{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Предсказание пола человека по фотографии" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Цель вычислительного эксперимента" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Целью данной работы является решение задачи классификации изображений, а именно предсказание пола человека по фотографии. В качестве целевого датасета будет взят известный датасет celebA, не раз появлявшийся в kernels на Kaggle." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Описание выборок" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "CelebFaces Attributes Dataset (CelebA) представляет собой внушительных размеров датасет с размеченными признаками для каждого из более чем 200 тысяч лиц знаменитостей. Изображения в этом датасете включают себя множество вариаций поз и фона, что непременно положительно скажется на обобщающей способности итоговой модели. Основные особенности данного датасета:\n", "* 10,177 личностей\n", "* 202,599 изображений лиц\n", "* 5 ориентиров (глаза, нос etc.) и 40 атрибутов (пол, makeup, цвет волос etc.) для каждого изображения " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Блок загрузки и предобработки выборок" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Для решения нашей задачи нам понабятся только сами изображения и один из соответствующих атрибутов: пол, который представлен колонкой \"Male\".\n", "Важно отметить, что датасет содержит картинки размера 218x178, что порождает 38804 признаков при разворачивании их в вектор. Это означает следующее:\n", "1. Метрические методы классификации (KNN) не будут работать из-за крайне высокой размерности + на построение KDTree внутри KNN может уйти колоссальное количество времени.\n", "2. Матрица размера 202,599x38,804 будет занимать > 10 гб в памяти, что делает копирующие операции с ней (например train_test_split) крайне затратными как в смысле времени, так и памяти.\n", "\n", "Поэтому в данной задаче необходим feature extraction: для каждой картинки нужно извлечь вектор информативных признаков. Для этого воспользуемся нейросетью InceptionV3. Возьмем ее предпоследний слой размерности 2048. Стоит отметить, что выборка будет иметь не только большой размер, но и, что более важно, все еще свыше 2,000 признаков, что делает фактически невозможным запуск KNN, поэтому в пайплайн предобработки для KNN было решено добавить PCA. Также не помешает центрировать данные с помощью StandartScaler. Разумеется, это нужно сделать перед PCA, чтобы еще и убрать bias." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "env: CUDA_VISIBLE_DEVICES=1\n" ] } ], "source": [ "!export CUDA_VISIBLE_DEVICES=1\n", "%env CUDA_VISIBLE_DEVICES=1" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import time\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from matplotlib import pyplot as plt\n", "from pathlib import Path\n", "from os import listdir\n", "from skimage import io\n", "from random import sample\n", "from tqdm import tqdm_notebook\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import cross_val_score, StratifiedKFold, train_test_split, GridSearchCV\n", "from sklearn.metrics import roc_auc_score, make_scorer\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.neural_network import MLPClassifier\n", "\n", "import keras, keras.layers as L\n", "import keras.applications as zoo\n", "from keras import Model\n", "\n", "IMG_DIR = Path('data/img_align_celeba/')\n", "\n", "from warnings import filterwarnings\n", "filterwarnings('ignore')\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Соберем нашу модель: InceptionV3 без головы." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "model = zoo.InceptionV3(include_top=True, weights='imagenet')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "layer_model = Model(inputs=model.layers[0].input,\n", " outputs=[model.get_layer('avg_pool').output])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Сначала загрузим изображения в черно-белой цветовой гамме, затем предскажем для них значения на предпоследнем слое и возьмем их как вектор признаков." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "img_files = sorted([img_file for img_file in IMG_DIR.iterdir() if not img_file.name.startswith('._')])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "278b2eeaf9434fd5a44eb1b8110610b3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=202599), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "img_features = []\n", "\n", "for img_file in tqdm_notebook(img_files):\n", " img_preprocessed = zoo.inception_v3.preprocess_input(plt.imread(img_file)[None] * 255)\n", " preds = layer_model.predict(img_preprocessed)\n", " img_features.append(preds)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "img_features_np = np.vstack(img_features)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "np.save('data/flatten_images_matrix.npy', img_features_np, allow_pickle=False)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "X = np.load('data/flatten_images_matrix.npy')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "X = StandardScaler().fit_transform(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Загрузим таргет для наших изображений." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "attrs = pd.read_csv('data/list_attr_celeba.csv')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "y = attrs['Male'].values\n", "y[y == -1] = 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "И применим PCA, проведя при этом небольшой анализ зависимости качества классификации KNN от n_components." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "\n", "n_components_range = [5, 10, 30, 50, 100, 150, 200, 300, 500]\n", "scores = []\n", "\n", "for n_components in n_components_range:\n", " X_pca = PCA(n_components=n_components).fit_transform(X)\n", " clf = KNeighborsClassifier(n_jobs=10)\n", " clf.fit(X_pca[:10000], y[:10000])\n", " scores.append(roc_auc_score(y[10000:12000], clf.predict(X_pca[10000:12000])))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(n_components_range, scores)\n", "plt.xlabel('n_components')\n", "plt.ylabel('roc_auc_score')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Возьмем наилучшее число компонент." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "X_pca = PCA(n_components=100).fit_transform(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. График анализа состава выборки" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Поскольку наши данные по сути представляют собой выходы промежуточных слоев нейросети на соответствующих картинках, стандартные методы для анализа признаков на выбросы или корелляцию не подойдут: пространство имеет достаточно высокую размерность, да и не очень понятно, что в этих терминах есть выброс. К тому же после PCA корелляция между признаками гарантированно будет нулевая. Зато на баланс классов посмотреть явно не помешает." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Number of samples, %')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(y)\n", "plt.title('Classes distribution')\n", "plt.xlabel('Class')\n", "plt.ylabel('Number of samples, %')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5832457218446291" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y[y == 0].shape[0] / y.shape[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Явного дисбаланса классов в выборке не наблюдается: нулевой и первый класс составляют 58% и 42% соответственно." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Список моделей" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* KNN (с варьируемым n_neighbors)\n", "* SVM (с различным C)\n", "* NN с одним скрытым слоем (варьируется число нейронов и число эпох)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "models2params = {\n", " KNeighborsClassifier(): {\n", " 'n_neighbors': [2, 5, 10, 20, 50, 100, 200]\n", " },\n", " SVC(kernel='linear', random_state=42, probability=True, class_weight='balanced', cache_size=10 ** 7): {\n", " 'C': np.logspace(-5, -2, 10)\n", " },\n", " MLPClassifier(random_state=42): {\n", " 'hidden_layer_sizes': [(size,) for size in range(20, 400, 10)]\n", " }\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Список функций ошибки, критериев качества" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Основным критерием качества в данной задаче является ROC-AUC. Для его максимизации будем пользоваться функцией потерь binary crossentropy aka logloss (ее on default использует MLPClassifier)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. Способ разбиения выборки на обучение-контроль" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Для валидации нашей модели будем использовать кросс-валидацию на 3-х фолдах. Также отложим тестовую выборку размера 0.1 от исходной для финальной проверки качества (примерно 20,000 объектов)." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, X_pca_train, X_pca_test, y_train, y_test = train_test_split(\n", " X, X_pca, y, test_size=0.1, shuffle=True, random_state=42, stratify=y)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "cv = StratifiedKFold(shuffle=True, random_state=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Теперь давайте проведем небольшой анализ. Мы знаем, что сложность инференса KNN сильно зависит от числа объектов, т.к. обучение KNN по сути есть lazy-learning: основная работа выполняется на этапе предсказания (вычисляются ближайшие соседи, расстояния до них etc.). В силу размеров датасета, обучение на нем целиком может сильно замедлить работу KNN, поэтому мы попробуем выяснить: как размер датасета влияет на итоговое качество и на время обучения. Качество будем мерять при помощи кросс-валидации на 3-х фолдах." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "def sample_n_objects(X, y, n):\n", " assert X.shape[0] == y.shape[0]\n", " idxs = sample(range(X.shape[0]), n)\n", " return X[idxs], y[idxs]\n", "\n", "def score_n_objects(clf, X, y, n):\n", " X, y = sample_n_objects(X, y, n)\n", " score = cross_val_score(clf, X, y, scoring=make_scorer(roc_auc_score), cv=cv)\n", " return np.mean(score), np.std(score)\n", "\n", "def test_model(clf, sample_sizes, X, y):\n", " scores = []\n", " stds = []\n", " times = []\n", " for sample_size in tqdm_notebook(sample_sizes):\n", " start = time.time()\n", " mean, std = score_n_objects(clf, X, y, sample_size)\n", " scores.append(mean)\n", " stds.append(std)\n", " end = time.time()\n", " times.append(end - start)\n", " \n", " scores = np.array(scores)\n", " stds = np.array(stds)\n", " \n", " plt.plot(sample_sizes, scores, label='mean roc_auc_score on folds')\n", " plt.fill_between(sample_sizes, scores - stds, scores + stds, label='68% confidence interval', alpha=0.3)\n", " plt.title('roc_auc_score of {} in dependecy on size of sample'.format(clf.__class__.__name__))\n", " plt.xlabel('size of sample')\n", " plt.ylabel('score')\n", " plt.legend()\n", " plt.show()\n", " plt.plot(sample_sizes, times)\n", " plt.title('computation time {} in dependecy on size of sample'.format(clf.__class__.__name__))\n", " plt.xlabel('size of sample')\n", " plt.ylabel('time')\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "78310502d8294efb9558951c8a7d32c9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=7), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sample_sizes = [100, 500, 1000, 5000, 10000, 20000, 50000]\n", "\n", "test_model(KNeighborsClassifier(n_jobs=10), sample_sizes, X_pca, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Прирост качества начиная с 10,000 объектов наблюдается незначительный, зато время инференса растет квадратично." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2c256039b35841ab9eeebc35d136269d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=7), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "test_model(MLPClassifier(), sample_sizes, X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Здесь время инференса растет линейно, прирост чуть более значителен." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1f0ccd01849a414892717312af034781", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[LibSVM][LibSVM][LibSVM][LibSVM][LibSVM][LibSVM][LibSVM][LibSVM][LibSVM][LibSVM][LibSVM][LibSVM][LibSVM][LibSVM][LibSVM]" ] }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "test_model(SVC(verbose=True, kernel='linear', cache_size=5 * 10**7), sample_sizes[:-2], X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Начиная с некторого объема данных SVM имеет кубическую сложность по числу элементов в выборке (если наблюдения не умещаются в кэш), поэтому вычисление SVM на очень большом объеме данных будет весьма затруднительно и не позволит провести много экспериментов по подбору параметров. Сейчас SVM считался на сервере, что позволило выделить под кэш 50гб, но такая опция не всегда доступна.\n", "\n", "В целом картина ясна: 10,000 объектов вполне достаточно, чтобы сравнить модели между собой, затем лучшую модель можно будет обучить на всем train подмножестве датасета." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 8. Таблица модели/выборки/критерии качества на разбиении со стандартным отклонением" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "subsample_size = 10000\n", "\n", "X_train_subsample, y_train_subsample = sample_n_objects(X_train, y_train, subsample_size)\n", "X_pca_train_subsample, y_pca_train_subsample = sample_n_objects(X_pca_train, y_train, subsample_size)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Приступим к обучению моделей и подбору гиперпараметров." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "893ff5bd196b4b769482d8afa3bee39d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=3), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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\n", 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model2gs = {}\n", "for model, params in tqdm_notebook(models2params.items()):\n", " param_name, param_values = list(params.items())[0]\n", " params = {key: [value] for key, value in model.get_params().items()}\n", " params[param_name] = param_values\n", " \n", " gs = GridSearchCV(model, params, scoring=make_scorer(roc_auc_score), cv=cv, n_jobs=-1)\n", " if isinstance(model, KNeighborsClassifier):\n", " gs.fit(X_pca_train_subsample, y_pca_train_subsample)\n", " else:\n", " gs.fit(X_train_subsample, y_train_subsample)\n", " if isinstance(param_values[0], tuple):\n", " param_values = np.array([value[0] for value in param_values])\n", " \n", " model2gs[model.__class__] = gs\n", " scores = gs.cv_results_['mean_test_score'] \n", " stds = gs.cv_results_['std_test_score']\n", " \n", " plt.figure(figsize=(10, 7))\n", " plt.plot(param_values, scores, label='mean roc_auc_score on folds')\n", " plt.fill_between(param_values, scores - stds, scores + stds, label='68% confidence interval', alpha=0.3)\n", " plt.title(model.__class__.__name__)\n", " plt.xlabel(param_name)\n", " plt.ylabel('roc_auc_score')\n", " plt.legend()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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modelbest ROC-AUCstd on folds
0KNeighborsClassifier0.8377790.003890
1SVC0.8889880.007362
2MLPClassifier0.8935420.007798
\n", "
" ], "text/plain": [ " model best ROC-AUC std on folds\n", "0 KNeighborsClassifier 0.837779 0.003890\n", "1 SVC 0.888988 0.007362\n", "2 MLPClassifier 0.893542 0.007798" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from collections import OrderedDict\n", "\n", "pd.DataFrame([OrderedDict((\n", " ('model', model.__name__),\n", " ('best ROC-AUC', gs.best_score_),\n", " ('std on folds', gs.cv_results_['std_test_score'][np.argmax(gs.cv_results_['mean_test_score'])])\n", ")) for model, gs in model2gs.items()])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Наилучший результат показала нейросеть, поэтому возьмем ее в качестве итоговой модели." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 9. Анализ выбранной модели на разбиении обучение-контроль" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Качество нейросети не очень сильно зависело от числа нейронов в промежуточном слое на малой части датасета, поэтому возьмем побольше нейронов из расчета того, что на большем числе данных модель сможет выучить более сложные закономерности." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Построим график зависимости функции ошибки на test-подмножестве датасета от значения структурного параметра." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "param_values = [50, 100, 250, 500, 750, 1000]\n", "\n", "clfs = [MLPClassifier(hidden_layer_sizes=hidden_layer_sizes).fit(X_train, y_train)\n", " for hidden_layer_sizes in tqdm_notebook([(size,) for size in param_values])]" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "No handles with labels found to put in legend.\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "scores = [roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1]) for clf in clfs]\n", " \n", "plt.figure(figsize=(10, 7))\n", "plt.plot(param_values, scores)\n", "plt.title('roc_auc_score on test sample in dependency on hidden_size')\n", "plt.xlabel('hidden_size')\n", "plt.ylabel('roc_auc_score')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "best_clf = clfs[4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Построим кривую обучения." ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "No handles with labels found to put in legend.\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 7))\n", "plt.plot(range(best_clf.n_iter_), best_clf.loss_curve_)\n", "plt.title('Learning curve of MLPClassifier')\n", "plt.xlabel('number of iterations')\n", "plt.ylabel('loss')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Итог: наилучшая модель дала ROC-AUC скор 97.1 на отложенной тестовой выборке." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 2 }