{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Результат:\n", "[[1 2 3 7 8]\n", " [4 5 6 7 8]]\n" ] } ], "source": [ "import numpy as np\n", "\n", "# Создаем матрицу (2D массив)\n", "matrix = np.array([[1, 2, 3],\n", " [4, 5, 6]])\n", "\n", "# Создаем вектор (1D массив)\n", "vector = np.array([7, 8])\n", "\n", "# Сконкатенируем каждый вектор матрицы с вектором\n", "result = np.column_stack((matrix, np.tile(vector, (matrix.shape[0], 1))))\n", "\n", "print(\"Результат:\")\n", "print(result)\n" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "137" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [], "source": [ "X = np.load('X.npy')\n", "Y = np.load('y.npy')\n", "X=X[-2:]\n", "Y=Y[-2:]\n", "np.save('X',X)\n", "np.save('y',Y)" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(29263, 624)" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(29265, 624)" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ ".shape" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(29265,)" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y.shape" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "\n", "from sklearn.linear_model import LinearRegression\n", "\n", "dat = np.load('embeddings.npy')\n", "data =np.column_stack((dat, dat))\n", "datay = np.ones((data.shape[0]))*5\n", "\n", "data1 = np.column_stack((dat[1:], dat[:-1]))\n", "datay1 = np.ones((data1.shape[0]))\n", "\n", "\n", "X = np.load('X.npy') \n", "Y = np.load('y.npy')\n", "\n", "\n", "\n", "X=np.concatenate((data,X))\n", "Y=np.concatenate((datay,Y))\n", "X = np.concatenate((data1,X))\n", "Y = np.concatenate((datay1,Y))" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(29263, 624)" ] }, "execution_count": 132, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4.5227014967230694e-05" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "logreg = LinearRegression()\n", "logreg.fit(X, Y)\n", "\n", "import pickle\n", "with open('logreg.pkl', 'wb') as f:\n", " pickle.dump(logreg, f)\n", "\n", "logreg.score(X, Y)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0:\ttest: 0.9786223\tbest: 0.9786223 (0)\ttotal: 51.1s\tremaining: 7m 39s\n", "1:\ttest: 0.9950170\tbest: 0.9950170 (1)\ttotal: 1m 13s\tremaining: 4m 54s\n", "2:\ttest: 0.9966407\tbest: 0.9966407 (2)\ttotal: 1m 35s\tremaining: 3m 42s\n", "3:\ttest: 0.9982912\tbest: 0.9982912 (3)\ttotal: 1m 56s\tremaining: 2m 55s\n", "4:\ttest: 0.9988039\tbest: 0.9988039 (4)\ttotal: 2m 18s\tremaining: 2m 18s\n", "5:\ttest: 0.9992459\tbest: 0.9992459 (5)\ttotal: 2m 39s\tremaining: 1m 46s\n", "6:\ttest: 0.9997030\tbest: 0.9997030 (6)\ttotal: 3m 1s\tremaining: 1m 17s\n", "7:\ttest: 0.9998173\tbest: 0.9998173 (7)\ttotal: 3m 22s\tremaining: 50.7s\n", "8:\ttest: 0.9998216\tbest: 0.9998216 (8)\ttotal: 3m 44s\tremaining: 24.9s\n", "9:\ttest: 0.9998608\tbest: 0.9998608 (9)\ttotal: 4m 5s\tremaining: 0us\n", "\n", "bestTest = 0.9998607928\n", "bestIteration = 9\n", "\n" ] }, { "ename": "", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", "\u001b[1;31mClick here for more info. \n", "\u001b[1;31mView Jupyter log for further details." ] } ], "source": [ "from catboost import CatBoostRanker,Pool\n", "from sklearn.model_selection import train_test_split\n", "\n", "X, X_test, Y, Y_test = train_test_split(X, Y, test_size=0.1, random_state=42)\n", "classes_test = np.ones(len(Y_test)).astype(int)\n", "test_pool = Pool(data=X_test, label=Y_test, group_id=classes_test)\n", "\n", "\n", "classes_train = np.ones(len(Y)).astype(int)\n", "train_pool = Pool(data=X, label=Y, group_id=classes_train,)\n", "\n", "\n", "cb = CatBoostRanker(iterations=10,)\n", "cb.fit(train_pool,eval_set=test_pool)\n", "cb.save_model('model.cbm')\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0.82140051]\n", " [0.91314228]\n", " [0.92991252]]\n" ] } ], "source": [ "import numpy as np\n", "from sklearn.metrics.pairwise import cosine_similarity\n", "\n", "# Пример данных\n", "matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n", "vector = np.array([0.5, 0.7, 0.3])\n", "\n", "# Вычисление косинусного сходства между матрицей и вектором\n", "similarity = cosine_similarity(matrix, vector.reshape(1, -1))\n", "\n", "print(similarity)\n" ] } ], "metadata": { "kernelspec": { "display_name": "cv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 2 }