.flake8

@@ -0,0 +1,2 @@
+[flake8]
+max-line-length = 120

.gitattributes

@@ -0,0 +1 @@
+* text=crlf

.gitignore

@@ -1,18 +1,103 @@
-# ---> JupyterNotebooks
-# gitignore template for Jupyter Notebooks
-# website: http://jupyter.org/
 
-.ipynb_checkpoints
-*/.ipynb_checkpoints/*
+# Created by https://www.toptal.com/developers/gitignore/api/python,pycharm+all
+# Edit at https://www.toptal.com/developers/gitignore?templates=python,pycharm+all
 
-# IPython
-profile_default/
-ipython_config.py
+### 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
 
-# Remove previous ipynb_checkpoints
-#   git rm -r .ipynb_checkpoints/
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
 
-# ---> Python
+# 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]
@@ -116,15 +201,7 @@ ipython_config.py
 #   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
 #poetry.lock
 
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
 __pypackages__/
 
 # Celery stuff
@@ -168,13 +245,13 @@ dmypy.json
 cython_debug/
 
 # PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  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/
 
-# ---> VisualStudioCode
+### VisualStudioCode ###
 .vscode/*
 !.vscode/settings.json
 !.vscode/tasks.json
@@ -188,3 +265,14 @@ cython_debug/
 # Built Visual Studio Code Extensions
 *.vsix
 
+### VisualStudioCode Patch ###
+# Ignore all local history of files
+.history
+.ionide
+
+# End of https://www.toptal.com/developers/gitignore/api/python,pycharm+all
+
+# JS
+node_modules/
+
+test.csv

.vscode/extensions.json

@@ -0,0 +1,13 @@
+{
+    "recommendations": [
+        "ms-python.black-formatter",
+        "ms-python.flake8",
+        "ms-python.isort",
+        "ms-toolsai.jupyter",
+        "ms-toolsai.datawrangler",
+        "ms-python.python",
+        "donjayamanne.python-environment-manager",
+        // optional
+        "usernamehw.errorlens"
+    ]
+}

.vscode/launch.json

@@ -0,0 +1,16 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "mai-service",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "run.py",
+            "console": "integratedTerminal",
+            "justMyCode": true
+        }
+    ]
+}

.vscode/settings.json

@@ -0,0 +1,38 @@
+{
+    "files.autoSave": "onFocusChange",
+    "files.exclude": {
+        "**/__pycache__": true
+    },
+    "editor.detectIndentation": false,
+    "editor.formatOnType": false,
+    "editor.formatOnPaste": true,
+    "editor.formatOnSave": true,
+    "editor.tabSize": 4,
+    "editor.insertSpaces": true,
+    "editor.codeActionsOnSave": {
+        "source.organizeImports": "explicit",
+        "source.sortImports": "explicit"
+    },
+    "editor.stickyScroll.enabled": false,
+    "diffEditor.ignoreTrimWhitespace": false,
+    "debug.showVariableTypes": true,
+    "workbench.editor.highlightModifiedTabs": true,
+    "git.suggestSmartCommit": false,
+    "git.autofetch": true,
+    "git.openRepositoryInParentFolders": "always",
+    "git.confirmSync": false,
+    "errorLens.gutterIconsEnabled": true,
+    "errorLens.messageEnabled": false,
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter",
+    },
+    "python.languageServer": "Pylance",
+    "python.analysis.typeCheckingMode": "basic",
+    "python.analysis.autoImportCompletions": true,
+    "isort.args": [
+        "--profile",
+        "black"
+    ],
+    "notebook.lineNumbers": "on",
+    "notebook.output.minimalErrorRendering": true,
+}

README.md

@@ -1,2 +0,0 @@
-# PIbd-33_Dyakonov_R_R_MAI
-

backend/__init__.py

@@ -0,0 +1,52 @@
+import importlib
+import os
+import traceback
+
+import matplotlib
+from apiflask import APIBlueprint, APIFlask
+from flask_cors import CORS
+
+matplotlib.use("agg")
+
+cors = CORS()
+api_bp = APIBlueprint("api", __name__, url_prefix="/api/v1")
+dataset_path: str | None = None
+
+
+class Config:
+    SECRET_KEY = "secret!"
+    SEND_FILE_MAX_AGE_DEFAULT = -1
+
+
+def create_app():
+    global dataset_path
+
+    # Create and configure app
+    app = APIFlask(
+        "MAI Service",
+        title="MAI Service API",
+        docs_path="/",
+        version="1.0",
+        static_folder="",
+        template_folder="",
+    )
+    app.config.from_object(Config)
+
+    dataset_path = os.path.join(app.instance_path, "dataset")
+    os.makedirs(dataset_path, exist_ok=True)
+
+    @app.errorhandler(Exception)
+    def my_error_processor(error):
+        traceback.print_exception(error)
+        return {"message": str(error), "detail": "No details"}, 500
+
+    # Import custom REST methods
+    importlib.import_module("backend.api")
+
+    # Enable REST API
+    app.register_blueprint(api_bp)
+
+    # Enable app extensions
+    cors.init_app(app)
+
+    return app

backend/api.py

@@ -0,0 +1,57 @@
+from apiflask import FileSchema, Schema, fields
+from flask import send_file
+
+from backend import api_bp, dataset_path
+from backend.service import Service
+
+
+class FileUpload(Schema):
+    file = fields.File(required=True)
+
+
+class ColumnInfoDto(Schema):
+    datatype = fields.String()
+    items = fields.List(fields.String())
+
+
+class TableColumnDto(Schema):
+    name = fields.String()
+    datatype = fields.String()
+    items = fields.List(fields.String())
+
+
+service = Service(dataset_path)
+
+
+@api_bp.post("/dataset")
+@api_bp.input(FileUpload, location="files")
+def upload_dataset(files_data):
+    uploaded_file = files_data["file"]
+    return service.upload_dataset(uploaded_file)
+
+
+@api_bp.get("/dataset")
+def get_all_datasets():
+    return service.get_all_datasets()
+
+
+@api_bp.get("/dataset/<string:name>")
+@api_bp.output(TableColumnDto(many=True))
+def get_dataset_info(name: str):
+    return service.get_dataset_info(name)
+
+
+@api_bp.get("/dataset/<string:name>/<string:column>")
+@api_bp.output(ColumnInfoDto)
+def get_column_info(name: str, column: str):
+    return service.get_column_info(name, column)
+
+
+@api_bp.get("/dataset/draw/hist/<string:name>/<string:column>")
+@api_bp.output(
+    FileSchema(type="string", format="binary"), content_type="image/png", example=""
+)
+def get_dataset_hist(name: str, column: str):
+    data = service.get_hist(name, column)
+    data.seek(0)
+    return send_file(data, download_name=f"{name}.hist.png", mimetype="image/png")

backend/service.py

@@ -0,0 +1,59 @@
+import io
+import os
+import pathlib
+from typing import BinaryIO, Dict, List
+
+import pandas as pd
+from matplotlib.figure import Figure
+from werkzeug.datastructures import FileStorage
+from werkzeug.utils import secure_filename
+
+
+class Service:
+    def __init__(self, dataset_path: str | None) -> None:
+        if dataset_path is None:
+            raise Exception("Dataset path is not defined")
+        self.__path: str = dataset_path
+
+    def __get_dataset(self, filename: str) -> pd.DataFrame:
+        full_file_name = os.path.join(self.__path, secure_filename(filename))
+        return pd.read_csv(full_file_name)
+
+    def upload_dataset(self, file: FileStorage) -> str:
+        if file.filename is None:
+            raise Exception("Dataset upload error")
+        file_name: str = file.filename
+        full_file_name = os.path.join(self.__path, secure_filename(file_name))
+        file.save(full_file_name)
+        return file_name
+
+    def get_all_datasets(self) -> List[str]:
+        return [file.name for file in pathlib.Path(self.__path).glob("*.csv")]
+
+    def get_dataset_info(self, filename) -> List[Dict]:
+        dataset = self.__get_dataset(filename)
+        dataset_info = []
+        for column in dataset.columns:
+            items = dataset[column].astype(str)
+            column_info = {
+                "name": column,
+                "datatype": dataset.dtypes[column],
+                "items": items,
+            }
+            dataset_info.append(column_info)
+        return dataset_info
+
+    def get_column_info(self, filename, column) -> Dict:
+        dataset = self.__get_dataset(filename)
+        datatype = dataset.dtypes[column]
+        items = sorted(dataset[column].astype(str).unique())
+        return {"datatype": datatype, "items": items}
+
+    def get_hist(self, filename, column) -> BinaryIO:
+        dataset = self.__get_dataset(filename)
+        bytes = io.BytesIO()
+        plot: Figure | None = dataset.plot.hist(column=[column], bins=80).get_figure()
+        if plot is None:
+            raise Exception("Can't create hist plot")
+        plot.savefig(bytes, dpi=300, format="png")
+        return bytes

data/boston_housing.csv

@@ -0,0 +1,507 @@
+crim,zn,indus,chas,nox,rm,age,dis,rad,tax,ptratio,b,lstat,medv
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lab2.1.ipynb

@@ -0,0 +1,162 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.feature_selection import mutual_info_regression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from imblearn.over_sampling import ADASYN\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Загрузка датасета с мобильными телефонами\n",
+    "df = pd.read_csv(\"data/mobile_prices.csv\")\n",
+    "print(df.columns)\n",
+    "\n",
+    "# Кодируем все строковые столбцы в числовые\n",
+    "label_encoders = {}\n",
+    "for col in df.select_dtypes(include=[\"object\"]).columns:\n",
+    "    le = LabelEncoder()\n",
+    "    df[col] = le.fit_transform(df[col])\n",
+    "    label_encoders[col] = le\n",
+    "\n",
+    "# Проверка на пропуски и \"зашумленные\" столбцы\n",
+    "noisy_features = []\n",
+    "for col in df.columns:\n",
+    "    if df[col].isnull().sum() / len(df) > 0.1:  # Если более 10% пропусков\n",
+    "        noisy_features.append(col)\n",
+    "print(f\"Зашумленные столбцы: {noisy_features}\")\n",
+    "\n",
+    "# Проверка на смещение\n",
+    "skewness = df.skew()\n",
+    "print(f\"Смещение: {skewness}\")\n",
+    "\n",
+    "skewed_features = skewness[abs(skewness) > 1].index.tolist()\n",
+    "print(f\"Сильно смещенные столбцы: {skewed_features}\")\n",
+    "\n",
+    "# Поиск выбросов\n",
+    "for col in df.select_dtypes(include=[\"number\"]).columns:\n",
+    "    Q1 = df[col].quantile(0.25)\n",
+    "    Q3 = df[col].quantile(0.75)\n",
+    "    IQR = Q3 - Q1\n",
+    "    lower_bound = Q1 - 1.5 * IQR\n",
+    "    upper_bound = Q3 + 1.5 * IQR\n",
+    "    outliers = df[col][(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
+    "    print(f\"Выбросы в столбце '{col}':\\n{outliers}\\n\")\n",
+    "\n",
+    "# Визуализация выбросов\n",
+    "numeric_cols = df.select_dtypes(include=[\"number\"]).columns\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "for i, col in enumerate(numeric_cols, 1):\n",
+    "    plt.subplot(len(numeric_cols) // 3 + 1, 3, i)\n",
+    "    sns.boxplot(data=df, x=col)\n",
+    "    plt.title(f\"Boxplot for {col}\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Логарифмирование признака 'Battery'\n",
+    "df[\"log_Battery\"] = np.log(df[\"Battery\"] + 1)\n",
+    "\n",
+    "# Заполнение пропусков\n",
+    "df[\"Battery\"] = df[\"Battery\"].fillna(df[\"Battery\"].mean())\n",
+    "\n",
+    "\n",
+    "# Функция для разбиения на train/val/test\n",
+    "def split_stratified_into_train_val_test(\n",
+    "    df_input,\n",
+    "    stratify_colname=\"y\",\n",
+    "    frac_train=0.6,\n",
+    "    frac_val=0.15,\n",
+    "    frac_test=0.25,\n",
+    "    random_state=None,\n",
+    "):\n",
+    "    if frac_train + frac_val + frac_test != 1.0:\n",
+    "        raise ValueError(\n",
+    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
+    "            % (frac_train, frac_val, frac_test)\n",
+    "        )\n",
+    "\n",
+    "    if stratify_colname not in df_input.columns:\n",
+    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
+    "\n",
+    "    X = df_input\n",
+    "    y = df_input[[stratify_colname]]\n",
+    "\n",
+    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
+    "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
+    "    )\n",
+    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
+    "    df_val, df_test, y_val, y_test = train_test_split(\n",
+    "        df_temp,\n",
+    "        y_temp,\n",
+    "        stratify=y_temp,\n",
+    "        test_size=relative_frac_test,\n",
+    "        random_state=random_state,\n",
+    "    )\n",
+    "\n",
+    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
+    "\n",
+    "    return df_train, df_val, df_test\n",
+    "\n",
+    "\n",
+    "# Разбиение на train/val/test\n",
+    "data = df[[\"Ram\", \"Price\", \"company\"]].copy()\n",
+    "\n",
+    "print(\"@data\", data)\n",
+    "\n",
+    "data = data.groupby(\"company\").filter(\n",
+    "    lambda x: len(x) > 4\n",
+    ")  # убираем классы с одним элементом\n",
+    "\n",
+    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
+    "    data, stratify_colname=\"company\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
+    ")\n",
+    "\n",
+    "print(\"Обучающая выборка: \", df_train.shape)\n",
+    "print(df_train[\"Ram\"].value_counts())\n",
+    "\n",
+    "print(\"Контрольная выборка: \", df_val.shape)\n",
+    "print(df_val[\"Ram\"].value_counts())\n",
+    "\n",
+    "print(\"Тестовая выборка: \", df_test.shape)\n",
+    "print(df_test[\"Ram\"].value_counts())\n",
+    "\n",
+    "# # Применение ADASYN для oversampling\n",
+    "# ada = ADASYN(n_neighbors=2)\n",
+    "# X_resampled, y_resampled = ada.fit_resample(df_train, df_train[\"company\"])\n",
+    "# df_train_adasyn = pd.DataFrame(X_resampled)\n",
+    "\n",
+    "# print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n",
+    "# print(df_train_adasyn[\"Ram\"].value_counts())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

poetry.toml

@@ -0,0 +1,2 @@
+[virtualenvs]
+in-project = true

pyproject.toml

@@ -0,0 +1,25 @@
+[tool.poetry]
+name = "mai"
+version = "1.0.0"
+description = "MAI Examples"
+authors = ["Aleksey Filippov <al.filippov@ulstu.ru>"]
+readme = "readme.md"
+package-mode = false
+
+[tool.poetry.dependencies]
+python = "^3.12"
+jupyter = "^1.1.1"
+numpy = "^2.1.0"
+pandas = "^2.2.2"
+matplotlib = "^3.9.2"
+flask = "^3.0.3"
+apiflask = "^2.2.0"
+flask-cors = "^5.0.0"
+
+
+[tool.poetry.group.dev.dependencies]
+ipykernel = "^6.29.5"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

readme.md

@@ -0,0 +1,55 @@
+## Окружение и примеры для выполнения лабораторных работ по дисциплине "Методы ИИ"
+
+### Python
+
+Используется Python версии 3.12
+
+Установщик https://www.python.org/ftp/python/3.12.5/python-3.12.5-amd64.exe
+
+### Poetry
+
+Для создания и настройки окружения проекта необходимо установить poetry
+
+**Для Windows (Powershell)**
+
+```
+(Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | python -
+```
+
+**Linux, macOS, Windows (WSL)**
+
+```
+curl -sSL https://install.python-poetry.org | python3 -
+```
+
+**Добавление poetry в PATH**
+
+1. Открыть настройки переменных среды \
+    \
+   <img src="docs/path1.png" width="300"> \
+    \
+   <img src="docs/path2.png" width="400"> \
+2. Изменить переменную Path текущего пользователя \
+    \
+   <img src="docs/path3.png" width="500"> \
+3. Добавление пути `%APPDATA%\Python\Scripts` до исполняемого файла poetry \
+    \
+   <img src="docs/path4.png" width="400">
+
+### Создание окружения
+
+```
+poetry install
+```
+
+### Запуск тестового сервиса
+
+Запустить тестовый сервис можно с помощью VSCode (см. launch.json в каталоге .vscode).
+
+Также запустить тестовый сервис можно с помощью командной строки:
+
+1. Активация виртуального окружения -- `poetry shell`
+
+2. Запуск сервиса -- `python run.py`
+
+Для выходы из виртуального окружения используется команду `exit`

run.py

@@ -0,0 +1,16 @@
+from backend import create_app
+
+app = create_app()
+
+
+def __main():
+    app.run(
+        host="127.0.0.1",
+        port=8080,
+        debug=True,
+        use_reloader=False,
+    )
+
+
+if __name__ == "__main__":
+    __main()

utils.py

@@ -0,0 +1,82 @@
+from typing import Tuple
+
+import pandas as pd
+from pandas import DataFrame
+from sklearn.model_selection import train_test_split
+
+
+def split_stratified_into_train_val_test(
+    df_input,
+    target_colname="z",
+    stratify_colname="y",
+    frac_train=0.6,
+    frac_val=0.15,
+    frac_test=0.25,
+    random_state=None,
+) -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame]:
+    """
+    Splits a Pandas dataframe into three subsets (train, val, and test)
+    following fractional ratios provided by the user, where each subset is
+    stratified by the values in a specific column (that is, each subset has
+    the same relative frequency of the values in the column). It performs this
+    splitting by running train_test_split() twice.
+
+    Parameters
+    ----------
+    df_input : Pandas dataframe
+        Input dataframe to be split.
+    stratify_colname : str
+        The name of the column that will be used for stratification. Usually
+        this column would be for the label.
+    frac_train : float
+    frac_val   : float
+    frac_test  : float
+        The ratios with which the dataframe will be split into train, val, and
+        test data. The values should be expressed as float fractions and should
+        sum to 1.0.
+    random_state : int, None, or RandomStateInstance
+        Value to be passed to train_test_split().
+
+    Returns
+    -------
+    df_train, df_val, df_test :
+        Dataframes containing the three splits.
+    """
+
+    if frac_train + frac_val + frac_test != 1.0:
+        raise ValueError(
+            "fractions %f, %f, %f do not add up to 1.0"
+            % (frac_train, frac_val, frac_test)
+        )
+
+    if stratify_colname not in df_input.columns:
+        raise ValueError("%s is not a column in the dataframe" % (stratify_colname))
+
+    if target_colname not in df_input.columns:
+        raise ValueError("%s is not a column in the dataframe" % (target_colname))
+
+    X = df_input  # Contains all columns.
+    y = df_input[[target_colname]]  # Dataframe of just the column on which to stratify.
+    z = df_input[[stratify_colname]]
+
+    # Split original dataframe into train and temp dataframes.
+    df_train, df_temp, y_train, y_temp = train_test_split(
+        X, y, stratify=z, test_size=(1.0 - frac_train), random_state=random_state
+    )
+
+    if frac_val <= 0:
+        assert len(df_input) == len(df_train) + len(df_temp)
+        return df_train, pd.DataFrame(), df_temp, y_train, pd.DataFrame(), y_temp
+
+    # Split the temp dataframe into val and test dataframes.
+    relative_frac_test = frac_test / (frac_val + frac_test)
+    df_val, df_test, y_val, y_test = train_test_split(
+        df_temp,
+        y_temp,
+        stratify=df_temp[[stratify_colname]],
+        test_size=relative_frac_test,
+        random_state=random_state,
+    )
+
+    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)
+    return df_train, df_val, df_test, y_train, y_val, y_test

utils_clusters.py

@@ -0,0 +1,100 @@
+import math
+from typing import Dict, List, Tuple
+
+import numpy as np
+from pandas import DataFrame
+from sklearn import cluster
+from sklearn.metrics import silhouette_samples, silhouette_score
+
+
+def run_agglomerative(
+    df: DataFrame, num_clusters: int | None = 2
+) -> cluster.AgglomerativeClustering:
+    agglomerative = cluster.AgglomerativeClustering(
+        n_clusters=num_clusters,
+        compute_distances=True,
+    )
+    return agglomerative.fit(df)
+
+
+def get_linkage_matrix(model: cluster.AgglomerativeClustering) -> np.ndarray:
+    counts = np.zeros(model.children_.shape[0])  # type: ignore
+    n_samples = len(model.labels_)
+    for i, merge in enumerate(model.children_):  # type: ignore
+        current_count = 0
+        for child_idx in merge:
+            if child_idx < n_samples:
+                current_count += 1
+            else:
+                current_count += counts[child_idx - n_samples]
+        counts[i] = current_count
+
+    return np.column_stack([model.children_, model.distances_, counts]).astype(float)
+
+
+def print_cluster_result(
+    df: DataFrame, clusters_num: int, labels: np.ndarray, separator: str = ", "
+):
+    for cluster_id in range(clusters_num):
+        cluster_indices = np.where(labels == cluster_id)[0]
+        print(f"Cluster {cluster_id + 1} ({len(cluster_indices)}):")
+        rules = [str(df.index[idx]) for idx in cluster_indices]
+        print(separator.join(rules))
+        print("")
+        print("--------")
+
+
+def run_kmeans(
+    df: DataFrame, num_clusters: int, random_state: int
+) -> Tuple[np.ndarray, np.ndarray]:
+    kmeans = cluster.KMeans(n_clusters=num_clusters, random_state=random_state)
+    labels = kmeans.fit_predict(df)
+    return labels, kmeans.cluster_centers_
+
+
+def fit_kmeans(
+    reduced_data: np.ndarray, num_clusters: int, random_state: int
+) -> cluster.KMeans:
+    kmeans = cluster.KMeans(n_clusters=num_clusters, random_state=random_state)
+    kmeans.fit(reduced_data)
+    return kmeans
+
+
+def _get_kmeans_range(
+    df: DataFrame | np.ndarray, random_state: int
+) -> Tuple[List, range]:
+    max_clusters = int(math.sqrt(len(df)))
+    clusters_range = range(2, max_clusters + 1)
+    kmeans_per_k = [
+        cluster.KMeans(n_clusters=k, random_state=random_state).fit(df)
+        for k in clusters_range
+    ]
+    return kmeans_per_k, clusters_range
+
+
+def get_clusters_inertia(df: DataFrame, random_state: int) -> Tuple[List, range]:
+    kmeans_per_k, clusters_range = _get_kmeans_range(df, random_state)
+    return [model.inertia_ for model in kmeans_per_k], clusters_range
+
+
+def get_clusters_silhouette_scores(
+    df: DataFrame, random_state: int
+) -> Tuple[List, range]:
+    kmeans_per_k, clusters_range = _get_kmeans_range(df, random_state)
+    return [
+        float(silhouette_score(df, model.labels_)) for model in kmeans_per_k
+    ], clusters_range
+
+
+def get_clusters_silhouettes(df: np.ndarray, random_state: int) -> Dict:
+    kmeans_per_k, _ = _get_kmeans_range(df, random_state)
+    clusters_silhouettes: Dict = {}
+    for model in kmeans_per_k:
+        silhouette_value = silhouette_score(df, model.labels_)
+        sample_silhouette_values = silhouette_samples(df, model.labels_)
+        clusters_silhouettes[model.n_clusters] = (
+            silhouette_value,
+            sample_silhouette_values,
+            model,
+        )
+    return clusters_silhouettes

visual.py

@@ -0,0 +1,242 @@
+from typing import Any, Dict, List
+
+import matplotlib.cm as cm
+import matplotlib.pyplot as plt
+import numpy as np
+from pandas import DataFrame
+from scipy.cluster import hierarchy
+from sklearn.cluster import KMeans
+
+
+def draw_data_2d(
+    df: DataFrame,
+    col1: int,
+    col2: int,
+    y: List | None = None,
+    classes: List | None = None,
+    subplot: Any | None = None,
+):
+    ax = None
+    if subplot is None:
+        _, ax = plt.subplots()
+    else:
+        ax = subplot
+    scatter = ax.scatter(df[df.columns[col1]], df[df.columns[col2]], c=y)
+    ax.set(xlabel=df.columns[col1], ylabel=df.columns[col2])
+    if classes is not None:
+        ax.legend(
+            scatter.legend_elements()[0], classes, loc="lower right", title="Classes"
+        )
+
+
+def draw_dendrogram(linkage_matrix: np.ndarray):
+    hierarchy.dendrogram(linkage_matrix, truncate_mode="level", p=3)
+
+
+def draw_cluster_results(
+    df: DataFrame,
+    col1: int,
+    col2: int,
+    labels: np.ndarray,
+    cluster_centers: np.ndarray,
+    subplot: Any | None = None,
+):
+    ax = None
+    if subplot is None:
+        ax = plt
+    else:
+        ax = subplot
+
+    centroids = cluster_centers
+    u_labels = np.unique(labels)
+
+    for i in u_labels:
+        ax.scatter(
+            df[labels == i][df.columns[col1]],
+            df[labels == i][df.columns[col2]],
+            label=i,
+        )
+
+    ax.scatter(centroids[:, col1], centroids[:, col2], s=80, color="k")
+
+
+def draw_clusters(reduced_data: np.ndarray, kmeans: KMeans):
+    h = 0.02
+
+    x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1
+    y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1
+    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
+
+    Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])
+
+    Z = Z.reshape(xx.shape)
+    plt.figure(1)
+    plt.clf()
+    plt.imshow(
+        Z,
+        interpolation="nearest",
+        extent=(xx.min(), xx.max(), yy.min(), yy.max()),
+        cmap=plt.cm.Paired,  # type: ignore
+        aspect="auto",
+        origin="lower",
+    )
+
+    plt.plot(reduced_data[:, 0], reduced_data[:, 1], "k.", markersize=2)
+    centroids = kmeans.cluster_centers_
+    plt.scatter(
+        centroids[:, 0],
+        centroids[:, 1],
+        marker="x",
+        s=169,
+        linewidths=3,
+        color="w",
+        zorder=10,
+    )
+    plt.title(
+        "K-means clustering (PCA-reduced data)\n"
+        "Centroids are marked with white cross"
+    )
+    plt.xlim(x_min, x_max)
+    plt.ylim(y_min, y_max)
+    plt.xticks(())
+    plt.yticks(())
+
+
+def _draw_cluster_scores(
+    data: List,
+    clusters_range: range,
+    score_name: str,
+    title: str,
+):
+    plt.figure(figsize=(8, 5))
+    plt.plot(clusters_range, data, "bo-")
+    plt.xlabel("$k$", fontsize=8)
+    plt.ylabel(score_name, fontsize=8)
+    plt.title(title)
+
+
+def draw_elbow_diagram(inertias: List, clusters_range: range):
+    _draw_cluster_scores(inertias, clusters_range, "Inertia", "The Elbow Diagram")
+
+
+def draw_silhouettes_diagram(silhouette: List, clusters_range: range):
+    _draw_cluster_scores(
+        silhouette, clusters_range, "Silhouette score", "The Silhouette score"
+    )
+
+
+def _draw_silhouette(
+    ax: Any,
+    reduced_data: np.ndarray,
+    n_clusters: int,
+    silhouette_avg: float,
+    sample_silhouette_values: List,
+    cluster_labels: List,
+):
+    ax.set_xlim([-0.1, 1])
+    ax.set_ylim([0, len(reduced_data) + (n_clusters + 1) * 10])
+
+    y_lower = 10
+    for i in range(n_clusters):
+        ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]
+
+        ith_cluster_silhouette_values.sort()
+
+        size_cluster_i = ith_cluster_silhouette_values.shape[0]
+        y_upper = y_lower + size_cluster_i
+
+        color = cm.nipy_spectral(float(i) / n_clusters)  # type: ignore
+        ax.fill_betweenx(
+            np.arange(y_lower, y_upper),
+            0,
+            ith_cluster_silhouette_values,
+            facecolor=color,
+            edgecolor=color,
+            alpha=0.7,
+        )
+
+        ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
+
+        y_lower = y_upper + 10  # 10 for the 0 samples
+
+    ax.set_title("The silhouette plot for the various clusters.")
+    ax.set_xlabel("The silhouette coefficient values")
+    ax.set_ylabel("Cluster label")
+
+    ax.axvline(x=silhouette_avg, color="red", linestyle="--")
+
+    ax.set_yticks([])
+    ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])
+
+
+def _draw_cluster_data(
+    ax: Any,
+    reduced_data: np.ndarray,
+    n_clusters: int,
+    cluster_labels: np.ndarray,
+    cluster_centers: np.ndarray,
+):
+    colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)  # type: ignore
+    ax.scatter(
+        reduced_data[:, 0],
+        reduced_data[:, 1],
+        marker=".",
+        s=30,
+        lw=0,
+        alpha=0.7,
+        c=colors,
+        edgecolor="k",
+    )
+
+    ax.scatter(
+        cluster_centers[:, 0],
+        cluster_centers[:, 1],
+        marker="o",
+        c="white",
+        alpha=1,
+        s=200,
+        edgecolor="k",
+    )
+
+    for i, c in enumerate(cluster_centers):
+        ax.scatter(c[0], c[1], marker="$%d$" % i, alpha=1, s=50, edgecolor="k")
+
+    ax.set_title("The visualization of the clustered data.")
+    ax.set_xlabel("Feature space for the 1st feature")
+    ax.set_ylabel("Feature space for the 2nd feature")
+
+
+def draw_silhouettes(reduced_data: np.ndarray, silhouettes: Dict):
+    for key, value in silhouettes.items():
+        fig, (ax1, ax2) = plt.subplots(1, 2)
+        fig.set_size_inches(18, 7)
+
+        n_clusters = key
+        silhouette_avg = value[0]
+        sample_silhouette_values = value[1]
+        cluster_labels = value[2].labels_
+        cluster_centers = value[2].cluster_centers_
+
+        _draw_silhouette(
+            ax1,
+            reduced_data,
+            n_clusters,
+            silhouette_avg,
+            sample_silhouette_values,
+            cluster_labels,
+        )
+
+        _draw_cluster_data(
+            ax2,
+            reduced_data,
+            n_clusters,
+            cluster_labels,
+            cluster_centers,
+        )
+
+        plt.suptitle(
+            "Silhouette analysis for KMeans clustering on sample data with n_clusters = %d"
+            % n_clusters,
+            fontsize=14,
+            fontweight="bold",
+        )