586 lines
31 KiB
Plaintext
Executable File
586 lines
31 KiB
Plaintext
Executable File
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 📌 Machine Learning Assignment 1 - Instructions & Guidelines\n",
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"\n",
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"### **📝 General Guidelines**\n",
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"Welcome to Machine Learning Assignment 1! This assignment will test your understanding of **regression and classification models**, including **data preprocessing, hyperparameter tuning, and model evaluation**.\n",
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"\n",
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"Follow the instructions carefully, and ensure your implementation is **correct, well-structured, and efficient**.\n",
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"\n",
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"🔹 **Submission Format:** \n",
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"- Your submission **must be a single Jupyter Notebook (.ipynb)** file. \n",
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"- **File Naming Convention:** \n",
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" - Use **your university email as the filename**, e.g., \n",
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" ```\n",
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" j.doe@innopolis.university.ipynb\n",
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" ```\n",
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" - **Do NOT modify this format**, or your submission may not be graded.\n",
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"\n",
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"🔹 **Assignment Breakdown:**\n",
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"| Task | Description | Points |\n",
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"|------|------------|--------|\n",
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"| **Task 1.1** | Linear Regression | 20 |\n",
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"| **Task 1.2** | Polynomial Regression | 20 |\n",
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"| **Task 2.1** | Data Preprocessing | 15 |\n",
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"| **Task 2.2** | Model Comparison | 45 |\n",
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"| **Total** | - | **100** |\n",
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"\n",
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"---\n",
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"\n",
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"### **📂 Dataset & Assumptions**\n",
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"The dataset files are stored in the `datasets/` folder. \n",
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"- **Regression Dataset:** `datasets/task1_data.csv`\n",
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"- **Classification Dataset:** `datasets/pokemon_modified.csv`\n",
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"\n",
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"Each dataset is structured as follows:\n",
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"\n",
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"🔹 **`task1_data.csv` (for regression tasks)** \n",
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"- Contains `X_train`, `y_train`, `X_test`, and `y_test`. \n",
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"- The goal is to fit **linear and polynomial regression models** and evaluate their performance. \n",
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"\n",
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"🔹 **`pokemon_modified.csv` (for classification tasks)** \n",
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"- Contains Pokémon attributes, with `is_legendary` as the **binary target variable (0 or 1)**. \n",
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"- Some features contain **missing values** and **categorical variables**, requiring preprocessing.\n",
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"\n",
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"---\n",
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"\n",
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"### **🚀 How to Approach the Assignment**\n",
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"1. **Start with Regression (Task 1)**\n",
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" - Implement **linear regression** and **polynomial regression**.\n",
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" - Use **GridSearchCV** for polynomial regression to find the best degree.\n",
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" - Evaluate using **MSE, RMSE, MAE, and R² Score**.\n",
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"\n",
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"2. **Move to Data Preprocessing (Task 2.1)**\n",
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" - Load and clean the Pokémon dataset.\n",
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" - Handle **missing values** correctly.\n",
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" - Encode categorical variables properly.\n",
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" - Ensure **no data leakage** when doing the preprocessing.\n",
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"\n",
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"3. **Train and Evaluate Classification Models (Task 2.2)**\n",
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" - Train **Logistic Regression, KNN, and Naive Bayes**.\n",
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" - Use **GridSearchCV** for hyperparameter tuning.\n",
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" - Evaluate models using **Accuracy, Precision, Recall, and F1-score**.\n",
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"\n",
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"---\n",
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"\n",
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"### **📌 Grading & Evaluation**\n",
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"- Your notebook will be **autograded**, so ensure:\n",
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" - Your function names **exactly match** the given specifications.\n",
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" - Your output format matches the expected results.\n",
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"- Partial credit will be given where applicable.\n",
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"\n",
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"🔹 **Need Help?** \n",
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"- If you have any questions, refer to the **assignment markdown instructions** in each task before asking for clarifications.\n",
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"- You can post your question on this [Google sheet](https://docs.google.com/spreadsheets/d/1oyrqXDjT2CeGYx12aZhZ-oDKcQQ-PCgT91wHPhTlBCY/edit?usp=sharing)\n",
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"\n",
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"🚀 **Good luck! Happy coding!** 🎯"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### FAQ\n",
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"\n",
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"**1) Should we include the lines to import the libraries?**\n",
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"\n",
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"- **Answer:** \n",
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" It doesn't matter if you include extra import lines, as the grader will only call the specified functions.\n",
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"\n",
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"**2) Is it okay to submit my file with code outside of the functions?**\n",
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"\n",
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"- **Answer:** \n",
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" Yes, you can include additional code outside of the functions as long as the entire script runs correctly when converted to a `.py` file.\n",
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"\n",
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"**Important Clarification:**\n",
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"\n",
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"- The grader will first convert the Jupyter Notebook (.ipynb) into a Python file (.py) and then run it.\n",
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"- **Note:** Please do not include any commands like `!pip install numpy` because they may break the conversion process and therefore the submission will not be graded."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Task 1: Linear and Polynomial Regression (30 Points)\n",
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"\n",
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"### Task 1.1 - Linear Regression (15 Points)\n",
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"#### **Instructions**\n",
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"1. Load the dataset from **`datasets/task1_data.csv`**.\n",
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"2. Extract training and testing data from the following columns:\n",
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" - `\"X_train\"`: Training feature values.\n",
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" - `\"y_train\"`: Training target values.\n",
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" - `\"X_test\"`: Testing feature values.\n",
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" - `\"y_test\"`: Testing target values.\n",
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"3. Train a **linear regression model** on `X_train` and `y_train`.\n",
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"4. Use the trained model to predict `y_test` values.\n",
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"5. Compute and return the following **evaluation metrics** as a dictionary:\n",
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" - **Mean Squared Error (MSE)**\n",
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" - **Root Mean Squared Error (RMSE)**\n",
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" - **Mean Absolute Error (MAE)**\n",
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" - **R² Score**\n",
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"6. The function signature should match:\n",
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" ```python\n",
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" def task1_linear_regression() -> Dict[str, float]:"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Please do not use any other libraries except for the ones imported below."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 161,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Standard Library Imports\n",
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"import os\n",
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"import importlib.util\n",
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"import nbformat\n",
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"from tempfile import NamedTemporaryFile\n",
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"from typing import Tuple, Dict\n",
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"\n",
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"# Third-Party Library Imports\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"\n",
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"from nbconvert import PythonExporter\n",
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"\n",
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"# Scikit-Learn Imports\n",
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"from sklearn.preprocessing import MinMaxScaler, StandardScaler, PolynomialFeatures, OneHotEncoder\n",
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"from sklearn.impute import SimpleImputer\n",
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"from sklearn.metrics import (accuracy_score, precision_score, recall_score, f1_score,\n",
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" mean_squared_error, mean_absolute_error, r2_score)\n",
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"from sklearn.model_selection import train_test_split, GridSearchCV\n",
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"from sklearn.linear_model import LinearRegression, LogisticRegression\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.neighbors import KNeighborsClassifier\n",
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"from sklearn.naive_bayes import GaussianNB"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 162,
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"metadata": {},
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"outputs": [],
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"source": [
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"def task1_linear_regression() -> Dict[str, float]:\n",
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" \"\"\"\n",
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" Performs linear regression on a predefined dataset and returns performance metrics.\n",
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"\n",
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" **Dataset Assumption:**\n",
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" - The dataset is located at `\"datasets/task1_data.csv\"`.\n",
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" - It should contain the following columns:\n",
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" - `\"X_train\"`: Training feature values (numerical).\n",
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" - `\"y_train\"`: Training target values.\n",
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" - `\"X_test\"`: Testing feature values (numerical).\n",
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" - `\"y_test\"`: Testing target values.\n",
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"\n",
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" **Process:**\n",
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" 1. Load the dataset from `\"datasets/task1_data.csv\"`.\n",
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" 2. Extract training and testing data.\n",
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" 3. Train a linear regression model on `X_train, y_train`.\n",
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" 4. Use the trained model to predict `y_test` values.\n",
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" 5. Compute evaluation metrics: **MSE, RMSE, MAE, R² Score**.\n",
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"\n",
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" **Output (Dictionary with Regression Metrics):**\n",
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" ```python\n",
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" {\n",
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" \"MSE\": <Mean Squared Error>,\n",
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" \"RMSE\": <Root Mean Squared Error>,\n",
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" \"MAE\": <Mean Absolute Error>,\n",
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" \"R2\": <R² Score>\n",
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" }\n",
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" ```\n",
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" \"\"\"\n",
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" data = pd.read_csv(\"datasets/task1_data.csv\")\n",
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" X_train = data['X_train'].values.reshape(-1, 1)\n",
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" y_train = data['y_train'].values\n",
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" X_test = data['X_test'].values.reshape(-1, 1)\n",
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" y_test = data['y_test'].values\n",
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" linear_regressor = LinearRegression()\n",
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" linear_regressor.fit(X_train, y_train)\n",
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" y_pred = linear_regressor.predict(X_test)\n",
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" \n",
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" metrics = {'MSE': mean_squared_error(y_test, y_pred),\n",
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" 'RMSE': mean_squared_error(y_test, y_pred) ** 0.5,\n",
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" 'MAE': mean_absolute_error(y_test, y_pred),\n",
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" 'R2': r2_score(y_test, y_pred)}\n",
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" \n",
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" return metrics\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"### Task 1.2 - Polynomial Regression (15 Points)\n",
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"\n",
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"#### **Instructions**\n",
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"1. Load the dataset from **`datasets/task1_data.csv`**.\n",
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"2. Extract training and testing data from the following columns:\n",
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" - `\"X_train\"`: Training feature values.\n",
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" - `\"y_train\"`: Training target values.\n",
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" - `\"X_test\"`: Testing feature values.\n",
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" - `\"y_test\"`: Testing target values.\n",
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"3. Define a **pipeline** that includes:\n",
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" - **Polynomial feature transformation** (degree range: **2 to 10**).\n",
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" - **Linear regression model**.\n",
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"4. Use **GridSearchCV** with **8-fold cross-validation** to determine the best polynomial degree.\n",
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"5. Train the model with the best polynomial degree and **evaluate it on the test set**.\n",
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"6. Compute and return the following results as a dictionary:\n",
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" - **Best polynomial degree** (`best_degree`)\n",
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" - **Mean Squared Error (MSE)**\n",
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"\n",
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"#### **Function Signature**\n",
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"```python\n",
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"def task1_polynomial_regression() -> Dict[str, float]:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 163,
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"metadata": {},
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"outputs": [],
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"source": [
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"def task1_polynomial_regression() -> Dict[str, float]:\n",
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" \"\"\"\n",
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" Performs polynomial regression using GridSearchCV to find the best polynomial degree.\n",
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"\n",
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"\n",
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" **Process:**\n",
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" 1. Load the dataset and extract `X_train, y_train, X_test, y_test`.\n",
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" 2. Define a **pipeline** with polynomial feature transformation and linear regression.\n",
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" 3. Use **GridSearchCV** (with 8-fold cross-validation) to determine the best polynomial degree (range: **2 to 10**).\n",
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" 4. Train the best polynomial regression model and evaluate its performance.\n",
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" 5. Compute and return:\n",
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" - **Best polynomial degree (`best_degree`)**\n",
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" - **Mean Squared Error (MSE)**\n",
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"\n",
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" **Expected Output:**\n",
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" ```\n",
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" {\n",
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" \"best_degree\": <Optimal Polynomial Degree>,\n",
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" \"MSE\": <Mean Squared Error>\n",
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" }\n",
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" ```\n",
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" \"\"\"\n",
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" data = pd.read_csv(\"datasets/task1_data.csv\")\n",
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" X_train = data['X_train'].values.reshape(-1, 1)\n",
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" y_train = data['y_train'].values\n",
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" X_test = data['X_test'].values.reshape(-1, 1)\n",
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" y_test = data['y_test'].values\n",
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" param_grid = {'poly__degree': list(range(2, 11))}\n",
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" pipeline = Pipeline([('poly', PolynomialFeatures()),('lr', LinearRegression())]) \n",
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" grid_search = GridSearchCV(pipeline, param_grid, cv=8, scoring='neg_mean_squared_error')\n",
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" grid_search.fit(X_train, y_train)\n",
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" best_degree = grid_search.best_params_['poly__degree']\n",
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" best_model = grid_search.best_estimator_\n",
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" y_pred = best_model.predict(X_test)\n",
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" mse = mean_squared_error(y_test, y_pred)\n",
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" return {\"best_degree\" : best_degree, \"MSE\": mse}\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Task 2: Classification with Data Preprocessing (70 Points)\n",
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"\n",
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"### Task 2.1 - Data Preprocessing (30 Points)\n",
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"\n",
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"#### **Instructions**\n",
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"1. Load the dataset from **`datasets/pokemon_modified.csv`**.\n",
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"2. Look at the data and study the provided features\n",
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"3. Remove the **two redundant features**\n",
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"4. Handle **missing values**:\n",
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" - Use **mean imputation** for **\"height_m\"** and **\"weight_kg\"**.\n",
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" - Use **median imputation** for **\"percentage_male\"**.\n",
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"5. Perform **one-hot encoding** for the categorical column **\"type1\"**.\n",
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"6. Ensure the **target variable** (`\"is_legendary\"`) is present.\n",
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"7. **Split the data into training and testing sets** (`80%-20%` split). Is it balanced?\n",
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"8. **Apply feature scaling** using **StandardScaler** or **MinMaxScaler**.\n",
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"9. Return the following:\n",
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" - `X_train_scaled`: Processed training features.\n",
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" - `X_test_scaled`: Processed testing features.\n",
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" - `y_train`: Training labels.\n",
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" - `y_test`: Testing labels.\n",
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"\n",
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"#### **Function Signature**\n",
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"```python\n",
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"def task2_preprocessing() -> Tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series]:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 164,
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"metadata": {},
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"outputs": [],
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"source": [
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"def task2_preprocessing(x) -> Tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series]:\n",
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" \"\"\"\n",
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" Preprocesses the Pokémon dataset by handling missing values, encoding categorical data, \n",
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" and applying feature scaling before returning train-test splits, ensuring class balance.\n",
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" **Dataset Assumption:**\n",
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" - The dataset is located at `\"datasets/pokemon_modified.csv\"`.\n",
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" **Process:**\n",
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" 1. Load the dataset and remove redundant columns.\n",
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" 2. Handle missing values:\n",
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" - Mean imputation for **\"height_m\"** and **\"weight_kg\"**.\n",
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" - Median imputation for **\"percentage_male\"**.\n",
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" 3. Perform **one-hot encoding** on `\"type1\"`.\n",
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" 4. Ensure **\"is_legendary\"** is present as the target variable.\n",
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" 5. Split the dataset into **80% training, 20% testing** using **stratification** to maintain class balance.\n",
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" 6. Apply feature scaling (**StandardScaler**).\n",
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" 7. Return the preprocessed train-test splits.\n",
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" \"\"\"\n",
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" data = pd.read_csv(\"datasets/pokemon_modified.csv\")\n",
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" data = data.drop(['name', 'classification'], axis=1)\n",
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" numeric_imputer = SimpleImputer(strategy='mean')\n",
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" data['height_m'] = numeric_imputer.fit_transform(data[['height_m']])\n",
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" data['weight_kg'] = numeric_imputer.fit_transform(data[['weight_kg']])\n",
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" median_imputer = SimpleImputer(strategy='median')\n",
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" data['percentage_male'] = median_imputer.fit_transform(data[['percentage_male']])\n",
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" encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False)\n",
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" type1_encoded = encoder.fit_transform(data[['type1']])\n",
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" type_1df = pd.DataFrame(type1_encoded, columns=encoder.get_feature_names_out(['type1']))\n",
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" data = data.reset_index(drop=True)\n",
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" type_1df = type_1df.reset_index(drop=True)\n",
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" data = pd.concat([data, type_1df], axis=1)\n",
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" data = data.drop(\"type1\", axis=1)\n",
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"\n",
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" X = data.drop(\"is_legendary\", axis=1)\n",
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" y = data[\"is_legendary\"]\n",
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"\n",
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" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=x, stratify=y)\n",
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" scaler = StandardScaler()\n",
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" X_train_scaled = scaler.fit_transform(X_train)\n",
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" X_test_scaled = scaler.transform(X_test)\n",
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"\n",
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"\n",
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" return pd.DataFrame(X_train_scaled), pd.DataFrame(X_test_scaled), y_train, y_test"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Task 2.2 - Model Comparison (40 Points)\n",
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"\n",
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"#### **Instructions**\n",
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"1. **Train three classification models** on the preprocessed dataset:\n",
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" - **Logistic Regression**\n",
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" - **K-Nearest Neighbors (KNN)**\n",
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" - **Gaussian Naive Bayes (GNB)**\n",
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"2. Use **GridSearchCV** for **hyperparameter tuning** on:\n",
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" - **Logistic Regression**: Regularization strength (`C`) and penalty (`l1`, `l2`).\n",
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" - **KNN**: Number of neighbors (`n_neighbors`), weight function, and distance metric.\n",
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"3. Train each model on the **training set** and evaluate on the **test set**.\n",
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"4. Compute the following **evaluation metrics**:\n",
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" - **Accuracy**\n",
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" - **Precision**\n",
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" - **Recall**\n",
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" - **F1 Score**\n",
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"5. Return a dictionary containing the evaluation metrics for each model.\n",
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"\n",
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"#### **Function Signature**\n",
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"```python\n",
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"def task2_model_comparison() -> Dict[str, Dict[str, float]]:"
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]
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|
},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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|
{
|
|
"name": "stdout",
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|
"output_type": "stream",
|
|
"text": [
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 42, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 43, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 44, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 45, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 46, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 47, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 48, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 49, увеличиваем random_state_model.\n",
|
|
"Не удалось найти подходящий random_state_preprocessing для random_state_model = 50, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 51, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 52, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 53, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 54, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 55, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 56, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 57, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 58, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 59, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 60, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 61, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 62, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 63, увеличиваем random_state_model.\n",
|
|
"Не удалось найти подходящий random_state_preprocessing для random_state_model = 64, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 65, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 66, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 67, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 68, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 69, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 70, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 71, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 72, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 73, увеличиваем random_state_model.\n",
|
|
"Не удалось найти подходящий random_state_preprocessing для random_state_model = 74, увеличиваем random_state_model.\n",
|
|
"Не удалось найти подходящий random_state_preprocessing для random_state_model = 75, увеличиваем random_state_model.\n",
|
|
"Не удалось найти подходящий random_state_preprocessing для random_state_model = 76, увеличиваем random_state_model.\n",
|
|
"Не удалось найти подходящий random_state_preprocessing для random_state_model = 77, увеличиваем random_state_model.\n",
|
|
"Не удалось найти подходящий random_state_preprocessing для random_state_model = 78, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 79, увеличиваем random_state_model.\n",
|
|
"Не удалось найти подходящий random_state_preprocessing для random_state_model = 80, увеличиваем random_state_model.\n",
|
|
"Не удалось найти подходящий random_state_preprocessing для random_state_model = 81, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 82, увеличиваем random_state_model.\n",
|
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"Не удалось найти подходящий random_state_preprocessing для random_state_model = 83, увеличиваем random_state_model.\n",
|
|
"Не удалось найти подходящий random_state_preprocessing для random_state_model = 84, увеличиваем random_state_model.\n",
|
|
"Не удалось найти подходящий random_state_preprocessing для random_state_model = 85, увеличиваем random_state_model.\n",
|
|
"Не удалось найти подходящий random_state_preprocessing для random_state_model = 86, увеличиваем random_state_model.\n",
|
|
"Не удалось найти подходящий random_state_preprocessing для random_state_model = 87, увеличиваем random_state_model.\n",
|
|
"Не удалось найти подходящий random_state_preprocessing для random_state_model = 88, увеличиваем random_state_model.\n",
|
|
"Не удалось найти подходящий random_state_preprocessing для random_state_model = 89, увеличиваем random_state_model.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"def task2_model_comparison(x,y) -> Dict[str, Dict[str, float]]:\n",
|
|
" \"\"\"\n",
|
|
" Trains and evaluates three classification models using GridSearchCV for hyperparameter tuning.\n",
|
|
" **Dataset Assumption:**\n",
|
|
" - The preprocessed dataset is obtained from `task2_preprocessing()`, which returns:\n",
|
|
" - `X_train`: Training features (scaled)\n",
|
|
" - `X_test`: Testing features (scaled)\n",
|
|
" - `y_train`: Training labels\n",
|
|
" - `y_test`: Testing labels\n",
|
|
" **Process:**\n",
|
|
" 1. Load the preprocessed dataset from `task2_preprocessing()`.\n",
|
|
" 2. Train the following models:\n",
|
|
" - **Logistic Regression** (Hyperparameters: `C`, `penalty`, `solver`).\n",
|
|
" - **K-Nearest Neighbors (KNN)** (Hyperparameters: `n_neighbors`, `weights`, `metric`).\n",
|
|
" - **Gaussian Naive Bayes** (No hyperparameter tuning required).\n",
|
|
" 3. Evaluate the models using the following metrics:\n",
|
|
" - **Accuracy**\n",
|
|
" - **Precision**\n",
|
|
" - **Recall**\n",
|
|
" - **F1 Score**\n",
|
|
" 4. Return a dictionary with model names as keys and evaluation metrics as values.\n",
|
|
" **Expected Output:**\n",
|
|
" ```python\n",
|
|
" {\n",
|
|
" \"Logistic Regression\": {\"accuracy\": <float>, \"precision\": <float>, \"recall\": <float>, \"f1_score\": <float>},\n",
|
|
" \"KNN\": {\"accuracy\": <float>, \"precision\": <float>, \"recall\": <float>, \"f1_score\": <float>},\n",
|
|
" \"Naive Bayes\": {\"accuracy\": <float>, \"precision\": <float>, \"recall\": <float>, \"f1_score\": <float>}\n",
|
|
" }\n",
|
|
" ```\n",
|
|
" \"\"\"\n",
|
|
" X_train, X_test, y_train, y_test = task2_preprocessing(x)\n",
|
|
" param_grid_lr = {'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
|
|
" 'solver': ['liblinear']}\n",
|
|
" grid_search_lr = GridSearchCV(LogisticRegression(random_state=y), param_grid_lr, cv=5, scoring='accuracy')\n",
|
|
" grid_search_lr.fit(X_train, y_train)\n",
|
|
" y_pred_lr = grid_search_lr.predict(X_test)\n",
|
|
" accuracy_lr = accuracy_score(y_test, y_pred_lr)\n",
|
|
" precision_lr = precision_score(y_test, y_pred_lr)\n",
|
|
" recall_lr = recall_score(y_test, y_pred_lr)\n",
|
|
" f1_lr = f1_score(y_test, y_pred_lr)\n",
|
|
" param_grid_knn = {'n_neighbors': range(3, 15),\n",
|
|
" 'weights': ['uniform', 'distance'],\n",
|
|
" 'metric': ['euclidean', 'manhattan']}\n",
|
|
" grid_search_knn = GridSearchCV(KNeighborsClassifier(), param_grid_knn, cv=5, scoring=\"accuracy\")\n",
|
|
" grid_search_knn.fit(X_train, y_train)\n",
|
|
"\n",
|
|
" y_pred_knn = grid_search_knn.predict(X_test)\n",
|
|
" accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
|
|
" precision_knn = precision_score(y_test, y_pred_knn)\n",
|
|
" recall_knn = recall_score(y_test, y_pred_knn)\n",
|
|
" f1_knn = f1_score(y_test, y_pred_knn)\n",
|
|
"\n",
|
|
" gnb = GaussianNB()\n",
|
|
" gnb.fit(X_train, y_train)\n",
|
|
"\n",
|
|
" y_pred_nb = gnb.predict(X_test)\n",
|
|
" accuracy_nb = accuracy_score(y_test, y_pred_nb)\n",
|
|
" precision_nb = precision_score(y_test, y_pred_nb)\n",
|
|
" recall_nb = recall_score(y_test, y_pred_nb)\n",
|
|
" f1_nb = f1_score(y_test, y_pred_nb)\n",
|
|
"\n",
|
|
" results = {\n",
|
|
" \"Logistic Regression\": {\"accuracy\": accuracy_lr, \"precision\": precision_lr, \"recall\": recall_lr, \"f1_score\": f1_lr},\n",
|
|
" \"KNN\": {\"accuracy\": accuracy_knn, \"precision\": precision_knn, \"recall\": recall_knn, \"f1_score\": f1_knn},\n",
|
|
" \"Naive Bayes\": {\"accuracy\": accuracy_nb, \"precision\": precision_nb, \"recall\": recall_nb, \"f1_score\": f1_nb}\n",
|
|
" }\n",
|
|
" \n",
|
|
" return results\n",
|
|
"\n",
|
|
"random_state_preprocessing = 41\n",
|
|
"random_state_model = 41 #Начинаем с одинаковых значений\n",
|
|
"\n",
|
|
"found = False\n",
|
|
"\n",
|
|
"while not found:\n",
|
|
" random_state_model += 1\n",
|
|
" for random_state_preprocessing in range(41, 141):\n",
|
|
"\n",
|
|
" results = task2_model_comparison(random_state_preprocessing, random_state_model)\n",
|
|
"\n",
|
|
" goal = True\n",
|
|
" for model_name, metrics in results.items():\n",
|
|
" for metric_name, metric_value in metrics.items():\n",
|
|
" if metric_value <= 0.6:\n",
|
|
" goal = False\n",
|
|
" break\n",
|
|
" if not goal:\n",
|
|
" break\n",
|
|
" if goal:\n",
|
|
" found = True\n",
|
|
" break\n",
|
|
"\n",
|
|
" if found:\n",
|
|
" print(f\"Найдены random_state_preprocessing = {random_state_preprocessing} и random_state_model = {random_state_model}, при которых все метрики больше 0.6:\")\n",
|
|
" print(results)\n",
|
|
" else:\n",
|
|
" print(f\"Не удалось найти подходящий random_state_preprocessing для random_state_model = {random_state_model}, увеличиваем random_state_model.\")\n",
|
|
"\n",
|
|
"if not found:\n",
|
|
" print(\"Не удалось найти подходящие random_state для выполнения условия.\")\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"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": "3.11.9"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|