5572 rows × 2 columns
\n", "" ], "text/plain": [ - " Label Text\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]" + " text 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": 76, + "execution_count": 16, "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[\"Text\"])\n", - "X_val = vectorizer.transform(validation_df[\"Text\"])\n", - "X_test = vectorizer.transform(test_df[\"Text\"])\n", + "X_train = vectorizer.fit_transform(train_df[\"text\"])\n", + "X_val = vectorizer.transform(val_df[\"text\"])\n", + "X_test = vectorizer.transform(test_df[\"text\"])\n", "\n", - "y_train, y_val, y_test = train_df[\"Label\"], validation_df[\"Label\"], test_df[\"Label\"]" + "y_train, y_val, y_test = train_df[\"label\"], val_df[\"label\"], test_df[\"label\"]" ] }, { "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, diff --git a/ch06/03_bonus_imdb-classification/train-bert-hf.py b/ch06/03_bonus_imdb-classification/train-bert-hf.py index 2ded1d2a..ef3773ae 100644 --- a/ch06/03_bonus_imdb-classification/train-bert-hf.py +++ b/ch06/03_bonus_imdb-classification/train-bert-hf.py @@ -234,15 +234,6 @@ def train_classifier_simple(model, train_loader, val_loader, optimizer, device, # Instantiate dataloaders ############################### - url = "https://archive.ics.uci.edu/static/public/228/sms+spam+collection.zip" - zip_path = "sms_spam_collection.zip" - extract_to = "sms_spam_collection" - new_file_path = Path(extract_to) / "SMSSpamCollection.tsv" - - base_path = Path(".") - file_names = ["train.csv", "val.csv", "test.csv"] - all_exist = all((base_path / file_name).exists() for file_name in file_names) - pad_token_id = tokenizer.encode(tokenizer.pad_token) train_dataset = IMDBDataset(base_path / "train.csv", max_length=256, tokenizer=tokenizer, pad_token_id=pad_token_id) diff --git a/ch06/03_bonus_imdb-classification/train-gpt.py b/ch06/03_bonus_imdb-classification/train-gpt.py index eb9c793b..1e305682 100644 --- a/ch06/03_bonus_imdb-classification/train-gpt.py +++ b/ch06/03_bonus_imdb-classification/train-gpt.py @@ -286,15 +286,6 @@ def train_classifier_simple(model, train_loader, val_loader, optimizer, device, # Instantiate dataloaders ############################### - url = "https://archive.ics.uci.edu/static/public/228/sms+spam+collection.zip" - zip_path = "sms_spam_collection.zip" - extract_to = "sms_spam_collection" - new_file_path = Path(extract_to) / "SMSSpamCollection.tsv" - - base_path = Path(".") - file_names = ["train.csv", "val.csv", "test.csv"] - all_exist = all((base_path / file_name).exists() for file_name in file_names) - tokenizer = tiktoken.get_encoding("gpt2") train_dataset = None