutorials/plot_crossconformal.py b/examples/classification/2-advanced-analysis/plot_crossconformal.py similarity index 99% rename from examples/classification/4-tutorials/plot_crossconformal.py rename to examples/classification/2-advanced-analysis/plot_crossconformal.py index c72e58fa3..881a43225 100644 --- a/examples/classification/4-tutorials/plot_crossconformal.py +++ b/examples/classification/2-advanced-analysis/plot_crossconformal.py @@ -1,7 +1,7 @@ """ -================================== -Cross-conformal for classification -================================== +========================================= +Cross conformal classification explained +========================================= In this tutorial, we estimate the impact of the training/conformalization split on the prediction sets and diff --git a/examples/classification/4-tutorials/plot_main-tutorial-binary-classification.py b/examples/classification/2-advanced-analysis/plot_main-tutorial-binary-classification.py similarity index 98% rename from examples/classification/4-tutorials/plot_main-tutorial-binary-classification.py rename to examples/classification/2-advanced-analysis/plot_main-tutorial-binary-classification.py index 733c0fe19..a83dca23e 100644 --- a/examples/classification/4-tutorials/plot_main-tutorial-binary-classification.py +++ b/examples/classification/2-advanced-analysis/plot_main-tutorial-binary-classification.py @@ -1,7 +1,7 @@ """ -=========================== -Tutorial for set prediction -=========================== +============================================================ +Set prediction example in the binary classification setting +============================================================ In this example, we propose set prediction for binary classification estimated by :class:`~mapie_v1.classification.SplitConformalClassifier` with the "lac" diff --git a/examples/classification/3-scientific-articles/plot_sadinle2019_example.py b/examples/classification/3-scientific-articles/plot_sadinle2019_example.py index 4109b55be..2fd99a9c8 100644 --- a/examples/classification/3-scientific-articles/plot_sadinle2019_example.py +++ b/examples/classification/3-scientific-articles/plot_sadinle2019_example.py @@ -1,7 +1,7 @@ """ -================================================ -Reproducing Example 7 from Sadinle et al. (2019) -================================================ +======================================================================================= +Least Ambiguous Set-Valued Classifiers with Bounded Error Levels, Sadinle et al. (2019) +======================================================================================= We use :class:`~mapie_v1.classification.SplitConformalClassifier` to reproduce Example 7 from Sadinle et al. (2019). diff --git a/examples/classification/4-other-notebooks/README.rst b/examples/classification/4-other-notebooks/README.rst new file mode 100644 index 000000000..5cb51be96 --- /dev/null +++ b/examples/classification/4-other-notebooks/README.rst @@ -0,0 +1,8 @@ +.. _classification_examples_4: + +4. Other notebooks +-------------------- + +This section lists a series of Jupyter notebooks hosted on the MAPIE Github repository that can be run on Google Colab: + + - `Deep learning with MAPIE on the Cifar10 dataset `_ \ No newline at end of file diff --git a/examples/classification/4-tutorials/README.rst b/examples/classification/4-tutorials/README.rst deleted file mode 100644 index 2724fa0a0..000000000 --- a/examples/classification/4-tutorials/README.rst +++ /dev/null @@ -1,6 +0,0 @@ -.. _classification_examples_4: - -4. Tutorials ------------- - -The following examples present pedagogical tutorials explaining how to use MAPIE on different classification taks. \ No newline at end of file diff --git a/examples/classification/4-tutorials/plot_main-tutorial-classification.py b/examples/classification/4-tutorials/plot_main-tutorial-classification.py deleted file mode 100644 index ab3dc44ff..000000000 --- a/examples/classification/4-tutorials/plot_main-tutorial-classification.py +++ /dev/null @@ -1,303 +0,0 @@ -""" -=========================== -Tutorial for classification -=========================== - - -In this tutorial, we compare the prediction sets estimated by the conformal -methods implemented in MAPIE on a toy two-dimensional dataset. - -Throughout this tutorial, we will answer the following questions: - -- How does the number of classes in the prediction sets vary according to - the confidence level? - -- Is the chosen conformal method well calibrated? - -- What are the pros and cons of the conformal methods included in MAPIE? -""" - -import matplotlib.pyplot as plt -import numpy as np -from sklearn.naive_bayes import GaussianNB - -from mapie.classification import SplitConformalClassifier -from mapie.utils import train_conformalize_test_split -from mapie.metrics.classification import ( - classification_coverage_score, - classification_mean_width_score, -) - -############################################################################## -# 1. Conformal Prediction method using the softmax score of the true label -# ------------------------------------------------------------------------ -# -# We will use MAPIE to estimate a prediction set of several classes such -# that the probability that the true label of a new test point is included -# in the prediction set is always higher than the target confidence level : -# ``P(Yₙ₊₁ ∈ Ĉₙ,α(Xₙ₊₁)) ≥ 1 - α``. -# We start by using the softmax score output by the base classifier as the -# conformity score on a toy two-dimensional dataset. -# -# We estimate the prediction sets as follows : -# -# * Generate a dataset with train, conformalization and test, the model is -# fitted on the training set. -# -# * Set the conformal score ``Sᵢ = 𝑓̂(Xᵢ)ᵧᵢ``, the softmax -# output of the true class for each sample in the conformity set. -# -# * Define ``q̂`` as being the ``(n + 1)(α) / n`` -# previous quantile of ``S₁, ..., Sₙ`` -# (this is essentially the quantile ``α``, but with a small sample -# correction). -# -# * Finally, for a new test data point (where ``Xₙ₊₁`` is known but -# ``Yₙ₊₁`` is not), create a prediction set -# ``C(Xₙ₊₁) = {y: 𝑓̂(Xₙ₊₁)ᵧ > q̂}`` which includes -# all the classes with a sufficiently high softmax output. - -# We use a two-dimensional toy dataset with three labels. The distribution of -# the data is a bivariate normal with diagonal covariance matrices for each -# label. - -centers = [(0, 3.5), (-2, 0), (2, 0)] -covs = [np.eye(2), np.eye(2)*2, np.diag([5, 1])] -x_min, x_max, y_min, y_max, step = -6, 8, -6, 8, 0.1 -n_samples = 1000 -n_classes = 3 -np.random.seed(42) -X = np.vstack([ - np.random.multivariate_normal(center, cov, n_samples) - for center, cov in zip(centers, covs) -]) -y = np.hstack([np.full(n_samples, i) for i in range(n_classes)]) -(X_train, X_conf, X_test, - y_train, y_conf, y_test) = train_conformalize_test_split( - X, y, train_size=0.6, conformalize_size=0.2, test_size=0.2 -) - -xx, yy = np.meshgrid( - np.arange(x_min, x_max, step), np.arange(x_min, x_max, step) -) -X_test_mesh = np.stack([xx.ravel(), yy.ravel()], axis=1) - -############################################################################## -# Let’s see our training data. - -colors = {0: "#1f77b4", 1: "#ff7f0e", 2: "#2ca02c", 3: "#d62728"} -y_train_col = list(map(colors.get, y_train)) -fig = plt.figure() -plt.scatter( - X_train[:, 0], - X_train[:, 1], - color=y_train_col, - marker='o', - s=10, - edgecolor='k' -) -plt.xlabel("X") -plt.ylabel("Y") -plt.show() - -############################################################################## -# We fit our training data with a Gaussian Naive Base estimator. And then we -# apply MAPIE in the conformity data with the LAC conformity score to the -# estimator indicating that it has already been fitted with `prefit=True`. -# We then estimate the prediction sets with different confidence level values with a -# ``conformalize`` and ``predict`` process. - -clf = GaussianNB() -clf.fit(X_train, y_train) -y_pred = clf.predict(X_test) -y_pred_proba = clf.predict_proba(X_test) -y_pred_proba_max = np.max(y_pred_proba, axis=1) -confidence_level = [0.8, 0.9, 0.95] -mapie_score = SplitConformalClassifier( - estimator=clf, - confidence_level=confidence_level, - prefit=True -) -mapie_score.conformalize(X_conf, y_conf) -y_pred_score, y_ps_score = mapie_score.predict_set(X_test_mesh) - -############################################################################## -# * ``y_pred_score``: represents the prediction in the test set by the base -# estimator. -# * ``y_ps_score``: represents the prediction sets estimated by MAPIE with -# the "lac" conformity score. - - -def plot_scores(n, confidence_levels, scores, quantiles): - colors = {0: "#1f77b4", 1: "#ff7f0e", 2: "#2ca02c"} - plt.figure(figsize=(7, 5)) - plt.hist(scores, bins="auto") - for i, quantile in enumerate(quantiles): - plt.vlines( - x=quantile, - ymin=0, - ymax=400, - color=colors[i], - ls="dashed", - label=f"confidence_level = {confidence_levels[i]}" - ) - plt.title("Distribution of scores") - plt.legend() - plt.xlabel("Scores") - plt.ylabel("Count") - plt.show() - - -############################################################################## -# Let’s see the distribution of the scores with the calculated quantiles. - -scores = mapie_score._mapie_classifier.conformity_scores_ -n = len(mapie_score._mapie_classifier.conformity_scores_) -quantiles = mapie_score._mapie_classifier.conformity_score_function_.quantiles_ -plot_scores(n, confidence_level, scores, quantiles) - -############################################################################## -# The estimated quantile increases with the confidence level. -# A low confidence level can potentially lead to a low quantile ``q``; the associated -# ``1 - q`` threshold would therefore not necessarily be reached by any class in -# uncertain areas, resulting in null regions. -# -# We will now visualize the differences between the prediction sets of the -# different values of confidence level. - - -def plot_results(confidence_levels, X, y_pred, y_ps): - tab10 = plt.cm.get_cmap('Purples', 4) - colors = {0: "#1f77b4", 1: "#ff7f0e", 2: "#2ca02c", 3: "#d62728"} - y_pred_col = list(map(colors.get, y_pred)) - fig, [[ax1, ax2], [ax3, ax4]] = plt.subplots(2, 2, figsize=(10, 10)) - axs = {0: ax1, 1: ax2, 2: ax3, 3: ax4} - axs[0].scatter( - X[:, 0], - X[:, 1], - color=y_pred_col, - marker='.', - s=10, - alpha=0.4 - ) - axs[0].set_title("Predicted labels") - for i, confidence_level in enumerate(confidence_levels): - y_pi_sums = y_ps[:, :, i].sum(axis=1) - num_labels = axs[i+1].scatter( - X[:, 0], - X[:, 1], - c=y_pi_sums, - marker='.', - s=10, - alpha=1, - cmap=tab10, - vmin=0, - vmax=3 - ) - plt.colorbar(num_labels, ax=axs[i+1]) - axs[i+1].set_title(f"Number of labels for confidence_level={confidence_level}") - plt.show() - - -plot_results(confidence_level, X_test_mesh, y_pred_score, y_ps_score) - -############################################################################## -# When the class coverage is not large enough, the prediction sets can be -# empty when the model is uncertain at the border between two classes. -# The null region disappears for larger class coverages but ambiguous -# classification regions arise with several labels included in the -# prediction sets highlighting the uncertain behaviour of the base -# classifier. -# -# Let’s now study the effective coverage and the mean prediction set widths -# as function of the ``confidence_level`` target coverage. To this aim, we use once -# again the ``predict`` method of MAPIE to estimate predictions sets on a -# large number of ``confidence_level`` values. - -confidence_level2 = np.arange(0.02, 0.98, 0.02) -mapie_score2 = SplitConformalClassifier( - estimator=clf, - confidence_level=confidence_level2, - prefit=True -) -mapie_score2.conformalize(X_conf, y_conf) -_, y_ps_score2 = mapie_score2.predict_set(X_test) -coverages_score = classification_coverage_score(y_test, y_ps_score2) -widths_score = classification_mean_width_score(y_ps_score2) - - -def plot_coverages_widths(confidence_level, coverage, width, conformity_score): - fig, axs = plt.subplots(1, 2, figsize=(12, 5)) - axs[0].scatter(confidence_level, coverage, label=conformity_score) - axs[0].set_xlabel("Confidence level") - axs[0].set_ylabel("Coverage score") - axs[0].plot([0, 1], [0, 1], label="x=y", color="black") - axs[0].legend() - axs[1].scatter(confidence_level, width, label=conformity_score) - axs[1].set_xlabel("Confidence level") - axs[1].set_ylabel("Average size of prediction sets") - axs[1].legend() - plt.show() - - -plot_coverages_widths( - confidence_level2, coverages_score, widths_score, "lac" -) - -############################################################################## -# 2. Conformal Prediction method using the cumulative softmax score -# ----------------------------------------------------------------- -# -# We saw in the previous section that the "lac" conformity score is well calibrated by -# providing accurate coverage levels. However, it tends to give null -# prediction sets for uncertain regions, especially when the ``confidence_level`` -# value is low. -# MAPIE includes another method, called Adaptive Prediction Set (APS), -# whose conformity score is the cumulated score of the softmax output until -# the true label is reached (see the theoretical description for more details). -# We will see in this section that this method no longer estimates null -# prediction sets by giving slightly bigger prediction sets. -# -# Let's visualize the prediction sets obtained with the APS method on the test -# set after fitting MAPIE on the conformity set. - -confidence_level = [0.8, 0.9, 0.95] -mapie_aps = SplitConformalClassifier( - estimator=clf, - confidence_level=confidence_level, - conformity_score="aps", - prefit=True -) -mapie_aps.conformalize(X_conf, y_conf) -y_pred_aps, y_ps_aps = mapie_aps.predict_set( - X_test_mesh, conformity_score_params={"include_last_label": True} -) - -plot_results(confidence_level, X_test_mesh, y_pred_aps, y_ps_aps) - -############################################################################## -# One can notice that the uncertain regions are emphasized by wider -# boundaries, but without null prediction sets with respect to the first -# "lac" method. - -mapie_aps2 = SplitConformalClassifier( - estimator=clf, - confidence_level=confidence_level2, - conformity_score="aps", - prefit=True -) -mapie_aps2.conformalize(X_conf, y_conf) -_, y_ps_aps2 = mapie_aps2.predict_set( - X_test, conformity_score_params={"include_last_label": "randomized"} -) -coverages_aps = classification_coverage_score(y_test, y_ps_aps2) -widths_aps = classification_mean_width_score(y_ps_aps2) - -plot_coverages_widths( - confidence_level2, coverages_aps, widths_aps, "aps" -) - -############################################################################## -# This method also gives accurate conformalization plots, meaning that the -# effective coverage level is always very close to the target coverage, -# sometimes at the expense of slightly bigger prediction sets. diff --git a/examples/classification/README.rst b/examples/classification/README.rst index 828a4bd1d..9a881c2db 100644 --- a/examples/classification/README.rst +++ b/examples/classification/README.rst @@ -1,4 +1,6 @@ .. _classification_examples: -Classification examples -======================= \ No newline at end of file +All classification examples +============================ + +Following is a collection of notebooks demonstrating how to use MAPIE in classification. \ No newline at end of file diff --git a/notebooks/classification/Cifar10.ipynb b/notebooks/classification/Cifar10.ipynb old mode 100755 new mode 100644 index 39b821af8..7870ca79f --- a/notebooks/classification/Cifar10.ipynb +++ b/notebooks/classification/Cifar10.ipynb @@ -1,1146 +1,4821 @@ { - "cells": [ - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "# Estimating prediction sets on the Cifar10 dataset\n", - "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." - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "ttlxlz86igUX" + }, + "source": [ + "# Estimating prediction sets on the Cifar10 dataset\n", + "The goal of this notebook is to present how to use :class:`mapie.classification.SplitConformalClassifier` on an object classification task. We will build prediction sets for images and study the marginal and conditional coverages." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "djASgfdOigUY" + }, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/scikit-learn-contrib/MAPIE/blob/master/notebooks/classification/Cifar10.ipynb)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZTlhivAGigUY" + }, + "source": [ + "### What is done in this tutorial ?\n", + "\n", + "> - **Cifar10 dataset** : 10 classes (horse, dog, cat, frog, deer, bird, airplane, truck, ship, automobile)\n", + "\n", + "> - Use :class:`mapie.classification.SplitConformalClassifier` 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": { + "id": "jka_m2M5igUZ" + }, + "source": [ + "## Tutorial preparation" + ] + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "install_mapie = True\n", + "if install_mapie:\n", + " !pip install mapie" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xjymeAiAigUZ" + }, + "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.classification import classification_coverage_score_v2\n", + "from mapie.utils import train_conformalize_test_split\n", + "from mapie.classification import SplitConformalClassifier\n", + "from mapie.conformity_scores.sets import NaiveConformityScore\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": null, + "metadata": { + "id": "IzGcITNOigUZ" + }, + "outputs": [], + "source": [ + "SPACE_BETWEEN_LABELS = 2.5\n", + "SPACE_IN_SUBPLOTS = 4.0\n", + "FONT_SIZE = 18\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OwNFOaenigUa" + }, + "source": [ + "## 1. Data loading" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HCPQRfRiigUa" + }, + "source": [ + "The Cifar10 dataset is downloaded from the `Tensorflow Datasets` library. The training set is then splitted into a training, validation and a conformalization 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", + "> - **Conformalization set**: used to conformalize the conformal scores in :class:`mapie.classification.SplitConformalClassifier`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KGrjz4qfigUa" + }, + "outputs": [], + "source": [ + "def train_valid_conf_split(\n", + " X: np.ndarray,\n", + " y: np.ndarray,\n", + " conf_size: float = .1,\n", + " val_size: float = .3,\n", + " random_state: int = 42\n", + "\n", + ") -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n", + " \"\"\"\n", + " Create conformalization and validation 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", + " conf_size: float\n", + " Percentage of the dataset X to use as conformalization set.\n", + "\n", + " val_size: float\n", + " Percentage of the dataset X (minus the conformalization 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 - conf_size - val_size), width, height, n_channels),\n", + " (n_samples * conf_size, width, height, n_channels),\n", + " (n_samples * val_size, width, height, n_channels),\n", + " (n_samples * (1 - conf_size - val_size), 1),\n", + " (n_samples * conf_size, 1),\n", + " (n_samples * val_size, 1).\n", + "\n", + " \"\"\"\n", + " (X_train, X_conf, X_val,\n", + " y_train, y_conf, y_val) = train_conformalize_test_split(\n", + " X, y, train_size=1-conf_size-val_size, conformalize_size=conf_size,\n", + " test_size=val_size, random_state=random_state\n", + " )\n", + " return X_train, X_conf, X_val, y_train, y_conf, y_val\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RuGTufGIigUa" + }, + "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, conf, 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_conf, X_val, y_train, y_conf, y_val = train_valid_conf_split(\n", + " X_train,\n", + " y_train\n", + " )\n", + "\n", + " X_train = X_train/255.\n", + " X_val = X_val/255.\n", + "\n", + " X_conf = X_conf/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_conf_cat = tf.keras.utils.to_categorical(y_conf)\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", + " conf_set = (X_conf, y_conf, y_conf_cat)\n", + " test_set = (X_test, y_test, y_test_cat)\n", + "\n", + " return train_set, val_set, conf_set, test_set, label_names\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Zz4MgYjJigUa" + }, + "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": null, + "metadata": { + "id": "PrUWxPhTigUa", + "outputId": "d3f666e7-a145-440a-cc03-aa6e98d24a44", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 341, + "referenced_widgets": [ + "e3c9d897dc5d4e8aaf6511f88dd9b38c", + "e3924cf327134005a811a6fd7777be85", + "c56070c3bc7647d0bed95f0781b200f3", + "2efe177d687e4764a9f80abcd6d20463", + "99e5ac05af7d441b9d39fc37b3267da3", + "91e45143b4494d9f8dc23f5d576723d1", + "e9eb7108ebec4f4aaf6abe4cc61cc404", + "5a851e1e83f743a89488220b2af1459f", + "e3a7016413754790807fd90a7593f28b", + "ef16b62c960b42b8b289fc61f7e70a87", + "b26dc30600b34011aa88a6ce6c455fb3", + "134168e259c44c42819159204d797c00", + 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"output_type": "stream", + "text": [ + "WARNING:absl:Variant folder /root/tensorflow_datasets/cifar10/3.0.2 has no dataset_info.json\n" + ] + }, + { + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading and preparing dataset Unknown size (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/tensorflow_datasets/cifar10/3.0.2...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e3c9d897dc5d4e8aaf6511f88dd9b38c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Dl Completed...: 0 url [00:00, ? url/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "134168e259c44c42819159204d797c00", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Dl Size...: 0 MiB [00:00, ? MiB/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4e86e0796c774c699cdf1b877f99e248", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Extraction completed...: 0 file [00:00, ? file/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "808147a9d26e4e508518313d91b64de8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Generating splits...: 0%| | 0/2 [00:00" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "train_set, val_set, conf_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_conf, y_conf, y_conf_cat) = conf_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": { + "id": "G5RzpMJRigUb" + }, + "source": [ + "## 2. Definition and training of the the neural network" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Xxl1v_LBigUb" + }, + "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 achieving a reasonable accuracy score and a fast training. The objective here is not to obtain a perfect classifier.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "syd9pjowigUb" + }, + "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": { + "id": "JhEqbyxCigUb" + }, + "source": [ + "## 3. Training the algorithm with a custom class called `TensorflowToMapie`\n", + "\n", + "As MAPIE asks for a model with `fit`, `predict_proba`, `predict` class attributes and the information about whether or not the model is fitted." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DUCiKdvrigUb" + }, + "outputs": [], + "source": [ + "class TensorflowToMapie():\n", + " \"\"\"\n", + " Class that aims 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": null, + "metadata": { + "tags": [], + "id": "Zw1kubsRigUb" + }, + "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": null, + "metadata": { + "scrolled": true, + "tags": [], + "id": "YqHicSqgigUb", + "outputId": "4f73f2ba-25c8-4ced-cbf1-5176d63778f0", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 92ms/step - accuracy: 0.2611 - loss: 1.9659 - val_accuracy: 0.4459 - val_loss: 1.5022\n", + "Epoch 2/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 84ms/step - accuracy: 0.4653 - loss: 1.4454 - val_accuracy: 0.5254 - val_loss: 1.2981\n", + "Epoch 3/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 84ms/step - accuracy: 0.5468 - loss: 1.2604 - val_accuracy: 0.5698 - val_loss: 1.1990\n", + "Epoch 4/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 82ms/step - accuracy: 0.5989 - loss: 1.1229 - val_accuracy: 0.6003 - val_loss: 1.1245\n", + "Epoch 5/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 80ms/step - accuracy: 0.6391 - loss: 1.0123 - val_accuracy: 0.6284 - val_loss: 1.0389\n", + "Epoch 6/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 91ms/step - accuracy: 0.6700 - loss: 0.9404 - val_accuracy: 0.6544 - val_loss: 0.9937\n", + "Epoch 7/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 83ms/step - accuracy: 0.6940 - loss: 0.8689 - val_accuracy: 0.6681 - val_loss: 0.9565\n", + "Epoch 8/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 82ms/step - accuracy: 0.7262 - loss: 0.7869 - val_accuracy: 0.6579 - val_loss: 0.9905\n", + "Epoch 9/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 82ms/step - accuracy: 0.7443 - loss: 0.7390 - val_accuracy: 0.6814 - val_loss: 0.9257\n", + "Epoch 10/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 91ms/step - accuracy: 0.7592 - loss: 0.6841 - val_accuracy: 0.6848 - val_loss: 0.9275\n", + "Epoch 11/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 82ms/step - accuracy: 0.7789 - loss: 0.6320 - val_accuracy: 0.6875 - val_loss: 0.9564\n", + "Epoch 12/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 81ms/step - accuracy: 0.7995 - loss: 0.5770 - val_accuracy: 0.6886 - val_loss: 0.9533\n", + "Epoch 13/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 82ms/step - accuracy: 0.8119 - loss: 0.5407 - val_accuracy: 0.6854 - val_loss: 0.9716\n", + "Epoch 14/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 80ms/step - accuracy: 0.8236 - loss: 0.4980 - val_accuracy: 0.6921 - val_loss: 0.9915\n", + "Epoch 15/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 80ms/step - accuracy: 0.8422 - loss: 0.4579 - val_accuracy: 0.6925 - val_loss: 1.0070\n", + "Epoch 16/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 82ms/step - accuracy: 0.8502 - loss: 0.4225 - val_accuracy: 0.6809 - val_loss: 1.0804\n", + "Epoch 17/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 91ms/step - accuracy: 0.8655 - loss: 0.3844 - val_accuracy: 0.6828 - val_loss: 1.0804\n", + "Epoch 18/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 82ms/step - accuracy: 0.8783 - loss: 0.3465 - val_accuracy: 0.6797 - val_loss: 1.1260\n", + "Epoch 19/20\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 90ms/step - accuracy: 0.8927 - loss: 0.3103 - val_accuracy: 0.6754 - val_loss: 1.1953\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": null, + "metadata": { + "id": "bWbIr-PpigUb", + "outputId": "c6ab0bde-7e94-4b7d-e325-34f3602a872c", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step\n" + ] + } + ], + "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": { + "id": "tJ56ipA3igUb" + }, + "source": [ + "## 4. Prediction of the prediction sets" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7zsevXMYigUb" + }, + "source": [ + "We will now estimate the prediction sets with the five conformal methods implemented in :class:`mapie.classification.SplitConformalClassifier` for a range of confidence levels between 0 and 1." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "id": "tpsTu6KhigUb" + }, + "outputs": [], + "source": [ + "method_params = {\n", + " \"naive\": (NaiveConformityScore(), False),\n", + " \"lac\": (\"lac\", False),\n", + " \"aps\": (\"aps\", True),\n", + " \"random_aps\": (\"aps\", \"randomized\"),\n", + " \"top_k\": (\"top_k\", False)\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "scrolled": true, + "tags": [], + "id": "JZIGNo92igUb", + "outputId": "3594228a-4a1b-4d6b-99eb-816ebabe6ee9", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 29ms/step\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step\n", + "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 12ms/step\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step\n", + "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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0.01)\n", + "\n", + "for name, (conformity_score, include_last_label) in method_params.items():\n", + " mapie = SplitConformalClassifier(estimator=cirfar10_model, confidence_level=confidence_levels, conformity_score=conformity_score, prefit=True, random_state=42)\n", + " mapie.conformalize(X_conf, y_conf)\n", + " y_preds[name], y_pss[name] = mapie.predict_set(X_test, conformity_score_params={\"include_last_label\": include_last_label})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "w1osqbn4igUc" + }, + "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": 62, + "metadata": { + "id": "wLWiaQDzigUc" + }, + "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": 63, + "metadata": { + "id": "Sgkrz4PxigUc" + }, + "outputs": [], + "source": [ + "nulls, coverages, accuracies, sizes = {}, {}, {}, {}\n", + "for name, (conformity_score, 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(confidence_levels)\n", + " ]\n", + " coverages[name] = classification_coverage_score_v2(y_test, y_pss[name])\n", + " sizes[name] = [\n", + " y_pss[name][:, :, i].sum(axis=1).mean() for i, _ in enumerate(confidence_levels)\n", + " ]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "id": "_9alS_w-igUc" + }, + "outputs": [], + "source": [ + "coverage_90 = {conformity_score: coverage[90] for conformity_score, coverage in coverages.items()}\n", + "null_90 = {conformity_score: null[90] for conformity_score, null in nulls.items()}\n", + "width_90 = {conformity_score: width[90] for conformity_score, width in sizes.items()}\n", + "y_ps_90 = {conformity_score: y_ps[:, :, 90] for conformity_score, y_ps in y_pss.items()}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2Vwrb68-igUc" + }, + "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": 65, + "metadata": { + "id": "NvwOVvQnigUc" + }, + "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": 66, + "metadata": { + "id": "XEpXrcd9igUc", + "outputId": "90ee23b1-5ea0-4bbd-fdac-50ce232598b4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Coverages Number of null sets Average prediction set sizes\n", + "naive 0.926 0 2.583\n", + "lac 0.912 0 2.347\n", + "aps 0.948 0 2.985\n", + "random_aps 0.915 99 2.650\n", + "top_k 0.949 0 4.001" + ], + "text/html": [ + "\n", + "
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CoveragesNumber of null setsAverage prediction set sizes
naive0.92602.583
lac0.91202.347
aps0.94802.985
random_aps0.915992.650
top_k0.94904.001
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Notice that the \"aps\" 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. Besides, top-k score's tendency to build larger prediction sets compared to the other methods is highlighted here." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "axXQsHxXigUc" + }, + "source": [ + "## 5. Visualization of the prediction sets" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "id": "5aMqbApjigUc" + }, + "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": 68, + "metadata": { + "id": "m24ky3aKigUc" + }, + "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", + " )\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "id": "yKozd1Y-igUc" + }, + "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": 70, + "metadata": { + "id": "k7TNtClNigUc", + "outputId": "11753524-2b2c-41ae-ea15-6a844dc2733c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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OzJkJ7Jny7HNAx16uPPo3X/rob1I81P+WLPW/Itnp33zpo79J8VD/W7LU/4pkp3/zpY/+JsVD/W/JUv8rkp3+zZc++psUD/W/Jauk+18FERWTatWqZc6fOHHCabmsy7LWkaLn6t+kTJkyGg8zD88//zzvvvsuAO+88w5PP/20w3L6/1D82rVrR6dOnQD49NNPMz/Xv3u50uj7pvTR91DRU/9beqj/FTH0fVP66Huo6Kn/LT3U/4oY+r4pffQ9VPTU/5Ye6n9FDH3flD76Hip66n9Lj5LqfxVEVEwaNWqEm5s5vHv27HFazrasSpUqlC9f/qK07UrVtGnTzPn8/E0aN25c7G26lI0aNYq3334bgEmTJvHcc885Lav/DxdH9erVAThw4EDmZ/p3L1cafd+UPvoeKlrqf0sf9b8i+r4pjfQ9VLTU/5Y+6n9F9H1TGul7qGip/y191P+K6PumNNL3UNFS/1v6lET/qyCiYuLn50fHjh0BWLp0qcMyVquVZcuWAdCjR4+L1rYr1VVXXUWtWrUA53+T+Ph41q1bB+hvkpuRI0fyzjvvAKYDGTVqVK7l9f/h4jh06BCQPR2d/t3LlUbfN6WPvoeKjvrf0kn9r4i+b0ojfQ8VHfW/pZP6XxF935RG+h4qOup/Syf1vyL6vimN9D1UdNT/lk4l0f8qiKgYDR48GIDVq1ezefPmHMvnzZuX+UcfNGjQRW3blchisWQe59mzZxMWFpajzOTJk4mLi8Pd3Z177733Irfw0jBy5MhsKezy6kBs9P+h4NLT07FarbmWWbVqFX/88QcAoaGhmZ/r371cifR9U7roe6hoqP+9+NT/irhG3zeli76Hiob634tP/a+Ia/R9U7roe6hoqP+9+NT/irhG3zeli76Hiob634uvVPe/Vik2qamp1mbNmlkBa/Xq1a0rV660Wq1Wa3p6unXu3LnWMmXKWAFrz549S7ill56IiAjr2bNnM181a9a0AtZRo0Zl+zw2NjZbvaioKGuVKlWsgLVx48bWLVu2WK1WqzU5Odn6ySefWL28vKyAdcSIESWxW6XeqFGjrIAVsL733nsu1dX/h4I7fPiwtUWLFtapU6daDx48aM3IyMhcdvToUesbb7xh9ff3twLW8uXLW0+dOpWtvv7dy5VG3zfFR/1vyVD/WzLU/4q4Rt83xUf9b8lQ/1sy1P+KuEbfN8VH/W/JUP9bMtT/irhG3zfFR/1vyVD/WzJKc/+rIKJidvjwYWtISEjmfzw/Pz+rj49P5vtWrVpZIyIiSrqZl5zatWtnHsPcXoMHD85Rd8uWLdYKFSpklgkMDLR6enpmvu/Ro4c1KSnp4u9UKXfkyJHMY+Tm5matXLlyrq+33347xzr0/6FgDh8+nO3ftZeXl7VixYqZHYftVadOHeu2bdscrkP/7uVKo++b4qH+9+JT/1ty1P+KuE7fN8VD/e/Fp/635Kj/FXGdvm+Kh/rfi0/9b8lR/yviOn3fFA/1vxef+t+SU5r7Xw1nVsxCQkLYtWsXr776Kk2bNsViseDp6Unr1q1555132LRpE0FBQSXdzCtK69at2bt3L8888wwNGjQgNTUVf39/OnXqxGeffcYvv/yCt7d3STez1MnIyMg2f/r06VxfcXFxOdah/w8FU61aNebNm8djjz1GmzZtqFixIjExMWRkZFCrVi169+7N559/zt69e2nVqpXDdejfvVxp9H1T+uh7qGDU/5Yc9b8irtP3Temj76GCUf9bctT/irhO3zelj76HCkb9b8lR/yviOn3flD76HioY9b8lpzT3vxarNY+B1kRERERERERERERERERERERE5LKmTEQiIiIiIiIiIiIiIiIiIiIiIlc4BRGJiIiIiIiIiIiIiIiIiIiIiFzhFEQkIiIiIiIiIiIiIiIiIiIiInKFUxCRiIiIiIiIiIiIiIiIiIiIiMgVTkFEIiIiIiIiIiIiIiIiIiIiIiJXOAURiYiIiIiIiIiIiIiIiIiIiIhc4RREJCIiIiIiIiIiIiIiIiIiIiJyhVMQkYiIiIiIiIiIiIiIiIiIiIjIFU5BRCIiIiIiIiIiIiIiIiIiIiIiVzgFEYmIiIiIiIiIiIiIiIiIiIiIXOEURCQiIiIiIiIiIiIiIiIiIiIicoVTEJGIiIiIiIiIiIiIiIiIiIiIyBVOQUQiIiIiIiIiIiIiIiIiIiIiIlc4BRGJiIiIiIiIiIiIiIiIiIiIiFzhFEQkIiIiIiIiIiIiIiIiIiIiInKFUxCRiIiIiIiIiIiIiIiIiIiIiMgVTkFEIiIiIiIiIiIiIiIiIiIiIiJXOAURiYiIiIiIiIiIiIiIiIiIiIhc4RREJCIiIiIiIiIiIiIiIiIiIiJyhVMQkYiIiIiIiIiIiIiIiIiIiIjIFU5BRCIiIiIiIiIiIiIiIiIiIiIiVzgFEYmIiIiIiIiIiIiIiIiIiIiIXOEURCQiIiIiIiIiIiIiIiIiIiIicoVTEJGIiIiIiIiIiIiIiIiIiIiIyBVOQUQiIiIiIiIiIiIiIiIiIiIiIlc4BRGJiIiIiIiIiIiIiIiIiIiIiFzhFEQkIiIiIiIiIiIiIiIiIiIiInKFUxCRiIiIiIiIiIiIiIiIiIiIiMgVTkFEIiIiIiIiIiIiIiIiIiIiIiJXOAURiYiIiIiIiIiIiIiIiIiIiIhc4RREJCIiIiIiIiIiIiIiIiIiIiJyhVMQkYiIiIiIiIiIiIiIiIiIiIjIFU5BRCIiIiIiIiIiIiIiIiIiIiIiVzgFEYmIiIiIiIiIiIiIiIiIiIiIXOEURCQiIiIiIiIiIiIiIpe80BmhWMZbGLdmXInUz6+QD0KwjLcwY8eMYt2OiIiIiIirPEq6ASIiIiIiIiIiIiIiInLxxCbH8u7v7zJ/33wORx7G3c2dqypcxd1N7uaJ9k/g5e5VoPWuDVvL8oPL2XJqC4ciD3Eu4RxxKXEE+QTRJLgJfa7uw8PXPIyvp2+u6zkYcZBJGyax/NByTsWeItA7kGuqXsOwa4bRt3Ffh3Vm7JjB0B+G5rutM26fweCWg3N8HpMcw8ebP2bh/oX8G/Evqemp1ChTg571ezKq4yhqlKmR722IiIiIXGoURCQiIiIiIiIiIiIiIle8WmVr0bBCQyr6VSzpphSrI1FHCJ0ZSlhUGAB+nn4kpyWz5eQWtpzcwqzds1g1aBVBvkEur/vtjW+z5N8lme/9Pf3xdvfmbMJZ1oStYU3YGj7Y9AFL71vKVRWucriOn//9mX7z+pGQmgBAGe8yRCRGsPzgcpYfXM7QlkOZftt0LBZLtnq+Hr5U9q+ca/tikmNITEsEoG31tjmW7z+3n56zemYeG293b3w9ffk34l/+/eNfZuycwaIBi+hap2u+j4mIiIjIpURBRCIiIiIiIiIiIiIicsX7qs9XJd2EYpeWkUbv73oTFhVG1YCqfNXnK7rV7UaGNYN5e+fx8E8Psz18O/ctvI8lA5fkvcILdKvbjZvq3USnWp2oX74+gd6BAJxPOM+3u79l9MrRHI46TJ85fdg9YjduFrds9Q9HHqb/vP4kpCbQsWZHvrj9C66qcBVxKXG8veFtJvw2gS93fMnVFa/m+Y7PZ6s7oOkABjQdkGv7mk1pxp4ze7i2xrU0rtQ427LE1ERu+fYWwqLCqOBbgU97f8ptDW/Dw82DAxEHePKXJ/nlwC/cMecO9j66VxmJRERE5LLklncRERERERERERERERERudTN3DGT3Wd2AzC//3y61e0GgJvFjQFNBzDt1mmAyQa06tAql9f/9LVP80T7J2hVtVVmABFABb8KPNH+CT64+QMA/jr7F78f+z1H/VfXvEp8ajxVAqqweODizGxFAV4BjO86nmHXDAPgtXWvEZkY6VLbNh/fzJ4zewB4qNVDOZZP3z6dQ5GHzPxt07mz0Z14uJnf4tcvX58FAxZQu2xtYpJjGLt6rEvbFhEREblUKIhIREREREREREREREQuKynpKby5/k2aT2mO/+v+BL0VRPevu/PLv784rRM6IxTLeAvj1ozLsSzkgxAs4y3M2DGDuJQ4Xl39Ks2mNCPwjUAs4y2Zw1+ByWgz8beJNJ7cGN/XfAl+O5hes3oVKCinqM3cOROAriFdua7mdTmW3930buqUqwPAV7uKPjPTtTWuzZw/HnM827L4lHjm/zUfgBFtRlDOp1yO+i9c/wJghiVbtH+RS9uevn06YAKSHGUssg3D1qB8A26/+vYcy308fHiy/ZMAzNk7h8TURJe2LyIiInIpUBCRiIiIiIiIiIiIiIhcNlLSU+j2VTdeWPUC+87tw8vdi6ikKFYeWkmvb3s5DBLKr/MJ52n9aWv+77f/45/z/2RmqrGJSIyg4xcdeWX1K+w7t4+0jDRSM1L55cAvdP+6O1P+nFKofRu3ZhyW8ZYcgUv5kZCawIZjGwDoWb+nwzIWi4Wb698MwPKDywvVVkfWHVmXOV+vfL1sy9YfXU9iWmKu7QspF0Kjio1M+w7lv33xKfHM3jMbgLub3E2AV0COMkeijgDkGOYsK9u241PjM4+liIiIyOVEQUQiIiIiIiIiIiIiInLZ+OTPT/jjxB9MvWUqsS/EEjk6kqNPH+WuxncBMH7teH78+8cCrXvc2nHEJMewcMBC4l6II3J0JMeeOUawfzAAD/34ENvDt+Pt7p1t+2FPhXHH1Xfw1NKnOJtwtsj21RX7zu4jw5oBQNPgpk7L2ZaFx4UTkRhR6O0mpiby7/l/eX3d6zy3/DkAOtfuTJtqbbKVsw01lt/27T2zN99tmLt3LrEpsQA8dE3OocyySrem52vZ7tO78719ERERkUuFR95FRERERERERERERERELg3RydFMv206D7R6IPOzmmVrMueuOXSd2ZXfjvzGi6te5LaGt7m87sTURH4b8hutqrbK/KxGmRoA/HHiDxbuXwjAJ7d8km37tcvVZl6/eYTODGX90fUF3LPCORl7MnO+epnqTstVD7QvOxl7kvK+5V3eVnhcOFXfrepwWe+rejPjjhlO2xfkE4Svp2+e7cu6P3mxDWXWNLgp7Wu0d1gmpFwI+87tY++ZvVitViwWS44yWQOdXNm+iIiIyKVCmYhEREREREREREREROSyUbNMTYa2HJrjczeLGy9f/zIAe8/uLVAmmZvr35wtgCgr23BZzrbv7ubOK51fcXmbWY0LHYd1rBXrWCsh5UJcqmvLxAPg5+nntFzWZbHJsU7L5cbd4k5l/8pU9q+Mj4dP5uf9GvdjUvdJDgOTbO3LrW1Zl2fdn9zsP7c/c+ixB1s96LRcrwa9ADgcdZhvd3+bY3lcShwfbf4o831Mcky+ti8iIiJyKVEQkYiIiIiIiIiIiIiIXDZCQ0IdZpEBuL729Xi4mUEatpzc4vK6O9bs6HSZbX25bb9z7c6Z27+cVfKvRPjIcMJHhpPwYgLHnjnGS9e/xE///ETzKc35dOunF60t07eZLETe7t7c3/x+p+WGthxKnXJ1ABi+eDhTt0zlfMJ5ktOS2XhsIz2+7sGpuFOZfz83ix6xiYiIyOVHZzgiIiIiIiIiIiIiInLZyDoc14V8PHyo4FsBgDPxZ1xed7B/sNNltvXld/sXW6BXYOZ8QmqC03JZlwV6Bzotl18Wi4UaZWow8YaJzLpzFqkZqYxYMoKd4Tsdti+3tmVdnnV/nElNT+WrXV8BcMfVd1DBz/mx9/fyZ/HAxdQuW5v41HhGLBlBxbcr4vOaDx2/6Mim45uY1G0S5XzKARDkG5Tn9kVEREQuNQoiEhERERERERERERERyQd3N/eSbkKBVQusljl/IuaE03InYu3LstYpCnc2upNaZWuRYc1g+vbpDtsXmRRJYmpinu3LT9t++uenzOCuh655KM/yjSs1ZveI3bzd/W1CQ0KpU64ODco3oH+T/qwdspanrn2KqKQoAK6qcFWe6xMRERG51Fz+OTNFREREREREREREROSKkTUI5kLJacmcTzwP5J5VqCCC/YP5+/zf+d7+xdaoUiPcLG5kWDPYc2YPPRv0dFhuz5k9AFQJqEJ53/JF3o7qgdU5Gn2UAxEHsn3eNLhptja0rd421/Y1CW6S57ZsgUp1ytXhxjo35qt9gd6BjOwwkpEdRuZY9ueJP0nLSAOgQ80O+VqfiIiIyKVEmYhEREREREREREREROSysfbIWqxWq8Nl646uywwCaVOtTZFu17a+3Lb/25HfMrd/sfl5+tGxZkcAlh5c6rCM1Wpl2cFlAPSo16PI22C1WjkcdRjIOVRap1qd8PXwNe074Lh9R6KOsO/cPtO+urm373jMcZYdMPsytOVQLBZLodoO8M2ubwBoX729MhGJiIjIZUlBRCIiIiIiIiIiIiIictk4Gn2UmTtn5vg8w5rB6+teB8ywVc0qNyvS7Q5oMiDP7U9cN7FIt+mqwS0GA7D68Go2H9+cY/m8v+ZxKPIQAIOaD3Jp3fkJjvpyx5eEx4UDEFo7NNsyfy9/+jbuC8CULVOITorOUf+tDW8BEOgVyB1X35HrtmbsmEG6NR13iztDWw3Nxx7kbtPxTUzZMgWAlzu/XOj1iYiIiJRGCiISEREREREREREREZHLRlnvsoxYMoLPtn5GUloSAMeij3HP/HtYHbYagIldiz6Yp32N9tzW8DaAzO0npyUDJrBowPcD+P3Y7/h5+hV4G+PWjMMy3oJlvIWwqDCX6w9uOZhmwc2wYqXv3L6sOrQKMAFO8/bO4+GfHgagZ/2e3Fg35/BfuW1//dH1dP6yM1/v/JrjMcezLfv3/L+MWTmG4YuHA1AvqB5DWg7Jsf4JoRPw9/TnVNwpen/Xm3/P/wtAfEo8E9ZOYOqWqYAJ4gnyDXK6n1arlS+2fwHATfVvokaZGvk4OvDhpg/5dve3nI47nfnZmfgzvLvxXbp/3Z3UjFSGXTOMW6+6NV/rExEREbnUeJR0A0RERERERERERERERIrKo20fZd3RdQxbPIzHfn6MAK8AIpMiM5e/fP3L9GnUp1i2/cVtX3DjVzey8/TOzO37e/kTlRSFBQv/6/U/Jm2YxJHoI8Wy/bx4uHnw4z0/0nVmV8Kiwuj2dTf8PP3IsGZkBly1qtKKWXfOKtD61x1dx7qj6wDw8fAhwCuA+JR4EtMSM8u0qNyCRXcvwtfTN0f9OkF1mNtvLv3m9WPd0XVc9b+rKOtdlriUONKt6YAZmmxUh1G5tuPXw79mDpv2UKuH8t3+1WGr+eHvHzLb7+XuRUxyDAAWLDzR7gk+uPmDfK9PRERE5FKjTEQiIiIiIiIiIiIiInLZ8HL3YtWgVbx+w+s0rNiQ5PRkynqX5cY6N7Jk4BL+74b/K7ZtV/CrwMYHNzI+dDxXV7waN4sbHm4e3Fz/Zlbcv4JH2z5abNvOr5ByIex6ZBevdn6VpsFNsWDB082T1lVb8073d9j00KZcs/w407pqa77u8zUPtnqQFpVbUNa7LFFJUbhZ3KgXVI9+jfsxu+9stg7bSki5EKfr6dWgF7se2cXD1zxMSLkQktKSCPINonvd7nzf73u+uP0LLBZLrm2Zvn06AJX9K9O7Ye9878Pw1sO5v/n9NKzQEC93L1LSU6gbVJehLYey8cGNfNTzI9wserQmIiIily+L1Wq1lnQjRERERERERERERERERERERESk5ChcWkRERERERERERERERERERETkCqcgIhERERERERERERERERERERGRK5yCiERERERERERERERERERERERErnAKIhIRERERERERERERERERERERucIpiEhEREREREREREREREqn0FCwWGDcuJJuiYiIiIjIZU9BRCIiIiIiIiIiIiIiIuLYggVw000QHAw+PlCnDgwfDgcOFHydycnw00/w+OPQpg2UKweenlCpEnTtCh9/DAkJrq9340ZwdzeBZxYLrFnjuNzJk/Dee3DvvdC8OVSpAl5eUKaMef/kk7B/f97b27ULhg41x8THBypUgE6dYMoUSE93vf0iIiIiJcxitVqtJd0IERERERERERERERGRHEJDYe1aGDtW2YguNqsVHnwQvvzSvHdzg4AAiIkx7/38YN486NXL9XV37w4rV9rfe3iAvz9ER9s/q18ffvnFTPMjKQlatcoe/LN6tfk3dKHvv4d+/bJvPzAQoqLMfoMJavrgA3j0UcfbmzwZnnrKHixUtqwJjkpKMu87dIClS816RURERC4RykQkIiIiIiIiIiIiIiIi2b39tj2AaOxYE+ATHW2CdDp0MJmC+veHw4ddX3dqKtSubQLDtm0zwTdRURARAW+9ZQKUDhyAnj3tQTl5GT/e3ra81KwJL79sgnxOn4aUFLPtpCRYsQJatzZtfPxx+P33nPWXLoUnnjABRN26me1GRUFcHPz4o8natHEjDB7swkERERERKXnKRCQiIiIiIiIiIiIiIqWTMhGVjMhIE+QTG2uGLps6Nefyxo0hPBzuuw++/tq19a9fD9deazIAOTJ7Ntxzj5n/+muzjdxs2wbt20NIiBkKrWdP87mzTER5iYyEGjVMoNRDD8Fnn2Vf3qYNbN0K1avDP/+YoKesli61t+G33+D6611vg4iIiEgJUCYiEREREREREREREREp/axWE8zRvj2UKWOGibruOvjmm7zrLlgAt94KlSuDl5eZ3norLFzovM6QIWCxmKnVCp9/Dp06QYUK5vMZM+xljx+HZ56BJk3MsFze3lCtmslo88wz8OefzrezZAn07WsCUry9ISgIOneGKVNMhpySsHChCSACeOGFnMuDguCRR8z8/PkQH+/a+jt1ch5ABCbDkW0YsNyOHZiMQUOHQloaTJsGPj6utcWRoCBo2NDMHz+efdnp0yaACMxQZxcGEAHcfDM0a2bmbdmcRERERC4BCiISEREREREREREREZHSLT0d+vSBYcNM1hmLxQwdtWkT3H+/yVTkSEoK3H23CdJZsgTOnYOAADNdsgTuvBMGDjSBKM5YrdCvHzz8sBnaymoFtyyPV3buhObN4YMP4K+/zNBc/v4mS8+2bebzyZNzrjcx0az31ltNkNPJkyYAJjoa1q0zASpdupisOK4KCzPHyGIpWAanFSvMtHFjk5HIEVumncREk1moKLm5gaenmU9Pz73sG2/Arl0mkOiGG4pm+2fPwt9/m/l69bIvO3LEPt+4sfN1NGpkpsuXF02bRERERC4CBRGJiIiIiIiIiIiIiEjpNnkyrFljsv/ExJhAm2PHoHdvs3ziRPj335z1XnwR5swxwTSvvALnz0NEhAkievFFU+a778wyZxYsgB9+gHfeMQE9ERFm+zfdZJY/95z5/JprTJBRaqopk5Rkhrp65x2ToehCw4bB999D3bowa5ZZZ3S0GULrhx/M55s2wQMPFObIFcyePWbatKnzMlmX7d1btNvfvdscQ7Bn9HFk71547TUIDjbHuTDS0+HUKfP3vvFG83fw9IQnnsi9Tl7LTpwoWCCYiIiISAlQEJGIiIiIiIiIiIiIiJRukZFmiK3Bg8HX13xWowbMm2eGDcvIgLlzs9c5cQI+/NDMjxkDEyZAuXLmfVCQCT559lnz/r33TACJI3FxZvlzz5lh1MBkM6pa1cxv3Gim//sfXHutCVgCM2xagwam3qhR2de5bp0Zhi042ARHDRxoX7ePD9x2G6xdazIaLVoEO3a4drwK6+RJM61e3XkZPz/78bSVLyqjR5tpuXImW5Mj6ekmwColxWR7Kl++YNu6+WbzN/PwMP+W+vY1QUw1a8KyZfZhzWxCQuzztmArR7IGVhX18REREREpJgoiEhERERERERERERGR0q1jR+jaNefn3t72jEC7dmVfNn8+pKWZoJwxYxyv9+WXzTpSU01WIEeCgmD4cOdtswXSOAtCcmT6dDO9914TrOJIjRr2fV62LP/rBhPoYrWaV0GGM4uNNVM/v9zL2ZbbyheFSZPgl1/M/FtvOQ8Oeu89+OMPM6zaPfcUfHvly0PlyubvbFO7Nnz0kRlO7kLBwdC6tZmfPNlkj7rQ/Pmwf7/9fUxMwdsnIiIichEpiEhEREREREREREREREq39u2dL6tWzUxtw1/ZbNlipm3b2rP8XCgoCNq0yV7+Qm3bmqxCztx6q5kOHmyyDq1da4bCys2GDWY6fTpUqeL8tXKlKXfkSO7ru1zMnQsvvGDmBw0yQ7458u+/MHasydQ0ZUrhtvnttxAebv79xMSYACAfH+jTxwyXFxeXs8748WZ69ix062YySyUnm3VMn24yJHl62su76XGciIiIXBp01iIiIiIiIiIiIiIiIqVbYKDzZR4eZpqamv3zM2fMNLchucBk/Mla/kLBwbnXnzTJZAyyDXsWGmqCltq0MYEuJ07krGMb3iomBk6fdv5KSjLl8gpKKmq2453Xdm3Lc/v75NfChSYzU0aGGVLMlq3pQlYrPPggJCbCxIkma1BRCQyEO++ETZtMhqiff3acyemWW8wQau7uJvisc2cTeFShAjz0kBlyb+JEe/msWY5ERERESjEFEYmIiIiIiIiIiIiIiDjj7p778nLl4NdfTTaa5583Q695eMDWrTBhAjRoAN99l71OerqZTpliH3Yst9eMGcWxZ87Zsjs5CoCySUiAqKjs5Qtq0SIYMMAMP9enD8yebQ8Ou9BXX5lj3aKFyfgTF5f9lZhoL5uYmPOz/ChXDoYONfOffea4zFNPwY4dMGKEaUvNmtCqFYweDbt3Q6VKppyXV9EGOomIiIgUIydnYCIiIiIiIiIiIiIiIpcwWwah48dzL2dbnlfGobx06mReYDIILV8OL79sAkoeeABuuAEqVzbLq1QxQ5SV1mHKmjaFPXvMy5msy5o0Kfi2Fi40AUSpqXDHHTBnjvMAIoDDh810504oWzb3dffqZaYtWpiAH1fYMljFxJgsVY7+fTRtCp984ri+bXi81q3B29u1bYuIiIiUEGUiEhERERERERERERGRy0+bNma6ZQtERzsuExVlD/Zo27botu3jA7fdBgsWmPdJSbB+vX15x45munhx0W2zKHXvbqb79sHRo47LLF1qpr6+9uApVy1YkD2AaO5c8PQs2LqK2qFD9vmAANfqJiXB99+b+UGDiq5NIiIiIsVMQUQiIiIiIiIiIiIiInL56dvXZLRJSoK33nJc5vXXITnZBK707ev6NtLSICPD+XJfX/u8W5ZHMsOGmemePWZIs9zEx0NKiuttK4w+fSAw0Ayl9uabOZdHRcHUqWa+b1/w93d9GwsXwt13mwCiPn3yH0A0blzuQ7+tXm0vu3q1+ezCLERpablvIzwcvvzSzLdrB35+ruyZGdbuzBkzjNn997tWV0RERKQEKYhIREREREREREREREQuP9Wrw1NPmfk334SxY03wC5jpK6/A22+b988+C1Wrur6N48ehQQOYOBG2b88enLJrF9x3n5n394cuXezLunSBoUPN/GOPwTPPZM98k5wMmzaZYJTatU1AiivCwsBiMa9x41zfr6AgMxQbmGChCRNMMBPAP/9A795w6pTZrwkTXN/+Dz/YMxD17XvxMxB16mTavXOnaYNNVBTMmgXXXWeOucXieP/i483fbMMG+3EB82+gf3/4+GPw8oIZMwoWYCUiIiJSQnIZVFZEREREREREREREROQS9vrrcOyYCVKZMMEE+5Qta4Y3s2UQuuce+L//K/g2Dh0yAUmvvALu7mb9cXH27EG2YJLy5bPXmzrVlP/8c/jgA/MKCDDBNFnbByaY5WIbNQr27zcZecaONccvIMA+NJyfnzmudeq4vu5nnrEH76xdCzVqOC/boYN9WLiiEh5u9mnsWPvfLD09+7B3AQHwySdw000566em2v9mAOXKQWKiCf4CqFDBBCOFhhZtu0VERESKmYKIRERERERERERERETk8uTlBXPmmOww06fDli0QGWmCPNq0gYcfNkNpFVT16vDjj2bYrN9/N5mJzpwxw6jVrw9du5psSA0aOG7bZ5/BAw/Ap5/CunVw8qQJRAkOhquvhs6d4a67zHYuNosFvvgCbrkFpk0zWXZiY01mpB49TJak+vULtu6sAVLnzuVeNiKiYNvIzcyZsGKFOeZHjsDZsyaLVHAwNGpk9m/oUOfZqfz9TYDa6tWwb5+p7+sLzZqZLE2PP54zaExERETkEmCxWq3Wkm6EiIiIiIiIiIiIiIiIiIiIiIiUHLeSboCIiIiIiIiIiIiIiIiIiIiIiJQsBRGJiIiIiIiIiIiIiIiIiIiIiFzhFEQkIiIiIiIiIiIiIiIiIiIiInKFUxCRiIiIiIiIiIiIiIhcfoYMAYvFTEVEROTKZrGY15o1Jd0SkVJNQUQiIiIiIiIiIiIiIiJXsgUL4KabIDgYfHygTh0YPhwOHCjcetetgw8/hMGDoWlT8PAwD3BDQ/O/jj/+MIFg9euDry94e0PNmtCnD/zwg+ttmj3b/iDZYoGwsLzrJCfD5Mlwww1QpYppQ9Wq0KEDjBkDx4653g4RkUvFjBkwbpyCby6GbdvgvvugRg17X9OnD/z6a+HWGxKSve9z9OrUybV1btwI7u55B2eFheW9bYsFXn459+3Fxpp/h82aQUAAlC0LbdvCu+9CSoprbZdceZR0A0RERERERERERERERKQEWK3w4IPw5ZfmvZubeTAXFgaffgrffAPz5kGvXgVbf+fOhWvfxInw6qumnQBeXuDpCcePm9eiRdCvH3z7rQlQysvZs/DEE661Yc8euOMOOHjQvPfwgMBAOH0awsPh99/h2mtNYJOIyOVoxgxYu9bMuxIEKq75/HMYMQLS0sz7smVNX7NokXmNHWuCaAqjTBkTkOtIhQr5X09Skjl/yMhwbfsVK5rAI0cCA53XO3LE/NuzBf76+ZkA3y1bzGvWLFi1CoKCXGuPOKRMRCIiIiIiIiIiIiIiIleit9+2BxCNHQvR0ea1f7/JspOQAP37w+HDBVu/ry+0awePPAKffWayHeXXr7/CK6+YAKLQUNi+3Ty0jIuDo0fhoYdMuXnz4OOP87fOJ5+Ec+fMvuXHv/+abR88aKZr1pg2RERAYiLs2gWvvQbVquV/v0RERC70+++mr0xLM4Grx45BVJQJfh0+3JQZPx7mzi3cdj780ATAOnq5kt1v/Hj7uYIr/vzT+fZHj3ZcJy0Nevc2AURVq8KKFRAfb85RZs82wUfbt5sMTlIkFEQkIiIiIiIiIiIiIiJypYmMNJl+wDygHDfOZCECaNgQFi82Q3fFx5tsQAURGwubN8OUKSbop0qV/Nf9+mszDQw0DzZbtjTDnYDJ+vPZZ3D99eZ9fh6q/vijedjYqZM9ACk3VqsZhu38efNAd+VK6NLFnkHB29sMqfLiiyZQSkREpKCefx7S002/MneuGc4MTHagqVPtQbijR5tyJWnbNnjnHTPM6CuvFP/2Zs6E3bvN/Pz50K2bmXdzgwEDYNo08/7nn002Iik0BRGJiIiIiIiIiIiIiMiladYs6NjRBJqULQvt25thuGzDX+Vlzx4YNgwaNDBDYwQEQPPm8NJLJmNNbmJj4c034brroHx5E1RSsybcfbfJKOBIWJgJhLFYzPzBg2b7deqY+iEhLux8IS1caPYB4IUXci4PCjJZEcA8tIuPd30bzoYsyY9Tp8z0qqvM8CuOtG1rpnFxua8rKsrsi7e3CT6yBSPlZvly83f08jIPKAuzLyIil6IZM8z3pW0os/Hj7X1Y1r7MxvbZmjVw5gw8+6z5Dvfzy/69Gxpq3uc2NNe4caZMbsOnHTtmgm9atjTnAL6+UK8e3H47fPWVyRyXXxkZZigxi8W015WsPIV16BCsX2/mR440w3ZeyNZPh4XBb79dtKblkJoKQ4ea7EDTpoGPT/Fvc+ZMM+3a1ZxzXejuu815FJi/uxSagohEREREREREREREROTSYrXCAw+YoSs2bjQBLu7usGWLyaozcGDe65g0CVq0MEElBw6YB4epqebX7q+/boKJtm93XHfHDmjc2DzU27QJYmJMgMrx4zBnjglseuON3Le/caN58PnZZ+Zhq6OHhrnJGpCU24NYZ1asMNPGjaF2bcdlevY008RE+wPOi6VuXTP95x9zfB35808zbdMm93U9+6wJSnrxRbj66vxt3/Yg8qabIDg4f3VERC4nvr5QubK9f/L3N++zvhwFWB44YPrQ9983gT4eHkXftq+/NgFKb78NO3eagCF/fzPc5Y8/mkxy+/fnb11JSXDXXSbjT1CQyTx3++2utSckJO+gJ2ds/THAzTc7LtOpkwmYBhPkWlLeeMMM5Tl0KNxwQ/FvLyEBNmww87ZzkgtZLPbjVpLH5jKiICIREREREREREREREbm0fPwxfPmlmX/8cROEExFhXuPGmUCe3LIITJ9uhgTx84PXXjMBJvHx5mHVli3mwdipU3DbbTmz3Jw6ZQJLjh+HO+805RMTTaDL6dNmaA93dxOwsmiR8zYMHw5NmphAmPh4s52L+fBrzx4zbdrUeZmsy/buLd72XGjYMDNUSWyseZi7Y4c9w9SxY2b5unVQsWLuQVQrVph/K40bw5gx+d++LWiqdWszpNlzz5kMF97eZpvdu8M335jsFSIil6MBAyA8HDp0MO9HjjTvs75q1sxZ75lnoFw5M7RUfLzpH//+u+jatWSJCRJKSjJBu+vWmX743DmzvXXr4OGHTSa5vERFQY8eJjtfzZrmu9+2vxeLrT8ODnYetOrubg+CLUx//M47UL26OTbly5vgpDffNEOc5mXvXnPOFBxs1lMQAwaY7Xp5QdWqcMstJiAsLc1x+X377P1sfs5XwsPNuaAUioKIRERERERERERERETk0pGUZIZUAbj/fhNQVLGieV+2LIwdawKEoqIc14+NNQ9CAb7/3gT7VKli3ru7m6CRZcvM9Phx+Pzz7PVfftkELQ0caIb5at3anqUhOBgmTDBZjiD34JYKFUy2g6xZdK66Kr9HofBOnjTT6tWdl/HzMw+Cs5a/WFq2hO++M0PMrVkDrVqZYVMCAqBWLTOU3f33w9atzjMpxcWZB8kWixnmLj8PlAGSk002CzAPv5s1g/feM5/5+5sHlCtXmu3fdpspLyIihpub+Y684QYzD0XXv6WlwRNPmKDSTp3g11/N1LYdLy/z/tNPTfBobo4fN2XXrTNBvRs35l2nOOSnP866vDD98d69pg/z9zeBQxs2mKyKjRvbM/44kp5uMkCmpMAHH5hAoIL44w/zN/T0NAE/P/8MgwaZYDDbMKZZZd3X3I5P1mUX+3zlMqQgIhERERERERERERERuXQsX27/lfmrrzouM2aMCThxZP58E2DUqpXJKOSIhwfcc4+ZX7bM/nlSEnz7rZkfPdp5GwcNMtOdO012Ikcef9wExBRUSIh5iGq1Fmw4s9hYM/Xzy72cbbmt/MXUv7/JJFSvnnmfkmKyTIAZei42NveMA6NHw5Ej8Mgj5gFlfmXNyPDBB+b9tGkmoCgiwvxNH3/cLF+yBEaNcmm3REQua/ffDzVqFM+6V6+Gw4fN/Pvv5z849EJ//WUyDu3da89mVJg2h4WZ/njNGtfrXoz++PbbYe5cEwSdmGj6tbNnzTEMCDABPbfcAocOOa7/3nsmAKhnT/v5UX75+MCIEebYxMSYV3y8Ga700UdNoO8ff5jtX5iRKOu+5nZ8si4rifOVy4yCiERERERERERERERE5NKxZYuZ1qwJ9es7LlO2rMkQ5Ijtl/b79pkMRM5eEyaYckeO2Otu3WoCicAMf+KsbpMm9jpZ62flSlDLlSg9HZ5+Gq67zmSYWLDAZCmIijLDzYSGmuHiOnY0GS8utG4dTJkC1aqZoVpckXWIsowMeOMNM3yar6/5rFIlkwHr1lvN+6lTzYNZEREp3v5t40YzrVIleyY/V2zYYDIQHTtmgmtWroSgoKJrY2n04YfQr5/pv2wqVjT97MqVJng6OtpxUPK//5osj/7+pl91VZUq8Mkn0KULBAbaP2/QACZPtg+Ntn07fPWV6+uXIqcgIhERERERERERERERuXTYgjXyGvbDWUYB2zAXSUkmo4yzV0yMKZeQkLMu5F43a/ahrPWzCg7Ovf3FzfYgz1n7bGzLsz74uxjee8889KxY0QQN9eljHkSWLWseUC9bZh5IJiTA8OEmS5FNYiI8+KDJCjF5MpQp49q2s+6rnx889pjjcrZsVKmpJjuGiIgUb/8WHm6mzoaxzI+XXzaZeJo3N9kJnWUuvFhKuj9u3x4GDDDzP/5o+k4bq9X0p4mJMHFi4Y67M08/bYYpBRMcnFXWfc3t+GRddrHPVy5DCiISEREREREREREREZErR3q6mQ4YYB8OLLdXWFjOumAeqOWnfmio43a4uxfXHuZPtWpmeuKE8zIJCSbzT9byF4stM8GgQY4fSFss8NxzZv7QIdixw75s0iSTOaFXL+jWDeLisr+Sk+1lExLMZ7YMU2AeQNoCj+rVA09Px21s3Ng+7yzjlIjIlaY4+zeLpfDrGDjQrGfXLnjllcKvr7Dy0x9nXV4c/fF115lpdDScP2///KuvTGa/Fi3ggQdy9qeJifayiYk5P8sPNzdo187MXzicWtZ9ze34ZF12sc9XLkMKIhIRERERERERERERkUuHLaAkvw/bLlSlipkWJOjDVreg9UuTpk3NdM8e52WyLss6RFtxO3/ennGqXj3n5Ro0sM8fPpxz/uefTUDQha9HHrGXbdLEfHb33dnX3axZ3u3Mmq2hKB5si4hcCTw8zDRr8OaFoqMdf16YPtzm4Ydh2jTzvf3GG/D88wVfV1Gw9cdnzsDZs47LpKfD/v1m/mL2x7b+dOdOkwnwwv60Vy972V69zGe2gKSi0KiRCTKC/J2vVKkC5csX3favUAoiEhERERERERERERGRS0ebNmZ67BgcPOi4TEwMbN3qeFnHjma6dSucOuXattu2BS8vM//TT67VLW26dzfTffvg6FHHZZYuNVNfX+jU6eK0C+wPDCH3B8VZh40r6uFLevQw04MHzXBljvz1l32+Tp2i3b6ISGlh+07OGjhZGEFBZnrsmPMymzc7/rxDBzMND4ctWwrehocfhunTzb69/bY9s11JsPXHYO93L7RhA8TGmnlb/1SUNm0y0zJloEKFol9/bjIy4I8/zPyFfamfn/28zdmxsVrNEKdQPMfmCqQgIhERERERERERERERuXR0725/APl//+e4zKRJzofT6NcPypUzgSHPPpv7Q9GMDPtwXgD+/mYYFIC33nIefGMTEZH78pLUp48JvLFa4c03cy6PioKpU818375m3y+WoCAICTHz337rPCPFlClmmnUoFIAZM3IfYu7LL+1lDx82ny1alH3d991nhjFLSIDJkx1v/623zNTPD2680cWdFBG5RNiGd8zaHxZGixZmumwZxMfnXP7rr/D7747rdu0Kdeua+WeegZSUgrdj6FDTH7i5wXvvwdNPF3xdhVG3rj1Q9913HQeu2vrp2rWhc2fX1p9X8Neff8KcOWa+d+/smfXGjcu9P1292l529WrzWdbhRfOz/Y8+sp9P3X57zuWDB9vX7yi4bN48+zBogwblvi3JFwURiYiIiIiIiIiIiIjIpcPXF155xczPnGke+p0/b97HxJjAotdfN4FCjpQrBx98YOZnz4ZbbjEPpTIyzGcZGSY7z7vvmiFDFi/OXv/116FaNTh3zgzZ8fXX9uwAYIYimT/fBOncc0/R7LMjYWHmQZ/FYh7yuSooCF5+2cxPnQoTJtgf5v7zj3mQeOqUCR6aMKFg24+LM8fJ9kpONp+npmb/PDIyZ90nnjDT48dNZoHff4e0NPMw8uBBuPde+0PPwYOhYkXXj0Fu6taFp54y8y++CJ99Zh965+xZePJJWLLEvH/+eXtgm4jI5cY23NbPP+c9lGh+9O9vAnfOnzf95PHj5vPERNOv9+njfEgqd3f43/9M37N+vQngXL/e3oenpMCaNSYQNGu2OGcGDTL9uLs7fPghPP54wTIuhYSYNoWGul4XTFCqu7sZNuzuu+3HOSICHn0UfvnFvJ80yZRzZftPPmn2a80a0y/bnD9vAni6dTP9cmBgwc4n8tKlC0ycaIKLsgZIHTxo+tlnnzXvmze3BwxlNXiwGWLUajVBzatWmc8zMkwA0cMPm/c9eyqgt4h4lHQDREREREREREREREREXPLUU7B9u3nw9+GH8PHHULasCSJKTzcP4Ly9zcNIRwYPNg8rn3rKPJj75RdTPiDArCPrQ66sv8gHqFoVVq6EO+4wwTaDBpmHoeXKmSCZrFkVunUr6j0vWqNGwf79JhPD2LEmWCggwJ75x88P5s4t+FBdjz/u+G+wcSNUqmR/X7u2CUrK6umnYc8e07Y//jBD2Hh4mOHkEhLs5bp2NX//4vDmmyY7wty5MGwYPPaYecgaGWl/yPzAA/agNhGRy9HgwSaw9sABqFXLfH/7+Jhl69dDjRqure+qq0wQ64QJZmjQn34yfXh8vAkWveMOE7g0caLj+j17moxzw4aZ7V9/vb0Pj4426wAYOTJ/7Rk40ATm3HefyTyXlmYy3V3Y/xenDh1MQO+IEbBggXmVK2f2x9bfjB1rArBcFRtr+uLJk80+lSljzluyBvBWrWr6uvr1i2R3sjl61PSTr7xijnPZsibYK2tAU/v2Zp89PXPW9/CAH380/X1YmDm38vMzQUS24N5WrWDWrKJv+xVKmYhEREREREREREREROTS4uYGX31lXtdea7ITpaXBNdeYh3Dffpv3Oh55BP7+2zxkbNHCPICMijIPIdu0MZlwVqxwnE2oUSPYtQumTTNZcipWNMFHVqt5ANevH3z6qXkgV5pZLPDFF/D99/Zh4pKSTFDPww+bjAi9epVM29zcTNuWLzdBYSEh5kFiaqp52Nmrl3lguHJl8Q215u5ush3Nmwc33WQe6MbGQnAw3HabyUQ0fbppq4jI5apBAzOU1G23mQCi8+fhyBHzsgXsuGr8eBMIfO215js8PR1atjR9+IIFjrPtZDVokAmCffppaNzY9A+Jiab/uuMOs+5GjfLfngEDTHZCT0/Ttw8bVrCMRIXx0EMmM+LAgVC9ugmYDQ42+7NqVcGzBD3yCIwebTIC1axp/mZxcWbdN95ohnLbt88+pFpRe+cd04bWraFyZXuwWK1aJuvU7NmwYYPJ8uhMSIg573r1VRNgZrGYv1Xr1mb9mzYpI2ARslitF/tfv4iIiIiIiIiIiIiIiIiIiIiIlCYKjRYRERERERERERERERERERERucIpiEhERERERERERERERERERERE5AqnICIRERERERERERERERERERERkSucgohERERERERERERERERERERERK5wCiISERERERgyBCwWMxURERERERG5XOn6V0RERC5gGW/BMt7CmrA1RV6/sOsWudg8SroBIiIiIiKlwoIFMG0abN8OMTFQtSr06AGjRkH9+oVb9/798NFHsGYNHDkCqalQoQK0bAkDB8K994Kbg/j+5GRYvhyWLYNNm+DAAYiPh3LloGlTuPNOePBB8PNzvN2TJ2H2bNi6FXbvhjNnICICfHwgJARCQ+HRR+Hqqx3XX7MGunbNe/8++wweeig/R0JERERERERKWnFd/65bB9u2mdfWreZaOD0dunQx15f5lZwMn38O8+fDX39BZCSULw916kDnzvDYY1CzZtHWDwkx1+v5ERIChw/nf39EREq5RfsXsSN8By2rtOSOq+8o6eZIKbRg3wKmbZ3G9lPbiUmOoWpgVXrU7cGojqOoX75g5w4nY08ye89stp7ayu7TuzkTf4aIxAh8PHwIKRdCaEgoj7Z9lKsrOrl3/Z8/TvzBJ39+wvqj6zkRe4IMawbB/sG0qdaGIS2GcPvVtzusl5aRxtqwtWw7tY2tp7ay7dQ2DkQcwIqVwS0GM+OOGblud8aOGQz9YWie+7ni/hV0q9stz3KliYKIREREROTKZrWaQJwvvzTv3dwgIADCwuDTT+Gbb2DePOjVq2DrnzEDhg0zgUMAHh7g6wvh4bB0qXl9/jksXgyBgdnr3norrFxpf+/hAf7+cO6cuQG7Zo0JTvrlF8c3ejduhOeey14/MBCiokxQ0e7dMHUqfPCBCSbKTeXKzpc5C2ISERERERGR0qO4r387dy58G/fsgTvugIMHzXvbdezp0+Y6+vff4dprnQcRFbR+pUqQlOS8XRkZcPasmW/btjB7KCJS6izav4iZO2cyuMVgBREVUMMKDQHw87y87pNarVYe/PFBvtxhzh3cLG4EeAUQFhXGp9s+5Zvd3zCv3zx6NXD93GHjsY08t9x+79rDzYNAr0CikqLYfWY3u8/sZuqWqXxw8wc82tbxveuJv03k1dWvYsUKgJe7F55unhyPOc7xmOMs2r+Ifo378W3fb/Fwyx4aczzmON2+Lnxwj5vFjUp+lZwu93b3LvQ2LjYNZyYiIiIiV7a337bfQB07FqKjzWv/fujQARISoH//gv3K8O+/7QFEzZvDb7+Zm5IxMebm40svmXK//QYvv5yzfmoq1K4N48aZX3ImJ5sAoIgIeOstE7xz4AD07On4ZmfNmma9S5eaG6YpKaZuUhKsWAGtW5ttPP64uZGam/Bw56+BA10/NiIiIiIiInJxFef1L5gfzLRrB488YjLW3nSTa/X//ddkzD140EzXrDHXrxERkJgIu3bBa69BtWpFX//PP3O/7p0yxV72wQdd2y8REbns7X98P/sf30+76u1KuilF6u2Nb2cGEI3tMpboMdFEj4lm/2P76VCzAwmpCfSf15/Dka6fO9QsU5OXr3+Zpfcu5fTI06S8nELE6AiSXk5ixf0raF21NakZqTz+8+P8fiznvetfD//KK6tfwYqV0JBQtg/fTtJLScS9GMfRp4/yUCuTOX/eX/P4ePPHDtsQ6BVIp1qdeKr9U8y8YyYtq7Qs0H6Ejwx3+rq+9vUur7OkKRORiIiIiFy5IiNh4kQzP3y4CdaxadjQZAdq3NjcMHz1Vfj6a9fWP3u2PQPRDz+YlOc2FSuabR85Yn7tOXcufPhh9voTJ5pfSHpccNoeFATPPw+1asE995hAou+/h/vuy16ufXvzupCXF3TrZgKJatQwN4q/+AKuu861/RMREREREZFLQ3Ff/wLExoK7u/39+vX5r2u1wuDBcP68yST0/ffZ1+XtDc2amVdx1M/L55+bae3a0L17wdYhIiJyCYlMjGTib+bcYXjr4YwLHZe5rGHFhiy+ZzGNP2lMeFw4r655la/7uHbu0L5Ge9rXyHnv2svdi251u7Hi/hXUeL8GCakJfLH9C66rmf3e9de7zPYCvQL54e4fKONdJnNZzbI1+ey2z/j7/N+sO7qOuX/N5ZnrnslWv1bZWkSPicZisWR+9sX2L1zah8uVMhGJiIiIXClmzYKOHU0a77JlTXDJp5+aG235sWCBGV6rcmUThFK5snm/cGHedX/4AW64AcqVM6nSW7SASZNMgM24cWCxmF8JXmwLF5qbnAAvvJBzeVCQ+QUlwPz5EB/v2vpPnTLTChWyBxBl1e6/X6fExeVc1qlTzgCirPr3tw+B9uefrrUNzP41NKl2OX7c9foiIiIiIiKlka5/cyru61/IHrTjquXLTYZcLy+YNs31dRW2fm6OHTPrBxg61AwDJyJSxCITI5m+bTr95/Wn2ZRmlH+rPD4Tfaj9QW0Gzh/IpuObnNYdsmgIlvEWhiwa4rTMjB0zsIy3EPJBSOZna8LWYBlvYebOmQDM3DkTy3hLtteasDU51rVg3wJu/fZWKr9TGa//86LyO5W59dtbWbjPeT95YRtn7JjBddOvo+ybZQl6K4huX3XjtyO/ZZZPy0jj480f0/rT1pR5owxl3yxLr1m92HZqm9NtAEQnRTNh7QSumXYNZd4og+9rvjT4uAEjFo/gUOShXOvahMeF8/jPj1Pnwzr4TPShyjtVuHfBvew/t99pndyOV34s+WcJfef2pfp71fGe6E3QW0F0/rIzU/6cQkp6SoHWWVgL9y8kNsWcO7zQKee5Q5BvEI+0NucO8/+aT3xKAc4dchHkG5Q5TNzx2Jz3rk/FmnvvV1W4KlsAUVZtq5khSONSct57d7O4ZQsgEjud6YiIiIhc7qxWeOABk6Vm40ZzI9DdHbZsMb8+zGsoqpQUuPtu6NsXliyBc+fMjdBz58z7O+8067Bl3LnQyJHmV4CrV5s06Z6e8NdfMHq0yYbjrF5+hIWZG7AWS/ZfUebXihVm2rix+TWhIz17mmliomu/ogSoW9dMz583bXXkjz/MtE0b19YN5salp6eZT093vf7Zs2bINYB69VyvLyIiIiIiUpro+te54r7+LayvvjLTm26C4OCLXz83M2ZARoa5Bn/ggaJdt4jIfz7c/CEP/fQQ8/6ax76z+zI/Pxp9lO/2fEeH6R34aPNHRbpNL3cvKvtXxsfDBwAfDx8q+1fO9vJy98osn5Kewt3f303fuX1Z8u8SziWcI8ArgHMJ51jy7xLunHsnA+cPJDU99/5uyKIhDP1hKFtObsFqtRKVFMWqw6u48asbWfzPYpLTkrnl21t4cumT7DmzhwxrBjHJMfxy4Beu//J6tp7c6nC9e8/spemUpoxdM5bt4dtJzUjF082TAxEHmLp1Ko0nN2b+X/NzbdvhyMO0mtaKyX9O5nTcaTzdPTkdf5pvd39Lq2mtWHpgqYtHOXeJqYn0m9ePW7+7lQX7FnAy9iQ+Hj5EJ0Wz7ug6Hv35UbrM6EJkYqTL6w6LCssMbhq3ZpzL9VccMucOjSs1pnY5x+cOPRuYc4fEtETWHy3ac4ez8Wf5+7y5d10vKOe967pB5t77P+f/ISY5xuE6/jxpfnjbploB7r1fwRREJCIiInK5+/hj+NKMW8zjj8OZMxARYV7jxsGcOeaXks68+KIpY7HAK6+YgJiICHMT9cUXTZnvvjPLLjR7Nrz7rpkfONBku4mMNL9+/PRTE0AzZUqR7q5L9uwx06ZNnZfJumzvXtfWf//94O9v5m+/3dyEtQX7nDtnjtk334Cfn/llqqt27zZ/C8h/Svb0dJMhacECuPFGM5SZpyc88UTu9Tp0ML/g9fGBmjXNTfUffsj/L3lFRERERESKm65/nSvu69/CsgUttW5tjvtzz5kfu3h7m+HAu3c3188ZGcVT3xmr1Qz/DdCjh7keFhEpBtUCqzG2y1i2PLyFhJcSiBgdQeJLiRx68hBPtX8KgGeXPcv2U9uLbJsdanYgfGQ4A5oMAGBAkwGEjwzP9upQs0Nm+RdXvcicvXOwYOGVzq9w/vnzRIyO4Nyoc7zYyfST3+35jldWO+gn//PD3z8wd+9cpt06jZgxMcS8EMP+x/bTumpr0jLSeOKXJxi5fCRbTm5h7l1ziXshjtgXYtny8BbqBdUjITWBp5Y+lWO9scmx9P6uN8djjlM9sDpLBi4h/sV4Yl6IYcfwHVxb41qS05O5d8G97Azf6bR9zyx7Bi93L5bft5z4F+OJfSGWzQ9tpllwM5LSkhjw/QCOxxRdRvdhi4fx/V/fUzeoLrPunEX0mGiix0ST8FICP9z9A3WD6rLp+CYe+PHiB7HuOWPOHZoGOz93yLps79nCnzukZ6RzKvYUC/Yt4MavbiQhNQFPN0+eaJfz3vWw1sNws7gRmxLL7bNvZ0f4Dqz/3as+Fn2MYT8NY93RdVT0q8i4LuMK3TZnziacpfWnrQl4PQDf13yp+2Fd7ltwX4GzUpUGCiISERERuZwlJcH48Wb+/vvNDdWKFc37smVh7Fjzi8ioKMf1T5yADz8082PGwIQJJiU7mFTnr70Gzz5r3r/3nn34LjA32mw3Vm0366pXN+99fODhh80N1EjXf0VRZE6eNFNbuxzx87Pvs618flWtCosXm19B7toF119v9r1sWahUCd56y/xKddMmaNvW9faPHm2m5cpBv365l735ZnMj3MMDqlUzQUC7d5sboMuW2Yc1c+b3382vLi0WczN8wQLT9ltucTwUm4iIiIiIyMWk69/cFff1b2EkJ8PRo2Y+Jsb8SOa998xn/v4mkGvlSvN3ve02U74o6+dm1Sp7ZuGHHir0roqIODOs9TDGhY6jdbXWmdl/LBYLdYLq8MHNH/Bo20dJt6Yz+c/JJdK+EzEn+HCz6SfHdBrDhK4TKOdTDjDDTr1242s8e63pJ9/7/b3MoaYuFJUUxWe9P2NY62H4evoC0LBiQ+bcNQcw2XP+9+f/+OHuH+jXpB+e7p5YLBZaV2vNp70/BWDDsQ05Ank++fMTDkcdxtPNk6X3LaVXg164WUwoRIsqLVh+33JCyoWQnJ7MS7++5HQ/E9MSWXrvUrrX65451FW76u1YOWgl5X3LE5Mcwxvr3ijIIcxh3ZF1fLPrG4L9g1kzeA0Dmw3MHJbLx8OH2xrextoha/H39GfR/kXsCN9RJNvNr5Ox5lygeqDzcwc/T7/Mfwe28gVx8zc3YxlvweP/PKj2XjX6zu3L7jO7qVmmJsvuW0bDijnvXbes0pLv+n5HgFcAa8LW0GpaK3xe8yHg9QBqfVCLWbtncX/z+9k6bKvTTEpFISE1gW2ntuHl7kWGNYPDUYeZtXsWXWd25YEfHiAtI63Ytl1cFEQkIiIicjlbvtyeqebVVx2XGTPG3NR0ZP58SEszy8eMcVzm5ZfNL/tSU+H77+2f79gBBw6Y+RdfNMEnFxo8GGrVyteuOBQSYm7WWq0FS+cea8Z0xs8v93K25bbyrggNhbVrza8hwRzPmP/Sq6anmwCcc+dcX++kSfDLL2b+rbegfPncy5cvD5Urm5vfNrVrw0cfQZcujuuUK2fS8W/ebIYBiIw0mYt27IAB5hdK/PIL3HOP6+0XEREREREpSrr+zd3FuP4tqKzBVR98YN5Pm2aunSMi4PRpk1kKzLByo0YVbf3cfP65mVaqZAKQRERKyC0NbgEo8iGj8mv+vvmkZaTh4+HDmE6O+8mXO7+Mt7s3qRmpfP/X9w7L1Cpbi4HNcg4vWq98PeqXrw/A9bWup1OtTjnKdKndBW93bwB2nd6VbdmcvSYI6a7GdznMnBPoHcjzHZ4H4JcDvxCdFO2wff0a96NRpUY5Pg/2D+aR1o9k21ZhTd8+HYB7m91LzbKOM93VKFODrnW6ArDswDKX1h9SLgTrWCvWsVbGhY5zuX2xyeZcwM8z93MH23Jb+YIo71ueyv6VCfKx37uuXbY2H/X8iC4hTu5dA/2b9GfF/SsyhztLSU8hPjUegNT0VGJTYolIjChwu3Jjyx6285GdJL2URMToCBJeTGDDAxvoVrcbAF/u+JJnlj5TLNsvTgoiEhEREbmcbdlipjVrQv36jsuULWsPcHFWv21bKFPGcZmgIGjTJnt5gG3bzNTT0wyF5YjF4jyA5XIxaRI0aWJ+xTljhvklZGysOVb9+5tfQ3bvDl9/nf91zp0LL7xg5gcNgmHD8q7z7bcQHm5uoMbEmBvkPj7Qpw/07u04m1DLlvD229Cunf1GssUCLVqYVP22IdAWL4Zff81/+0VERERERIqarn8vXVmHGMvIgDfeMNe5viZDBZUqmcxSt95q3k+daoaqK6r6zkREwKJFZn7QIPP3FREpRociDzFy+Uhaf9qacm+Ww32CO5bxFizjLfT6thdAkQ6l5YotJ02/17Za28xsORcK8g2iTTXTT245tcVhmTbV2mRm+LlQZf/KmdtwxN3NnYp+JstgZKI9gDQlPSUzqMgWvOFI93rdAciwZrDt1DaHZW6oc4PT+rZl5xPPczjysNNy+bXh2AbABBNVeaeK09fKQysBOBJ9pNDbLK2+7fst4SPDiRgdQcyYGOb3n4+Phw995vSh93e9iUvJee86PSOdp5c+zXXTr8PN4saC/gs49dwpokZHsX7oekJDQlm0fxEdv+iYeQyLUo96PRgXOo7mlZvj7WGC29zd3OlQswPL7lvG7Q1vB+CTLZ/w7/l/i3z7xUlBRCIiIiKXM9tNsdzSlQPUqFE09bPehDt71kwrVAAvL+d181p3cQoMNNOEhNzL2ZbbyufXvHkmXb6np0mBPniwuaEdEGBuXH/3nUmnnp4OTz4J58/nvc6FC+Hee82N0b59Yfp019pk24877zTDqNWsCT//XLBfsr7+uv1XvLYbqyIiIiIiIiVB17+5K+7r38LIui0/P3jsMcflbEN6p6bC6tVFV9+Zr7+2D32mocxEpJgt3LeQxpMb8+7v77Lt1Daik6MJ8Aog2D84W4YWW5aVi+1MvOn3qpfJvS+rUaZGtvIXCvRy3r94uHmYMt55l0nNSM38LCIxgnRrumlfLkNv2dqWW/tyq591353Vd4Vt+K+Y5BhOx592+kpKSwLMsFkXk+3vkNd2bctz+7u5ut07G93Jpoc2UbNMTX7+92fGrRmXo9x7v7/Hh5s/pKJfRdY/sJ4+jfpQJaAKZX3K0rFWR5bdt4wutbuQkJrA8MXDSUlPKZL25YebxY13erwDmKC1n/756aJtuygoiEhEREREipeTX5aUCtWqmemJE87LJCRAVFT28vn1jrlQ4JZboFHONLiAPY16VJQJNMrNokVmGLG0NJNBaPZs8PBwrU1ZlSsHQ4ea+c8+c71+QAA0/S898KFDBW+HiIiIiIjI5eBKvv4tjMBAe/anevWcZ/xp3Ng+fyRLNobC1nfG9qOdjh3h6qvzLi8iUkDnE84z5IchJKcnc0OdG1gzeA0JLyYQPSaa0yNPEz4ynHn95pV0M6WIpWeYwKcpt0zJHHYst9eMO2Zc1PZVCzTnAidinZ87JKQmEJUUla18USnnU46hLc2968+25bx3/c7v5t77oOaDCPYPzrHcYrHw3HXPASbL147wHUXavrzUL18/M3PWochL6965gohERERELmfB/50853aTMLfltvrH80iTa1senOVkvVIlMz13DlJyifLPq23FyRYAs2eP8zJZlzVp4tr6//rLTOvVc16mQQP7/OFc0uAuXGiGP0tNhTvugDlzChdAZGP7JWxMTP7SuYuIiIiIiJRGuv7NXXFf/xZWs2Z5l7Fa7fMXBmwVtv6F/vwTdu8288pCJCLF7Od/fyYmOYYgnyB+uucnuoR0wdfTN1uZ8Lhwp/Vt2XlsGWsciU6KLlQbbUEaeQ2nZlvuKKijuJT3LY+7xT3b9h3JusxZ+3ILmDkRY19WFPtXJaAKAEeiSucwZU2DzbnDnjPOzx2yLmtSqejPHWzZn2KSY7JlfzqfcD7zfb3yzu+9N6hgv/deFEPQXSkURCQiIiJyOWtjxqDm2DE4eNBxmZgY2Lo19/pbtkC0kwvNqCizHKBtlvGqr7nGTFNTYeNGx3WtVvjtN6fNL3bdzTjY7NsHR486LrN0qZn6+kKnTq6t3+2/0+3cfuF4+rR93lm6+AULTAYiWwDR3LnOf1npqqwZhAICXKsbF2e/yVynTtG0R0REREREpCB0/Zu74r7+LawePcz04EFzHB2x/VAHcl6DFrb+hT7/3EwDA6Ffv9zLiogU0rGYYwA0rNgQP08/h2VWHl7ptL5tqDPbehzZfGKz02VuFnMP04rVaZk21Uw/ueXkFqcBSVFJUWw5afrJttXaOixTHLzcvWheuTkAqw47z/S+8pA5hm4WN66peo3DMqvDnA93aVtW3rc8dYIKfy+0Y62OACz+d3Gh11Ucutc15w77zu7jaLTjc4elB8y5g6+HL51qFf25Q9YMPgFe9nvXtn+zkHsQ1uk4+733ohpuLb8ORhzkXMI5AOqUu7TunSuISERERORy1r07BJmLSP7v/xyXmTQJEhMdL+vb12S7SUqCt95yXOb11yE52QS19O1r/7xlS6hf38y/+Wb2X/zZfPNN/lKIF5c+fcwNQavVtPFCUVEwdaqZ79sX/P1dW7/tRvIvvzjfzylT7PPXXZdz+cKFcPfd5iZonz6uBRClpeW+PDwcvvzSzLdrB34X3KRw9DfL6uWXzb8NgNtvz1+bREREREREioOuf3NX3Ne/hXXffea4JiTA5MmOy9j+Ln5+cOONRVs/q4QEM3w4wD33XPxjISJXnLLeZQH45/w/DrMJ7Qjfwbe7v3Vav0WVFgD8eeJPjkXnDCTad3YfC/YtcFq/jLcZEtI2LJUjfRv1xcPNg6S0JN7a4LiffH3d6ySnJ+Pp5knfRn0dlikudze9G4Dv//reYeacuJQ4Jm2YBECvBr0o61PW4Xrm7Z3H3+f+zvH5uYRzTNs6DYABTQYUSZuHXTMMMNl8pvw5Jdey8SnxpKTnku2wGPS5ug+BXoFYsfLm+pznDlFJUUzdYs4d+jbui7+Xa/1lWkbu967D48L5coe5d92uertsAXZBvkGElAsB4Ns93zoNbJuyxRxXN4sb7aq3c6l9ubHmcd/carUyasWozG3fetWtRbbti0FBRCIiIiKXM19feOUVMz9zJjz9NJw/b97HxJgbq6+/DuXKOa5fvTo89ZSZf/NNGDvW3FgEM33lFXj7bfP+2WehalV7XYsFxo8388uWweDBcPKkeZ+UBNOnw/Dh9pu8BREWZrZjscC4ca7XDwoygTBgbpZOmADx8eb9P/9A795w6pS5YThhguvbf+IJM42NhZtuMschOdl8duKE+XtMMhevdO0KrVplr//DD/YMRH37up6BqFMn0+6dO7P/EjMqCmbNMkFLZ86Y9jvav8aN4f33zS9VMzLMZ1arSel+333w4Yfms549oVu3/LdLRERERESkqOn6N3fFff0LJlvtuXP2l+36NzU1++eRkTnr1q1rP/4vvgiffWb/0crZs/Dkk7BkiXn//PM5j2Vh62c1d675NwMaykxELooe9XrgZnEjIjGCexfcmzlsVkp6CnP3zqXH1z0I9HKeRaX3Vb0J8AogNSOV/t/3zwyCSU1P5Yf9P9Dt6265BnjYhq1ad2Qd+8/td1imepnqPNXefM++uf5Nxq4emxl0FJUUxSu/vsLbG00/+ex1z1I1sKrD9RSXEW1GUKdcHVIzUuk5qye//PsLGVZzP3P36d3c9M1NHI46jLe7NxO7TnS6Hh8PH26edTMrD63MDBT588SfdPuqG+cSzhHoFciYTmOKpM1dQrowtOVQAB77+TGeWfpMtsw7yWnJbDq+iedXPE/tD2pnG84rP8KiwrCMt2AZb2HcmnEuty/IN4iXO5tzh6lbpjJh7QTiU8y5wz/n/6H3d705FXcKf09/JoTmPHfIa/udvujEhLUT2Bm+k9R0+73rqKQoZu2axXXTr+NM/BksWByu/4l25t778Zjj9PimB78f+520jDSsVisHIw5y74J7mbN3DgCDWwymol/FHOuITormXMK5zFdqhmlHcnpyts8vDFI6En2Edp+1Y9qWaRyKPJT5byXDmsGm45voOasnC/cvBGB46+E0rNgw94NdyliseYVJiYiIiMilLSMDhgyBr782793coGxZc0MsPd1kufH2NjdZBw+GGTOy109JgfvvNzfRstaPjrYHltxzj6nvKMDlmWfggw/MvMVibtjGxZmbiDfcAO3bwxtvmCAbW+r0/AoLs6cgHzu2YDdSrVZ48EF7Rh53dzOsly19vZ8fzJsHvXoVbPuvvgoTJ9p/iermZtYZF2cv06wZrFgBlStnr1u3Lhz+b6zmihVN25zp0MEMe5ZVSIj9l67u7ubvlp6ePTV/QAB88on5G1/IYrHPe3pCmTLmF5lZf7nbq5f5haazodhEREREREQuFl3/5q64r3+HDDHHJi+1a5v1XSg9HQYOtB9/T09zrRkZab+mfuABEyBkGz68KOvbXH89rF8PzZubH+WIiFwEY1aOyZbhp6x3WRJSE0jNSKVOuTpMvGEi9y64FwDr2JyP96dvm87DPz2cOSRZoFcgyenJpKSncG2Na7mv2X08/svj1C5bm7Cnw7LVjUyMpOH/GnI24SwAFf0q4u9pgo5m3zWba2tcC5igpvsX3s/cveZ71s3iRlnvskQnR2cG7NzT9B5m3jETT/fs/eSQRUOYuXMmg1sMZsYdMxweg9AZoaw9spaxXcYyLnScwzIhH4RwJPoIX97+JUNaDsm2bM+ZPdz8zc2ciDVBWD4ePni5exGTbAJDvd29+ebOb7ir8V051msZb+6DfnHbF7z464uEx4Xj5+mHm8WNuJS4zPrz+8/nlqtucVp/9eDVhIaE5ntZSnoKjy15jM+3f575WYBXAJ5untmOK8DxZ45TvUx1h8fFkbCoMOp8aPru3I5pbqxWKw/++GBmRiB3izsBXgFEJ5tzBz9PP+b1m0evBjnPHfLavu1vaVtvWZ+ypGekZ64bzLH4pNcn3N8i573rDGsGD/34UGbbADzcPPBy9yIhNSHzs64hXfnpnp8cBtLZ/s3lpUvtLqwZssbhvoH5txHoHUhscizJ6cmZnw9tOZRPe3+Kh5tHntsoTZSJSERERORy5+YGX31lXtdea36dmZZmhtqaOhW+dZ4KFwAvL5gzB77/3mScqVDBZNapUMG8X7DArMNZhpz33zdlQkPNzbvkZGjUyPyCc9ky+y8fnf0atLhZLPDFF2b/bOnvk5LMTc2HHzY3DB3dQM2vCRPg999h6FC46irw8THrr1TJpE+fMgX+/DNnABHYb1KD+bXm6dPOXxEROevPnAkvvQSdO0ONGma7iYkQHAxdusBrr5lfnDoKIAL49FPT7ubNoXx5c+PdYoF69cyN859/Nr/kVACRiIiIiIiUBrr+zV1xX/8Wlru7Of7z5plAq3LlzPEPDobbbjPXn9OnOw8AKmx9gL//NgFEoCxEInJRvdntTb664yvaVW+Hr4cvqRmp1C9fnxc7vcj24dupFlgt1/oPXvMgSwYu4YY6N1DGuwxpGWlcVeEq3rzxTdYOWZtrJqIg3yB+G/obdze9m+qB1YlOiuZI9BGORB/JNryal7sXc+6aw/f9vqdn/Z5U8K1AbEosFXwr0LN+Txb0X8C3fb/NEUB0sTQNbsreR/cyrss4WlZpiYebB8lpydQLqscjrR9h76N7HQYQZVUnqA7bh2/nsbaPUcmvEinpKQT7B3NP03vYPny7wwCiwvBy9+Kz2z5j4wMbGdJyCPWC6pGekU5cShzB/sGEhoTyaudX2fXILpcCiIqKxWLhi9u/4Pt+39O9bneCfINISkuidtnaPHzNw+x8ZKfDAKL8mHnHTF66/iU61+5MjTI1SEpLIjEtkWD/YLrU7sJrN7zGP4//4zCACEwQ2xe3f8Hy+5Zzd9O7CSkXgoebB6npqVQNqEqvBr2YdecsVg5a6fJQa3mp7F+Zj3t+zMBmA2lcqTFlvMsQlRSFp7snV1e8mgdaPsD6oev54vYvLrkAIlAmIhEREREpaR07wsaNJtjGlnpeRERERERE5HKj618RERERKeWUiUhERERESs7ateYGKsDNN5dsW0RERERERESKi65/RUREROQSoCAiERERESlejz0GM2ZAeDjYkmBGRcG0aXD77eb9DTdA27Yl1UIRERERERGRwtP1r4iIiIhc4jScmYiIiIgUr5YtYedOM+/tDX5+5iaq7TS0cWNYvhyqX/wxnUVERERERESKjK5/RUREROQSpyAiERERESleP/4IixbB5s1w+jRER0OZMtCkCdx5JwwbZm6sioiIiIiIiFzKdP0rIiIiIpc4BRFJoczYMYOhPwyldtnahD0dViJtCIsKo86HdQA4/NRhQsqFlEg7RERERERERC41lvEWAFYPXk1oSGjJNkZEREREREQub0OGwMyZMHiwGQL0YtcXkTx5lHQDRKTkLdi3gGlbp7H91HZikmOoGliVHnV7MKrjKOqXr1+odR+KPMR7v7/HikMrOBZ9DE93T+qXr0+/xv14qv1T+Hr65lp/1+ldvL/pfdaEreFU7Cn8vfxpVLER9za7l2Gth+Hu5u6wni3ALS8r7l9Bt7rdHC5Ly0hj5o6ZzN47m53hO4lMisTHw4eQciHcEHIDT7Z/knrl6+V9EEREhBk7ZhAWFUZoSGipeECZkp7C//74H9/t+Y5/zv9DekY6dYLqcOfVdzKyw0gCvQMLtN6E1ATWhq1l66mtbDu1ja2ntnI0+igAY7uMZVzouHyvKzwunMl/TObnAz9zOPIwiWmJBPsH06hiI0JDQnnuuufwdPfM17oeWfwI07ZOA8hX8PePf//IZ9s+488TfxKRGEEFvwq0q96OR1o/Qs8GPfO9DyIil7PS1rddzrad2sZ7v7/HmrA1nE04S3nf8lxb41qeaPcEN9S5oVDrjkmO4ePNH7Nw/0L+jfiX1PRUapSpQc/6PRnVcRQ1ytTItf7puNO8v+l9lvy7hMORhwEIKRfC7Q1vZ2SHkQT5Bjmtu+7IOrad2sa28G1sPbmV/ef2k25Np0vtLqwZsiZf7S+ucxoRESmEGTMgLAxCQ82rpKWkwP/+B999B//8A+npUKeOyYw0ciQEFrKviI2Fd9+F+fPh8GFwd4erroK774YnngAvr7zXER4OkyfDzz+bdSQmQnAwNGpkjuFzz4HnBde/a9eaoeG2bIFDh+DcOYiLg6Agk/mpTx94+GHwzf3+MwcPwqRJZl2nTpnjcc01JmtU374FPiwiIiJOWa3wxRfmnGHvXtPv1awJt94Ko0dD5cqFW39B+/7z501WxVWrYNs2OHIE0tKgUiVo08YEbvXpk/f2V6yAzz6zZ2a0WKBqVbjuOtO/duniuN62bfDTT7B1q2n32bMQE2OyOl59NfTqBSNGQPnyBT82ki/KRCSFsnDfQl5Y9QLVy1Rn1aBVJdIGZSIqOKvVyoM/PsiXO74EwM3iRoBXADHJMQD4efoxr988ejXoVaD1L9i3gPsX3k9CagIAgV6BpFvTM983rNCQXwf/SrXAag7rT/5jMk8tfYp0azoAZb3LkpyeTFJaEgAdanZg6b1LHd4UtQURuVncqORXyWkb5/Wbx/W1r8/xeWRiJD1n9WTzic2ZnwV6BZKYlkhaRhoA3u7efN3na/o16ZefwyEickULnRHK2iNrXQ6kKQ6RiZHc+NWNbA/fDpjvc3c398z+qXbZ2qwdspba5Wq7vO41YWvoOrOrw2Wu7PucPXMYtnhYZp/s4+GDl7tX5nuAyNGRlPMpl+e6Vh9ezY1f3YgVc9qfWxBRekY6gxcNZtbuWQBYsFDOpxwxyTGZ/fET7Z7go54f5Ws/REQuZ6WpbyuM0p6J6PNtnzNiyYjM67Cy3mWJSY7J7NcKc/z3n9tPz1k9CYsKA8w5ga+nL1FJUQCU8S7DogGL6FrHcd++4egG7phzB+cSzgHmGtrd4k5sSiwAVQKqsPL+lTQJbuKwvu3YXyi/QUTFeU4jIiKFEBpqAlzGjoVx40q2LZGRcOONsN30FXh7myCfBNNXULu2aWvtAvYVR46Y/Q0LM+/9/MyDyuRk875VK/MgMsh5UC1z5pgHijH/Xe/6+JjAoxj79S+RkVCuXPZ6t94KS5bY3/v7m2l8vP2zOnVg6VIT1OTIzz9Dv37241GmjAlEysgw74cOhenTzcNPEREpGoXNJPTCC7BwoQlmeeONom5d8UtOhttvh2XLzHsPD9P3xcWZ9xUqmGWtWxds/YXp+z09TdCQjY+PqZu1b+3ZE77/3vHwrFarCfKZNs3+mS2YNzHR/tkzz8B77+Ws//jjJqg46/Y9PU3Ask3FiibQ6brrHO+/FAm3km6AXNr6NOrD/sf3l1gAkRTO2xvfzgwgGttlLNFjookeE83+x/bToWYHElIT6D+vf+avKV2x6/QuBs4fSEJqAq2qtOLPh/8k5oUY4l6IY+2QtdQvX5+/z//N7bNvJ8OakaP+0gNLeeKXJ0i3ptOtbjf2P7afqDFRxL0Qx493/0iwfzAbj21k8KLBubajZpmahI8Md/pyFEAE8PSypzMDiMZ1Gce5UeeIeSGGpJeSWDN4DU0qNSE5PZnBiwZzIuaEy8dHRERKzr0L7mV7+HbKeJdhzl1zSHgpgfgX41l+33KqBlTlSPQRen/Xm/SM9AKtP8gniBvr3MioDqP4ru93VAmo4lL9eXvnMXDBQGKSYxh2zTD2PrqXxJcSiR4TTcyYGH4b8hvPXPsMnm55ZyFKSE3g4Z8exsPNgzbV2uRZ/uVfX84MIHqq/VOcHXWWiNERRI+J5p3u7+Dh5sHHf3zMR5sVRCQiIsXv92O/88jiR0jLSOOOq+/g2DPHiBoTxdlRZxneejgA49eOZ+7euS6vOzE1kVu+vYWwqDAq+FZgfv/5xL0YR+ToSP594l961u9JTHIMd8y5g+Mxx3PUPxV7ittm38a5hHOElAth1aBVxL0QR8wLMWwfvp221doSHhdOz1k9iU+Jd9AC8PXwzcz091nvz7ip3k0u7UNxn9OIiMhl4N57zUPEMmVMsE5CgnkQuHy5yQhw5Aj07m0Cf1yVlmbqhoWZda1YYdadkACzZ5ssB9u3w333OV/HvHkwcKAJGBo2zJ6NITrafPbbb+ZB44VZiAC6dYOPPjJZC2JizMPXuDiTkeijj8xDy8OHzUPmjJz3nzl8GPr3N+3t2BH+/ttsNzoaXn3VlPnyS3j7bdePjYiIFJ833oD9+y/NACIw/dqyZaZv+9//TN8ZGwt//mmy7Zw/bwJlswbTuqIwfX9aGrRrB598YjL1JSaavvXwYXjwQVPml19g+HDH254xwx5AdNddJptQQoJ57d9vgqcA3n/fBIJdqF070+/+/rsJhkpMNMchNtYEnlWqZPr5O+4w/bUUGwURiVyhIhMjmfjbRACGtx7OuNBxBHgFANCwYkMW37OYKgFViE+N59U1r7q8/glrJ5Ccnoy/pz+LBy7OfHBpsVjoXLsziwYswt3izpaTW5i1a1aO+i//+jJWrFQPrM4Pd/9Aw4oNAXB3c6d3w97MvGMmAAv3L2TdkXUFOgbOJKclM2fPHAAGtxjM2NCxVPCrkLn9LiFd+OHuHwBITEtk8T+Li3T7IiJSfFYdWsUvB34BYNqt0+jfpD9uFnNK3L1ed+b3nw/A7jO7mbFjhsvrv77W9USMjmDloJVM6j6Ju5vejbe7d77rn4o9xfDFw8mwZvBuj3eZ1nsajSs1zlwe6B3I9bWv572b3sPfyz/P9b206iUORh7k+Y7P06SS4ywINucSzvH+pvcBuOPqO/jg5g8y+z9/L3+e6/Acz133HABj14zNlhVJRESkODy/8nnSrek0C27G3LvmZg4tVsGvAlNvnZoZdDN65WiXA2Wmb5/OochDZv626dzZ6E483DwAqF++PgsGLKB22drEJMcwdvXYHPXf/f1dIhIjcLO4saD/Am6ocwOW/7IUtKzSksUDF1PGuwzHYo7x3u8OfmEJxL4Qy+aHNjPl1ik8dM1DLgUeF/c5jYiIXAZWrTIP+sA80OvfH9z+eyTUvbsZfgxg9+6CZYKYOdPUBbOubt3MvJsbDBhgf4j488+mLRc6dco8hMzIMMOhTZsGje3XvwQGwvXXm0wF/g6uf59+2gyX1qpV9mFZKlQwn3/wgXn/11/mYeSFXn3VPFStUgUWL7ZnKwoIgPHjTVATwGuvmQeZIiIihfXPP/Dpp2Z+wgR47DH7sJ9t2pgMe76+ZpjPggSxFrbv//VXMwTZiBFQt67985AQ+Pxze/DQN9/AsWM563/1lZnWr2+GUmvQwL6sYUMTPGxb71wHPwYaNMgMt3bttdkzEAYEmGXffGPenzlj+m4pNgoiEiITI5m+bTr95/Wn2ZRmlH+rPD4Tfaj9QW0Gzh/IpuObnNadsWMGlvEWQj4IybFs3JpxWMZbCJ0RCsD8v+bT4+seBL8djNt4N8atGQeY4cgs4y1YxlsIiwrj3/P/MmTREGq8VwPvid7Uer8Wjyx+hJOxJwu0f5uOb2L0itFc/+X11P6gNj4TfSj3Zjmu/fxa3lr/FnEpcU7r2tq1JmwNscmxvPzry1z9v6vxfc2XCpMqcOu3t7L5+Gan9W2W/LOEvnP7Uv296nhP9CborSA6f9mZKX9OISU9pUD7VVgL9y/MTLH+QqcXciwP8g3ikdaPAOZv5+yXk46kZ6Sz9MBSAAY2G+hwuLImwU0yh0mzZUOyOR13mq2ntgLwaNtH8fPMmRLv5vo30yy4mcP6hRWZFElyukm56yxrQ73y9Sjva8bczO3fkIhIaXYs+hjPr3iellNbUvbNsvi+5ku9j+px++zb+WrnV5nDR9ocjjzMW+vf4uZvbuaqj6/C/3V/Al4PoPHkxjy99GmORh/NsQ3bucLaI2sBky3A1r9m7f8vlpk7TRBq3aC6DGgyIMfy62pelzmUy1e7vnJ5/e5u7oVq30ebPyIyKZJWVVrxzLXPFGpdm45v4qM/PuKqClfxcueX8yy/6tCqzP5vVIdRDss83/F5AKKSoli0f1Gh2icicqlytW/Lel15Jv4Mzy57lqs+vgq/1/yyDWcVOiMUy3hL5rWyIxdeZzviav+emwxrBiMWj8Ay3oLfa378sP+HfNctrEORh1h/dD0AIzuMxNM9ZwYC27VsWFQYvx35zaX1L/nXDH/SoHwDbr/69hzLfTx8eLL9kwDM2TuHxNREh/VvrHMjraq2ylE/2D+YwS1M5twZO2c4bENhzhuK+5xGROSyc+wYPP88tGwJZcuaB3T16plfxH/1FSRd0D8ePgxvvQU332yCS/z9zQOsxo1N8MrRnNe/zJhhhr1aa84RGD/evM/6sg37dTHMNH0FdeuaoJ4LXXedGYoM7A/9CrL+rl0dDyly991mODFn6//oIxOc06qVycpQ1K691j5//IKsgvHx9gepI0bkHCoNzHA5YDIgLFpU9O0TEblczJljhriqXNlk1ylXzgSP3HabGZrqwj72Qt9/b/qj8uXNEFktW8KHHzrOIgdmODSLxUwvFBpqlo0bBykp8Oab0Ly56ceDgkwgjS3IpiR8843JABQQYAJeL5S1z/76a9fXX9i+v6vjobwz2bIRAWzZknP5qVNm2qKFGabtQp6e5u8L9uHbXJFb3y5FSkFEwoebP+Shnx5i3l/z2Hd2X+bnR6OP8t2e7+gwvUOhh6x4btlz3DXvLlYeWklaRlrmr+MutPn4Zlp/2pqZO2cSnRyNu8WdYzHHmLZ1Gs2mNGPbqW0ub/u66dcxaeMk1h9dz7mEc/h5+hGdHM3mE5sZs2oM7T9vz5n4M7mu41TsKa759BpeW/caR6KP4GZxIyIxgiX/LqHzjM4sP7jcYb3E1ET6zevHrd/dyoJ9CzgZexIfDx+ik6JZd3Qdj/78KF1mdCEy0fVfMmQNvsrtJrMzKw6tAKBxpcbULud4zOueDXqa/UhLzLx5mx/nE88TnxqfuX5nGlVsBMBvR37LdiP7SPSRzPlc61cy9Z0d/4Kq7F8Zf0/z65YtJx10gsDBiINEJEYAzgONRERKs693fs1V/7uKtze+zc7TO0lKS8Lf05+j0Uf58e8fGbxoMPvP7c9WZ+gPQxmzagzLDi7jSPQRfD18SUxLZN+5fXy4+UOaT2meo7/w9fClsn/lzKG3/D39qexfOdvL3ZL/B2i2B7e2h7GusvV/N9e7OTNbwIV61jf93/qj63M8MCxutod89zW/z2n78iM5LZkHfngAq9XKp7d+io+HT5518tP/lvctT7B/MFD0/a+IyKWioH3bgYgDNJ/SnPc3vc+xmGOZWW+KUkH6d2eS0pK4a+5dTN06lSCfIFYOWukw2CY3IR+E5Bn05MyKgysy52+uf7PDMp1qdSLQy2QecLVfOhJl+r38XLPGp8az4diGAtc/FHmIAxEHXGpfXkr7OY2ISKny9dcmEOjtt2HnTvMw09/fBAL9+CMMHmyG2Mhq6FAYM8YMN3LkiAk6SkyEffvMg83mzWH9BfdLfX3tD1DBbKNy5ewvdxcCSG1BSRYLrFnj+n6v+K8vvflmsw5Hepq+gvXrzf7lV0ICbNiQfR0XsljMtsEMoXIh28PL++5z3r7CWJcle329etmXZd1fZ+0PCYFGpi932H4REYEHHjBBo0uXmuwwPj6QmgoHDsBPP8Hjj5usOs48/jj062e+s61W8928c6cJ2B06tODtSkkxGfJeeMH03V5eEBUFK1dCr14myKgg1qyx980FyeJn65s7d3acZQ/s/dKRI2aozYKsvzj6fjB/XxtHw6HZsgzt3GmGRrtQairs2GHm2xTg2WpufbsUKQURCdUCqzG2y1i2PLyFhJcSiBgdQeJLiRx68hBPtX8KgGeXPcv2U9sLtP6tp7by3qb3GN1xNKdHniZidATxL8YztGXOL//hi4dTJ6gOmx/aTOwLscS/GM+y+5ZRq2wtIhIj6DOnD7HJsS5tv/dVvZlz1xxOPXeK+BfjiRgdQcKLCSzov4CGFRry19m/eGTxI7mu47GfH8PL3YtfB/1K/IvxxL0Qxx8P/UHDCg1JSU9h2E/DyLDmjIgdtngY3//1PXWD6jLrzllEj4kmekw0CS8l8MPdP1A3qC6bjm/igR8fcGmfisKeM3sAaBrc1GmZrMv2nt1boO3kllI+3ZqeOc0awJbv+v8tOxF7wmkg1tmEs7T+tDUBrwfg+5ovdT+sy30L7sv1wbPFYmF4a5OSb+bOmYxfM57zCeczt7k2bC23zzY3z/s17keXkC5O1yUiUhot+WcJgxcNJiktiY41O7Ju6DoSX0rk3PPniH8xnnVD1/HwNQ/j5e6VrV7LKi2Z3Gsy/zz+T2b55JeT2fzQZm6ufzPRydEM+H5AtodUA5oOIHxkOB1qdgBMJoHwkeHZXjXL1rwo+30+4TzhceaiNT/9X4Y1g33nHPdPxeFw5OHMzIutq7Zm9+ndDJw/kKrvVsV7ojc13qvBgO8HsOHohjzWZIYV3XduHw+2erBA/VR++t/dZ3a7vF4RkctBQfu2Z5Y9QzmfcqwatIr4F+OJeSGGvx938YZgLgravzsSlRRFj697sHD/QmqWqcn6B9Zn7u/FYrtmDfYPzgxgvZC7mztXV7waKMQ1qzXva1aA3acd93v5uebNrX5BlPZzGhGRUmXJEhMklJQEHTuah0+JiXDunMlGs24dPPywfSgRm5YtTfaEf/6xl09ONkN83HwzREebX/hnffg2YIB5UNrhvz5z5EjzPuur5sW5/uX8eftD26bO+4rMZRkZ5iFrfu3bZ88QkZ/1h4dDRIT988OH4eR/Iw+0bm2GVRk4EKpWBW9vqFHDHM8NeV//ZpOYCP/+C6+/Ds+Z4bjp3Dnng8o9e3K2Mbf27y3YeYaIyGVt/Xr48kszXNZbb5m+JzbW9K/nzplA3MGDc/axNj/+CJ99ZoatjIw0r3Pn4KGHzPKvvjLDaxXEJ5/AH3/A1KmmTZGRJnj4rrvM8vHjzfYvNlt/kp++J2v5/Cjuvh+yBzU3a5Zz+YgRZnrgANxzj5na/P23GV7t0CETAJTfLITJySaT4//+B/ffbz6rXx9693at7eISBREJw1oPY1zoOFpXa515M9FisVAnqA4f3PwBj7Z9lHRrOpP/nFyg9celxPHstc/yZrc3qeRfCQBvD2+H2W883DxYcf8K2lVvl9mOHvV6sPTepXi5e3E0+ihTt0x1afs/3vMj/Zv0p0pAlczPfD196dOoD6sGrcLb3ZtF+xc5HIIla7tWD15N1zpdcbO4YbFYaFu9LfP6zQPML/d/P5Z9XOV1R9bxza5vCPYPZs3gNQxsNpAy3mUAkxb9toa3sXbIWvw9/Vm0fxE7wne4tF+FZXtIWT2wutMyfp5+lPMpl618flTwrUCAVwAAe87ucVrOdlP4wvWHlAtxWOZCWW8SO2tfQmoC205tw8vdiwxrBoejDjNr9yy6zuzKAz88QFqGg0hY4LUbX2NQi0EAjFs7jopvV6Tsm2Xxec2H0JmhJKYl8la3t/iu73dO2yciUhqlZaTxxC9PYMVKp1qd+HXwr3Sq1SkzS6CXuxedanXi096f5vhlve28oEGFBpnlPdw8aFe9HYvvWUzzys05GXuS+fvmX/T9yo+sfUX1Ms77v6x9Y0GHUy2If87/kzm/4dgG2nzWhu/2fEd0UjQ+Hj6ciD3B3L1zuf7L6/m/tf/ndD3bT21n0sZJVPavzNs98j92dn763/C4cM4nmsDai3lsREQuB24WN1YOWskNdW7I7EevqnBVkay7MP37hY7HHKfTF51Yd3QdTSo1YeODG/OsUxxOxuV9zQr2Pt3VfsnW7+09sxer1eqwjLNr1qz1C3LNW1il/ZxGRKTUSEszQ4VYrdCpk3kQ2amTedgJ5qFmp07w6admmLKsPvgAHn3UDMdiK+/hAe3aweLFJhPRyZP2IbFKm5NZvver59KXZl120oW+orDr/8d+/cuGDSbI57vvTHCWjw+cOAFz58L118P/Ob/+BcwDU1tWCD8/k3XqpZfMQ8fevWHhQuftDwoyGaTyar8rx0ZE5EqxcaOZdutmhgwtX96+rEIF6NHDZOupVs1x/chImDbNBJOUKWOv99lnJsAUTN9QENHRJpBo+HB79pyaNc3Qa507m/cvvliwdRdUbKx5Qensm/MSFQVvvGHmr78eGjbMWaZ3b3j/fXOO9f335jzKz8+8rr7aBCGNGGECvGx/c2d8fEzf7uNjhkd94gnzb6ZjR1i1ygQdS7FREJHk6ZYGtwC4NJxVVm4WN0Z3Gp2vso+0ecThLwwbVWrEXY1NdOjsvbML1A5HqpepTosqLbBiZeOxjU7LDWs9zGG7mlVuRp1yZlznXad3ZVs2fft0AO5tdq/TDAs1ytSgax0zvuSyA8tcantIuRCsY61Yx1oZFzrOpbpAZkYnP0+/XMvZlruSAcrdzZ2b6t0EwHe7v+NQ5KEcZbac3JIt3XxMckzmfLB/MK2rmhOEyX9OJjopOkf9+X/Nz5aGP2t9sGfY2vnITpJeSsrMQLXhgQ10q9sNgC93fMkzSx1Huvp4+PB57895u/vbmcMUxCTHZAYdJaQmEJEYQXJ6ct4HRESkFFl9eDWHow4D8P5N7+crG0F+uLu5c3M9k6a8oOcM+TGk5ZDM/i80JNSlurEp9r4st/4v6zJXMyAWRmSSPaveK6tfoVpgNVbcv4K4F+OIHhPN3kf3EhoSihUrr655lQX7FuRYR1pGGg/8aIJkP+r5UWYwcH7cUOcGvN3Nxddr615zWOa13+yfX9j3iohI7u5vfj81ytQolnUXVf/+19m/6DC9A3vP7s3MZlSYNoc9HYZ1rJU1Q9a4XNfla9YU1/rsXg16AXA46jDf7v42x/K4lLhsQ7tf2O/Z6q8JW+MwS+Cx6GN8tfMrp/ULo7Sf04iIlBqrV5uMN2B/oFUU3N3tw3RdOKRZURoyxARAWa0QGupa3dgs3/t+ufSlWZfFutBXFHb9kVmyyr/yinnAvGIFxMWZB79795p9tlrh1VdhQc7r30zu7vbh4rIOs9KvH0yalP2h9oVtya3tWZe7cmxERK4U5cqZ6dmzjoe2ykvNmiZTkSO33Wamu3Y5Xp6fdTsaDs3NDV5+2czv3Wsy4bnC1jdZraafdkVp75tzk5FhsgCdOmX62v/9z3nZp582/Xbwf8/VExPtmRtTUux9fV6qVDF9e9Zh37p2NYHetWrlr91SYAoiEgAORR5i5PKRtP60NeXeLIf7BHcs4y1Yxlvo9a25MXY85niB1l2/fH2nqccvdEOdG5wvCzHLdp3eRWp6ar63n2HN4Nvd33Lbd7dR6/1a+L7mm7lvlvEW/jjxB5D7/rWv3t7psmqBJoI2IjEi2+cbjpmbiNO3T6fKO1WcvlYeWgmYbEaXk5c7v4yXuxfJ6cnc9M1N/PzvzySkJhCbHMv3f33Pbd/dhoebR2Z52y9kbcaHjgfMcGTdvu7GuiPrSE5LJiIxgunbpvPAjw9kBvc4qt+jXg/GhY6jeeXmeHuYB6Lubu50qNmBZfct4/aGZjiyT7Z8wr/n/83R/sORh2n9aWtGrRhF38Z92fLwFmJfiOXo00eZcfsMLFh4a8NbdP6yM3EpcUVz0ERELgJb0GyVgCq0qeb6uMPrjqxjyKIhXP2/qwl4PSBbnzpp4ySg4OcMV7qsQ6NarVbm959Pt7rdMvu4xpUa89M9P2VmVxy/dnyOdby5/k12hO/g1qtupX+T/i5tv6JfRZ5s/yQAKw6t4L4F97H/3H5S01M5Gn2UMSvHMPnPyZn974V9r4iI5K5jzY7Ftu7C9u8AG45uoNMXnTgWc4zbG97OykErCfINKspmlipDWw7N/FHQ8MXDmbplKucTzpOclszGYxvp8XUPTsWdyrxuvbDfe+baZwjyMcfnzrl3MnvPbGKSY0hMTWT5weX0+KZHtsy36jdFREqALUNClSo5h7PKj3XrzAPCq6+GgAB7thuLxQSnABzX9W+BZNivf7FaTUanbt3sWZ8aN4affjJ/OzDDzjhTqZJ9uLiEBDh2zGQi+uknkzHq00+Lbz9ERK5kN95oAkq2bzeZaaZPtwfv5kfbtqZPdcSWvSgiwvHyvISGOl/39deb7IIAW7YUbP1XmqeeMpkYwQz32ry543IJCWY40ltvNYE+y5ebILOzZ81848bw9dcms2NeAWJhYaZvj4uD06fhnXdgxw5T99VXi3LvxAGPvIvI5W7hvoXcM/+ebBlVyniXwcfDBwsWUtJTiEyKJD41vkDrz28AEeSeptyWojstI42IxAgqB1TOc30JqQnc+u2trA5bnfmZl7sX5X3LZz4Ai0iMIDUjlfgU5/sX6B3odJnthmJqRvbAJluq8JjkmHz94jAhNSHPMkUp0DuQiMSIPLdrW57bMXCkZZWWfNPnGwYvGsyBiAPc8u0t2bfvFch7N73HE788AZDj5vQtV93CBzd9wHPLn2PLyS10ntE52/LK/pV56fqXGL1ytMP6uXGzuPFOj3f44e8fyLBm8NM/P/Hsdc9mLk/PSOf22bez+8xuBrUYxMw7ZmYuC/AKYHDLwbSt3pZrpl3D1lNbeWv9W/zfDXmk1RURKSXC48y4yLXL5hxWNC+jV4zODBQCcLe4E+QTlJntIC4ljvjU+AKfMxS3QC97X5Zb/5d1mav9X2Fkbd+NdW/kmqrX5CgT4BXAY20f45XVr7Dr9C5Ox53OPCf66+xf/N9v/0eAVwCf9PqkQG14/cbXORZzjNl7ZjNr9yxm7Z6Vbfm1Na6lZeWWTN06NfPBqYiI5I8r18auKkz/bvPyavNrzOaVmzO//3zc3dyLpG0FZeuD833N6uVan+3v5c/igYvpNasXR6KPMGLJCEYsGZG53IKFSd0mMWnjJM4lnMtxzVm9THUW3b2IPnP6cCb+DPfMvyfbci93Lyb3msywxcMA165Z81Laz2lEREqNcNM/UrsA/ePo0fZAITDZboKC7NmM4uIgPt68SqPALN/7Cbn0pVmXBbrQVxR2/Vnnb7wRrsl5/UtAADz2mMlUtGuXeYBYOY9nAhYL1KgBEyeadfbta4ZNad8eWrTIuf3c2p51uSvHRkTkSlGvHnz+OTzyCPz+u3mBCe7s2hUGDjQZhZwF8+T23WoL8knNf1KJbHIbzsvHxwybdvo0nDlTsPUXRGnvm50ZOdKeeej99+GBB5yXHTXKDEfasKEJxs6aIbB7dzOMbMuWZljTxx4zZfIjOBiee84EgF13nRnqtF07E6wkxUI/g7rCnU84z5AfhpCcnswNdW5gzeA1JLyYQPSYaE6PPE34yHDm9ZtXqG24W0ruxuNrv73G6rDV+Hr48v5N73Pk6SMkvZTE+efPEz4ynPCR4bSvYbIMWbEW6bbTM0zqvim3TMkcdiW314w7ZhTp9vNiy6B0IvaE0zIJqQlEJUVlK++Kfk368ddjf/Hcdc/RplobapWtRbPgZjzW9jF2jdhFk0pNMsteVeGqHPWfuvYpdjyygxFtRtCicgtqlqlJqyqtGN1xNLtH7KaSXyXA3Jx19WZ5/fL1qehXESDHcGvLDy5n9xmTwnDkdSMd1m9cqTG3XGUCo+bvK6Vjn4uIOGBxdtGWhxUHV2QGED3a5lF2j9hN8svJRIyOyOxTn7nWDBFptRZtn1pUsvZlJ2Kc939Z+8aC9H8FZQuYBmhUsZHTco0rNc6cz5rJ8LGfHyMlPYWXrn+JIN8g4lLisr1s2RCsWDM/uzC7o4ebB9/1/Y4lA5cwoMkArq54NbXL1ub6Wtfz0c0f8duQ30hIMxeajvpuERFxrjiDcgrav2c1sNlALFjYdXoXr6x+pQhaVTjVAvK+ZgV7n16QPrtxpcbsHrGbt7u/TWhIKHXK1aFB+Qb0b9KftUPW8tS1T2VeEzvq9zrX7sz+x/YztstYOtTsQO2ytWlUsRFDWg7hz4f/zBxK21n9girt5zQiIqVGQfvHFSvsAUSPPmqGOklONtkQbBlvnjHXv5TS69/MDA4AJ3LpS7Muq+ZCX1HY9Wd9uNvI+fUvje3XvxxxMZP/nXeaLAgZGSY7Rla2tkRG2odYccTWfleOjYjIleTee83389SpJgNNzZom68zcuXDHHdClC8QU3dDOl7TAQHvQTmnsmx15/nl4910z/847ZqgyZ2Jj7dn/HnssewCRja8vPP64mV+/3vUgrnbtTCASKNNgMVMmoivcz//+TExyDEE+Qfx0z0/4eeYcI9H2i8aL4UTsCRpWbOh42X83xjzcPCjv62AcYwdm750NwKtdXuXpa592WKa49q9KQBWORB/hSFTpHKasaXBT9pzZw54ze5yWybosa8CPK0LKhfBOj3ccLpu31wSoVQ2oSki5EKft/OQWx9kUtpw0aQZbV22dOWRZUfjr7F+Z8/XK13NarkH5BgAcjnIhPaOISAmzDYXl6jCas/eYPvWmejcx+ZbJDstczHOGgqjgV4EqAVUIjwvPV//nZnHLNZinqDWu1Bh3izvp1tzHEM8apGXBflP8cKTpj15Y9QIvrHrBaf2j0UcJfMNcsL5/0/sOz5F6NehFrwa9HNa39b8danbItZ0iIuIaW5bbpLQkp2Wik6Idfl7Q/j2rh695mNDaoQxfPJw31r9BWkYak7pPyrtiMWka3BSAM/FnOBt/lkr+lXKUSc9IZ/+5/UDBr1kDvQMZ2WEkIzvk/AHJnyf+zAzCddbvVfKvxLjQcYwLHZdjme2a19PNk7bV2haofY6U9nMaEZFSwzYUlqvBJ7PN9S833WSG7HAkvHRf/1Khgtn/8HDY47yvyFzm5pZ7MM+FGjUydTIyzDp69sx9/VWqQPks9/QbNzbZndJzv/7NFqRVkKCw6tXh6FE4cCD7502bZm9jWyf9tK39TQp2niEickUoXx6GDzcvgIMHTYait94ymWbGjYP33ru4bcotiCY5Gc6fN/PBxZcx2KEmTWDTpvz1zbby+VXUff+oUSZwCExw9XPP5b79f/6BtP+G9K7n/NkqDRrY5w8fdv1vYAtEvrBvlyKlTERXuGMxxwBoWLGhwwAigJWHV1609qw+vNr5sv+GJGteuTme7p75Wt+xaLN/raq0crg8LCqMAxHF8yXTsVZHABb/u7hY1l9Y3et2B2Df2X0cjT7qsMzSA0sB8PXwpVOtTkW6favVmjlEyqAWg1yun5SWxPf7vi9w/YMRBzmXcA6AOuXqZFvmZrF/NeYWBHY6/jTgetp8EZGSZHsAFh4XnhkMkh+2cwZnfarVauXXsF+d1rd9t5Z0liJb/7fs4DKnbbH1f51qdcLX0/eitc3Hw4fOtc3wnfvO7XNazhbsasHiNAi3uGw/tT1z+wXpf0VELidF3bfZhruy9bmObD6x2eHnBe3fL/Rw64eZftt03CxuvL3xbZ5blsdNwmLUvV73zHlb33yhDcc2EJsSC0CPej2KvA3f7PoGgPbV2xcok9A3u039Oxvdib+Xf5G2rTSf04iIlBod/gsADQ+HLS70j8f+64tbOb7+xWqFX51f/+LmZi9Xkrr/15cuW+a8LUv/62M7dTLZAfLLzw86dsy+jgtZrWbbAD0u6Kd9fKCzuf5ln/PrX/7678eeFguEhOS/fbbtH/7vx58XDteSdX+dtf/IEXvbLmy/iIg4V68evPGGGc4MTIa/i23tWud937p19mCXNm0uXpvA3jevW+d8yDFbv1S7thkWrCDrL2zfP3Jk9gCiUaPy3rZblrCT3AK4T5+2zxdkuNBDhwpeV/JNQUT/iY2NZdy4cTRr1oyAgADKli1L27Zteffdd0lJSSnp5hWbst5lAfjn/D8Of+24I3wH3+7+9qK1Z+rWqZmBHVn9fe5vvv/LBIwMaDIg3+sr62P2b+fpnQ6Xj1k5pgCtzJ9h1wwDzC//pvw5Jdey8SnxpKRf3H9nfa7uQ6BXIFasvLn+zRzLo5KimLplKgB9G/ct8hue7296n52nd1LWuyxPtn/S5frPr3ieM/FnqF22Nvc3vz/bsrxu4lutVkatMB2em8WNW6/KPmbmNVXtY3BP2eL4bxceF87CfQsBuK7mdS63X0SMK7X/LUldQ7pSN6guAM8seybf/U9eferULVNzDA+ZVRnvMgCZQ4KUlMEtBgNwMPIg8/7KOWTr5uObMwOXBzW/+EEyQ1sOBWDVoVVsO7Utx/K4lDg+2WIy9LWv0T5bVoawp8NyHTrVtu+1y9bO/MxZpkZHElITGLFkBAB3Nb6LqyteXdDdFJESpv63aBR139aicgsAlh1YRnxKfI7lvx7+ld+P/+6wbkH7d0eGthrKl7d/iZvFjfc2vcfTS58u8LoKo25Q3cwfs7z7+7s5huAEMq9la5etnRmIW1Q2Hd+UeT34cueXXa4//6/5/Pj3j3i4eTCmU9Hfeyjt5zQikpP63xLQtSvUNf0jzzwD+T3OZc31LzsdX/8ydar9AZYjZcw5AlFR+dtecRls+goOHoR5OfsKNm+G1f/9qHdQAfoK2/pXrzbrutC8efbj5Gj9Q831L6tWwbac17/ExcEn/2Wob98eKmXJSmh7+JubL7+0Z4wKDc2+zN8f+vY181OmQLSDbI9vvWWmgYFmSB4RuSSp/y1Gycm5L7cFqLiVQDjC0aMwc2bOzzMy4PXXzXzjxtCs2cVt1733mkx8sbHwv//lXB4WZs+IeP/9OZfnpSj6/pEjsw9hlp8AIoCrr7b/zT//3HFfnZ5uH4YsKCh7kFR6et4B2KtWwR9/mPkL+3YpUgoiAo4cOULz5s0ZP348e/bswWq1kpyczJYtWxg5ciTXXnstkZGRJd3MYtGjXg/cLG5EJEZw74J7M4cMS0lPYe7eufT4usdFzbKSmp5K96+78+eJPwET7LHy0Epu+uYmktOTqVmmJo+0eSTf67u5/s0ATPxtIgv2LchMQ3448jAD5w9k7t65BPkEFf2OAF1CumQ+CHzs58d4Zukz2R6uJqcls+n4Jp5f8Ty1P6jNmXjXxn0MiwrDMt6CZbyFcWvGudy+IN+gzBuhU7dMZcLaCZk3qv85/w+9v+vNqbhT+Hv6MyF0QoG2/+yyZ1lxcEW2lPt/n/ubR5c8ynPLn8OChcm9JlMtMOeYm/Ep8Tyz9Bk2HN2Q7Qb69lPb6T+vPx//8TFe7l7MuGNGjgCnI9FHaPdZO6ZtmcahyEOZQUUZ1gw2Hd9Ez1k9WbjfBAANb/3/7N15nJ11ef//9332ObNmJstk31iSEBBwL6IouKBiXVqt1Upbq7ZVq1+Xfqv91WoXl4rL91uXb1tFxKK2iNQNEEVpQRRZhUBYQhKyT5LZZ85+3/fvj8mEBHLOdQUmJ8t5PX3kQcy55rrvcy+f6/P53J855x1P+Aq9c5eeu38C/wu//oLe9+P3acf4DklTn4B03Ybr9PyvPV+j5VEFCvS+57yv/oEGUFcr19+jKZlI6gsXfkGBAt285Wadf/n5unnLzYriSNJUH+DGzTfqzd9980Ff7/iylVM19doN1+rv//vv97fNI6URffymj+vd175bfW19dbc7/ZUk12y4Zn9/48m47O7L9tefGzffeNg/f/6K83XhSVMfs/72H7xdV9535f73fsPGG/Ta/3ytJOn0uafrD8/8wye1/eHisPYW9u7/M52/UC0c9O8TlYkn/OybzniTnrXwWYoV63X/+TrdsPGG/T+/fs96vepbr9KuiV1KBAn944v+8bDfv+XWbbfq4zd9XPfvuX//A+hKWNF1G67T8y59nm7dfqsWdy3WF19e5yP9ARzzqL8zZ6Zq27TXn/Z6JYKEBouDeuNVb9S2sW2SpGK1qK/f/XW95j9eU/ervZ9sfa/nLU97i77xmm8oGST1f279P3rXNe96Up+4tOzzyxR8LNB5l5132D8rSZ+64FNKBkn9ZuA3+r2rfm//cR4qDunPf/TnunbDtZKkf3rxPymZSB729v/Pr/6PvnnvNzUw8dhvQu6e3K3P3PIZvfgbL1Y1qurtZ7/9Cb94Mu1jN35M33vgexosDO7/ty2jW/Q3P/sbvfGqN0qS/u68v9OZ/Wce8ucnKhMH9Q3K4dQkfDWqHvTvw8Un3pNPtU8DoLmov0dJMjn1gC4IpJtvls4/f+q/0VR7qUpFuvFG6c1vfuwTbyTpZVPjX117rfT3fy9N7pubHBmZevD47ndPfWVIPdNflXXNNY2/TsVy2WVT+x4EU/t5uM4//7GvGXv726ceJk6/9xtukF47VSt0+unSH/7h4W//4ounfjaOpxbk3HDD1L9H0dS23va2qf9/4YVT+/J4b3qT9KxnHfzz0/u3fr30qldNLQJKJKR/fNz49+abpz7J6BvfkLZtO/i1hx+W/uqvHvtanZUrD/3+/u7vphYT7dwpXXTR1M9JU+f77/5uarGYJP1//9/Ug04Axx3q7xH2rndJr3+9dNVV0u4DnnFOTEy1oZdfPvX/X/GK5u9bd7f0Z38m/du/SaV9H6Kxdav0xjc+tojmH/7h8PPeeONjtfGyyw7/5089daomS9Lf/M3UQtbpxWx33DF1rIrFqa8lO9TiHWv7T7X2/+VfPraA6LOftb/C7EBtbdKf/MnU3++8c6q23nvv1PajSLrnHunlL5duuWUq5r3vneqrTdu6depTIP/lX6YWIR84B7F1q/TJT0q//dtT/97bO7VAHEdM6mjvwNFWq9V00UUXafPmzZo/f74uv/xyXXDBBYqiSFdeeaXe9ra36a677tKb3/xm/ehHP3LnjaJIO3bsUGdnp4In8129TTIvPU9/8fS/0Odv/7y+u/67+u7676o7261CtaBqVNXSrqX6m3P+Rn9y7dRNPzY2dtDPF4tFSVOLfR7/WnnfCtQwDJ/w2oHGx8f3//1z539O7/npe/SsrzxLHekORXGkQm3q49y6s926/BWXS2VprDx2yJ8fHx/XWOKx1/73M/+3rt9wvXYXdut1//k6pRIptafbNVqeWtTykXM+op89+jPdvO1mlcvluvs5OTlZ97Vw3/c2H+rn/+n5/6SwFurydZfr87d+Xp+/9fPqSHcolUhprDK2f4JPkiYmJjSm+sfp8Q583432vZF3rH2H7t15r/79vn/X3974t/q7//47dWQ69h+ffCqvy15xmfqSfU/I79n+pXddqs/96nOSpr7yqxbVVKxNXTPt6XZ95kWf0UXLLjrkz46URvYfM2nq/Jdqpf2Tqr25Xv3bhf+ms3vPPuS+3bbjNt22Y2oxWjaZVUemQxOVif0/L0lvPu3N+odz/uGQ27/s5ZfpNd99jTaPbtbnfvU5fe5Xn1NHukOFWmH/eUsGSX38BR/XWb1nPanjfyyK41jj4+NasGCBEkdjdThaRqvX36PtnHnn6Msv/bLe89P36OYtN+vcr52rbDKr9nS7xipj+xfd/ukZf6qx7FT79urlr9alCy/VLdtv0Udu/Ij+9sa/VXe2e389e+nyl+qMuWfo07d++pC1/3UrX6fP3PIZbRjaoCWfX6LZbbOVS+UkSde9/jot7Fzo2vfpvofUuD438uUXf1mvGnuV7tl9j17/ndcrl8wpEST29zmWdC3RNy/6piYnnvgpEJ7tn/nVM7Vl7IlfFfrpWz6tT9/y6f3///fX/L6+/NInfuLdv7/i3/Wq77xKDww9oAu+cYHyqbzSyfT++pxOpHXJiy7RM/qecVjvv1qd+gSHQ/Xbpj2y+xH99c/+Wn/9s79WoEA9uR6NlccUxlP9nTV9a/Tt3/62cmHuhKl9xwLqL5qF+juzDre2WXWrP92vDz7rg/rUrZ/SDx76gX7w0A/Une3WZHVStaimV658pVbPXl231j6Z+t5o/1659JX6twv/TW+/7u364m1fVKFU0OfO/9xhnePphUfWvEA9a7vX6nPnf07v+9n7DpqzGCuPKdZU7r96zl/pZYtfdsj81vZ/suEn+tEjU9d6LplTJpnRWGUqLlCgd5z5Dn3y+Z+su+9X3X+VPvrfH5U0NX5OBAlNVKcWCacTaf3tOX+rdz7tnXV//s9+/Gf65v1P/PTnW7beojmffuzTFpZ0LdG9b733CXFPpU8D6i+ah/p7lJ1zjtJf/rJy73mPgptvls49V3E2q7i9XcHYmIJ9vyU/8ad/qmi6vX71q5W/9FKlbrlF+shHFP/t3049jBwbUxBFqr70pYrOOEPZT39atTBU4XHtfOJ1r1P7Zz6jYMMGxUuWKJ49e+rruyRNXned4oW+8W+6WNT0l4xMTk4qfDJjsC9/We2vepWS99wjvf71inM5KZFQsO8rVKIlSzT5zW8qnnxirfBsP7jiCrW/8pVKbNkiXXCB4nxeiiIF+x7Yhmecockvf1mqs+/Bv/+78q96lZIPPPDYz6fTCvZ9MlCcTqt0ySWqPuMZB+VITk6q/aabpr4ORlKcy02d00JBwQHj9vD001W44grF1apUfdynGvb1KXXZZWq7+GIFN90knXKK4u5uaWJCwb45/8qb36zSO95Rd/9x+Ki/aBbq75GXKxSUufLK/Z94E3d0SMnk/jZckmrPfa4K7373Qe1orlpVRlKlWlWpTvs6XYOiONbE42Ia/Xw+DJWSVH7rW5X85S+VevvbFb/znVJ7u4IDPiGw/MEPqnz++YfdvicnJzX9sQbFYlHVJ1MfPvYx5R96SKkbbpD+/M8Vv+c9Ui6nYN9z16i3V4Vvf1uR9IT9c23/Sdb+YOtWdX56au46TiQUf/KTUwt36qi8+92q/MXjvmnmr/9a+fXrlfrpT6e+Nu266xRns1P5D/jkqurv/I6Kj7sugvFxdf7mN9KfTn2YSJzJKO7sVFAqKThgX6OlS1X4xjcU5fPU58NwuPU3iJ/Mr5KdQL761a/qT/atirvlllv03Oce/LVE3/rWt/T7+76z8ac//anOP9SK+UPYtm2bFi9ePLM7eySdIelZkuZJiiWNSlov6ReS5kv6w31xH33cz50p6dWSRiR9/nGvnbfvz2ZJlzXYdo+k9+77++clJSWdK2mFpLykgqSHJf23dMg1No//+ZHHvd61bz9OktQuqShpu6RfS3pEU+9tmaQb9/050Ef3/feyfe/jUBr9/LRFkp4uaamkTk19BlhB0l5Jj0q6X9LhfRDRwe+70bY9Vkt6hqR+SVlJE5o6Nr+QNPQUtv9MTR33uZI6JEWaOj8Pa+r4N2rbE5J+S9JySbM1de6q+/bnoX0/X6zzsylJZ2vquPfv+9mcpNq+bW6VdNe+/zaS1tR5O3Xfe8hJCjV1fzwq6TZJA3V/+ri2detWLVq06GjvBk5g1N9jRI+kZ0taue/v0lQNGNBUP+A+TbV701KSnidp7b74qqZq2W8k3SHpBWpc+xft+/lFkto0VfOlQ9fves7UVN9DalyfLUlN9X1Ol9QnKZA0rKn3/UtJ9T6N17P99+qx49nI3ZL+y9i/tfv2LyVpXNImSb/S4fcbpKn9PlOH7rdN69LUNbFUU++hTVP1dkBT18PdmqrnOCKovzjSqL9HgKe2fXTffy+Tr26doamx1PT4fK+kOzVVa8+TPc7u0eHVd2v/Vkv6HU29tzsk/cDxHqa9d98+NNpfj/mSnqupsXdeUklT47lfa6o2Ptntn6SpvsBCTY1Zk5qqt49q6r1uO8TPHOh0Sav27V/Hvn8bk7Rx37498dvaD/ZqTdVmy4jq1+4n26fBftRfHGnU32PDUknvkfSSfX8PJO2UdK+kqyRdKenAL7XJSvorSW/UVPkpSHpA0tcl/aukj2iqhN4o6YWH2N6zJX1o33/7NDXNqH25HnXu88V6rHydp6np8ScjLendmnovp2hq2nWTpO9K+oymSt9T2X6HpA9Ieq2mpnIjTU3ffkvSP2tq6qCRjKR3SXrDvv1rk7RD0s8kfU5TXZdDbfO39+3X9LR2n6bK3i5NdZ2ukvQd2UPYFZL+t6QXa6qkj2tq6vhfNHWMcGRQf3GkUX+PvBWSXq6pOrhaU21xh6Ye4/1GU3Xgcj2xHf6aph6tXibpj+rknq5BmzVVW7w//3NN1YaPSvq4pPdL+v19+1qVdLukz0q61nx3h/YCPfY49A811S94st6qqfe5VlOPH7dK+qGkT6n+1K93+0+m9i/V4U21f1TSx+q89jpJb9bUI9a5mpra2K2pYfLXJF1TZ59fpanz92xJCzT1eDiUtEdT19T3JH1TU1MCeHK89bflFxE9//nP10033aQXvvCF+tnPfvaE1+M41sqVK7Vp0ya95S1v0dcP9f2JhzA6Oqqenh6tevWXlUy31Y2LorDua4fLu941cDz5iQLn06GEvf9L589v+HohsVv/0z310aLnDPyz2sK5dWN3DdYb0hwsSKbtIPku/SBwrMZzZdKT+vj3Q27Pubp5JldBB55Vic5ckeM4JLz7PpPvcQa35znXgfcbJeMnfiz/4yVCX8lcsqDBRy1LqpYL+v6XfkcjIyPqnv7+d+AIONL191N//VHl9v2W36F422PPiux02vfBjnHsqa2+/SqV7KdB3hqQTts105srDGeuW5lI2Nssl33fW57NZsyYVNI+16Gz35ZO2cfUmysKHf02Z7fNdR7d5dcOPNTXyhxKoVgwY3z3z2OfENmIu3/kaCdm6oovlcv6yKc/Sf3FEXek6++9D21VZ2dX3bjo8b/9XcedDzZaFTLl0R07Xbl2D9ttzKqTHj8leWhtabtdiyZHzRhJGtptrU6Rdu4dNGMkaemCHjMmnfT1V+6/91CP6Q42tGeXK5dn7uHkNWeYMVseta8HSRqbcNQT+WpT//x5Zszq1WtcuebOOfRX0B0ok/LtV09v4/GcJLW3t5sxkjSw21rhJG3dY8dIUq1ij0dH92525Zozp2bGnLX2Wa5c48XGfbLJiUm9+PyLqL844o50/f2d3/mdhuO6Utn3aWSTk0/8uuUnxEz4ftu76hirecflnjFrZ3ePK1d7p32ve8cxE47j1d3la1v6emebMeWyb1WoZ77AM5Kp1Xz9tpSjhjWanzlQMmnnClK++dTIMYas1HzzqWFkH4sw8l3Plapjvyq+uY7R0REzZnCPrz9ZLdjH4sBPZm6kVGqcK4oi7d09TP3FEXek6+83/uNHyufr971LznnLamT3f7OOuUZJSnqeZzofS2245y4zZtN9d7pypSftNmbhrB5Xrvmz5zR8/WX/+U31b9uqn6w6TT9dc3rD2DHHfPDL3vp2134lHGUglbRrQCZjz2NLvvnNWtV3DZaKdt+hVLLH25LvEWpbW/11Ewfq7bW/SnTubLsPJUn5vL3NfC7rylVzvMmCY35CksbH7H764Lgv164dexrvU2FSf/QHr3XX35b+OrNCoaBf/OIXkqQLp78f8HGCINDLXvYyffnLX9b111/vzj198ybTbUqm8/XjjtFFREHg3C/HIqJ0tvEEVjp47MZNpduUTtY/Xsm0b78CT0F1DlRncvEMi4j2xbGIaN/2fL21wLWIyJfLuh/3b7PFP4YTR1Yz6m8ul1Nb0xYR+QZxM7mIKHC0WDO7iMjXxoSOBS9enmOfcO5XNmsPAFKOiUjPAhXJd0y9uXyLiGZuYfSMLiJyTABLvnvjRF9ENI36iyOpGfW3s7NLXV31FxGFzkVE+fYOM6atwWTtgbKOZ0Oe7UnORUSyJ4AlqdhWf9w7LZfzTRTl83Yu7yIiT810T2o65h48k4eefZKkTMU+9t5FRNms/bDTc9wl36Ie7yKijg77WvXESNKE42FhftL3sLDm6EdV2nwPkPN5+zx2dvru/zg5cxPwwJPVjPqbTqcbts1h5HuA5PkFmVTK+Us0jn65d1zuWaTi/eUez1jNO45JV+xc3prpqXXe4+UbHzrmZh2/2CP5zo/3OHiOfcLRH5O0/yu5GydzjjMj+1h4FxHFjucusXOkmXJc90lnHyPyHHvnV49546i/OJKaUX/z+Xa1NxhLJlK+xZ/V0LGIyDn/nAwcizGdi4hyOXus5m3fMxW77cs5c+WNmjndBqWSSWXTjXNmHYuIGp3jg7Y7U4uIHL8MKzkXETkXpSYTdj3x9gs811feuYjIM7bt7Ox05Wpvt8fvM7mIKOU4ppKk2D5gZUeMJOXbvQu9nH08V9QJav369Yr2/er22rVr68ZNv7Zr1y4NDQ2pt/eJv0VWLpcP+m2Aet93DwBAq6P+AgDQfNRfAACaj/oLAEDzUX8B4Klxrjc8Me3YsWP/3xcuXFg37sDXDvyZA33iE59Qd3f3/j98HyYAAIdG/QUAoPmovwAANB/1FwCA5qP+AsBT09KLiMbHx/f/vdHHUB/42oE/c6APfehDGh0d3f9n69atM7ejAACcQKi/AAA0H/UXAIDmo/4CANB81F8AeGpa+uvMZlI2m3V9dzGeKB/366LC9aoVfN8PCgDANOovAADNR/0FAKD5qL8AADQf9ff4cuObLta6jZuP9m4Ax72W/iSizs7O/X8vFAp14w587cCfAQAAh4/6CwBA81F/AQBoPuovAADNR/0FgKempT+JaMGCBfv/vn37dp1xxhmHjNu+ffshf8YjjiPFcdQgoMFrhyl2xkVB4AiquXJ15ZNmTEfet0J3y54RMyZ2vstgBo+r5xwFnmMqKfAs2/O8Rd/m5NwtH8dxcJ8fV5RzjWPsvfIdqWYsyBfoTZVwvMdcMnTlmtfbuCNcLs3kRQMcWjPqbxiGCsP690U6nXbnsdRqvprpaZMb9hkOkEza9Td2to+VSsWMaWtrc+VKJOzjWq1WnbnsOuD9LSBPnfZsz3OsJN+x92xPkiLHNVELfddNHNvXaldXlyvXaaedZsY0mqQ50Lp195gxkbNr57l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+ }, + "metadata": {} + } + ], + "source": [ + "plot_prediction_sets(X_test, y_test, y_pred_proba, y_ps_90, 5, label_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OeeuIWT0igUc" + }, + "source": [ + "## 6. Calibration of the methods" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e9bBo5hHigUf" + }, + "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": 71, + "metadata": { + "id": "GX6jMs3SigUf", + "outputId": "f1bcbffb-0acc-492d-f8cc-bfc67b8d391b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "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, (conformity_score, include_last_label) in method_params.items():\n", + " axs[i].plot(confidence_levels, var[name], label=name)\n", + " if i == 1:\n", + " axs[i].plot([0, 1], [0, 1], ls=\"--\", color=\"k\")\n", + " axs[i].set_xlabel(\"Target coverage: confidence_level\")\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": { + "id": "U9Yk4NgEigUg" + }, + "source": [ + "The two only methods which are perfectly calibrated for the entire range of confidence level values are the \"lac\" and \"random_aps\". 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 conformalized coverages or non-empty prediction sets but with larger marginal coverages is entirely up to the user." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ahhxTGEEigUg" + }, + "source": [ + "## 7. Prediction set sizes" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "id": "gjQcQzxbigUg", + "outputId": "4e903d10-698f-4073-b9c9-50b2b8dd2646", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 358 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "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": { + "id": "MJfdvQNcigUg" + }, + "source": [ + "## 8. Conditional coverages" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fk1O1mCMigUg" + }, + "source": [ + "We just saw that all our methods give marginal coverages always larger than the target coverages for confidence level 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": 73, + "metadata": { + "id": "76qE6Va4igUg" + }, + "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 confidence_level, 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_v2(y_test_trunc, y_set_trunc)[0]\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": 74, + "metadata": { + "id": "F2rDBYtqigUg" + }, + "outputs": [], + "source": [ + "class_coverage = get_class_coverage(y_test, y_ps_90, label_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "id": "sg2TUWkcigUg", + "outputId": "16856361-f787-45f0-910b-ca7dd4a7f230", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 535 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 75 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "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": { + "id": "khUFgaEJigUg" + }, + "source": [ + "We can notice that the conditional coverages slightly vary between classes. The only methods whose conditional coverages remain valid for all classes are the \"aps\" and \"top_k\" ones." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "id": "pXJjGUYdigUg" + }, + "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": 77, + "metadata": { + "id": "rJqQ3b-jigUg" + }, + "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": 78, + "metadata": { + "id": "OUJfVQaqigUg" + }, + "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": 79, + "metadata": { + "id": "f8Dx_Q8TigUg", + "outputId": "c0a9d90f-08ac-4721-bc5d-71f8d790b6b3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 912 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "plot_confusion_matrix(\"aps\", y_ps_90, label_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OZa0rjQ6igUg" + }, + "source": [ + "Thanks to this confusion matrix we can see that, for some labels (as cat, deer, dog and bird) 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 as with the \"dog\" label. In this case, the reverse is also true. 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Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/scikit-learn-contrib/MAPIE/blob/master/notebooks/classification/Cifar10.ipynb)\n" - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "### What is done in this tutorial ?\n", - "\n", - "> - **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" - ] - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## Tutorial preparation" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "install_mapie = True\n", - "if install_mapie:\n", - " !pip install mapie" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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.classification 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" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "SPACE_BETWEEN_LABELS = 2.5\n", - "SPACE_IN_SUBPLOTS = 4.0\n", - "FONT_SIZE = 18\n" - ] - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## 1. Data loading" - }, - { - "metadata": {}, - "cell_type": "markdown", - "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`" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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['lac'].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" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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)" - ] - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## 2. Definition and training of the the neural network" - }, - { - "metadata": {}, - "cell_type": "markdown", - "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 achieving a reasonable accuracy score and a fast training. The objective here is not to obtain a perfect classifier.\n" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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" - ] - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "## 3. Training the algorithm with a custom class called `TensorflowToMapie`\n", - "\n", - "As MAPIE asks for a model with `fit`, `predict_proba`, `predict` class attributes and the information about whether or not the model is fitted." - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "model = get_model(\n", - " input_shape=(32, 32, 3), \n", - " loss=CategoricalCrossentropy(), \n", - " optimizer=Adam(), \n", - " metrics=['accuracy']\n", - ")" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "cirfar10_model = TensorflowToMapie()\n", - "cirfar10_model.fit(model, X_train, y_train_cat, X_val, y_val_cat)" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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" - ] - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## 4. Prediction of the prediction sets" - }, - { - "metadata": {}, - "cell_type": "markdown", - "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. " - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "method_params = {\n", - " \"naive\": (\"naive\", False),\n", - " \"label\": (\"label\", False),\n", - " \"aps\": (\"aps\", True),\n", - " \"random_aps\": (\"aps\", \"randomized\"),\n", - " \"top_k\": (\"top_k\", False)\n", - "}\n" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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)\n", - " y_preds[name], y_pss[name] = mapie.predict(X_test, alpha=alphas, include_last_label=include_last_label)" - ] - }, - { - "metadata": {}, - "cell_type": "markdown", - "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 \\%." - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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()}" - ] - }, - { - "metadata": {}, - "cell_type": "markdown", - "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 \\%. " - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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)" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "summary_df" - }, - { - "metadata": {}, - "cell_type": "markdown", - "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 \"aps\" 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." - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## 5. Visualization of the prediction sets" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "plot_prediction_sets(X_test, y_test, y_pred_proba, y_ps_90, 5, label_names)" - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## 6. Calibration of the methods" - }, - { - "metadata": {}, - "cell_type": "markdown", - "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. " - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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])" - ] - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "The two only methods which are perfectly calibrated for the entire range of alpha values are the \"label\" and \"random_aps\". 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." - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## 7. Prediction set sizes" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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)" - ] - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## 8. Conditional coverages" - }, - { - "metadata": {}, - "cell_type": "markdown", - "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." - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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", - " " - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "class_coverage = get_class_coverage(y_test, y_ps_90, label_names)" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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])" - ] - }, - { - "metadata": {}, - "cell_type": "markdown", - "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." - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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", - " " - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "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')" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "plot_confusion_matrix(\"aps\", y_ps_90, label_names)" - }, - { - "metadata": {}, - "cell_type": "markdown", - "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 as with the \"cat\" label. In this case, the reverse is also true. However, for the deer, the cat label is often included within the prediction set while the deer is not." - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "" - } - ], - "metadata": { - "interpreter": { - "hash": "c701d105863f3b19d95155354c5cd7eba8f6824e73339ef8c56a1f0753fbe4df" - }, - "jupytext": { - "formats": "ipynb,md" - }, - "kernelspec": { - "display_name": "mapie-notebooks", - "language": "python", - "name": "mapie-notebooks" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file