mirror of
https://github.com/rasbt/LLMs-from-scratch.git
synced 2025-11-03 19:30:26 +00:00
278 lines
7.9 KiB
Plaintext
278 lines
7.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "8968a681-2db1-4840-bb73-7d6c95986825",
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"metadata": {},
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"source": [
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"<table style=\"width:100%\">\n",
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"<tr>\n",
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"<td style=\"vertical-align:middle; text-align:left;\">\n",
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"<font size=\"2\">\n",
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"Supplementary code for the <a href=\"http://mng.bz/orYv\">Build a Large Language Model From Scratch</a> book by <a href=\"https://sebastianraschka.com\">Sebastian Raschka</a><br>\n",
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"<br>Code repository: <a href=\"https://github.com/rasbt/LLMs-from-scratch\">https://github.com/rasbt/LLMs-from-scratch</a>\n",
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"</font>\n",
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"</td>\n",
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"<td style=\"vertical-align:middle; text-align:left;\">\n",
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"<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>\n",
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"</td>\n",
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"</tr>\n",
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"</table>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8b6e1cdd-b14e-4368-bdbb-9bf7ab821791",
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"metadata": {},
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"source": [
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"# Scikit-learn Logistic Regression Model"
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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": 1,
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"id": "c2a72242-6197-4bef-aa05-696a152350d5",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"100% | 80.23 MB | 4.37 MB/s | 18.38 sec elapsed"
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]
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}
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],
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"source": [
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"!python download-prepare-dataset.py"
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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": 14,
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"id": "69f32433-e19c-4066-b806-8f30b408107f",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"train_df = pd.read_csv(\"train.csv\")\n",
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"val_df = pd.read_csv(\"validation.csv\")\n",
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"test_df = pd.read_csv(\"test.csv\")"
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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": 16,
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"id": "0808b212-fe91-48d9-80b8-55519f8835d5",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>text</th>\n",
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" <th>label</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>The only reason I saw \"Shakedown\" was that it ...</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>This is absolute drivel, designed to shock and...</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Lots of scenes and dialogue are flat-out goofy...</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>** and 1/2 stars out of **** Lifeforce is one ...</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>I learned a thing: you have to take this film ...</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" text label\n",
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"0 The only reason I saw \"Shakedown\" was that it ... 0\n",
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"1 This is absolute drivel, designed to shock and... 0\n",
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"2 Lots of scenes and dialogue are flat-out goofy... 1\n",
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"3 ** and 1/2 stars out of **** Lifeforce is one ... 1\n",
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"4 I learned a thing: you have to take this film ... 1"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"train_df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fae87bc1-14ca-4f89-8e12-49f77b0ec00d",
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"metadata": {},
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"source": [
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"## Scikit-learn baseline"
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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": 17,
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"id": "180318b7-de18-4b05-b84a-ba97c72b9d8e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.feature_extraction.text import CountVectorizer\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.metrics import accuracy_score"
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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": 20,
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"id": "25090b7c-f516-4be2-8083-3a7187fe4635",
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"metadata": {},
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"outputs": [],
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"source": [
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"vectorizer = CountVectorizer()\n",
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"\n",
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"X_train = vectorizer.fit_transform(train_df[\"text\"])\n",
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"X_val = vectorizer.transform(val_df[\"text\"])\n",
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"X_test = vectorizer.transform(test_df[\"text\"])\n",
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"\n",
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"y_train, y_val, y_test = train_df[\"label\"], val_df[\"label\"], test_df[\"label\"]"
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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": 22,
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"id": "0247de3a-88f0-4b9c-becd-157baf3acf49",
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"metadata": {},
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"outputs": [],
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"source": [
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"def eval(model, X_train, y_train, X_val, y_val, X_test, y_test):\n",
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" # Making predictions\n",
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" y_pred_train = model.predict(X_train)\n",
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" y_pred_val = model.predict(X_val)\n",
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" y_pred_test = model.predict(X_test)\n",
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" \n",
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" # Calculating accuracy and balanced accuracy\n",
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" accuracy_train = accuracy_score(y_train, y_pred_train)\n",
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" balanced_accuracy_train = balanced_accuracy_score(y_train, y_pred_train)\n",
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" \n",
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" accuracy_val = accuracy_score(y_val, y_pred_val)\n",
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" balanced_accuracy_val = balanced_accuracy_score(y_val, y_pred_val)\n",
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"\n",
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" accuracy_test = accuracy_score(y_test, y_pred_test)\n",
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" balanced_accuracy_test = balanced_accuracy_score(y_test, y_pred_test)\n",
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" \n",
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" # Printing the results\n",
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" print(f\"Training Accuracy: {accuracy_train*100:.2f}%\")\n",
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" print(f\"Validation Accuracy: {accuracy_val*100:.2f}%\")\n",
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" print(f\"Test Accuracy: {accuracy_test*100:.2f}%\")"
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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": 23,
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"id": "c29c6dfc-f72d-40ab-8cb5-783aad1a15ab",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training Accuracy: 50.01%\n",
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"Validation Accuracy: 50.14%\n",
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"Test Accuracy: 49.91%\n"
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]
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}
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],
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"source": [
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"from sklearn.dummy import DummyClassifier\n",
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"\n",
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"# Create a dummy classifier with the strategy to predict the most frequent class\n",
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"dummy_clf = DummyClassifier(strategy=\"most_frequent\")\n",
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"dummy_clf.fit(X_train, y_train)\n",
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"\n",
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"eval(dummy_clf, X_train, y_train, X_val, y_val, X_test, y_test)"
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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": 24,
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"id": "088a8a3a-3b74-4d10-a51b-cb662569ae39",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training Accuracy: 99.80%\n",
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"Validation Accuracy: 88.62%\n",
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"Test Accuracy: 88.85%\n"
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]
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}
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],
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"source": [
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"model = LogisticRegression(max_iter=1000)\n",
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"model.fit(X_train, y_train)\n",
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"eval(model, X_train, y_train, X_val, y_val, X_test, y_test)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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