From d6e07349bc09eeaffd20be751c5e70289e17b756 Mon Sep 17 00:00:00 2001 From: Anton Romanov Date: Thu, 13 Feb 2025 11:25:06 +0400 Subject: [PATCH] Add local notebook --- MDA/.gitignore | 259 ++++++++++++++++++ MDA/project/.gitignore | 259 ++++++++++++++++++ MDA/project/Main.ipynb | 513 +++++++++++++++++++++-------------- MDA/project/requirements.txt | 6 + MDA/project/run.bat | 8 + 5 files changed, 838 insertions(+), 207 deletions(-) create mode 100644 MDA/.gitignore create mode 100644 MDA/project/.gitignore create mode 100644 MDA/project/requirements.txt create mode 100644 MDA/project/run.bat diff --git a/MDA/.gitignore b/MDA/.gitignore new file mode 100644 index 0000000..259965b --- /dev/null +++ b/MDA/.gitignore @@ -0,0 +1,259 @@ + +# Created by https://www.toptal.com/developers/gitignore/api/python,pycharm+all +# Edit at https://www.toptal.com/developers/gitignore?templates=python,pycharm+all + +### PyCharm+all ### +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff +.idea/** +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/**/usage.statistics.xml +.idea/**/dictionaries +.idea/**/shelf + +# AWS User-specific +.idea/**/aws.xml + +# Generated files +.idea/**/contentModel.xml + +# Sensitive or high-churn files +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml +.idea/**/dbnavigator.xml + +# Gradle +.idea/**/gradle.xml +.idea/**/libraries + +# Gradle and Maven with auto-import +# When using Gradle or Maven with auto-import, you should exclude module files, +# since they will be recreated, and may cause churn. Uncomment if using +# auto-import. +# .idea/artifacts +# .idea/compiler.xml +# .idea/jarRepositories.xml +# .idea/modules.xml +# .idea/*.iml +# .idea/modules +# *.iml +# *.ipr + +# CMake +cmake-build-*/ + +# Mongo Explorer plugin +.idea/**/mongoSettings.xml + +# File-based project format +*.iws + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# SonarLint plugin +.idea/sonarlint/ + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties + +# Editor-based Rest Client +.idea/httpRequests + +# Android studio 3.1+ serialized cache file +.idea/caches/build_file_checksums.ser + +### PyCharm+all Patch ### +# Ignores the whole .idea folder and all .iml files +# See https://github.com/joeblau/gitignore.io/issues/186 and https://github.com/joeblau/gitignore.io/issues/360 + +.idea/* + +# Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-249601023 + +*.iml +modules.xml +.idea/misc.xml +*.ipr + +# Sonarlint plugin +.idea/sonarlint + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintainted in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# End of https://www.toptal.com/developers/gitignore/api/python,pycharm+all + +temp/ +data/ +darts_logs/ diff --git a/MDA/project/.gitignore b/MDA/project/.gitignore new file mode 100644 index 0000000..259965b --- /dev/null +++ b/MDA/project/.gitignore @@ -0,0 +1,259 @@ + +# Created by https://www.toptal.com/developers/gitignore/api/python,pycharm+all +# Edit at https://www.toptal.com/developers/gitignore?templates=python,pycharm+all + +### PyCharm+all ### +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff +.idea/** +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/**/usage.statistics.xml +.idea/**/dictionaries +.idea/**/shelf + +# AWS User-specific +.idea/**/aws.xml + +# Generated files +.idea/**/contentModel.xml + +# Sensitive or high-churn files +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml +.idea/**/dbnavigator.xml + +# Gradle +.idea/**/gradle.xml +.idea/**/libraries + +# Gradle and Maven with auto-import +# When using Gradle or Maven with auto-import, you should exclude module files, +# since they will be recreated, and may cause churn. Uncomment if using +# auto-import. +# .idea/artifacts +# .idea/compiler.xml +# .idea/jarRepositories.xml +# .idea/modules.xml +# .idea/*.iml +# .idea/modules +# *.iml +# *.ipr + +# CMake +cmake-build-*/ + +# Mongo Explorer plugin +.idea/**/mongoSettings.xml + +# File-based project format +*.iws + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# SonarLint plugin +.idea/sonarlint/ + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties + +# Editor-based Rest Client +.idea/httpRequests + +# Android studio 3.1+ serialized cache file +.idea/caches/build_file_checksums.ser + +### PyCharm+all Patch ### +# Ignores the whole .idea folder and all .iml files +# See https://github.com/joeblau/gitignore.io/issues/186 and https://github.com/joeblau/gitignore.io/issues/360 + +.idea/* + +# Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-249601023 + +*.iml +modules.xml +.idea/misc.xml +*.ipr + +# Sonarlint plugin +.idea/sonarlint + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintainted in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# End of https://www.toptal.com/developers/gitignore/api/python,pycharm+all + +temp/ +data/ +darts_logs/ diff --git a/MDA/project/Main.ipynb b/MDA/project/Main.ipynb index 907cd4d..9316c3c 100644 --- a/MDA/project/Main.ipynb +++ b/MDA/project/Main.ipynb @@ -1,211 +1,310 @@ { - "metadata": { - "kernelspec": { - "name": "python", - "display_name": "Python (Pyodide)", - "language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "python", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8" - } + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7669f948-8d8e-460f-a6b4-e4358db9ec58", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n", + "from statsmodels.tsa.holtwinters import ExponentialSmoothing" + ] }, - "nbformat_minor": 5, - "nbformat": 4, - "cells": [ + { + "cell_type": "markdown", + "id": "b0fa9e7b-c123-4b1e-8918-2397e77429c8", + "metadata": {}, + "source": [ + "Определим функцию сглаживания временного ряда, она понадобится позже.\n", + "\n", + "Например:\n", + "1. Метод скользящего окна. Параметр window задает ширину окна усреднения значений.\n", + "\n", + " ``return ts.rolling(window=5).mean()``\n", + "\n", + "2. Метод экспоненциального сглаживания (метод Хольта-Уинтерса)\n", + "\n", + "\n", + " варианты для параметра trend: \"add\", \"mul\", \"additive\", \"multiplicative\", None\n", + "\n", + "\n", + " варианты для параметра seasonal: \"add\", \"mul\", \"additive\", \"multiplicative\", None\n", + "\n", + "\n", + " seasonal_periods задает для модели предполагаемый интервал сезонности, когда ВР будет иметь похожие уровни и тенденции\n", + "\n", + " ``model = ExponentialSmoothing(ts, trend=\"additive\", seasonal=\"additive\", seasonal_periods=5)``\n", + " ``return model.fit().fittedvalues``" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b1525171-5f8b-410e-8b04-599757b1afe1", + "metadata": {}, + "outputs": [], + "source": [ + "def smooth_time_series(ts):\n", + " model = ExponentialSmoothing(ts, trend=\"additive\", seasonal=\"additive\", seasonal_periods=5)\n", + " return model.fit().fittedvalues" + ] + }, + { + "cell_type": "markdown", + "id": "6e2859e5-739e-4b49-8999-4a54aeebb903", + "metadata": {}, + "source": [ + "Для чтения из файла необходимо взять временные ряды из предложенных наборов и сохранить в csv файл как показано в примере, сформировав датасет" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "64bdc662-a64e-43be-a3d6-f4327b29f477", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = pd.read_csv(\"ts2.csv\", delimiter=\";\")" + ] + }, + { + "cell_type": "markdown", + "id": "345981a3-8b6d-42dc-b5ab-aed2bdcce8d8", + "metadata": {}, + "source": [ + "после того как файл был прочтен, можно вывести содержимое временных рядов в консоль" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c228b5b9-5bfb-4786-887d-857d9b3409c7", + "metadata": {}, + "outputs": [ { - "id": "7669f948-8d8e-460f-a6b4-e4358db9ec58", - "cell_type": "code", - "source": "import matplotlib.pyplot as plt\nimport pandas as pd\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\nfrom statsmodels.tsa.holtwinters import ExponentialSmoothing", - "metadata": { - "trusted": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": "Matplotlib is building the font cache; this may take a moment.\n" - } - ], - "execution_count": 1 - }, - { - "id": "b0fa9e7b-c123-4b1e-8918-2397e77429c8", - "cell_type": "markdown", - "source": "Определим функцию сглаживания временного ряда, она понадобится позже\nНапример:\n1. Метод скользящего окна. Параметр window задает ширину окна усреднения значений.\n\n ``return ts.rolling(window=5).mean()``\n\n2. Метод экспоненциального сглаживания (метод Хольта-Уинтерса)\n\n\n варианты для параметра trend: \"add\", \"mul\", \"additive\", \"multiplicative\", None\n\n\n варианты для параметра seasonal: \"add\", \"mul\", \"additive\", \"multiplicative\", None\n\n\n seasonal_periods задает для модели предполагаемый интервал сезонности, когда ВР будет иметь похожие уровни и тенденции\n\n ``model = ExponentialSmoothing(ts, trend=\"additive\", seasonal=\"additive\", seasonal_periods=5)``\n ``return model.fit().fittedvalues``", - "metadata": {} - }, - { - "id": "b1525171-5f8b-410e-8b04-599757b1afe1", - "cell_type": "code", - "source": "def smooth_time_series(ts):\n model = ExponentialSmoothing(ts, trend=\"additive\", seasonal=\"additive\", seasonal_periods=5)\n return model.fit().fittedvalues", - "metadata": { - "trusted": true - }, - "outputs": [], - "execution_count": 2 - }, - { - "id": "6e2859e5-739e-4b49-8999-4a54aeebb903", - "cell_type": "markdown", - "source": "Для чтения из файла необходимо взять временные ряды из предложенных наборов и сохранить в csv файл как показано в примере, сформировав датасет", - "metadata": {} - }, - { - "id": "64bdc662-a64e-43be-a3d6-f4327b29f477", - "cell_type": "code", - "source": "dataset = pd.read_csv(\"ts2.csv\", delimiter=\";\")", - "metadata": { - "trusted": true - }, - "outputs": [], - "execution_count": 3 - }, - { - "id": "345981a3-8b6d-42dc-b5ab-aed2bdcce8d8", - "cell_type": "markdown", - "source": "после того как файл был прочтен, можно вывести содержимое временных рядов в консоль", - "metadata": {} - }, - { - "id": "c228b5b9-5bfb-4786-887d-857d9b3409c7", - "cell_type": "code", - "source": "print(dataset)", - "metadata": { - "trusted": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": " first second third\n0 3 2.500000 1\n1 2 2.833333 2\n2 3 2.966667 3\n3 3 3.066667 4\n4 4 4.383333 5\n5 6 6.466667 6\n6 5 5.416667 7\n7 5 5.433333 8\n8 5 5.583333 9\n9 5 5.683333 10\n10 5 5.150000 11\n11 5 5.633333 12\n12 6 6.033333 13\n13 12 12.866667 14\n14 6 6.783333 15\n15 6 6.500000 16\n" - } - ], - "execution_count": 4 - }, - { - "id": "e53b79b2-74e0-492d-bcab-5f3c47c25175", - "cell_type": "markdown", - "source": "Для выполнения первой части задания необходимо подобрать метод и параметры сглаживания временного ряда. Для этого выше реализован пример функции ``smooth_time_series``", - "metadata": {} - }, - { - "id": "74674c95-9af6-4b5a-ab08-30fe89c4e05e", - "cell_type": "code", - "source": "sds = dataset.apply(smooth_time_series)\n\nplt.plot(sds)\nplt.plot(dataset)\nplt.legend(['sm first', 'sm second', 'sm third', 'first', 'second', 'third'])\nplt.show()", - "metadata": { - "trusted": true - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": "
", - "image/png": 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" - }, - "metadata": {} - } - ], - "execution_count": 19 - }, - { - "id": "01e366c4-d7c0-4a01-85b7-f24c4383766d", - "cell_type": "markdown", - "source": "Выполним нормализацию. Варианты: MinMaxScaler; MaxAbsScaler; RobustScaler; StandardScaler", - "metadata": {} - }, - { - "id": "e609b470-19b7-4f52-af0d-b2643e926779", - "cell_type": "code", - "source": "df = pd.DataFrame(MinMaxScaler().fit_transform(sds), columns=sds.columns)\nprint(df)", - "metadata": { - "trusted": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": " first second third\n0 0.032607 0.000000 0.000000\n1 0.000000 0.057355 0.066665\n2 0.113045 0.085473 0.133331\n3 0.452163 0.447628 0.199999\n4 0.169570 0.209032 0.266668\n5 0.306525 0.276186 0.333334\n6 0.273918 0.333541 0.399998\n7 0.386963 0.361659 0.466664\n8 0.726082 0.723814 0.533332\n9 0.443488 0.485218 0.600001\n10 0.580444 0.552372 0.666667\n11 0.547837 0.609727 0.733331\n12 0.660882 0.637845 0.799997\n13 1.000000 1.000000 0.866665\n14 0.717407 0.761404 0.933334\n15 0.854362 0.828558 1.000000\n" - } - ], - "execution_count": 6 - }, - { - "id": "8d7324a5-0610-4ac6-b270-1f0eedf00c2f", - "cell_type": "markdown", - "source": "Расчет степени корреляции между сглаженными временными рядами", - "metadata": {} - }, - { - "id": "5e1850c7-17c7-49ae-9b71-594934054b03", - "cell_type": "code", - "source": "matrix = df.corr()\nprint(matrix)", - "metadata": { - "trusted": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": " first second third\nfirst 1.000000 0.992092 0.904237\nsecond 0.992092 1.000000 0.916556\nthird 0.904237 0.916556 1.000000\n" - } - ], - "execution_count": 7 - }, - { - "id": "cbe8bb83-5958-4e9c-8963-ad04d5653eb8", - "cell_type": "markdown", - "source": "Для примера сравним корреляцию для несглаженных временных рядов", - "metadata": {} - }, - { - "id": "07268810-4221-49b0-8c59-a07470f15a4e", - "cell_type": "code", - "source": "matrix2 = dataset.corr()\nprint(matrix2)", - "metadata": { - "trusted": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": " first second third\nfirst 1.000000 0.990651 0.704796\nsecond 0.990651 1.000000 0.722877\nthird 0.704796 0.722877 1.000000\n" - } - ], - "execution_count": 8 - }, - { - "id": "4c5a24e1-0734-4aaa-9212-56eadadd273f", - "cell_type": "markdown", - "source": "Визуализация матрицы корреляции:", - "metadata": {} - }, - { - "id": "d3e09b25-63ec-4c52-9013-19f4be727ffb", - "cell_type": "code", - "source": "plt.imshow(matrix, cmap='Blues')\nplt.colorbar()\nvariables = []\nfor i in matrix.columns:\n variables.append(i)\nplt.xticks(range(len(matrix)), variables, rotation=45, ha='right')\nplt.yticks(range(len(matrix)), variables)\nplt.show()", - "metadata": { - "trusted": true - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": "
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" - }, - "metadata": {} - } - ], - "execution_count": 9 + "name": "stdout", + "output_type": "stream", + "text": [ + " first second third\n", + "0 3 2.500000 1\n", + "1 2 2.833333 2\n", + "2 3 2.966667 3\n", + "3 3 3.066667 4\n", + "4 4 4.383333 5\n", + "5 6 6.466667 6\n", + "6 5 5.416667 7\n", + "7 5 5.433333 8\n", + "8 5 5.583333 9\n", + "9 5 5.683333 10\n", + "10 5 5.150000 11\n", + "11 5 5.633333 12\n", + "12 6 6.033333 13\n", + "13 12 12.866667 14\n", + "14 6 6.783333 15\n", + "15 6 6.500000 16\n" + ] } - ] -} \ No newline at end of file + ], + "source": [ + "print(dataset)" + ] + }, + { + "cell_type": "markdown", + "id": "e53b79b2-74e0-492d-bcab-5f3c47c25175", + "metadata": {}, + "source": [ + "Для выполнения первой части задания необходимо подобрать метод и параметры сглаживания временного ряда. Для этого выше реализован пример функции ``smooth_time_series``" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "74674c95-9af6-4b5a-ab08-30fe89c4e05e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sds = dataset.apply(smooth_time_series)\n", + "\n", + "plt.plot(sds)\n", + "plt.plot(dataset)\n", + "plt.legend(['sm first', 'sm second', 'sm third', 'first', 'second', 'third'])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "01e366c4-d7c0-4a01-85b7-f24c4383766d", + "metadata": {}, + "source": [ + "Выполним нормализацию. Варианты: MinMaxScaler; MaxAbsScaler; RobustScaler; StandardScaler" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e609b470-19b7-4f52-af0d-b2643e926779", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " first second third\n", + "0 0.032607 0.000000 0.000000\n", + "1 0.000000 0.057355 0.066665\n", + "2 0.113045 0.085473 0.133331\n", + "3 0.452163 0.447628 0.199999\n", + "4 0.169570 0.209032 0.266668\n", + "5 0.306525 0.276186 0.333334\n", + "6 0.273918 0.333541 0.399998\n", + "7 0.386964 0.361659 0.466664\n", + "8 0.726082 0.723814 0.533332\n", + "9 0.443488 0.485218 0.600001\n", + "10 0.580444 0.552372 0.666667\n", + "11 0.547837 0.609727 0.733331\n", + "12 0.660882 0.637845 0.799997\n", + "13 1.000000 1.000000 0.866665\n", + "14 0.717407 0.761404 0.933334\n", + "15 0.854362 0.828558 1.000000\n" + ] + } + ], + "source": [ + "df = pd.DataFrame(MinMaxScaler().fit_transform(sds), columns=sds.columns)\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "id": "8d7324a5-0610-4ac6-b270-1f0eedf00c2f", + "metadata": {}, + "source": [ + "Расчет степени корреляции между сглаженными временными рядами" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5e1850c7-17c7-49ae-9b71-594934054b03", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " first second third\n", + "first 1.000000 0.992092 0.904237\n", + "second 0.992092 1.000000 0.916556\n", + "third 0.904237 0.916556 1.000000\n" + ] + } + ], + "source": [ + "matrix = df.corr()\n", + "print(matrix)" + ] + }, + { + "cell_type": "markdown", + "id": "cbe8bb83-5958-4e9c-8963-ad04d5653eb8", + "metadata": {}, + "source": [ + "Для примера сравним корреляцию для несглаженных временных рядов" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "07268810-4221-49b0-8c59-a07470f15a4e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " first second third\n", + "first 1.000000 0.990651 0.704796\n", + "second 0.990651 1.000000 0.722877\n", + "third 0.704796 0.722877 1.000000\n" + ] + } + ], + "source": [ + "matrix2 = dataset.corr()\n", + "print(matrix2)" + ] + }, + { + "cell_type": "markdown", + "id": "4c5a24e1-0734-4aaa-9212-56eadadd273f", + "metadata": {}, + "source": [ + "Визуализация матрицы корреляции:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d3e09b25-63ec-4c52-9013-19f4be727ffb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(matrix, cmap='Blues')\n", + "plt.colorbar()\n", + "variables = []\n", + "for i in matrix.columns:\n", + " variables.append(i)\n", + "plt.xticks(range(len(matrix)), variables, rotation=45, ha='right')\n", + "plt.yticks(range(len(matrix)), variables)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/MDA/project/requirements.txt b/MDA/project/requirements.txt new file mode 100644 index 0000000..7ce1665 --- /dev/null +++ b/MDA/project/requirements.txt @@ -0,0 +1,6 @@ +jupyter==1.0.0 +JupyterLab== 3.1.0 +matplotlib==3.9.2 +pandas==2.2.3 +scikit-learn==1.5.1 +statsmodels==0.14.4 \ No newline at end of file diff --git a/MDA/project/run.bat b/MDA/project/run.bat new file mode 100644 index 0000000..69e85db --- /dev/null +++ b/MDA/project/run.bat @@ -0,0 +1,8 @@ +python -m venv .venv + +call .venv\Scripts\activate.bat + +pip install -r requirements.txt + +call jupyter lab +