{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "te5uCzPlhBWx" }, "source": [ "# Processing the data (PyTorch)" ] }, { "cell_type": "markdown", "metadata": { "id": "eiQ3FKJDhBW0" }, "source": [ "Install the Transformers, Datasets, and Evaluate libraries to run this notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HedrkDuchBW2" }, "outputs": [], "source": [ "#let's install the necessary libraries from Hugging Face\n", "!pip install datasets evaluate transformers[sentencepiece] transformers[torch]" ] }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login\n", "#authenticate into the huggin face\n", "notebook_login()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "2459586b900d4972893416101cf66518", "bb63af0e6e2d44d39dea0c7fb50f2675", "b84acaf57645444a93e8344e62b02f37", "0adacec9605245e8813c1eb6c375f97c", "3556f7c20dd942d9845a172d2fb3a308", "c9c4f33186b34af5bfe4b23f5a94cd92", "d9fce65fdc0f4f4e99511a95049b0627", "1dfa79c17ca34ccb8a15887162536305", "20b45c6e63d94306a2cd368e6dfc0719", "7853cc389609486e9774a6327cbc6d7f", "0bfa8f38be0c4d5e8b1a8f2a21623e2f", "f363053a53f442379a9be069597c70dc", "1c90fe7a114140a9b32d8107c2af011c", "b45e9bbed2c34078813f4d8be11ba856", "784ba2ebff4d451485ba312e0dd065e7", "74757608b6d945b186ce03761a04a067", "6f292a0a27234aaebaa90c47edbaa6bd", "d35e9d78443840a6af111a3e3245184d", "589080843ecd489488d86c10bc3c3d67", "404e6261b69646be85dd78a0fb1dc3cc", "adafd48f91e54326a4a5b2bb297e3d47", "56a61a4c28de4eb9833e571d4067c4c4", "954f23b0ee9249df81dd30725c9954b0", "ec75914e1c264260a9f573728880474a", "e998f2b2d75d4f3887c59669603793df", "eea3bf1ec05c4ba28cd5f1baa44bcfd8", "1b22a95ee6ff4fdca56a966d35c46ddf", "83c197270ac54969af02fa998fec28f1", "21453473c9f04ef992fe35419f6afff9", "3980e52353a14d538c1dc4b1b45d1396", "1fa7b92f0588403bb146f602942baea6", "20169140dbe04320b1a7d090d738f273" ] }, "id": "Z4IolwVzaQQh", "outputId": "7f4c4beb-0cdf-4549-cfb9-6496f50c7118" }, "execution_count": 137, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='
] 41.00M 40.1MB/s in 1.0s \n", "\n", "2024-04-28 15:34:18 (40.1 MB/s) - ‘drugsCom_raw.zip.1’ saved [42989872]\n", "\n", "Archive: drugsCom_raw.zip\n", "replace drugsComTest_raw.tsv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n", " inflating: drugsComTest_raw.tsv \n", "replace drugsComTrain_raw.tsv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n", " inflating: drugsComTrain_raw.tsv \n" ] } ] }, { "cell_type": "code", "source": [ "from datasets import load_dataset\n", "\n", "#save the data into a csv file with both splits\n", "data_files = {\"train\": \"drugsComTrain_raw.tsv\", \"test\": \"drugsComTest_raw.tsv\"}\n", "# \\t is the tab character in Python\n", "drug_dataset = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\n", "\n", "drug_dataset" ], "metadata": { "id": "yifIkdVzj1kS", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "a0b787f7-ed71-4825-d638-378c0b198dac" }, "execution_count": 108, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['Unnamed: 0', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n", " num_rows: 161297\n", " })\n", " test: Dataset({\n", " features: ['Unnamed: 0', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n", " num_rows: 53766\n", " })\n", "})" ] }, "metadata": {}, "execution_count": 108 } ] }, { "cell_type": "code", "source": [ "#rename columns\n", "drug_dataset = drug_dataset.rename_column(\n", " original_column_name=\"Unnamed: 0\", new_column_name=\"patient_id\"\n", ")\n", "drug_dataset" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EOOatck3rwmY", "outputId": "a2443b2e-db62-45a0-87d5-320d69b08404" }, "execution_count": 109, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n", " num_rows: 161297\n", " })\n", " test: Dataset({\n", " features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n", " num_rows: 53766\n", " })\n", "})" ] }, "metadata": {}, "execution_count": 109 } ] }, { "cell_type": "code", "source": [ "#remove rows where the condition feature has no data\n", "drug_dataset = drug_dataset.filter(lambda x: x[\"condition\"] is not None)" ], "metadata": { "id": "cZzYLJphrw6W" }, "execution_count": 110, "outputs": [] }, { "cell_type": "code", "source": [ "#turn it into lowercase text\n", "def lowercase_condition(example):\n", " return {\"condition\": example[\"condition\"].lower()}\n", "\n", "drug_dataset = drug_dataset.map(lowercase_condition)" ], "metadata": { "id": "MVsier2fsGr8" }, "execution_count": 111, "outputs": [] }, { "cell_type": "code", "source": [ "#create a feature called 'review length' because we will later remove reviews that are too short\n", "def compute_review_length(example):\n", " return {\"review_length\": len(example[\"review\"].split())}\n", "\n", "drug_dataset = drug_dataset.map(compute_review_length)" ], "metadata": { "id": "1YS2yrZgsGgK" }, "execution_count": 112, "outputs": [] }, { "cell_type": "code", "source": [ "#keep only reviews with at least 16 letters\n", "drug_dataset = drug_dataset.filter(lambda x: x[\"review_length\"] > 15)\n", "print(drug_dataset.num_rows)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qQk-ReJssefI", "outputId": "e1587a33-b6e4-4a7b-fcd7-2c9b8f2fb6f4" }, "execution_count": 113, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'train': 151236, 'test': 50396}\n" ] } ] }, { "cell_type": "code", "source": [ "drug_dataset" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DyNIhmXc6GQg", "outputId": "e1807a4e-66c0-419d-c7da-e5eaeab995f5" }, "execution_count": 114, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n", " num_rows: 151236\n", " })\n", " test: Dataset({\n", " features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n", " num_rows: 50396\n", " })\n", "})" ] }, "metadata": {}, "execution_count": 114 } ] }, { "cell_type": "code", "source": [ "import html\n", "\n", "#remove html characters from the review feature\n", "new_drug_dataset = drug_dataset.map(\n", " lambda x: {\"review\": [html.unescape(o) for o in x[\"review\"]]}, batched=True)" ], "metadata": { "id": "Hzu-0TCCs7tr" }, "execution_count": 115, "outputs": [] }, { "cell_type": "code", "source": [ "#create three splits (train, validation and test) with what we have\n", "drug_clean = new_drug_dataset[\"train\"].train_test_split(train_size=0.8, seed=42)\n", "\n", "# Rename the default \"test\" split to \"validation\"\n", "drug_clean[\"validation\"] = drug_clean.pop(\"test\")\n", "\n", "# Add the \"test\" set to our `DatasetDict`\n", "drug_clean[\"test\"] = new_drug_dataset[\"test\"]\n", "\n", "drug_clean" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_YpST6_3yBCA", "outputId": "21252016-7685-44b0-8c29-79031b276811" }, "execution_count": 116, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n", " num_rows: 120988\n", " })\n", " validation: Dataset({\n", " features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n", " num_rows: 30248\n", " })\n", " test: Dataset({\n", " features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n", " num_rows: 50396\n", " })\n", "})" ] }, "metadata": {}, "execution_count": 116 } ] }, { "cell_type": "code", "source": [ "drug_clean['train'].features" ], "metadata": { "id": "92I1TbyMs7-_", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "8c7fbcb9-cc0d-4831-af30-5b7e6c18905c" }, "execution_count": 117, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'patient_id': Value(dtype='int64', id=None),\n", " 'drugName': Value(dtype='string', id=None),\n", " 'condition': Value(dtype='string', id=None),\n", " 'review': Value(dtype='string', id=None),\n", " 'rating': Value(dtype='float64', id=None),\n", " 'date': Value(dtype='string', id=None),\n", " 'usefulCount': Value(dtype='int64', id=None),\n", " 'review_length': Value(dtype='int64', id=None)}" ] }, "metadata": {}, "execution_count": 117 } ] }, { "cell_type": "code", "source": [ "from transformers import AutoTokenizer, DataCollatorWithPadding\n", "\n", "#let's tokenize the review feature, which is the one we'll use as predictive feature\n", "raw_datasets = drug_clean\n", "checkpoint = \"bert-base-uncased\"\n", "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n", "\n", "def tokenize_function(example):\n", " return tokenizer(example[\"review\"], truncation=True)\n", "\n", "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n", "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)" ], "metadata": { "id": "6z0WrmVrV5Fw", "colab": { "base_uri": "https://localhost:8080/", "height": 113, "referenced_widgets": [ "8815bc4f84334c66a72bef825a716be6", "12347ee2672b4b4ba3c8671c725352f5", "6e1cf29ae5b54b6f87366ab6e66f905b", "dcf16678502b4a19901aa56d8a16a9f4", "0606e216d28149feb818c8e7919cf987", "664a737eea6847c4acc62c2169b13675", "56304f48a0de4c45b940828de823a749", "56eb06a4ce794bc68310219e8c8891ec", "fa7119d5141a4856a0369d42cc8c0846", "3642d8ebdf0a4a649b2ebe309bad02a5", "6c4c1bb481eb40639f92efeb694dc8d1", "e87b26d89be642348040ae7e03c75fec", "2c6bf5514fe74289954d26b01753ba08", "5aa9a0f8dd4a4296a2e14011aecaa1c8", "d6f72b92b863462ba31f65439c385fb0", "a82d0e6bb393410ca7325d94f05c1dc5", "13a407daf96e49e5b4ddd51712e33644", "5faa4e2f8598445ebdebb2138d1db076", "0485a3579d5544a9a36cbd81e34a3f6c", "aa13c976a702422ea532a766810d6e2c", "4a078d05cee54316acc4d7bd27481369", "927ce83ff4b04b6c977e6b5c32295b8b", "5ba5166459f24d09ae9d2474ea6d6731", "dbd8c86d43654b48a7ec0842dbc3b9bc", "a32fdfb0ff0b429688f24195008b61be", "ae8bc40b6c3a427eafba8236e83e4b5c", "e0da74ab9e214156a3b6bad7165a1475", "33637cc35432432db50363a247cc9b0a", "fb54e06787714635bafdc9b7f941d64d", "fef842e408574a4998ee140a0907b2b3", "25d1e01786ed47188e3caa184130dbd0", "0fb40e51d1c7484b9478e3dae9134e82", "b30c7d2540f945ebaa37ee75e7373cf1" ] }, "outputId": "233d5e71-1fce-4968-f654-b05192d26de2" }, "execution_count": 118, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/120988 [00:00" ], "text/html": [] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n", "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n" ] } ] }, { "cell_type": "code", "source": [ "import numpy as np\n", "#given that we get all the logs from the trainer, we'll select the label with the highest likelihood with an argmax function\n", "preds = np.argmax(predictions.predictions, axis=-1)" ], "metadata": { "id": "DgBVUDfQzZSM" }, "execution_count": 140, "outputs": [] }, { "cell_type": "code", "source": [ "from evaluate import load\n", "#let's define a comput_metrics function that will return accuracy, precision, recall and f1 for our training\n", "def compute_metrics(eval_preds):\n", " logits, labels = eval_preds\n", " preds = np.argmax(logits, axis=-1)\n", "\n", " # Load metrics with zero_division parameter set to 1\n", " metrics = {\n", " 'accuracy': load('accuracy'),\n", " 'precision': load('precision', config={'zero_division': 1}),\n", " 'recall': load('recall', config={'zero_division': 1}),\n", " 'f1': load('f1', config={'zero_division': 1})\n", " }\n", "\n", " # Compute each metric, omitting the 'average' argument for accuracy\n", " results = {}\n", " for metric_name, metric in metrics.items():\n", " if metric_name == 'accuracy':\n", " result = metric.compute(predictions=preds, references=labels)\n", " results[metric_name] = result['accuracy']\n", " else:\n", " result = metric.compute(predictions=preds, references=labels, average='weighted')\n", " results[metric_name] = result[metric_name]\n", "\n", " return results" ], "metadata": { "id": "6x4ETEv1Fr_2" }, "execution_count": 141, "outputs": [] }, { "cell_type": "code", "source": [ "#let's train the model right away\n", "trainer.train()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 348 }, "id": "7WZ77bmbG5Zt", "outputId": "b8100baf-99fc-425c-9a5b-e74ab3562d29" }, "execution_count": 142, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [339/339 02:18, Epoch 3/3]\n", "
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EpochTraining LossValidation LossAccuracyPrecisionRecallF1
1No log1.1375110.7747040.7301480.7747040.744985
2No log0.5595480.8853750.8674840.8853750.872829
3No log0.4307890.9209490.9060860.9209490.910576

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n", "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n", "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=339, training_loss=1.01418788313514, metrics={'train_runtime': 138.9845, 'train_samples_per_second': 19.513, 'train_steps_per_second': 2.439, 'total_flos': 245512156913712.0, 'train_loss': 1.01418788313514, 'epoch': 3.0})" ] }, "metadata": {}, "execution_count": 142 } ] }, { "cell_type": "code", "source": [ "#it's good. Let's push the model to the hugging face hub\n", "trainer.push_to_hub()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 70 }, "id": "DxFz2gWSMUI6", "outputId": "9e83f368-3084-4168-c828-1665b934e769" }, "execution_count": 143, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "CommitInfo(commit_url='https://huggingface.co/Marcuswas/bert-drug-review-to-condition/commit/d6f99808a6fad08574389e154a44694bbd24ad5d', commit_message='End of training', commit_description='', oid='d6f99808a6fad08574389e154a44694bbd24ad5d', pr_url=None, pr_revision=None, pr_num=None)" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 143 } ] } ], "metadata": { "colab": { "provenance": [], "gpuType": "T4" }, "language_info": { "name": "python" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "2459586b900d4972893416101cf66518": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "VBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "VBoxView", "box_style": "", "children": [ "IPY_MODEL_adafd48f91e54326a4a5b2bb297e3d47", "IPY_MODEL_56a61a4c28de4eb9833e571d4067c4c4", "IPY_MODEL_954f23b0ee9249df81dd30725c9954b0", "IPY_MODEL_ec75914e1c264260a9f573728880474a" ], "layout": "IPY_MODEL_d9fce65fdc0f4f4e99511a95049b0627" } }, "bb63af0e6e2d44d39dea0c7fb50f2675": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_1dfa79c17ca34ccb8a15887162536305", "placeholder": "​", "style": "IPY_MODEL_20b45c6e63d94306a2cd368e6dfc0719", "value": "


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