From ee0920396cc75551e69a5bbb277a029b68dffdba Mon Sep 17 00:00:00 2001
From: Ludovic Moncla <moncla.ludovic@gmail.com>
Date: Thu, 17 Nov 2022 20:34:32 +0100
Subject: [PATCH] Create Predict.ipynb
---
notebooks/Predict.ipynb | 2096 +++++++++++++++++++++++++++++++++++++++
1 file changed, 2096 insertions(+)
create mode 100644 notebooks/Predict.ipynb
diff --git a/notebooks/Predict.ipynb b/notebooks/Predict.ipynb
new file mode 100644
index 0000000..ab25086
--- /dev/null
+++ b/notebooks/Predict.ipynb
@@ -0,0 +1,2096 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#\n",
+ "\n",
+ "## 1. Setup the environment\n",
+ "\n",
+ "### 1.1 Setup colab environment\n",
+ "\n",
+ "#### 1.1.1 Install packages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "pwmZ5bBvgGNh",
+ "outputId": "fce0a8bf-1779-4079-c7ac-200ebb2678c5"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install transformers==4.10.3\n",
+ "!pip install sentencepiece"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 1.1.2 Use more RAM"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "WF0qFN_g3ekz",
+ "outputId": "f3a5f049-24ee-418f-fe5e-84c633234ad8"
+ },
+ "outputs": [],
+ "source": [
+ "from psutil import virtual_memory\n",
+ "ram_gb = virtual_memory().total / 1e9\n",
+ "print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
+ "\n",
+ "if ram_gb < 20:\n",
+ " print('Not using a high-RAM runtime')\n",
+ "else:\n",
+ " print('You are using a high-RAM runtime!')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 1.1.3 Mount GoogleDrive"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "vL0S-s9Uofvn",
+ "outputId": "4b7efa4d-7f09-4c8e-bc98-99e6099ede32"
+ },
+ "outputs": [],
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount('/content/drive')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8hzEGHl7gmzk"
+ },
+ "source": [
+ "### 1.2 Setup GPU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "dPOU-Efhf4ui",
+ "outputId": "121dd21e-f98c-483d-d6d1-2838f732a4e2"
+ },
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "\n",
+ "# If there's a GPU available...\n",
+ "if torch.cuda.is_available(): \n",
+ " # Tell PyTorch to use the GPU. \n",
+ " device = torch.device(\"cuda\")\n",
+ " print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
+ " print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
+ "\n",
+ "# for MacOS\n",
+ "elif torch.backends.mps.is_available() and torch.backends.mps.is_built():\n",
+ " device = torch.device(\"mps\")\n",
+ " print('We will use the GPU')\n",
+ "else:\n",
+ " device = torch.device(\"cpu\")\n",
+ " print('No GPU available, using the CPU instead.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wSqbrupGMc1M"
+ },
+ "source": [
+ "### 1.3 Import librairies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "SkErnwgMMbRj"
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd \n",
+ "import numpy as np\n",
+ "\n",
+ "from transformers import BertTokenizer, CamembertTokenizer\n",
+ "from torch.utils.data import TensorDataset, DataLoader, SequentialSampler"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "c5QKcXulhNJ-"
+ },
+ "source": [
+ "## 2. Load Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#path = \"drive/MyDrive/Classification-EDdA/\"\n",
+ "path = \"../\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!wget https://projet.liris.cnrs.fr/geode/files/datasets/EDdA/Classification/LGE_withContent.tsv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_LGE = pd.read_csv(path + \"data/LGE_withContent.tsv\", sep=\"\\t\")\n",
+ "data_LGE = df_LGE[\"content\"].values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_LGE.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_LGE.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3. Load model and predict\n",
+ "\n",
+ "### 3.1 BERT / CamemBERT"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model_name = \"bert-base-multilingual-cased\"\n",
+ "#model_name = \"camembert-base\"\n",
+ "model_path = path + \"models/model_\" + model_name + \"_s10000.pt\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def generate_dataloader(tokenizer, sentences, batch_size = 8, max_len = 512):\n",
+ "\n",
+ " # Tokenize all of the sentences and map the tokens to thier word IDs.\n",
+ " input_ids_test = []\n",
+ " # For every sentence...\n",
+ " for sent in sentences:\n",
+ " # `encode` will:\n",
+ " # (1) Tokenize the sentence.\n",
+ " # (2) Prepend the `[CLS]` token to the start.\n",
+ " # (3) Append the `[SEP]` token to the end.\n",
+ " # (4) Map tokens to their IDs.\n",
+ " encoded_sent = tokenizer.encode(\n",
+ " sent, # Sentence to encode.\n",
+ " add_special_tokens = True, # Add '[CLS]' and '[SEP]'\n",
+ " )\n",
+ " input_ids_test.append(encoded_sent)\n",
+ "\n",
+ " # Pad our input tokens\n",
+ " padded_test = []\n",
+ " for i in input_ids_test:\n",
+ " if len(i) > max_len:\n",
+ " padded_test.extend([i[:max_len]])\n",
+ " else:\n",
+ " padded_test.extend([i + [0] * (max_len - len(i))])\n",
+ " input_ids_test = np.array(padded_test)\n",
+ "\n",
+ " # Create attention masks\n",
+ " attention_masks = []\n",
+ "\n",
+ " # Create a mask of 1s for each token followed by 0s for padding\n",
+ " for seq in input_ids_test:\n",
+ " seq_mask = [float(i>0) for i in seq]\n",
+ " attention_masks.append(seq_mask)\n",
+ "\n",
+ " # Convert to tensors.\n",
+ " prediction_inputs = torch.tensor(input_ids_test)\n",
+ " prediction_masks = torch.tensor(attention_masks)\n",
+ " #set batch size\n",
+ "\n",
+ " # Create the DataLoader.\n",
+ " prediction_data = TensorDataset(prediction_inputs, prediction_masks)\n",
+ " prediction_sampler = SequentialSampler(prediction_data)\n",
+ "\n",
+ " return DataLoader(prediction_data, sampler=prediction_sampler, batch_size=batch_size)\n",
+ "\n",
+ "\n",
+ "\n",
+ "def predict(model, dataloader, device):\n",
+ "\n",
+ " # Put model in evaluation mode\n",
+ " model.eval()\n",
+ "\n",
+ " # Tracking variables\n",
+ " predictions_test , true_labels = [], []\n",
+ " pred_labels_ = []\n",
+ " # Predict\n",
+ " for batch in dataloader:\n",
+ " # Add batch to GPU\n",
+ " batch = tuple(t.to(device) for t in batch)\n",
+ "\n",
+ " # Unpack the inputs from the dataloader\n",
+ " b_input_ids, b_input_mask = batch\n",
+ "\n",
+ " # Telling the model not to compute or store gradients, saving memory and\n",
+ " # speeding up prediction\n",
+ " with torch.no_grad():\n",
+ " # Forward pass, calculate logit predictions\n",
+ " outputs = model(b_input_ids, token_type_ids=None,\n",
+ " attention_mask=b_input_mask)\n",
+ "\n",
+ " logits = outputs[0]\n",
+ " #print(logits)\n",
+ "\n",
+ " # Move logits and labels to CPU ???\n",
+ " logits = logits.detach().cpu().numpy()\n",
+ " #print(logits)\n",
+ "\n",
+ " # Store predictions and true labels\n",
+ " predictions_test.append(logits)\n",
+ "\n",
+ " pred_labels = []\n",
+ " \n",
+ " for i in range(len(predictions_test)):\n",
+ " # The predictions for this batch are a 2-column ndarray (one column for \"0\"\n",
+ " # and one column for \"1\"). Pick the label with the highest value and turn this\n",
+ " # in to a list of 0s and 1s.\n",
+ " pred_labels_i = np.argmax(predictions_test[i], axis=1).flatten()\n",
+ " pred_labels.append(pred_labels_i)\n",
+ "\n",
+ " pred_labels_ += [item for sublist in pred_labels for item in sublist]\n",
+ " return pred_labels_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = torch.load(model_path, map_location=torch.device('mps'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if model_name == 'bert-base-multilingual-cased' :\n",
+ " print('Loading Bert Tokenizer...')\n",
+ " tokenizer = BertTokenizer.from_pretrained(model_name)\n",
+ "elif model_name == 'camembert-base':\n",
+ " print('Loading Camembert Tokenizer...')\n",
+ " tokenizer = CamembertTokenizer.from_pretrained(model_name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_loader = generate_dataloader(tokenizer, data_LGE)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "_fzgS5USJeAF",
+ "outputId": "be4a5506-76ed-4eef-bb3c-fe2bb77c6e4d"
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "8WEJjQC7I8mP"
+ },
+ "outputs": [],
+ "source": [
+ "df_LGE = pd.read_csv(\"LGE_withContent.tsv\", sep=\"\\t\")\n",
+ "data_LGE = df_LGE[\"content\"].values\n",
+ "\n",
+ "\n",
+ "#pred_labels_, true_labels_ = evaluate_bert(data_eval, labels, model, batch_size)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 206
+ },
+ "id": "9qJDTU-6vzkk",
+ "outputId": "1b279f0e-7715-4d23-f524-08e8ba327f6c"
+ },
+ "outputs": [],
+ "source": [
+ "df_LGE.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "71-fP61-OOwQ",
+ "outputId": "ef08b49e-0a9f-4653-e303-3163250af35b"
+ },
+ "outputs": [],
+ "source": [
+ "df_LGE.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "lFFed2EAI8oq"
+ },
+ "outputs": [],
+ "source": [
+ "def generate_prediction_dataloader(chosen_model, sentences_to_predict, batch_size = 8, max_len = 512):\n",
+ "\n",
+ " if chosen_model == 'bert-base-multilingual-cased' :\n",
+ " print('Loading Bert Tokenizer...')\n",
+ " tokenizer = BertTokenizer.from_pretrained(chosen_model)\n",
+ " elif chosen_model == 'camembert-base':\n",
+ " print('Loading Camembert Tokenizer...')\n",
+ " tokenizer = CamembertTokenizer.from_pretrained(chosen_model)\n",
+ "\n",
+ " # Tokenize all of the sentences and map the tokens to thier word IDs.\n",
+ " input_ids_test = []\n",
+ " # For every sentence...\n",
+ " for sent in sentences_to_predict:\n",
+ " # `encode` will:\n",
+ " # (1) Tokenize the sentence.\n",
+ " # (2) Prepend the `[CLS]` token to the start.\n",
+ " # (3) Append the `[SEP]` token to the end.\n",
+ " # (4) Map tokens to their IDs.\n",
+ " encoded_sent = tokenizer.encode(\n",
+ " sent, # Sentence to encode.\n",
+ " add_special_tokens = True, # Add '[CLS]' and '[SEP]'\n",
+ " )\n",
+ "\n",
+ " input_ids_test.append(encoded_sent)\n",
+ "\n",
+ " # Pad our input tokens\n",
+ " padded_test = []\n",
+ " for i in input_ids_test:\n",
+ "\n",
+ " if len(i) > max_len:\n",
+ " padded_test.extend([i[:max_len]])\n",
+ " else:\n",
+ "\n",
+ " padded_test.extend([i + [0] * (max_len - len(i))])\n",
+ " input_ids_test = np.array(padded_test)\n",
+ "\n",
+ " # Create attention masks\n",
+ " attention_masks = []\n",
+ "\n",
+ " # Create a mask of 1s for each token followed by 0s for padding\n",
+ " for seq in input_ids_test:\n",
+ " seq_mask = [float(i>0) for i in seq]\n",
+ " attention_masks.append(seq_mask)\n",
+ "\n",
+ " # Convert to tensors.\n",
+ " prediction_inputs = torch.tensor(input_ids_test)\n",
+ " prediction_masks = torch.tensor(attention_masks)\n",
+ " #set batch size\n",
+ "\n",
+ "\n",
+ " # Create the DataLoader.\n",
+ " prediction_data = TensorDataset(prediction_inputs, prediction_masks)\n",
+ " prediction_sampler = SequentialSampler(prediction_data)\n",
+ " prediction_dataloader = DataLoader(prediction_data, sampler=prediction_sampler, batch_size=batch_size)\n",
+ "\n",
+ " return prediction_dataloader\n",
+ "\n",
+ "\n",
+ "\n",
+ "def predict_class_bertFineTuning(model, sentences_to_predict_dataloader):\n",
+ "\n",
+ "\n",
+ " # If there's a GPU available...\n",
+ " if torch.cuda.is_available():\n",
+ "\n",
+ " # Tell PyTorch to use the GPU.\n",
+ " device = torch.device(\"cuda\")\n",
+ "\n",
+ " print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
+ "\n",
+ " print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
+ "\n",
+ " # If not...\n",
+ " else:\n",
+ " print('No GPU available, using the CPU instead.')\n",
+ " device = torch.device(\"cpu\")\n",
+ "\n",
+ " # Put model in evaluation mode\n",
+ " model.eval()\n",
+ "\n",
+ " # Tracking variables\n",
+ " predictions_test , true_labels = [], []\n",
+ " pred_labels_ = []\n",
+ " # Predict\n",
+ " for batch in sentences_to_predict_dataloader:\n",
+ " # Add batch to GPU\n",
+ " batch = tuple(t.to(device) for t in batch)\n",
+ "\n",
+ " # Unpack the inputs from the dataloader\n",
+ " b_input_ids, b_input_mask = batch\n",
+ "\n",
+ " # Telling the model not to compute or store gradients, saving memory and\n",
+ " # speeding up prediction\n",
+ " with torch.no_grad():\n",
+ " # Forward pass, calculate logit predictions\n",
+ " outputs = model(b_input_ids, token_type_ids=None,\n",
+ " attention_mask=b_input_mask)\n",
+ "\n",
+ " logits = outputs[0]\n",
+ " #print(logits)\n",
+ "\n",
+ " # Move logits and labels to CPU\n",
+ " logits = logits.detach().cpu().numpy()\n",
+ " #print(logits)\n",
+ "\n",
+ " # Store predictions and true labels\n",
+ " predictions_test.append(logits)\n",
+ "\n",
+ " #print(' DONE.')\n",
+ "\n",
+ " pred_labels = []\n",
+ " \n",
+ " for i in range(len(predictions_test)):\n",
+ "\n",
+ " # The predictions for this batch are a 2-column ndarray (one column for \"0\"\n",
+ " # and one column for \"1\"). Pick the label with the highest value and turn this\n",
+ " # in to a list of 0s and 1s.\n",
+ " pred_labels_i = np.argmax(predictions_test[i], axis=1).flatten()\n",
+ " pred_labels.append(pred_labels_i)\n",
+ "\n",
+ " pred_labels_ += [item for sublist in pred_labels for item in sublist]\n",
+ " return pred_labels_\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "O9eer_kgI8rC",
+ "outputId": "94ea7418-14a8-4918-e210-caf0018f5989"
+ },
+ "outputs": [],
+ "source": [
+ "data_loader = generate_prediction_dataloader('bert-base-multilingual-cased', data_LGE)\n",
+ "#data_loader = generate_prediction_dataloader('camembert-base', data_LGE)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "sFpAwbrBwF2h",
+ "outputId": "8d210732-619d-41f0-b6e2-ad9d06a85069"
+ },
+ "outputs": [],
+ "source": [
+ "p = predict_class_bertFineTuning( model, data_loader )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "51HF6-8UPSTc",
+ "outputId": "26bff792-eb8d-4e1a-efa4-a7a6c9d32bf9"
+ },
+ "outputs": [],
+ "source": [
+ "len(p)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "rFFGhaCvQHfh"
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "qgJ-O4rcQHiI",
+ "outputId": "bfe93dd6-4d89-4d5c-be0d-45e1c98c6b14"
+ },
+ "outputs": [],
+ "source": [
+ "# Il faudrait enregistrer l'encoder, \n",
+ "# sinon on est obligé de le refaire à partir du jeu d'entrainement pour récupérer le noms des classes.\n",
+ "encoder"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "QuST9wJoQHnS"
+ },
+ "outputs": [],
+ "source": [
+ "p2 = list(encoder.inverse_transform(p))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "6ek7suq9QHqE",
+ "outputId": "6636983a-7eba-48c8-d884-f8fb437294dc"
+ },
+ "outputs": [],
+ "source": [
+ "p2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "XvdDj5PBQHtk"
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "t39Xs0j7QHXJ"
+ },
+ "outputs": [],
+ "source": [
+ "df_LGE['class_bert'] = p2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 206
+ },
+ "id": "-VZ7geRmQHaD",
+ "outputId": "350a4122-5b1f-43e2-e372-2f628f665c4a"
+ },
+ "outputs": [],
+ "source": [
+ "df_LGE.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3xkzdkrKQHwA"
+ },
+ "outputs": [],
+ "source": [
+ "df_LGE.to_csv(\"drive/MyDrive/Classification-EDdA/classification_LGE.tsv\", sep=\"\\t\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "machine_shape": "hm",
+ "name": "EDdA-Classification_BertFineTuning.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3.9.13 ('geode-classification-py39')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
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+}
--
GitLab