@@ -1,59 +1,50 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "8b6e1cdd-b14e-4368-bdbb-9bf7ab821791",
"metadata": {},
"source": [
"# Scikit-learn Logistic Regression Model"
]
},
{
"cell_type": "code",
"execution_count": 75 ,
"id": "b612c4c1-fa3c-47b9-a8ce-9e32f371e160 ",
"execution_count": 1 ,
"id": "c2a72242-6197-4bef-aa05-696a152350d5 ",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sms_spam_collection/SMSSpamCollection.tsv already exists. Skipping download and extraction.\n "
"100% | 80.23 MB | 4.37 MB/s | 18.38 sec elapsed "
]
}
],
"source": [
"import urllib.request\n",
"import zipfile\n",
"import os\n",
"from pathlib import Path\n",
"\n",
"url = \"https://archive.ics.uci.edu/static/public/228/sms+spam+collection.zip\"\n",
"zip_path = \"sms_spam_collection.zip\"\n",
"extract_to = \"sms_spam_collection\"\n",
"new_file_path = Path(extract_to) / \"SMSSpamCollection.tsv\"\n",
"\n",
"def download_and_unzip(url, zip_path, extract_to, new_file_path):\n",
" # Check if the target file already exists\n",
" if new_file_path.exists():\n",
" print(f\"{new_file_path} already exists. Skipping download and extraction.\")\n",
" return\n",
"\n",
" # Downloading the file\n",
" with urllib.request.urlopen(url) as response:\n",
" with open(zip_path, \"wb\") as out_file:\n",
" out_file.write(response.read())\n",
"\n",
" # Unzipping the file\n",
" with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n",
" zip_ref.extractall(extract_to)\n",
"\n",
" # Renaming the file to indicate its format\n",
" original_file = Path(extract_to) / \"SMSSpamCollection\"\n",
" os.rename(original_file, new_file_path)\n",
" print(f\"File download and saved as {new_file_path}\")\n",
"\n",
"# Execute the function\n",
"download_and_unzip_spam_data(url, zip_path, extract_to, new_file_path)"
"!python download-prepare-dataset.py"
]
},
{
"cell_type": "code",
"execution_count": 76 ,
"execution_count": 14 ,
"id": "69f32433-e19c-4066-b806-8f30b408107f",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"train_df = pd.read_csv(\"train.csv\")\n",
"val_df = pd.read_csv(\"validation.csv\")\n",
"test_df = pd.read_csv(\"test.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "0808b212-fe91-48d9-80b8-55519f8835d5",
"metadata": {},
"outputs": [
{
"data": {
@@ -76,280 +67,56 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Label </th>\n",
" <th>Text </th>\n",
" <th>text </th>\n",
" <th>label </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ham </td>\n",
" <td>Aight text me when you're back at mu and I'll ... </td>\n",
" <td>The only reason I saw \"Shakedown\" was that it ... </td>\n",
" <td>0 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ham </td>\n",
" <td>Our Prashanthettan's mother passed away last n... </td>\n",
" <td>This is absolute drivel, designed to shock and... </td>\n",
" <td>0 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ham </td>\n",
" <td>No it will reach by 9 only. She telling she wi... </td>\n",
" <td>Lots of scenes and dialogue are flat-out goofy... </td>\n",
" <td>1 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ham </td>\n",
" <td>Do you know when the result. </td>\n",
" <td>** and 1/2 stars out of **** Lifeforce is one ... </td>\n",
" <td>1 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>spam </td>\n",
" <td>Hi. Customer Loyalty Offer:The NEW Nokia6650 M... </td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5567</th>\n",
" <td>ham</td>\n",
" <td>I accidentally brought em home in the box</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5568</th>\n",
" <td>spam</td>\n",
" <td>Moby Pub Quiz.Win a £100 High Street prize if ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5569</th>\n",
" <td>ham</td>\n",
" <td>Que pases un buen tiempo or something like that</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5570</th>\n",
" <td>ham</td>\n",
" <td>Nowadays people are notixiquating the laxinorf...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5571</th>\n",
" <td>ham</td>\n",
" <td>Ard 4 lor...</td>\n",
" <td>I learned a thing: you have to take this film ... </td>\n",
" <td>1 </td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5572 rows ×
"</div>"
],
"text/plain": [
" Label T ext\n",
"0 ham Aight text me when you're back at mu and I'll ... \n",
"1 ham Our Prashanthettan's mother passed away last n... \n",
"2 ham No it will reach by 9 only. She telling she wi... \n",
"3 ham Do you know when the result. \n",
"4 spam Hi. Customer Loyalty Offer:The NEW Nokia6650 M...\n",
"... ... ...\n",
"5567 ham I accidentally brought em home in the box\n",
"5568 spam Moby Pub Quiz.Win a £100 High Street prize if ...\n",
"5569 ham Que pases un buen tiempo or something like that\n",
"5570 ham Nowadays people are notixiquating the laxinorf...\n",
"5571 ham Ard 4 lor...\n",
"\n",
"[5572 rows x 2 columns]"
" t ext label \n",
"0 The only reason I saw \"Shakedown\" was that it ... 0 \n",
"1 This is absolute drivel, designed to shock and... 0 \n",
"2 Lots of scenes and dialogue are flat-out goofy... 1 \n",
"3 ** and 1/2 stars out of **** Lifeforce is one ... 1 \n",
"4 I learned a thing: you have to take this film ... 1"
]
},
"execution_count": 7 6,
"execution_count": 1 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv(new_file_path, sep=\"\\t\", header=None, names=[\"Label\", \"Text\"])\n",
"df = df.sample(frac=1, random_state=123).reset_index(drop=True) # Shuffle the DataFrame\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 77,
"id": "4b7beeba-9f3a-45f0-b2dc-76bb155a8f0e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Label\n",
"ham 4825\n",
"spam 747\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"# Class distribution\n",
"print(df[\"Label\"].value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 78,
"id": "b3db862a-9e03-4715-babb-9b699e4f4a36",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Label\n",
"spam 747\n",
"ham 747\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"# Count the instances of 'spam'\n",
"n_spam = df[df[\"Label\"] == \"spam\"].shape[0]\n",
"\n",
"# Randomly sample 'ham' instances to match the number of 'spam' instances\n",
"ham_sampled = df[df[\"Label\"] == \"ham\"].sample(n_spam)\n",
"\n",
"# Combine the sampled 'ham' with all 'spam'\n",
"balanced_df = pd.concat([ham_sampled, df[df[\"Label\"] == \"spam\"]])\n",
"\n",
"# Shuffle the DataFrame\n",
"balanced_df = balanced_df.sample(frac=1).reset_index(drop=True)\n",
"\n",
"# Now balanced_df is the balanced DataFrame\n",
"print(balanced_df[\"Label\"].value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 79,
"id": "0af991e5-98ef-439a-a43d-63a581a2cc6d",
"metadata": {},
"outputs": [],
"source": [
"df[\"Label\"] = df[\"Label\"].map({\"ham\": 0, \"spam\": 1})"
]
},
{
"cell_type": "code",
"execution_count": 80,
"id": "2f5b00ef-e3ed-4819-b271-5f355848feb5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training set:\n",
"Label\n",
"0 0.86612\n",
"1 0.13388\n",
"Name: proportion, dtype: float64\n",
"\n",
"Validation set:\n",
"Label\n",
"0 0.866906\n",
"1 0.133094\n",
"Name: proportion, dtype: float64\n",
"\n",
"Test set:\n",
"Label\n",
"0 0.864816\n",
"1 0.135184\n",
"Name: proportion, dtype: float64\n"
]
}
],
"source": [
"# Define split ratios\n",
"train_size, validation_size = 0.7, 0.1\n",
"# Test size is implied to be 0.2 as the remainder\n",
"\n",
"# Split the data\n",
"def stratified_split(df, stratify_col, train_frac, validation_frac):\n",
" stratified_train = pd.DataFrame()\n",
" stratified_validation = pd.DataFrame()\n",
" stratified_test = pd.DataFrame()\n",
"\n",
" # Stratify split by the unique values in the column\n",
" for value in df[stratify_col].unique():\n",
" # Filter the DataFrame for the class\n",
" df_class = df[df[stratify_col] == value]\n",
" \n",
" # Calculate class split sizes\n",
" train_end = int(len(df_class) * train_frac)\n",
" validation_end = train_end + int(len(df_class) * validation_frac)\n",
" \n",
" # Slice the DataFrame to get the sets\n",
" stratified_train = pd.concat([stratified_train, df_class[:train_end]], axis=0)\n",
" stratified_validation = pd.concat([stratified_validation, df_class[train_end:validation_end]], axis=0)\n",
" stratified_test = pd.concat([stratified_test, df_class[validation_end:]], axis=0)\n",
"\n",
" # Shuffle the sets again\n",
" stratified_train = stratified_train.sample(frac=1, random_state=123).reset_index(drop=True)\n",
" stratified_validation = stratified_validation.sample(frac=1, random_state=123).reset_index(drop=True)\n",
" stratified_test = stratified_test.sample(frac=1, random_state=123).reset_index(drop=True)\n",
"\n",
" return stratified_train, stratified_validation, stratified_test\n",
"\n",
"# Apply the stratified split function\n",
"train_df, validation_df, test_df = stratified_split(df, \"Label\", train_size, validation_size)\n",
"\n",
"# Check the results\n",
"print(f\"Training set:\\n{train_df['Label'].value_counts(normalize=True)}\")\n",
"print(f\"\\nValidation set:\\n{validation_df['Label'].value_counts(normalize=True)}\")\n",
"print(f\"\\nTest set:\\n{test_df['Label'].value_counts(normalize=True)}\")"
]
},
{
"cell_type": "code",
"execution_count": 81,
"id": "65808167-2b93-45b0-8506-ce722732ce77",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training set:\n",
"Label\n",
"ham 0.5\n",
"spam 0.5\n",
"Name: proportion, dtype: float64\n",
"\n",
"Validation set:\n",
"Label\n",
"ham 0.5\n",
"spam 0.5\n",
"Name: proportion, dtype: float64\n",
"\n",
"Test set:\n",
"Label\n",
"spam 0.5\n",
"ham 0.5\n",
"Name: proportion, dtype: float64\n"
]
}
],
"source": [
"# Define split ratios\n",
"train_size, validation_size = 0.7, 0.1\n",
"# Test size is implied to be 0.2 as the remainder\n",
"\n",
"# Apply the stratified split function\n",
"train_df, validation_df, test_df = stratified_split(balanced_df, \"Label\", train_size, validation_size)\n",
"\n",
"# Check the results\n",
"print(f\"Training set:\\n{train_df['Label'].value_counts(normalize=True)}\")\n",
"print(f\"\\nValidation set:\\n{validation_df['Label'].value_counts(normalize=True)}\")\n",
"print(f\"\\nTest set:\\n{test_df['Label'].value_counts(normalize=True)}\")"
"train_df.head()"
]
},
{
@@ -362,35 +129,35 @@
},
{
"cell_type": "code",
"execution_count": 82 ,
"execution_count": 17 ,
"id": "180318b7-de18-4b05-b84a-ba97c72b9d8e",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import CountVectorizer\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.metrics import accuracy_score, balanced_ accuracy_score"
"from sklearn.metrics import accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": 83 ,
"execution_count": 20 ,
"id": "25090b7c-f516-4be2-8083-3a7187fe4635",
"metadata": {},
"outputs": [],
"source": [
"vectorizer = CountVectorizer()\n",
"\n",
"X_train = vectorizer.fit_transform(train_df[\"T ext\"])\n",
"X_val = vectorizer.transform(validation _df[\"T ext\"])\n",
"X_test = vectorizer.transform(test_df[\"T ext\"])\n",
"X_train = vectorizer.fit_transform(train_df[\"t ext\"])\n",
"X_val = vectorizer.transform(val_df[\"t ext\"])\n",
"X_test = vectorizer.transform(test_df[\"t ext\"])\n",
"\n",
"y_train, y_val, y_test = train_df[\"L abel\"], validation _df[\"L abel\"], test_df[\"L abel\"]"
"y_train, y_val, y_test = train_df[\"l abel\"], val_df[\"l abel\"], test_df[\"l abel\"]"
]
},
{
"cell_type": "code",
"execution_count": 84 ,
"execution_count": 22 ,
"id": "0247de3a-88f0-4b9c-becd-157baf3acf49",
"metadata": {},
"outputs": [],
@@ -414,16 +181,12 @@
" # Printing the results\n",
" print(f\"Training Accuracy: {accuracy_train*100:.2f}%\")\n",
" print(f\"Validation Accuracy: {accuracy_val*100:.2f}%\")\n",
" print(f\"Test Accuracy: {accuracy_test*100:.2f}%\")\n",
" \n",
" print(f\"\\nTraining Balanced Accuracy: {balanced_accuracy_train*100:.2f}%\")\n",
" print(f\"Validation Balanced Accuracy: {balanced_accuracy_val*100:.2f}%\")\n",
" print(f\"Test Balanced Accuracy: {balanced_accuracy_test*100:.2f}%\")"
" print(f\"Test Accuracy: {accuracy_test*100:.2f}%\")"
]
},
{
"cell_type": "code",
"execution_count": 85 ,
"execution_count": 23 ,
"id": "c29c6dfc-f72d-40ab-8cb5-783aad1a15ab",
"metadata": {},
"outputs": [
@@ -431,13 +194,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Training Accuracy: 50.00 %\n",
"Validation Accuracy: 50.00 %\n",
"Test Accuracy: 50.00 %\n",
"\n",
"Training Balanced Accuracy: 50.00%\n",
"Validation Balanced Accuracy: 50.00%\n",
"Test Balanced Accuracy: 50.00%\n"
"Training Accuracy: 50.01 %\n",
"Validation Accuracy: 50.14 %\n",
"Test Accuracy: 49.91 %\n"
]
}
],
@@ -453,7 +212,7 @@
},
{
"cell_type": "code",
"execution_count": 86 ,
"execution_count": 24 ,
"id": "088a8a3a-3b74-4d10-a51b-cb662569ae39",
"metadata": {},
"outputs": [
@@ -461,13 +220,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Training Accuracy: 99.81 %\n",
"Validation Accuracy: 95.27 %\n",
"Test Accuracy: 96.03 %\n",
"\n",
"Training Balanced Accuracy: 99.81%\n",
"Validation Balanced Accuracy: 95.27%\n",
"Test Balanced Accuracy: 96.03%\n"
"Training Accuracy: 99.80 %\n",
"Validation Accuracy: 88.62 %\n",
"Test Accuracy: 88.85 %\n"
]
}
],
@@ -476,22 +231,6 @@
"model.fit(X_train, y_train)\n",
"eval(model, X_train, y_train, X_val, y_val, X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "34411348-45bc-4b01-bebf-b3602c002ef1",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "5a9bc6b1-c8b9-4d4f-bfe4-c5a4a8b0c756",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
@@ -510,7 +249,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6 "
"version": "3.11.4 "
}
},
"nbformat": 4,
rasbt